INFORMATION TO USERS This reproduction was made from a copy o f a docum ent sent to us for microfilming. While the most advanced technology has been used to photograph and reproduce this docum ent, the quality o f the reproduction is heavily dependent upon the quality o f the material subm itted. The following explanation o f techniques is provided to help clarify markings or notations which may appear on this reproduction. 1. The sign or “ target” for pages apparently lacking from the docum ent photographed is “ Missing Page(s)” . I f it was possible to obtain the missing page(s) or section, they are spliced into the film along with adjacent pages. This may have necessitated cutting through an image and duplicating adjacent pages to assure complete continuity. 2. When an image on the film is obliterated with a round black mark, it is an indication o f either blurred copy because o f movement during exposure, duplicate copy, or copyrighted materials th a t should n o t have been filmed. For blurred pages, a good image o f the page can be found in the adjacent frame. If copyrighted materials were deleted, a target note will appear listing the pages in the adjacent frame. 3. When a map, drawing or chart, etc., is part o f the material being photographed, a definite m ethod o f “sectioning” the material has been followed. It is customary to begin filming at the upper left hand com er o f a large sheet and to continue from left to right in equal sections with small overlaps. If necessary, sectioning is continued again—beginning below the first row and continuing on until complete. 4. For illustrations that cannot be satisfactorily reproduced by xerographic means, photographic prints can be purchased at additional cost and inserted into your xerographic copy. These prints are available upon request from the Dissertations Customer Services Department. 5. Some pages in any docum ent may have indistinct print. In all cases the best available copy has been filmed. University Microfilms International 300 N. Zeeb Road Ann Arbor, Ml 48106 8503213 H affar, Imad AR A DISCRETE TIME MANAGEMENT MODEL FOR COMPARING MECHANICAL CUCUMBER HARVESTING SYSTEMS IN MICHIGAN Michigan State University University Microfilms International 300 N. Zeeb Road, Ann Arbor, Ml 48106 Ph.D. 1984 PLEASE NOTE: In all c a s e s this material h as been filmed in the best possible way from th e available copy. Problems encountered with this do cu m en t have been identified here with a check mark , V 1. Glossy photographs or p a g e s _______ 2. Colored illustrations, paper o r prin t______ 3. P hotographs with dark b a ck g ro u n d ______ 4. Illustrations a re poor co p y _______ 5. P a g e s with black marks, not original copy_______ 6. Print show s through a s th ere is tex t on both s id e s of p ag e _______ 7. Indistinct, broken or small print on several pages 8. Print ex ceed s margin req u irem en ts______ 9. Tightly bound copy with print lost in spine_______ 10. C om puter printout p ages with indistinct print 11. P a g e (s)____________ lacking w hen material received, and not available from school or author. 12. P a g e (s)____________ seem to b e missing in numbering only a s text follows. 13. Two pages num bered . ^ ^ '______ . Text follows. 14. Curling and wrinkled p a g e s ______ 15. O ther_______________________________________________________ _________________ University Microfilms International A DISCRETE TIME MANAGEMENT MODEL FOR COMPARING MECHANICAL CUCUMBER HARVESTING SYSTEMS IN MICHIGAN By I mad AR H a f f a r A DISSERTATION in Submitted to Michigan S t a t e U n i v e r s i t y p a r t i a l f u l f i l l m e n t of the requirements f o r t h e d e g r e e of DOCTOR OF PHILOSOPHY Department of Agricultural 1984 Engineering ABSTRACT A DISCRETE TIME MANAGEMENT MODEL FOR COMPARING MECHANICAL CUCUMBER HARVESTING SYSTEMS IN MICHIGAN By I mad AR H a f f a r Three in mechanical Michigan model wa s separate were sets of determined The second was owning of the simulated included of dollar harvesting daily and of fruit determine size day. hectare weight studied of study time. and The CUCHARV. determine the The cost of based on system. dollar grade returns diminished The o u t p u t and d a i l y i n t wo harvesters. also total per grade and t h e t r a f f i c a b i 1ity mo d e l wa s a l s o the sequence locations sequences weather fruit net The by t h e included per the farm of average cost fruit and t o t a l harvesting. A field three daily the algorithm to harvesting calculated per mo d e l model. a function of the systems experiment with a computer flow the on t h a t average number cost using per as was c o n c e r n e d a cash value dynamics state obtained The f i r s t parameters and o p e r a t i n g The mo d e l the fruit harvesting a discrete parameters experiments. plant cucumber using using experiment determination program compared constructed and mo d e l once-over were on t h e of go/no-go in Michigan used in t h e net harvest and f o r mo d e l to outcome. days established on a d a i l y three soil study the to basis types. for These sensitivity of I mad AR H a f f a r The mo d e l and revealed wa s validated a future farmers and systems analysts promise processors and and as for t wo locations to aid as an aducational in Michigan a decision tool tool for researchers. APPROVED: D r . \3ai? an E e , Committed Chairman r Dr . Do n a l d Department M. E d w a r d s Chairman for the T h i s wo r k i s d e d i c a t e d t o Go d , The Mo s t M e r c i f u l , The Mos t B e n e f i c i e n t , b l e s s i n g s o f Whom o n l y ma de reach t o t h i s page ACKNOWLEDGMENTS My h e a r t i e s t only in provides every thanks me w i t h wo r k the I do b u t cess. My d e e p e s t mother for being to care, also wishes the my l o v i n g most in making also loving a nd globe. To t h e m , all love and our to grant to acknowledge: a continuous I also ** * The ** * like National me w i t h that ma de t h i s Ga r y R. tance of the that and a true is suc­ my f a t h e r sacrificing give patience and parents myself on a n d my happiness and the E s ma y , Dr. H. Price and tributions to facilities professor, moral, who p r o v i d e d and f i n a n c i a l knowledge that ma ny o f my c o l l e a g u e s for influenced C. A. Rotz, serving their this very his s how me much friendly spirit my a t t i t u d e s . Dr. A. a s m e mb e r s helpful dissertation. iii and assis­ to acknowledge teach me w i t h time Dr. for and Research the positively M. Scientific scholarship technical, I deeply Dr. for possible. taking applied lack. mittee wo r k valuable has to them a supreme Council Van E e , my m a j o r and f o r still *** would providing a very only it who n o t mercy. Lebanese for Lord I can extended this ask Hala, encouragement, helps are wife, K. Srivastava, a nd on my g u i d a n c e c o m­ and c o n s t r u c t i v e con­ *** Mr. Jack Hobson, information tices for on t h e and f o r providing commercial serving as the much o f the cucumber outside essential harvesting examiner prac­ during my o r a 1 e x a mi n a t i o n . *** Dr. S. ments * * * Mr . Sargent, of t h i s with *** J e a n wo r k of t h i s during in A very graduate I am v e r y a very special students Michigan State proud for to very constructive providing research the Terrell, his editorial c o m­ farm for dissertation. Ray K e n n e y , parts of for model the and f o r impressive of the goes typist, data land he p r o v i d e d me who p r o d u c e d this manner. to the Agriculture University, be the his validation. artistic thanks s ome o f a very a me mb e r o f . faculty, staff, and Engineering Department cooperative team t h a t TABLE OF CONTENTS LI ST OF TABLES Vi i i LI ST OF FIGURES xi i CHAPTER 1 INTRODUCTION ........................................................................................... 1.1. Purpose 1.2. Scope o f of the the Study Study 1 ...................................................... 1 ........................................................... 3 2 OBJECTI VES ................................................................................................ 17 3 LITERATURE REVIEW .............................................................................. 19 3.1. G e n e r a 1 ........................................................................................ 19 3.2. B a c k v i e w on t h e D e v e l o p m e n t o f M e c h a n i c a l C u c u m b e r H a r v e s t i n g ...................................................... 20 Ma ke s a n d C o n c e p t s o f O n c e - O v e r C u c u m b e r H a r v e s t e r s on t h e M a r k e t ......................................... 21 Q u a l i t y and R e c o v e r y o f F r u i t in OnceO v e r C u c u m b e r H a r v e s t i n g ......................................... 22 3.5. "Threshing Concept" 25 3.5. Ma n a g e m e n t of the 3.3. 3.4. Harvester Cucumber Plant Moisture Stress .. ..................... 25 3.6.1. Effect ................. 26 3.6.2. E ffe ct of Soil Physical Condition a n d M i n e r a l Co n t e - n t ................................. 28 E f f e c t of P la n t P o p u l a t i o n S p a c i n g -Cu 1 t i v a r ......................................... 30 Effect 32 3.6.3. 3.6.4. of Cucumber of Temperature ........................... i v 3.6.5. 3.6.6. H a r v e s t I n d i c e s o f t h e C u c u mb e r P l a n t ...................................................................... 36 ........................................................ 37 Plant Gr owt h 3.8. Cucumber Production/Harvesting 3.9. Economic Performance Model s Mo d e l s ... 39 ..................................................... 44 T r a f f i c a b i 1 i t y a n d A v a i l a b l e F i e l d Work Days ............................................................................................... 49 3.10.1. A n a ly s is of P r e c i p i t a t i o n F r e q u e n c y ............................................................ 51 Soil ...................... 53 3 . 1 1 . Sys t ems Approach in A g r i c u l t u r e E n g i n e e r i n g ............................................................................ 59 3.10.2. 5 33 3.7. 3.10. 4 E f f e c t o f C h e m i c a l Gr o wt h R e g u l a ­ t o r s and En d o g e n o u s P l a n t H o r mo n e s .............................................................. Moisture 3.11.1. The 3.11.2. Farming MODEL DEVELOPMENT Systems Budget Methodology ..................... 59 ................... 66 ............................................................................. 70 Systems 4.1. System Definition 4.2. System Description 4.3. Model Hypothesis 4.4. Model 4.5. Da t a Research ............................................................. 70 ........................................................... 73 ..................... 74 Relations ................................................................... 80 Requirement ................................................................ 119 DATA COLLECTION ................................................................................... 122 5.1. 5.2. Plant Growt h and Description Parameters 5.1.1. Experimental 5.1.2. Da t a Gathering 5.1.3. Data Analysis Machi ne Performance vi ............................................. Setup 122 .................................. 122 ............................................. 130 ................................................ 133 ........................................................ 175 10 REFERENCES SUGGESTIONS FOR FURTHER RESEARCH ............................. 252 ..................................................................................................................... 254 APPENDICES A A LI ST OF THE MODEL COEFFI CI ENT MATRICES B A TABULATION OF EQUATION COEFFI CI ENTS IN THE CASH FLOW COST ANALYSIS .................................................... C 271 273 WEATHER SIMULATION Cl L istin g of P r o g r a m DATATAP ............................... 275 C2 L istin g of P r o g r a m CUCWEAT ............................... 27 6 Daily T r a f f i c a b i 1i t y Sequence of the Mont h o f A u g u s t P e r A r e a P e r Y e a r P e r S o i l Ty p e ............................................................................ 278 Daily T r a f f i c a b i 1 i t y Sequence of the Mo n t h o f A u g u s t f o r L o c a t i o n 2 3 9 5 P e r Y e a r P e r I n i t i a l F i e l d C a p a c i t y ................. 28 3 C3 C4 D ... MODEL SIMULATION D1 Program L i s t i n g ............................................................. 285 D2 V a r i a b l e S e t 1 ................................................................. 29 7 D3 V a r i a b l e S e t 2 ................................................................. 298 D4 V a r i a b l e S e t 3 ................................................................ 300 D5 V a r i a b l e S e t 4 ................................................................. 301 D6 V a r i a b l e S e t 5 ................................................................ 304 D7 V a r i a b l e S e t 6 ................................................................ 305 D8 V a r i a b l e S e t 7 ................................................................ 306 ......................... 307 G u i d e ..................................................... 308 D9 Flowchart Sy mbo l D10 Program User D11 Pr ogram Sample Description Output vi i ............................................ 315 LI ST OF TABLES 1 A C o m p a r i s o n o f Some A d v a n t a g e o u s a n d D i s a d v a n ­ t a g e o u s F e a t u r e s o f t h e Cucumber H a r v e s t e r s in M i c h i g a n ..................................................................................................... 2 USDA C l a s s i f i c a t i o n 3 Breakdown o f Cucumber s Pe r S i z e Grade Wei ght a n d Numbe r a t 2 4 - H o u r I n t e r v a l s f o r Two F i e l d s i n M i c h i g a n ............................................................................................ 4 The D o l l a r V a l u e o f F i e l d s A a n d B a s a F u n c ­ t i o n o f Ti me .......................................................................................... 5 E f f e c t o f I n i t i a l F i e l d S t a t e on t h e A v e r a g e K i l o g r a m a n d D o l l a r R e t u r n P e r Ha P e r H a r v e s t S t a r t i n g Day P e r H a r v e s t i n g S y s t e m .............................. 6 E f f e c t o f P r i c i n g S t r u c t u r e on t h e A v e r a g e D o l l a r R e t u r n P e r Ha P e r S t a r t i n g Day P e r H a r ­ v e s t i n g S y s t e m .................................................................................... 7 A L i s t o f Some o f t h e P h y s i c a l P r o p e r t i e s o f Four Ge nera l S o i l T e x t u r e s in P e r c e n t of Tot a l D e p t h ............................................................................................................. 8 A L i s t o f Some o f t h e P h y s i c a l P r o p e r t i e s o f F o u r G e n e r a l S o i l T e x t u r e s i n t h e U p p e r 150-mm L a y e r ............................................................................................................. 9 A L i s t o f t h e T r e a t m e n t s Per Bl ock and T h e o r e t ­ i c a l P l a n t P o p u l a t i o n P e r T r e a t m e n t ........................... Stand of Cucumber Counts Grades Population ... 10 A Summar y o f 1 1 A R ep re s e n ta tio n of the Average, Standard Devi­ a t i o n , a n d Numbe r o f P l a n t s P e r Se x i n P e r c e n t o f T o t a l P l a n t Numbe r f o r E v e r y P o p u l a t i o n . . . . 12 An O u t l a y o f t h e A n a l y s i s T a b l e f o r F r u i t E n t r y a n d E x i t D a t a ....................................................................................... vi i i Per Size .............. 13 A Summar y o f a T w o - F a c t o r A n a l y s i s o f V a r i a n c e o f t h e Mean o f F r u i t Number p j j ( P e r 20 P l a n t s ) E n t e r i n g t h e P l a n t s on a D a i l y B a s i s P e r P o p u l a t i o n .................................................................................... 136 A Summa r y o f a T w o - F a c t o r A n a l y s i s o f V a r i a n c e o f t h e Mean o f F r u i t Number p j j ( P e r 9 . 3 m2 ) E n t e r i n g t h e P l a n t s on a D a i l y B a s i s P e r P o p u l a t i o n ............................................................................................... 137 A L i s t of t h e Values Assigned to t h e Set of I n d i c a t o r V a r i a b l e s R e p r e s e n t i n g D i f f e r e n t Days 162 A R e p r e s e n t a t i o n o f t h e C a l c u l a t e d V a l u e s and A n a l y s i s F a c t o r s f o r t h e P a r a m e t e r s in t h e L i n e a r R e g r e s s i o n Model ( B o t h M u l t i p l e a n d S i m p l e ) Per P o p u l a t i o n f o r S i z e G r a d e 1A ................ 164 A Representation o f t h e C a l c u l a t e d V a l u e s and A n a l y s i s F a c t o r s f o r t h e P a r a m e t e r s in t h e L i n e a r R e g r e s s i o n Model ( B o t h M u l t i p l e a n d S i m p l e ) Per P o p u l a t i o n for Size G r a d e 1B ................ 165 18 Per 166 19 A Representation o f t h e C a l c u l a t e d V a l u e s and A n a l y s i s F a c t o r s f o r t h e P a r a m e t e r s in t h e L i n e a r R e g r e s s i o n Model ( B o t h M u l t i p l e a n d S i m p l e ) Per P o p u l a t i o n for Size Grade 3A 167 A Representation o f t h e C a l c u l a t e d V a l u e s and A n a l y s i s F a c t o r s f o r t h e P a r a m e t e r s in t h e L i n e a r R e g r e s s i o n Model ( B o t h M u l t i p l e a n d S i m p l e ) f o r S i z e G r a d e s 1A, 1B, a n d 2 ....................... 171 A Representation o f t h e C a l c u l a t e d V a l u e s and A n a l y s i s F a c t o r s f o r th e P a r a m e t e r s in t h e L i n e a r R e g r e s s i o n Model ( B o t h M u l t i p l e a n d S i m p l e ) f o r S i z e G r a d e s 3A, 3B, a n d 4 ....................... 172 Summar y o f F ruit Size F r u i t Dy n a mi c B e h a v i o r Relations for G r a d e 1A ..................................................................... 176 Summar y o f Fru it Size F r u i t Dy n a mi c B e h a v i o r Relations for G r a d e s 1B a n d 2 ..................................................... 177 Summar y o f Fru it Size F r u i t Dy n a mi c B e h a v i o r Relations for G r a d e s 3A a n d 3B .................................................. 178 Summa r y o f F ruit Size F r u i t Dy n a mi c B e h a v i o r Relations for G r a d e 4 ....................................................................... 179 14 15 16 17 20 21 22 23 24 25 Population for Size ix Grade 2 .................. 26 27 28 29 30 The Me a n , S t a n d a r d D e v i a t i o n , a n d P a r a m e t e r s o f t h e A n a l y s i s o f V a r i a n c e o f F r u i t R e c o v e r y Per H a r v e s t e r S y s t e m P e r G r a d e .................................................... 182 P e r c e n t o f F r u i t Recovery Per H a r v e s t i n g System i n Kg o f H a r v e s t e d F r u i t P e r Kg o f H a n d - P i c k e d F r u i t ............................................................................................................. 183 Mean a n d S t a n d a r d D e v i a t i o n o f F i e l d P e r f o r m ­ a n c e P a r a m e t e r s P e r H a r v e s t e r Ty p e .............................. 185 Summa r y o f t h e F r e q u e n c y o f E x i s t e n c e o f a T r a f f i c a b l e Day O v e r a P e r i o d o f T w e n t y Y e a r s P e r L e v e l o f I n i t i a l i z a t i o n ................................................. 195 Summa r y o f t h e A c t u a l a n d G e n e r a t e d Days i n A u g u s t 1983 i n Two R e g i o n s Values 32 Base and V a r i a b l e V a l u e s o f t h e D i f f e r e n t I n p u t V a r i a b l e S e t s P e r Run ................................................................. 212 33 Base ................................ 213 34 A Summa r y o f t h e A v e r a g e P e r H e c t a r e o f R e c o v ­ e r e d F r u i t V a l u e ( A ) , H a r v e s t i n g C o s t ( B ) , Net D o l l a r R e t u r n ( C ) , a n d t h e T o t a l Numbe r o f H a r ­ v e s t i n g Da ys ( D) P e r H a r v e s t S t a r t i n g Day P e r P o p u l a t i o n P e r H a r v e s t i n g S y s t e m ................................... 215 A v e r a g e Ne t R e t u r n s P e r H e c t a r e P e r S t a t e o f F i e l d P e r H a r v e s t S t a r t i n g Day f o r t h e T h r e e H a r v e s t i n g S y s t e m s ......................................................................... 219 The R a t e o f C h a n g e o f A v e r a g e Ne t R e t u r n s P e r H e c t a r e B e t w e e n H a r v e s t S t a r t i n g Da ys 1 a n d 2 a n d Da ys 4 a n d 5 , R e s p e c t i v e l y , P e r S t a t e o f F i e l d P e r H a r v e s t i n g S y s t e m ................................................. 220 A v e r a g e Ne t R e t u r n s P e r H e c t a r e P e r P r i c i n g S t r u c t u r e P e r H a r v e s t S t a r t i n g Day P e r H a r v e s t ­ i n g S y s t e m ............................................................................................... 225 The R a t e o f C h a n g e o f A v e r a g e Ne t R e t u r n s P e r H e c t a r e B e t w e e n H a r v e s t S t a r t i n g Da ys 1 a n d 2 a n d Da ys 4 a n d 5, R e s p e c t i v e l y , P e r P r i c i n g S t r u c t u r e P e r H a r v e s t i n g S y s t e m ...................................... 226 E f f e c t o f T r a f f i c a b i 1 i t y S e q u e n c e on t h e A v e r ­ a g e Ne t R e t u r n s P e r H e c t a r e P e r S y s t e m P e r H a r v e s t S t a r t i n g Day .................................................................... 22 8 36 37 38 39 the Values of Different the Input Input x Variables Variables Per Run 197 31 35 of Nonworkable in Michigan. 211 40 41 42 43 44 45 46 A v e r a g e Ne t R e t u r n s P e r H e c t a r e P e r H a r v e s t i n g S y s t e m P e r Far m S i z e P e r H a r v e s t S t a r t i n g D a y . . 232 E f f e c t of I mp ro vi ng t h e C a p a c i t y of System T h r e e o n t h e A v e r a g e Net D o l l a r R e t u r n P e r H e c ­ t a r e Per P l a n t P o p u l a t i o n Per H a r v e s t System P e r H a r v e s t S t a r t i n g Day ......................................................... 23 4 A c t u a l a n d S i m u l a t e d F r u i t Number ( x 1 0 “ 3 ) P e r Day P e r G r a d e P e r H e c t a r e f o r Fa r m C a s e 1 U s i n g H a r v e s t i n g S y s t e m 1 ....................................................................... 23 7 A c t u a l and S i m u l a t e d F r u i t Wei ght ( Kg / Ha ) and D o l l a r V a l u e P e r Day P e r G r a d e f o r Far m C a s e 1 and R e c o v e r e d R e t u r n s Us i n g H a r v e s t i n g S y s t e m 1 ( Da y 5 ) ....................................................................................................... 238 A c t u a l a n d S i m l u a t e d F r u i t Numbe r ( x 1 0 ” 3 ) P e r H e c t a r e P e r G r a d e P e r Day a n d t h e R e c o v e r e d Number U s i n g H a r v e s t i n g S y s t e m s 2 a n d 3 ( Da y 4 ) 240 A c t u a l and S i m u l a t e d F r u i t We i g h t ( K g / H a ) Pe r G r a d e P e r Day a n d t h e R e c o v e r e d W e i g h t U s i n g H a r v e s t S y s t e m s 2 a n d 3 ( Da y 4 ) ...................................... 241 A c t u a l and S i m u l a t e d R e c o v e r e d D o l l a r R e t u r n s Pe r H e c t a r e P e r Gr a de P e r H a r v e s t i n g S y s t e m and t h e Value and M a g n i t u d e o f D u r a t i o n from A c t u a l 242 LI ST OF FIGURES FIGURE 1 A Vi ew o f t h e Wil. de H a r v e s t e r 2 A Vi ew o f t h e CUKE H a r v e s t e r 3 A Vi ew o f t h e MSU H a r v e s t e r 4 A Vi ew o f the Different .. 9 5 A S c h e m a t i c L a y o u t o f t h e O n c e - O v e r Cucumber H a r v e s t S y s t e m ................. ...................................................................... 71 A Schematic R e p r e s e n t a t i o n of t h e Once-Over C u c u m b e r H a r v e s t i n g S y s t e m Model - P a r t I : F r u i t R e c o v e r y R e s p o n s e ............................................................ 75 A Schematic R e p r e s e n t a t i o n of t h e Once-Over C u c u m b e r H a r v e s t i n g S y s t e m Model - P a r t I I : T o t a l M a c h i n e C o s t a n d Ne t H a r v e s t R e t u r n s R e s p o n s e .................................................................................................... 76 Number o f F r u i t E n t e r i n g S i z e G r a d e 1A P e r 20 P l a n t s V e r s u s Ti me i n Days f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ........................................................ 138 6 7 8 9 10 11 12 Size ............................................ 5 ............................................... 5 .................................................. 5 Grade Cucumbers F r u i t Number o f S i z e G r a d e 1A P e r 20 P l a n t s V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ........................................................................... 141 F r u i t Number o f S i z e G r a d e 1B P e r 20 P l a n t s V e r s u s Ti me i n Days f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ........................................................................... 142 F r u i t Number o f S i z e G r a d e 2 P e r 20 P l a n t s V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ........................................................................... 143 F r u i t Number o f S i z e G r a d e 3A P e r 20 P l a n t s V e r s u s Ti me i n Days f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ........................................................................... 144 xi i 13 14 15 16 17 18 19 20 21 22 23 24 25 26 F r u i t Numbe r o f S i z e G r a d e 3B P e r 20 P l a n t s V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ............................................................................ 145 F r u i t Numbe r o f S i z e G r a d e 4 P e r 20 P l a n t s V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ............................................................................ 146 F r u i t Numbe r o f S i z e G r a d e 1A P e r 9 . 3 m2 V e r s u s Ti me i n Days f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ............................................................................................ 147 F r u i t Number o f S i z e G r a d e 1B P e r 9 . 3 m2 V e r s u s Ti me i n Days f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ............................................................................................. 148 F r u i t Numbe r o f S i z e G r a d e 2 P e r 9 . 3 m2 V e r s u s Ti me i n Da ys f o r the Three D i f f e r e n t Plant P o p u l a t i o n s ............................................................................................ 149 F r u i t Number o f S i z e G r a d e 3A P e r 9 . 3 m2 V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ............................................................................................ 150 F r u i t Number o f S i z e G r a d e 3B P e r 9 . 3 m2 V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ............................................................................................ 151 F r u i t Numbe r o f S i z e G r a d e 4 P e r 9 . 3 m2 V e r s u s Ti me i n Da ys f o r th e Three D i f f e r e n t Plant P o p u l a t i o n s ............................................................................................. 152 Numbe r o f F r u i t E n t e r i n g S i z e G r a d e 18 P e r 20 P l a n t s V e r s u s Ti me i n Days f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ................................................. 153 Numbe r o f F r u i t E n t e r i n g S i z e G r a d e 2 P e r 20 P l a n t s V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ................................................. 154 Numbe r o f F r u i t E n t e r i n g S i z e G r a d e 3A P e r 20 P l a n t s V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ................................................. 155 Numbe r o f F r u i t E n t e r i n g S i z e G r a d e 3B P e r 20 P l a n t s V e r s u s Ti me i n Da ys f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ................................................. 156 Numbe r o f F r u i t E n t e r i n g S i z e G r a d e 4 P e r 20 P l a n t s V e r s u s Ti me i n Days f o r t h e T h r e e D i f f e r e n t P l a n t P o p u l a t i o n s ................................................. 157 Fl o w C h a r t 189 of Program CUCWEAT ............................................ xi i i 27 Fl ow C h a r t CUCHARV ................................................................... 200 28 A v e r a g e R e c o v e r e d F r u i t Numbe r ( S i z e 1A) P e r H e c t a r e P e r H a r v e s t S t a r t i n g Day P e r P o p u l a t i o n P e r H a r v e s t S y s t e m .......................................................................... 216 A v e r a g e R e c o v e r e d F r u i t Numbe r ( S i z e 4) Per H e c t a r e P e r H a r v e s t S t a r t i n g Day P e r P o p u l a t i o n P e r H a r v e s t S y s t e m .......................................................................... 217 Average F r u i t Numbe r P e r H e c t a r e ( S i z e 1A) P e r S t a t e o f F i e l d P e r H a r v e s t S t a r t i n g Day f o r t h e T h r e e H a r v e s t i n g S y s t e m s ......................................................... 221 Average F r u i t Numbe r P e r H e c t a r e ( S i z e 4 ) Per S t a t e o f F i e l d P e r H a r v e s t S t a r t i n g Day f o r t h e T h r e e H a r v e s t i n g S y s t e m s ...................................................... . 222 Average F r u i t Numbe r ( 1 A) P e r H e c t a r e P e r H a r v e s t S t a r t i n g Day P e r S y s t e m .................................... 229 Average F r u i t Numbe r ( S i z e 4 ) P e r H e c t a r e Per .................................... H a r v e s t S t a r t i n g Day P e r S y s t e m 230 29 30 31 32 33 of xi v CHAPTER 1 INTRODUCTION In t h e in over and North of Michigan it cucumbers 1981). States, thirty-nine hectares. try, United is Other the has in t h e leading Carolina, fifty been estimated grown cucumbers over are states Wisconsin, states a major that U.S. are grown on a b o u t state for seventeen grown in quantity 72,000 this indus­ percent Michigan in cucumber California, in the (USDA, production and of include Virginia (Brown, 1980 ). About strictly seventy grown slices, chips, duction of green for or brine stock Stat i s tie s , wa s percent of processing relish. stock 238 x In wa s the either 1981, 100.8 103 c wt nation's into x 103 t o n s , only (Michigan dynamic. many a r e a s , vesting. devoted are pickles, cucumber while pro­ the Agriculture 1981). Cucumber in whole Michigan 1.1. very cucumbers production and Much r e s e a r c h especially In t h e toward Purpose early the processing '60s, production technology and d e v e l o p m e n t in t h e field attention of of have the Study is been of mechanical har­ wa s e x c l u s i v e l y a mechanical done cucumber harvester. This prompted labor this goal was achieved, further determine the me a n s recovering quantity input. his of revenues and expand habits result, development in cucumbers and just the Michigan Since the This shift hand (1) The to conducted quality interest in to increase almost both In a d d i ­ the capacity. harvest and and e n e r g y operation. production and been of con­ As a pickling ninety-five to percent return of the and c a s h during the harvesting total ber size grades the different the lack the cucumber number or and t h e in t h e return has harvesting. availabil­ with mechanical field is l ow harvest­ attributed, management process. This deci­ has of: harvester weight harvester harvesters; industry dissatisfaction improper of hand increase the due t o of an industry cash back cucumber to knowledge percent of low q u a l i t y factors, and Michigan shift because and This before the a major labor quality a mong o t h e r primarily were a change to of When a lower expense mechanical zero '70s, resulted system. sions from mi d experiencing cucumber processor cost availability. a better production inflicted rising acreage. been of with a few y e a r s , increased studies by a f a r m e r his by t h e uncertain cucumbers coupled sumer ing for was the ity relatively This tion, of its was farm higher and research of th e field recovery various capacity in cucum­ for (2) The ability the total at (3) The skill the The ( T) (5) The (6) time to field at and t h e to grade change (7) (8) of The r e c o g n i t i o n at the of The capability day knowing and price labor type forecast (T + a T ) ; of a f i e l d as have the and dollar different state available on a d a y - t o - d a y the number a field its harvest different the ( aT ) ; processor; to return a time of peak q u a n tity of the by t h e the at a function between t h e of machine, of Michigan har­ relative cost; at the optimal of the field, size grades; and, wo r k d a y s basis during the season. The study of discrete state mo d e l these is factors the and t h e i r subject 1.2. vest. state required schedule sequence Michigan in t h e between to to All both grade equilibrium dollar The a b i l i t y harvesting for size intervals return and t h e the structure in afield change return balance peak the time dollar demanded and its of weight per dynamic in t h e farmer fruit of equipment the of the the state total different perception vested the (T); predict knowledge return size predict number and t h e a given of (4) to mechanical follows Currently, the matter simulation of Scope of harvesting once-over there are of pickling the Study type brands a thesis. cucumbers or d e s tr u c tiv e t wo h a r v e s t e r this in in har­ on t h e Michigan Harter) either market, and namely, a Wilde o wn e d a Cuke* (Figures by t h e farmer (previously 1 and or 2). leased to known as Cart- Harvesters are hi m by t h e proces­ sor. The u n i f o r m i t y critical out to in a once-over adapt Some o f width, the this practices research such pollination, hybrid fruit and set "small current we ed that chemical pickles" of population and spacing the harvester ery of the and Engineering et al., Table 1982). 1 summarizes tageous features of (McCollum, have set activity al., done fruit 1981; of that would and d e v e l o p e d to of Michigan Figure s ome o f the the three * T r a d e n a me s a r e u s e d f o r a n d do n o t i m p l y e n d o r s e m e n t . the for vine, referred to The c o n ­ a clear a high cucumbers. State 3 s hows breed 1970). created by t h e and an a 1 1 - g y n o e c i o u s provide grade be d 1971; on t h e Tompkins, technology cultural geometry, bee et been utilization me d i u m s i z e Department 1981, irrigation, studies this was d e s i g n e d carried study a compatible harvester been p o llin a to r s — a technology of very the for small has is included (Baker, development Research to uniformly and development harvesting. control Other and once-over application, hormones, with plant has plant 1966). varieties variety as as set harvest. cucumber fertilizer Morrison, in f r u i t need recov­ The new Agricultural University ( Van Ee, MSU h a r v e s t e r . advantageous and-disadvan­ harvesters. identification purposes only r ( ) Figure 1. The W i l d e Harvester. Figure 2. The Cuke® H a r v e s t e r . ms Figure 3. The MSU H a r v e s t e r . Table 1. A C o m p a r i s o n o f Some A d v a n t a g e o u s H a r v e s t e r s in Mi c h i g a n . Mach i n e Br a nd Cuke and D i s a d v a n t a g e o u s of the Cuc umb e r Disadvantages Advantages 1. 1. I t i s l i g h t e r i n w e i g h t and mo u n t e d on t h e 3 - p o i n t h i t c h wh i c h p r o v i d e s a b e t t e r t r a c t a b i l i t y and l e s s c o m­ paction . 2. 2. I t h a s a w o b b l i n g c u t t e r b a r wh i c h e l i m i n a t e s l o t s of t h e p r od u ct d i r t . 3. It unloads it s product d i r e c t l y i n t o a t r u c k wh i c h i m p r o v e s i t s field efficiency. 3. Features The h a r v e s t e r i s d r i v e n b a c k w a r d s demandi ng a p r o f e s s i o n a l o p e r a t o r and a m o d i f i c a t i o n o f t h e t r a c t o r ' s p o we r t r a n s m i s s i o n a nd s t e e r i n g systems. I t d e s t r o y s mo s t o f t h e s m a l l e r fru i t . I t n e e d s a t r u c k t o a c c o mp a n y i t i n t h e f i e l d t h u s r e q u i r i n g mo r e e n e r g y and l a b o r and c a u s i n g s o i l c o m p a c ­ t i o n by t h e t r u c k . Wi I d e 1. The p r o d u c t i s d i r e c t l y u n l o a d e d i n t o t h e b i n (no need of a t r u c k ) . 1. It destroys fruit. most o f t h e MSU (Threshing concept) 1. I t r e c o v e r s mo s t o f s m a l l and medi um s i z e f r u i t ( h a s a h i g h e r p o te n tia l d o l l a r return per ha). 1. More m e c h a n i c a l c o m p o n e n t s ( h i g h e r i n i t i a l and m a i n t e n a n c e c o s t s ) . 2. Heavier in w e i g h t . small Cucumbers several n a me s size and A c o mmo n l y Figure These price 1B, 2) percent while value. be very grade coincide timely by t h e size/area ferent size determines size grades ent the does starting varying day systems (Table price a dynamic fruit ber number, size, tribution 5). 4). which price machine type. the acreage size not hectare. with to of as dif­ The m a c h i n e per day. Conse­ by d i f f e r ­ s a me initial change changes total the fruit This grades of s a me h a r v e s t also 6). is different the the must fruits/ harvested with day w i l l percent various of ( 1 A, very does per the a field leads value or a high­ grades no, (number o f of f r u i t (Table the return T), coincide through The d o l l a r harvested value and t h e i r operation recovery This and t h e among t h e time adopted harvesting field and t h e structure growth have tonnage from invariably usually fruit highest the that C o mmo n l y , smaller the various grades. the percent total size into and t h e i r grades crop of peak d o l l a r not the the these is highest 3 and grades, system the a certain and t h e harvesting state cucumbers state at fruit type quently, (Tables classes from most f a r m e r s the value, graded among t h e processor. for with maxi mum c r o p determined the paid oversized fruit bought with is vary 2 presents the As a r e s u l t , necessarily To o b t a i n are are The n u m b e r o f known g r a d i n g 4 s hows contract purposes standards Table cucumbers preharvest est pickling classes. USDA ( 1 9 8 1 ) . diameters; low, grade for differentiating processors. by t h e grown is when prompted in t h e fruit time by cucum­ number d i s ­ progresses. Table 2. USDA C l a s s i f i c a t i o n of Cucumber Si ze Grades. Size Grade* Fruit Diameter (mm) Fruit Diameter (in) 1B 2 3A 3B 12.7-20 20-27 27-38.1 38.1-44.4 44.4-50.8 fc"-3A" V'-W' 1 V - 1 ' / 2" 1A *A11 f r u i t s w i t h a d i a m e t e r g r e a t e r t h a n a r e c o n s i d e r e d an o v e r s i z e o r a c u l l . 5 4 . 0 mm v r - iv 1%" - 2" 4 50.8-54.0 2 1/<11 9 Figure 4. A Vi ew o f the Different Size Grade Cucumbers. Table 3. Da t e Br e a k d o wn o f Cu c u mb e r s p e r S i z e f o r Two F i e l d s i n M i c h i g a n . * N1A N1B N2 N3A N3B Gr a de We i g h t and Number a t N4 W1A 24-Hour Intervals W1 B W2 W3A W3B W4 F i e l d A** 7-17-71 378 516 538 173 32 22 19.5 87.4 326.5 195.0 48.7 48.7 7-18-71 324 430 580 334 54 32 14.6 63.4 336 350.0 82.9 68.2 7-19-71 215 334 334 398 269 44 9.7 48.7 214.5 453.3 463.0 121.8 7-20-71 151 289 420 346 247 141 9.7 43.9 234.0 351.0 400.0 302.1 Field B** 8-14-71 289 334 346 269 193 54 19.5 63.4 195.0 292.4 312.0 11 2 . 1 8-15-71 269 334 324 247 259 119 19.5 63.4 185.0 273.0 398.0 258.3 213, 1973, *Research Repori Mi c h i g a n S t a t e N. x 100 = Number o f g r a d e s i z e W. x 10 We i g h t o f g r a d e s i z e **Dat a are estimated at University. i in f r u i t s p e r h e c t a r e . i i n kg p e r h e c t a r e . a 70% by w e i g h t m a c h i n e r e c o v e r y . Tabl e 4. Size T h e . D o l l a r Value o f Fi el ds A and B as a Function of Time. 1A IB 2 3A 3B 4 330 / Kg 3 3 0 /Kg 1 9 ^ /Kg 13^/ Kg 80/ Kg 40/ Kg Gr a d e Price/Grade* Total $ Va l u e p e r Ha. Potential $ Va l u e p e r Ha. ** F i e l d A ($/Ha) Date 7-17-71 64.35 289.40 620.35 253.50 39.00 19.50 1286.10 1837.30 7-18-71 48.20 209.20 638.40 455.00 66.50 27.30 1444.60 2063.70 7-19-71 32.00 160.70 407.55 589.25 370.40 48.70 1608.60 2298.00 7-20-71 32.00 144.80 444.60 456.40 320.00 121.80 1519.80 2171.20 Field B ($/Ha) 8-14-71 64.35 209.20 370.50 380.10 250.00 45.00 1319.15 1884.50 8-15-71 64.35 209.20 351 . 50 355.00 318.40 103.30 1401.45 2002.00 *Listed **Total prices are those Val ue = P o t e n t i a l a d o p t e d by a l o c a l Mi chi gan g r o we r in 1982. Va l u e x 70% ( e s t i m a t e d m a c h i n e r e c o v e r y ) . Table 5. E f f e c t of I n i t i a l F i e l d S t a t e on t he Average Kilogram and D o l l a r Return Per Ha Per Har vest S t a r t i n g Day Per Ha r v e s t i n g System. I n i t i a l Field State ( F r u i t n u mb e r / s i ze g r a d e / 9 . 3mz ) Harvest S t a r t i ng Day KG/Ha $ / Ha KG/Ha $/ Ha KG/Ha $/ Ha SYSTEM 1 SYSTEM 2 SYSTEM 3 1A IB 2 3A 3B 4 72 = 50 = 52 = 20 = 5 = 1 1 2 3 4 5 6 7233 9647 12270 15005 17803 20616 993 1151 1293 1417 1521 1605 6522 8 58 5 10857 13242 15694 18170 985 1124 1249 1356 1446 1516 88 1 4 11196 13742 16377 19053 21730 1321 1469 1591 1697 1778 1839 1A 1B 2 3A 3B 4 = 65 = 50 = 65 = 20 = 5 = 1 1 2 3 4 5 6 7927 10607 13503 16458 19415 22332 1106 1276 1420 1537 1628 1697 7166 9433 11927 14497 170 8 8 19660 1096 1242 1366 1465 1541 1597 9645 12250 15019 1 7830 2 0 6 33 23390 1448 1598 1717 1808 1873 1918 1A 1B 2 3A 3B 4 = = = = = = 63 48 70 22 5 1 1 2 3 4 5 6 8433 11248 14254 1 7287 20292 23233 1172 1341 1482 1591 1674 1734 7613 9989 12576 15213 17849 20446 1158 1303 1422 1513 1581 1629 10916 12911 15767 18638 21478 24253 1514 1660 1773 1855 1912 1948 1A 1B 2 3A 3B 4 = = = = 60 45 52 34 = 7 = 2 1 2 3 4 5 6 9514 12230 14945 1 7662 20360 23018 1187 1313 1415 1499 1567 1621 8 44 9 10790 13154 1 5541 1 7926 20284 1154 1263 1350 1421 1478 1521 11044 13637 16230 18823 21393 23918 1481 1590 1674 1738 1787 1822 Table 6. E f f e c t of P r i c i n g S t r u c t u r e on t he Average D o l l a r Return Per Ha Per S t a r t i n g Day Per Ha r v e s t i n g System. PRI CI NG STRUCTURE ( $/KG/Grade) Harvest S t a r t i ng Day SYSTEM 1 SYSTEM 2 SYSTEM 3 Ave r a ge $/ Ha Structure 1: 1A = 0 . 4 1B = 0 . 4 2 = 0.24 3A = 0 . 0 8 3B = 0 . 0 4 4 = 0 1 2 3 4 5 6 1050 1101 1146 1169 1175 1164 1103 1143 1174 1189 1187 1170 1400 1410 1401 1376 1339 1291 Structure 2: 1A = 0 . 3 5 1B = 0 . 3 5 2 =0.21 3A = 0 . 1 1 3B = 0 . 6 4 = 0 1 2 3 4 5 6 1066 1174 1255 1309 1336 1342 1085 1 176 1244 1287 1306 1305 1420 1507 1564 1592 1594 1576 Structure 3: 1A 1B 2 3A 3B 4 = 0.33 = 0.33 =0.19 = 0.13 = 0.08 = 0.02 1 2 3 4 5 6 1106 1276 1420 1537 1628 1697 1096 1242 1366 1465 1541 1597 1448 1598 1717 1808 1873 1918 Structure 4: 1A 1B 2 3A 3B 4 = 0.31 = 0.31 =0.19 = 0.15 = 0. 1 = 0.04 1 2 3 4 5 6 1198 1425 1631 1810 1963 2092 1163 1359 1537 1690 1821 1932 1540 1747 1927 2078 2 20 5 2309 14 In of s ome c a s e s , fruit/grade and at somet imes of ranges from t e n more. In rapid with 1968, field jectory to to overmaturity actually (Tables very return prediction time for can and to predict for both aid the the farmer farmer time axis period The rain the farm of should (24-hour starting two. tra­ field losses. size, the uncertainty. size Wh i l e mo r e a field size should state grade of is The o p t i mu m in are due to least distribu­ be d o n e the field, cucumbers. as d i s c r e t e at sampling b e c ome s desired type, for a nd t r a n s p o r t a t i o n , and/or be r e c o r d e d the processor. losses prediction machine interval) with situation Thi s the These flow of different also field processor it plant. and t h e weight, assists while the prediction is or value-time on t h e of prices cash a day in d e c i d i n g regulation the just cucumber equipment, and or may go f r o m o p t i ­ harvested of on t h e in a con­ change a mechanically shortages based maturity fruit, reduce into percent n u mb e r o f unforeseen mi x forty a field a unique to tion the in favorable the harvesting, and percent in we i g h t under as culls scheduling labor ten 3-6). of essential but that or exhibits increase high cucumbers, to ( n umb e r in good c o n d i t i o n s percent, indicated field from f i v e Daily may be a s Si ms The a b i l i t y dollar it a c ucumber percent hours. thirty pickling of changes twenty growth, mum m a t u r i t y Ea c h T) twenty-four of state time up t o period ditions the points a five- to The on t h e six-day day. compl e x reaches when e n c o u n t e r i n g its peak value, a 15 workable day value. If is the ficability), (depending growth the day the determining is nonworkable crop on t h e dynamics) value initial by t h e Thus, knowledge of t h e least ten further not days, assist have t o ficiently In addition, basis sary and because trigger the not the of the ten field the the of day of day wi t h the structure. to pass workable. of sampling, this interval crop again crop period is peak cr op at will The s e q u e n c e be d e f i n e d discrete decrease and t h e the since or traf- workability harvest. days, the are should small cucumber does, suf­ value. on a d a y - t o - d a y This intervals is neces­ required f r om one m a t u r i t y to stage to other. The v a l u e cost of of equipment the will that field capacity. of owni ng the be crop Thus, with balance situations value. but high with of the The n e t a higher harvester performance, in and o p e r a t i n g l ow r e c o v e r y a harvester harvester should Unde r c e r t a i n cost with mat ch of of crop exceed a harvester tion the harvesting. harvesting, of with include a time the sequence sequence as of this harvester increase future scheduling to the may e i t h e r state attaining (limited conditions be mo r e t h a n large in time starting in factor ownership, is harvesting returns capacity recovery selection delayed crop field the but also may l ower a func­ and o p e r a t i o n cost. In d e t e r m i n i n g value of mo n e y the should cost of the be c o n s i d e r e d . harvester, This is the time needed because 16 of the nation's pricing structure The in rapid due integration to in both inflation and simulation of mo d e l defines thesis. The mo d e l shall be d y n a m i c , and t i m e invariant as the mo d e l includes n u mb e r and t y p e , sampl e is the The mo d e l output a simulated includes the harvesting the plant n u mb e r ber d i s t r i b u t i o n for defined of the population, per the total of per size ten days. period present system. of value cost of preceding linear, of of this The field, sampling data grade 9.3 weight, grade per in dollar, input machi ne (a m2 ) . and harvested The o u t p u t owni ng factors deterministic, (1973). state size and t h e policies. objectives and includes fruit e c o n o my the by Ca d z o w farm s i z e , fruit the and t a x time to a discrete changes n u m­ area also and o p e r a t i n g the CHAPTER 2 OBJ ECTI VES As an involved initial in t h e objectives (1) we r e To d e f i n e of (2) for once-over the system, the following thesis: mechanical harvesting The d e f i n i t i o n inputs, factors system shall include system components, and p r e ­ output; grade the fruit To d e t e r m i n e vine the effective cucumber To d e t e r m i n e ber this cucumbers. To d e t e r m i n e size (4) selected the once-over (3) integrating harvesting system given dicted toward cucumber pickling the step the field harvesters percent per capacity (Wilde, recovery of of Cuke, the the three MSU); different harvester; daily for a defined entry area of as new f r u i t to the cucum­ a function of plant population; (5) To d e t e r m i n e per (6) area (7) To c o n s t r u c t t i me ; the weight per simulates daily for different To e s t a b l i s h fruit the the n u mb e r o f plant fruit between grade population a linear fruit regression growth size grade populations; relations per per the fruit per as a nd day; prediction dynamics n u mb e r mo d e l a function that of (8) To u s e cost (9) a cash of cal on the To w r i t e basis the utilizing the under This algorithm different model ed algorithm various of the between mo d e l in the present harvesting 1-30 season weather mo d e l in sensitivity that work­ Michi gan) and soil be e n c o u n ­ analysis; mathematically system components; different combinations of (mechani­ data will value equipment; sequence August harvesting harvesting a computer To v a l i d a t e Mi chi gan period cucumber the mo d e l characteristics. in t h e predict and o p e r a t i n g a day-to-day relates (11) for once-over tered to a deterministic days physical (10) owni ng To b u i l d able f l o w mo d e l of locations system in inputs; and , (12) To i mpl ement use. the mo d e l for on-line interactive computer CHAPTER 3 LITERATURE REVIEW 3.1. The p r o d u c t i o n processing started into whol e in t h e provided of September for selective, pickles, hand n u mb e r picking that cucumber mechanical been the for (Stout, crop et me n t have me nt of for required to harvesting indicate value al., been wa s 1963). that as paid for responsible a mechanical cucumber pay as one-half plus mi dThe been f r om 370 t o It the and interest al., in labor in t h e (Stout has expense et labor while value seventy-five harvester a n umb e r o f extra (Stout, expense the 19 and and Br own e s t i m a t e d harvesting 1959) . until man-hours/acre. high for Mi chi gan requiring 1980, costs Harvest relish 150 m a n - h o u r s / a c r e five farmer and always on y i e l d s In commerci al laborers. has operation 1980). needed the direct of m i g r a n t t r a n s p o r t i n g , and h o u s i n g Some r e p o r t s the al., harvesting recruiting, of et in from August depending for chips, grew wel l cucumbers stoop-labor harvesting customary crop of Mi chi gan slices, empl oyment 860 m a n - h o u r s / h a / y e a r (Rotz, in Cu c umb e r s a large labor-intensive, pickings cucumbers 1890s. additional Ge ner a 1 and of for 1 9 63 ) . percent 1962 procure­ develop­ Ries, 20 3.2. The f i r s t ber harvester 1967). B a c k v i e w on t h e D e v e l o p m e n t o f M e c h a n i c a l C u c u mb e r H a r v e s t i n g k nown wa s ma d e Dur i ng t h e at developing et al., attempt 1950s, their 1980). by H. to develop J. Hei nz growers in own m u l t i p i c k In 1957, Stout Co. mechanical carried out machines. The search revealed cucumber harvesters and a vine trainer wer e invented, University harvester cal Another constructed, personnel wa s cucumber harvester Two y e a r s proved that of harvesting Stout (a) the indicated problems of in of 1 95 8 tested testing need (Stout, the of for et by of al., Bingley, the wa s 1963). stages 1959). of of trainer State The a mechani­ al., in 1957- harvester unsatisfactory alternative Some o f approach for vine MSU m u l t i p i c k considering harvest et ( Rot z, experimental various components harvester al., multipick the wor ke d harvester by M i c h i g a n et cucum­ (Stout, states a pneumatic (Stout, experimental the and 1945 a search four in and t e s t e d a combination 1959. and harvester in several existing development. a mechanical as the met hods inherent described by include: Accumulative d a ma g e to plants with a resultant decrease in y i e l d ; (b) Inadequate near (c) the Inability fruit mechanical base to of the r e move from c e r t a i n component s for removing all the fruit plant; and retrieve commerci al varieties; marketable set 21 (d) Lowe r y i e l d s by t h e (e) of luxuriant Limited plants or the As a r e s u l t State approach over or first five use pick from t h e because s a me of the destructive of Canadian approach is required the vine growth necessity of repeatedly problems, the multipick (Stout, a once-over the 1966 still (Humphries, under for 1967). European 1981). Some o f States, na mel y t h e 1981). These markets commerci al some 1981; these harvesters t wo Wi l d e and existing in t h e ket Prior the to Cuke Mi c h i g a n year 1979; are t wo o n c e - o v e r m o u n t e d known ma k e s 1977). harvester (MAF, 1972, con­ The m u l t i ­ in 1974, once­ Some f o r t y - Ma k e s a n d C o n c e p t s o f O n c e - O v e r C u c u mb e r H a r v e s t e r s on t h e Mar f cet once-over al., Mi c h ­ The wa s 3.3. several et built research 1971, the 1967). 19 6 3 s e a s o n . (Stout, 1968, to harvester wer e at harvest exclusively harvesters in researchers Threadgill, are is and, 1972; and today. when poor; harvesters by t wo m a n u f a c t u r e r s There r ow s p a c i n g plants. during cucumber states of above type prototype harvest is wa s d e v o t e d and e v a l u a t e d Perkinson, wi de soil abandoned and a t t e n t i o n once-over Southern the anchorage University field structed the capacity harvesting igan of machi ne; Pulling (f) because model s Ko r me r , on t h e et produced in t h e brands (Kor mer , harvesters and t wo United are al., United et al., the only States mar­ additional 22 self-propelled and FMC ( W e s t e r n Hilliker, Al l but of differ lifted allow In harvesters slightly are cut the of about 1971, over to This problems caused raw p r o d u c t and 1972a). that lower cucumbers four and c o n v e y i n g between inches between of the period with Leaders in systems. ground sets of diameter them wi t h in q u a l i t y (Marshall, surface, closely that the and q u a n t i t y earlier birth of acres) the (less recovery processed Mi chi gan harvested al., wer e once-over do n o t vines Michigan's system occurred than six to the of quality pickle a set of harvested of in a and the (Marshall, industry har­ et sus­ cucumbers hand-harvested 1972b). once-over problems years) and h a n d l i n g than cucum­ mechanically and h a n d l e d and q u a n t i t y et of Q u a l i t y and R e c o v e r y o f F r u i t O n c e - O v e r C u c u mb e r H a r v e s t i r T g The d e v e l o p m e n t gave percent time in t h e mechanically the of subsequent 3.4. it 1967; s a me p r i n c i p l e below t h e fed (24,500 adoption has but and pass acreage short s ome o f roots eighty-five relatively wer e Stout, on t h e handling from t h e i r cucumbers production pected 1967; Blackwelder 1980 ) . vested. al., existed: operate in t h e i r harvester, rollers the ( Br own , Far m E q u i p m e n t , these into related harvesters 1972). The v i n e s ber once-over of c ucumber labor cost in harvesters and procurement, new p r o b l e m s over crop. problems These solved the quality 23 initiated standing a new r e s e a r c h and processing bers. In recovery wa s systems 1971, of of three on t h e sizes we r e ried out to harvester percent 1B , w h i l e also to the 2A, 2B, twenty the and reel fifty-percent percent the hand-harvested studied the Some o f their over percent of seventy the of lifting d a ma g e wer e of wer e all also the car­ different accounted pickles for sizes 1A a n d conveyor inflicted ninety we r e on p i c k l e al., 1972b). Their st ems attached to Marshall, influencing the the crops study the fruit c ompar ed ones. conclusions The g r e a t e r average in m a c h i n e - h a r v e s t e d experiment, factors et n umbe r o f mo r e role and t h e that Studies rolls s ma s he d c ucum­ 1B p i c k l e s twelve physical a nd pickling that of ground. 3 (Marshall, that In a n o t h e r (a) on t h e under­ cucumber h a r v e s t e r s found percent The p i n c h pick-up and once-over Th e y a l s o the broken for revealed the loose of reported An a d d i t i o n a l determine the responsible sizes vines. towards postharvesting, harvest al., twelve components. of et different 1A a n d found harvesting, once-over percent. size still of the Marshall, sixty-seven percent was improving program o r i e n t e d the et al., recovery (1972a) of the fruit. were: pinch roll force, the greater the recovery . (b) The c h o i c e ating the of pinch conditions. greater the roll coverings The h e a v i e r need for rough the depends we e ds rolls. on t h e and/or oper­ vines, 24 (c) Wilted vines had higher detachment force than nonwilted vines. Research was harvesting was had t w e n t y to afternoon vested the of in t h e et al. fruit Faster ery, vine did the of the highest speeds the wer e apron not In of to varied study allowed versus wa s not so t h a t mechanical i mpact on t h e directly related existed area trend green by S a r i g towards handling forty in stock. and increased to Broken less fruit the conducted by the sixty and It vines wa s ground fed recov­ roll strength Wh i l e defects his in handling, a func­ that and pickup onto brine no stock such fruits percent source with we r e relation indicated formation and studies (1976) s ma s he d as 1974). He l d ma n five in freely carried than percent. found studies and recovery pinch Further bloater har­ of recovery carpel fruit. mechanical (1975) steps. percent to when A study increase whe n t h e the quality that in t h e effect. ( AEI S 2 9 1 , analyzed internal st ems and speed. attained cucumbers 1972b). speed, on t h e Hooper that al., whi ch harvested percent a s mal l pickup lag whe n thirty apron 1973, revealed day d u r i n g indicated between conducted coordinated did et a significant recovery of " s m a 11 p i c k 1e s " was orientation, ground twenty on This have st ems (Marshall, speeds wer e and percent performance roll Studies tion morning time Mee ha ni c a 1l y - h a r v e s t e d fifteen by w e i g h t while texture twenty-two (1980). pinch d o n e on t h e performed. and o n l y harvester Rotz, also over in t h i s a definite increases in reduced from wer e whe n t h e floor 25 surface rubber on w h i c h the (Marshall, Research fruit et was al., harvested studied concepts and potato was found lifters. Researchers cel develops detachment pedicel reel (MSU, also with al., 3.5. In fruit Van detachment and pinch rolls field and recovered mo r e 59%, and sizes 1, 2, the lower 3, fruit of the foam of research water obtained, the and t h e that the It jets none fruit needed the at pedi­ pedicel greater losses the the Cu c u mb e r and consisted between Wi l d e concept" pickup to the 23%, projects cucumber of three the 62%, have a nd of been as of apron In 1982, harvester was significantly Ee, et plant pairs pickup conventional ( Va n a new harvester. The p r o t o t y p e than Harvester tested cucumber Ma n a g e me n t behavior The that developed respectively research loads pressure Concept" inserted tested. 3.6. ine al., 88% c o m p a r e d and Nu me r o u s vine indicated "threshing smal l in was angle on a c o n v e n t i o n a l designed high success the the beaters size wi th 1972a). Ee, et a commercial 76%, to mechanism. counter-rotating of the "Threshing 1981, dirt cucumbers. use studied force, et reduce s ome Results detachment covered 1975a). respect force. (Marshall, the Wh i l e feasible to pickling as was 1972b). conducted mechanically such was d r o p p e d the machine: 89% f o r al., grade 1982). Cu c u mb e r conducted a function Plant to of exam­ its 26 management. izer Th e y include application, plant tural practices on t h e plant (Morrison and growth and y i e l d the spacing, yield Ries, to effect plant growth 1967). Other temperature, variety, (O'Sullivan, 1980; and muc h c o n c e r n has chemical fruit growth set and Wh i l e the cucumber on t h e fruit plant, of fruit at or Effect water 1982, stress of of of stress and plants also length and cul­ related climate, activity 1970). study and have stress, bee on pickling in cucumber vine wa s completely of In a d d i t i o n , the role plant of hor mone s on as n u mb e r time to the wer e e i t h e r been of the conducted rate harvest. best the of the time of As a of harvest intuitive existing or data. Stress Kr et chman studied cucumbers. length inhibited and not node one after as the effects The y f o u n d after a reduction but have purely given concerning and y i e l d especially the reduced experienced node few s t u d i e s analysis and growth growth, Moisture Ortega increase plants fertil­ cucumber investigations on t h e near harvesting on c o n v e n t i o n a l In the b e e n ma ny recommendations once-over 3.6.1. to the studies and e n d o g e n o u s relatively enlargement based rate have o f ma nage me nt the and Martin, type, crop y i e l d . dynamics result, for regulators there effects given of moisture photoperiod, been soil population, and irrigation, Connor of t wo n u mb e r we e k o f that of pronounced fruiting Control rate as the water weeks. in gr owt h of in of the vine 27 stressed water plants. stressed affected than Fruit plants; the tials we r e lower smal l stressed that fruit yield plants. leaves in t h e yield test, There high wer e result Cummi ns a n d stressed plants set growing under age of fifteen days the fruit plants stressed plants. pared in fruiting higher production tion or the wer e in less water poten­ than in t h e fruits use leaf area not stress had poorly developed observed stress. for inches six in days the fruit pattern, and t o t a l and g r o w t h c hamber smaller for water no s i g n i f i c a n t fruit use ( Lo o mi s days to affect on t h e on t h e high ( m l / c m 2 )» use wer e or c om­ for rate was Fruit Moder at e Cradall, aver­ l ow water grade an grow from plants. total high before environments fruiting and the T r a n s p i r a t ion pattern. effect water lower stressed on t h e rate and and t h e Howeve r , fruit transpiration area The that diameter for cucumbers. high three indi­ l ow s t r e s s e d plants. also of leaf leaves, on t h e an a v e r a g e t wo (1975) to fewer fruits significantly seasonal c ompar ed stressed required nonfruiting did and conditions and l ow m o i s t u r e to greenhouse and high experiment, water and reduced fruits Kr et chman as vines Kr et chman and o n l y In a n o t h e r and stress fewer wa s stage stressed of fruit plants area, larger stressed reduced on t h e plants. leaf water shorter smaller set severely Osmot i c Cummi ns wer e g r e a t l y wa s t h e the ones. large was fruits. under wer e rate however, smaller In a s i m i l a r cated growth consump­ moisture n u mb e r 1977). of 28 Wa t e r auguria water L. use absorption growth. (1980), plant gen Similar highest decreased are produced 3.6.2. plant the wer e populations Mc Co l l u m a nd a mo n g c u c u m b e r s an e x p e r i m e n t Miller treated of nutrient Th e y a t t r i b u t e d capacity the of cucumber severe nutritional plants h a d mo r e nutritional was only 160 lb. vegetative stress, about one the per of growth to and as wer e a t t a i n e d and nitro­ irrigation, whe n c u c u m b e r s density. s mal l to growth set fruit, the than in t h e those nitrogen in pickling remarkable even well under fertilized with n u mb e r o f plants appeared nutrient of of a fairly Further, amount) differences levels and y i e l d Although increase N/ A ( a n e x c e s s i v e ma xi mum r a t e that plant. a nd significant plant different plant stress. as yields found uptake cucumbers. absorption Condition (1971) with fruit by O ' S u l l i v a n nitrogen high E f f e c t of So i l P h y s i c a l and Mi n e r a l C o n t e n t of (64 p l a n t s / m 2 ), t i s s u e for at water that flower irrigation population d e ma n d irrigation not highest increasing with of during stages reported effects on y i e l d increased early progressed, wer e Cucumi s He o b s e r v e d maxi mum v a l u e Wh i l e t h e with an that ( 1949 ) . and t h e findings application indicating its development populations. the common g h e r k i n by H a l l fertilization, who o b s e r v e d nitrogen the reached As f r u i t declined. for wer e d e s c r i b e d development, at curves s ome fruit treated stunted. accumulation set with The occurred 29 about that fifty nitrogen treatment me n t days at had 201 preplant or of (268 k g / h a ) color) direct leaf Fe, and at N. reduced. harvest. Tissue N rates no p r e p l a n t anion N, that kg/ha while the N fertilizer accumulation (shape not N fer­ practices composition of K, had a of the Ca , Mg, to plants Na c o n c e n t r a t i o n little tissue of preplant compar ed had in t h e of rate concentration received 201 highest source concentration in t i s s u e f r o m 67 t o the on t i s s u e of The p e r ­ generally fertilization rates in a s l i g h t l y evaluation The nutrient no t r e a t ­ The a d d i t i o n for wer e N fertilizer. influence a preplant plant. quality ratios mineral higher per observed preplant resulted higher Fruit Nitrogen Sidedressing and N. on t h e Mn w e r e received cation little influence fertilizer that had total tissue wer e while yields. flowers lengthrdiameter by p r e p l a n t used N03 -N a n d fruit as N03 -N t h a n 134 k g / h a pistillate preplant and influenced tilizer of l ower (1977a) cucumber 134 k g / h a , gave up t o off-shape to and t i s s u e 67 a nd 268 k g / h a of Cantliffe addition yield between n u mb e r centages and higher N fertilizer greater seeding. fertilizer rates between after was influence on (Cantliffe, 1977b ) . Bishop, applied al. (1969) observed fertilizers s h o we d P to importance than approximately adequate. ing the et N or 50, Smittle rate of K. 100, and Th e y a nd that be o f greater indicated 50 k g / h a , Williamson N fertilization to yield that responses relative N, P, respectively, (1977) suggested partially to a nd K at should be increas­ alleviate the 30 effects ity. of soil Th e y c o n c l u d e d not restricted eighty percent strength tractor sue of NOg a t 3.6.3. Several al., growth 1981). this Plant cultural fruit shape sion, and have and Wi d d e r s , trend, and c o l o r , level, kPa; at however, a soil produced reduction by a of tis ­ a 25-35% y i e l d l e n g t h :diameter ratio. been conducted to plant cultivars (Kretchman, 1982; however, commercially practices. percent N0^ c u c u m b e r wa s 500 compaction availabil­ P o p u 1 a t i o n - S p a c i n g - C u 11 i v a r adopted under the than inhibited in f r u i t and The g e n e r a l cultivars soil experiments Price wa s of a fifty-percent a decrease of nutrient growth less Mechanical in plant root of introduced 1982; the strength a comparable and on t h e by s o i l resulted Effect ne wl y that 850 k P a . wheel reduction, and compaction percent distribution Johnson, 1979; has to been utilized Compar i son has fruit plant et Price, c ompar e the populations included off-shape of evaluate fruit, yield, sex e x p r e s ­ a mong t h e various s i ze g r a d e s . Cantliffe where cucumbers mier," wer e 50,000 plants per (10 as ha tons ha. Phatak ( C u c u mi s g r o wn per (1975) sativus ha of Ov e r L.), population out ha ( 4 6 x 46 cm s p a c i n g ) 50,000 to with 100,000 Yields common c o m m e r c i a l and densities as increasing and an e x p e r i m e n t "Bounty" eight increased the carried at x 10 cm s p a c i n g ) . per densities per and to ranging 850,000 dollars plant 250,000 to "Pre­ per from plants ha a nd population 500,000 populations, at plants 100,000, 31 150,000, 200,000, increase as days did not tons per ha plant lower The n u mb e r plant affect plant of per lower ha. Length :d i a m e t e r population. percent and uniformity) reported of by P u t n a m Another cluded that was obtained the r ow a n d cluded of that study the the the time ma d e and was dollar kilogram the a genetic inheritance of yields did not in harvest for four but slower with fruit cultivar. ha) than per in ha). increasing of "Premier" 50,000 wer e plants unaffected populations or the higher plants ratios "Bounty" in per 200,000 population, of doubled did color Similar not per by affect (green quality results we r e (1963). ma d e by M o r r i s o n consistent plant suggested of ha, plants diameter plant spacing of fruit as had t h e destructive the mos t Miller Ries per and to (1981) woul d c ompar ed and Q u i s e n b e r r y time r e q u i r e d plants criterion cultivars that reach yield be for to (1976) determines anthesis. in con­ fluctuation harvest accurate con­ hectare McSay least v a r i a n c e a mong c u l t i v a r s the (1967) 2 4 0 mm b e t w e e n Ells p i c k l i n g cucumber ones. and dollar was rows. wh e n a s i n g l e yield wa s decreased fruit either numbe r comparing observed Varying 24 0 mm b e t w e e n over or to ratios highest whe n ha y i e l d 850,000 plant off-shape De l a y sizing plant lowest per (50,000 to Length wer e the Fruit densities the per (250,000 to fruit at plants increased. dollars produced. population. plant 250,000 population populations the and 32 3.6.4. Effect Le wi n (1971) conditions perature of wer e of T e mp er at u r e reported 65° F and irrigation, carried out 55°F t o an e x p e r i m e n t cultivars wer e g r o wn at days and 30°C D: (9 h o u r s high intensity (LI), comparable days the t wo at the at Total flower periods the for high present minate and nine-hour compact high minate high we r e had gynoecious A similar of pickling cucumber plants but had D: 2 0 ° C in t h e a nd at days at when weights. photo­ fifteen-hour No p i s t i l l a t e or the Variation wer e twelve-hour monoeci ous regime 43 l ow shorter fresh the determinate N for forty-three fifty-nine higher cultivar Plants after wer e a nd photoperiods LI). cultivar. cultivar gynoecious on an long in cultivar indeter­ photoperiod photoperiod sex e x p r e s s i o n of an a nd indeter­ cultivar. experiment fruiting a nd inter­ ( 1 9 75 ) HI + 3 h o u r s Al l cultivars on al . and t h r e e development temperature no e f f e c t (30°C 9 hours r egi me regime. et and monoeci ous regimes greatest on t h e constant indeterminate determinate monoeci ous temperature temperature nodal indeterminate the a nyctotem- HI + 6 h o u r s l ow t e m p e r a t u r e flowers the (HI), regime. numbe r s 85 ° F and Lowe r , 64 d a y s ) temperature for at of enlargement a determinate 9 hours stages photoperiods wer e wher e 13° C N f o r and fruit 75°F u n d e r temperature l ow t e m p e r a t u r e g r o wn and best of respectively. monoecious at the a phototemperature mittent intensity that genetically lines was conducted wher e parthenocarpic and wa s under determined parthenocarpic nonparthenocarpic different 33 thermo-photoperiods. MSU 3 6 4 G, produced both thermo-photoperiod thenocarpic temperatures yield of for was associated resulted parthenocarpic the genetically high night 3.6.5. types et in of in e a r l i e r fruits of et lines and al., t wo intermedi­ i.e., for numbe r s Th e y yield be b e s t lines strong greater 1977). pressure might these parthenocarpic fruiting (Rudich, or l ow n i g h t of we r e femaleness, ma xi mum s e l e c t i o n develop al. (1952) Cucurbit to occur studied flowers. that axils flower completely the the sug­ in exerted but flowers wer e called on t h e vine existed The buds dried development Continued following whe n a u n i f o r m was m a i n t a i n e d . in t h e i r Higher intensity indicated tended These Yield high under temperatures. vines temperature bore yield. at under either lines nonpar- production was g r e a t e s t involving true all E f f e c t o f C h e m i c a l Gr o wt h R e g u l a t o r s a n d E n d o g e n o u s P l a n t Ho r mo n e s ' expression squash fruits the line, under genetically Conversely, parthenocarpic Nitsch, not with fruits wa s e s p e c i a l l y Hybrids parthenocarpic that the a n d t wo h e r m a p h r o d i t i c ate femaleness parthenocarpic a n d mo r e than Thi s (18°C). (12°C). p a r e n t a l lines gested Gy 3 . parthenocarpic temperatures of earlier treatments line, night The g e n e t i c a l l y of the out of leaves mal e "underdeveloped an a r e a sequence while whi ch sex observation pattern first of light flower and generally type still mal e of of whi ch did small. flowers." contained nor ma l 34 ma l e and flowers female liar of ma l e the These in flowers size, greenish, s a me mi ght produce whi c h When t h i s was usually strongly percent of female described to Sandu, et al. growth promoter than in t h o s e GA-like dwarf this GAg. tall those in of growth both was wa s g r e a t e r of mature was found petals in in the plants. similar s o me w h a t the enlarged having subsequent in Finally, an e x t r e m e l y the smaller increased b e c a me without instead The f e m a l e separate. was towards been growth femaleness in a g i v e n of pol­ the a higher of t he level tall However , found seedlings GA-like shoot the in t h e of highest shoot to of of the and authentic between dwarf than content and level tips seedlings tall cultivar plants; similar the was auxin-like and m a t u r e Furthermore, in d a r k - g r o w n of nodes) controlled. cucumber activity tips (expressed n u mb e r o f and e n v i r o n m e n t a l l y dwarf. in hand, chromatographically The d i f f e r e n c e material flowers promoter cultivar, fraction vine seedlings the other pecu­ no p o l l e n . successively open. ma l e inhibited. be g e n e t i c a l l y (1972) petals bore not a fruit both further, greenish whi ch less with into the and did mo r e o r happened, of had while a flower developed The p r o g r e s s in and lengthened on t h e ovary, containing b e c a me f or med area, the ones flowers" last reduced, linated. vine the vine whi ch yellow mal e especially ovary, appeared and t h o s e in by an a r e a As t h e bright "inhibited flowers followed flowers. usual size, vine only dwarf of a nd in GA-l i ke cucumber, 35 but as in light high in t h e Wa t k i n of g r o wn seedlings, dwarf and as in t h e Cauliffe naphthaleneacetic the wa s nearly investigated ( NAA) , the a synthetic an a u x i n ( Cuc umi s applied on the transport sativus L.). inhibitor, In t h e NAA a n d c h l o r f l u r e n o l position NAA was of applied mo v e d into auxin was the ovary applied after on t h e ovary, detected When NAA was placed peduncle stem s e c t i o n s stigma or ovary. inhibited of intact wa s plant flurenol the auxin in the from t h e ovary Similar Beyer (1972) to quantities plant that peduncle the of ovary to peduncle may be t h r o u g h the restruction may t r i g g e r fruit observations wer e and al. Ries, c h 1 o r f 1 u r e n o 1 by 3 , et set and reported (1978) chlor- NAA mo v e me n t o u t regulation increased in in t h e sections: the The the resulted that ovary. in on t h e NAA mo v e me n t reduced of placed and excised whe n pieces. NAA was tissue. the Howeve r , than whe n when quantity mo r e a c c u m u l a t e d in t h a t to depending peduncle, of Th e y c o n c l u d e d by c h l o r f 1 u r e n o l mo v e me n t hours. and o t h e r NAA o u t similar c ucumbe r chamber, a smal l significant ovary set C h 1o r f 1u r e n o 1 a p p l i c a t i o n s applied ovary. in t h e on t h e mo v e me n t o f accumulation twenty-four in exogenously growt h stigma, and (Chlorflure- set fruit In t h e on t h e 1 4 C NAA w e r e and enhanced application. directly in f r u i t greenhouse, involvement auxin, methyl-2-chloro-9-hydroxyfluorene-9-carboxylate nol), twice tall. (1980) acid activity auxin of of fruit auxin growth. by Q u e b e d e a u x when set accumulation subsequent substituting 3 a - d i h y d r o - 2 - ( p - m e t h o x y p h e n y 1 ) - 8H- of and 36 pyrazolo ( TRI A) , [5, 1-a] i s o i n d o l -8-1 , respectively. Owe n s , et al . ( 1980) (AVG) at 50, 100, Aminoethoxyvinylglycine induced in the type staminate of water nificant late to nodes and flowers greenhouse. Bo t h used effect on t h r e e the time prepare on t h e staminate bearing to ( DPX1840 ) a n d node at occurred staminate flowers. 2 0 0 ppm AVG w e r e chlorotic and cucumber AVG s o l u t i o n s the Plants for total ten a sig­ from p i s t i l ­ treated about lines and t h e had conversion a n d on that 2 0 0 ppm application whi ch first reported gynoecious of the 1- T r i a c o n t a n o l n umbe r o f with days 100 after application . Ethephon cious applied (all-male) pistillate line flowers types. The d e g r e e tion ethephon of cation. the In t h e three- induction growth dr a wn and of and C u c u mi s stage four-leaf pistillate et and Indices the grades those was 4). the the the time of concentra­ of color appli­ fifty ppm a t for inhibition conclusions Amchem ( 1 9 6 9 ) , to pheno­ treatment ma r ke d of we r e Sadhn (1972). C u c u mb e r 1966, produce on t h e best al. as to an a n d r o e - monoeci ous Parallel et and/or In at (1969), recommendations 3B o r of without 1973). Cantliffe, on n u m b e r L. depended growth flowers of caused a concentration Miller Harvest based of stage al. and of to spray sativus conversion (1978), Mo s t are of greenhouse, by Mc Mu r r a y 3.6.6. (size of a foliar analogous (Augistine, Das vest or as Plant the of best time of har­ oversized fruit Put nam p r o p o s e d that the 37 stage of growth cucumbers usually appearance of gested that dollar yield mos t for Chao-shun, occurred grade the mature ma xi mum y i e l d index obtain grade (1969), of 4). and to Ries a yellow Miller Ennis, et harvesting following and development (size al. t wo d a y s Morrison effective the fruit et 3A f r u i t . mos t wa s about in o n c e - o v e r al. sug­ highest color and the (1967) the of on t h e Hu g h e s (1979) (1969), indicated t that the tion of ma xi mum r e t u r n fruit greater from f o u r t e e n Mo s t provide of the planning. to per than t wo thirty-one these farmer inches and processor the and growth of cucumber weight and little cultural as in d i a m e t e r instantaneous wa s inputs fruit and a function with known modeling three plant on t h e its of interaction of environmental thate that (Curry, 1971; et al., approaches the dynami c Baker, Farazdaghi, 1983, et 1976; approach produce converted 1981; al., 1982a, Edwards, to Smi t h and time for the effect change of and in Gr o wt h followed cultural, a net Loewer, Wei s s , Hodges, Mo d e l s in described the and photosyn- accumulated 1982b; 1979; not time. generaly to did development physiological, processes consequently 1971a; was The f i r s t various system enough Plant growth. propor­ ranged and about 3.7. One o f whe n t h e percent. we r e environmental n u mb e r occurred indices In a d d i t i o n , the acre dry 1981; 1968; 1977; matter Wal ker , Arkin, Laison and 38 Thornley, 1982; probabilistic The of 1971; the aging delay as whi ch the growth the was time Exa mpl e s (1972), of of to fluid (1974), the application crops, the Similar pickling model s and Kumar of as cycle, and/or wer e such, elements the t wo succes­ by C o r r i e (1979), response surface techniques field recommendations by S mi t h can and and (1982) laboratory sug­ to models. be d e t e r m i n e d Les s less experimen­ w e r e ma d e for and (1978). Lo we r by 1981). Baker a result, from divided (Smith, process to accumulated between Mi s h o e plant view­ regimes regard, in use ma na ge me nt wo r k d o n e (1981), thus restricted algorithm the et the In t h i s costly cucumbers As to representing ma nage me nt function parameters less interact systems. crop, include for a life difference these (1973). parameters, with site, These growth gested accurately simple wa s as Curray with a segment approach sensitive networks was d e r i v e d . and tation. delay of sensitive processes given climate, suggested Soribe identify the (1983) between Fridley how m u s t Sme r a g e elapsed this Wh i l e 1971). areas, the control. Stapleton, calculated elements 1971). 1971b; 1982). base al., al., approach data et we r e e i t h e r et analogous physiographic wa s model s (Curry, over (Gleason, mo d e l physiological l u mp e d c o m p o n e n t s specific sive and These (Sowell, defined question result The t h i r d the the Hesketh, development ing approach yield, this 1977). deterministic partition, produce al., or second a defined energy Vanderlip, experiments 39 3.8. The Cucumber P r o d u c t i o n / H a r v e s t i n g literature modeling cucumber available we r e to harvesting. In 1976, evaluated interval as fruit et to t wo fruit al., and on t h e r a n d o m n u mb e r weight relations gm f o r respectively. fruit to 1.01, 1.4, 1, a nd 2, This mo d e l planting to stochastic val, and 3. 1.7 d a y s , the harvest. a nd of to grade to mo d e l was Harvest returns weather date of 1, 2, mo d e l and days for a gen­ a a s s ume d by Chen in production to be grades 1979. system simulated as planting the and for size during new 4, fruit wer e date the of and required wa s was of 127.0, 3, planting, conditions wer e number - 61.2, developed cucumber the The another whol e simulating a function 27.2, respectively, extent distribution and o n e . n umb e r o f devel­ and t h e stochastic, numbe r s in op t i mu m h a r v e s t was be for Another size was a large frequency the plant fruit f r om one function and the Yields pickings. zero a s s ume d grade studied regime. consequently between to a mo d e l picking yield cucumber Another acreage, the The a v e r a g e advance pick The mo d e l wer e size of the 1975). arrival developed research Mo s t o f pertained successive predict erated 154.0 al., a function cucumber (Chen, et and in a m u l t i p l e between constructed the stochastic Chen, cucumber y i e l d s little production/harvesting. oped model s multiple contained Models from a inter­ growth season . bers O'Sullivan and at intervals 24-hour Colwell (1980) for harvested periods from pickling six to cucum­ eleven 4° days over date on y i e l d , and t o a three-year average identify o pt i mum yield harvest ( MT / h a ) date and isfactory criterion that between study the value, in t h e to to that average indicating value average indicate yield number ) wer e measures of cate also linear grade crop percent bers did sidered not to date. by w e i g h t follow be and and harvest 1 and also a consistent satisfactory indicators The 5 cucum­ that {% by these be u s e d to indi­ between y i e l d 2 and and of date. 4 cucumbers grade pattern be a s a t ­ grade The r e l a t i o n s h i p of between linear woul d indicating by n u mb e r identify wa s value grade could harvest relationship o p t i mu m ( R2 > . 6 8 ) to ( $ / MT) and g r a d e distribution o p t i mu m h a r v e s t The of distributions, be u s e d field. crop effect and g r a d e could (% by n u mb e r a n d by w e i g h t ) bers and crop criteria ( R2 > . 9 0 ) relationships period we r e 3 cucum­ not con­ o p t i mu m h a r v e s t date. An e c o n o m i c used, in c o n j u n c t i o n exami ne of the pickling grade economi c cucumbers distribution. vesting wa s of wa s harvesting opt i mum. tified for involving with three both delayed and s h o wn t h a t , to timely average earlier criteria or of or for premature measures harvesting. by $1,091 later, c ucumber was to harvest of once-over cucumber size har­ production Profitabil­ a nd $722 f o r respectively, whi ch ma x i mi z e d conventional methodology experiments, several profitability on t h e days Harvest wa s simulation from f i e l d of on y i e l d sensitive reduced data effects It cucumbers, extremely ity/ha mo d e l profit production we r e a nd than iden­ 41 production based on g y n o e c i o u s c h i o r f 1u r e n o l . harvest timing criteria wer e on cultivars and classified ventional between t h e s e occurred as 1981). Sinnot (1945) and fruits sisted of as initial l o we d by o n e o f fruit size each had little chiefly portion of s ma 1 1 - f r u i t e d in the rate results duration was rapidly was and t o relatively than Gr o wt h the patterns developing over a thirty-day Ni ne cultivars s h o we d that a all involving gynoe­ lesser value was of in all size was lower, that growth but was longer as mainly than reflected growth size. rate When g r o w t h was a t t a i n e d the races, duration factors, fol­ final of but con­ rate large-fruited fruit final con­ cases In affected extent for exponential rate fruit and He c o n c l u d e d to cycle the cucumber o v a r i e s Gr o wt h constant seasons, rate Ma x i ­ (Colwell of Environmental mo r e size itself s a me . of cucumbers fruit period we r e v o l u me duration. high, when t h e essentially graphing six 27.3% of systems. whe n 5 4 . 6 % o f decrease. growth effect with o p t i mu m h a r v e s t methods of t i m e . its ones. of the relation by the was phase of gradual in and model ed in The c o m p a r a b l e met hods a function an determined 1. in t h e production chlorflurenol production O'Sullivan, and in p r o d u c t i o n grade and t r e a t m e n t differences profitability/ha occurred mum m a r g i n s cious Substantial cultivars at beginning studied. sections two- wer e or studied by p h o t o ­ three-day intervals with Analysis of t h e fruit pollination of grew the in (day 0). photographs length at a 42 constant growth mo r e rate wa s end of over the fairly growth slightly the the growth percent fruit t wo o t h e r tively The wo r k once-over cessive sons tinuous field (relative size established estimated and also for estimated entering advancing the wa s also the plant from one fruit relation at time size peduncle longest fruit types types growing had pro­ earlier days, a case fields than respec­ by t h e incident and between at the and t h e to and or suc­ 1972 sea­ Con­ radiation, state of wa s n u mb e r with the of fruit mo d e l was The mo d e l between time and a s s ume d weight T. n u mb e r o f another on four. estimator. grade s ome 2 0 0 regression relationship In successive dynamics) linear size grade The count squares point. sampl ed 1971 three obtained. T+1 in wer e in t h e usually proportional every the processors, development least a nd pickling The n u mb e r o f A multiple the at Pickling is pickle temperature, fruit grade. using (1973) 1974). field but slightly ends had t h e fourteen harvest described a fixed weight to the of 1983). harvested wer e to be a d e q u a t e l y in each Patel wa s than stopped versus Mi chi gan of humidity they twelve al., recording relative length. because per types in prior et market change by at b l o s s o m end growth, t wo just samplings the The p a t t e r n there than of mechanically days that section Saltveit, done (Holtman, daily to and with except period. period types, ( We h n e r cooperation at Fresh thirty-day shorter thirty-day center fruit. highest the uniform in t h e mo r e the duced during of count The mo d e l new f r u i t fruit those at time 43 T (Holtman, perature, al., relative considered Their et to be statistically The d a i l y humidity, inputs significance The c l i m a t i c 1974). to and the observations incident fruit radiation development wa s analyzed via influence on f r u i t development verified The f o l l o w i n g (Holtman, relations least et wer e al., of tem­ we r e process. square addition. rate was not 1974). hypothesized and e s t i ­ ma t e d : W. ( T ) =E • N j ( T ) [3.1] Wj ( T + a T) =E • N j ( T + a T) [3.2] Nj ( T + a T) =N j ( T ) [3.3] Xj ( T + a T) =C - N j f T ) + B • U( T ) + S X ( T + a T) [3.4] where : W. ( T ) =w e i g h t of f r u i t per grade size i at time T N.(T) =n u m b e r of f r u i t per grade size i at time T AT Xj ( T + a T) =a d v a n c e m e n t =n u mb e r but U( T ) E in of period fruit in a smaller =c o n s t a n t equal =a d i a g o n a l estimated of size grade grade to at i day a t day T 6 f o r T>0 p e r 100 f t 2 6x6 m a t r i x using 1 day least wh o s e v a l u e s square wer e estimator met hod C = 6x6 m a t r i x entries with 6 hypothesized nonzero T+ a T 44 i Thus, at the time n u mb e r knowledge T can of the new f r u i t constant. gation the rate grades and entering well of as sideration the in size s a me t i m e new g r a d e at time be c o n s i d e r e d however, the plant that the grade i and t h e T+1. The the input as of the dynamics mo d e l behavior is estimate always field day for by t h e i r did under not of a investi­ indicated per be e x p l a i n e d the the data new c u c u m b e r s not mo d e l a t any t i m e explained that In a d d i t i o n , plant at fruit so on. of IB c o u l d distribution. into analysis formation 1A a n d n u mb e r o f weight Patel the = can t h e n be n o t e d , In 1 9 7 3 , as its T+1 ) T+ 2 , should the advancing (at time f or time It of predict fruit new s t a t e state = c o l u mn m a t r i x 1A 1B 2 3A 3B 4 that size initial take into different con­ plant populations . The mo d e l cent maturity percent of estimated and was e s t i m a t e d of size based fruit 2 to on a n , wher e is grade total initial Mj =25%, It in wa s number. , per­ defined as the The mo d e l was maturity range M. Mf = 50%. Performance concerned and 2. and f i n a l criterion opportunities a dummy v a r i a b l e fruit E c o n o mi c often ma xi mum p r o f i t a b i l i t y investment the and 3.9. The f a r m e r size using about the in e v a l u a t i n g comparing least cost or capital choices a mo ng wa y s 45 to use this his capital stage is Several the (Walrath, cost of researchers 1977). the categories: 1975; Hunt, labor has been included as part of the operation cost (Kepner, et al., 1979; Timeliness Yearbook ( 1982) activity at are maximized. due to biological needs one o p e r a t i o n , however, as the in t h e ability a time that the that tions. Some o p e r a t i o n s Others, particularly of timing perishable perform and may h a v e seeding products, to and q u a n t i t y arise to 1982). a machine from any very in machi ne to product opera­ timeliness o f ma ny c r o p s may h a v e yields losses reduction zero and an crop operations untimely near of reduced operations, of machine plants, may be a t t r i b u t e d ASAE, system Engineers quality costs machinery Agricultural and p l a n t i n g improper quality highly into In c o n s i d e r i n g defined of cost (Bowers, improper t i l l a g e with this and t i m e l i n e s s Timeliness associated at labor, is such concern operation. spreading three 1977). special harvesting suggested machine, Of costs. harvest high of timeliness costs . The date relationship varies with f r om one y e a r Eng i n e e r s the to factor crop per operations. ( 1982) (fractional acre-day of A linear an o p t i m u m d a t e is crop operation, another. Yea r b o o k loss between Hu n t crop, reduction for reduction a s s ume d and and estimates in y i e l d s ome the operation and even A g r i c u 1t u r a 1 of timeliness or value of specific crops and in y i e l d (reported the location, (1977) present delay) value of the by H e t z , crop 1982). the after 46 Timeliness cost the loss in t h e the crop has the fact that grades 1981; cost and whi ch the have costs. variable of independent and repair dollar fruits grow into and and (Hunt, are costs variable (Ayers and al., estimated fairly The closely wer e (1970, index of mos t Hi nz, as taxes, of both Hunt, of proposed how w e l l and are serv­ and t i m e . in t h e fixed category fixed and/or researchers 1977; Kepner, operations either operating housing, use these was The 1982). farming being use, Depreciation both cost vari­ use. time use. Handbook, however, 1981) with by s e v e r a l Estimates of machi ne included 1982; selected nor mal (indication due t o and lubrication, variable Engineers 1975, after size costs of with is suggested class, as O'Sullivan, year fuel, calculating classified average to on c a l e n d a r (1974). and investment, in t h e for is cucumber fixed be a f u n c t i o n 1979; This proportionally associated for been Amer i can by Wh i t e Renoll machi ne have requirements operation high. cost higher defined progresses return. into depreciation Me t h o d s Boehlje, 1979; Fuel are been time independent The c o s t s seem t o repair costs are on m a c h i n e r y use. as (Colwell divided dependent has 1980). increase often, 1977). price Colwell, costs costs category or peak and m a i n t e n a n c e mo s t per its a lower are of However , et value costs interest harvesting crop Fixed insurance ice, cucumber O'Sullivan while cucumber reached Machi nery able of wer e requirements l ow, suggested average, to fit conditions. the use adapted of a farm a specific 47 field is for the field capacity He s u g g e s t e d better for for that estimate determining Agriculture (1978), relations hours cost. D e g a r mo , of al. et the recovery and uniform annual costs (1977 ), Wh i t e agricultural quadratic of costs (1977) correlate total in the an of a me t h o d cost of initial this covers expected percent suggested The y d e f i n e d whi ch to introduced than regres­ farm machinery. techniques basing rather cost in in mos t (1981) Wal rat h cost. Procedures available using repair al. in a cost. in p e r c e n t accumulated (1979) of transformation hours conditions. bulletins. cost farm machinery al and machi ne result Hu n t established on a r e p l a c e m e n t a capital equivalent (1982), et can are ( 1982), (1971) Mayfield, estimating depreciation Ha n d b o o k operating to capacity and e x t e n s i o n logarithmic operating for et index operational field Williams calculating and o p e r a t i n g this a machine's machi ne in field use of describing accumulated initial the textbooks The y u s e d the specific of a nd Fairbanks, sion of machi nery) Engineers Aye r s machinery use cost. the cost use as an depreciation and interest. Harsh, operator as operating et al. the time cost and is defined opportunity machinery. be on an h o u r l y labor (1981) or inversely When h i r e d annual directly cost labor of is for to to used, machine machine a farm time On an h o u r l y proportional proportional operator labor basis. cost used the for cost basis, may total operating productivity. 48 When is labor is hired independent S c h wa b of c u s t o m wo r k the district ability. the in an annual s a me rate costs basis are values during categories tractors cost to or with and real of indicated high that at original Hel wers and Wa t t s wer e (1980). The mo d e l (both found replacement. cultural their to (1981) type and the cost A cash retain the and of on t h e Finner the inflation groups use of costs estimate inflation to the of "as is" five combines. rates, a very mo s t substan­ price. used a financial and i n c o me t a x The mo d e l costs pre-1981 tax influence mo d e l all of under on w h i c h Schoney to of modification includes of of avail­ analysis basis three replacement Another per equipment machine high machinery. be t h e productivity. a function rates inflation the cost were the likely labor rates mo d e l and of annual rates a setting a statistical for of dollars). portion I n c o me t a x e s Smi t h expressed farm t r a c t o r s estimates and The m o d i f i c a t i o n consistent incorporate time machinery are determine l aw) to total r e c o mme n d e d combi nes to and and t h e (1981) periods of results mo d e l state (constant developed tial These approach (1981) The Michigan. inflation. interest operating a range Bar t hol omew of basis, suggested in t h e traditional periods machine (1980) for on an a n n u a l was machinery wa s presented included the effects of for by into was t h e n by m a c h i n e law and the determining introduced f l o w mo d e l discounting cost Rotz, annual e mp l o y e d age. 1981 o p t i mu m by Mo o r e analysis et inflation. al. tax and of agri­ (1981). This mo d e l was 49 incorporated 1982; Hetz, Liuer the into cost cucumber and Hu g h e s we r e based harvester, Hass an and referred tractors state inputs tures of soil the important. whi ch as weather is their estimated cucumbers. The and o p e r a t i n g trucks, and hired a labor. with blowing can out of the the their only mechanical to without of weather mechanical on t h e on t h e soil machi ne consequence to whe r e The t r a c t a b i l i t y differ that condition move on t h e soil. place be u s e d t r a f f i c a b i 1ity the However , according pointed fea­ ground are also in d e s i g n and in desired soil the function (Tulu, mos t operation of 1973). unusual field rains, freezing t e mp e r a t u r e s , high snow c a n , in a few instances, mechani s ms and h y d r a u l i c defined the performance winds, control. owni ng a direct takes to material Torrential steering of functions d a ma g e t o machines. tion the whi ch interferes gathering (1975) properties. (1980) and cost pickling farm machinery Equi pment affects Hu n t (Muhtar, (1979) of t r a c t a b i 1i t y ) as field wo r k construction Colwell transportation significant and on t h e perform a given and harvesting Broughton and o t h e r of model s T r a f f i c a b i 1 i t y and A v a i l a b l e F i e l d Work Days' to satisfactorily causing (1972) once-over 3.10. (also f a r m ma na ge me nt 1982). of estimates other and c o n v e y o r s , control, Bu t interfere and c a u s e generally, mos t a loss i mp e d e with of tractors lubrica­ traction a nd crop a nd 50 implements weather on are capable conditions. soils of Instead, and crops that The m o i s t u r e state of important 1971). factor For and with soil that a workable and crop available day inflated on t h e in o r d e r timeliness costs costs al., 1977). days suitable data have and y e a r , parameters days location and each we e k days is permit are and is to a smal l (1) be the et data using needs field the literature observed 1973). wo r k high and t h e (Elliot, Two c a t e g o r i e s data likelihood complement in m a c h i n e r y pres­ integrated al., for mos t Ka mp e n , past, between of the a farmer suitable an a b u n d a n c e work. can (Batterham, weather ( Va n though, anticipate machinery of of operations. operations balance range usually observations properly generated of for weather et concerning wor kday a location and soil 1982). category depends throughout soil type ranked, determined (Williams soil perception, arrived reported: estimates machi ne days field (Hetz, suitable or a wi de effects n u mbe r o f for The f i r s t field overinvestment (2) the management, is and over machinery to of of has is machine weather There been crop conditions For e f f i c i e n t information the precise anticipated it control affecting a mo r e ent, operating to the crop (Fulton, and under the on a c t u a l et season al., different 1978). for of probability to of a specific 1976). mi n i mu m n u m b e r b e ma d e a c c o r d i n g Edwards, observation The d a t a for suitable levels an a c c e p t a b l e to risk 51 The second assuming various generally s o me moisture tions weather consider initial applied category are date. for each the soil days is as answers from r e p e a t e d are site- 3.10.1. of and (1966) precipitation is a device the itation Wi s e r its or for of process, suggested out come reported frequency studying three the short the machine field work. lead working on t h e of condi­ season state is of are The v a r y i n g to probabilistic Mos t simulations 1 980 ) . Frequency use of analysis. process. probability of Mo n t e Carlo A Mo n t e C a r l o stochastic The m e t h o d wa s urn Bernoulli, analysis model s Polya, the independent period the in a nd We a t h e r of at change operations days. (Hunt, the calculated for application consists to whe n crops derived a passing an a r t i f i c i a l namel y: of are model s and procedure. Precipitation mathematical study applicable of These soils factors simulations s o i 1- s p e c i f i c in physical for of observations statements as days n u mb e r o f Analysis Wi s e r for suitable the day favorable counted statements content accounting The n u m b e r o f or inputs. soil from we at he r simulated. crop and Mathematical a moisture entered simulated moisture resulting to utilizes of a nd of me t h o d mo d e l of a applied the precip­ Ma r k o v Ma r k o v c h a i n , probability met hods chain. since events precipitation fre­ quency. Feyerherm, mo d e l for et al. establishing (1966) utilized probabilities of the Ma r k o v c h a i n sequences of we t a nd 52 dry days a ny specified ability in Mi c h i g a n . was sequence the we r e Thi s probability used author to to concluded the and it a wi de over for of and d r y dry and Several days types exhibited for six of model ed Mi chi gan solving bility the of the dry et or we t good of mo d e l mi ght recorded equation are using with the a pro­ of single observed S t r o mme n of precipitation an incomplete and c h a r t s days. apply available. and p r e d i c t i n g and d r y a and t h e y data. distribution tables to of probabi1i t i e s computed, correlations be u s e d regime frequencies dry days we r e The suggested required observed (1964) sequences. (1977) divisions we t transition probability al. types He p r e s e n t e d of the f r o m ma ny s t a t i o n s frequency a sequence transition and c u m u l a t i v e from t h e quadratic in sequence, n consecutive climatic we r e of probabilities derived gamma e q u a t i o n . in and the in t h e by We i s s when o n l y significantly probabilities (1974) days days day. or drought all remaining involved prob­ and t r a n s i t i o n how many d a y s Ma r k o v c h a i n Ami r , initial presented results area. the an for was for rainfall probability day, sequence, given the determining we t with day length that first no m a t t e r mo n o g r a p h from t he cedure we t the distribution station and d r y the a given probability, indicate with true for A convenient probability days, we t For a s p e c i f i e d previous relating of associated held sequence. from t h e the associated probabilities sequence. In c a l c u l a t i n g that helped the proba­ 53 Swaney, et al. oped a mo d e l soybean data based of on d a i l y utilized in profit. The u s e profit Soil In 1965, soil profile. c omput ed antecedent estimating potential only inflow surface occurrence that was soil occurred, the of of current index Saxton, et t r a n s p i r a t i on soil-plant al. is in process. soil using actual surface. of of subtracted Surface of a the water from p r e ­ runoff was a mo d e l Ligon, a growth moisture daily developed et al. (1965) chamber deficiency for and e x c e s s . evapotranspiration reduced by f a c t o r s whe r e for measurable evapotranspiration by was was precipi­ precipi­ taken as value. (1974) nearly in On d a y s content p r e c i p i t a t i o n - and t h e approach, estimated potential wa s infiltration. dryness. the the runoff e v a p o t r a n s p i r a t ion tation the and o u t e v a p o t r a n s p i r a t ion and one-half into In a s i m i l a r tation decision-making water precipitation The y c o n c l u d e d resulted mo v e me n t o f a function investigated data gathered moisture determine Sha w ( 1 9 6 3 ) . rainfall daily Estimated as of we r e t h e n expected the Precipitation to These d a t a historical the weather Budget Sha w m o d e l e d by e s t i m a t i n g ( 1 98 2 ) d e v e l ­ b a s e d on t h e counts during Moisture soil cipitation of al. incorporated forecasts. frequency period et The y decisions prediction 3.10.2. modeled. weather of Mi s hoe , growth. irrigation from a t w e n t y - y e a r lower ( 1982 ) a n d indicated equivalent If water to is that potential available readily evapo­ energy available, at 54 mos t of plant this energy surfaces, is and water a mong energy Actual Thus, division and t h e Baier the basis mo r e use and from s t a n d a r d of basic piration ration ( AE) and such for runoff, drainage, on t h e AE: PE r a t i o Rutledge and moisture zones by and Baier content in strength Alberta woul d below f i e l d used of soils total also on a z o n e - b y - z o n e This m e t h o d wa s budgets potential actual evapotrans­ evapotranspi­ available types soil (1966) soil of to shear soil-drying atmospheric divided the d e ma n d soil moisture budget estimate the strength and c o n c l u d e d The y mois­ Adjust­ rates at soil obtained that into moisture Th e y required for required shear moisture a good six developed soil f r o m c 1 i m a t o 1o g i c a 1 r e c o r d s . soil a n d ma de incorporated. (1968) the energy upon. capacities. different different be d e v e l o p e d capacity. the different Ma c Ha r d y zone values of wer e Robertson in each calculated and of acted the a new t e c h n i q u e data. ma xi mum o f subdividing sinks. from p o t e n t i a l is moisture taking divided circumstances. of e s t i m a t i n g that or attained. is energy meteorological as a possible effect these soil is energy on t h e presented soil previous zones and t h e water (1965) several curves, of meteorological than as of daily concepts, ( P E) one a matter into me n t s age of this evapotranspiration location from t h e evapotranspiration depending is b e c o me s estimation evaporation available, sinks, Robertson versatile ture not actual e v a p o t r a n s p i r a t ion for is evapotranspiration estimating for potential When a d e q u a t e several used contents also t i l l ­ at correlation or with 55 observed of days available moisture that to of sinkage. Rutledge one and having content data. to it AMj and Tulu, developed by considered and for every previous of and t h e of soil, wo r k o f water the concept Ma c Ha r d y t o working days design from w e a t h e r effect to as profile. applied combi ned day and m o i s t u r e available and moisture the coefficient the soil wo r k resistance a suitable (1974) Rutledge the occur the al. soil angle defined cm o f indicated rolling refined percent et the precipitation available a s s ume d t o < C. mm o f fifteen evaporation, wa s (1971) ninety-five top a nd percent ma xi mum s o i l Th e y than deformation, and the zones. thrust, (1968) ninety-five as frictional al. 2.5 determine The mo d e l tation, and (1973) tractability a mo d e l than than in t h e Tul u et Ma c Ha r d y less less capacity of Mo r e y , used three soil soil whe n convenient internal of was top mo r e sinkage, modul us tillage capacity was cohesion, cohesion for in t h e mo d e l related soil water content their whi c h suitable of define precipi­ a wo r k d a y , if: i = 1, 2, 3 soil layers [3.5] where : AMj = fractional (the mo d e l available divided from s u r f a c e , Cj Values = available for AM^ we r e the water moisture the next soil 1.8", capacity calculated by: of of ith surface and ith soil into the layer next layer 1.2" 3.0") 56 AM _ actual 1 and available moisture ma xi mum a v a i l a b l e values for Cj wer e of moisture a s s ume d of ith layer ith |-3 6 -j layer as C1 = C2 = 0 . 9 5 '0.98 for fall and spring tillage and planting C3 = < o p e r a t i o n s .1.0 The t o t a l agreed for corn combining n u mb e r o f wo r k days well comparison with the s h o we d observed that mo r e were m i s s e d . The a u t h o r s fact that the mo d e l they wer e reported Baier (1973) snow c o v e r the upper This the in three notation first the plastic third £95% o f with use of the (1970) observation capacity limit. s o me wh a t other and and as by t h e wetter to the wo r k d a y s while full days. wo r k as a day the or with conditions notation This machinery and r e c o mme nd e d hand, as days was d u e conditions long the no in SM 9 0 / 9 5 . ( £ 9 0 %) in may p r e v a i l average of situation shallow soil cm d e p t h . Selirio an of conditions capacity. lighter 0-5.1 as mo d e l a day-by-day this moisture surface as but wo r k d a y soil zones field that record a field estimated but and the On t h e farm by t h e percent partial dry allow field give to woul d Frisby believed referred is tion. defined ten defined zones cultivation than not the wo r k d a y s , as these cent in determined zones, zone, second similar did and wi t h as index Li nk of Br own the (1972) use possible (1968) of reported ninety soil utilized per­ cultiva­ the soil 57 Jensen, mo d e l that cients plant as (1971) to and al. a linear percent of included heat. cover, type of relative heat energy decrease in t h e was n u m b e r o f wo r k d a y s and not and Baier drainage total (1979) but wer e divided into ture exchange incorporated between into a runoff variable tration, soil itation their model . while or that it Relative humidity, and sunshine on o r percent influenced the moisture Rosenberg, al . ( 1974 ) . probabilities et al. The mo d e l under of (1982) was and the of to drainage Dy e r in The regard zones and t h e mois­ also (1980) it used to detention, soil to soil was related whe n extended mo d e l field. increased in u p p e r indi­ cover Johnson surface by surface the infil­ a nd whe n precip­ infiltration temperature, used a variety good zones, Th e y runoff about loss in t h e approach six, decreased increased. mo d e l by d i f f u s i o n Na t h storage The y f o u n d increased, zones utilized also differences. than model . surface interception. rather these the in type con­ The mo d e l percent capacity and wo r k d a y s with a comparable crop two, and plant, favorable a s s ume d on a s o i l drainage ignoring was type was from t h e i r n u mb e r o f It soil followed variable increased. factor. upon t h e on t h e the results that dependent crop The a drainage was a the included the in The c o e f f i ­ A similar cover coefficients depletion. (1977). surface crop moisture represented latent et soil a function canopy Elliot, al. estimated wer e verted cated et wind, r a d i a t i o n , surface soil layers. the wo r k o f determine conditions. directly Tulu, suitable et day The c o n d i t i o n s 58 included six field operation Michigan on f o u r included nonharvest types we 1 1 - d r a i n e d , a n d wer e wheat, alfalfa. clay l oa m, Soil also sandy type field to for a given days fifteen soil, if incorporated (1982) and The into et tension and through to categories Three navy types harvest bean, soybean selected we r e and clay, found As t h e soil to influence type changed suitable drainage on c l a y soil a condition days had existed. the by suitable less i mpact The mo d e l f a r m ma na g e me nt mo d e l s days from c l a y increased reduced but the on a was by H e t z (1982). (1982) established hydraulic could mo d e l and tion rate tive stages tion schedule water in be u s e d the relationships conductivity to quantify mo v e me n t with plant of wa t e r and soil of soil water. available solutes in and soils. L o o mi s between soil we r e Poor other al. a nd relations water such Mu h t a r Rawls, water percent operation soils. corn, location, percent. by t w e l v e sandy work. locations sandy. and d r a i n a g e for about and five on we 1 1 - d r a i n e d , s o me w h a t included: l oa m, at Field drained The f o u r suitable sandy soils. operations poorly operations and of categories of Crandall cucumbers of growth. for irrigations. upper The during and the cucumbers cm o f ( Kc ) of the water vegetative sixty-four ninety ratio studied Th e y c o n c l u d e d pickling forty-eight in t h e (1977) the that and the involved percent soil reproduc­ best r e mova l of the profile consumptive consump­ water irriga­ of available between use to 59 evaporation pan loss reached early harvest season. fifty percent of upper thirty next thirty Since the be cm o f c m, very In t h i s total the and little effective ninety the study, a mo u n t soil ten of profile, percent water rooting c m. a ma xi mum o f cucumbers water from t h e A similar for extracted from t h e percent from t h e next thirty c m. from below n i n e t y cucumbers observation the c ons ume d thirty was e x t r a c t e d depth 1 .5 d u r i n g was cm, considered was ma d e to by V i t o s h (1977). 3.11. 3. 11. 1. Sys t ems The Syst ems Appr oach growing ment. This component s has industry size, of in stimulate processes, and stages. that interact tions." process The to perform systems of of its a given concept its parts of was an e v i d e n t defined objects, vi ews any need by M a n e t s c h function or compl ex e n t i t y whole to a nd components, func­ or interrelationships, a unified of operations, called objective and application individual functions, in industry's The as continu­ advance­ interdependence, c o me s many and and t e c h n i c a l processes. A system "a c o l l e c t i o n in t e r m s integration 1959) . as the dynami c a mong t h e agriculture and (1977) very specialization, coordinate Park trends alternative research is complexity, created towards availability systems in Engineering Met hodol ogy The a g r i c u l t u r a l ously in A g r i c u l t u r e (Basselman, a nd 60 Many d e f i n i t i o n s sented was in t h e given with cal the synthesis optimized with with encompassing engineering and of produce farm. that to the of processes its Spedding those systems embracing very The or systems system at tively a nd any to guide to that field that of they the academic analysis p h e n o me n o n or a f t e r harvest purpose from s i n g l e - c e l l processes. as and also are l e a s t one component s c o u l d area of organisms and on t h e systems contain at to a particular biological agricultural the necessary t h a t isperformed the " Sys t e ms systems." are on agricultural agricultural be v e r y the to simply eco­ living large, world, or whol e crops. approach particular that in t h e modification a considerable herds, operations are s ys t e m as a biological sense ranging production as a an man/machine a process referred have the crops The s i z e small, flocks, as and are physi­ A mo r e or growth of intellectual whi ch concerned whi ch crop environment in t h e component. to during (1975) whi ch field is performance control, pre­ definition (1976): complex, and been by Wymor e of a crop limited criteria." given concerns defined He r e f e r r e d or feedback was have engineering the accepted scale, a particular occurs to of professional, large an o p e r a t i o n crop without primary (1965) collection or but " Sys t e ms analysis respect is the Li nk and engineering A simple (1965): definition discipline design systems literature. by W i l s o n systems, of changes assumes time in t h e can that the state be e x p r e s s e d system can of an e c o ­ quantita­ be d e s c r i b e d in 61 mathematical formance is terms goal whi c h measured. goal, and and (Ebersohn, Thi s as goals The s y s t e m s feasibility are do n o t evaluation, is the ma t c h consists abstract evaluation is being sis, and of both phase range of of all input phases: and a set capable is comprised of Park, of of program needs problem f o r m u l a t i o n , viable satisfy­ whom a d e v e l o p m e n t parameters. and Controlled undesired and Bailey referred to (1963) synthesis. It involved selecting in the that in t h i s was used desired analy­ and g e n ­ solutions systems the part is referred should 1974). (Faidley the to the the and identified for Alexander and as inputs systems inputs from t h e of be and c o n ­ well identification of systems part as and e n v i r o n m e n t a l E s ma y , as recognition variables outputs results analysis case, includes and o u t p u t (Faidley sis, generates alternative identification consideration stated desired 1974). desired provide for identification, Syst ems sideration people the implementation ( Manet s ch (system concepts) This a broad Es may, system the designed. systems eration of five modeling, The f e a s i b i l i t y needs desired major 1979). the if the of and o p e r a t i o n ing with system a per­ performance 1979). implementation, solutions its (Dixon, design, alternative syst em has c ompar ed ma de t o approach Ea c h system o p e r a t e s measurement adjustments performance the 1976). and as systems approach outputs whi c h system. Di x o n (1979) modeling phase. manipulation of Analy­ a mo d e l 62 of a system t h a t ever, mus t is ma ke e s t i m a t e s In a n a l y z i n g can be used. ations oped in e x i s t e n c e . is approaches components with the real prerequisite knowledge of able, mu s t (Fridley the and abstraction of central framework to study study the modeling oping real second already is of system 1968). not to 1983), a considerable often Wh i l e any impossible a mo d e l solution can (Rountree, be used 1977). design system that does system before it not is approach me a n s to exist. may n o t (Faidley develop and The o b j e c t i v e established. have Thi s is allows be in d e v e l ­ and t h e systems been E s ma y , a mo d e l mu s t is existing may o r to Abstract The f i r s t controlling on t h e impractical and Syst ems the or in a systems " an the extent programs, using avail­ is so o b t a i n e d applications. whi ch is represent new d e v e l o p m e n t in o p e r a t i o n A on a s u b s y s t e m implementing analyzing system i ma g e o r m o d e l , (Swartzman, to reality t wo m a j o r of representation. is system. in attempts situ­ devel­ mo d e l is research of is al., knowledge A spatial relies it et a simulation undertake system" a mo d e l correspondence (Naylor, that whi ch problem, proved has and that because a systems ultimately oped of research model s If techniques a variety whereby ma k i n g 1974). a system points of of frequently salient use world inputs. Holtman, Systems procedure to how­ inputs. simulation applicable a one-to-one necessary one various One t e c h n i q u e analyst, n o n c o n t r o 1 1 a b 1e a system, a simulation whi ch of A system of devel­ 1974). a needed to the evaluate system a 63 designers inputs of to test the associated inputs design is the to (Dixon, Preparing systems with similar inputs theory a s y s t e m mo d e l suitable demonstrating is and subjecting nature of the system. Since reality, they abstraction is onto one there and is that model s Dalton (1982) analogue, serve the their scale. to inflexible be Th e y are reality. objects s a me also can include is 1982). to modeling physical to explore of and degrees correspondence of detailed of abstrac­ are elements of model s of ma ny e l e m e n t s real degree different mo s t whi c h from t h e purpose their At t h e as the representations their of form simulation, be o b t a i n e d to part s y s t e m may b e h a v e . inputs level, model. or called An a r r a y according technical ma p p e d level, in reality model. categorized and The y a r e as expensive real but are are not mo r e ma de t o types: usually system except physical very three model s models. experimentation model s are into Iconic the usually so t h a t mo d e l model s symbolic. appearance Anal ogous in t h e but defined way t h e simplified general in t h e evaluate The e v a l u a t i o n mos t (1968) might according of the the various are a one-to-one elements iconic, results ranked part the modeling to mo s t is a mathematical a mo d e l (Dalton, At t h e in of vary available, tion. use analysis Chestnut a system result and t o system d e s i g n . systems representing The system 1979). engineering. for of t he good is for The y t e n d time-consuming. representations abstract, look pre­ like since the the objects of 64 being represented. depends well the on how w e l l the behavior analogue. mo d e l are and but and of mination can the of be u s e d manageable Mo d e l s mo d e l in acts This mod e l s forces to is that of general, ma na ge me nt of a n d how in a symbol i c type its can as research be dr a wn to be sys­ problems beyond the the identifica­ r e 1a t i o n s h i p s a nd the provides The that decision and d e s i g n of of or requires s ome o b j e c t i v e decision rules. function F o r mo s t a comprehen­ of the individual the the construction system to fashion in an a t t e m p t derivation a decision may be c o n c e r n e d (synthesis). rela­ 1971). p r o b l e m may be t h e assist that an deter­ these and o p e r a t i o n reducing a normative will for circumstances, (Wright, in f or ms capabilities a me a n s proportions rules structure In t h e s e descrip­ for may be s o c o m p l e x the between descriptive Many s y s t e m s of a n u mb e r of for a framework and in When u s e d functional problems. an o p t i m a l systems satisfactory used control interactions applications. are decision corresponds definition distinction worker. a mo d e l solve in t h e syst em components s y s t e m may be of and analogous be u n d e r s t o o d system by s y m b o l s . rigorous understanding research real construction normative tionships. sive its a basic purposes, tion the can of 1982 ) . Mo d e l s tive of analogy The o b j e c t s and ( Da 1 t o n , ways, the represented flexible, tematic The e f f e c t i v e n e s s maker with of in ma k i n g both system A normative mo d e l thus to different evaluate decision-making problems, the to 65 objective function Descriptive problems, tion. will model s so t h e y are do Simulation, be c o n c e r n e d not not usually however, decision-making problems. behavior system yet, to of the by e x p e r i m e n t i n g problems and can Blackie, uses under with et and al. (Wright, Hetz, (1968) model , defined setting tem), and then performing experiments indicated that simulation is Although tation to attempt the the to lack the of model s sort of data of can is to as whereas i mpr ove the a mo d e l The t e r m model s to in for verified "validation" used the (Gordon, purpose is systems system design assumptions; of approximate 1971, the solutions reported a real in De n t as a technique situation on t h e model " (sys­ The y a two-phase is the opera­ whi ch in be a m a j o r models, that it role is mo d e l verification 1969). thus was the or limi­ me r e in t e r m s lacking in A mo d e l relation relation to validation is validated in constructed, absolute truth. particularly relevant for wher e t h e control. of (Suttor, to synthesis and/or to a useful information reality relation of play evaluating simulation study set satisfactory of of to describes may p r o v e The p r o c e s s stage func­ merely essentially 1967). referred solving and e x p e r i m e n t a t i o n . development develop highlighting up a mo d e l modeling wi t h mode l s simulation involves involving utility. 1982). "that tion or an o b j e c t i v e descriptive a given the profits concerned include The mo d e l be o b t a i n e d 1979, Naylor, primarily with objectives These are objectives to can 66 often of be a c h i e v e d reality mi n e (Wright, this stage whi ch parameters influence analysis, to eters inputs and followed the that performance, determine if which there render of the controllable constraints gross and c o n t r o l l a b l e are the simplifications placed best analysis, inputs and combinations Optimization whi ch upon t h e of satisfy system deter­ signifi­ of param­ unstable. combinations inputs mos t to a stability system by s y s t e m o p t i m i z a t i o n . and are come a s e n s i t i v i t y system specification eters model s 1971). After cantly the with This results system the (Faidley in param­ needs and is given Es ma y , 1 97 4 ) . The f i n a l mentation three design, phases whe n a new s y s t e m existing system be upon t h e required changes 3.11.2. in t h e phases to to are control achieve economi c in n a t u r e . referred to as of Systems being the changes (Faidley always When an implementation the be and design required. mo d e l adequately indicates respond E s ma y , to This will the 1974). Research characterized biological s ome g o a l For developed. may o r may n o t system to systems attempting is system approach— imple­ and o p e r a t i o n — a r e modeled, environment Fa r mi ng environment being extent in t h e Fa r mi ng is is implementation depends the implementation, required and of this bioeconomic systems whi ch reason, systems by t h e is in fact that man an u n c e r t a i n predominantly they (Wright, are frequently 1971). In 67 manipulating or offset ture this its perceived and f u n c t i o n diversion of a mo u n t of inputs arises from t h e family with qualities ma nner of energy the decisions land, given maxi mi ze the by a l t e r i n g system over come the struc­ through of 1977). A specific f ar mi ng by a s m a l l different capital, knowledge household of the is family and system farming quantities Thi s attainment rate or and ma n a g e me n t the the the farmer enterprises. the to regulation taken labor, tries and t h e allocating and o f f - f a r m which, farmer agricultural (Pigram, to the limitations flows respect of livestock, will environment, to a nd crop, performed in a possesses, goal(s) ( No r ma n , 19 7 8 ) . Fa r mi ng approach the to family that of smal l and to are and is provide ( Es ma y, attitudinal farmers in research a voice and ( Nor ma n , 1978). economi c performance m e n t may o c c u r injection t e ms of ( Me n z , It via interdisciplinary the farm family starts with the whi ch research programs. a mo r e to a whol e prescription of It barriers research of an institutional, attempts elements 1980). as is towards 1982). shaping advocated ( FSR) focused substantial disciplinary research research development farmer there mational, systems priorities Fa r mi ng immediate farm of to the system. whole new t e c h n o l o g y infor­ the voice in c ommodi t y systems and assistance i mpr ove premise political, inhibit systematic smal l technical Su c h way farmers and improve­ new s y s t e m s into and existing or via sys­ 68 The o b j e c t i v e of i mprovement of approach farming years: to first straints existing the this, systems farming systems farming involved Following farming attempts system and focusing Knipscheer, the are technologies smal l well for farmers, being of There are the than t wo t y p e s and d o w n s t r e a m research from e x p e r i m e n t tions the in major a relatively research farmers and FSR, but area. to only systems region or area. design after stations, and i mprovements the research of ( FSR) : solu­ i mpr ovement farming information that the FSR u s e s approach whereby systems programs farming system have should (1982) be h o l i s t i c stated in its that the farming approach, but system not in systems, been analyzed . Es may in from u p s t r e a m research in p a r t i c u l a r constraints of t e a m wo r k t o g e t h e r i mpr ove c ommodi t y of Downst r eam f a r m i n g research FSR u s e s through prototype research and problems Upst ream find con­ appropriate on a g r i c u l t u r a l level modify, Downst r eam 1978). to the i mprovement farming stations recent productivity 1982). ( No r ma n , a farm design, experiment order is the and these of ( Es ma y , a multidisciplinary diagnose, a local of of in farming achieved general constraints large systems to the farm f a m i l i e s is the identified. develop i mprovement enhancement rather are overcoming This on t h e upstream to for thus evolved described, ma d e t o 1981). attention is is A standard has objectives m e t h o d s / t e c h n o 1o g i e s / s y s t e m s ( Ma i z systems. research in me et i n g research research necessarily 69 designed to approach might adaptation holistic change of entire cannot a particular systems be of are areas without ditions. Gi ve n a with research this problem. environments guiding result obscure the the development leaving the fine individuals in lationships exist scheer, 1982). the location is over of to s o ma ny The such specific local needs a whi ch but are me t hod to and con­ for coping as for to emphasizes or systems, institutions ( Me nz of do n o t Important t wo m e t h o d s geo­ relevant whi ch technologies, systems is, parameters "locations" second that farming met hods identify locations. the a nd with large availability, systems "preliminary" between to new s y s t e m s of but specific; cost-effective of An FSR development technology resource existing tuning the satisfactorily priorities. of of modification definition research study usually find formulation system. farm system. One m e t h o d and o f in t h e the limited mus t to type implemented graphical systems farming be p a r t i t i o n e d understanding Fa r mi ng they the or interre­ and Kni p- CHAPTER 4 MODEL DEVELOPMENT 4.1. At t h e of pickling (a) plant, ure 5 s h o ws time harvest, cucumbers (b) rectangular of soil stands system (inside the box). The c i r c l e s bo x are the between of c omponent arrows are the The p l a n t fruit per changes (label tion and grade as 2) the on t h e effect is and its line into that and Fig­ (outside the out system from t he direction numbe r s The separates respectively. the system system. represent and o u t p u t s , The harvester. environment arrows other. harvesting of t h i s box describe Definition subsystems: (c) boundary this The The of above effect these labels. subsystem and t h e three from within circles per of the box) inputs determines day. a function potential plant for the system once-over representation (subsystems). arrows one consists a schematic b ox the and w e a t h e r , the components System The of time plant population. fruit and production a function Since represented by t h e n u mb e r T (sampling date), then of the the weight and population is dynami cs influenced potential. of plant an initial by w e a t h e r This management , system produc­ cultivar, stimulus fruit in a sampling effect of weather 70 n u mb e r o f and area is on d a y plant INPUT FARM SIZE WEATHER RELATED INFORMATION NUMBER &TYPE OF HARVESTER SYSTEM BOUNDARY OUTPUT PLANT TOTAL #/DAY HAR VESTER TOTAL WT/DAY TOTAL $/DAY PRICE STRUCTURE SAMPLING DATA PLANT POPULATION Figure 5. A Sc he mat i c EATHE -► MACHINE COST NET HARVEST RETURN Layout of t h e O n c e - O v e r Cu c u mb e r H a r v e s t S y s t e m. 72 production potential population dynami cs encountered the total within system can the initial yield a nd t h e is latter day of the harvesting weight per day. The n u mb e r of vesting capacity capacity per grade plant fruits control on f r u i t inputs stimulus. Howe ve r , influenced by p l a n t of the operation. machinery The the mo r e equipment are ever, that stand for involved vesting a nd percent in t h e size governs the term determines of in t h e equipment, the starting size regulate grade the type This per total decides farm har­ the of the different of the field of the mo r e of the is the and/or should used in mechanical operation. transport, the and Thi s labor type a nd harvesting cost of trips The h i g h e r bulky It the the a nd potential transport equipment "harvester" of affects n u mb e r 4). with cost grade, (Label production along the produce the state trucks. harvest size recovery or components the farm defines produce) transportation the per wa g o n s needed the their potential. v o l u me type, 1). by c o n t r o l l i n g larger the while v o l u me o f fruit-carrying and returns day, harvest harvesters and t h e i r harvesters (label production (especially nu mb e r per harvest The d a y o f thus it) be e s t a b l i s h e d operation, n u mb e r a n d of directly harvesters total the as system should the the affecting inputs . The n u mb e r o f size factors be c o n c e i v e d the potential population, (and the yield n u mb e r is trips by and per be n o t e d , h o w­ this to case harvesting includes the (operators). system har­ 73 soil We a t h e r (as characterized provide the workable field. Since function of conditions achieved machine the day not cost ownership (label 3). harvest early the maturity to time late constraint levels) given (time variables solution due to time at syst em can of the the discrete to The linear, as at order the and t h a t time vi ewed discrete that intervals. can time and t i m e also is the harvesting yield) this are of influences along a can be the with harvesting the workable yield is harvesting determined influences decide the process by w e a t h e r farmer's harvest field weather as at a day different forecast. a discrete in t i m e ) time system with a general difference equation. has in t h e sampling DT a n d is period equal as to being is not an at is discrete out come on a s t i m u l u s (time This an i n h e r e n t l y interest The is operation the to the points T+DT d e p e n d s and h a r v e s t e r s — and t h u s be d e s c r i b e d invariant data) Description system the interval) system (response be the attain System first fact behavior referred are thus also of might a certain 4.2. This weather (and whether If n u mb e r A farmer (total considered complete In a d d i t i o n , a day is for availability We a t h e r latter then decision. or 2). situation. cost— required a certain the day d e t e r m i n e s whe n t h e within dynamics harvest, (label custom o p e r a t e d , their of geoclimatic conditions/days production on t h a t or harvester the by t h e is only (also one day. d yna mi c time T) , independent 74 variable control crete in t h e indicator signal output behavior in unit relations the step can equation). system T i me , process function however, operation. illustrating be s u m m a r i z e d is in t h e the a A dis­ input- following equation : r ( T ) = f {* • S( T - 1), T1 [4.1] wher e : r(T) = s y st e m ' s response at time T n = 2 ®. = s e t o f b e h a v i o r c o e f f i c i e n t s i =1 1 system process o p e r a t i o n S ( T- 1 ) = the system's stimulus at in t h e time T-1 (first system can also order del a y ) T = time According regarded time), to as the above being causal equation, l u mp e d (response and d e t e r m i n i s t i c (one at (each the independent T depends stimulus on be variable at a a stimulus at T^T), determines a unique r e s p o n s e ). The n a t u r e of fruits) one, whe r e involved of d e ma n d s mo r e the that than ( Cadzow, tem is one 1973; 4.3. A possible cucumber this fruit system stimulus Ma ne t s c h Mode l mo d e l schematically distribution be r e g a r d e d and/or and described by response Park, and cucumber Figures a vector are 1979). Hypothesis h y p o t h e s i s of t h e as (grades 6 and Description harvest 7. The sys­ MATURITY FACTOR P L A N T PO P U L A T IO N HARVESTING SYSTEM TYPE POPULATION N1A(T i 1) P R IC E STRUCTURE P1A N1A(T) FI A R1A C IB FI li 1) R D 1 B (T t 1) R W 1 B ( T •- 1) R N 1 B (T i 1) R IB M1B P2 N2 (T f 1) 1 R N 1 A ( T . 1) Pi B N1B(T) RD 1A(T R W 1 A { T r I) C IA C2 R D 2 (T t 1) R W 2 ( T r 1) N2(T) F2 R2 R N 2 ( T t 1) P3A N3A(T t 1) R D 3 A ( T - 1) R W 3 A (T r 1) C3A N3A(T) F3A R3A P3B M23A C3B N3B (T < 1) N3B (T) R N 3 B (T 1 1) R3B P4 C4 R D 4 (T t 1) R W 4 (T r 1) N 4 (T r 1) N4(T) F4 R4 R N 4 (T + 1) M4 MODEL PARTITION Figure 6. A Sc he mat i c Mo d e l — P a r t R e p r e s e n t a t i o n o f t h e Onc e - Ove r I: F r u i t Rec ove r y Re s p o n s e . Cu c u mb e r H a r v e s t i n g Sys t em TR A N SPO R TA TIO N COST HARVEST SYSTEM TYPE H O IA IT < RW1AI EH— H 3 — ^ §3 HDIb R W lb TOTAL M A C H IN E COST (T . 1) EH—- Q — • 0 R D3A ( RW3AI R1—- 0 — 0 NET HARVEST RETURNS (T i 1) RD3B ( RW3B ( R]—- 0 ——0 8W 4 Eil— Llj—■^3 M O D E L P A R TITIO N Figure 7. A Sc he ma t i c Mo d e l — P a r t R e p r e s e n t a t i o n o f t h e O n c e - O v e r Cu c u mb e r H a r v e s t i n g S y s t e m II: T o t a l Ma c h i n e Co s t a nd Net H a r v e s t R e t u r n s R e s p o n s e . inside with of its the rectangular endogenous genous components) tangle partitions 7, respectively, tration. This and line vi ewed and components. are at located the end resemble partition outcome, box d e l i n e a t e s both as one a multiplication fraction) of the inside value ation (either pooling square into or quantity it. it. triangle typifies from t he model , Quantities box to resemble entries variables the and/or Patel (1973). quately of is the left mo d e l and The mo d e l behavior, depending of right or the of for a and oper­ a an o p e r a t i o n . of into the the or A out arrow. rectangle respectively. box s t a n d or quantities exit, direction sides box square from e i t h e r after entry the a s umma t i on all exiting value this for of input-output, or Other exogenous control the dyna mi c indicators. initiator, based on Thi s whi c h a previous theory describes theory defined the plant established behavior to by a n u m b e r - n u m b e r , n u m b e r - w e i g h t fruit text). The t h e o r y factors as plant towards stands on t h e below t h e described set partition by a w h o l e net and the mo d e l (either positive) the at final 6 and illus­ process an e n d o g e n o u s the above are the (exo­ rec­ Figures inside heading or of squares The q u a n t i t i e s a circle affect mo d e l The The A circle negative box. output-input be j o i n e d block. system's environment this beginning not can represent the outside temporary does figures The mo d e l and a the size grades excluded population, the (see Section effects weather, of 3.8 such cultivar, of by be a d e ­ relation this exogenous a nd plant 78 management . plant population dyna mi c factor, and t h e case simulated at to certain addition, the cost pling fruit for size to is the stage of (maturity An a c t u a l dicted done of other from t h e a function from t h e recovery fruit n u mb e r for mo d e l growth a control maturity fruit also is skipping exit used during there as fruit in the assumes a passage grade 1B. an o v e r ­ the total returns. of the 9.3 This grade. the 2, of from t h e growth in d e t e r m i n i n g T. as The mo d e l grade of population n u mb e r m2 , g i v e n predicted fruit rate (I(T), fruit estimation The rate and t h e plant of 0S(T )). and the the is sam­ carried fruit passage fruit Ni Mi f r o m entry This, fruit n u mb e r a nd e x i t in t u r n , maturity factor). (recovered) potential by m u l t i p l y i n g fruit fruit harvest time fruit a nd o u t maturity area effect a dummy v a r i a b l e by e s t i m a t i n g at one and o u t allows a sampling fruit is of 1A t o net starts into fruit the population harvest. essential a function into of grade Ni ( T+1) i s grade fruit mo d e l n u mb e r every stage of the coefficients, rate factor the and t h e T+1 f o r plant behavior stages grade— a step time vi ews the for size The mo d e l out It includes description entry-exit period In at the define s ome f r u i t machi ne however, The m a t u r i t y of size in affecting subsystem. that model, behavior. indicator this This per fruit fruit the fruit grade Ri RNi(T+1). to n u mb e r n u mb e r n u mb e r at of the grade time Ni ( T+1) attain The p e r c e n t per T+ 1 . by t h e actual fruit is- p r e ­ Thi s is percent recovered recovery per 79 grade is mented. a characteristic of These perform generally harvesters distinct percent A conversion of the tor recovered is also lation. its input The d o l l a r the and is to the weight FS. T+1) are basis to harvested spent on t i m e fruit of of storage component as is the to a result, the price and dollar fac­ plant is of weight Thi s the popu­ attained every price the (both and Pi is grade a mo d e l structure capacity and reduces is As t h e will p a y mo r e is, the day on t h e as vol ume, to machi ne and d o l l a r return $) time m2 T+1). harvest The hours in hec­ a function of the eventually faster. the to the the harvester Thi s unloading field from a 9.3 capacity increases, the factor proportional and day recovered (at field per by t h e total in t u r n , be f i l l e d trips per and inversely fruit the tonnage machi ne returns RDi ( T+1) in t o n s acreage the harvested value the dependent stage. weight, harvester yield harvester growth Thi s a grade. RDi ( T+1) by m u l t i p l y i n g interpolates ( T+1) Thi s the of RWi ( T+1 ) ($/Kg). provide in gr ams . fruit weight dollar harvested acreage tares/hour. type the total the tonnage derived and t h e factor by c o n v e r t i n g per be a f u n c t i o n of and estimate RWi ( T + 1) resembling harvested RWi ( T+ 1 ) This used imple­ farmer. The t o t a l time differently to recovered a vector harvester is to price of recovery n u mb e r value corresponding offered (at fruit type fruit Ci hypothesized by m u l t i p l y i n g by factor the causes trucks capacity. bulk the which, 80 The n u mb e r o f fruit on d a y ( T+1) transportation This capacity recovered ber of total weight for cost that both $/ha basis. return is of day. carry the is of the per day multiplied determine per harvested tonnage The n u mb e r cost the capacity harvested to MC. the of truck T+1. by t h e required trucks CT t o transportation factor for returns and cost It variable return and day per hectare the represents should costs are T+1 is grade) harvesting per variable) by m u l t i p l y i n g returns. and dollar the fixed This dollar harvest sum o f (both calculated fixed (sum o f the T+1 by t h e cost is to the The n e t total this by a f a c t o r machine to n u m­ is determine the cost. operation return and t h e on t i m e multiplied truck a function trucks The m a c h i n e vest is required T in t r u c k / k i l o g r a m s trucks further trucks the total the be n o t e d , dollar total on the a harvest by t h e The m a c h i n e all of here, calculated the har­ ratio diminished cost. for for cost harvested acreage. 4.4. According Figures 6 and to 7, the the Mode l mo d e l Relations schematic representation following relations can be in derived: N 1A( T + 1 ) = F 1 A * N1 A ( T ) + I ( T ) [4.2] N1B ( T + 1 ) = F 1 B * N1 B( T) [4.3] + M11 A * N1 A ( T ) 81 N2(T+1) = F2 * N1B(T) + M1B * N1B(T) + M21A * N1A( T) [4.4] N3A(T+1) = F3A * N3A(T) + M2 * N2(T) [4.5] N3B(T+1) = F3B * N3B(T) + M23A I N3A(T) [4.6] N4(T+1) = F4 * N4(T) + M3B * N3B(T) + M13A * N3A(T) [4.7] OS(T+1) = N4(T) * M4 + OS( T ) [4.8] where: N1A ( T + 1 ) . N4 ( T + 1 ) are grade at N4 ( T ) are day that are expected grades and the of data); 2 and 3B ( M21A, s ome 4, to the plant into fruit fruit of 9 . 3 m2) p e r M1B, fruit n u mb e r grade grades (to stand size 1A) fruit F and equations: T+1 ; and, for and the fruit related rate exit 1B F4 a r e the remains are new f r u i t from t h e 4). to T is grades 0S(T) of day into T that T M3B, pass F3B , a n d and at (It into day M23A, 3A w i l l I(T) at grade T=1 day (from grade M are per going at and m2 ) p e r M13A, day 1A a n d grade 9.3 grade M2, at without per day respectively plant (in N3 B( T + 1 ) . N3 A( T ) ,N3 B( T ) , a n d M21A. another at an o v e r s i z e following of n umbe r The p r o p o r t i o n s the N3A( T+1) , M1 3 A ) ) ; F 1 A . F 1 B . F2 , F 3 A, s a me g r a d e entry of (in respectively, within and M11A, fruit of constants fruit to proportion the n u mb e r proportion transformed that N2( T + 1 ) . N1 A ( T ) , N1B ( T ) , N 2 ( T ) , n u mb e r input M4 a r e predicted T=1 ; the (sampling and the N1B ( T+1 ) . e a c h o t h e r in 82 F1A = 1- M1 1 A - F1B = 1- MlB [4.10] F2 = 1- M2 [4.11] F3A = 1- M1 3 A - F3B = 1- M3B [4.13] F4 = 1- M4 [4.14] In a m a t r i x marized as M21A M23A notation. (Neter the Ni (T+1) and fruit matrix a nd grades i s equal at X is 1 Fi Mi 4.7 can be s u m­ 1974): + L [4.15] both day T+1 a n d = ” N1A“ N1 B N2 N3A N3B N4 1 £ and through T, a 6x1 c o l u mn matrix respectively. of This to : a 6x6 m a t r i x cients 4.2 Wa s s e r ma n , Ni ( T ) a r e Ni a C [4.12] Equations N i ( T+1 ) = X * N i ( T ) where: [4.9] with (see constant Appendix ma t r i x equa 1 t o : "I ( T f| L = 0 0 0 0 0 entries A) , and covering L is a 6x1 the coeffi­ c o l u mn 83 Equations Fi 4.9 = H - M1i through 4.14 can be summar i zed as: [4.16] - M2i wher e : Fl F 1A F 1B F2 F3A F3B _ F4 _ * 1 1 1 1 1 1 H = The relation T+1 is: > describing R N i ( T + 1 ) = Ri the Ml i Ml 1 A M1B M2 = M13A M3B _ M4 _ M2 i "M2 1 A 0 0 = M23A 0 0 recovered fruit n u mb e r at day [4.17] * Ni(T+1) whe r e : RNi(T+1) = Ri = RN1 A ( T+1 RN1B ( T + 1 RN2 (T+1 RN3A( T + 1 RN3 B ( T + 1 RN4 (T+1 a 6 x6 d i a g o n a l entries each the recovered f r u i t g r a d e / 9 . 3 m2 a t T+1 matrix describing harvester per the with six percent grade (see number/ nonzero recovery Appendix by A) . 84 The w e i g h t to n u mb e r R~Wi ( T+1 ) = Cl relations are: [4.18] * RNi ( T+1 ) whe r e RWi(T+1) Ci = RW1A ( T+1 RW1 B ( T+1 RW2 (T+1 RW3A( T + 1 RW3B( T+1 RW4 (T+1 the per = a 6x6 d i a g o n a l entries fruit recovered grade per matrix resembling n u mb e r to with the fruit we i ght (grams) 9 . 3 m2 a n d T+1 six nonzero conversion factor weight i n gr a ms 9.3 m2 ) a t day recovered weight by t h e from (see Ap p e n d i x A ) . The d o l l a r culated return per grade by m u l t i p l y i n g the (per T+1 is cal­ price structure: R D i ( T + 1 ) = Pi [4.19] * R Wi ( T + 1 ) / 1 0 0 0 whe r e RDi(T+1) Pi = RD1 A ( T + 1 RD1B ( T + 1 RD2 (T+1 RD3A( T + 1 RD3 B ( T + 1 RD4 (T+1 the d o l l a r return T+1 f o r 9 . 3 m2 = a 6x6 d i a g o n a l resembling grade, as Appendi x the matrix price offered A) . with (in by t h e six per grade entries $/kilogram) receiver per (see at 85 The t o t a l area dollar return on d a y T+1 per grade per harvested is : [4.20] TRDi ( T+1 ) = FS * RDi ( T+ 1 ) and : FS = HEC * 1 0 7 5 . 2 [4.21]* HEC = MAC * HOURS * TNH whe r e : TRD1A( T+1 TRD1B( T+1 TRD2 (T+1 TRD3 A( T+ 1 TRD3B( T+1 TRD4 (T+1 TRDi ( T+1 ) HEC = t o t a l 1075.2 harvested = conversion MAC = h a r v e s t e r HOURS = t o t a l as af u n c t i o n the fruit time, a v a ria b le over time. field capacity capacity n u mb e r growth of material field the (hectares per in hour) 4.25: ( 1075.2 hectares) m2 t o Kg/ Ha hectares/hour and field is used changes in capacity v o l u me (MAC) c a l c u l a t e d based and t h e n converted is on to by: MAC = TAREA -r TTIME TAREA = TANK r (in on T+1 dyna mi c hour) T+1 Kg/9.3 harvesters capacity per at capacity harvester Thi s (tons is from hours of dollar return/grade/ harvested acreage at acreage factor harvest TNH = t o t a l Since total = total T+1 [4.22] * 6 Z RWi ( T + 1 ) / 1 0 0 0 ) [4.23] * Th e f o l l o w i n g r e f e r e n c e s a r e u s e d i n E q u a t i o n s 4 . 2 1 Bower s, 1975; Hunt , 1979; Ke pne r , e t a 1 . , 1980. 86 TTIME = FI ELD + TRANS + STOP + UNLOAD [4.24] FI ELD = TAREA t ( SPEED x WI DTH/ 10 ) [4.25] whe r e : TAREA = t h e total area (ha) requried to TTIME = t h e total time (hr) that spent start TANK = t h e of a tank harvester filling bulk was to storage fill a next the tank from t h e start capacity in Kg ( t a n k capac i t y ) FI ELD = t h e actual vesting TRANS = t h e to unload STOP = t h e UNLOAD = t h e It by t h e by t h e stops time produce time of fruit r ow, by t h e spent by t h e clean operator by t h e SPEED =the average harvester WIDTH =the harvester width 1000 =a conversion factor 10 =a conversion factor UNLOAD, standardized from SPEED, and har­ Equations and heading truck to the pinch to turn rolls, at a nd (hr) in unloading (hr) operation s p e e d ( Km/ h r ) ( 2 . 1 3 m) 4.22 WIDTH a r e harvester harvester produce noted in (hr) spent the while collection harvested be harvester harvester the can STOP, the end rest spent from t he idle the spent TAREA ( h r ) time and time f r o m gm t o from Km- m/ h r t o H a / h r through constants e x p e r i m e n t a 1ly d e t e r m i n e d . Kg 4.25 that whi ch The can value TRANS, be 87 TAREA, however, tional to and the directly machi ne 4.23 the (MAC) is The t o t a l of to is inversely recovered MAC. propor­ by t h e harvester As TAREA d e c r e a s e s , the decreases. the harvesters' 1 3 50 Kgs (60 tanks bu), that then are Equation as: + . 6 RWi 1= 1 n u mb e r o f required to generally be w r i t t e n whi ch weight also capacity TAREA = 1255 truck fruit proportional market can a variable total capacity Since on t h e is trucks carry the [4.26] T+1 ) and/or total the n u mbe r o f harvested trips crop per on d a y T+1 is : TRUCK = I R Wi ( T + 1 ) / 1 0 0 0 * FS * T [4.27] [4.28] wher e : TRUCK = n u m b e r o f Tc The = truck relation in trucks capacity Equation needed on d a y T+1 ( Kgs ) 4.27 can be rewritten as: TRUCK = (1075. 2 * 1000)* . f RWi (T+1) * HEC * -=L1 —1 Ic [4.29] TT * Tc 88 whe r e : TT = 1 . 0 7 5 2 Tc is (11,000 not c ombi ned result, is a constant, generally capacity the that on d a y o wn s (or interest those T+ 1 . If n 1 a n d n 2> and . 1 1R Wi ( T + 1 ) and Kg - me d i u m t r u c k ) The f a r m e r trips * HEC * value a truck(s) is n u mb e r of in Kg - to have t o the ma k e t o of t r i p s / t r u c k r e s p e c t i v e l y , and t h e i r of Tc1 truck). a standard him). knowi ng be large either leased may be mo r e woul d can e i t h e r Tc2 ( 2 2 , 0 0 0 a truck trucks the or its As a n u mb e r of the receiver sizes 1 and capacities are 2 Tc1 Tc 2 , t h e n : s 1 * n 1 * T c 1 + s 2 * n 2 * Tc 2 = TT where s 1 and s2 are respectively, from t h i s that equation (either large at and one zero types is or of then using data Since distance, justify cannot is the following farmer that solves if felt then be the n. degree time, of assumption the In c a s e one the in for 2, be n o t e d type farmer of can be has this truck set both equation a c o m p l e x mo d e l delivery and accuracy required 1 and can quantity estimated gains sizes It solution speed, used: has requires complexity wa s of hand. other elaborate accurately that on farmer One a p p r o a c h cycle trucks has the for a mo r e required. it the n u mb e r o f o r me d i um) trucks, that the [4.30] of time, and other standardized. woul d not fully the model , the 89 whe r e ¥ is Tc^TCj. a s s ume d Thi s that if the take twice transport Equation the 4.30 ¥ equal 2 and assumption farmer the to n s a me can of load of the be w r i t t e n the n u mb e r s trips [4.31] resembles follows had e q u a l n u mb e r * n2 the logical of trucks of the smaller larger sized ratio of observation it woul d sized one. then truck to As s u c h , as: TT = 2 s 1 * n 2 * T c 1 + s 2 * n 2 * T c 2 or [4.32] n 2 = TT -r ( 2 s 1 * T c 1 + s 2 * T c 2 ) TT t Tc 2 ( s 1 + s 2 ) The c a l c u l a t e d and t h e value Thi s value of mo d e l s 1 a nd either be o w n e d , to the n2 can be r e p l a c e d in Equation 4.31 n 1 determined. assumes trucks related of s2 that that he h a s leased, n u mb e r o f or the farmer on h a n d . custom k n o ws numb e r o f These t r u c k s rented. custom r e n t e d the s 1 and trucks can s 2 are by: s RENT 1 + OWN 1 + LEAS E 1 [4.33] S 2 = RENT2 + 0WN2 + LEASE2 [4.34] a nd whe r e 0WN1, 0WN2, RENT 1 , RENT2 , LEASE1 , a nd LEASE2 a r e the 90 owne d, rented, and leased trucks of sizes 1 and 2, respec ti vely . Equation 4.30 can t h e n be r e w r i t t e n as: TT = Tc1C(RENT1*nl +LEASE1*L1 )+(0WN1*(n1-N1- L 1) ) ] + [4.35] Tc 2 [ ( RENT2*N2+LEASE2*L2 )+(0WN2*(n2 -N2 -L2 ) ) ] whe r e : N1 = n1 [4.36] N2 = J M I f U n2 [4.37] L1 = L E-| - E 1 * n 1 [4.38] L2 = LE^ SE2 * n 2 [4.39] and Np N2 , out by t h e n u mb e r o f , and custom trips L2 r e p r e s e n t rented by a l l and the the leased trucks n u mb e r trucks for truck of out trips of carried the sizes total 1 and 2, respecti vely. The t o t a l truck cost on d a y T+1 is calculated by: TOTAL TRUCK COST = COST OF CUSTOM RENT TRUCK + COST OF [4.40] OWNED TRUCK + COST OF LEASED TRUCK and 91 CUSTOM RENT = Q1 * TR1 + Q2 * TR2 + N1 * DL1 + N2 * DL2 [4.41 ] whe r e : * * Tc1 [4.42] Q2 = RENT2 * N2 * T c 2 [4.43] Q1 = RENT1 TR^, TR2 = t h e transportation truck of sizes ($/ 2 2 .5 DL^ , DL2 = c o s t The c a l c u l a t i o n s commercially a tonnage expense owni ng the and truck The t o t a l culated n u mb e r of per a n o wn e d 4.39 of driver bushel) for truck and 4.41 follow renting adding to of the on 0 P 1 and sizes cost T+1 . 1 and truck it. is This the cost and cost of o wn e d trucks of the each cost size. sum o f varies truck) of 0P2 a r e 2, truck the a truck it the the on the as extent on T+1 cost of can its then sum p e r truck This represented is the cost of size on ownership sizes for T+1 , of use. be c a l ­ by t h e by: [4.44] operating respectively, of a function OWNED TRUCK = (0P1+0W1 J0WN1 + (0P2+0W2 )0WN2 wher e sizes ($/trip) custom of trucks rent respectively through (type truck a custom a driver. by m u l t i p l y i n g of a truck procedure operating size 2, respectively Equations (cents hiring The c o s t hiring 2, adopted basis of in 1 and for Kg) of 1 and rate and 1 and the o wn e d t r u c k s 0W1 a n d 2, 0W2 a r e respectively, 92 The f i x e d / v a r i a b l e by mo s t agricultural ma r y a d v a n t a g e 1979; Kepner, 1982). o wn e d et Fixed shelter, and use and and labor. al., cost) vary f l o w me t h o d s e e ms methodology and piece of Oliver equivalents). multiplying a function reduced of years to with In p r e s e n t owni ng to and c o mp o u n d value present for used, is al. costs of flow is cost capital, a net equipment. future factor n u mb e r the the given costs are present In t h e by recovery of capital n u mb e r considered. all a (annual determined an a n n u i t y over by of costs and t e h the This approach annual cost model , (1981). initial rate machi ne following by a c a p i t a l a mo u n t annual of In t h i s cost cost interest analysis, the equal factor interest value operating of the is costs a cash established breaking by a n equivalent et taxes, maintenance, suited. is Hunt , a machi ne a mount inflation, equivalent the the pri­ Yearbook, interest, repairs, by R o t z , 1975; Variable to me t h o d The Engineers how l o n g analysis recovery of (Bowers, used. of capital When m u l t i p l i e d is is series initial whi ch counted of into The c a p i t a l years. cost (1974) factor. is it on be b e t t e r The a n n u a l the depend effect of analysis. depreciation, a previously equipment cost proportion to a c o mmo n l y u s e d Agriculture presented follows for lubrication, the the Smi t h in fuel, me t h o d methodology 1980; and is simplicity include analysis basic its how muc h including cash is costs include When it than me t h o d engineers insurance rather (operation of cost dis­ value cost cash flow of 93 analysis ments, method, and t h e intrest for inflated present ology fits rates In down for by et payment; labor down value replace before al., the of use and fuel costs costs in t h e elimination three are of different and one pay­ and are discounted Changes fuel; loan depreciation are 1981). the annual Current they include labor, and payment , ownership. Rot z machinery, addition, of costs (Rotz, proposed and cost future value actual remaining the to the to method­ tax bene­ inflation interest considered in rate. present value. The c a s h flow analysis categorizes the costs into the f o l l o w i n g :* (1 ) Owner shi p c o s t (2) Taxes, (3) Repairs (4) Fuel (5) Labor of insurance, and and m a i n t e n a n c e and lubricants The c o s t of machi ne the down p a y m e n t for the the end o f present purchase its and payment s * Th e operating ana l y s i s . are shelter is of plus the salvage all life. in future is determined principal machi ne therefore in t h e ownership mi nus and the The down present and n o r m a l l y s et of e q u a t i o n s d e s c r i b i n g a truck shall also apply to interest remaining pa yment value terms. sum payment s value occurs represent the the as t h e at in t h e An n u a l a uniform c o s t o f owni ng harvester cost and series of costs multiplying a single p r e s e n t -worth recovery can mus t whi ch cost is inflated (Rotz, et to al., to by t h e the The m a c h i n e r y be value be c o n v e r t e d payment factor, factor. sum w h i c h present whi ch present by uniform-series- reciprocal remaining future value of the value value is capital a single and d i s c o u n t e d to 1981). OWNERSHIP [4.45] where : DP = down payment P = principal and m = loan in y e a r s i term = annual n = machine related to P = ( TI C - value age the DP) be loan (assumed rate machinery RV = r e m a i n i n g to interest discount a = annual P is ( assumed the ( nu mb e r initial pa yment to be 5 years) ( 8%) inflation of 1) of rate ( 1 0 %) machi ne analysis cost of the years, machine 10 y e a r s ) by: [ R{ -1-+ r ) m ] ( 1 + R ) m-1 [4.46] whe r e : TI C = i n i t i a l R = bank The remaining cost interest value of of the rate the equipment ($) ( 4%) equipment RV i s c omput e d by: 95 [4.47] RV = RV1 ( RV2 ) n ( TI C ) wher e to RV1 a n d 0.8 and 1981). RV2 a r e 0.84, the remaining respectively, Equation the be factors automotive and e q u a l (Rotz, et al., Thus: RV = 0 . 8 ( 0 . 8 4 ) 1 0 ( T I C ) cost for value 4.43 can be = 0 . 1 4 ( TIC) rewritten as: OWNERSHIP = P [ ( 1+1 )m~J ] i(1+i)m - 0 . 1 68 ( TI C ) The t a x , shelter insurance, mod e l e d as to cost normally portion of Since this cost in c u r r e n t future cost and d i s c o u n t e d is the is a constant equipment. inflated and [4.48] initial to a mi nor cost value, present of it mu s t value by : TIS = S(TIC) .3. (— [4.49] )j wher e : TI S = c o s t S = tax of tax, factor insurance, (portion of and initial or : TIS = 0 .0 1 (TIC)(11.33) = shelter 0 . 1 1 3 ( IC ) cost, 0.01) 96 Repair equipment and m a i n t e n a n c e use. T he y a r e RM = ( T I C ) RCI S costs are described a function of the as: [ ( T i ^ ) RC2 . ( J u s e ( jj)-.L) ) RC2] ( ^ J [4.50] J whe r e : RM = r e p a i r a n d maintenance cost RC1 , RC2 = r e p a i r a n d maintenance constants 0.055 Tuse and = cumulative ( as sumed RM1 and RM2c a n initial cost Labor in whi ch requirement up a n d for delivery labor cost annual to is of 2, is by t e n machine automotives equipment IC1 (hrs) and I C2 f o r the respectively. value taking the to hrs/yr) percent to for the in c u r r e n t operation the of when u s i n g 1 and calculated increased use be c o n s t a n t truck sizes harvest is respectively, be c a l c u l a t e d of cost the 1.8, and e q u a l for place. to allow field. the year The labor for setting The e q u a t i o n is: LABOR = 1 . 1 ( W ) ( U S E ) ( 1 + C ) X [4.51] wher e : in W = wa g e rate and e q u a l base year rate to 4 ($/hr) according to a (1981 ) * Re f e r t o Appendi x B f o r t h e v a l u e s Equations 4.43 through 4.55 t h a t are of t he c o e f f i c i e n t s time de pe nde nt . 97 USE = operation C = annual X = current The final calculated hours inflation year cost based in (1982) include a fuel meter, total rather than the of fuel the fuel cost is cost of factor (hrs/season) ( 6%) and lubrication. similar explained based (1981). then cost value FF b a s e d power, increased is f u e l and year consumption year of labor methodology equipment a base lubrication that process ye ar (1981) on a c u r r e n t Changes in t h e rate - base is cost. price spent on to liters cost per To i n c l u d e by f i f t e e n calculated et trip on t h e lubrication percent. al. kilo­ per based is labor by R o t z , on k i l o m e t e r s a fuel It The cost, fuel and by: FUEL = 1 . 1 5 ( F P ) ( K I L 0 ) ( F L ) ( 1 + b ) x [4.52] whe r e : FUEL = f u e l and FP = f u e l price equal KILO = f u e l to lubrication to according 0.18 and to a base dollars/1 iter consumption 0.19 cost/truck/trip 0.24 rate of year diesel fuel trucks ( L/ Km) and equal 1 and 2, L/Km f o r b = fuel The t o t a l fuel traveled inflation cost per rate truck per truck sizes trip ( 12%) size and for r e s p e c t i ve l y FL = k i l o m e t e r s (1982) on T+1 is: 98 TOTAL FUEL1 = 1 . 1 5 ( 0 . 1 8 ) ( 0 . 1 9 ) ( FL)( 1 + 0 . 1 2 ) X( n 1-N1- L 1) [4.53] = 0 . 0 4 ( F L ) ( 1 . 1 2 ) x ( n 1-N1- L 1 ) TOTAL FUEL2 = 1 . 1 5 ( 0 . 1 8 ) ( 0 . 2 4 ) ( F L ) ( 1 + 0 . 1 2 ) X(n«-N9-L~) * 1 d [4.54] = 0 . 0 5 ( F L ) ( 1 . 1 2 ) x (n2 -N2-L2 ) wher e TOTAL FUEL1 pertr uc k of sizes The o w n i n g and TOTAL FUEL2 a r e 1 and cost can by m u l t i p l y i n g it Thus, OW12 c a n OW' 1 fuel cost in equal annual cost respectively, be e x p r e s s e d by t h e total capital on T+1 . recovery factor ( CRF) . be c a l c u l a t e d by: + T I S 1) CRF * CU1 [4.55] OW 12 = ( OWNERSHIP2 + T I S 2 ) CRF * CU2 [4.56] OW' 1 and 2, the = ( 0WNERSHI P1 wher e : CRF = f t * 1 +R) n - 1 ( 1+R ) n -1 OW' 1, OW' 2 = t h e and [4.57] cost 2, CU2 = F r a c t i o n cucumber farm owni ng t h e respectively, harvest CU1, of over of the sizes entire season of truck harvest processes respectively CRF = c a p i t a l trucks use process for truck ( as s umed recovery (hours) to factor out in of sizes the all 1 and be 40%) the 2, 1 99 Since it is f a r m on a d a i l y appropriate that basis. basis during express This can to the measure the cost the harvest of be a c h i e v e d net i n c o me o f season, owni ng the it is trucks the mo r e on by: 0W1 = OW' 1 x FA [4.58] 0W2 = OW12 x FA [4.59] wher e the to desirable FA i s total the fraction cucumber The o p e r a t i o n of acreage planted land cost T+1 on harvested on t h e on T+1 from farm. is: 0P1 = RM1 * CRF * FA * CU1 + LABOR * FA + TOTAL FUEL1 [4.60] = FA(RM 0P2 = RM2 * CRF* CU1 + LABOR) + TOTAL FUEL1 * CRF * FA *CU2 + LABOR * FA + TOTAL FUEL 2 [4.61] = FA(RM * CRF * CU2 + LABOR) + TOTAL FUEL2 It can labor the of be o b s e r v e d cost fact that whether The is equal the they cost from t h e for both workers are in above are truck sizes. paid by t h e operation of l e as e d t r u c k s t wo e q u a t i o n s or that This hour is the due to irrespective not. isc a l c u l a t e d by: COST OF LEASEDTRUCKS = (CL1 * LEASE1 + CL2 *LEASE2)FA [4.62] + (LEASE LAB0R1 + LEASE FUEL1) (LEASE 1) + (LEASE LAB0R2 + LEASE FUEL2)(LEASE2) 100 where : CL1, CL2 = c o s t of sizes LEASE LAB0R1, LEASE LAB0R2 = cost 1 and of leased LEASE FUEL1, LEASE FUEL2 = cost the CL i s calculated inflated to based current CL 1 = LR1 on lease value * (1 + G) J CL2 = LR2 * (1 i + G) J leasing 2, of fuel leased rates of respectively hiring trucks a truck drivers (in and for the $/trip) lubricant for trucks followed in 1981 a nd by: [4.63] where : LR1 , LR2 = t h e lease equal to months) rate based $1500 and for truck on t h e $2400 sizes year per 1981 season 1 and 2, and (3 respec­ tively G = lease The c o s t trip of of hiring 1981 and rate inflation a driver inflated to is LLB i s equals $20 p e r LEASE the the of leased based rates = LLB(1 labor at 12% on w a g e s per by: +C)X based on [4.64] 1981 a nd trip. FUEL1 procedure cost a s s ume d calculated current LEASELABOR 1 = LEASE LAB0R2 wher e and and LEASE described FUEL2 c a n in E q u a t i o n s be 4.53 calculatedfollowing and 4. 54 but 101 replacing n 1- N 1- L 1 and A final use in r emar k harvest harvester (usually increased n 2 - N2 -l_2 by L 1 a n d about the system type requires size 1), cost concerns 2 ( Cu k e h a r v e s t e r ) . a truck then truck L2 , r e s p e c t i v e l y . to FL i n a cc ompa ny Equation the Since truck this it in t h e field 4.52 should be by A: A = [TC t I RWi (T+1 ) * 1 .0 7 5 2 ) ] x 10 4 2.1 [4.65] where : A = increase while 2.1 = total in addition, Equation 4.41 The c o s t TMACOS1 , 2, and 3, dures the mo d e l h a r v e s t e r in the field harvester of harvest and of the cucumber h a r v e s t e r system calculated the by 25% i n 2). TMAC0S3 f o r cost. Km-m increased and o p e r a t i n g truck a portion usually system is (m) f r o m Ha t o TR2 a r e respectively, for the factor owni ng TMAC0S2, as the truck width (for discussed s p e n t by the TR1 a n d of kilometers accompanying 10 = c o n v e r s i o n In the This total b a s e d on cost dollar is harvester types 1, the proce­ represented return of TMC0S_ = . TRDi ( T+1 ) * MC s 1=1 T+1 in by: [4.66] where: TMC0Ss = t o t a l machine system types s = 1- 3 cost 1, 2, per day and 3, (T+1) per harvester respectively, as 1 02 MC = p o r t i o n The t o t a l leased cost. machi ne cost are cost the machi ne harvesters vesters This of is not total is dollar the only. sum o f the is to This rented in t h e represented return due cucumber on cost the T+1 of covering o wn e d fact that harvesting cost of THLS = leased har­ system. by: TMAC0Ss = COST OF LEASED HARV + COST OF OWNED HARV The and harvesters on T+1 HLS (1+G)J * NH * FA + [4.67] is: HLD (1+C)X * HOURS *NH [4.68] wher e : THLS = t o t a l harvester(s) harvester HLS = h a r v e s t e r type for NH = n u mb e r HLD = c o s t of of of o wn e d lease rate to cost ( S = 1, for 1981 base $850/season leased harvesters to a driver 1981 harvesters base is ($) 2, every S =1 , hiring according The c o s t system type according season lease for year or T+1 for per 3) harvester year rate system ($1000/ S=2 ) used the on T+1 harvester rate calculated on ($4.65/hour) by: COST OF OWNED HARVESTER = (OPERATION + OWNERSHIP) NMAC [4.69] where: OPERATION = o p e r a t i o n cost maintenance, and fuel, includes and labor repairs and 103 OWNERSHIP = o w n e r s h i p taxes, cost and insurance, NMAC = n u m b e r o f o wn e d The o w n e r s h i p tions 4.45 cost (OWNMAC) through 4.48 is with DP = 20% o f includes and shelter harvester(s) calculated the ownership, following following Equa­ changes: IC RV1* = 0 . 7 RV2 = 0 . 9 thus RV = 0 . 2 4 ict + icH IC = where Equation ance, and I Ct = initial cost of a tractor I CH = i n i t i a l cost of a harvester 4.49 is used shelter Similarly, for to a cucumber Equation repair and m a i n t e n a n c e l owi ng changes : calculate 4.50 cost of is the cost harvester followed the of tax, ( TI SMAC) . to harvester determine with RMMAC = RMt + RMh *In c a l c u l a t i n g t he r e p a i r f a c t o r s of a bean c ommuni c a t i o n s ) . the the fol­ [4.70] RMt - ( ICt ) RC, RMh + ( I Ch )RCi .Si [ ( ^ insur­ - « ) RC2 - ! > RC2 ] [«.71] ( j ^ j [4.72] c o s t of a cucumber h a r v e s t e r , p u l l e r are used (Rotz, p e r s o n a l 104 whe r e : RMMAC = r e p a i r s and m a i n t e n a n c e cost of a cucumbe r cost of the harvester RM^, RMj_| = r e p a i r s and m a i n t e n a n c e and h a r v e s t e r , RC1 , RC2 = 0 . 0 2 5 , 1.6 tractor respectively for tractor; 0.2, 1.6 for harvester RMMAC i s the increased necessary Equation 10% f o r adjustments 4.51 (LABMAC) f o r W, HOURS. by is whi c h Fuel and harvester required followed is set lubricant for for type the of tractor. determining at $4.26/hour, cost is 2 because a nd calculated labor cost USE, set by: FUELMAC = 1 . 1 5 ( TYPE) (FP)(KW)(FF)(HOURS) ( 1+6)x [4.73] where : FUELMAC = f u e l and lubricant cost on T+1 f o r the harvester KW = t r a c t o r (75 kilowatts) FF = f u e l consumption f a c t o r (0.28 TYPE = f u e l consumption f a c t o r and e q u a l s and 1. 1 4.69 for harvester respectively. This because slight d e ma n de d Equation powe r can of the by t h e be w r i t t e n types variable as: liter/Kw-hr) 1, 2, is types. 1, and 0.9, 3, included differential harvester at powe r 105 COST OF OWNED HARVESTER = [FUELMAC + LABMAC + (CRF * (RMMAC + OWNMAC + TISMAOFA)] * NMAC The n e t harvest return, HARVNET, on T+1 is [4.74] calculated by : HARVNET = I TRDi(J+1) (1 - MC) - TOTAL TRUCK COST on T+1 [ 4 . 7 5 ] HARVNET i s attainable (allow harvester on for a go/no-go Ne t whe r e T+1 harvest w is is basis the only if field conditions t r a f f i c a b i 1i t y ). This are is workable represented by: f HARVNET, w = 1 = \ [ 0 , w = 0 returns notation trafficable standing ( w= 1 ) The w o r k a b i l i t y or not condition for whether the field on ( w=0) . is a function of four vari­ ables: w = f {s o i 1 , p l a n t , machine, weather} where : soil = soil ity type. through (especially off). mi n e water A variable the soil These the soil inflow soil physical texture, properties water and affecting trafficabil- properties drainage, consequently conditions outflow). and run­ deter­ (balance of 106 plant = plant soil in condition. water turn tion, Ma c h i n e consumptive a function growth = a variable machine under A variable stage, whi ch to of plant and soil This variable is type of operation the field machi ne variable root the ground ability field cover. of a operation budget. a function to of the be p e r f o r m e d machine's is differentia­ moisture in t u r n and t h e Thi s percent a given a determined affects use. defines perform that by physical parameters . Wea t he r = weather water This conditions, content movement , and percent affected field of the p a r a m e t e r i z e d , and The harvester one because pore tion) of evaporation can function humidity, the weather flat involving air percent of water). precipitation, relative variable location pattern soil humidity, is of also the c o mmo n l y there. can variables for can this for obtained move me nt , sunshine. be g r o u p e d , particular be c l a s s i f i e d subsurface (essential those of This geographic standardized replace and a function sunshine. affecting and o u t f l o w temperature, mentioned operation its is by t h e and observed Several (inflow variable air a variable as blade. Readings determining by u s i n g a soil engaging of open evapotranspira- the temperature, A crop model . mathematical relative coefficient also 107 essential utilized growth can to the stand stage, and be c h o s e n ground the for is A soil this mo d e l existence 1982; evapotranspiration for of plant at of a complete a later a go/no-go et evaporation are tent upper based used tural equipment is layer (Rutledge and using First, historic A no-go lengths. bility model s generate of the machinery year, is period expected levels. soil. the soil The operation on and soil pore moisture of by t h e generally sequences data divided of con­ of this agricul­ moisture proceeded of daily and y e a r of are occur. in o r d e r wo r k d a y s are to at of t wo days weather periods days in go/no-go cannot into go/no-go n u mb e r based Hetz, 1968). operation of 1974; selection a d a y when c o n d i t i o n s sequences each Tulu, daily in and t h e precipitation determined have climatological the The s ummed f o r mat es Mc H a r d y , they field step, that the since utilized t r a f f i c a b i 1ity and the primarily is Relationships c omput e fact day r e p r e s e n t s efficient second runoff, to on t h e T r a f f i c a b i 1i t y stages. 1982). (complete harvest. approach 1973; be coefficient stage during (Baier, 150 mm o f is stage field This growth can differentiation, differentiation, budget day al., and level root content root cover. plan;t growth determining drainage, in t h e ground on a l a t e moisture for indices percent and Rosenberg, texture, of based cover) plant calculation records. such t h a t In t h e specified grouped establish selected and esti­ proba­ In o r d e r to requirements s o i l budget (1) (3) of Model i ng this the the as a function Dropping the the fact of These betw een the are specific ma de changes August thawing that time and/or heat a daily of and 1-30 in t h e include: and not not a day the of of day o v e r a n u mb e r o f This contrary to cumulative probability period. approach is illustrated this in t h e with information In a d d i t i o n , by f i e l d content. workability a years' distribution for 1 day. based common a p p r o a c h The r e a s o n of t r a f f i c a b i 1ity the past behavior determined moisture Thi s dealing As s u c h , field distribution the conditions solely by c r o p 1 day. dynami c essential. is as increments drying. certain is with and/or is not a period system's varies probability on a f r e q u e n c y of the crop units workability Us i ng length subprograms t r a f f i c a b i 1ity time into some c h a n g e s approach. specific due t o regarding (4) model , period is the procedures year. Setting soil these accounting whol e (2) fit over change following of recorded of data. utilizing a a specific in t he example, g i ven : (a) a field days (b) (null a 0.7 this to with profitable a machinery 10 d a y s (c) operation system with fulfill cumulative period. the a time constraint returns after a total of 10 this period); capacity requiring workable days job; probability of for The f a r m e r of his yields attainable tial. for given If harvest, days the the of the fall farmer at will get not the Thi s because of the nature with at virtually In t h i s to whi ch the no o r model, be t r a f f i c a b l e * SO I LM( T + 1) rest fruit very zero of of This the (or the passes yield is operation, is very e s s e n ­ field is ready nonprofitable) period fruit percent thirty-percent days whe n t h e is workable. development into l ow m a r k e t a field of seventy harvesting a time or rate capacity. nonworkable whether rapid attain location return, is only In a c u c u mb e r location these can machine of period. the case his regardless nonworkable however, in t h i s and t h e an o v e r s i z e grade value. condition on d a y T+1 is a s s u me d if: < Z [4.76] where: SO I LM( T + 1) = soil moisture layer on d a y Z = quantity available the Available of water manent a soil wilting *Tukey,et water holds point al. soil content in t h e equal to water field either capacity capacity 0.99 between field capacity Baier and defined soil 0.95 of as the a mo u n t ( FC) and per­ Broughton, (1973); the ( FC) is ( Hassan of (AWX) o r (AWC) (1974); 150 mm T+1 capacity (PWP) upper Shaw 1975). (1963) 1 10 Field soil has capacity after been stored eight free completed. water hours after is whi c h a plant has defined as soil 150- mm l a y e r . i i SOILM- Table moisture content (as and textures upper type has no water emptying water use. is, FC i s saturated ceased. moisture some o f t h e in Mi c h i g a n . moisture the of longer of in of macropores part, the forty- the and the regain in the measured Permanent content left soil wilting soil turgor at ( Hassan 1975). g r o wn A soil or plant moisture the a mo u n t remaining for wilt 7 lists general the practically will Broughton, Table Thi s the as drainage available drainage four defined subsurface point and is on = 1, 2, on w h i c h 8 s h o ws budget properties pickling these approach T+1 for a certain 3, and 4) . ( T + 1 ) = SOILM- 1>J physical of cucumbers properties suggests location that j are for the the soil for a soil is: - (T)+WATIN- (T+1)-WAT0UT- ( T + 1 ) * >J the J J [4.77] WATINj(T+1) = PRECEPj(T+1) - RUNOFFj(T+1) [4.78] WATOUTj(T+1) = EVPTRANj(T+1) + DRAINj(T+1) [4.79] where: SOILM. , ( T + 1) = soil moisture content tain location j on T+ 1 f o r a cer- 1 *J in mm f o r a soil type i Table 7. A L i s t o f Some o f t h e P h y s i c a l P r o p e r t i e s General Soi l Te x t u r e s in P e r c e n t of Tot al ( R a w l s , e t a 1 . , 1982 ) . S o il Text ure Permea b i 1ity Total Porosity Field C a p a c it y (mm/hr) (%) 14 Sandy Loam 25 (%) 43 Loam 13 47 S i l t Loam 8 Sandy Clay Loam 2.5 Permanent Wi l t i ng Point (%) o f Four De p t h Total Available M ois tur e (mm/M)* 6 120 22 10 170 49 27 13 190 51 31 15 210 *R ea d il y a v a i l a b l e m o i s t u r e i s a p p r o x i m a t e l y 75% o f t o t a l a v a i l a b l e m oisture. Table 8. i A L i s t o f Some o f t h e General S o i l T e x t u r e s S oil T e x tu re P h y s i c a l P r o p e r t i e s o f Four i n t h e U p p e r 150- mm L a y e r . Total Porosity Field C a p a c it y (%) (%) Permanent Tota 1 Wi l t i n g Available P o in t M oi st ur e (mm/150mm) (%) 1 Sandy Loam 65 21 9 18 2 Loam 71 33 15 26 3 S i l t Loam • 74 41 20 29 4 Sandy Clay Loam 77 47 23 32 112 (wher e l o a m, i =1, l oa m, l oam s o i l , S 0 I L Mi i (T) = soil 2, silt type (i) and 4 for l oa m, and a sandy sandy clay respectively) moisture 1 »J 3, content per 150 mm s o i l on T p e r location (j) soil in t h e upper layer WA T I N . ( T + 1 ) = w a t e r i n f l o w (mm) on T+1 J P R E C I P j ( T + 1 ) = p r e c i p i t a t i o n (mm) on T+1 RUNOFFj ( T + 1) = water lo s t WATOUTj ( T+1 ) = by r u n o f f on T+1 (mm) w a t e r o u t f l o w (mm) on T+1 EVPTRAN^( T + 1 ) = water lo s t T+1 by e v a p o t r a n s p i r a t i o n on (mm) DRAI N. ( T+1 ) = w a t e r (mm) d r a i n e d f r o m t h e s o i l a t J Us i ng t h e p r o c e d u r e s i n B a i e r and R o b e r t s o n ( 1 9 6 6 ) , He t z (1982), and Pair, et al. (1976), then: EVPTRAN. (T+1) = EVAP - (T+1) * PAN. * CROP * ZONE J T+1 [4.80] *J whe r e : EVAP- ( T + 1 ) = Pan e v a p o r a t i o n p e r l o c a t i o n ( j ) on T+1 J (mm) u s i n g a US W e a t h e r S e r v i c e c l a s s A pan . PANa = Pan c o e f f i c i e n t class is A. This considered tion CROP = c r o p and e q u a l is to from a f r e e coefficient, (1977) and Tul u because to 0.7 pan b e mo r e t h a n water pa n evaporation the evapora­ surface. suggested (1974) for to by V i t o s h equal 0.8 for a 1 13 maturity stage 20% c o v e r betw een to harvest ZONE = z o n e coefficient, cucumbers. percent of from t h e Equation 4.80 can then This J soil is tion from tion is a s s u me d to content reaches permanent defined as no that off by be is or enter to whi ch soil the is to the c omi ng layer. as: sum o f [4.81] wilting soil. by t h e on t h e soil researchers lack of essential is attained for the lost reports Runoff all the has is of soil can water by s u r f a c e of by and w a t e r been literature. determination in mm/ hour ) . wh e n Runoff is temporarily additional available weather moisture water any either Evapotranspira- held with by e v a p o r a ­ Saturation water surface. hourly lost soil of is in t h e the point. As s u c h , soil water by p l a n t s . temporarily run­ a s s ume d This is precipitation, runoff as s ume d (based on in t h i s as : RUNOFFj ( T+1 ) = 0 . 1 and refers * 0.34 whenever Saturation the permeability mo d e l zero filled received several due are ponded the ma xi mum a m o u n t water m a t r i x . longer 150-mm s o i l and t r a n s p i r a t i o n the macrospaces for J Evapotranspiration the 0.6 e v a p o t r a n s p i r a t ion be r e w r i t t e n EVPTRAN. ( T+1 ) = E V A P . ( T + 1 ) at coefficient total upper estimated Equation 4.48 can be * PRECIPj(T+1) rewritten as: [4.82] I 14 WAT IN j ( T + 1) The w a t e r for a period excessive deep in t h e of subsurface moisture is space drains period, as field the [4.83] macrospores hours. is held temporarily Duri ng t h i s out from t h e upper is assumed, however, It percolate defined and soil zones. hour will * PRECI Pj ( T + 1 ) forty-eight moisture twenty-four ity = 0.9 only into half deeper difference capacity. of the Th u s soil that soil D R AI N ^ ( T + 1 ) i s to in a porosity Drainable total all layer drainable zones. between period, poros­ pore calculated by: ^0.5 * (TPORE.-FCJ, SOILM,. I I . (T) > FC, 1JJ 1 [4.84] DRAIN.. (T+1) = 0 , S0ILMi>:j(T) 1 FCt where : TPORE. = s o i l FC j Thus, than = field whe n t h e field total pore space as i = 1 , 2, i = 1, 2, 3, c a p a c i t y as soil moisture content capacity, DRAI N. ( T + 1) and on d a y assumes 3, and 4 4 T is greater a value of 22.0, J 19.0, 16.5, and 15.0 mm f o r soil soil moisture types 1, 2, 3, and 4, ,(T), is respectively. An i n i t i a l needed for moisture tion of T to content 4.77. today's culate day the This on simulate a fie ld T+1 can t h e n moisture moisture content content, for and tomorrow, 1 >J condition. be c a l c u l a t e d content content SOILM. is then used simulation and so o n . The soil using Equa­ as the proceeds A point value to in cal­ time 1 15 with a defined the fixed soil moisture Day T is chosen simulation. day of June. This period of mo n t h capacity the and started. however, 0.5 and soil In t h e the 1, is due t o the loss through calculation variables respectively, is justified by t h e less at a partial crop the totally stage moisture dr a wn deeper There a mo ng has and is several of on a i r w ere We a t h e r elements ever, we r e three Service as only taken to those into Mu s k e g o n , Van B u r e n . the that than upper have and Kent, field has the yet to the Ottawa, and in These 3) Barry, In t h i s cucumber that pattern This Great several Mi chi gan in weat her model , how­ production wer e g r o u p e d and be humidity, by t h e much h o m o g e n e i t y involved 2, of t he As a r e s u l t , 1974). to established. relative 1982). ( St r ommen, at locations. and g r o u p e d as a s s ume d since effect temperature, is Moreover, s u mme r w e a t h e r geographical values 15th. maturity. been in t h e July is not budget, as s ume until full not selected j=1, its moisture 150-mm z o n e , variation counties (as soil ZONE s h a l l consideration. categories at first this evapotranspiration provide possible during fact (Nurnberger, carefully the 1st attributed mo v e me n t as from June Michigan's been initiate evapotranspiration of cover woul d a wi de precipitation areas (1) roots that by e v a p o t r a n s p i r a t i o n from t h e partially Lakes lost fact to mo d e l generally CROP a n d This all is needed in t h i s the soil is into included: Allegan, Kal amazoo, a nd 116 (2) Saginaw, and (3) Midland, Gratiot, Isabella, Mo n t c a l m, Clare. Ionia, and Clinton, Shiawassee, Livingston, I ng h a m, Eaton, Jackson. The c l i m a t i c lected Ba y , data for from South tological the Haven, stations, three La ke sets City, respectively of counties and East are Lansing (Nurnberger, col­ clima- personal c o m­ munication). For the a certain probability PCDw] field that in a day location T is j with workable is a soil given type i, by: = NDw/ N [4.85] wher e : P[ Dw] = the probability able). of An e v e n t elementary descriptive Nqw = n u mb e r o f their teen A sampl e to the of of all twenty data twenty defined as the T is work­ collection characterized by some elementary a sampl e space possible outcomes space is N) the belong defined distinct of as to the out comes experiment. experiment's 1962-1981. complete out the event Dw. Dw ( d a y feature. (having N = the betw een is experiment of equal an e v e n t outcomes times collection N is of and sampl e represents These y e a r s records years ha d of wer e space. the n u mb e r selected evaporation. August 7th of years because Thus, workable, if for of four­ 117 example, future then to the probability be t r a f f i c a b l e location and soil absolute decision the strategies tainty are is that (1982) are defined uncertainty. the sense a given a situation mos t likely these that cucumber due the a certain have to an is production 0.7, a zero, then rather it. being r i s k . Harsh, et al. to deal with equally of mo r e t h a n given a long­ one-day a probability system, however, basis. Moreover, is terms, than outcomes of a function event a Uncer­ a n d ma xi mum e x p e c t e d an o u t c o me In o t h e r ma na ge me nt ma xi ma x, involved adverse uncertainty. a chance as known establish counteract were a day-to-day probability. day in seldom wher e d i f f e r e n t states, they with ma d e on yields to maxi mi n, criteria is alternative outcomes included strategy a trafficable harvest adapted and criteria term arising e mp l o y risk to decision of risk with needs on f i v e Al l are day given time to values. decisions future referred These In t h e in is states, level. best as likely decisions) this percent, A farmer mu s t uncertainty (in events When t h e s e reported seventy in d e a l i n g farmer possible. adverse, of certainty. criteria Moreover, expecting type. The o c c u r r e n c e with is of not if the the partialized the over p r o b a b i l i t y of nonwor ka bl e day d u r i n g a seventy percent, return. If of risk should a farmer and is to intelligently uncertainty, be c o n s i d e r e d are: then the deal four with general the problems areas that 1 18 (1) Kn o wi n g his (2) Evaluating ability and w i l l i n g n e s s all decisions in terms the probabilities to of take risks; alternative actions; (3) Estimating events (4) affecting Developing tainty payoff managerial whi ch are to or odds associated decisions; strategies applicable to to the with and, counteract farmer's uncer­ individual env i r o n m e n t . Farmers differ undertake widely various in t h e i r risks. established does not unfavorable outcomes A young have as willingness the does farmer s a me attempting ability an o l d e r , and a b i l i t y to to to get withstand well-established farmer. To s t u d y t h e harvest schedule, generated using ficable days and years the order. woul d the per woul d t h e n from t h e that model. year woul d of the percentiles whi ch as of type then be c o u n t e d count t r a f f i c a b i 1i t y , the of trafficable years. on A u g u s t a synthetic be nontraf- t r a f f i c a b i 1it y classified starts on t h e woul d The n u m b e r o f a decreasing range soil sequences August sensitivity be u t i l i z e d days The fifteenth sequence for sensi­ available for farmers analysis. We a t h e r mo s t in and synthetic mo n t h o f arranged be c h o s e n tivity the location weather and t w e n t y - f i f t h sequence in per of however, the To t e s t sixtieth effect of forecasts forecasts the can Mi chi gan either be are generally cucumber long or production short term. counties. Thr ough These years 1 19 of experience, farmers of trafficable days the farmer shall t r a f f i c a b i 1it y with the have given variables classified as parameter. directly Da t a essential being either by t h e mo d e l user conditions. The local variables mo d e l are to that are major As s u c h , enter ten the days daily starting machine performance (3) machi ne cost (4) weather parameters generally per fed specific however, condition. standardized, utilized this a stored variables parameters, constants for or and mo d e l his are Those stored by t h e to within algorithm. can be g r o u p e d parameters parameters needed proportion is transformed of to under the fruit growth per grade per fruit/area population plant n u mb e r another population n u mb e r o f ( I ( T) ) per factor are parameters The per are simulation categories: growt h parameters mo d e l those stored required (2) grade are be a u t o m a t i c a l l y plant The type. to of the input and determined, (1) (2) an factors into four (1) mo d e l sequences Requirement for input The p a r a m e t e r s The soil a period The parameters the forecast sampling . The those to farm in t h i s for 4.5. own their be a s k e d sequence learned at grade at T+1 ( M1 - , growth stage. entering the per growth category day T t h a t M2 . ) p l a n t on d a y stage. are: per T+1 120 (3) The n u mb e r oversize (4) of grade at The c o n v e r s i o n fruit weight stage ( Ci ) . for (1) Percent fruit (2) The a v e r a g e me r c i a 1 l y The t i m e bulk (4) correlating per grade per fruit per performance at from t h e plant into an population the a nd per growth includes: harvester whi ch n u mb e r per grade harvesters are (Ri). run c o m- ( s peed ). required the time by t h e UNLOAD, exiting ( 0 S ( T ) ). recovery speed storage spent T+1 machine from t r a n s p o r t truck, T+1 factor on The c a t e g o r y (3) fruit/area of by e a c h fruit needed tank to from t h e to the harvesters harvester field unload truck, for type the a nd stops for to the fruit the a to-andunloading from t h e average in t h e field time ( TRANS, S T OP ) . The a v e r a g e total harvest hours spent in t he field/day ( HOURS) . The m a c h i n e (1) contains: annual cumulative hours that a truck is annual cumulative hours that a harvester ( Tus e ) . The a v e r a g e used is ( USE ) . The w e a t h e r for category The a v e r a g e used (2) cost parameters a harvester include ( on a g o / n o - g o the t r a f f i c a b i 1ity basis).* These are conditions stored * T he s e q u e n c e o f g o / n o - g o d a y s ( f o r a p e r i o d o f c a n be r e p l a c e d by i n p u t f a c t o r s f e d d i r e c t l y by t h e r e p l a c i n g t h e need f o r l o c a t i o n , d a y , and s o i l t y p e . 10 d a y s ) user 121 per soil day (over type. the period of August The r e q u i r e m e n t s for 1-31) per location calculating those per parame­ t e r s are : (1) Daily precipitation (2) Daily pan A e v a p o r a t i o n The input factors (1) Fruit n u mb e r (2) The the (3) price The t y p e The of (7) trucks owned) of $/ Kg) T per per grade (Ni(T)). grade, as offered harvesters on hand by The c o s t 2, of both initial the farmer of (TIC1, year The a p p r o x i m a t e the receiving The initial (rented, ( RENT1, leased, 0WN1, LEASE1, ( TR1 , rent truck of TR2) . driver for truck sizes 1 and a truck TI C2) whe n o r i g i n a l l y for and d a y ) sizes that (in 1 and the km) 2, mo d e l between paid for per for harvester is run. the by t h e forecast the for sampling (harvester farmer (IC^, a period day. of by respectively. field and plant. paid with a custom DL2 ) . distance cost T r a f f i c a b i 1ity for a truck ( DL1 , cost (9) starting on h a n d capacities rate hiring (in originally are respectively respectively The that LEASE2 ) . The d a t e (11) ( EVAPj ( T+1 ) ) . NH) . (8) (10) j are: n u mb e r o f The t r a n s p o r t a t i o n 2, ( P RECI P - ( T+1 ) ) _ location m* ) on d a y (in and t o t a l s i z e s 1 and (6) per j (Pi). RENT2 , 0WN2, (5) location mo d e l 9.3 fruit n u mb e r o f and/or the (in rece i ver ( TYPE, (4) for per + tractor) IC H ) . ten days CHAPTER 5 DATA COLLECTION 5.1. 5.1.1. Experimental The d a t a in a joint and for Horticulture effects of machinery cucumber include of are vine disease in The study wa s Brothers farm near fed a clay with phorus returns and * Ta mo r C a s t l e seed of " T a mo r " in I(T), while determinate resistance of and University. was t h e study of He ml oc k, l oam s o i l of 4.5 C* . t wo and length was the Ta mor h a s a are high in content. s u mme r and 1983 on t h e Ke nny The f a r m was high fertilizer and C a s t l e p i k a r e s u p p l i e d companies, r e s p e c t i v e l y . 122 text growth a shortened varieties pH 6 . 8 Plant varieties. Michigan. Preplant Vi n e and pickling this produces seed in of the nutrition, commerci al and Both female mineral "Castlepik." Castlepik vine. Engineering State and these collected Mi c h i g a n t wo 0S(T), wa s Agriculture section conducted potassium. parameters on g r o w t h , selecting type, the experiment defined M l . , M2 j , Parameters growth betw een density cultivars: internode plant this harvested concern n o r ma l the Departments plant parameters Gr o wt h Setup experiment The o b j e c t i v e ma i n Plant levels consisted by t h e rain of phos­ of As g r o w a n d 123 330 Kg/ Ha 4-10-32, A pre-emergence Kg / Ha ) was was with When o n l y seeds, however, of the and them. seed and to the beds pick the assure carried out of block twelve on design treatments, per block the r ow s p a c i n g treatment Hal f of mineral wer e wa s each growth July design planted plot study, parameters. with seeds, r o ws with with a rapid planting 7 00 mm were a l s o speeds were was m o n i t o r e d for the 35 0 mm b e t w e e n spacings consisted s h o wn on three beds 70, at 100, 50 mm i n germination speed of 3.5 operation. Plant­ 6th. as (33.5 dealing the four by t h r e e with filled An o p e r a t i n g with a combination nutrition experiments moisture + 6.5 vacuum ground-to-rotation planting seed. Heat h of 5 wer e The t h r e e during The e x p e r i m e n t a l domi zed of the filled r ow s p a c i n g s of (4.5 forming a spacing and the ratio adequate of r o ws w e r e ma de o f up p l a t e . The d e p t h a five-row w ere 3, Prefar seeding. betw een with L/ Ha 7 - 2 8 - 3 . 320 and after using 1, within Km/ h r wa s m a i n t a i n e d was r o ws planters Three development ing out and Alanap planters five by v a r y i n g 2 0 0 mm. order carried of rows. obtained of immediately When a l l consisted betw een 28-0-0, a 350-mm s p a c i n g 2.25 m wide. beds combination applied Seeding planter 110 Kg/ Ha of a completely blocks. Ea c h in 9. Table block The t wo cultivars within the r ow s p a c i n g . m x 2.25 while m) was the mechanical rest by t wo was to m plot. plant utilized harvesting between Ea c h 67 m x 2 . 2 5 devoted ma de treatments of a rectangular wa s ran­ and for plant the Tabl e 9. A L i s t of t he per T reatm ent T r e a t m e n t s p e r B l o c k and T h e o r e t i c a l (in Pl a n t s / Ha ) . * Between t h e Row S p a c i n g (mm) Culti var Tamor Castlepik (Castle 2012) Plant Population Wi t h i n t he Row S p a c i n g (mm) 70 100 200 Tota 1 Treatm ents p e r Bl o c k 350 420,000 285,000 142,000 . 3 700 210,000 142,000 71 , 0 0 0 3 350 420,000 285,000 142,888 3 700 210,000* 142,000* 71 ,000* TOTAL *Data c o l l e c t i o n me n t s o n l y . 3 12 for plant gr owt h p a r a m e t e r s was limit ed to those treat­ 125 On l y spacing one cultivar ( 7 0 0 ' mm) w e r e experiment. This between-the-row at the time toring turb of the the it T and ratio did exit from t he of the growth spacing within that vine, production parameters wer e ( 7 0 0 mm) , one of expected the w ere collected cultivar dis­ vines. yields of fruit to the entry the As s u c h , one (Castlepik a c o mme r ­ s a me p r o c e d u r e s grown. and be m a i n l y Finally, under on be e q u i v a l e n t . rate the moni­ to cultivars, hypothesized follows having the and vegetation potential to r ow cultivar an a c c u r a t e betw een spacings. plots this without the the parameters a thinner allow while for betw een that branching was within-the-row under plant betw een the r ow 2012), and three and ma r ke d per treat­ r ow s p a c i n g s . These m plot plots treatmental plot side (above and days after emer ged true experimental the was whi ch growth fact be d i f f e r e n t hold the dynamics believed not one woul d and yields and provide crawling A 3 m x 2.25 ment. growth for the This the cially-adopted whi ch to harvest. T+1 m i g h t fact due woul d wa s This function was plant betw een selected spacing natural Mor eover , days of (Castlepik) with below seeding (July wer e 21). was selected selected in t h e a 2 m x 2.25 it). A stand middle m border count plot was whe n almost all seedlings Al l plants per plot* of were each on e a c h taken had fifteen completely counted, a nd t h r o u g h o u t t he d e s c r i p t i o n t h a t f o l l o w s in t h i s t e x t , a p l o t s h a l l r e f e r t o t h e 2 m x 2 . 2 5 m e x p e r i m e n t a l p l o t on whi c h t h e p l a n t g r o w t h p a r a m e t e r s wer e c o l l e c t e d . 126 the average ized the of four replicates in d e t e r m i n i n g crop stands the per emer ged plants Of t h e s e plants, some not borne true counted as they em erged from t h e gence wa s the theoretical emergence fully of theoretical defined and as had plants wer e the w ere plots picked expression ber of of the flowers, A gynoecious flowers on all a plant with ten counted and the at per ha) plants was tenth stage random from t h e parameters) subsamples plants mal e was of and some m a l e female, but it by observed ha) by t h e was within-the-row after plot. by c o u n t i n g whi ch (outside The sex the n u m­ positions. bore female beyond Ten The p l a n t s each. and t h e i r none 10). boundary plants that most of t h e (August A predominantly flowers plant full population every five as per plot determined wa s d e f i n e d nodes. per total emer­ population. determined node plant the by u s i n g every fruit percent by d i v i d i n g calculated that and d i v i d i n g the The t h e o r e t i c a l for total average Similarly, (in the or plants sense miss A percent soil Those in t h e to the recorded. from t h e time. plants plants spacings both plant w ere enough population growth i n t wo T a b l e 10 s h o ws count comput e d population picked for late m2 t o population. reached the emerged sex e x p r e s s i o n plants u til­ stand. emerging harvest. wa s between-the-row Plant at population. plant the and crop by c o n v e r t i n g 6.75 plants em erged plot still soil at calculated per w ere nonfully period (from p l a n t per leaves w ere development expected comput e d population. Al l had wa s t h e n the female plant fifth was Table 10. A Summary of Stand Counts per P o p u l a t i o n . P la nt Spacing Average Total Pl a n ts (mm) 70 Average Fully Emerged Pl a n ts (p l t s / 6 .7 5 m 2 ) Observed Population (Full Emergence) (plts/ha) T he o re ti c a l Population ( p i t s / 6 . 75m2 ) Average Nonemerged P la n ts (plts/6.75m2 ) A B C = A-B D E F G = Dt E 79.75 118,148 210,000 58 56 67 99,259 142,000 72 70 28.5 71 , 0 0 0 71 , 0 0 0 66 59 82.25 ( 5 . 1 )* 200 69.25 (1.73) 2.25 (1.41) (2.63) 31 . 75 3.25 (5.56) *Standard deviation (2.06) of t he average. 127 100 2.5 (p l a t s / H a ) Percent Percent Full Total Emergence Emergence (%) (%) 128 node. only A monoecious mal e plants flowers with plant beyond a common c ompar ed ence four blocks at per (2) Accept using of t h r e e the following: hypothesis, a mon g t h e three nificantly different in There PF, was a n o n s i g n i f i c a n t three the to the total error. Thi s effect on t h e total experiment vari- carried me a n s w ere of not out sex sig­ populations. contribution effect of three a n d t wo o b s e r ­ The a n a l y s i s , populations of in t h e analysis HQ , t h a t the carrying M) populations concluded expression one The n u m b e r s a t wo- way block. null the (GY, per the as node. population a = 0.05, (1) fifth sex e x p r e s s i o n w ere vations defined the populations having was of the eliminated out c ome block the (blocks block w ere homogeneous). (3) The e x p e c t e d plant percentages p r e d o m i n a n t 1y f e m a l e , 18%, respectively. standard percent Five selection chosen the tagged, later behavior used per and for plant influence of set middle daily other fruit, ma r k e d in of the the per plot plots. fruit plants per sex of wer e in plants fruit n u mb e r plants row t o The w ere eliminate plants primordia also Those replicate. Al l and population.) plot. and t h e inner 17%, average, by a f l a g , every 12th, 65%, the every determination adjacent and for spacing Augus t of n u mb e r n u mb e r n u mb e r e d w ere 11 p r e s e n t s the plants, the was ma d e on to and within-the-row from t h e starting total nonmoneci ous selected, w ere of w ere g y n o e c i o u s , and mo n e o c i o u s (Table deviation, that were could be Tabl e 11. A Re p r e s e n t a t i o n of t he Sex i n P e r c e n t o f T o t a l "^ ^ -^ P la n t Plant Sex S p acirT ^ ^^^ 70 mm A v e r a g e , S t a n d a r d D e v i a t i o n , a nd Number o f P l a n t Number f o r E v e r y P o p u l a t i o n . Gynoe ci ous A verage* SD Predomi nantly %** Female Plants per Monoec i o u s A verage SD % Average SD % 25.75 0.5 64 7.75 0.96 19 6.5 0.5 17 100 mm 26 1 . 41 65 6.75 0.5 17 7.25 0.96 18 200 mm 26 0.82 65 6.25 0.5 16 7.75 0.96 19 *Average **Percent = a v e r a g e of 2 s ubs a mpl e s x 5 p l a n t s x 4 b l o c k s . = n u mb e r o f p l a n t s p e r s e x i n 40 p l a n t s o f d a t a . 130 observed. ease its numbe r s The f l a g recognition per na mel y t o daily. plot ranged collect the a n y d a ma g e c r o wn of the A V-slot grade classification. gauge 3B, was and mi n e to 4) visual whether or stage. plant measured 5.1.2. the plant of to The and w ere me ant the s a me a plastic The t a g plant pocket wa s also to tied slot 1B, 2, the it wa s size observations the plants entered (August 3A, 15). basis 3B, 4, scale a fruit w ere had into eight grade fruits to precisely of such carried out to Fruit the n u mb e r from t h a t date deter­ gauge. determine fruit three ( 3 A, grade V-slot stage cate­ circular the this daily and o v e r s i z e ) using entered its . growth days after the behavior wa s t h e n and o v e r the next Gathering fruit following to an u n d e r ­ and Another difficult shape stage scaled grades. grade circular was upper more growth whether period. Da t a Daily The grade on a d a i l y seven-day the increased. five in of determine fruit to The p l a n t s tagging the to fruit its not had fruit by r a i n . used 1A, fruit with to placed cucumber since whi ch belonged Daily used wa s entered (undersize, the c r own vegetation n u mb e r o f was fruit resembling the plant. sized gories to from one caused gauge ha d tied whe n The n u mb e r t a g resist the was n u mb e r procedure: behavior was determined utilizing 131 (1) Al l fruit ified to on d a y either The f r u i t wa s resistant, tify that entry day tern the T. plant fruit on d a y The t a g grade that of grade. the fruit 1A , 1B, 2, through lower 6, side (as bore For example, to be t r a c e d ( A) Determination and the 1A o r 1B ) on a new such was on kept period. natural counted, the it pat­ n u mb e r T=4 i n categor­ s a me whi ch d a y on w h i c h 44 step it wa s indicated the step allowed the that 1 placed from both in grades numbe r s N u m b e r i n g wa s in resembled g r a d e 3A ( t h e 4 wereg i v e n on t h e rainfall following informa­ collected: fruit per n u mb e r replicate numbe r s o f grade T. wa s it Determination of plant iden­ orientation. protect of r ow s p a c i n g to as gathering to and size. as fruit keep t h e plant the respectively). tion to and t h e 3 B, a n d This Al l following b e l o n g e d on d a y tag no t a g a n u mb e r on fruit the bore class­ its wa s m e a n t counted data T =1 —6) the of with been one. entire and t a g g e d 3A, a water that and and petiole were t a k e n also sunshine. per the counted to entering T+1 wa s 1B a c c o r d i n g already entangling (1). ( B) had during vine 16) The t a g g i n g Any f r u i t precautions of on t h e tape. fruit recorded, and 1A o r tagged ized, the grade T+1 wa s a n e w l y Extra Al l (August glossy on d a y on t h e (2) T=1 to whi ch per grade on d a y per plant T. fruit entering they entered the (whether 132 ( C) Determination day on d a y c a me f r o m Two p e r s o n s This given to in all fruit grade time have e l a p s e d one g r a d e whi ch it on was t r a n s - size data taken time before the and the day's fruit basis. the growth concern daily was time treatments. was t h e r e f o r e next to 4). measure Special plant this on a d a i l y to for period that 3B o r s a me t r e a t m e n t data from t h e grade treatments. the hour to exiting and t h e in c o l l e c t i n g twenty-four leaving grade collecting the follow schedule of (either minimized parameters the grade were fruit T+1. Determination an o v e r s i z e (4) the T and t r a c i n g ferred ( D) of A ensured measurements to wer e taken . (5) Five plants treatment taken to density wer e per on t h e f r om one and wer e the These laboratory wher e recorded. Thi s n u mb e r to was of first second wer e sampl e the in we r e plot a plastic counted, weight for five plants T, second and plot on e v e r y box, brought weighed, sealed, to the and establishing relationship plant per on d a y from t h e then the the per we r e plants The f r u i t s necessary fruit of boundary gathered fruits precautions by s e l e c t i n g T+1 . plots disturbing behavior done they boundary Special effect r ow on picked, tagged. fruit was the from a n o t h e r plant and in selecting the overall This r ow from t h e replicate. minimize treatment. then picked the on d a y T. 133 5.1.3. Data A n a l y s i s The e x p e r i m e n t a l analysis mo d e l variance. (1) A. - the vest Bj the These seven (2) of levels - the (as levels 1, and 1 - (as 3 for mo d e l d e ma n d e d a three-factor that the analysis of were: day of of this follow i = 1 —7) T= d a y three 2, of effect for data factors effect T (as setup harvesting. representing day the factor time of had har­ 7). plant j=1-3) This population. Thi s representing plant spacings 2 0 0 mm, 100 mm, factor had populations and 70 mm, four blocks r e s p e c t i v e 1y . (3) - the wi t h recording the of the suggested to factor analysis cell. This of data the the drop of and block factor had the to wa s block the basis experiment, required, effect approach fact was that This time-of-day variable As s u c h , effect treated A. B j effect on t h e (as was as treatment the i = 1—7 , blocks follow j =1-3). was mo d e l a t wo- n observations important that for the sampl e replicate we r e during for the have blocks, every treat­ The a s s u m p t i o n based to a s s ume d t o The f o u r per recording o n l y one per replicates insignificant analysis encountered experiment. four and daily a homo g e n e o u s d e ma n d e d per whe r e the with extremely be u t i l i z e d we r e cell this variance measurement. therefore, of sensitivity. a h o mog e n e ous me n t This parameters wa s d u e plants) eliminate daily nature on a d a i l y experiment (five effect. K= 1 - 4 . Due t o was block on t h e that earlier 134 analysis of recorded by o t h e r yield the sex studies.* analysis researchers Table outlay 5.1.3.1. for Analysis of the Fruit fruits entry and entering the following variance. The analysis wa s ma de on a p l a n t - a n d - a r e a the five plants was by m u l t i p l y i n g four fractional replicates. area basis 4.56 for (9.3 plant n umbe r s we r e e mer ged plants percent of Thi s per 9.3 Tables 13 a n d variance of daily rate m2 p e r and area of fruit plants basis, entry was 2, then it and by 3, exit the data. on a d a i l y rate to This fruit daily twenty was d o n e the 3.82, average to and Figure plants Those 10) by t h e and d i v i d i n g it by analysis to an fully (Table s u mma r y o f to average extrapolated n u mb e r e n t e r i n g twenty of entering 1.63, population ( 83%) analysis respectively. the respectively. per the by f o u r . 14 s h o w t h e fruit uptake/ of a two-factor for by m u l t i p l y i n g nonmoneci ous the 1, plant The f r u i t m2 ) by m u l t i p l y i n g reached fruit whe n c a l c u l a t i n g n u mb e r populations nutrient and e x t r a p o l a t e d n u mb e r out comes twenty. a plant basis. counted the findings Entry analyzed eliminate the fruit in and a representation wer e plants of involved I ( T ), entry to experiment 12 s h o ws of The n u mb e r o f basis, expression the 8 versus of plant on illustrates time in *H. P r i c e , I . W i d d e r s , R. B a u g h a n , a n d N. B l a k e l y . 1983. U n p u b l i s h e d r e p o r t s p r e s e n t e d a t t h e a n n u a l PPI m e e t ­ i n g , Mi chi gan S t a t e U n i v e r s i t y , Eas t L a n s i n g , Mi c h i g a n . Table 12. An Out l ay of the Ana l ysi s Table f o r Fruit Ent r y and E x i t Data. A. (Days) 1 ( 8 /1 5 ) EL ( Popul at i on o C\J m C_) c_> c_> CQ CO c CO CO < < 2 (8/ 16) 3 ( 8 /1 7 ) 4 ( 8 /1 8 ) 5 (8/19) 6 (8 / 2 0 ) 7 (8/21) Table 13. A Summary o f a Two- Fact or A n a l y s i s of Var i ance of t he Mean o f F r u i t Number (Per 20 P l a n t s ) E n t e r i n g t he P l a n t s on a D a i l y Basis Per P o p u l a t i o n , FACTOR B: POPULATION j FACTOR A: J DAYS i =1 i =2 i =3 i =4 i =5 i =5 i =7 =1 ( 2 0 0 mm) 13 ( m11) 27 („21) 21 (u31) 5 U41) 7 (p51) 2 (y61) 0 (p71) j =2 ( 1 0 0 mm) 15 (y12) 9 (j.22) 9 (y32) 8 (u42) 3 (p52) 1 (p62) 0 (p72) j =3 ( 7 0 mm) 11 (n13) 9 (u23) 14 (u33) 7 (u43) 0 ( u53) 1 ( m63) 0 (p73) Row E f f e c t (A) Col umn E f f e c t (B) F ratio 9.3 3.542 De gr e e s o f f r e e d o m 6 2 F 0.95 0.7 0.901 Mai n e f f e c t f o r Factor A at the i t h l e v e l <*i a r e not a l l equal Mai n e f f e c t f o r Factor B at the j t h l e v e l 3j a r e not a l l equal Ac ce pt n u l l h y p o t h e s i s Hq t h a t : Tukey' s Mu l t i p l e Co mpa r i s on 3.08 2.43 Interaction Effect (AB) 1 .4369 12 1 .92 Interaction ’ ij effect = 0 ( no i n t e r a c t i o n ) - Table 14. A Summary o f a Two- Fact or A n a l y s i s o f Var i ance of t he Mean of F r u i t ( Per 9.3m2 ) E n t e r i n g t he Pl ant s on a D a i l y Basis Per P o p u l a t i o n . FACTOR B: FACTOR A: POPULATION i =1 mm) i i =3 =2 DAYS i =4 j =1 (200 j =2 ( 1 0 0 mm) 57.13 ( y 12) 34 .3 8 ( y22) 34 .3 8 (u32) 30 .5 8 („42) j =3 ( 70 mm) 50.16 (ii13) 41.05 (y23) 63.8 5 (|i33) 3 1 .9 Mi . 21.19 (M11) 44 42.83 ( M1 . ) ( y 2 1 ) 34.25 (u31) 8.4 (A) i =5 (p 4 1 ) 11.4 (p43) 39.81 ( y 2 . ) 44 .1 6 ( | i 3 . ) 23.5 3 ( y 4 . ) Row E f f e c t Number p . , J Col umn E f f e c t i i =7 =6 3 .2 5 ( m61) 0 (u71) 11.48 (M52) 3.8 3 ( m62) 0 ( m72) ( m51) 0 ( m53) 4 . 5 ( m63) 0 (p73) 7. 6 ( m5. ) 3 .8 6 ( p 6 . ) 0 (u7.) (B) Interaction Effect (AB) F ratio 2.6 De gr ee s o f f r e e dom 6 F 0.95 0.7 0.901 1 .92 Mai n e f f e c t f o r Factor A at the ith level a i are not a l l equal Mai n e f f e c t f o r Factor B at the j t h l e v e l pj a r e a l l equal Interaction effect T ,j - 0 3.08 - - Ac c e p t n u l l h y p o t h e s i s HQ t h a t : Tukey' s Mu l t i p l e Co mpa r i s on 0.42 1 .26 63 12 ( no i n t e r a c t i o n ) 30 population 1 population 2 population 3 18 12 138 FRUIT NUMBER 24 0 1 2 3 4 5 6 7 TIME (DAYS) Fi gur e 8. Number of F r u i t E n t e r i n g Si z e Grade 1A Per 20 Pl ant s Versus Time in Days f o r t he Three D i f f e r e n t Pl an t Popu1a t i o n s . 139 days for vations (1) (2) the three can Al l plant be ma de f r o m fruit entering grade 1A b e f o r e There was ior. This points populations. The f o l l o w i n g obser­ Tables 13 a n d 14 a n d Figure 8 plant had t o pass onto going into no s p e c i f i c was the other trend illustrated by t h e curves of wa s e x p l a i n e d by t h e need of enough fruit to photosynthates could be in moderate ent decreasing cantly basis trend. affected the plants. At h i g h b e c o me s a greater growth light. ma s s was Thus, of that to enable a greater leaf to the the fruit, plants large 2 ha d differ­ signifi­ 1 on a p l a n t plant to of within than petiole and growt h for plants' tissues axillary from h i g h e r the fruit c ompet e the basis density vegetative plant plant s t e m and lamina, revealed continuously dry ma tte r percentage into As a r e s u l t , the fruit valley population populations, priority partitioned expense tissues. plant the population new f r u i t of on w h i c h and a generally theory nodes insignificnatly that Thi s assimilate peak with mo r e time. into while behav­ valley them wer e partitioning in o r d e r mo r e to however, points The f a c t by t h e plant the 3, and versus that "manufactured" explained peak transfer 1 and the differences on a d a i l y the Observing between fruit-entry entry produce and t o populations differences the to gr ow and m a t u r e . curves is set fruit through grades. in t h e in t h e . sun­ bio­ at stem density 140 stands produced 1983, the unpublished reason X-axis fruit entry of basis that the (Table rate the plant wa s o b s e r v e d Analysis of plotted grade 20, sions could both plants n u mb e r new f r u i t ) on an basis be s a me (peak and time of the twenty in fruit time a plant a similar of fruit A (days). basis. (in plants Figures the and 9 per twenty the next days) in The f o l l o w i n g figures variance the Factor T and e n t e r i n g against utilizing points). n u mb e r o f days) The n u mb e r o f as Relations per (in trend an a r e a N u mb e r - N u mb e r on d a y under of and respectively. followed the levels grade however, valley in a plant in each and s hown, behavior difference versus 25, an a r e a could a mong t h e analysis behavior It plotted be dr a wn t wo-way Al l of whi ch whe n c o m p a r e d one g r a d e 21 t h r o u g h on production peak, populations had t h e fruit on d a y T+1 wa s the each 3) differ­ respectively. leaving Figures (population significantly Fruit 9.3 m2 wa s wa s on b o t h of plants after supported not significant The n u mb e r through (zero 14). on a p l a n t was 5.1.3.2. (1) three entry entering Thi s also density point (Widders, 5. a mong t h e There petioles theory plant the touched area grade high even observed of the fewer This a valley ent per data). had The r a t e (4) that and continuously on d a y (3) larger and t h e fruit conclu­ outcomes n u mb e r per basis: general different trend in f r u i t populations. Thi s ■O population 1 30 -A population 2 -q population 3 FRUIT NUMBER 24 12 0 1 2 3 4 5 6 7 TIME (DAYS) Fi gure 9. F r u i t Number of Si ze Grade 1A Per 20 Pl ant s Versus Time in Days f o r the Three D i f f e r e n t P l a n t P o p u l a t i o n s . O--------O population 1 &-------- & population 2 O--------□ population 3 ro 0 1 2 3 4 TIME (DAYS) Figure 10. F r u i t Number o f S i z e Ti me i n Days f o r t h e Populations. Gr a d e Thr ee 1B P e r 20 P l a n t s Different Plant Ve r s u s 35 O-------- O population 1 A -------- A population 2 □ --------□ population 3 FRUIT NUMBER 28 21 14 143 7 0 1 2 3 4 5 6 7 TIME (DAYS) Fi gur e 11. F r u i t Number of Si ze Grade 2 Per 20 Pl ant s Versus Time in Days f o r t he Three D i f f e r e n t Pl ant Populations. 20 O____ q population 1 A -------A population 2 □ ------- □ p opulation3 16 12 8 4 1 0 2 3 4 5 6 7 TIME (DAYS) Fi gure 12. F r u i t Number of Si ze Grade 3A Per 20 Pl ant s Versus Time in Days f o r the Three D i f f e r e n t Pl an t Popul at i ons . 15 □ population 1 .& population 2 12 145 FRUIT NUMBER ■□population 3 1 0 2 . 3 4 5 6 7 TIME (DAYS) Figure 13. F r u i t Number o f S i z e Ti me i n Days f o r t h e P o p u 1a t i o n s . Gr a d e 3B P e r 20 P l a n t s Thr ee D i f f e r e n t P l a n t Ve r s us q q population 1 ^ ____ A population 2 □ -------Q population 3 1 0 2 4 3 5 6 7 TIME (DAYS) ure 14. F r u i t Number o f S i z e Ti me i n Days f o r t h e Gr a d e 4 P e r 20 P l a n t s Thr ee D i f f e r e n t P l a n t Ve r s us 80 O -------O population 1 A ------ A population 2 □ ------ □ populations FRUIT NUMBER 64 48 32 16 TIME (DAYS) Fi gure 15. F r u i t Number o f Si ze Grade 1A Per 9 . 3 Versus Time in Days f o r the Three D i f f e r e n t P l a n t P o p u l a t i o n s . O -------O population 1 70 A ------ A population 2 FRUIT NUMBER □ -------□ populations 42 28 148 14 TIME (DAYS) Fi gur e 16. F r u i t Number of Si ze Grade 1B Per 9 . 3 m? Versus Time in Days f o r the Three D i f f e r e n t P l a n t P o p u l a t i o n s . O------ O population 1 85 A ------ A population 2 □ ------ □ populations FRUIT NUMBER 68 34 149 17 TIME (DAYS) Fi gur e 17. F r u i t Number of Si ze Grade 2 Per 9 . 3 m2 Versus Time in Days f o r the Three D i f f e r e n t P l a n t P o p u l a t i o n s . 55 O------ O population 1 A ------A population 2 FRUIT NUMBER 44 □ ------ □ populations 33 22 1 50 TIME (DAYS) Fi gur e 18. F r u i t Number of Si ze Grade 3A Per 9 . 3 m2 Versus Time in Days f o r t he Three D i f f e r e n t P l a n t P o p u l a t i o n s . 55 O ------O population! A ----- A population 2 FRUIT NUMBER 44 □ ------□ population 3 33 22 TIME (DAYS) Fi gur e 19. F r u i t Number of Si ze Grade 3B Per 9 . 3 m2 Versus Time in Days f o r the Three D i f f e r e n t P l a n t P o p u l a t i o n s . 35 O------ O population 1 A ------ A population 2 FRUIT NUMBER 28 □ ------ □ population 3 14 152 TIME (DAYS) Fi gur e 20. F r u i t Number of Si ze Grade 4 Per 9 . 3 m2 Versus Time in Days f o r the Three D i f f e r e n t P l a n t P o p u l a t i o n s . /K 30 - O O O po p u latio n 1 A------& po p u latio n 2 - 18 - 12 - 6 - □ p o p u latio n 3 153 FRUIT NUMBER Q 24 1 0 2 3 4 5 6 7 TIME (DAYS) Figure 21 . Number of F r u i t E nt e r i n g Si ze Grade 1B Per 20 Pl ant s Versus Time in Days f o r the Three D i f f e r e n t Plant Populations. 0— 30 0 p opulation 1 A ------ A p opulation 2 □ ------□ p opulation 3 12 - 0 1 54 FRUIT NUMBER 24 1 2 3 4 5 6 7 TIME (DAYS) Fi gur e 22. Number of F r u i t Ent e r i n g Si ze Grade 2 Per 20 Pl ant s Versus Time in Days f o r the Three D i f f e r e n t Pl an t Populations. A 0 — 0 p o p u latio n 1 - 20 - 15 - 10 - 5 - A a p o p u latio n 2 □ o p o p u latio n 3 1 55 FRUIT NUMBER 25 0 TIME (DAYS) Figure 23 Number of F r u i t Ent e r i n g Si ze Grade 3A Per 20 Pl ant s Versus Time in Days f o r the Three D i f f e r e n t Pl an t Popu1at i o n s . O------O population 1 A----- A p o p u latio n 2 10 □ ------ q p o p u latio n 3 6 4 1 56 FRUIT NUMBER 8 2 0 1 2 3 4 5 6 7 TIME (DAYS) Fi gur e 24. Number of F r u i t E nt e r i n g Si ze Grade 38 Per 20 Pl ant s Versus Time in Days f o r the Three D i f f e r e n t Pl ant Popul at i ons . O po p u latio n 1 10 -A po p u latio n 2 ■Q po p u latio n 3 FRUIT NUMBER 8 6 4 2 0 1 2 3 4 5 6 7 TIME (DAYS) Fi gur e 25. Number of F r u i t E nt e r i n g Si ze Grade 4 Per 20 P l a n t s Versus Time in Days f o r the Three D i f f e r e n t Pl an t Populations. 158 was d e s c r i b e d as a function time nounced of peak decrease. of ferent than basis. It the also the similar, other populations plant smoother plant growth fruit stage. t wo d a y s grades 3A a n d t wo d a y s Fruit n u mb e r the one plant population The c u r v e s versus time for dif­ maintained in all higher be o b s e r v e d was the 1 and in at a 2 and plants 3. dur­ s l owed example, after peak 3. entered had g r a d e It also population 2 had 1 and 3. a function basis, mo r e reversed lower fruit plant plant on a d a i l y on an increase of in area popula­ basis. basis, fruit pop­ whi ch n u mb e r as increased. the we r e day significantly a significant the populations populations significantly reflected for for was under resulted 2, tions observation This Population On a p l a n t - c o u n t This could whi ch ulation. had rate on a p l a n t - c o u n t at population wa s and negative) time after value, development curve 3B f r u i t after It as gradual a significantly populations. that and n u mb e r a pro­ 1 generally positive populations. a growth reached however, (both affected fruit peak Population plant also n u mb e r increase, revealed growth in by a c o n t i n u o u s , of not increase high Thi s (3) the other ing f r u i t (2) wer e slope grades the rate populations. a steeper than until followed The decrease a general fruit still n u mb e r in increasing size when grades data 3B a n d collection 4 159 ceased. It n u mb e r o f (4) could fruit An i n c r e a s i n g size grade observed at this the thate per 1 A— stage 7. the than acted the plant Consequently, of fruit the size and 1A, whi ch The f r u i t that to to n u mb e r o f multiple linear fruit in regression was by t h e and photosyn- grade in to the fluid larger reabsorbtion to under­ 1B ( s i g n i f i c a n t ) in t h e curve that especially 1A ( n o n s i g n i f i c a n t ) grade fact flow of fruit rising to on d a y 7. i (as i = 1—6 ) p e r population grade i +1 on d a y was grade i on d a y of be resulted fruits s ome n u mb e r could fruit, peak later. fruit time This bi omass smaller size another grade fruit. there resulted per the the 7 or a mo r e d e m a n d i n g plant grade fruit n u mb e r transferred the size fruit grade as and o t h e r ones. of 3— v e r s u s other of curve size that be on d a y was e x p l a i n e d larger mo r e t r a n s l o c a t i o n from t h e in t h e Thi s however, could population fruit, sink grade trend on d a y c r o wn be c o n c l u d e d , T+1 T by t h e related general model : Yj = e0 + B1Xj1 + B2 Xj 2 + B3Xj 3 + B4Xj 4 + B5Xj 5 + B6 Xj 6 + ej [5.1] whe r e : Yj = response ferred Xj i Xj 2 = fruit ’ xj 3 * Xj in to the jth level grade i +1 from g r a d e n u mb e r 4 * xj variables) 5 in grade i at - fruit day » Xj5 = i n d i c a t o r utilized to n u mb e r t r a n s ­ i on d a y T+1 T variables distinguish ( dummy between days 1 60 The r e s p o n s e value of Y, , function and it wa s of this equal mo d e l was the expected to: J E( Yj ) = B0 + B1Xj1 + B2Xj 2 + B3Xj 3 + B4Xj 4 + B5Xj 5 + B6 Xj 6 Indicator variables classes of 1974). The y w e r e for me a n s a qualitative used any s i g n i f i c a n t tions per Table 15 l i s t s indicator are grade per the quantitatively variable in this population for to in t he and day level the Wa s s e r ma n , number-number to combination jth identify quantitatively f r om one and t h e every (Neter mo d e l differences days variables to [ 5 -2 ^ the value response test rela­ next. of for the every day . The d a t a the six days assigned between square to the representing we r e the days. the standard and To f i n d a general that to (as establishing n u mb e r time. the throughout essential the wa s error solution set in one mo d e l . of of for of equal and the F significance, grade of it the zero. mo d e l per distinguish least was d e t e r m i n e d . days, wa s grade solved, to that determined. popula­ wa s t h e r e f o r e null Thi s hypothesis woul d described population, In T signif­ and e v e r y i=2-6) per me a n s for values every acceptance behavior The wer e the regression relations correlation prove one p. B, given to wer e then parameter coefficient the number-number variables The mo d e l of icance, tion combi ned indicator estimator addition, all fruit result the regardless HQ in number of Table 15. A L i s t o f t h e Va l ue s As s i g n e d t o t h e R e p r e s e n t i n g D i f f e r e n t Da y s . a 1 ue Day Xj 2 Set o f Indicator Variables Xj 3 Xj 4 XJ 5 XJ 6 1 0 0 0 0 0 2 1 0 0 0 0 3 0 1 0 0 0 4 0 0 1 0 0 5 0 0 0 1 0 6 0 0 0 0 1 162 To e l a b o r a t e is given. linear Suppose in it regression number-number then on t h i s be t o both mo d e l for essential could in whether equations solution was behavior test principle, 1 woul d 1 and the (describing day to be u s e d days both the test to 2. slope days following whether describe The and 1 and exampl e s ame the fruit solution woul d the 2) the Y intercept wer e equal. The be: E ( Y j ) = 0q + + 83(0) + + ^5(0) + = s0 ♦ SjXj, The solution for day E ( Y . ) = Bg + 2 woul d be: + 32 ( 1) + ^ 3 ^ ® ) + ^ 4 ( 0 ) + 8 5 ( 0 ) + ^ 6 ^ ® ) = B0 + ®1Xj 1 + b2 = (Bfl + S2 ) + B j Xj , Since the necessary slope and for both equations was e q u a l sufficient condition for days to be e q u a l zero at the was true, woul d be grade per desired then valid the to general describe was not sion models true, should for however, confidence linear the days day in mo d e l level. 1 and the equal If 1Xj 1 relations separately linear for per If regres­ day 2 ( E ( Yj ) = B0 2 + fi2 Xj 2 ^ ’ to this 2 simultaneously. t wo g e n e r a l a both E( Y. ) = bq + number-number be e s t a b l i s h e d and mo d e l then &2 * s s t a t i s t i c a l l y be t h a t probability population this ( E ( Yj ) = woul d the (0 ^ , 1 This 163 should be f o l l o w e d whether the behavior should if on t h e b 2 =B3 , t h e n well. be and dard 3A, is standard then to the day, to 18, the arrived per | TI l t d The null day. for size also with the the hypothesis for on d a y grades of 1A, the T value. the 1B, stan­ Thi s & by HQ w h e r e day 3 as solution calculated b2 For e x a m p l e , present its the case, established 19 p r e s e n t along verify describe behavior The t a b l e s to In t h i s that mo d e l case to so o n . the by d i v i d i n g deviation. this used population parameter at be a c c e p t e d linear and respectively. of be and describe 17, mo d e l in Bi d e s c r i b i n g general 15 , test could 2 following the linear error value in. d a y utilized Tables general 2, mo d e l be e q u a l 2 could by a n o t h e r its B^O can if: - «/2, n-p) [5.3] where : t = tabulated ec = c o n f i d e n c e level t distribution (0.05) n = total n u mb e r of cases p = total n u mb e r of parameters 1 and studentized inear t (0.975, Wa s s e r ma n , (b) (24) in t h e general 17 ) = 2 . 1 1 , t ( 0 .975, 23 ) = 2 . 0 7 (Neter and 1974). population. regression test r e g r e s s ion Two r e g r e s s i o n including per The the model s first wa s indicator mo d e l that wer e calculated a multiple variables. best described per grade regression The the second per mo d e l wa s behavior. a simple Va l ue Table 16. Plant Population A Re p r e s e n t a t i on of the C a l c u l a t e d Values and Ana l ysi s Factors f o r the Parameters in the Li nea r Regression Model (Both M u l t i p l e and Si mpl e) Per Pop ul at i on f o r Si ze Grade 1A. Regression Model Mu 11 i pi e R2 = 0 . 8 3 Ana l ys i s Factors Va l u e Standard T Error Va 1 ue Standard T Error Va 1 ue Standard T Error Va 1 ue Standard T Error Va 1 ue Standard T Error Va l u e Standard T Error 8o *1 1.15 3.34 0.34 0.68 0.12 -3.17 2.19 -1 . 4 4 0.83 5.69 *3 % *5 S6 5.42 4.66 1. 16 -1 . 4 9 4.39 -0.34 -2.0 4.19 -0.47 -5.3 4.2 -1.27 -6.95 4.2 -1.65 -5.15 -3.38 3.05 - 1.11 P2 1 S i mp l e R2 = 0 . 7 5 Multiple R2 = 0 . 7 3 1 . 77 2.1 0.86 0.10 8.04 0.55 0.09 5.67 1. 54 2.64 2.1 2.1 -2.7 2.13 0.75 1. 27 -1 .2 -5.15 2.16 -2.4 0.34 2.87 -1.04 1. 04 3.0 0.34 -3.0 2.87 -1.04 -1.7 2.96 -0.57 2.6 -2.4 p S i mp l e R2 = 0 . 6 2 Multiple R2 = 0 . 7 3 -1 . 4 5 1 . 54 -0.94 0.74 2.6 0.29 0. 71 0.12 5.93 0.65 0.14 4.55 3 Si mpl e R2 = 0 . 6 8 -1 . 4 8 1 . 38 -1 . 57 0.77 0.11 6.88 Table 17. Plant Population A Repr e sent at i on of the C a l c u l a t e d Values and Ana l y s i s Factors f o r the Parameters in the Li nea r Regression Model (Both M u l t i p l e and Si mpl e) Per Pop ul a t i on f o r Si ze Grade 1B. Regression Mode 1 Multiple R2 = 0 . 8 7 An a l y s i s Factors 60 B1 Va 1 ue Standard T Error -3.3 2.89 -1.14 0.13 6.4 Va 1 ue Standard T Error - 2.6 1 . 61 - 1.6 0.92 0. 91 10. 9 Va 1 ue Standard T Error 0. 51 1 . 46 0.37 Va 1 ue Standard T Error -1 . 54 0.90 -1.7 0.8 Error 1.38 2. 51 0.55 -1.23 1 . 79 -0.68 0.6 0.86 B2 *3 P4 P5 *6 1. 83 4.07 0.45 5.48 5.2 1 . 05 6. 81 4.35 1 . 56 1. 28 3.9 0.32 -0.86 3.9 -0.22 -4.63 2.03 - 2.01 6.39 2.44 0.16 -1 . 3 4 2.23 -0.61 -2.26 2.06 -1.09 -1.81 -0.59 3.23 -0.18 -4.6 3. 21 -1.43 -6.41 3.43 -6.81 3.33 -2.04 -9.2 3.25 -2.82 1 S i mp l e R2 = 0 . 8 4 Mu 11 i p i e ? R2 = 0 . 8 8 S i mp l e R2 = 0 . 8 3 Multiple R2 = 0 . 6 4 S i mp l e R2 = 0 . 4 8 Va l u e Standard T Va l u e Standard T Error 0.82 0.105 7.76 2.02 -0.89 0.87 0.08 10.37 0.16 5. 1 0.149 4.009 -1 . 8 6 Plant P o p u 1a t i o n A Re p r e s e n t a t i on of the C a l c u l a t e d Values and Ana l ysi s Factors f o r the Parameters in t he Li nea r Regression Model (Both M u l t i p l e and Si mpl e) Per Pop ul at i on f o r Si ze Grade 2. Regression Mode 1 Mu l t i p i e 1 R2 = 0 . 8 4 S i mp l e R2 = 0 . 8 4 Error Va l u e Standard T Error Va l u e Standard T Error Va l u e Standard T Error Va l u e Standard T Error Va l u e Standard T Error -0.13E-12 0.88 -0.15E-12 -0.54 0.951 -0.57 62 0.64 0.038 16.7 0.34E-12 1.25 0.27E-12 B5 B3 B4 2.31 -0.41 - 6 .6 1.33 1.6 1.73 1.8 - 0.22 -0.15 B6 -4.5 1.42 -3.16 0.57 0.04 13-27 0.18E-12 0.43 1.5 0.109 2.14 0.12 4.00 0.37 - 0.02 0.45 0.79 0.61 - 0.02 7.37 -0.8E-13 - 2 .6 1.2 1.84 2.14 2.7 3.06 2.63 0.39 0.69 0.69 -0.96 0.15E-12 0.19 0 .68 0.41 4.7 6.9 2.4 0.14 3.8 3.9 4.2 4.11 CO • R2 = 0 . 5 3 Va 1 ue Standard T Bo I LU CO • o1 Mul t i pl e Analysis Factors 1 O -O 18. • Table 0.64E-13 2.35 0.18 0.1 1.11 1.66 0.56 1.04 0.297 1.6 0.105 0.62 2.81 0 S i mp l e R2 = 0 . 4 5 Mu l t i p i e R2 = 0 . 5 3 *3 S i mp l e R2 = 0 . 4 5 Table 19. Regression Model . Analysis Factors eo s, S2 63 CO. Plant Population A Repr e sent at i on of the C a l c u l a t e d Values and Ana l ysi s Factors f o r the Parameters in the Li nea r Regression Model (Both M u l t i p l e and Si mpl e) Per P opul at i on f o r Si ze Grade 3A. S6 0.27 I *5 Mu l t i p i e R2 = 0 . 8 4 Va l u e Standard T Error Va l u e Standard T Error Va l u e Standard T Error Va 1 ue Standard T Error Va l u e Standard T Error Va 1 ue Standard T Error 0.59 0.31 0.31 -0.18 0.31 0.09 0 .88 0 .88 0.9 0 .88 1.16 -0.3E -1 3 6.27 0.35E-13 0.3E-13 - .0 2 0.3E-13 0.23 -0.13E-1 0 .6 -0.29 - 0.86 0.66 0.81 -0.1E-13 0.62 1 S i mp l e R2 = 0 . 8 4 Mu l t i p i e R2 = 0 . 5 3 0.24 0.57 -0.57 10.75 0.3E-14 0.143 0.9E-14 0.9E-14 0.45 0.7E-14 0.08 0 .6 0 .6 2.68 0.15E-13 0.15E-13 -.43 0.104 1.34 -1.05 0.7E-13 -0.29 - 0.8 6 0.104 0.66 0.81 -0.43 -1.05 2 S i mp l e R2 = 0 . 4 5 Multiple 3 R2 = 0 . 5 3 S i mp l e R2 = 0 . 4 5 - 0.16 0.14 0.21 - 0.76 0.031 4.26 0.3E-14 0.143 0.9E-14 0.9E-14 0.45 0.08 0 .6 0.6 0.7E-14 1.68 0.15E-13 0.15E-13 - 0.16 0.14 0.21 - 0.76 0.031 4.26 1.34 0.7E-13 168 transformation of the and of regression three sets a - exponential model: b - model: logarithmic c - parabolic The coefficient R2 ) was of independent the lations scored and model s mo d e l : of determined simple variable wer e was E(Yj) = B„ ^ L n X j , E ( Yj ) - B0 + (regression for model s linear three regression grades), R2 a much h i g h e r in value and model. the simple than those out, generated: determination the carried , + B 2Xj , coefficient— compar ed In a l l cases to (popu­ linear regression of other the that three mode 1 s . The f o l l o w i n g analysis (1) in conclusions Tables In a l l cases 16 t h r o u g h the the logical simple the n u mb e r in grade The the to axis. no f r u i t following i t . R2 i n t h e simple R2 i n the variables in the of in the line This was so zero then pass the from t h i s contribution regression of followed through if the be fruit to smaller This the model. the zero, grade always model. multiple This should was Y intercept pass because regression multiple the both to then regression the from t h e models. for multiple the elimination grades), regression i wa s could and insignificant necessity of one be linear origin since (2) to be d r a w n 19: (populations S 0 wa s p r o v e d and could was the than due indicator 169 (3) The p a r a m e t e r s belonged to the collection change in that later (days the the fruit the influence we r e days 5 and of during ). 6 behavior maturity significant of This the entered the larger the of b. j) data interpreted by t h e number- number later size than period wa s fruit the (other growth fruit as stages dominated a nd the behav i o r . A simple fruit was regression number-number due grade to the fact 4 on d a y s specific section this 5.1.3.3. of An a n a l y s i s fruit the only. 6 of Fruit approach mo d e l model. weight n u mb e r that size describe 3B a n d along the 4. entering model, will to grades started Thi s situations, number-number fruit for utilized Thi s size with those in a later be p r e s e n t e d chapter. Analysis Fruit was behavior 5 and describing mo d e l per wa s Yj ' 60 + Bf Xj 1 similar followed (in grade Nu mb e r - We i g h t grams) per to in that the Relations of the fruit wa s m o d e l e d as fruit weight-number a function of population: + s 2 Xj 2 + e3Xj 3 + ‘ j [ 5 -4] whe r e : Yj = response Xj1 = f r u i t Xj £, Xj ^ in n u mb e r the per = indicator between jth level - fruit weight i n g r a ms grade variables populations. The utilized to distinguish respective combination 1 70 values of X j 2 » X^ 3 , w h i c h (1,0), (0,1), stood for we r e equal to populations 1, ( 0 , 0 ), 2, and 3, respecti vely. The of response Y, , and function it was of this mo d e l equal to: B 1XJ1 + B2 Xj 2 wa s t h e expected value «J E(Yj) = Bq The mo d e l the + was parameter solved, was error of e, cient of correlation the n u mb e r and data trate the the determined. on wer e this was data collected per in t he grades. The least however, a function a daily square In a d d i t i o n , determined. grade we r e and of time analysis points mo d e l could estimator the a nd was that Tables regression following It a sum o f basis. values linear [ 5 . 5] T significance, model , not calculated parameters size of relation thus and t h e F significance, generation + B3 Xj 3 the during sampling) individual 20 a n d 21 factors for coeffi­ weight- (day of the standard a s s ume d the of illus­ for the the six be o b s e r v e d fruit from thi s analysis : (1) There was a high correlation between fruit n u mb e r and we i g h t . (2) The c o n t r i b u t i o n was insignificant. general the of simple fruit the Thi s linear population resulted regression number-weight populations plant in t h e mo d e l relationship simultaneously. to mo d e l adoption that for the the of described three a Table 20. Fr u i t S i z e Gr a de A Repr e sent at i on of the C a l c u l a t e d Values and Anal ysi s Factors f o r the Parameters in the Li nea r Regression Model (Both M u l t i p l e and Si mpl e) f o r Si ze Grades 1A, 1B, and 2. Re g r e s s i on Mode 1 Multiple R2 = 0 . 8 5 Anal ysi s Factors &0 Va l u e Standard T Error -6.16 4.78 -1.3 12. 9 0.88 14. 7 Va l u e Standard T Error -4.3 2.8 - 1.6 12. 7 0.76 16. 6 -7.27 6.63 30.63 1. 2 &2 B3 -1.04 4.2 -0.25 3.99 4.5 0.89 S t u d e n t i zed t - Di s t r i bu t i on 2.02 1A S i mp l e R2 = 0 . 8 4 Va 1 ue Standard Error -3.39 7. 1 -0.47 7.2 171 Multiple 2.02 2.01 R2 = 0 . 9 3 1B S i mp l e R2 = 0 . 9 2 Mul t i pl e R2 = 0 . 9 3 Va l u e Standard T Error -8.66 4.85 -1.8 30.67 1. 2 24.7 Va l u e Standard T Error -21.5 20.46 -1.1 72.7 3. 1 23.7 Va 1 ue Standard T Error -35.1 1 4. 1 -2.48 73.6 2.9 2.53 3 S i mp l e R2 = 0 . 9 3 2.01 -7.6 18.8 -0.4 -21.8 19. 1 -1.14 2.02 Table 21. Fruit S i z e Gr a de A Re p r e s e n t a t i on o f t he C a l c u l a t e d Values and Ana l y s i s Fact ors f o r the Parameters in the Li nea r Regression Model (Both M u l t i p l e and Si mpl e) f o r Si ze Grades 3A, 3B, and 4. Re g r e s s ion Model Multiple R2 = 0 , 9 5 Analysis Factors Val ue Standard T e 0 & 1 Error -11.4 22.2 -0.5 111 4.6 23.9 Error -17.4 19.4 -0.9 112 4.9 22.7 2 e 19.5 19.4 -1.6 e 3 -32.2 19.9 1. 0 S t u d e n t i zed t - D i s t r i but ion 2.04 1A S i mp l e R2 = 0 . 9 3 Multiple R2 = 0 . 9 8 Va l u e Standard T Va l u e Standard T Error 14.4 34.4 0.42 141.3 6.0 23.5 Error 34.5 20.2 1.7 139.2 5. 1 27.2 Error 12.9 31.8 0.4 194.0 7.9 24.6 Error 18.6 39.9 0.47 198.2 9. 1 21.78 2.04 21.2 30.19 0.62 18.8 30.5 0.42 2.11 1B S i mp l e R2 = 0 . 9 8 Multiple R2 = 0 . 9 8 Val ue Standard T Val ue Standard T 3 Si mpl e R2 = 0 . 9 8 Va l u e Standard T 2.09 -1.57 34.9 -0.04 6.28 35.2 0.17 2.2 1 73 (3) The d a t a that wer e passed best fitted through the in a simple origin linear regression (nonsignificant Y- i n t e r c e p t ). 5.1.3.4. Analysis After the experiment, vested sion and of plots sampl es fruit woul d rapidly ber. A linear fruit weight fruit wer e for in size It wa s gain in the the weight grade weight size correlation however, that this woul d or probably cient of fruit weight mo d e l period if a on d a y weight on d a y net fruit weight exit size grade with R2 = 0 . 8 2 . after 4 on d a y correlation period only T. T of on d a y wa s exiting T+1) T+1 to day we r e these the (in It fruit s a me grams) should the only. data n u m­ the to be n o t e d , collected A n o t h e r mo d e l (either chosen. logarithmic This coeffi­ by c o r r e l a t i n g fruit the the The mo d e l s a me c l a s s with regres­ correlating T. obtained the that using harvest T+1 on t h e fruit on d a y fitted Al l previous fitted be h a r ­ fruit maintaining was the to days. exiting 4 on d a y was the four grade was end o f remained however, grade longer determination the the while mo d e l s how b e t t e r exponential) 4 of observed, high a three-day of n u mb e r o f exiting of at fruit a period revealed over harvested regression of Exit oversize correlating insignificant. the Fruit of exami ned model s n u mb e r of diminished (resulting weight of the by in a fruit 1 74 5.1.3.5. Maturity Since it all data wa s e s s e n t i a l observations used and This index a general tion of observations to into n a me d Factor utilize the should only maturity one. maturity description the a me t h o d a universal as w e r e ma d e on a d a i l y the factor interpret A dummy factor be u t i l i z e d of to or for plant maturity this in the daily indicator mo d e l dynamics. proceeded basis, the was index. and not as The d e t e r m i n a ­ following manner: (1) Al l days n u mb e r with or specific conditions number -number for relations either wer e weight- ma p p e d per popu­ lation . (2) Several indices combinations of total we r e (3) (4) The were of calculated number-number distribution also index ma p p e d with as in conditions Step behavior of the was or fruit a distinct specific The m a t u r i t y of utilizing different weight-weight grades. These percents indices (1). matching selected of to that for represent the the plant. factor selected was MF a n d equal to: MF = N1A(T)+N1B( T) / N 1A( T) +N1B( T) +N2 (T)+N3 A( T) +N3 B( T) +N4 (T) To e l a b o r a t e (Table 16) observed the could that behavior on t h i s be u s e d while up t o daily this day 5, point, as population an e x a m p l e . grade the had It 2, could a regression behavior in grade days [5.6] 1A be explaining 5 a nd 6 175 needed a special condition. woul d not be p o s s i b l e stage wa s in. knowledge o f fruit. a nd It This the could , MF wa s 6 figure could tive mo d e l stage or equal the randomly the that shifting the days plant it growth lack started thirty-five in plant general plant in t h e s e Plant of setting during days percent. to the later was 5 This condition in a p r e d i c ­ in t h i s maturity plants we r e for The MF i s of is classified 2 holds less or 1, 40,000, describing true equal 2, and 80,000, only to 3, on 90 here, and respectively. Recovery fruit recovery first behavior density Ma c h i n e the relations Populations Fruit in the relation 1 and hectare, collected experiments. is a plant per Harvester This 50 p e r c e n t . resemble Behavior s ummar i ze For e x a m p l e , population than 25 Dy n a mi c behavior. basis. The d a t a (Ri) day t h e however, whether 5.2. 5.2.1. of the to behavior 22 t h r o u g h plant greater 110,000 whi ch be u t i l i z e d dynami c a condition and or Su mma r y o f plant the because in by k n o w i n g Tables if whi ch model , not. 5.1.3.6 . the tell be r e a l i z e d , then described is day less that to In a p r e d i c t i v e Performance per s u mme r o f experiment grade 1982 per harvester i n t wo measured the separate recovery of Table 22. Summary o f RELATION* TITLE NUMBER 1 Fruit Dynamic Behavior R e l a t i o n s f o r Fruit Si ze Grade 1A. NUMBER CONDITION RELATION Function** N1 A(T+1) = 0.17 * N. , ( T) + 40 1A 1A + 44 + 24 + 8 + 4 + 0 4. 2 4 .2 4 .2 4 .2 4.2 4 .2 Equation M aturity Index Population MF>90% 90%>M >55% 55%>MF>35% 35%>MF>25% 25%>MF>20% MF<20% 1 1 1 11 12 MF>95% 95%>MF>60% 60%>MF>35% 35%>MF>30% 30%>MF>25% MF<25% 2 2 2 2 2 2 1 2 3 4 5 6 1 1 1 + 24 + 8 N1A(T+1) = 0.27 * N1a (T) + 4 + 0 4.2 4.2 4 .2 4.2 4. 2 4 .2 1 N. . (T+1) = 0.23 * N. . (T) + 40 1A 1A + 4 + 2 + 8 + 4 + 0 + 0.42 * N1 B(T) 4. 2 4 .2 4.2 4 .2 4.2 4. 2 13 14 15 16 17 18 MF>65% 65%>MF>60% 60%>MF>35% 40%>MF>35% 35%>MF>30% MF<30% 3 3 3 3 3 3 2 W1a (T+1) = 12.7 * N1A(T+1) 4.18 19 ALL ALL 1 N1A(T+1) = 0.29 * N (T) + 40 1A 1A + ^ * 1 = number-number r e l a t i o n ; 2 = wei ght -number r e l a t i o n * * Refer to Chapter 4, Sect i on 4 of t h i s t e x t . 7 8 9 10 Table 23. Summary of Fruit 1 1 Fruit Si z e Grades 1B and 2. NUMBER RELATION* TITLE NUMBER 1 Dynamic Behavior R e l a t i o n s f o r CONDITION RELATION Function** Equation M aturity Population Index 1 MF>55% N1B(T+1) = 0.08 * N1 B(T) + 0. 6 * N 1 A(T) 4. 3 20 + 0 .8 3 * N U (T) 4 .3 21 MF<55% 1 N. (T+1) = 0.13 * N b (T) + 0. 5 * N . a (T) 4. 3 22 MF>60% 2 + 0.71 * N1A(T) 4. 3 23 MF<60% 2 N1B(T) + 0 . 5 3 * N 1A(T) 4.3 24 MF>65% 3 + 0.65 * N1A(T) 4 .3 25 MF-^65% 3 N1b (T+1) = 0 .4 * 2 W1b(T+1) = 30.67 * N1B(T+1) 4.18 26 ALL 1 N2 (T+1) = 0.43*N2 (T)+0.92*N1B(T)+0.23*N1A(T) 4 .4 27 MF>55% 1 + o * n 1a ( t ) 4. 4 28 25%60% 2 + o * n 1a ( t ) 4. 4 31 MF<60% 2 N2 (T+1) = 0 . 7 * N 2 ( T) +0. 6*N b (T)+0.24*N1a(T) 4.4 32 MF>65% 3 + o * n 1a ( t ) 4. 4 33 35%25% 1 1 N3 a (T+1) = 0.86 * N3 A(T) + 0.35 * Ng (T) 4 .5 38 ALL 2 1 N3 A(T+1) = 0.86 * N3 a (T) + 0 .3 4 .5 39 ALL 3 2 W3 a (T+1) = 112 4.18 40 ALL ALL 1 N3 B(T+1) = 0.68 * N3 b (T) + 0.32 * N3 A(T) 4. 6 41 MF<55% 1 N3 B(T+1) = 0.48 * N3 B(T) + 0.28 * N3 A(T) 4. 6 42 MF>55% 1 N3 B(T+1) = 0.60 * N3 B(T) + 0.07 * N3 A(T) 4. 6 43 MF<60% 2 N3 B(T+1) = 0.42 * N3 b (T) + 0.06 * N3 A(T) 4. 6 44 MF>60% 2 N3 B(T+1) = 0.90 * N3 B(T) + 0.14 * N3 A(T) 4. 6 45 MF<60% 3 N3 b (T+1) = 0.78 * N3 B(T) + 0.09 * N3 ft(T) 4 .6 46 MF>60% 3 W3 B(T+1) = 139.2 *N 3 B(T) 4.18 47 ALL ALL 1 1 1 2 N3 A( T + 1 ) = 0 . 4 Equation * N2 (T) * N 3 A(T+1) * 1 = number-number r e l a t i o n ; 2 = number-wei ght r e l a t i o n * * Refer to Chapter 4, Sect i on 4, of t h i s t e x t . Tabl e 25 . Summary of F r u i t Dynamic Behavi or R e l a t i o n s f o r F r u i t Si ze Grade 4. CONDITION NUMBER RELATION* TITLE NUMBER RELATION Function** Equation M aturity Index P opulation n4 ( t+ d = 0.32*N3B(T )+ 0 .2 8 * N 3A(T)+N4 (T) 4 .6 48 MF>55% 1 n4 ( t +D = 0.52*N3B(T )+ 0 .3 2 * N 3A(T)+N4 (T) 4 .6 49 MF<55% 1 ) = 0. 4 *N3B(T )+ 0 .0 7 * N 3A(T)+N4 (T) 4 .6 50 MF>60% 2 n4 ( t + d = 0.58*N3B(T )+ 0 .0 8 * N 3A(T)+N4 (T) 4.6 51 MF<60% 2 n4 ( t+ d = o . io * n3B( t )+ o * n3a ( t )+ n4 ( t ) 4 .6 52 MF>60% 3 n4 ( t + d = 0.22*N3b (T )+ 0 .0 5 * N 3a (T)+N4 (T) 4 .6 53 MF<60% 3 2 w4 ( t + d = 198.2 * N4 (T+1) 4.18 54 ALL ALL 3 W0S( T) = 0 - 55 MF>35% ALL W0S( T+1) = Wq s (T) + 0.11 * W4 (T) - 56 MF<35% ALL 1 1 1 n4 ( t+ i * 1 = number-number r e l a t i o n ; 2 = number-wei ght r e l a t i o n ; * * Refer t o Chapter 4, Sect i on 4, of t h i s t e x t . 3 = weight-weight r e l a t i o n 180 both the MSU p r o t o t y p e and m o d i f i e d experiment was Chesaning, Mi chi gan ( Hobs on experiment recorded the harvester and carried done at was mo r e appropriate the derived experiment wer e recovery for The f i r s t ized design 17 m x 2 . 1 and a Wi l d e experiment twenty first part grade from t h e then uniform This vines per plot graded The wa s into was wa s This hand South mo d e l experiment Georgia. Since utilize a set data, results of divided et a l. of plots, t wo determining done all f r o m an random­ was subplots: plot 4.25 the hand- was, in m long. potential by p u l l i n g the of (1982). a completely each second coefficients Ea c h r e p l i c a t e into it of the wer e e x t r a c t e d hand-harvested picking This the second MSU p r o t o t y p e to Instead, was The all fruit. the The fruit vines The f r u i t was and w e i g h e d . second harvest carried while in consisted for the Thi s farm, 1983). harvester. by R o t z , t wo e q u a l plot. both harvester The utilized and of replicates. machine-harvested. divided farm this Weisenberger Marshall, Wi l d e included. m and e a c h turn, per for reported with and from Mi chi gan not research the recovery Rodenberry parameters earlier at a commercial was fruit out Cu k e * h a r v e s t e r s . plot of plots out leaving wa s u s e d as if to no by c a r e f u l l y the vines prepare the harvester interruptions picking on t h e all ground. for a w e r e ma d e . fruit from t h e Al l * Th e h a r v e s t e r wa s m o d i f i e d by t h e Au n t J a n e ' s p i c k l e c o m p a n y ; t h u s , i t may n o t n e c e s s a r i l y r e p r e s e n t a c o m m e r c i a l Cu k e h a r v e s t e r . 181 mechanically-harvested and then lected Table wei ghed after and for the me a n fruit recovery grade. Table 27 s u m m a r i z e s harvesting kilogram of system A one-way a. = 0 . 0 5 . significance the Cuke h a r v e s t e r utilized parison. (1) and fruit than returns hand wer e MSU p r o t o t y p e (2) There Cuke (3) wer e and harvested It wa s a s s ume d of fruit then recovery of the system per recovery per by h a n d was in t h i s study. out at used to per harvesters. t-test col­ test grade for The c o n ­ for me a n c o m- • following: significantly The Cu k e less less grade 1 harvester than those of the harvester. MSU a n d 2, also fruit carried we r e in significantly no s i g n i f i c a n t grades was studentized harvesting. also harvested yield bins studies. fruit harvested MSU p r o t o t y p e the wer e deviation of potential difference concluded MSU p r o t o t y p e of contrasts a pairwise, The t e s t The the separate harvester percent variance linear of per Fruit field of in quality standard Kg/ha) the food kilograms the Multiple and and fruit. analysis the trast in potential a s s ume d to r e p r e s e n t (in collected Random s a m p l e s brine me a n o f per was graded. grading 26 s u m m a r i z e s fruit differences hand-harvested between returns for MSU a n d fruit size 3 A , 3B , a n d 4 . size descriptive in t h i s grade for size study 1 in that both grades the percent harvesters 1A a n d r 'e c o v e r y wa s e q u a l l y 1B , s e p a r a t e l y . Ta b l e 26. The Mean, S t a n d a r d D e v i a t i o n , A n a l y s i s of Va r i a n c e of F r u i t S y s te m p e r Gr a d e ( i n K g / h a ) . and P a r a m e t e r s o f t h e R ecovery p e r H a r v e s t e r MSU Pro totyp e Degrees o f Freedom (n -p ) 184.2 (90 .6 ) 465.1 (180.8) 2832.8 (1024.6) 1760.33 (829.5) 3A 3592.2 (750.9) 3B F ru it Grade 1 2 4 Hand Harvest Cuke+ H arv ester F Ratio CAL TAB 37 86.71 3.25 2139.5 (1023.8) 37 7.98 3.25 3174.32 (741.9) 3225.37 (534.9) 37 2.84 3.25 3728.1 (1675.2) 3401.8 (1727.1) 3176.4 (1277.0) 37 0.77 3.25 1544.26 (815.0) 1377.0 (870.1) 1337.1 (852.9) 37 0.46 3.25 791.0 (168.3)* * S t a n d a r d d e v i a t i o n o f t h e me a n . ** F - r a t i o f o r a o n e - w a y a n a l y s i s o f v a r i a n c e w i t h 3 l e v e l s o f factor A (harvester systems). + T r a d e na me s a r e u s e d f o r i d e n t i f i c a t i o n p u r p o s e s o n l y and do n o t i mp l y e n d o r s e m e n t . 183 Table 27. P e r c e n t of F r u i t Recover y per H a r v e s t i n g System i n Kg o f H a r v e s t e d F r u i t p e r Kg o f H a n d - P i c k e d Fruit. ‘''■■''■^.Harvester ^ S y s’tGm F ru it G ra d e < ' ^ ^ Type 1* H a rv e s te r System Type 2 H a r v e s te r System Type 3 H a rv e s te r System 1 32% 23% 59% 2 67% 62% 76% 3A 80% 88% 90% 3B 80% 91% 86% 4 80% 89% 87% * T y p e 1, 2 , a n d 3 h a r v e s t e r s y s t e m s s h a l l be u s e d t o s t a n d f o r a W i l d e , C u k e , a n d MSU p r o t o t y p e h a r v e s t e r s , respectively. T r a d e n a me s a r e u s e d f o r i d e n t i f i c a t i o n p u r ­ p o s e s o n l y a n d do n o t i m p l y e n d o r s e m e n t . 184 5.2.2. Harvester Field Per f or mance Twe nt y o b s e r v a t i o n s different season. locations idle defined of time ( UNLOAD) , time as a tank ( STOP) the filling The s p e e d to standard also were this deviation the system of determined the start the time that in the capacity each harvester determining of the (11,000 actual harvester the cycle by d i v i d i n g data by 8 . 3 3 . a me d i u m t r u c k tank. to Thus, of type it in from t h e wa s start the time the and me a n This took per har­ data types from variable. a Type It 2 har­ wa s a s s ume d a me d i u m t r u c k observed a truck. and a nd r e c o r d i n g per for me a n These harvester start har­ The t a b l e STOP. variable. was o f all by t h e deviation spent per by m e a s u r i n g K m/ h r . and utilized capacity if speed, period 28 s h o ws t h e a next time such spent standard UNLOAD, wa s since into cycle spent time speed harvester Kg) , both determined the as ( TRANSPORT) , determined Table me a n that of dividing by t h e filling tank was tracing approach in start. a tank deviation the harvester of directly of the and t h e by t i m e unloading then The TRANSPORT, A different vester period. distance. average variables cycle it the those type 1982 h a r v e s t time harvester of the transportation a next dividing cover s h o ws vester to harvester during included time of the a 4 5 - m r ow a n d vester per total ma de p e r Mi chi gan The o b s e r v a t i o n s unloading and in wer e harvesters The me a n idle and n u mb e r by t h a t a Type time and of standard ( STOP) w e r e standard was we r e deviation reached by a commerci al 2 harvester 181.6 Tabl e 2 8. Mean and S t a n d a r d H a r v e s t e r Ty p e . Deviation of Field Pe r f or ma nce Parameters per Unload Time (UNLOAD) Min Id le Time (STOP) Min Cycle Time Percent E ffic ie n c y (Km/hr) T ransport Time (TRANS) Min Min % 1 3.13 (0.18) 1 . 37 (2.65) 6 .2 1 (1.41) 1. 24 (0.96) 21. 41 (3.81) 59 2 2.04 (0.06) — — 1. 97 (0.71 ) 21. 81 (4.42 ) 91 3 2. 31 (0.15) 1 .1 2 (0.24) 8 .2 (3.27) 1. 26 (0.52) 25.8 (2.44) 59 Performance Parameters Type H arvester Speed 186 minutes is (SD = 3 6 . 8 2 ) a s s ume d utes that (181.6 this fill per cycle efficiency efficiency. a medium-sized harvester r 8.33) Percent field to was if field a hectare-per-hour basis, the in cycle time. lated the sense, by d i v i d i n g total cycle efficiency the fruit 5.2.3. spent in in t h e maturity the eleven subjective due t o the hours a m) , (2) efficiency This wa s to efficiency TRANSPORT, UNLOAD, it from was Field it min­ tank. stand for calculated efficiency Harvesting carried per It out day wa s to wa s on used, was calcu­ and STOP by 100. This a function of county and growers (from nature fact that included: availability of as am t o and that (1) (from a receiving assisted survey variable. could Wh i l e replies records. extend hours forecast the we r e This har­ (up t o circumstances. room a t that generally the accurate additional rainfall type. counties) p m) , hours I n g h a m, HOURS was 9:00 constraining average harvester a fixed lacked four the from t h e s ome f a r m e r s to certain Gratiot replied in per agents revealed 10:00 Hour s determine ( HOURS) be e s t a b l i s h e d from one given circumstances day, of Shiawassee, to mo d e l and Ionia, surveyed in t h i s percent 21.81 a commercial a variable extension not had spent then stage. surveying. vesting 1:00 was have subtracting Mi chi gan HOURS c o u l d wa s and however, harvesting Jackson, very sum o f Determination Several equal the time was, A survey all for it used Wh i l e this woul d truck, for the These the next processing 187 plant, call and for (3) too another smal l a remaining operation on t h e 5.3. Except trucks of general all directly into and average annual harvesters equipment mathematical (Tuse) the and ma nage me nt next ma na ge me nt parameters the.model equations. harvesters wer e or hours farmers suggested The a v e r a g e wer e to of use of records As a r e s u l t , to be e i t h e r through annual 300 and Parameters lacked parameters. calculated ( USE) Ma n a g e me n t total ( USE) , field day. Harvester for (Tuse) unharvested fed preestablished use of 150 h o u r s , trucks respec­ tively. 5.4. The d a t a no-go days Mi chi gan Agriculture years essential for the included These d a t a for generating different daily evaporation obtained from t h e and Mi chi gan We a t h e r wer e location. These Lake and recorded were: 2935 7690 f o r South for East Ha ven. and buffer-out Pan A e v a p o r a t i o n written basis. using A computer Fortran soil we r e go, types in Department The d a t a coded of for the tape per 4502 The d a t a included, daily for precipitation inches). recorded algorithm IV c o m p u t e r of Lansing, the (in Workability precipitation. on a s e p a r a t e , (in data and Service. c 1 i m a t o 1o g i c a 1 r e c o r d s , The c l i m a t o l o g i c a l and Mi chi gan a mo ng o t h e r inches) sequences locations wer e 1961-1980 City, Field on a c o n t i n u o u s , ( DATATAP) wa s langguage to decode the 188 data per blocks. location and A listing of The o u t p u t and of this evaporation 21,840 d a t a (2) silt another l o a m, the and (3) clay for soil The a l g o r i t h m computer language chart this of and for the years The computation location 1961-1980 ( LOCATE) , precipitation tional started (PRECI P), variables wer e read from t h e was the area in ( T YP E ) , whi ch was the soil type. the array of field capacity point ( PWP ) , data decisions of the carding soil moisture stored ( FC), and the rest saturation area of the drainage (AREA = LOCATE) specified ( SAT) . and input wer e be the to data. the variables day ( DAY) , Two a d d i ­ directly: simulated, selected loop soil included: wilting with allowed be e x e c u t e d and f r o m an permanent ( 6 <. MON < 8 ) IV June-August. These A data per conditions selected ( DRN) , of 26. ( EVAP ) . to into A flow ( MON) , algorithm. rate and mo n t h s the Us i ng parameters within of terminal Mi chi gan fed Fortran Figure mo n t h and e v a p o r a t i o n whi ch was C2. of (1) l o a m , location in from t a p e ( YEAR) , ( AREA) , type, in mont hs by r e a d i n g year per day, a total wer e: trafficable the mont h, sequence Appendix daily during the written presented simulated year, output August in in A p p e n d i x C1. types generate listed and mo n t h l y location, This wa s a l s o is given soil mo n t h o f is algorithm The a l g o r i t h m of the yearly the per l oa m. (CUCWEAT) t o days is included The t h r e e trafficable type. into precipitation sets. algorithm it algorithm algorithm and record rearrange t wo the while data dis­ 189 START D A T A : S A T , DRN _ _ _ _ _ _ _ F C , PWP READ AREA TYPE READ: L O C A T E . YEAR M O N , DAY P R E C I P , EVAP EVAP DATA TAPE AREA: LOCATE 6 < MON < 8 Figure 26. Fl ow C h a r t of Program CUCWEAT. 190 S0ILM(T-1)=0.9*FC (TYPE) DAY = 1 RUN(T) = 0 DRAIN(T) = 0 INFI L( T)=PRECI P*22 .86 EVPTRANC T)=8.6*f*EVAP SMOIS=SOILM(T-1) IN FI L( T) h. SMOIS < FC(TYPE) SMOIS £ SAT(TYPE) + EVPTRANC T) DRAIN(T)=0 RUN(T) = 0 RUN(0 ^ SMOIS-SAT (TYPE) DRAIN(T) = s o il m C T) 48 JOAY > 17 OUTPUT RETURNS 'NONTRAFFICABILITYi STATEMENT Figure 27. / Continued. DAY WORKABLE 204 CAL. VARIABLE SET 6 OUTPUT VARIABLE SET 6 OUTPUT: NEW DATA CAL. VARIABLE SET 7 OUTPUT VARIABLE SET 7 Figure 27 . INPUT NEW DATA Continued. 205 entry statement bers. only and respectively The a l g o r i t h m whe n t h e required entered. The input set are listed 2 and The a l g o r i t h m input issues algorithm then calls Subroutine sections. an input data The have return to input data the original s u mma r y whe n a r u n is ma k e run according to the algorithm advances hand o f It also as a function each then the This computes of the cost their of D3 . has the is posi­ the next step and input data into six the 2, and When a l l allows at a in the user the a date total o wn e d total Next , the loop, a new h a r v e s t s c h e d u l e the the user point is woul d data. "JDAY" n u mb e r o f The trucks and "S2." and t r a c t o r cost. looping is harvester called like to counter "S1" to of also input respectively, c ombi ned in whi ch program e x e c u t i o n . the the algorithm s ta r ts section subroutine initializes 1 and variable negative, changed. route calculate capacities is subroutine and mo n t h o f to in are answer for o n l y few change s day statement operator the the or algorithm then the If asks completely executed The a l g o r i t h m on C. statement Appendix answer all printing. with in n u m­ data. be c o r r e c t e d corrected, entry previous mo v e s o n t o input entered categorized the C reorganizes to been a new the the data whether subroutine subroutine needs If algorithm a s u mma r y o f are checks correctly. prints the and e x p l a i n e d then the of variables data however, another data the tive, reading for five synthesized loops. with a In new 206 harvest starting variable set day. 3 (Appendix headings. The n t h e looping completed, If the the is answer is algorithm, negative, cost, through the the and previous is algorithm for each A is set net at the FARM t h e area dynamics behavior is sampling). ables HN, the harvested per hectare that that on t h a t tonnage calculating loop the of t h e starts by starting initializing executed on t h a t day. calculated when day and t h e capacity numbe r o f to The the is looping day o f loops that through the harvest all 1, weight, total T. day is from greater however, vari­ These are: and d o l l a r is area return on the essential trucks than the in c a l c u l a t i n g transportation SI ZE fruit-time harvested This the FARM a t calculated. utilized on d a y by returns, and d i m i n i s h i n g is 11 a r e fruit the through by number, run. answer done and another If takes day When upon harvest l o o p mo v e s When t h e output called it is A. for as a variable for required on day. The c a l c u l a t i o n tion is the terminated. a harvest The TAREA, fruit 11 i s day. expected DW, is It all The ma xi mum- n u m b e r o f harvested 1 (day of FN, with C is executed. returns. ten. This prompts subroutine is initializing subroutine algorithm a s muc h t i m e first calls algorithm loop. ( FARM=0 ) . time the by by p r i n t i n g a simulator algorithm algorithm field algorithm the starts D4 ) and a new r u n however, harvest loop positive, and Subroutine of The of plant of population fruit dynamics and m a t u r i t y proceeds indicator as a func­ value. 207 Variable set rithm then (Appendix is 4 is mo v e s t o D6 ) . updated. JDAY. The return's This determine is output. then This If the statement day is subroutine both variable B. set is (Appendix 08). Appendix algorithm flow c h a r t . date (in variable the the s u mma r y o f out set 5 mo n t h a n d d a y ) by c h e c k i n g carried if value of the the day is instead. completed subroutine D9 d e s c r i b e s a program its loop, calculates D7) and Appendices and of The a l g o ­ nontrafficable, a nontraffic- (Appendix 6 guide only has This out D5). values the prints issued algorithm upon program us er is (Appendix the stage, carried algorithm When t h e comput e d At t h i s trafficable. ability then D10 a n d set symbols D11 calls and o u t p u t s variable the it 7 of the include sampl e o u t p u t , a respec- ti vely. 6.3. The f o l l o w i n g mode 1 Mode l assumptions utilized during the execution : (1) Realistic (2) Model data wer e execution September was input o wn e d o n e t y p e (4) The f a r m e r kne w t h e Al l following harvesters chased in the out model. between July 15 a n d 15. The f a r m e r days to carried (3) (5) we r e Assumptions the that harvesting trafficabiIity of system. sequence for the ten sampling. and o t h e r last of harvesting ten-year period. machinery we r e pur­ '208 (6 ) The initial during (7) The cost represented that cost entered run paid for purchase. initial sented machinery machinery a portion of the total tion wa s e q u i v a l e n t to that period by t h e divided that of per initial the total cost. land farm repre­ This harvested size. por­ on CHAPTER 7 MODEL SENSI TI VI TY ANALYSIS 7.1. A sensitivity genous variables importance analysis tion the in t h e function of system wa s performed that point of with is, will logic change of The the A sensitivity output input extreme test the has been in t r y i n g analysis variables the varied input wa s t h e n view of its effect on t h e output, value per dollar (2) Aver age harvesting cost (3) Average net return (4) Average fruit dollar n u mb e r simula­ values of mo d e l in studied as to a analyze of the except of Aver age the stability. input (1) exo­ their testing the sensitivity all to results. rigorously and by w h i c h regard using of t h e by f i x i n g The c h a n g e process a me cha ni s m f o r change behavior. the simulation variables the the the to provides of is evaluated of ma t he mat i cal The r a t e time. are extremes; exogenous terms analysis relative also Ge ne r a 1 one studied mo d e l at a from including: hectare. per hectare. per (size hectare. 1A) p e r * Th e s e n s i t i v i t y a n a l y s i s i s m e a n t t h e mo d e l d y n a m i c s o n l y a n d n o t f o r t h e p a r t i c u l a r h a r v e s t i n g system. 209 hectare. to t e s t the endorsement e f f e c t of o f any 210 (5) Average f r u i t number The o u t p u t was studied system the five for harvesting variable run. Tables and t h e base It to a set three 33 values per should of be systems. setting of the initial percent to that fact the a total f a r m o wn e d logical approach since the size A final Mi chi gan mo d e l wa s jected bulk mo r e of capacity, harvest the section time. harvest formance the of handling. this harvesting of three n u mb e r wer e and altered each was the per variable set starting field harvester carried with its A test out an This led to of the by f o u r , hectares. prior of t he Since to the i mpr ovement and t h u s the generally returns of was d u e one-quarter cost the harvester initial This capacity collected the only harvester. includes day. chapter) Ha). refer for the resembled ten to system resembled only affected used that initial the prototype is value. (40 the data the This cost prototype whi ch of a run cost field concerns wa s t h e r e f o r e run be n o t e d farmer field 19 8 4 r e s e a r c h , that per and t h e harvesting true dividing on f i e l d system also the University based the that per model ed the remar k handling field of State here, by t h e of days values machinery of and run. should twenty-five the values r u n s — one It basis s h o ws s how t h e noted, three 31 and/or 32 a n d per h e c t a r e . starting Table sets 4) on a d a i l y harvest systems. input (size the rate in of pro­ of the machi ne d e ma n d e d a function machi ne (discussed i mpr oved as this per­ another bulk Ta bl e 31 . Va r i a bl e Run # 20 25 Val ues o f t h e Popul a t i on (POP) Different Farm Si ze ( i n Ha) (FS) Input S t a t e of Fi e l d (SF) Variables Pr i c i n g St r uct ure (PS) per Run. Trafficabili t y SEQ (TS) 212 Table 32. Va r i a b l e SF** Ba s e a n d Variable 1* 3 4 3 4 Different Input Value 2=30, 1A=60, 1B=55, f r u i t s / 9 . 3 m2 /g r a d e 1A=45, 1B=45, 2=30, 1A=50, 1B=50, 2=35, 2=40, 1A=55, 1B=50, 1A= 0 .3 3 , 1 B= 0 .3 3 , d o lla rs/k g /g ra d e 1A=0.25, 1B=0.2 5 , 1A=0.30, 1B=0.30, 1A=0.4, 1B=0.40, 3A=18, 3B=20, 4=4 3A=20, 3A=30, 3A=30, 3B=12, 3B=18, 3B=20, 4=8 4=12 4=18 2 = 0 .1 8 , 3A =9.13, CO 1* 2 the o• o 3 4 of II CO CO 1* 2 TS Val ues Run . Level 2 PS Variable Sets per 4=0.04 2 = 0 .2 , 3A=0.15, 3B=0.1, 4=0.08 2 = 0 .3 , 3A=0.1, 3B=0.08, 4=0.08 2 = 0 .1 5 , 3A=0.1, 3B=0.05, 4=0 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 , 0 , 1, 1, 1, 1, 1, 1, 1, 0 , 0 , 0 , 0 , 0 , 1 , 1, 0 , 0 , 1 , 1, 1, 1 1, 0 , 1 1, 1, 0 1, 1, 1 *Refers to base val ues ( i n i t i a l s e t t i n g ) . * * S F , PS, a n d TS r e f e r t o s t a t e o f f i e l d , p r i c i n g s t r u c ­ t u r e , and t r a f f i c a b i 1 i t y s e q u e n c e , r e s p e c t i v e l y . 213 Table 33. Ba s e Values of 10 Ha Fa r m S i z e 8 -1 5 -8 4 Da t e Capacity 1 1 0 Initial Cost $6 , 0 0 0 Va 1 ue Va r i a b l e Distance to Processing Plant 200 Owned H a r v e s t e r I n i t i a l C ost $9 , 0 0 0 2 Trucks: Own Rent L ease Own Km 1 0 0 Initial C ost $7,000 Rent: Rent : $0.15 $25 T r a n s p o r t Rat e D r i v e r Cost Nu mb e r o f Owned Harvesters *Values Variables. Capac i t y 1 Truck: Own Re n t Lease Own Input Va 1 ue Variable* Run the apply 1 only for T r a n s p o r t Rat e D r i v e r C ost Numbe r o f L e a s e d Ha r ve s t e r s a 10-hectare farm size. $0.15 $25 0 214 7.2. A s u mma r y o f fruit value, total n u mb e r o f lation per Figures per It of wa s one started dropped this the to at fluctuation harvesting population that dollar per as w ere in per plant and t h e day per Table 34. fruit starting popu­ n u mb e r day for net as density increased returns as significantly value 1 both first t wo levels a function different , and t h e 2 in the 3 but Mor e over , fruit value populations, of from net population progressed. in steady and than time the 3 maintained starting recovered in t h e was o b s e r v e d for recovered return, listed populations value cost net of respectively. a higher a lower hectare are 4, returns in per average fluctuation another Density population 1A a n d The n e t day t o return per be o b s e r v e d less time. system system Plant days show t h e grades can cost, harvesting 28 a nd 29 of average harvesting size there the harvesting harvesting fruit Effect and while both value and l ow r e t u r n s early in t h e cost. There harvest the was schedule, returns. vesting value a general system as time then This wa s 3, whi ch advanced. uted to the cost of harvesting increased was also increase beyond of a return not the started and f i n a l l y for high a nd declined increase in fruit v o l u me until the fruit level. drop in a drop population The optimal of peak, case (especially the a function in trend return 1, in was a stage in har­ return attrib­ when t h e transportation) The increase harvester field in c o s t capacity. Tabl e 34. POP 1 A Summary o f t h e A v e r a g e P e r H e c t a r e o f R e c o v e r e d F r u i t Va l u e ( A ) , H a r v e s t i n g C o s t ( B ) , Net D o l l a r R e t u r n ( C ) , and T o t a l Number o f H a r v e s t i n g Days (D) P e r H a r v e s t S t a r t i n g Day P e r P o p u l a t i o n P e r H a r v e s t i n g S y s t e m . Harvest Starting Day 1 2 3 4 5 2 1 2 3 4 5 3 1 2 3 4 5 SYSTEM 1 SYSTEM 2 SYSTEM 3 A B C D A B C D A B C D 1305 1565 1623 1493 1257 529 567 614 657 700 776 998 1009 836 557 3 3 4 4 5 1264 1495 1651 1630 1481 537 559 587 615 643 727 936 1064 1015 838 3 3 3 3 3 2180 1701 1273 953 787 721 775 784 734 683 1460 925 488 219 104 7 1140 1552 1754 1828 1803 520 550 577 600 621 619 2 1178 1229 1182 3 3 4 4 1118 1437 1643 1743 1740 531 547 564 578 591 587 890 1079 1157 1150 3 3 3 3 3 1709 1997 1878 1694 1362 611 674 722 728 678 1098 1322 1156 966 684 5 1002 1056 1349 1362 1301 1319 515 535 544 553 565 540 814 818 748 743 2 1020 3 3 3 3 1108 1127 1168 1196 527 534 539 546 533 493 574 588 622 643 3 3 3 3 3 1555 1610 1470 1487 1479 581 603 612 634 656 973 1007 859 854 823 5 5 5 8 8 8 8 6 7 7 7 6 6 POP 1 POP2 RUN12 RUN11 SYSTEM I V W Y 9339994 SYSTEM2 B B B SYSTEM 3 llllllllll s 3 Z B55B 30- 15- i I 0- rj = M 75- 1 3 4 POP 3 n HARVEST STARTING DAY RUN 13 A7+/..9+A 0711 2 3 4 HARVEST STARTING DAY Figure 2 8. Average Recovered F r u i t Number ( S i z e 1A) Per Hectare Per Harvest S t a r t i n g Day Per Popul at i on Per Harvest System. DO CD 750 44489999999921 B./+.+^+^^ POP I POP 2 RUN12 RUN II 038257797916 06 z fa iso- fa *#= % ■= £§l_ 0 -*P- SYSTEM I a d l 2 750 SYSTEM 3 $ --- — "* 1 ^ 3 HARVEST STARTI NG 41 y r-*TZ 4 *4— ■ 5 OAT POP3 RUN 13 2 3 4 HARVEST STARTING DAY Fi gur e 29. Average Recovered F r u i t Number ( S i z e 4) Per Hectare Per Harvest S t a r t i n g Day Per Popul at i on Per Harvest System. PO 218 As t i m e the progressed harvester because the of a n d mo r e spent the unloading mo r e time increased trucks. v o l u me to n u mb e r This a uniform thus a uniform time the maintaining of of the case fruit period to develop, harvesting it ha d to in pay t o population v o l u me 3, increase, essential to finish harvest. 7.3. The refer the state to the day o f this case variety with of Effect a field total also or to The refer production fruit smal l count stood state with to plant vigor and per harvest Table net starting 36 s h o ws t h e hectare 4 and between 5 per 30 a nd (for average 31 size harvest day of for field the 1A a n d starting day An three of starting per grade on days fruit three be u s e d a function field and per 1 a l ow, state referred 2 field returns and per state harvesting of 35 systems. system. per An Table and n u mb e r state sizes net in of date. both average harvesting the m2 p e r harvesting respectively) for to production. the Field mo d e l sampling of of in t h i s count hectare average 4, fruit State could as per change of initial count potential of field an e a r l y a high 9.3 vigor grade for used per of plant return rate illustrate grades to harvesting state fruit state cucumber field s h o ws t h e a term practices. initial high wa s n umb e r o f sampling. a high, large per the trips not rate allowed finish wa s whi ch m a i n t a i n e d was per days Figures hectare of field systems. 219 Table 35. A v e r a g e Ne t Per H a r v e s t Systems. Va r i a b l e per Run # Harvest S t a r t i ng Day S F 1/ 1 1 1 2 3 4 5 SF2/17 1 2 3 4 5 SF3/18 1 2 3 4 5 SF4/19 1 2 3 4 5 Ret ur n Per H e c t a r e Per S t a t e o f F i e l d S t a r t i n g Day f o r t h e T h r e e H a r v e s t i n g AVERAGE NET RETURN ( $ / H a ) SYSTEM 1 SYSTEM 2 SYSTEM 3 665 1048 1223 1275 1228 63 3 936 1 125 1203 1196 1 09 8 1322 1 156 966 7 62 647 929 1 03 4 1 05 8 1003 598 858 1117 1001 1069 1058 928 765 521 975 1 184 12 7 4 1252 1161 894 1 1 19 1268 1308 1269 1466 1 198 1 11 3 1303 1388 1354 1 23 4 1017 1238 1395 1431 1384 1601 1248 96 7 702 463 1021 1001 732 488 220 Table 36. The R a t e o f C h a n g e o f A v e r a g e Ne t R e t u r n s P e r H e c t a r e B e t w e e n H a r v e s t S t a r t i n g Da ys 1 a n d 2 a n d Da ys 4 a n d 5 , R e s p e c t i v e l y , P e r S t a t e o f F i e l d Per H a r v e s t i n g Sys t em. ^ " " \ J R a t e o f Change DAYS 1 and 2 DAYS 4 and 5 S t a t e o f F i e l c T ''''^ ^ ^ System 1 System 2 System 3 System 1 System 2 System 3 SF1 383 3 03 22 4 - 47 - 7 -204 SF2 28 2 260 - 96 - 55 -11 -244 SF3 209 225 -268 - 91 -39 -244 SF4 190 221 -353 -120 -49 -239 80 1^^63781673308 SF I SF2 RUN 11 RUN 17 45 30 FRUI T NUM BIR^HoXIO 60 SF3 SF4 95 RUN 19 RUN 18 60 i m sm si SYSTEM 3 tllllllllllll 45 30 FRUIT N UMB E R / H o X 10 ’ system SYSTEM 2 2 3 4 HARVEST STARTING OAY Fi gur e 30. 5 2 4 HARVEST STARTING DAY Average F r u i t Number Per Hect ar e ( S i z e 1A) Per S t a t e o f F i e l d Per Harvest S t a r t i n g Day f o r the Three H a r v e s t i n g Systems. SF I SF 2 RUN II R U N 17 SYSTEM I W « ! system 2 tm m m system 3 minium FRUIT N UM B f R/H o X 1 0 ' soo r,*£ M RUN IB RUN 19 31 738697^737 ro ro 999999999994 r\j FRUI T NUMBER/HoXIO 3 ^999905 2 H A R V E S I S T A R T I N G DAY Figure 3 1. 3 4 H A R V E S T S T A R T I N G DAY Average F r u i t Number Per Hect ar e ( S i z e 4) Per S t a t e o f F i e l d Per Harvest S t a r t i n g Day f o r t h e Three H a r v e s t i n g Systems. 223 The r a t e was c omput e d from t h e change rate than change that It ferent to In a l l the state of at higher woul d other cases, woul d start could t wo the at systems, mo r e yet Finally, the magnitude was observed 1 and A positive one d a y . returns second day by r a t e to of an day g r e a t e r the states of rate of the not change field field, harvest Syst ems of and the schedule rate 2, respectively. applicable to System maturity restore this trend for average net grade could rate decrease the was was of as as A or a producing S y s t e m 3. of time System In 3 pro­ fact that System 3 fruit than the lower. be e x p e c t e d harvest returns and d e c r e a s e 3. indicator fruit capacity posi­ and c h a n g e d grade size was 1 and plant level was d i f ­ harvesting size the This referred on t h e s mal l of the initial fruit when t h e r e 2. to to of the to it attributed its returns beginning Syst ems smal l a high wa s and d i v i d i n g the in was to hectare day that a high tend This net of however, recover higher with on t h e per day. states end returns value value different the net the net the first the field gressed. with however, potentially cultivar average beginning trend, average wa s e q u a l be o b s e r v e d a mong t h e negative This of on t h e could at of final whi c h trend systems. tive on t h e time, increasing change by s u b t r a c t i n g value of of of other As a r e s u l t , from System 3 t o wa r d s period. an increasing change of trend, average net returns of time for both a slower rate of a function reflected returns increase 224 as a function After the time trend continued as of reached decreasing, a function of Pricing $/ Kg) returns of per whi ch harvesting the the intermediate of that net average dollar a nd harvest day t wo. at per refer of decrease to the 5, the average per harvest Table per 38 s h o ws hectare price net the between respectively, the favored per rate harvest pricing at lowest structure A price grade second with the or structure third smaller starting day). harvest size 3 returned and a nd grade the highest structure return four positively harvest an e a r l y pricing net structure affected 2 when t h e System hectare pricing pricing size however, cases, The the 1 and (either the to returns. fruit best, structure Among a l l that Systems 3 performed five. magnitude Structure used system. net large wa s day the one. system. be o b s e r v e d affected also increasing rate structure 4 and day ing pricing 2 and returns Syst em 3, however, 37 r e p r e s e n t s harvesting net fruit. Table per the a pricing an a slower a term 1 and favored System is returns could was Pricing net per definitely of average days structure It grade. per change of starting Effect hectare day peak, reflecting structure per starting its trend time. 7.4. (in whe n t h e was t h a t harvest three of start­ 225 Table 37. A v e r a g e Ne t R e t u r n s P e r H e c t a r e P e r P r i c i n g S t r u c t u r e P e r H a r v e s t S t a r t i n g Day P e r H a r v e s t i n g System. Variable per Run # Harvest S t a r t i ng Day PS1/20 1 2 3 4 5 PS2/ 21 1 2 3 4 5 PS3/22 1 2 3 4 5 PS4/ 23 1 2 3 4 5 AVERAGE NET RETURN ( $ / Ha ) SYSTEM 1 SYSTEM 2 SYSTEM 3 778 1274 1545 1 66 2 1662 633 936 1 125 12 0 3 1 196 1098 1322 1 156 966 6 84 973 1493 16 2 4 15 4 6 1403 782 1 179 1 44 7 1584 1625 1314 1669 1 5 78 1391 1048 485 730 792 767 674 945 1361 1 50 6 1 46 2 13 6 8 1465 1719 1436 1 153 802 6 65 1048 1223 1275 12 2 8 416 608 700 709 652 780 853 614 430 231 226 Table 38. The R a t e o f C h a n g e o f A v e r a g e Ne t R e t u r n s P e r H e c t a r e B e t w e e n H a r v e s t S t a r t i n g Da y s 1 a n d 2 a n d Da ys 4 a nd 5 , R e s p e c t i v e l y , P e r P r i c i n g S t r u c t u r e Per H a r v e s t i n g Syst em. DAYS 4 and 5 DAYS 1 and 2 — - ^ Ra t e o f Change P r i c i n g St ruct ure^------ Syst em 1 Syst em 2 Syst em 3 Syst em 1 Syst em 2 Syst em 3 496 303 224 PS2 520 397 355 -143 41 -343 PS3 245 416 2 54 - 93 -94 -351 PS4 383 192 73 - 47 -57 -199 0 - 7.0 -282 PS 1 227 7.5. Effect of The t r a f f i c a b i 1 i t y go/no-go days sampling day. sequence on t h e starting day average per for fruit system 40 average ated and by t h e random t o soil mediate and mi ni mal percent over the three ut i 1i of of and t h e i r 4, per illustrate starting the day respectively. a function of the area, we r e g e n e r ­ sequences wer e selected intermediate traffic- bad t r a f f i c a b i 1 i t y . with harvest used an during fifty the sequences and t h e sequences with of t r a f f i c a b i 1ity 33 random t e r m s period of of harvest was sequence hectare 1A a n d These wer e The y e a r s used the three An i n t e r ­ to mo n t h soil refer of percentages (starting with to a Augus t types and t w e n t y - f i v e these at and t h e percent, wer e t h e n August 15) zed . observed in t h e vol ume. This field for stage. day grades cases per per the starting 32 a n d The f o u r stations, The g e n e r a l or hectare t r a f f i c a b i 1i t y twenty-year selected Figures case a bad y e a r respectively. returns sequence t wo and one weather effect model. represent year the type. weather ability s h o ws size The t r a f f i c a b i 1 i t y climate, days net Sequence to ten per fruit refers of system. n u mb e r for sequence a period Table per T r a f f i c a b i 1i t y nonoptimal per change allowed either This effect in the an e a r l y , resulted average system. of the fruit n u mb e r harvesting optimal, in t h e net t r a f f i c a b i 1i t y return dollars and sequence consequently systems to be late of a respective hectare per its in t h e or per fruit was maturity optimal starting 228 Table 39. E f f e c t o f T r a f f i c a b i 1 i t y S e q u e n c e on t h e A v e r a g e Ne t R e t u r n s P e r H e c t a r e P e r S y s t e m P e r H a r v e s t S t a r t i n g Da y . AVERAGE NET RETURN C$ / Ha ) Variable per Run # Harvest S t a r t i ng Day SYSTEM 1 SYSTEM 2 SYSTEM 3 TS1/24 1 2 619 1002 3 4 5 1 17 8 1229 1 182 5 87 890 1079 1 157 1 1 50 1098 1322 1 156 96 6 684 1 2 544 935 2302 2503 2071 5 16 830 1759 2859 2765 1321 1846 1352 1046 7 60 544 2291 231 0 1743 1 174 857 2248 2329 1755 1 124 1601 1832 1339 1024 729 2852 2518 1819 2017 1878 2379 21 07 1528 2326 2253 2024 1831 1329 1024 738 TS2/25 3 4 5 TS3/26 1 2 3 4 5 TS4/27 1 2 3 4 5 TSt TS 2 RUN 24 RUN 25 O SYSTEM I K M x 0 1 45 a SYSTEM SYSTEM 3 Itllllllll a i 3 Z 30 3cr *35 TS4 RUN 2 6 229 n RUN 2 7 60 O at < a i l 30 3 C K IS I Al 2 3 4 4 H A R V E S T S T A R T I N G DAY Fi gur e 32. Average F r u i t Per System. Number (1A) H A R V E S T S T A R T I N G DAY per Hect ar e Per Har vest Starting Day 375 TSI TS 2 Run 24 Run 25 SYSTEM I 225. SYSTf M 2 SYSTEM 3 z> z iso. U m m m J fL 0TS3 375Run 26 230 o is m 2 3 s 4 HARVEST STARTING DAY Fi gur e 33. Average F r u i t Per System. Number ( S i z e 4) I 2 3 4 5 HARVEST STARTING DAY Per Hect ar e Per Har vest S t a r t i n g Day 231 7.6. Far m s i z e harvest It around Table to be The we r e equal the land to harvested for carried the at the initial cost set at respectively. It increased, average lative cost As t h e seasonal capacity within the revealed result field of farm net capacity three, dollar size returns net out average dollar fruit was o b s e r v e d be f a s t e r , System 3, to to Syst ems to The r a t e grow of 2. a nd dollars, size the cumu­ harvester finish to however, 1 and farmer. additional returns. the ishing that additional allowing was hectare the to harvesters analy­ farm a mong t h e the margin. the per of a profitability thousand as beyond margin), day. farm by t h e fact harvester beyond compar ed that the per a n d t wo t r u c k s four return reached of that o wn e d the hectare sensitivity one and was t h i n n e d inability thus land wa s d u e t o (ability diminished early, starting be o b s e r v e d This profitable the harvest total of two, capital acreage. the could well. of per assumption day. intervals. returns variable to farm net size the wishes sampling total time farm such, as different the with the a larger per out farmer with size of increased of the farm percent the that per ten wer e Farm Si z e starts average to harvester that be a p o r t i o n system runs of period 40 r e p r e s e n t s harvesting As the may o r may n o t arranged sis refers Effect the acreage This finish in field wa s the v o l u me returns in t h e dimin­ case of a 232 Table 40. Variable per Run # F S1/11 . 5 (10 Ha ) A v e r a g e Ne t R e t u r n s P e r H e c t a r e P e r H a r v e s t i n g S y s t e m P e r Fa r m S i z e P e r H a r v e s t S t a r t i n g Da y. Har ve s t S t a r t i ng Day 1 2 3 4 5 FS2/14 ( 4 Ha ) 1 2 3 4 5 FS3/15 ( 6 Ha) 1 2 3 4 5 FS4/16 ( 8 Ha) 1 2 3 4 5 AVERAGE NET RETURN ( $ / H a ) SYSTEM 1 SYSTEM 2 SYSTEM 3 87 9 1272 1 45 8 1517 1478 849 1 155 1348 1428 1423 1386 1641 1497 1309 351 7 98 1059 1187 1230 259 708 990 1 143 1217 752 143 1349 1421 1388 523 1043 1262 1 34 2 1329 5 44 897 1 194 1359 1415 1056 1389 1445 1435 1339 771 1 143 1394 1496 1489 7 14 1052 1314 1459 1501 1253 1555 1508 1444 1252 1002 1 233 7.7. The f i e l d by setting of System test net the capacity its 1. E f f e c t of Improving t he of H a r v e s t i n g System T unloading of starting size six all other It ing ever, at The hectares wa s used be the it s h o wn t h a t both also total capacity, the wa s c a r r i e d mo r e compar ed out at to (Table test while and the all A farm 33). its to harvest­ finish mo r e population one stages available a ten-hectare per populations. populations at average maintaining System 3 r e c o v e r e d 1 in to increased required all that out 41). 32 a nd returns System of carred on t h e i mpr ovement time to population Tables System 2 in stages recovered i mpr oved test this mo r e t h a n harvest (see cases, than plant system average the the returns early net reduced recovered system the was t h e n in t h i s constant equivalent variable per harvest In a l l net this hectare S y s t e m 3 was m o d i f i e d a rate analysis per wi n d o w a nd harvesting. to day could average per variables dramatically time modifying returns harvest of harvesting A sensitivity effect dollar of Capacity t wo a nd and t h r e e . with an capacity, farm How­ size. when Table 41. E f f e c t o f Improving t he Capaci t y of System Three on t he Average Net D o l l a r Return Per Hectare Per Pl ant Pop ul at i on Per Harvest System Per Harvest S t a r t i n g Day. Ha r ves t Plant S t a r t i ng P o p u 1 a t i on Day 1 1 2 3 4 5 1 2 3 4 5 3 1 2 3 4 5 total SYSTEM 2 A* B C D A B 820 1641 2174 2277 2093 659 691 730 733 814 161 950 1444 1504 1279 1 2 2 3 3 879 1257 1611 1758 1719 678 696 721 748 776 820 1326 1572 1677 1686 659 679 695 708 720 161 647 878 969 966 1 2 2 2 2 838 1195 1512 1697 1774 820 1321 1645 1647 1693 659 676 690 698 707 161 645 955 949 986 1 2 2 2 2 832 1068 1131 1156 1196 SYSTEM 3 C D A B C D 201 1375 1722 1967 1933 1743 682 705 736 772 804 674 1016 1231 1162 939 2 2 2 943 2 2 2 2 2 676 689 704 718 730 161 506 808 979 1043 2 2 2 2 2 1241 1634 1874 1996 562 937 1159 1266 1265 2 2 201 0 679 697 715 730 • 745 676 685 690 696 703 156 384 440 460 494 2 2 2 2 2 1263 1472 1477 1441 1457 679 690 696 701 709 584 782 781 740 748 *A, B, C, and D r e f e r t o f r u i t v a l u e , h a r v e s t i n g n u mb e r o f h a r v e s t i n g d a y s , r e s p e c t i v e l y . 562 890 1010 cost, net dollar return, a nd t h e 3 3 2 2 3 2 2 2 2 2 234 2 SYSTEM 1 CHAPTER 8 MODEL VALIDATION 8.1. Validation is c ompar ed fication 1 ) the "accuracy" simulated or the judgment; research, areas. value of lated values The it fit" each of of Total weight The these we r e the t wo c a s e is and ( between with steps: ) measure the the process i n t wo 2 veri­ a me a n s o f logic proceeds mo d e l in t h e validation Far m 1983 on t h e farms average per of actual a nd utilized for for return, we r e deviation mentioned chosen wer e dollar hectare percent selected of variables determining fruit the n u m­ the actual from t h e the as the level simu­ of mo d e l . 8.2. Ca s e process areas, of previously pr ogr ammi ng intentions. "closeness kilogram comparison accuracy of comparison validation. and used a simulation results. In t h i s ber, not by w h i c h A verification subjective of process data fitness selection mo d e l field construction involves ( the process. comparing mo d e l to is Genera 1 1 validation Ke nny Brothers' Ca s e Far m 1 data we r e collected farm near 235 He ml oc k, in Summer Michigan. 236 La nd c o n d i t i o n s , procedures Section wer e 1. experimental similar Planting to was and resulted an a p p r o x i m a t e per hectare. by H a r r i s 35 0 mm w i t h i n The Seed variety the of s a me fruit the actual simulated ma r y o f dollar the presented process It in the a mo u n t nitude than wa s of was that predict accuracy other Chapter "Regal," 5, 500 mm b e t w e e n density dynamics of the This 80,000 which was plants supplied and n u mb e r is in per presented simulated per grade S y s t e m 3 wa s daily Chapter hectare in fruit Table 43. collected presented fruit data wer e day be o b s e r v e d of dollar four value -$48/Ha of the a total that value percent, dollar per Table weight for in A day 42. Far m the of A s u m­ ( Kg/ Ha) Case used 5. and 1 is harvesting the wa s average the percent between actual percent deviation seventeen. ($19/acre), actual. while with hectare and the return simulated in The a m o u n t a simulated However , deviation mo d e l within the of devi­ return was lower able a four ma g­ to percent range. amount . referred in r ow s p a c i n g . ( $ / Ha) p e r The m a g n i t u d e the at collection day f i v e . could returns ation in at average actual value out plant procedure s u mma r y o f and described the was and d a t a C o mp a n y . Parameters following those carried r ow s p a c i n g in setup, to terms, of The t o t a l the it deviation level deviations measured at the of was the the magnitude the absolute output deviation of value deviation micro-level. per grade of In without Table 42. Actual and Si mul at ed F r u i t Number ( x 1 0 ~ 3 ) Per Day Per Grade Per Hec t ar e f o r Case Farm 1 Using H a r v e s t i n g System 1. 3A 1B 1A \ 6 \r a d e 3B \ 0 a y\ . ACTUAL A C II I A l SIMULATED ACTUAL SIMULATED 112 112 64- 64 72 73 72 80 32 56 60 64 54 48 88 102 10 21 20 48 42 32 29 80 89 26 30 24 ACTUAL SIMULATED SIMULATED ACTUAL SIMULATED ACTUAL STMULATEO 11 237 TOTAL 16 ACTUAL 11 22 32 33 28 24 20 11 14 104 D3C 105 Table 43. Actual and Si mul at ed F r u i t Weight ( Kg/ Ha) and D o l l a r Value Per Day Per Grade f o r Case Farm 1 and Recovered Returns Using Ha r v e s t i n g System 1 (Day 5 ) . 1A '.Q ra d e 1B 2 3A 30 4 O a y V ALIIIAt SIMULAI1I) ACfUAl S im iA U D ACTUAL SIMIILAICD ACIUAL SIMULAIfO 1 1216 1422 2054 1984 0 0 0 0 0 2 776 927 2516 2480 1378 2912 736 1232 1056 3 620 812 1056 1488 5401 5304 1794 2352 2212 920 4 490 533 608 899 5052 4682 2776 3360 2742 2224 ACTUAL ^ ACIUAL S1HULAI1D 0 0 0 0 0 0 0 0 1227 1463 TOTAL •j IMUIAKD ACIIIAl 5 L'a 11 a r Va 1 u e 46 39 15 127 248 42 at day 5 1516 1664 273 13 3730 3136 485 82 2780 3329 266 300 1366 1571 55 408 10,114 9438 1 ,136 222 63 1088 A* -2 40 27 -77 -44 8 -48 II* 2 40 17 77 44 8 188 • A , 8 r e f e r to the amount and magnitude (a b so lu te value o f the amount) o f the d i f f e r e n c e in dollar value of the actual and simulated r e t u r n s . 239 countering deviation their was additive seventeen, accurate prediction in t o t a l per less than of hectare one effects. a level the n u mb e r and seven 8.3. Validation s a me t i m e (see that Section collected included three both out Syst ems 2 and the 3. weight (day 4) per hectare ( Kg/ Ha ) using actual per grade and per and m a g n i t u d e fruit day n u mb e r fruit (day 4) per grade per day the recovered system. 3. It Table of total fruit for harvest respectively. weight per hectare The wa s percent five deviation and fifteen of s h o ws Sys t ems fruit weight 46 p r e s e n t s lists deviation fourteen also return The p e r c e n t and wa s simulated recovered deviation seventeen and simulated also we r e data It return the collected harvesting dollar the harvesting day. and t h e at Harvesting actual and 2 and Thi s simulated wa s wer e out parameters both using actual wer e weight. per the harvesting data utilizing lists simulated weight carried harvesting. grade Sys t ems deviations respectively. dyna mi c 45 harvest kilogram an Fa r m 2 44 p r e s e n t s per reflected of hectare 3, Table and fruit and magnitude The p e r c e n t Fa r m 2 w e r e to n u mb e r 3. Table the prior fourth still performance Daily days fruit recovered Cas e percent percent, Case machine on t h e n u mb e r 2 and the for 5.2.1). carried fruit data whi ch returns. fruit percent The per the hectare value from a c t u a l . n u mb e r the percent per Syst ems 2 and total fruit for harvest Table 44. Actual and Si mul at ed F r u i t Number ( x 10 -3 ) Per Hect ar e Per Grade Per Day and t he Recovered Number Using H a r v e s t i n g Systems 2 and 3 (Day 4 ) . \G ra d e 1A IB 3A 2 3B ACTUAL SIMIIIAICD ACTUAL SIMULATED ACTUAL SIMULATED ACIUAL SIMULATED ACTUAL 1 191 191 158 158 36 38 12 12 1 2 149 97 180 116 60 106 13 21 3 117 64 154 92 63 112 33 42 SIMULATED ACIUAL SIMULATED 1 0 0 5 2 1 1 10 4 A fi TOTAL ACIUAL 4 c-.r. n (S*'> <) 11 9 19 15 27 20 48 39 38 41 52 64 240 D ,iy \ 4 SIMULATED 33 52 20 15 6 5 127 148 35 59 18 13 8 7 177 202 Table 45 . 1A \G rarJp X OayN^ Actual and Si mul at ed F r u i t Weight ( Kg/ Ha) Per Grade Per Day and t he Recovered Weight Using Har vest Systems 2 and 3 (Day 4 ) . ACIUAL 3A 2 IB SIMULATED SIMUL AIE 0 22 4 SIMULATE0 ACIUAL SIMULATED ACIUAL SIMULATED 1138 1080 257 139 0 0 5300 1395 1890 844 278 147 198 5600 4147 3780 1504 556 734 1188 ACIUAL SIMULATED l 1 150 2190 1941 2054 2055 2628 2 1014 1164 1424 1508 2972 3 875 888 1069 1196 3339 ACIUAL 38 TOTAL ACTUAL 4 (SYS ?) 4 (SYS 1) SIMIIIAICD 112 108 310 195 1759 2600 3174 4680 3401 2085 1376 990 10,132 10,658 196 240 744 507 2139 3200 3225 5310 3176 1807 1336 1386 10,816 12,450 Tabl e 46. A c t u a l a nd S i m u l a t e d R e c o v e r e d D o l l a r S y s t e m a nd t h e Va l u e a nd Ma g n i t u d e o f 3A 2 1D 1A R e t u r n s Per H e c t a r e Per D e v i a t i o n from A c t u a l . ACTUAL SIMULAltO ACTUAL SIMULATED actual SIMULAltO 37 36 102 64 317 468 ACTUAL 3B Harvesting 4 TOTAL SIMIIIAICD ACTUAL SIMUIA1CD ACTUAL STMULATCD ACTUAL SIMULATCP 608 272 167 56 40 1197 1383 SVSIfM ? uni t a h 413 Pf IIUtN -1 11* SVSI1M 5 non ar Cf IllPO 1 65 79 -38 151 195 -105 -16 186 38 151 195 105 16 506 245 167 385 576 419 690 254 145 55 53 1421 1712 A 14 -78 19 1 271 -109 2 291 0 14 78 191 271 109 2 665 *A,B r e f e r to the amount and magnitude (a bso lu te value o f the amount) o f the d i f f e r e n c e in d o l l a r value of the actual and simulated r e t u r n s . 242 A* 243 Systems per 2 and hectare senting of The 3, $291 percent value per magnitude and (1) to (2) (3) the remarks The mo d e l and twenty could could a plant responded realistic tendency. attributed This with resulted respective the in ($266/acre) the 2 and on t h e fields yield better deviation the wa s d u e t o counted This to in The a mo u n t actual dollar mo d e l repre­ 3, value in amount . was respectively. Model Validation validation, the with different a plant from t h a t variety used model. The mo d e l increase this 2, value be d r a w n : be u s e d the of the R e ma r k s dollar System $665 Syst ems of potential establish The for of of amount. percent deviation out come in actual deviated General Utilizing following of t h e forty-seven 8.4. The a mo u n t ($1 1 6 / a c r e ) hectare representing percent forty-two respectively. deviated sixteen dollar System 3, with in improper the fact fruit with l ow f r u i t Cas e having that undersized grade fruit weight. a higher Far m 2 c o u l d initialization size a high data numbers. fruit 1A d u r i n g n u mb e r be we r e sampling. c ompar ed with a CHAPTER 9 SUMMARY, CONCLUSI ONS, AND RECOMMENDATIONS 9.1. Mec ha ni ca l Mi chi gan mo d e l wer e wa s wer e parameters. and compar ed simulated experiments study and three wa s included concerned grade cost of plant with owni ng The d a i l y fruit grade size vesting of system. field to The the and of each determine inputs the dollar diminishing it by t h e The output included value cost the per harvester. per of for included systems. set exit the harvesting and 244 and harvester subprogram to mo d e l also Thes e experiment These of the determined study per Two with entry second and capacity return the fruit wer e The mo d e l dynamics. of in CUCHARV. concerned recovery flow model. the parameters. fruit dollar by f e e d i n g rate determination systems systems number - number populations. a cash determined was fruit and t h e and o p e r a t i n g net determine plant-fruit daily effective contained to The p a r a m e t e r s the by w e i g h t and t h e The mo d e l the harvesting percent of plant. different out time algorithm experiment relations from t h e mechanical the carried harvesting a discrete by a c o m p u t e r The f i r s t number-wiehgt cucumber using determination parameters to once-over Summa r y of daily the was per the har­ average 245 per hectare weight ing. to per and t o t a l grade A field determine basis for types. study the the in Mi chi gan and f o r in t h e ever, the for wa s This determine only deviation t wo optimal time set of mo d e l should be used the sors to produce. labor as other, a function The mo d e l soil to level, of wa s to on sixteen the be daily needs location. hectare from t h e wa s pre­ actual location, and t w e n t y as a ma nage me nt the how­ percent any system one numbe r , and differ­ time. by f a r m e r s and t h e m an e a r l y chance and t o the handle this harvesting behaves weight, to operation Howeve r , conditions utilized tool harvesting conditions. input while respectively. start each Michigan. second second accredit since give time. to per deviation in location, in t h e In t h e input also the woul d a nd e q u i p m e n t a function of could anticipate Thi s wel l mo d e l analysis return be u t i l i z e d a specific as three in t h e first and t h r e e , given ently as on a d a i l y locations implemented percent could not in t h e location. types mo d e l i n t wo simulated four percent system over sensitivity used wer e net first the encountered validated o n e wa s total harvester the harvest­ outcome. a nd t h r e e value at wer e of wa s e s t a b l i s h e d locations types with mo d e l and f r u i t cost days type dicted average go/no-go Harvester average and n u mb e r of effect, The mo d e l The total fruit sequence sequences harvest t wo farm of t r a f f i c a b i 1ity These net and t h e three their per to and proces­ v o l u me plan flow of of for produce The mo d e l especially of time could for also systems dynamics be vi e wed analysts, during the as for an e d u c a t i o n a l illustrating harvesting experiments (1) There to (2) that increase, progressed. size grades The r a t e and at trend the of at Thi s whi ch we r e dr a wn construction stabilize time the new f r u i t was set of the not population on an a r e a significantly on a p l a n t - c o u n t plant density The length set new f r u i t was me n t as time plant Fruit best model . fruit plant the fruit plant development The r a t e as densities. on t h e this n u mb e r decrease all basis. affected period increased in whi ch as t h e was stage Howeve r , rate and plant whe n m e a s ­ decreased suggested in as be mo r e and to correlated the n u mb e r on a p r e v i o u s rate of the harvest time linear fruit day harvesting at the of we r e model . on a l a t e r case higher constraints. regression in t h e develop­ relations n u mb e r to increased. fruit the flexible number-weight by a s i m p l e continued density As a r e s u l t , regard number-number explained to a plant plant by a s l o w e r progressed. densities The mo d e l the the accompanied wi n d o w wa s in field increased. of This plant basis. the and t h e n different a function plant of wa s o b s e r v e d three from t h e potential a peak, primarily ured (4) the wa s a g e n e r a l population (3) to effect Conclusions conclusions led the period. 9.2. The f o l l o w i n g tool, a day t o 247 number-number model. relation, however, the n u mb e r fruit In t h e the for The c o e f f i c i e n t fruit size ties. grades decreased correlation wa s weight-number (5) The c o n s t a n t relations (6 ) through the Plant mode l s tributed we r e There was grade to ing the size was high grade case wa s the in as with all plant of the the densi­ the plant increased. The fruit insignificant simple model. plant stage. neither per trend signifi­ linear density In t h e per regres­ separate fruit significantly and rela­ to f r om one grade. of the fruit move skipping'any grade This wa s the for s ome f r u i t grade 1B w h i l e grade whi ch 2. densities. skipped This case size was o b s e r v e d The r a t e con­ general without 1A, size number-weight next not the system. the factors all pass As a r e s u l t , coefficient, established to stage of in regression line development per mo d e l linear coordinate coefficients growth the the correlated though, regression and g r o wt h a general grade plant the of however, progressed. in t h e number-number to wa s different size by t h e allowed plant tions size fruit wer e d e v e l o p e d per three increased, highest affected relations, (7) correlation and origin in t h e grade of a number-weight relation. density cantly weight s a me d a y . and t h e described This of the (Y i n t e r c e p t ) model . sion fruit The c o r r e l a t i o n density case of this a mong fruit as all' time in enter­ three skipping was 248 not following populations. early stages a general trend Moreover, this of A similar fruit, tion whi ch to size followed density plant trend skipped grade wa s o n l y different observed development at harvest wa s o b s e r v e d in s o me size 4. a general a mong t h e grade 3B i n However , trend wa s d e c r e a s i n g of the rate increasing and t h e plant in t h e time. size its plant grade 3A transforma­ of as skipping the growth plant stage was increasing . (8 ) There wa s exiting from t h e previous weight no c o r r e l a t i o n day in of and t h e a smaller relation and t h a t plant between between size grade n umb e r o f n u mb e r size the the of grade. exiting fruit fruit in a The w e i g h t - fruit 4 on a p r e v i o u s on o n e d a y d a y was highly c o r r e 1ated . (9) There wa s growth stage result, size (10) s ome in t h e s ome o f grade Co mme r c i a l grade fruit the than with increase statistical types all the of not fruit harvesters recovery vester highest plant in size at the later density. grade 1B plant As a mo v e d t o 1A. fruit the reabsorbtion the recovered MSU e x p e r i m e n t a l harvesters, in t h e comparisons t wo be c o m p a r e d fruit could and t h r e e . to fewer the other smal l size harvester. nevertheless, size only grade. be ma de Harvester harvesters type The increased Mor eover , a mong one because har­ could of the 249 nature (11) of the data wer e The values obtained different three (12) (13) of its longer unloading The level of and 90% o f field go/no-go days location and the soil Simulation (1) the we r e Harvester because whi ch of type the de ma n d e d a (at 95, on t h e and occurrence the soil in e a r l y mo n t h type of of of had days b e c a me heavier. results from t h e of August. days affected in the Michi gan. decreased mo d e l no s i g ­ sequences significantly go/no-go trafficable June 100, as suggest the the texture follow­ trends : The fluctuation tion of time simulated in t h e decreased returns wer e net as simulated plant another in populations return started at a higher dropped There was early in t h e finally for to a lower a general a drop population in 1 the density a nd 1 value value trend harvest returns significantly day t o but (2) period initialization capacity) during of a cycle system, moisture sequences The n u mb e r of capacity handling whe r e circumstances. harvesters. field out time. soil effect of ing bulk nificant wa s c a r r i e d different three lowest of that components a mo n g t h e nature The in the had t h e n u mb e r (14) experiment of as returns. , harvesting time increased. Thi s system The from one , and t h e net in population 3 progressed. simulated then a func­ different than l ow, schedule, 2 as a return wa s 3, not whi ch returns peak, the and case started 250 high and d e c l i n e d The in (3) increase fruit return return wa s value as time attributed to the a stage ing (especially fruit transportation ) increased beyond the optima1 level. wa s mo r e har­ time than simulated much net higher harvest system than returned at or in the fruit return of average in f r u i t wa s maturity a respective net dollars three we r e optimal the starting the t r a f f i c a b i 1i t y allowed the it The However , whe n harvest­ the whe n t h e selected. value to systems. type systems of s a me the time. change whe n harvester l ower of This field late much sensitive harvesting other d a y wa s effect vol ume. in t h e the of a nonoptimal The g e n e r a l observed other returns starting harvest its the cost increase until Harvestingsystem t h r e e whe n t h e advanced. v o l u me vest (4) in in at n u mb e r Thi s optimal or hectare systems to be optimal, resulted in t he nonoptimal, per was consequently an e a r l y , stage. per and harvesting either sequence starting simulated day per system. 9.3. The f o l l o w i n g results (1) of To u s e the R e c o mme n d a t i o n s recommendations wer e suggested by t h e simulation: every condition ing a different set of outputs . set of as a separate inputs situation and e x p e c t i n g requir­ a specific (2) A l ow p l a n t fruit, and current woul d late also to the grade fruit, large (4) for available provide the grade A me d i u m p l a n t fruit, an for with an or i mpr oved fruit, and an strategies, size grade The c o s t than to fifteen condition fruit d e ma n d a n d me d i u m- harvesting concept" of small, we r e harvester capacity. high high price intermediate the for Thi s with woul d me d i u m, price intermediate woul d bulk and the me d i u m s i z e harvesting harvester tank included smal l provide period concept" storage strategy of the harvesting "threshing modified however, size a high grade period. Bo t h me d i u m a n d s mal l fruit. of that smal l early mixture the fruit. of tank grade for Thi s expected grade price An a l t e r n a t i v e density, the a very density, and size fruit. r e c o mme n d e d plant size storage large systems. "threshing wer e capacity. (5) and bulk if high a homogeneous size grade larger of we r e r e c o mme n d e d harvesting density, r e c o mme n d e d the price be r e c o m m e n d e d A l ow p l a n t size high harvesting technology belonged (3) density, an o wn e d of harvester a leased hectares woul d o n e whe n t h e per harvester. be land less expensive exceeded ten CHAPTER 10 SUGGESTIONS FOR FURTHER RESEARCH (1) To d e v e l o p a mo d e l that encompasses production-harvesting-processing cucumber s (2) in To d e v e l o p capacity complete s yst em of pickling Michigan. a mo d e l and the its that fruit optimizes recovery the per harvester unit of field time per harvester. (3) To s t u d y the cultural, effect of plant and ma n a g e me n t production, practices protection, on t h e plant fruit dynamics. (4) To mo d e l the from t h e plant production (5) To s t u d y rate of as fruit entry a function of and time fruit exit and t h e to and plant practices. the role of plant cultivars on t h e fruit dynami cs . (6 ) To c o m p a r e pickling as (7) (a) the net cucumber a function of systems environmental per of mechani cal to that of hand once-over harvesting time. To d e t e r m i n e nubs returns the effect conditions grade; 252 of cultural on t h e practices development of and 253 (b) To mo d e l dynami cs of To s t u d y t h e economi c influence of in t h e plants; (c) (8 ) (a) nub the fruit oversized (b) in t h i s actually d u mp e d fruit returns s c h e d u 1 i ng . mo d e l from t h e farmers effect of and of the presence produce. practiced) on t h e Study t h e dollar in t h e and, To i n c l u d e (as nub p r o d u c t i o n a transportation for processing land; this carrying penalty back t h e plant to be and, penalty consequently on t h e the net harvesting REFERENCES REFERENCES Agriculture Engineering Information Series. 1974. Harvest­ ing c u c u mb e r s m e c h a n i c a l l y . Agriculture Engineering De p a r t me n t , Mi chi gan S t a t e U n i v e r s i t y , East L a n s i n g . I n f o r m a t i o n S e r i e s 291. A m i r , I . , J . A r n o l d , a n d W. B i l a u s k i . 1977. A procedure f o r d e t e r m i n i n g p r o b a b i l i t i e s o f d r y a n d we t d a y s . Canadian A g r i c u l t u r e E n g i n e e r i n g 1 9 ( 1 ) : 2 - 5 . Amer i can S o c i e t y o f A g r i c u l t u r a l E n g i n e e r s . 1982. Agricul­ t u r e ma c h i n e r y management . A gr ic ul tu re Engineers Year­ book EP391: 2 2 7 - 2 3 0 . St. J oseph, Michigan. Am Chem P r o d u c t s . t he cucumber. 1969. Regulation Bioscience 19(2): of sex e x p r e s s i o n 141-142. A l e x a n d e r , J . , and J . B a i l e y . 1963. Syst ems mathematics. P r e n t i c e H a l l , New J e r s e y . in engineering Anon. 1975. P i c k l e r e s e a r c h a t Mi chi gan S t a t e U n i v e r s i t y . A g r i c u l t u r e E x p e r i m e n t S t a t i o n , Mi chi gan S t a t e U n i v e r ­ s i t y , East Lansing. Research Report 277: 12-15. A r k i n , G . , R. V a n d e r l i p , a n d J . g r a i n sorghum growt h model . 19(4): 622-630. Ritchie. 1976. A dyna mi c T r a n s a c t i o n s o f t h e ASAE A r k i n , G . , W. R o s e n t h a l , a n d W. J o r d a n . 1983. A sorghum l e a f a r e a model . Amer i can S o c i e t y of A g r i c u l t u r a l E n g i n e e r s , P a p e r No. 8 3 - 2 0 9 8 . S t . Joseph, Michigan. A u g u s t i n e , J . , L. B a k e r , a n d H. S e l l . 1 9 7 3 . F e ma l e f l o w e r i n d u c t i o n on a n d r o e c i o u s c u c u m b e r . J o u r n a l o f t h e Ame r ­ i c a n S o c i e t y o f H o r t i c u 1t u r a 1 S c i e n c e 9 8 ( 2 ) : 1 9 7 - 1 9 9 . A y e r s , E . , a nd M. B o e h l j i . 1979. costs. I owa S t a t e U n i v e r s i t y Ames , I o w a . Es t i ma t i ng farm machinery E x t e n s i o n B u l l e t i n P M- 7 1 0 . A y r e s , G . , a n d D. W i l l i a m s . 1981. Estimating fie ld capac­ i t y of farm ma c hi ne s . M a c h i n e r y Ma n a g e me n t S e r i e s P M- 6 9 6 . C o o p e r a t i v e E x t e n s i o n S e r v i c e , I owa S t a t e U n i v e r s i t y , Ames , I o w a . 254 255 B a i e r , W. , a n d G. R o b e r t s o n . 1966. A new v e r s a t i l e s o i l moisture budget. Canadi an J o u r n a l of P l a n t S c i e n c e 46: 299-315. Baier, W. 1 9 7 3 . E s t i m a t i o n o f f i e l d wo r k d a y s i n C a n a d a from t h e v e r s a t i l e s o i l m o i s t u r e b u d g e t . Canadian Agriculture Engineering 15(2): 84-87. Baker, L. 1 9 8 1 . U p d a t e on l i t t l e t a b l e Gr owe r , J u n e , r e p r i n t . Baker, D. 1 9 8 1 . P l a n t growth Engineering 62 (9 ) : 17-18. pickles. modeling. Ame r i c an Vege­ Agriculture Baker, F. 1 9 8 2 . P a r a m e t e r s e n s i t i v i t y in p l a n t p r o c e s s models. Ameri can S o c i e t y of A g r i c u l t u r a l E n g i n e e r s . P a p e r No. 8 2 - 4 5 7 0 . St. Joseph, Michigan. B a r t h o l o m e w , R. 1981. tion. Transactions Fa r m m a c h i n e r y c o s t i n g u n d e r o f t h e ASAE 2 4 ( 4 ) : 8 4 3 - 8 4 5 . infla­ Basselman, J. 1959. Syst ems e n g i n e e r i n g in a g r i c u l t u r e . A g r i c u l t u r e E n g i n e e r i n g J o u r n a l 4 0 ( 1 1 ) : 663, 685-686. B a t t e r h a m , R . , D. B r o w n , a n d P. Van D i e . 1973. e n g i n e e r i n g and m e t e o r o l o g i c a l d a t a r e q u i r e d e c o n o m i c mo d e l o f f a r m m a c h i n e r y s e l e c t i o n . Agricultural Engineering 15(2): 88-92. Ag r o no mi c i n an Canadian B i n g l e y , G. W. , R. K. L e o n a r d , W. F. B u c h e l e , B. A. S t o u t , a n d S . K. R i e s . 1962. Mechani zed cucumber h a r v e s t i n g . A g ri c u l t u r a l Engineering 43(1): 22-25. B i s h o p , R . , E. C h i p m a n , a n d C. M a c E a c h e r n . 1969. E f fe ct of n i t r o g e n , p h o s p h o r u s a n d p o t a s s i u m on y i e l d s a n d n u t r i ­ e n t l e v e l s in l a m i n a e and p e t i o l e s o f p i c k l i n g c u c u mb e r s . Canadi an J o u r n a l of So i l S c i e n c e 49: 2 9 7 - 3 0 4 . Blackwelder Manufacturing B o w e r s , W. Ma c h i n e 1975. Machi ner y management . Fundamentals of Operation. D e e r e a n d Co mp a n y , M o l i n e , I l l i n o i s . B r o w n , G. K. cultural 1980. crops. Co mp a n y , Ri o Vista, California. Harvest mechanization s t a t u s ASAE P a p e r No. 8 0 - 1 5 3 2 . C a d z o w, J . 1973. Discrete I n c . , New Y o r k . time systems. for horti­ P r e n t i c e - H a 11 C h a o - S h u n , S . , a n d E. H u m p h r i e s . F r u i t - s e t p a t t e r n s of pic kl in g cucumbers. T r a n s a c t i o n s o f t h e ASAE 1 2 ( 3 ) : 522-523. 256 C a n t l i f f e , D. 1977a. Ni t r o g e n f e r t i l i z e r r e q u i r e m e n t s of p i c k l i n g c u c u m b e r s g r o wn f o r o n c e - o v e r h a r v e s t I. E f f e c t on y i e l d a n d f r e s h q u a l i t y . J o u r n a l o f Ame r i c an S o c i e t y o f H o r t i c u 1t u r a 1 S c i e n c e 1 0 2 ( 2 ) : 1 1 2 - 1 1 4 . C a n t l i f f e , D. 1977b. Ni t rogen f e r t i l i z e r r e q u i r e m e n t s of p i c k l i n g c u c u m b e r s g r o wn f o r o n c e - o v e r h a r v e s t I I . E f f e c t on p l a n t t i s s u e m i n e r a l n u t r i e n t c o n c e n t r a t i o n . J o u r n a l of t h e Amer i can S o c i e t y of H o r t i c u l t u r a l S c i e n c e 102(2): 115-119. C a n t l i f f e , D . , R. R o b i n s o n , a n d S. S h a n n o n . 1972. Pr omo­ t i o n o f c u c u m b e r f r u i t s e t a n d d e v e l o p m e n t by c h l o r f l u renol. Hortscience 7(4): 416-420. C a n t l i f f e , D. , a n d S. P h a t a k . 1975. Plant population s t u d i e s w i t h p i c k l i n g c u c u m b e r s g r o wn f o r o n c e - o v e r h a r ­ vest. J o u r n a l o f t h e Ame r i c an S o c i e t y o f H o r t i c u l t u r a l Science 100(5): 464-466. C h e n , L . , R. S o w e l l , a n d E. H u m p h r i e s . 1976. A simulation mo d e l f o r m u l t i p l e h a r v e s t i n g o f p i c k l i n g c u c u m b e r s . J o u r n a l o f A g r i c u l t u r a l E n g i n e e r i n g R e s e a r c h 21: 6 7 - 7 5 . C h e n , L. 1979. S i m u l a t i o n of cucumber p r o d u c t i o n ing f o r o n c e - o v e r h a r v e s t i n g . T r a n s a c t i o n s of 22: 4 5 0 - 4 5 3 . schedul­ t h e ASAE C h e s t n u t , H. Wi l e y a n d John 1968. Sons, Syst ems e n g i n e e r i n g New Y o r k . methods. C h e n , L . , C. M i l l e r , a n d R. S o w e l l . 1975. Simulation model s f o r h a r v e s t i n g of p i c k l i n g c u c u m b e r s . J o u r n a l of Ame r i c an S o c i e t y o f H o r t i c u l t u r a l S c i e n c e 1 0 0 ( 2 ) : 176179. C o l w e l l , H. 1978. Mechani cal h a r v e s t i n g o f p i c k l i n g cucum­ b e r s — economi c a s p e c t s . M i n i s t r y o f A g r i c u l t u r e and F o o d , P a p e r No. 7 8 - 0 6 9 . O n t a r i o , Canada. C o l w e l l , T . , and J . O ' S u l l i v a n . 1981. Ec onomi c s o f h a r v e s t timing f o r o nc e- ov er h a r v e s t i n g of cucumbers. Journal o f t h e Amer i can S o c i e t y o f H o r t i c u I t u r a 1 S c i e n c e 1 0 6 ( 2 ) : 163-167. C o n n o r , L . , a n d E. M a r t i n . 1970. The e f f e c t o f d e l a y e d p o l l i n a t i o n on y i e l d o f c u c u m b e r s g r o wn f o r m a c h i n e h a r ­ vests. J o u r n a l o f Ame r i c a n S o c i e t y of H o r t i c u 1t u r a 1 Science 95(4): 456-458. C o n t r o l Da t a C o r p o r a t i o n ( CDC) . 1980. CDC o p e r a t i n g s y s ­ tems. F o r t r a u Ex t e n d e d V e r s i o n - 4 R e f e r e n c e Manua l . NOS 1, N0S/ BE 1, SCOPE 2 . Sunnyvale, C a l i f o r n i a . 257 C o r r i e , W. , a n d D. B o y c e . 1972. A dyna mi c pr ogr a mmi ng me t h o d t o o p t i m i z e p o l i c i e s f o r t h e m u l t i s t a g e h a r v e s t o f c r o p s w i t h an e x t e n d e d m a t u r i t y p e r i o d . J our na l of A g r i c u l t u r a l E n g i n e e r i n g R e s e a r c h 17: 3 4 8 - 3 5 4 . Cuke Inc., Box 4 5 2 , Me n d o t a , Illinois 61342. Cu mmi n s , T . , a n d D. K r e t c h m a n . 1975. Moisture s t r e s s r e l a ­ t i o n s t o g r o w t h and d e v e l o p m e n t o f t h e p i c k l i n g c u c u m­ ber. D e p a r t m e n t o f H o r t i c u 1t u r e , Oh i o A g r i c u l t u r e R e s e a r c h and De v e l o p me n t C e n t e r B u l l e t i n , 2 3 - 2 4 . C u r r y , R. 1971. Dy n a mi c s i m u l a t i o n o f p l a n t I. Devel opment o f a mo d e l . Transactions 14(5): 946-949. growth. Part o f t h e ASAE C u r r y , R . , a n d L. C h e n . 1971. Dy n a mi c s i m u l a t i o n o f p l a n t growth. Part II. Incorporation of actual d a i l y weather and p a r t i t i o n i n g o f n e t p h o t o s y n t h a t e . T r a n s a c t i o n s of t h e ASAE 1 4 ( 6 ) : 1 1 7 0 - 1 1 7 4 . Dalton, J. Science 1975. Study of a g r i c u l t u r a l s yst ems . P u b l i s h e r s L t d . , Essex, England. D a l t o n , J . 1982. Ma n a g i n g a g r i c u l t u r a l s y s t e m s . Science P u b l i s h e r s L t d . , Essex, England. De g a r mo , E . , economy. York. J . Canada, Sixth ed. Applied Applied a n d W. S u l l i v a n . 1979. Engineering M a c m i l l a n P u b l i s h i n g C o . , I n c . , New D e u t , J . , a nd M. B l a c k i e . c u l t u r a l management. Essex, England. 1979. Applied Department of A g r i c u l t u r e . statistics. Box 3 0 0 1 7 , Syst ems a n a l y s i s in a g r i ­ Science Publishers, Ltd., 1982. Mi chi gan a g r i c u l t u r e L a n s i n g , Mi chi gan 48909. D e V r i e s , P. 1977. E v a l u a t i o n o f s i m u l a t i o n mode l s c u l t u r e and b i o l o g y — C o n c l u s i o n s o f a w o r k s h o p . c u l t u r a l Sys t ems ( 2 ) : 9 9 - 1 0 3 . in a g r i ­ Agri­ Di xon, J . 1979. Sy s t e ms mo d e l i n g and s y s t e m s e n g i n e e r i n g . Seminar p r e p a r e d f o r p r e s e n t a t i o n in t h e P e o p l e ' s Republic of China. U n i v e r s i t y o f I d a h o , Mo s c o w, I d a h o . D y e r , J . , a n d W. B a i e r . 1979. We a t h e r - b a s e d e s t i m a t i o n of f i e l d workdays in f a l l . Canadian A g r i c u l t u r e E n g i n e e r ­ i n g 21 : 1 1 9 - 1 2 2 . Ebersohn, J. 1976. A c o m m e n t a r y on s y s t e m s s t u d i e s agriculture. A g r i c u l t u r a l Sys t ems ( 1 ) : 17 3 - 1 8 4 . in 258 E d w a r d s , D. 1979. A mo d e l Bot any 44: 5 2 3 - 5 3 5 . for leaf growth. Annals of E l l i o t , R . , W. L e mb k e , a n d D. H u n t . 1977. A simulation mo d e l f o r p r e d i c t i n g a v a i l a b l e d a y s f o r s o i l t i l l a g e . T r a n s a c t i o n s o f t h e ASAE 2 0 ( 1 ) : 4 - 8 . Ells, J . , a n d A. Mc S a y . 1981. Yield comparisons of p i c k ­ l i n g c ucumbe r c u l t i v a r t r i a l s f o r o n c e - o v e r h a r v e s t i n g . H o r t s c i e n c e 16( 1 2 ) : 1 8 7 - 1 8 9 . E n n i s , D. , and J . O ' S u l l i v a n . 1979. review. J o u r n a l o f Food S c i e n c e C u c u mb e r q u a l i t y — A 44 ( 1 ) : 186-189. E s ma y , M. 1982. The f a r m i n g s y s t e m s r e s e a r c h a p p r o a c h i n the a g ric u ltu ra l engineering f i e l d . P a p e r p r e p a r e d as p a r t o f t h e MSU T i t l e XI I T a s k F o r c e on F a r m i n g S y s t e m s research. Mi chi gan S t a t e U n i v e r s i t y , Ea s t L a n s i n g , Michi gan. F a i d l e y , L . , a n d M. E s ma y . 1974. guide to technology t r a n s f e r . c u l t u r a l E n g i n e e r s , P a p e r No. Michigan. F a i r b a n k s , G . , G. L a r s o n , using farm machi nery. 98-101. Sy s t e ms a n a l y s i s as a Amer i can S o c i e t y of A g r i ­ 74-5093. St. Joseph, a n d D. C h u n g . Transactions 1971. of t he Cost of ASAE 1 5 ( 1 ) : F a r a z d a g h i , H . , G. E d w a r d s , H. A y e r s , G. B u b e n z e r , a nd L. M a s s i e . 1982a. A m a t h e m a t i c a l mo d e l f o r l e a d a n d canopy p h o t o s y n t h e s i s under d i f f e r e n t l e v e l s of i r r a d i a n c e a n d COp c o n c e n t r a t i o n . Ame r i c an S o c i e t y o f A g r i ­ c u l t u r a l E n g i n e e r s , P a p e r No. 8 2 - 4 5 7 8 . St. Joseph, Mi chi gan . F a r a z d a g h i , H . , A. K a s c h a n i , a n d N. A s a d i . 1982b. A mathe­ m a t i c a l mo d e l f o r s e a s o n a l d r y m a t t e r p r o d u c t i o n o f c r o p s u n d e r d i f f e r e n t n i t r o g e n and i r r i g a t i o n w a t e r s u p ­ plies. Ame r i c an S o c i e t y o f A g r i c u l t u r a l E n g i n e e r s , P a p e r No. NAR 8 2 - 3 0 3 . St. Joseph, Michigan. F e y e r h a r m , A . , L. A r k , a n d W. B u r r o w s . 1966. Probabilities o f s e q u e n c e s o f we t a n d d r y d a y s i n M i c h i g a n . Mi c h i g a n A g r i c u l t u r a l E x p e r i m e n t a t i o n , East La ns i ng, Michi gan. FAM I n c . Canni ng Machi nery Division, Col umbus , Ohio. F r i d l e y , R . , and J . H o l t m a n . 1974. Simulation of s t r a w ­ b e r r y p r o d u c t i o n in C a l i f o r n i a t o e v a l u a t e a l t e r n a t e harve st system. T r a n s a c t i o n s o f t h e ASAE 1 7 ( 6 ) : 1 0 9 4 1098. 259 F r i d l e y , R. , and J . H o l t ma n . 1974. P r e d ic ti n g the s o c i o ­ e c o n o m i c i m p l i c a t i o n s o f m e c h a n i z a t i o n by s y s t e m s analysis. T r a n s a c t i o n s o f t h e ASAE 1 7 ( 5 ) : 8 2 1 - 8 2 5 . Frisby, J. 1970. E s t i m a t i o n o f good wo r k i n g d a y s f o r t i l l a g e in c e n t r a l M i s s o u r i . Transactions ASAE 1 3 ( 3 ) : 6 4 1 - 6 4 3 . F u l t o n , C. 1976. f i e l d wo r k i n 1045-1047. E x p e c t e d n u mb e r o f I owa. Transactions suitable of the days s u i t a b l e f o r o f t h e ASAE 1 9 ( 6 ) : Gardiner, J. Personal communication. Department of S t a t i s t i c s and P r o b a b i l i t y , Mi c h i g a n S t a t e U n i v e r s i t y , East Lansing, Michigan. G l e a s o n , C. 1982. Large a r e a y i e l d e s t i m a t i o n / f o r e c a s t i n g using p l a nt proc ess models. Amer i can S o c i e t y o f A g r i ­ cultural Engineers. P a p e r No. 8 2 - 4 5 6 9 . St. Joseph, Michi gan. G o r d o n , G. 1969. System s i m u l a t i o n . E n g l e w o o d C l i f f s , New J e r s e y . Prentice Hall, H a l l , W. 1949. The e f f e c t s o f e m a s c u l a t i o n i n r e l a t i o n n i t r o g e n supply d u r i n g t he ontogeny of t he g h e r k i n . Ame r i c a n J o u r n a l o f Bo t a n y 36: 7 4 0 - 7 4 6 . to H a r s h , S . , L. C o n n o r , a n d G. S c h w a b . 1981. Ma n a g i n g t h e farm b u s i n e s s . P r e n t i c e - H a 11 , I n c . , E n g l e w o o d C l i f f s , New J e r s e y . H a s s a u , A . , a n d R. B r o u g h t o n . 1975. Soil moisture c r i t e r i a f o r t r a c t a b i 1i t y . Canadian A g r i c u l t u r a l En g i n e e r i n g 17(2): 124-129. H e l d m a n , D . , D. M a r s h a l l , L. B o r t o n , a n d L. S e g e r l i n d . 1976. I n f l u e n c e o f h a n d l i n g on p i c k l i n g c u c u m b e r q u a l ­ ity. T r a n s a c t i o n s o f t h e ASAE 1 9 ( 6 ) : 1 1 9 4 - 1 1 9 6 , 1 2 0 0 . H e l m e r s , G . , a n d M. W a t t s . 1981. The e f f e c t o f i n f l a t i o n a n d i n c o me t a x on m a c h i n e r y c o s t s a n d o p t i m u m r e p l a c e ­ ment. Ame r i c an S o c i e t y o f A g r i c u l t u r a l E n g i n e e r s . P a p e r No. 8 1 - 1 5 1 4 . S t . J o s e p h , Michi gan. H e s k e t h , J . , D. B a k e r , a n d W. D u n c a n . 1971. y i e l d and g r o wt h i n c o t t o n . Respiration balance. Cr o p S c i e n c e 11: 3 9 4 - 4 0 8 . S i m u l a t i o n of and t h e c a r b o n H e t z , E. 1982. A f i e l d m a c h i n e r y s e l e c t i o n mo d e l f o r w h e a t p r o d u c e r s i n t h e An d e s P r e - C o r d i 11 e r a o f s o u t h c e n t r a l Chile. PhD d i s s e r t a t i o n , M i c h i g a n S t a t e U n i v e r s i t y , East La n s i n g , Mi chi gan. 260 H i l l i k e r , F. vester. 1972. G r o s s r e t u r n s e d g e up w i t h a Cuke V e g e t a b l e Cr o p Ma n a g e me n t ( 2 ) : 1 0 - 1 5 . har­ H o b s o n , J . , a n d D. M a r s h a l l . 1 9 8 3 . Unpublished r e p o r t p r e ­ s e n t e d a t t h e a n n u a l PPI m e e t i n g , M i c h i g a n S t a t e U n i v e r ­ s i t y , East La ns i ng, Michi gan. H o d g e s , T . , a n d E. K a n e m a s u . 1979. Model i ng d a i l y dr y m a t t e r p r o d uc t i on of w in te r wheat. Ag r o n o my J o u r n a l 974-978. 69: H o l t m a n , J . , A. P a t e l , F. P a n o l , a n d B. C a r g i l l . 1974. A m a t h e m a t i c a l mo d e l t o s c h e d u l e c u c u m b e r h a r v e s t . Trans­ a c t i o n s o f t h e ASAE 1 7 ( 5 ) : 8 6 1 - 8 6 3 . H o o p e r , A . , D. me t h o d f o r cucumbers. M a r s h a l l , L. B a k e r , a n d D. H e l d m a n . 1972. measurement of c a r p e l s t r e n g t h in p i c k l i n g ASAE P a p e r No. 7 2 - 3 7 9 . A H o o p e r , A. 1973. The e f f e c t o f i m p a c t on g r e e n s t o c k c a r p e l s t r e n g t h a n d b r i n e s t o c k q u a l i t y f o r c u c u m b e r s , Cuc umi s s a t i vu s . T h e s i s f o r MS d e g r e e , D e p a r t m e n t o f A g r i c u l t u r e E n g i n e e r i n g , Mi chi gan S t a t e U n i v e r s i t y , East La n s i n g , Michigan. H u i z , W. 1982. E s t i m a t i n g f arm machi ne o p e r a t i o n c o s t s . Cooperative Extension Service, U niv ers ity of Arizona, Tucson, A r i z o n a . Hull, C . , a n d N. N i e . 1981. S t a t i s t i c a l package for the s o c i a l s c i e n c e s , Updat e 7 - 9 . M c G r a w - H i l l , New Y o r k . H u m p h r i e s , E. 1 9 6 8 . Devel opment of a m u l t i p i c k harvester. T r a n s a c t i o n s o f t h e ASAE 1 1 ( 5 ) : cucumber 628-630. H u m p h r i e s , E. 1 9 7 1 . A second g e n e r a t i o n m u l t i p l e - p i c k cucumber h a r v e s t e r . T r a n s a c t i o n s o f t h e ASAE 1 4 ( 5 ) : 886-889 . H u m p h r i e s , E. 1 9 8 1 . C u c u mb e r m e c h a n i z a t i o n : removal. T r a n s a c t i o n s o f t h e ASAE 2 4 ( 4 ) : Cr own f r u i t 833-837. H u m p h r i e s , E . , a n d E. B e a s l e y . 1974. Mu 11 i p 1e - p i c k c u c u r n ber h a r v e s t i n g : S t a t u s and p o t e n t i a l . J o u r n a l of P i c k l e Pak S c i e n c e 4 ( 1 ) : 1 - 7 . H u n t , D. State 1977. Fa r m p o w e r University Press, and m a c h i n e r y Ame s , I o wa . management. I owa H u n t , D. 1980. We a t h e r d a t a us e in f i e l d m a c h i n e r y o p e r a ­ tions. Amer i can S o c i e t y of A g r i c u l t u r a l E n g i n e e r s , P a p e r No. 8 0 - 4 5 1 9 . St. Joseph, Michigan. 261 J e n s e n , M. , J . W r i g h t , a n d B. P r a t t . 1971. Estimating soil m o i s t u r e d e p l e t i o n f r om c l i m a t e , c r o p and s o i l d a t a . T r a n s a c t i o n s o f t h e ASAE 1 4 ( 6 ) : 9 5 4 - 9 5 9 . Johnson, J. 1979. I n f l u e n c e o f h a r v e s t f r e q u e n c y and t h o r o u g h n e s s on y i e l d a n d r e t u r n s f r o m p i c k l i n g c u c u m ­ bers. MS t h e s i s , Oh i o S t a t e U n i v e r s i t y , C o l u m b u s , O h i o . K e p n e r , R . , R. B a i n e r , E. B a r g e . m a c h i n e r y , 1978. Third e d . Westport, Connecticut. 1979. P r i n c i p l e s of farm AVI P u b l i s h i n g Co mp a n y , I n c . K r e t c h m a n , D. 1975. Plant population - c u ltiv a r - nitrogen r e l a t i o n s t o y i e l d and r e t u r n s f r om m e c h a n i c a l h a r v e s t of p ic k li n g cucumbers. D e p a r t m e n t o f H o r t i c u l t u r e , Oh i o A g r i c u l t u r a l R e s e a r c h and De v e l o p me n t C e n t e r B u l l e t i n ( a b s t r a c t ) : 25. K r e t c h m a n , D . , M. J a m e s o n , a n d C. W i l i e r . 1982. C u c u mb e r c u l t i v a r e v a l u a t i o n t r i a l s — 1982. Oh i o S t a t e U n i v e r ­ s i t y , Oh i o A g r i c u l t u r a l R e s e a r c h a n d D e v e l o p m e n t C e n t e r , Woost er. H o r t i c u l t u r e S e r i e s No. 5 2 6 . Ko r me r , K . , and S. K l e i s i n g e r . 1981. T e n d e n c i e s in cucum­ b e r and c a b b a g e m e c h a n i z a t i o n i n E u r o p e . ASAE P a p e r No. 81-1557. Ku ma r , R . , a n d J . G o s s . Comput e r s i m u l a t i o n o f h a r v e s t i n g a lf a lf a seeds. T r a n s a c t i o n s o f t h e ASAE 2 4 ( 5 ) : 1 1 3 5 1 140. L a i s o n , R. , and J . T h o r n e l y . sion in cucumber. Annals 1982. A mo d e l f o r l e a d o f Bot any 50: 4 0 7 - 4 2 5 . expan­ Lewi n, I . 1970. E f f e c t o f p h o t o p e r i o d and n y c t o p e r i o d t e m p e r a t u r e s a n d m o i s t u r e s t r e s s on f r u i t e n l a r g e m e n t t h e c u c u m b e r , Cu c umi s s a t i v u s L. CV. M i n i - C u k e . PhD d i s s e r t a t i o n , P e n n s y 1v a n i a S t a t e U n i v e r s i t y . L i g o n , J . , G. B e n o i t , a n d estimating occurrence excess. Transactions L i n k , D. tion. Paper Link, A. of of in E l a m. 1965. Procedure for s o i l m o i s t u r e d e f i c i e n c y and t h e ASAE 8 ( 2 ) : 2 1 9 - 2 2 2 . 1965. A systems approach to farm machinery s e l e c ­ Amer i can S o c i e t y o f A g r i c u l t u r a l E n g i n e e r s , No. 6 5 - 1 6 1 . St . Joseph, Michigan. D. 1968. Research needs f o r farm machinery s c h e d u l ­ ing. Ame r i c an S o c i e t y o f A g r i c u l t u r a l E n g i n e e r s , P u b l i ­ c a t i o n P ROC- 4 6 8 : 2 8 - 3 2 . St. Joseph, Michigan. 262 L i n e r , H . , a n d G. H u g h e s . 1972. C o s t s a nd r e t u r n s v e s t i n g cucumbers wi t h t h e o n c e - o v e r me c ha ni c a l vester. E x t e n s i o n C i r c u l a r 520, Nor t h C a r o l i n a U n i v e r s i t y , R a l e i g h , Nor t h C a r o l i n a . of h a r ­ har­ State L o o m i s , E . , a n d P. C r a n d a l l . 1977. Wa t e r c o n s u m p t i o n o f c ucu mb e r s d u r i n g v e g e t a t i v e and r e p r o d u c t i v e s t a g e s of growth. J o u r n a l o f t h e Ame r i c an S o c i e t y of H o r t i c u l ­ t u r a l Science 102(2): 124-127. Lo we r , R . , J . M c C r e i g h t , and 0 . S m i t h . 1975. a n d t e m p e r a t u r e e f f e c t s on s e x - e x p r e s s i o n cucumber. Abstract. Hor t S c i e n c e 1 0 ( 3 ) : Photoperiod and g r o w t h o f 24. M a n e t s c h , T. J . , a n d G. L. P a r k . 1979. Sy s t e m a n a l y s i s and s i m u l a t i o n w i t h a p p l i c a t i o n s t o e c o n o mi c and s o c i a l s y s ­ tems. Part I. D e p a r t m e n t o f E l e c t r i c a l E n g i n e e r i n g and System S c i e n c e , Mi chi gan S t a t e U n i v e r s i t y , East Lansi ng Mi chi gan . M a r s h a l l , D. , B. C a r g i l l , a n d J . L e v i n . 1971. Mechani cal h a r v e s t i n g and h a n d l i n g o f p i c k l i n g c u c u m b e r s . Recovery and e v a l u a t i o n o f q u a l i t y . ASAE P a p e r No. 7 1 - 3 4 7 . M a r s h a l l , D. , B. C a r g i l l , a n d J . L e v i n . 1972a. Factors i n f l u e n c i n g r e c o v e r y of p i c k l i n g cucumbers h a r v e s t e d mechanically. ASAE P a p e r No. 7 2 - 1 5 0 . M a r s h a l l , D. , B. C a r g i l l , a n d J . L e v i n . 1972b. P h y s i c a l a nd q u a l i t y f a c t o r s o f p i c k l i n g c u c u m b e r s a s a f f e c t e d by mechanical h a r v e s t i n g . T r a n s a c t i o n s o f t h e ASAE 1 5 ( 4 ) : 604-608, 612. M a r s h a l l , D. , B. C a r g i l l , J . L e v i n , a n d L. B a k e r . 1972c. The e f f e c t o f m e c h a n i c a l h a r v e s t i n g a n d h a n d l i n g on p i c k l i n g cucumber q u a l i t y . ASAE P a p e r No. 7 2 - 8 8 5 . M a y f i e l d , W. , G. H i n e s , a n d L. R o b e r t s . f o r e s t i m a t i n g farm machinery c o s t s . t h e ASAE 24 ( 6 ) : 1 4 4 6 - 1 4 4 9 . Mc C o l l u m, R . , a n d C. M i l l e r . 1971. a n d n u t r i e n t r e m o v a l by p i c k l i n g Amer i can S o c i e t y o f H o r t i c u l t u r e 1981. A new m e t h o d T r a n s a c t i o n s of Yield, n u t r i e n t uptake, cucumbers. J o u r n a l of Science 96(1): 42-45. Mc Mu r r a y , A . , a n d C. M i l l e r . 1969. The e f f e c t o f 2 c h 1 o r o e t h a n e p h o s p h o n i c a c i d ( e t h r e l ) on t h e s e x e x p r e s ­ s i o n a n d y i e l d s o f C u c u mi s s a t i v u s . J o u r n a l of t he Ame r i c an S o c i e t y o f H o r t i c u l t u r a l S c i e n c e 9 4 ( 4 ) : 4 0 0 4 02 . Me n z , K. 1980. U n i t f a r m s and f a r m i n g s y s t e m s r e s e a r c h . The 11T A e x p e r i e n c e . A g r i c u l t u r a l S y s t e ms ( 6 ): 4 5 - 5 1 . 263 Me n z , K. , a n d H. K n i p s c h e e r . 1981. The l o c a t i o n s p e c i f i c ­ i t y problem in f a r mi n g systems r e s e a r c h . Agricultural Syst ems ( 7 ) : 9 5 - 1 0 3 . Mi chi gan S t a t e U n i v e r s i t y . 1975. H a r v e s t i n g cucumbers mechanically. Cooperative Extension Service, Extension B u l l e t i n E-859. Mi c h i g a n S t a t e U n i v e r s i t y Comput e r L a b o r a t o r y . 1981. I n t e r a c t i v e s y s t e m u s e r ' s g u i d e f o r scope, h u s t l e r . Mi chi gan S t a t e U n i v e r s i t y , Ea s t L a n s i n g , Mi c h i g a n . M i l l e r , C . , a n d G. H u g h e s . 1969. Harvest indices for p i c k ­ l i n g cucumbers in o n c e - o v e r h a r v e s t e d s y s t e m s . Journal o f t h e Ame r i c a n S o c i e t y o f H o r t i c u l t u r a l S c i e n c e 94: 485-487. M i l l e r , J . , and J . Q u i s e n b e r r y . 1976. I n h e r i t a n c e of time o f f l o w e r i n g and i t s r e l a t i o n s h i p t o c r o p m a t u r i t y in cucumber. J o u r n a l of t h e Ame r i c an S o c i e t y o f H o r t i c u l ­ t u r a l Science 101(5): 497-500. M i n i s t r y o f A g r i c u l t u r e a n d Food ( MAT) . Mechani cal h a r v e s t ­ ing of c u c u mb e r s . O n t a r i o M i n i s t r y o f A g r i c u l t u r e and Food F a c t S h e e t Ag d e x 1 5 6 / 5 1 , O n t a r i o , C a n a d a . M i s h o e , J . , J . J o n e s , a n d G. G a s c h o . 1979. Harvesting s c h e d u l i n g o f s u g a r c a n e f o r o p t i mu m b i o m a s s p r o d u c t i o n . T r a n s a c t i o n s o f t h e ASAE 2 2 ( 6 ) : 1 2 9 9 - 1 3 0 4 . J i s h o e , J . , J . J o n e s , D. S w a n e y , a n d J . W i l k e r s o n . 1982. Usi ng c r o p mo d e l s f o r ma n a g e me n t . I n t e g r a t i o n of weather data. Amer i can S o c i e t y of A g r i c u l t u r a l E n g i n ­ e e r s , P a p e r No. 8 2 - 4 5 6 7 . St . J ose ph, Michigan. Mo o r e , J . , W. B r a n t , a n d E. machinery cost a n a l y s i s Agricultural Engineers, Michigan. Smith. 1980. A computerized program. Ame r i c an S o c i e t y of P a p e r No. 8 0 - 1 0 1 6 . St. Joseph, Mo r e y , R . , G. Z a c h a r i a h , a n d R. P e a r l . h a r v e s t i n g and h a n d l i n g s i m u l a t o r . ASAE 1 4 ( 2 ) : 3 2 6 - 3 2 8 . 1971. A corn growth T r a n s a c t i o n s of the M o r r i s o n , F. 1966. C u l t u r a l and e n v i r o n m e n t a l p a r a m e t e r s fo r me ch anic al ly h a r v e s t e d cucumbers. PhD d i s s e r t a t i o n . Mi chi gan S t a t e U n i v e r s i t y , Ea s t L a n s i n g , Mi c h i g a n . M o r r i s o n , F . , a n d S. R i e s . 1967. Cultural requirements for o n c e - o v e r me c h a n i c a l h a r v e s t of cucumbers f o r p i c k l i n g . A m e r i c a n S o c i e t y o f H o r t i c u 1t u r a 1 S c i e n c e 9 1 : 3 3 9 - 3 4 6 . 264 M u h t a r , H. 1982. An e c o n o m i c c o m p a r i s o n o f c o n v e n t i o n a l and c o n s e r v a t i o n t i l l a g e s y s t e m s in t h e s o u t h e a s t S a g i n a w Bay c o a s t a l d r a i n a g e b a s i n . PhD d i s s e r t a t i o n . Mi chi gan S t a t e U n i v e r s i t y , Ea s t L a n s i n g , Mi c h i g a n . N a t h , S . , a n d W. J o h n s o n . 1980. Devel opment of a s o i l m o i s t u r e mo d e l t o p r e d i c t s o i l m o i s t u r e a n d t r a c t a b i l i t y for harvesting. A g r i c u l t u r e M e c h a n i z a t i o n in As i a 11(1): 73-78. N a y l o r , T . , H. J o s e p h , L. B l a n t i f y , D. B u r d i c h , a n d K. Ch u . 1968. Comput e r s i m u l a t i o n t e c h n i q u e s . J o h n Wi l e y a nd S o n s , New Yo r k . N e t e r , J . , a n d W. W a s s e r m a n . cal models. Irwon, I n c . , Ni e, 1974. Applied l i n e a r Home wood, I l l i n o i s . statisti­ N . , C. H u l l , J . J e n k i n s , K. S t e i n b r e n n e r , a n d D. B e n t . 1975. S t a t i s t i c a l package for the s o c i a l s c i e n c e s . Second e d . M c G r a w - H i l l , New Y o r k . N i t s c h , J . , E. K u r t z , J . L i v e r m a n , a n d F. We n t . 1952. d e v e l o p me n t of sex e x p r e s s i o n in c u c u r b i t f l o w e r s . Ame r i c an J o u r n a l o f Bot a ny ( 3 9 ) : 3 2 - 4 3 . The No r ma n , D. 1978. Fa r mi ng s y s t e m s r e s e a r c h t o i mpr ove t h e l i v e l i h o o d of smal l f a r m e r s . Ame r i c an J o u r n a l of A g r i ­ c u l t u r a l Ec onomi c s ( 6 0 ) : 8 1 3 - 8 1 8 . N u r n b e r g e r , F. Personal me n t o f A g r i c u l t u r e , Lansing , M i ch i g an . communications. Mi chi gan Mi chi gan Wea t he r S e r v i c e . Depart­ East O r t e g a , D . , a n d D. K r e t c h m a n . 1982. Wa t e r s t r e s s e f f e c t on p ic k li ng cucumbers. J o u r n a l o f t h e Amer i can S o c i e t y of H o r t i c u l t u r a l Science 107(3): 409-412. O'Sulliv an, J. 1980. I r r i g a t i o n , s p a c i n g and n i t r o g e n e f f e c t s on y i e l d a n d q u a l i t y o f p i c k l i n g c u c u m b e r s g r o wn for mechanical h a r v e s t i n g . Canadian J o u r n a l of P l a n t S c i e n c e 60: 9 2 3 - 9 2 8 . O ' S u l l i v a n , J . , a n d T. C o l w e l l . 1980. E f f e c t of h a r v e s t d a t e on y i e l d a n d g r a d e d i s t r i b u t i o n o f p i c k l i n g c u c u m ­ bers h a r ve st e d o n c e- o ve r. J o u r n a l o f t h e Ame r i c an S o c i e t y of H o r t i c u l t u r a l S c i e n c e 10 5 ( 3 ) : 4 08- 412. Owe n s , K. , G. T o l l a , a n d C. P e t e r s o n . 1980. I n d u c t i o n of s t a m i n a t e f l o w e r s on g y n o e c i o u s c u c u m b e r by a m i n o e t h o x y v i n y 1g 1u c i n e . H o r t s c i e n c e 15 ( 3 ) : 2 5 6 - 2 5 7 . P e r k i n s o n , F . , a n d E. H u m p h r i e s . 1972. I n t e r a c t i o n s bet ween a m u l t i p l e - p i c k h a r v e s t e r and c u c u mb e r v i n e s . Transac­ t i o n s o f t h e ASAE 1 5 ( 3 ) : 4 0 4 - 4 0 5 . 265 Pigram, J. 1977. A g r i c u l t u r a l systems A g r i c u l t u r a l Systems ( 2 ) : 3- 15. P r i c e , H. 1981. Depart ment of East La n s i n g , in t r a n s i t i o n . P i c k l i n g cucumber c u l t i v a r t r i a l , 1981. Horticulture. Mi chi gan S t a t e U n i v e r s i t y , Mi chi gan ( u n p u b l i s h e d d a t a ) . P r i c e , H. , and I . W i d d e r s . 1982. C u l t i v a r a nd c u l t u r a l p r a c t i c e e v a l u a t i o n f or cucumbers. An n u a l PPI Ad Hoc P i c k l e R e s e a r c h Commi t t ee M e e t i n g . Mi c h i g a n S t a t e University. P u t n a m , A. 1963. H o r t i c u l t u r a l a s p e c t s conce r ne d with t he p r o d u c t i o n of p i c k l i n g cucumbers f o r o n c e - o v e r h a r v e s t . MS t h e s i s . Mi c h i g a n S t a t e U n i v e r s i t y , E a s t L a n s i n g , Mi chi gan . Q u e b e d e a u x , B . , a n d E. B e y e r . 1972. Chemically p a r t h e n o c a r p y i n c u c u m b e r by a new i n h i b i t o r transport. Hortscience 7(5): 474-476. induced of auxin R a w l s , W. , D. B r a k e n s i c k , a n d K. S a x t o n . E s t i m a t i o n of water p r o p e r t i e s . T r a n s a c t i o n s o f t h e ASAE 2 5 ( 5 ) : 1316-1320. soil R e n o l l , E. 1 9 7 0 . A me t h o d f o r p r e d i c t i n g f i e l d m a c h i n e r y e f f i c i e n c y and c a p a c i t y . T r a n s a c t i o n s o f t h e ASAE 13(4): 448-449. Renoll, E. 1 9 7 5 . F i e l d m a c h i n e r y i n d e x us e and a p p l i c a ­ tion. T r a n s a c t i o n s o f t h e ASAE 1 8 ( 3 ) : 4 9 3 - 4 9 6 . Renoll, E. 1 9 8 1 . P r e d i c t i n g machi ne f i e l d c a p a c i t y f o r s p e c i f i c f i e l d and o p e r a t i n g c o n d i t i o n s . Transactions Of t h e ASAE 2 4 ( 1 ) : 4 5 - 4 7 . R i e s , S . , T. R i c h m a n , a n d V. W e r t . 1978. Gr o wt h a n d y i e l d of c r o p s t r e a t e d wi t h t r i a c o n t a n o 1 . J o u r n a l of the Ame r i c a n S o c i e t y o f H o r t i c u 1t u r a 1 S c i e n c e 103: 3 6 1 - 3 6 4 . R o t z , A . , J . B l a c k , a n d P. S a v o i e . 1981. A machinery c o st mo d e l w h i c h d e a l s w i t h i n f l a t i o n . Ame r i c an S o c i e t y o f A g r i c u l t u r a l E n g i n e e r s , P a p e r No. 8 1 - 1 5 1 8 . St. Joseph, Michi gan. R o t z , C. A . , D. H e r r i n g o n t , G. B r o wn , R. L e d e b u h r , a nd D. M a r s h a l l . 1982. Me c h a n i c a l h a r v e s t i n g of s mal l p i c k l i n g cucumbers with c o n v e n t i o n a l h a r v e s t e r s . Trans­ a c t i o n s o f t h e ASAE 2 5 ( 1 ) : 1 3 - 1 6 . Rountree, J. aspects. 1977. S y s t e m s t h i n k i n g — Some f u n d a m e n t a l A g r i c u l t u r a l S y s t e m s 2: 2 4 7 - 2 5 4 . 266 R u d i c h , J . , L. B a k e r , a n d H. S e l l . 1977. P a r t h e n o c a r p y in C u c u mi s s a t i v u s L. a s a f f e c t e d by g e n e t i c p a r t h e n o c a r p y , t h e r m o - p h o t o p e r i o d , and f e m a l e n e s s . J o u r n a l of the Ame r i c an S o c i e t y o f H o r t i c u l t u r a l S c i e n c e 1 0 2 ( 2 ) : 225228. R u t l e d g e , P . , a n d F. M a c h a r d y . 1968. The i n f l u e n c e o f w e a t h e r on f i e l d t r a c t a b i l i t y i n A l b e r t a . Canadian A g ri c u lt u ra l Engineering 10(2): 70-73. S a d h u , M. , a n d P. D a s . 1978. E f f e c t o f e t h r e l on t h e g r o w t h , f l o w e r i n g , and f r u i t i n g f o t h r e e c u c u r b i t s . J o u r n a l of H o r t i c u l t u r a l S c i e n c e 53: 1-4. S a n d h u , M. , R. L o c k a r d , C. G r u n w a l k , a n d L. S t o l t z . 1 9 7 2 . Endoge nous g r o wt h r e g u l a t o r s in d w a r f and t a l l c u c u mb e r . J o u r n a l of t h e Amer i can S o c i e t y of H o r t i c u l t u r a l Science 97(3): 387-389. S a r i g , Y. , L. S e g e r l i n d , D. M a r s h a l l , J . L e v i n , a nd D. H e l d m a n . 1975. The e f f e c t o f c u c u m b e r h a n d l i n g brine stock q u a l i t y . ASAE P a p e r No. 7 5 - 6 5 0 4 . S a x t o n , K. , H. J o h n s o n , a n d R. S h a w. t r a n s p i r a t i o n and s o i l m o i s t u r e . ASAE 1 7 ( 3 ) : 6 7 3 - 6 7 7 . S c h o n e y , R . , a n d M. on u s e d m a c h i n e 292-295. Finner. values. on 1974. Mo d e l i n g e v a p o T r a n s a c t i o n s of the 1981. The i m p a c t o f i n f l a t i o n T r a n s a c t i o n s o f t h e ASAE 2 4 ( 2 ) : S c h w a b , G. 1980. R a t e s f o r c u s t o m wo r k i n Mi chi gan S t a t e U n i v e r s i t y , A g r i c u l t u r a l B u l l e t i n E-458, East Lans i ng, Michi gan. Michigan. Facts, Extension S e l i n o , I . S . , a n d W. M. Br o wn . 1972. E s t i m a t i o n of s p r i n g wo r k d a y s f r o m c l i m a t o l o g i c a l r e c o r d s . Canadian A g r i ­ c u l t u r e Engineering 14(2): 79-81. S h a w, R. H. 1963. E s t i m a t i o n of s o i l m o i s t u r e under c o r n . A g r i c u l t u r a l a n d Home E c o n o m i c s E x p e r i m e n t S t a t i o n R e s e a r c h B u l l e t i n 5 2 0 , I owa S t a t e U n i v e r s i t y o f S c i e n c e a n d T e c h n o l o g y , Ame s , I o w a . S h a w, R. 1965. Es t i ma t i on of from m e t e o r o l o g i c a l d a t a . 39(4): 393-402. f i e l d wo r k d a y s i n t h e s p r i n g I owa S t a t e J o u r n a l o f S c i e n c e S i m s , W. L . , M. B. Z a h a r a . 1968. Gr owi ng p i c k l i n g c u c u m ­ bers f o r mechanical h a r v e s t i n g . Agriculture Extension Service. U n i v e r s i t y o f C a l i f o r n i a , AXT - 2 7 0 . 267 Sinnot, bit E. 1945. The r e l a t i o n o f g r o w t h t o s i z e i n c u c u r ­ fruits. Ame r i c an J o u r n a l of Bot any 32: 4 3 9 - 4 4 6 . S m e r a g e , G. 1983. R e p r e s e n t a t i o n o f d e v e l o p m e n t d e l a y in population models. Ame r i c an S o c i e t y o f A g r i c u l t r u a l E n g i n e e r s , P a p e r No. 8 3 - 1 0 0 3 . St . Joseph, Michigan. Smith, E., model . and 0. Lo e we r . 1981. ASAE P a p e r 8 1 - 4 0 1 3 . A n o n s p e c i f i c cr op growth S t . J o s e p h , Michi gan. S m i t t l e , D . , a n d R. W i l l i a m s o n . 1977. Efficiency, root g r o w t h , y i e l d , and f r u i t s h a p e o f p i c k l i n g c u c u m b e r s . J o u r n a l of t h e Amer i can S o c i e t y of H o r t i c u l t u r a l Science 102(6): 822-825. S o r i b e , F . , a n d R. C u r r y . 1973. S i m u l a t i o n of l e t t u c e g r o w t h i n an a i r - s u p p o r t e d p l a s t i c g r e e n h o u s e . Journal o f A g r i c u l t u r e E n g i n e e r i n g r e s e a r c h 18: 1 3 3 - 1 4 0 . S o w e l l , R . , T. L i a n g , a n d D. L i n k . 1971. S i m u l a t i o n of expected crop r e t u r n s . T r a n s a c t i o n s o f t h e ASAE 1 4 ( 1 ) : 383-386. S p e d d i n g , C. Ac a d e mi c 1975. Press, The b i o l o g y New Y o r k . of agricultural S t a p l e t o n , H . , a n d R. M e y e r s . 1971. Model i ng for cotton: The c o t t o n p l a n t s i m u l a t i o n . o f t h e ASAE 1 4 ( 5 ) : 9 5 0 - 9 5 3 . S t e e l , R . , and J . T o r r i e . 1980. Principles of s t a t i s t i c s . A biometrical approach. M c G r a w - H i l l Book Co mp a n y , New Y o r k . systems. subsystems Transactions and p r o c e d u r e s Second e d . S t o u t , B. 1967. A mechanical h a rv e st in g system for p i c k ­ l i n g c u c u m b e r s i n t h e USA. S e c o n d H o r t i c u l t u r a 1 Me c h a n i c a t i o n C o n f e r e n c e , Kecskemet , Hungary. S t o u t , B . , a n d K. R i e s . 1959. A p r o g r e s s r e p o r t on t h e d e v e l o p m e n t of a m e c h a n i c a l cucumber h a r v e s t e r . Michi­ gan A g r i c u l t u r e E x p e r i m e n t S t a t i o n , Mi c h i g a n S t a t e U n i v e r s i t y , East Lansing 4 1 ( 3 ) : 699-718. S t o u t , B . , K. R i e s , a n d A. P u t n a m . 1963. The f e a s i b i l i t y of a o n c e - o v e r m e c h a n i c a l cucumber h a r v e s t e r f o r p i c k ­ ling cucumbers. Q u a r t e r l y B u l l e t i n , Mi chi gan A g r i c u l ­ t u r a l Ex p e r i me n t S t a t i o n , Mi chi gan S t a t e U n i v e r s i t y , East Lansing 4 5 ( 3 ) : 407-416. 268 S t o u t , B . , M. D e l o n g , D. P e t t e n g i l l , a n d S. R i e s . 1964. o n c e - o v e r m e c h a n i c a l cucumber h a r v e s t e r f o r p i c k l i n g cucumbers. Q u a r t e r l y B u l l e t i n , Mi chi gan A g r i c u l t u r a l Ex p e r i me n t S t a t i o n , Mi chi gan S t a t e U n i v e r s i t y , Eas t Lansing 4 6 ( 3 ) : 420-430. A S t r o m m e n , N. 1974. Mo n t h l y p r e c i p i t a t i o n p r o b a b i l i t i e s f o r c l i m a t i c d i v i s i o n s in Mi c h i g a n . Mi chi gan De p a r t me n t of A g r i c u l t u r e , Mi chi gan Wea t he r S e r v i c e . S u t t o r , R . , a n d R. tion. Journal Cr o m. 1964. Comput e r mo d e l s and o f Fa r m E c o n o m i c s 4 6 : 1 3 4 1 - 1 3 5 0 . simula­ S w a n e y , D . , J . M i s h o e , J . J o n e s , a n d W. B o g g e s . 1982. Us i n g c r o p mo d e l f o r m a n a g e m e n t — I m p a c t o f w e a t h e r c h a r ­ a c t e r i s t i c s on d e c i s i o n s . Amer i can S o c i e t y of A g r i c u l ­ t u r a l E n g i n e e r s , P a p e r No. 8 2 - 4 5 6 8 . St. Joseph, Michi gan. S w a r t z m a n , G. 1973. N o t e s on m o d e l i n g a nd s y s t e m s a n a l y s i s a p p l i e d t o e c o s y s t e m s and a g r i c u l t u r e s y s t e m s . Agricul­ t u r e Research Counci l, U n i v e r s i t y of Reading, England. T h r e a d g i l l , E . , S. Wi n d h a m, a n d D. S i b l e y . 1977. Perform­ a n c e o f mu 1 1 i p 1e - p i c k c u c u m b e r h a r v e s t e r s . Transactions o f t h e ASAE 20 ( 4 ) : 6 2 6 - 6 3 0 . T o m p k i n s , D . , a n d D. S h u l t e i s . 1970. Usi ng gr o wt h r e g u l a t ­ ing s u b s t a n c e s t o a d a p t p i c k l i n g cucumbe r s t o me c h a n i c a l harvesting. J o u r n a l o f t h e Ame r i c an S o c i e t y of H o r t i cu 1 t u r a 1 Sc i e n c e s . T u l u , M. 1973. S i m u l a t i o n o f t i m e l i n e s s and t r a c t a b i l i t y con dit ion s for corn production systems. PhD d i s s e r t a ­ tion. Mi c h i g a n S t a t e U n i v e r s i t y , E a s t L a n s i n g , M i c h i g a n . Tulu, M. Y. , J . B. H o l t m a n , R. B. F r i d l e y , a n d S . D. P a r s o n s . 1974. T i m e l i n e s s c o s t s and a v a i l a b l e wo r k i n g d a y s — shelled corn. T r a n s a c t i o n s o f t h e ASAE 1 7 ( 1 0 ) : 7 9 8 - 8 0 0 . United S t a t e s standards Department for grades of A g r i c u l t u r e . 1966. U. S. of p i c k l e s . S e c t i o n 52, 1964. United S t a t e s Department of A g r i c u l t u r e . 1981. Vegetables: 1981 a n n u a l s u mma r y a c r e a g e , y i e l d , p r o d u c t i o n and value. Cr o p R e p o r t i n g B o a r d , S t a t i s t i c a l R e p o r t i n g S e r v i c e , Vg 1 - 2 ( 8 1 ) . V a n d e r l i p , R . , a n d G. A r k i n . 1977. Simulating accumulation and d i s t r i b u t i o n o f d r y m a t t e r i n g r a i n s o r g h u m. Ag r o n o my J o u r n a l 6 9 : 9 1 7 - 9 2 3 . 269 Van E e , G . , R. L e d e b u h r , a nd I . H a f f a r . 1981. De vel opme nt and t e s t i n g o f a s ma l l p i c k l e t h r e s h i n g me c h a n i s m. A m e r i c a n S o c i e t y o f A g r i c u l t u r a l E n g i n e e r s , P a p e r No. 81-1556. S t . J o s e p h , Michi gan. Van E e , G . , R. L e d e b u h r , a n d I . H a f f a r . 1982. De vel opment and t e s t i n g o f a " t h r e s h i n g c o n c e p t " c u c u mb e r h a r ­ vester. Amer i can S o c i e t y of A g r i c u l t u r a l E n g i n e e r s , P a p e r No. 8 2 - 1 5 6 0 . St. Joseph, Michigan. Van Kampen , J . 1971. Fa r m m a c h i n e r y s e l e c t i o n a n d w e a t h e r uncertainty. S y s t e ms A n a l y s i s i n A g r i c u l t u r e Ma n a g e ­ ment. John Wi l e y, S i d n e y , A u s t r a l i a . 294-329. V i t o s h , M. 1977. I r r i g a t i o n s c h e d u l i n g f o r f i e l d c r o p s and vegetables. Mi chi gan S t a t e U n i v e r s i t y E x t e n s i o n B u l l e ­ t i n E-1 110 . East Lansi ng, Michigan. Wa l k e r , J . , and of p l a n t s : 14(5): 945. W. S p l i n t e r . Introduction. 1971. Mathematical modeling T r a n s a c t i o n s o f t h e ASAE W a l r a t h , A. 1977. Ev a l u a t i o n of invest ment United S t a t e s Department of A g r i c u l t u r e . Ha n d b o o k No. 3 4 9 . opportunities. Agriculture W a t k i n s , J . , a n d D. C a n t l i f f e . 1980. Regul at i on of f r u i t s e t i n Cuc umi s s a t i v u s by a u x i n a n d an a u x i n t r a n s p o r t inhibitor. J o u r n a l o f t h e Ame r i c an S o c i e t y of H o r t i ­ c u l t u r a l Science 105(4): 603-607. We h n e r , T . , a n d M. S a l t v e i t . 1983. Photographic analysis of c ucumber f r u i t e l o n g a t i o n . J o u r n a l o f t h e Ame r i c an So c ie ty of H o r t i c u l t u r a l Science 108(3): 465-468. W e i s s , G. 1 9 6 8 . E q u a t i o n s f o r t h e age s t r u c t u r e o f g r o w ­ ing p o p u l a t i o n s . B u l l e t i n of Mathematical Bi ophysi cs 30: 4 2 7 - 4 3 5 . W e i s s , L. 1 9 6 4 . S e q u e n c e s o f we t o r d r y d a y s d e s c r i b e d a Ma r k o v c h a i n p r o b a b i l i t y m o d e l . Mo n t h l y We a t h e r Re vi e w 9 2 ( 4 ) : 169- 1 7 6. W e s t e r n Far m E q u i p m e n t J o u r n a l . 1967. p i c k e r pe r f o r ms in C a l i f o r n i a f i e l d 63(17). Mechani cal p i c k l e demonstration. Whi t e, R. 1 9 7 4 . Fuel r e q u i r e m e n t s f o r s e l e c t e d f a r m i n g operations. Mi chi gan S t a t e U n i v e r s i t y , C o o p e r a t i v e E x t e n s i o n S e r v i c e , E x t e n s i o n B u l l e t i n E-780, East Lansing, Michigan. by 270 W h i t e , R. 1978. De t e r mi n i n g c a p a c i t i e s of farm machines. Mi chi gan S t a t e U n i v e r s i t y , C o o p e r a t i v e E x t e n s i o n S e r v i c e , E x t e n s i o n B u l l e t i n E- 1 2 1 6 , S F - 1 4 . East Lansing Michi gan. Wi l d e Manufacturing Co mp a n y , Inc., Bailey, Mi chi gan 49303. Williams, D. , a n d W. E d w a r d s . 1 9 7 8 . F i e l d wo r k d a y s i n I owa . E s t i m a t e d n u mb e r s u i t a b l e . M a c h i n e r y Ma n a g e me n t S e r i e s , P-695. C o o p e r a t i v e E x t e n s i o n S e r v i c e , I owa S t a t e U n i v e r s i t y , Ame s , I o w a . W i l s o n . W. E. 1965. Concepts of e n g i n e e r i n g design. M c G r a w - H i l l , New Yo r k . W i s e r , E. 1966. Mo n t e C a r l o tion frequency analysis. 9(4): 538-542. system met hods applied to p r e c i p i t a ­ T r a n s a c t i o n s o f t h e ASAE W r i g h t , A. 1971. Fa r mi n g s y s t e m s , mo d e l s and s i m u l a t i o n . S y s t e ms A n a l y s i s in A g r i c u l t u r a l Ma n a g e me n t . De n t a nd B l a c k i e , e d . , 1979. J o h n Wi l e y a n d S o n s , New Yo r k . Wy mo r e , A. W. 1976. interdisciplinary Syst ems teams. e n g i n e e r i n g methodology f o r J o h n Wi l e y a n d S o n s , New Y o r k . APPENDI CES APPENDIX A A LI ST OF THE MODEL COEFFI CI ENT MATRICES 271 APPENDIX A A LI ST OF THE MODEL COEFFICIENT MATRICES F 1A 0 0 0 0 0 Ml 1A F 1B 0 0 0 0 M2 1 A M1B F2 0 0 0 0 0 M2 F3A 0 0 0 0 0 M23A F3B 0 0 0 0 M13A M3 B F4 R1 A 0 0 0 0 0 0 R1 B 0 0 0 0 0 0 R2 0 0 0 0 0 0 R3A 0 0 0 0 0 0 R3B 0 0 0 0 0 0 4A Ill C1 A 0 0 0 0 0 0 C1B 0 0 0 0 0 0 C2 0 0 0 0 0 0 C3 A 0 0 0 0 0 0 C3B 0 0 0 0 0 0 C4 P 1A 0 0 0 0 0 0 P 1B 0 0 0 0 0 0 P2 0 0 0 0 0 0 P3 A 0 0 0 0 0 0 P3B 0 0 0 0 0 0 P4 APPENDIX B A TABULATION OF EQUATION COEFFI CI ENTS THE CASH FLOW COST ANALYSIS IN 273 APPENDIX B A TABULATION OF EQUATION COEFFI CI ENTS THE CASH FLOW COST ANALYSIS _______________________________ L i s t Co l u mn of Te r ms _______________ Numbe r A B IN Description Yearj J = 1 ’ n > and at wher e (' S ei^0QRC2 n=10 a = 1 0 % » i = 8 °^ ' U S e (1J0~00 RC2) f o r (T~ S0 1(O JOORC2 - T”-5-e -(ij000)~ C~ ) f o r ( 1+C) X as x=j and c=6 % ( 1+ b ) x as x =j and b=12% — as m= J a n d - - —— a s m= j i ( 1+ i ) m and R= 4% i=8 % u s e = 3 0 ° h r s > RC2=1.8 use=150 h r s > RC2=1.6 A B 1 1 .0 2 0.11 0.05 1 2 1 . 04 0.29 3 1 . 06 4 C D E F . 06 1.12 1 . 04 0.93 0.10 1.12 1 . 25 0.53 1 . 78 0.54 0.18 1 . 19 1. 41 0.362 2.58 1.10 0.85 0.26 1.26 1. 57 0.281 3. 31 5 1. 13 1 .22 0.37 1. 34 1.76 0. 231 3.99 6 1. 15 1 .66 0.47 1 . 42 1 . 97 0. 191 4.62 7 1. 17 2.14 0. 61 1. 50 2.21 0.167 5. 21 8 1. 29 2.69 0.73 1. 59 2.48 0.149 5.75 9 1.22 3.29 0.89 1 . 69 2.77 0.134 6.25 10 1. 24 3.93 1 .02 1 . 79 3. 11 0.123 6. 71 11 . 3 3 16.72 4.68 H 274 TOTAL G APPENDIX C WEATHER SIMULATION C1 Listing of Pr ogr am DATATAP C2 Listing of Program C3 Daily Per C4 for T r a f f i c a b i 1ity Ar e a Daily CUCWEAT Per Ye a r Per T r a ffic a b i1ity Location 2395 Per Sequence Soil the Mo nt h o f August the Mo n t h o f Augus t Type. Sequence Ye a r of Per of Initial Field Capacity. 275 APPENDIX C1 Listina of Proaram DATATAP PROGRAM DATATAP(INPUT.OUTPUT,TAPE 1 0 , TAPE20) C THIS PROGRAM DECODES INTO A BUFFERIN FORMAT C THE CLIMATOLOGICAL DATA NEEDED FOR A MICHIGAN C WORKABILITY MODEL C C*****************INITIALIZATI O N ** * * ** * * * * * * * * * * * * * * DIMENSION BUF ( 5 . 3 1 . 1 2 ) INTEGER YEAR,DAY,AREA C ************************************************* PRINT 1 1 FORMAT(*ENTER AREA CODE*) READ*,AREA C AREA COULD BE 2 3 9 5 4 5 0 2 OR 7 9 6 0 C C DO 1 N - 1 , 3 , 00 2 Y " 'E\ A RR- 6- 611. , 8 0 C | BUFFER IN ( 1 0 , 0 ) (BUF ( 1 , 1 , 1 ) , BUF ( 5 , 3 1 , 1 2 ) ) C C C C THIS STATEMENT BLOCKS THE DATA STARTING WITH THE FIRST BINARY WORD IN THE DATA BLOCK TILL THE FIFTH WORD OF THE DAY 31 MONTH 12 t F (UN IT ( 1 0 ) ) 6 , 7 , 9 C UNIT IS A DATA PROCESSING CHECK § DECODE (I*, 1 0 , BUF ( 1 , 1 , 1 ) ) LOCATE C C LOCATE IS ENCODED AND USED TO MATCH THE AREA 10 FORMAT (I 4) C C C C IF (LOCATE.NE. ARE A)GO TO 2 0 0 3 MON - 6 , 8 DO 4 D A Y - 1 , 3 1 15 12 C 4 3 2 I 9 II 7 I F ( MON. EQ. 6. ANO. DAY. EQ. 31 ) GO TO L OECOOE ( 4 ? , 1 5 , BUF (1 .OAY.MONl 1 PRFr.iF FORMAT(20X, F A . 2 , 1 4 X , F 3 • 2) WRITE ( 2 0 , 1 2 ) LOCATE.YEAR,MON, DAY,PRECIP,EVAP FORMAT ( 1 4 , 1 X, 3 12 . 9 X. FL. I . 1 4 X . F 3 - 2 ) CONTINUE CONTINUE CONTINUE CONTINUE PRINT 11 FORMAT (*OATA NOT PROCESSED*) STOP ENO 276 APPENDIX C2 L i s t i n g o f P r o g r a m CUCWEAT PROGRAM CUCWEAT( I H P U T , O U T P U T , T A P E 2 0 . T A P E 30 ) C T H I S PROGRAM CALCULATES THE PROBABILI TY OF WORK OAYS C IN CERTAIN LOCATIONS IN MICHIGAN OVER A 2 0 YEARS OF C WEATHER DATA FOR THE MONTH AUGUST(CUCUMBER HARVEST C SEASON IN MICHIGAN) C C C* * * * * * * * * * * * * * * * * I N I T I A L I Z A T I O N * * * * * * * * * * * * * * * * * * * * * * * * * C DIMENSION NWORK ( 4 0 , 1 0 0 ) , SO ILM ( 2 0 0 0 ) , DRAIN ( 2 0 0 0 ) , EVPTRAN ( 2 0 0 0 ) , + R I N F I L ( 2 0 0 0 ) , RUN ( 2 0 0 0 ) , SAT (3 ) . DRN ( 3 ) , PWP (3 ) , F C (3 ) INTEGER Y E A R , T Y P E , A R E A , O A Y . T DATA DATA OATA DATA (SAT (TYPE) . T Y P E - 1 , 3 ) / 7 1 . . 7 4 . . 7 7 . / ORN . , T Y P E, ) . T Y P E - 1 , 3 / 1 9 . . 16. 5. 15-•// PWP (TYPE , T Y P E - 1 . 3 / 1 5 • , 2 0 . , 2 3 . / I.FC ( T Y P E ) , T Y P E - 1 , 3 7 / 3 3 . . A 1 . . 4 7 . / C c********************************************************** C c PRINT 1 FORMAT ( *ENTER THE AREA CODE AND SOI L TYPE*) REAO* , AREA, TYPE 1 C C TM0IS-0.95*FC(TYPE) DO 5 1 - 1 , 1 8 4 0 READ( 2 0 , 1 0 ) L O C A T E . YE A R , M O N , DA Y , P R E C I P . EV A P FORMAT (I 4 , 1 X , 3 1 2 , 9 X , F 4 . 1 , 1 4 X , F 3 . 2 ) 10 C C c c IF ( D A Y . N E . 1 . AN D . M O N . NE . 6 ) GO TO 2 T-l RUN ( T ) - 0 . DRAIN(T)- 0 . SOILM ( T - I ) - F C ( T Y P E ) C C C C C 2 SO I L M- S O I L MO I S T U R E , F C - F I ELD CAPACITY PWP-PERMENANT WILTING POI NT . DR IAN-ORAINAGE SAT- SATURATI ON, DRN- OAILY RATE OF DRAINAGE P R E C I P - OA I L Y P R E C I P I T A T I O N , E V A P - D A I L Y EVAPORATION RINFIL(T)-PRECIP*0.9 * 2 5 •4 EVPTRAN ( T ) - 0 . 3 4 * E V A P * 2 5 . 4 C SMOI S - S OI LM ( T - 1) +RI NF I L ( T ) - E V P T R A N ( T ) C C 3 4 88 IF ( S M O I S . GT. FC ( T Y P E ) ) GO TO 3 DRAIN ( T ) - 0 . RUN ( T ) - 0 . GO TO 88 I F ( S M O I S . L E . S A T ( T Y P E ) ) G O TO 4 R U N ( T ) - S M O I S - S A T (TYPE) DRAIN ( T ) - O R N (TYPE) GO TO 88 RUN(T)- 0 . I F ( S M O I S . G T . F C ( TYPE) +ORN ( T Y P E ) ) DRAIN ( T ) - D R N ( T Y P E ) IF ( S M O I S . L E . FC ( TYPE) +ORN ( T Y P E ) ) DRAIN ( T ) - S M O I S - F C ( T Y P E ) CONTINUE C SO I L M ( T ) - S O I L M ( T - 1 ) + R I N F I L ( T ) - E VP T RA N ( T ) - D R A I N ( T ) - R U N ( T ) IF (SOILM (T) . GT. PWP ( T Y P E ) ) G O TO 6 SOILM (T) - PWP (TYPE) CONTINUE I F ( MON. £ Q . 8 . AN O. S O I L M ( T) . GT. TMO I S ) NWO R K ( DA Y , Y E A R ) - 0 I F ( M O N . E Q . 8 . ANO. SO ILM (T) . LE. TMOI S) NWORK ( DA Y , Y E A R ) - 1 T -T + l C C 5 100 CONTINUE WRITE( 3 0 , 1 0 0 ) FORMATT* YEAR 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 277 200 C C r 300 WRITE ( 3 0 , 2 0 0 ) FORMAT ( * DAYS*) DO 7 DAY-1,31 WRITE , 3 00 f) DAY, NWORK (DAY, 61 ) , NWORK (DAY , 62 , NWORK (DAY, 63 ) , NWORK (DA fM(3300.3 + Y . 6 1 . ) .NWORK ( D A Y , 6 5 ) , NWORK (DAY, 6 6 ) .NWORK ( DAY, 6 7 ) .NWORK (DAY, 6 8 ) , +NWORK DAY, 69 ) , NWORK (DAY, 7 0 ) , NWORK ( DAY, 7 1 ) , NWORK ( DAY, 72 ) , +NWORK DAY, 7 ;3 ) , NWORK DAY, 71. , NWORK' D ‘ A Y , 7 5‘ .NWORK DAY, " ' (day , )7;7 ) . NWORK(DAY , 78 .NWORK ( D A Y , 7 9 ) .NWORK ( DAY, ) +NWORK( DAY, FORMAT * * , 1 2 , 5 X , 2 0 ( 1 1 . 2 X ) CONTINUE WRITE ( 3 0 , 3 0 0 ) AREA, TYPE FORMAT ( / , 3 X , * A R E A COOE - * , 11*. 3 X , * S 0 I L TYPE *,11) v*JV>JKJN>K)NJroK)ruiN4Njro — — —. — —- — — —- — — o j Vaj Ki r o r o r o Mr o r v j r o MNi □ AREA —OVOCX>»4CTVn-tN>JN>—OVOCO-J 0*01 Jr\>»to —OVOOO'-J —> -< c/i CODE —---- 0 0 0 0 0 0 ———o ————————————o o o ——— ————————————— .———————— ———— * 2395 — O O —— — O O —— — — — — — — — O O —— O O —— — — — OOO ——O O ———O O O O ———————————————————— -< m > 70 on on N> SOIL TYPE to OO — — — — — — 0 11 — — — 0 — 0 0 0 — — — — — OOOOO — — '— — — — — — — — — — — — — ——O O ———O O O —— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — o — — — — — — — — — — — — — — — — — — — — — — O O O — o — — — — — — — — — — — — 0 0 0 — — O O O O — — — — 0 0 — 0 0 O om II N> V/4 ON VO VJ 1 ON ON ON *•*4 ON OO 0 — — — O O — o -< m -< co > 70 — — — o O o o o o o — — — O —— — — — — — — — — — — O O O —— — ——————————————————————————————— - 0 0 — - 0 0 - 0 0 ——— — 0 — —— 0 0 0 0 — 0 0 — — 0 0 — — — 0 0 0 0 0 — — 0-- — — — -O O O — — -I -< — on N> ON C4 — ON vn - — — 0 0 — 0 0 — 0 0 — *U m II ON ON - C- 0 0 — — — — - — T~ ON VO ———————————o o —————————————————— — — — — 0 o Ut ON \J1 — — —————— —— - o v o o K i o v n tv > »r s > -o u) o > ja \ji4 rv J M - ov oo > ^ (r iji4 r\A » iN j -> m > ON •to > 70 — — — — — ——————————————o ————— ON ON ON —O O O O —————ON oo — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — *4 ON ON — — — — — — — — — — — — — — w ^ ^4 00 O O —- —- —O ——O O O O ——————O O O ——0 0 ——0 — — — — — — —— — O O O O — — — — — —O O O — — — 0 0 — 0 0 — — — — —— — —— — — — — — o — — o —— —— — —— o — — — — — — — — NJ VO —. o — — — — — O —— O O O O — — — — — — O O — — — O O — — O — oo o ————————O O O O ———————O O ———0 0 —0 0 —— APPENDIX C3 Da i i y T r a f f i c a b i 1 i t y S e q u e n c e f o r t h e Mont h o f Au g u s t Pe r Ar e a P e r Ye ar P e r S o i l Type oo -vl VO 00 o CD 279 YEAR 61 DAYS lg 11 12 1 1 1 1 1 1 19 20 21 22 U 2 2 2 2 29 3° 62 63 64 65 66 67 68 69 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 A RE A CODE - 2395 S O IL TYPE 70 71 72 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 1 0 1 1 1 1 73 74 75 76 77 78 79 80 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 ' ro ro r^ro isjro ro ro ro ro —————————— kj — o u > o o ^ i a v n P"\>i ro — o c o — O ud oo- j cr u i r v i 70 i/% m — —o o o o o o o — —o o — o o o — — — o —— — —— o ro ———O O O O ——————————O O O —————O O O — o o o o — — — —— —— — — o o —o o o — — — — — —— — — — — — — — — — — —o o o — —— — — — —— — — — —— — — —— — — o o o o o o — — —o o o o o o — — — — — — — o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o — r~ u cr* o o o o o — o o —— — —— — cr* p- cr* vn o> cr* o o o o — — — —— — — —— —— — — —— — —— — — — — — — — — — — — — — — m cr* Oo TYPE H C T* ro SOIL — — u o o o o o o o —— o cr* 1*502 ro ———— a -o v £ )a s J o \n p \> » N i-o u )o o N i< ru iP v > J N > -o v i)0 (> * j< n n p v it* > -> -< CO -< m 70 ———O O O O O O ——0 0 —0 0 0 ———O — ———————— — — —O O O O — —— — — — — — — — o o o — — — — — — o o — — — - o ro CODE n u P" Vn -< TO i> o rsjroroMN>roroN>MM — — — — — — — — — — o oo^-i a \ n r u > t o — > -< AREA > o o o o —————————o o ——o o ———————————— cr* Oj — —— — — — o o o — — — — — —— — — — — — — — — —— — — o o o cr* o o o o o o o — — — —— —o o — — — —o o o o o —— — — — — — cr* vn o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o — cr* cr* o o o o o — — —o o — — — —— — —— — — — — o o o — — — — — — p- 00 O o o oo < r\ - 00000— 00000- 00000000- oo — r-» ■o— ——O O — OOO — o o o o o -o -o o o o — oo- \D r--. u> r>* ——o o o o o o o o o o o -4 _ _ 0 0 0 —————O ———————————————O O ——O IN r-. ^ O ———■ ------------------------------- ^ — ---------------- —o o — —o o o o o o — —o —————————o o o o --- p— ———————o o ——————————o o o o o o o o —— r-. o r^ . ————o o o o ———————————o o ———o o o o o \0 vO —o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o m vO ———————o o o o o o ———o o o —————o o o o o o o -t vO o( o o ———————————————————o o o —————— m vO ————————————o o o —o o —————————o o o o CM vO ———OOO —————OOO —1— —————— o o o o ——— ,_ vO a: «< UJ >- ——————————O O ——0 0 0 —0 0 0 —0 0 0 0 0 0 0 —— to >Jmro roNJhJNJroNJfofoN>ro —— — o -< (/> -< m nj t*Jtvjtvjroeo to N>N»t*j t o —o u ) —Ot£> oo-*4o v i ————— —— to — - OU) o o o oo-4crvn m CODE CODE NlCr*J»^V»N)-OVOO(SI(J,O l ^ N - > cr* ro ro - ON U) O OO- —o ——— —————o —— on OOOO ———————————————————— cr* o o o o o o o —————o o o —————o o o o ——————— cr* cr* cr* H -»o O O ——O ————————————————————————— -u ro ————— ——— —— ———— —————— —— ————— — — — — ————— —------ —-------■ —— (Ti cr* oo N ---------- KI — — — — — ------■— - 0 0 0 0 0 — 0 — — — — — — — — — — — — — — — VJ ———O O O O O ——————————O O O —O O O O —O O O — ^ TYPE -< — o — o o o o — — — — — — — - o o o o — — o 00 - 00 o o — — — o o o o — — — — — — — — - 0 —0 0 0 —0 —————— — ——— — —— — — — o —————0 0 —0 —— — — — — — — — — — — —— — — — — — — —— — — — — — — — — — — — v| V/1 O O O O O — — — — — — o o — — - — — — — o — — 0 0 - 0 — ------------ * — — — — — — o — — — — — — 0 — — — — 0 0 0 0 — o o - o o o O O O O O O O O O O O O O O O O O O O O O O O O O O O O ——— OOOOOOOOOOOOOOOOOOOOOOOOOOOOOO— — — — — — — O O O O — — — — — — O O — — — — — — — — — — — — — — — 00 — 0 — — o ro 00 I\> VI XT ^4 —I -v l or* o o o — 0 — — 0 — — — ——— —0 — 0 — O O O O O O O O — — — — ■^4 VI "-4 U3 -Vi O O O O O O O O O O O O O O O O O O O O O O O O O O O O — — — OO O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O — o "-i — — — — — — — — c r* O O O —0 —0 0 ——————O O —0 —0 0 0 0 0 0 0 0 ———— O' oo XT- OO O ————OO —————————— cr* cr* o VI O'* cr* U> ro 4 o v m A iw -o v D a > > i< jv n rv jro -o v D a )v j(ro ifx a M -> -< u% -< m > 73 CODE ------------ O O O O O O ——O O ————————————O O O ——— O' — = 2395 O' ro CfN —o o ———o o —————————o o ——o o —————o o o V>J ——o o ———o o o o ———————————————————— ^O V n4T A )N )-> -< (A ;o J» o ———o o o o o o ——o o ————————————O O O ——— o o m II —OO———OO—————————OO——OO—————OOO ro V*J ——OO———OOOO————————————————o o —— VO vn O ——OOO —--- .——OOOOO —————————————o o l/l o ———————o o o ———o o o ————o ———————o o — r~ 0 0 —0 0 0 ————————————————————————— H -< TP m II vn -o ON — ON ro ON VM ON Xr ON vn ON ON ON V4 ON 00 ON VO Vi VM — vl ro VI Ui —1 -p- -< m o ————0 —0 0 0 ——o o o o ——— o o o o o o ———o o — Vj ro ro 00 Vi CO VO ——o o o —————————o o o o o —————————o o — vj o o o —o o o o o o o ————o o o o o o —————o o o o — Vi XT vn VJ ON ON vi "Vl 03 o o ————o o —o o o o o ——————o o o —o o o —o o — v| VO ———————————o o —o o —————o o ————o o —— o o ————o o —o o o o o ——————o o o —o o o —o o — 00 o APPENDIX C4 D a i l y T r a f f i c a b i 1 i t y S e q u e n c e o f t h e Mont h o f A u g u s t f o r L o c a t i o n 2395 P e r Ye a r P e r I n i t i a l F i e l d C a p a c i t y Vi oo vi VO oo o AREA OJ\ >j r of oNJN>N>r or or or or o — o ~>OvoocKiatji^>JNJ*-ovi)aKi(Tin4ruiN»^ovDaKJcrtiir\>JN»-i> -< in -< m za COOE cr* 7&90 cr* N cr* . Va* —0 0 0 ———OOOO————————————— o o o o o o o —— — — — 0 0 0 — cr* Jr- - ————o o o o — ON Ul SOIL TYPE cr* cr* cr* VI cr* 00 u _ 0 0 0 0 0 0 0 0 ————————— —————— ——— — —— — — O -------------- -OOOOOO — — —— . - — o o o -O O O O —OOO— ——————————O ———————— —————————————0 0 ——OOO———--- .—OOOO— O O O ————O O ————————— *————0 0 0 —0 0 — M Kj J VI -Vj VJi 0N O O O O O —O O ——————O O —0 —0 0 0 0 0 0 0 0 ———— 00 O O O O O O O O O O O O O O O O O O O O O O O O O O O O ——— v| 00 VI VO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOO— OO O 284 ———OOOOO —- — — —— — ———— cr* u> vi o *v| APPENDIX D MODEL SIMULATION D1 Program Listing D2 Variable Set 1 D3 Variable Set 2 D4 Variable Set 3 D5 Variable Set 4 D6 Variable Set 5 D7 Variable Set 6 D8 Variable Set 7 D9 Flow C h a r t Sy mbol D10 Program User D11 Program Sample Description Gui de Output 285 APPENDIX D1 Listing of P r o g r a m CUCHARV PROGRAM CUCHARV ( i N P U T . O U T P U T , T A P E 10) £ it is is is is is i: is is i: is is is is is i: is is i: is is is is is is is is is is is it is is is is is isis is is is is is is is is it is is is is is C C C C C * * ■'! * * C * is is is K A * * PROGRAM CUCHARV BY IMAO AR HAFFAP. - S P R I N G 198A AGR. S N G . O E P T . M l C H . S T . UN I V . i s * :::: is is is is is is is is is is is i s : : is is is is is is is is is is is is is is is is is is * is is is is is is is is isis CT H I S PROGRAM C AL CUL ATES THE COS T- RE TURNS 0? H A RV E S TI NG C P I C K L I N G CUCUM3ERS I N M I C H I G A N . I T C A R R I E S OUT THE C OPERATI ON ON A D U L Y 3AS i 3 ANO R EVEALS A 0 A I ■_Y C SI MU L A T E D P R E D I C T I O N S OF THE T O T A L ANO AVERAGE F R U I T C NUMBER. WE I GH T ANO DOLLAR VALUE . I T ALSO P R E D I C T S "HE C COST OF OWING ANO OP E RA T I N G THE H A R V E S T I N G MACHI NERY C S Y S T E M . U S I N G A CASH FLOW MODEL . T HE COST IS THEN C D I M I N I S H E D FROM THE RETURNS ANO AN EXPECTED D A I L Y NET C RETURN IS PR E D I C T E D . T H I S G I V E S THE FARMER A ONE WAY C D E C I S I O N MAKI NG A I D FOR SCHE DUL I NG THE DAY TO START THE C PROCESS. Q isis is is is is is isis is is is is is is i s i s is is is is is is is is is is is is is is is is is is is is PROGRAM I N I T I A L ! Z A T I ON is is is i s i s is i s is i s i s is i s i s is i s is is i s is C** is is is is is i s i s is is is is is is is is isis is DIMENSION R 1 A { J ) , R I 3 ( 3 ) . R 2 ( 3 ) . R 3 A ( 3 ) . * 3 3 : 3 ) . 3 ^ 3 ) . +FN I A ( 15) , FN I 3 ( I 5) . F N 2 ( I 5 ) . F N 3 A 15) . F N 3 3 0 5) . F N U / I J ) . w w o s C1 53 . w i a ( i j ) . w 13 ( 15) , w 2 d p ) . W3A ( 15/ . W33 (15) . wi. ( ; 5 ) . + N I ( t o r , T W ( I 5 ) . S TOP ( 3 ) . F U E L ( 3 / . H L S ( 3 ) . +TRANS ( 3) , SPEED ( 3 ) . UNLCAO ( 3) I NTEGER AjO ULs0 u 50 LoO TYPE.POP.OAY.T C TYPE I S THE HARVESTER TYPE C POP I S THE PL A N T P OP U L A T I ON I NO I GATOR C T IS THE P L A NT DYNAMI CS COUNTER L30 L«0 500 5IQ 520 REAL MA T , L E A S E I . L E A S E S , NYEAR , L3AHA.R . + N R E N T I , N R E N T 2 . NOWN 1 , N 0 W N 2 . NOWNHAR, N H , LABOR I . L A 3 0 R 2 C C C C ^*0 jfO 5 oO MAT I S THE F R U I T M A T U R I T Y I NOEX LEASE I . L E A S E 2 . A R E THE NUMBER OF LEASED TRUCKS OF C A P A C I T Y I 5 2 R E S P E C T I V E L Y LEAHAR I S THE NUMBER OF LEASED HARVESTERS C NRENT 1 , N R E N T 2 . N O W N I , NOWN2 . ARE THE C OWNED TRUCKS OF C A P A C I T Y I 0 2 NUMBER OF RENTED H q F 30 3 OO 610 o 20 530 ?*0 ANO 6 50 C L A B O R ! . L A 8 0 R 2 . I S THE COST OF TRUCK L A 3 0 R ? 5 R TRUCK S I Z E C NOWNHAR, N H , ARE THE NUMBER OF OWNED ANO TOTAL NUMBER Or HARVESTERS C R I A , R I B . R 2 , R 3 A , R 3 3 . R L , A R E THE PERCENT OF C PER HARVESTER TYPE PER F R U I T GRAOE DATA DATA DATA OATA FRUIT ?I 8 7 10 RECOVERY 720 A ( TYPE) , T Y P E * j , 3 ) / O . 3 2 . 0 . 2 3 . 0 . 5 9 / ... , 3 ( TYPE) . T Y P E - l . 3 ) / 0 . 3 2 . 0 . 2 3 . 0 . 5 5 / (R2 ( TYPE) . T Y P E - 1 . 3 ) ' 0 . 6 Z . 0 . b Z . 0 . 7 = / R3A(TYPE), T Y P E - I,3 ) / O . 3 . 0 . 3 . 0 . 9 / ?33 i TY P E) . T Y P E - 1 . 3 ) / O . 3 . 0 . 9 1 . 0 . 3 6 / ( TYPE) , T Y P E = I , 3 ) / O . 3 . 0 . 3 9 , 0 . 3 7 / . STOP ( TYPE) . T Y P E - T . 3 ) / ! . 2 U . I . 9 ? . I . 2 6 / S3,3/1 .075. I -0752/ (UNLOAO ( TYPE) . T Y P E - ! , 3) / 3 . 2 I , 0 . 0 . 1 3 . 2 / (TRANS ( TYPE) . T Y p t f - 1 , 3) /' 1 • 37 . 0 . 0 , I . 1 2 / SPEED ( TYPE) , TY P E = ' . 3) / 3 ■ I 3 . 2 . OU . 2 . 3 ' / HLS ( TYPE) , T Y P E * I , 3/ / 1 50 . 0 . 1 5 0 . 0 , I 5 0 . 0 / FUEL ( TYPE! . T Y P E - I , 3/ / ' • 0 , 0 . 3 . I . I / C S T O P . U N L O A O , T R A N S . S P E E D . A R E " H E T I M E SPENT PER HARVESTER C TYPE I N I O L E . U N L O A O I N G , A N O F R U I T TRANSPORT " 0 7 H£ TRUCK I N C ANO THE SPEED IN KM/ HR PER HARVESTER SoO 70 o30 d THE FIELD o UJ m o 33 TO o CJ 73 50 ro o -1 ro O O 73 Z o H H O r id O Z •o s: -h — 4/1 73 U G 1 ' m ^ JZ X> — p O Z J>Z -i z o o Z P’ z O M - |C O V J1X * "J 70 p i 50 73 cr 3JZ c Z X33 Z in cr ^ -1 U) X rnni 73 — z o — o X-1 o 33 o o o 50 u m 3) O Z #5- H • z per — “CVJI 33 c z 3 J n 'U in r — o -------- j_ 3 3 U r n t/ii/i m 5 0 < 1 - ------------ ( o z z x u Ji- H O m Z c u o U' C O r — v ji V t o ­ ol Z - | co X - |X * z I o x m — z o -O ;*» — x ri rn UJ m u —| l O C 73 m M IC ) Z 33 cn o ■n •i Z —- X- I" V . l/l Cl > 3) z ft ICO z o z "Uv_*J —U> 1 -V J1 o o O H X — o -i-i 73 — i> o Z — cnu -| O — 500 H ro VO 3J *U m » — J—— CO o z H -< X ■iiv^>rn l~ OO vnx > 50 < m CO H ni 3J to -l-l — 3J O — 2* -1 3J3J >N » - i* m o r~ i- — z - 50 *0 Cl 0 33 3) 1>------O Z Z H H 3 J - J -J — 0*J1 « 3J ro » o t~ —• - 31 T) m x > — o z H cj — (Ji OVJl —* • H — O (O “1 X 3J3J m 3j X* — o z JJ H « ru j n ivji > iJ t to ni — • z o £ z — a CD -1 33 *U in 3) i> — O Z « —IVjJ -< C ■ovn rn « Z o c z X J> 50 O O CDO -I Z C O Z n i --------< — Z .-.|N |P 1 n -in r O J 3 J '0 0 — 31 t * r n 3 ))* —vji >• — O ' O Z i/ ica o —i -1 -H Z OO* —X VOZv>j u PICJ —c o — I* —.—.vn ro — —> • v^j — " 03) tr - -2.x* VJl Z 33 O '— —c o in — Co — 05 (0 (N \£ 3 '~ ' > |i n O X . O X * -< 01-0 ‘u V>J OJ CV^j 33* “I - C1C o o ro VTlio \-. O U l *■-1— C IO '— ooVDZ tn r-j O' |NJ f - lo — __ z u tz -1 z o -1 X rn O 1> —1 •j. «• «• jj- J> LA "O"U'O U JJ333)3333 __ u ZZZZ33 . oi __ L/l *i- »*• it H* Z r-vj h tx n u i— VJ4VJI z vji !!• 3*’ «• :!• H X m u 33 O O :u j> zt t/i 3> * *o z o 13 73 O O rn L/l CA — z Cl z cr cZ OJ z “ -1 V£>. v jiZ •J. *». ”■ V Jl - O'— O'" U)Z Cl ;u ~ z u — V/ J- - , f OJ vn1^ -1 **■ -c o- 'iooz— UX-- 33 X 1 > |— H U) rn* ■»» in Z O — z — -1 Jr~ — N > -1 — O in r n 3Jrj : t> 73 33m < in - | l/l X - |r n Pi :u x j-» 33 m L/» 1 PI ;u r~ m i- C IO ■ u -l rnX :u m *< o > t -< — i” z UJ X in J> cr OCA -< o — z ri 1> r“ r» cr r>-H in z p i o H X* PI z fi o i/i • 1 ___ u II i/i J> * .. — PI 50 1 Ji* »•* X 3> w' l> z o 1” ID Cl D* u i> o av- i vo*^ JJ;!• - -I ;u o o r i oc ;*• nj n OJZ LA— -I 33 • £- — VJl'-' O'-^j *n 0 0z VD — to Jj. jf :i; n z co-^ V O— V Jl .~ . o i: z Z -< in 4 ro - — — I Z3) » (/■CJ — ro fM'ji rn _ M 3)3>v^»33 3J 0 33 F*in 33 J>—VJ«i> — O Z O 'O Z S --I ouUVJIVO '»»VjJ oco z — 73VJl-------VJl |vj t» o * - fvJ v£j.-. -in TO 3J*U P i 33 — O Z O'Z -* J Z CO — :uto o c~- o- in T J O VO U in VJ»— J> H O I 'm rn (O 3) u m33 > — O o 73 ■H : l* p» -< ■•1 O cr - u: m cr 3> 33 “I in cn O pi X j» < z *—J"—J vT O ' O nO ' U ' O ' U - O ' O ' O * j »v/ iv ji »j i v j 4Vj »v j i v j »v j »v j i r f f r - r t ' r 4-~ r * -f'o * o > v -M O J O iO J O » O J O J O J i i i J M u t J i o N h J i J i J — — — — — — - O O v ^ O O O O O O O k O O r - iO W i r o j ro —' O ' O O ^ l O » J i C O J l - j — C>VD C O - 1 0 ‘ J I J o i l M — C jv ( i O > ~ J O U i t v >j M — O v U O > - l O V H | t o » m — C K D O O - J d V / i C \ * > w — O v O C O - l O VJl r v i f ' * - * O v O O » - I O ‘J i r o M - ' O O O O C i O O O O o OOOOOOOGOOOOOOOOOOOOOOOOOOOCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOQOOOOGOOOOOO 1 ‘ O CD Z S it- m in t/1 ♦*. 1 f\3 00 CTi 287 10 CON T I N U E c *** * * c it is is tt it P R I N T THE R E V I E W OF I N P U T OATA I F ( N W I S H . E O . 0 ) GO TO I t PRINT 385 ’ PRINT I , P R I N T 25 P R I N T 15 P R I N T 35 PRINT - 5 P R I N T 5 2 5 • ?N I A { i ) . ?N ! 3 ( I ) . F M2 H ) . F N 3 A { I ) . ENJS ( I ) . E N A ■;!) P R I N T 5 3 5 , S I Z E . /MON. O A Y . N Y E A R . N I ( I ) . N I ( Z) . N I ( 3) , N I ( L ) . + N I ( 5 ) , N I (b) . N i ( 7 ) . N I (3) . N I H O ) P R I N T 5 - 5 , NOWN1 . LEASE 1 , NRENT 1 . T I C ! PR I NT 5 ' 5 . T R I . 0 1 I P R I N T 5 o 5 . N O W N 2 . L E A S E 2 . N R E N T 2 . T I C2 PRINT 5 5 5 . T R 2 . 0 L 2 PRINT 5 / 5 . ? I A . P 1 3 , P 2 .P 3 A .P 3 3 .P L P R IN T 5 9 5 . T Y P E . NOWNHAR.LEAHAR.THIC P R I N T 6 0 5 . P O P . F L . N W I S H , NUMRUN END THE DATA I N P U T r AR M= 5 l 2E NH=NOWNHAP. -rl EAHAR C ***** II C9 WRITE WR I TE WRITE WRITE WR I T E WR I T E WR I T E WR I T E rNI (Li ITE WR I T E WRITE WRIT W R IT WRI T WRIT THE REVIEW S EC T I ON Or INPUT OATA TO LrN TAPE 10 10.385) 10.15) 10.25 10.15 10.35 10.-5 1 0 . 5 2 ? ) F N 1 A ( I ) , ? ' l ! 3 I ) . ■ N 2 ( j ) , r N 3A ( I ) t N 5 3 ( I ) , ? N L . I ) I2 3 -5 o 7 3 9 123L?o739 12 3-56 739 I2 3 -5 o 7 3 9 125-50739 . S 3 5 ) S i Z £ . M O N . O A Y . N Y E A R . N I ( 1 ! . N I ; 2 ) . N I (3) , ‘,■'? S . NNII ( 7 ; . N I (3) . N I ( 9 ) . N I ( 10 ) . N I ( 5 / NI (o) ( 1 0 . 5 - 55) ) NOWN I . L E A S E I , N R E N T 1 , 7 1 Cl 5 5 ) TR 1 . 0 L 1 NOWN2 . L E A S E 2 . N R E N T 2 . T I C 2 TR2.0L2 P 1 # , ? ! 3 . ? 2 , ? 3 A .? 3 8 . ? T Y P E . NOWNHAR. L E A H A R , T H I C P O P , r L , N W I S H , NUMRUN C IN ITIALIZIN G C 3ETWEEN J U L Y IF (HON. IF HON. I F (HON . E JOAY . W H I C H A COUNTER OF 15 ANO SEPTEMBER 15 THE 3AYS JOAY-OAY-I JOAY-OAY-17 9) J O A Y - O A Y + L d HI C = 0 . - * T H I C T I C“ 0 . 6 >'«TH I C C 51.52 ARE THE TOTA L NUMBER OF TRUCKS ON HANO S 1 - N R E N T l +NOWN i w-LEASE 1 52-NRENT2+N0WN2+LEASS2 c * it * * * * ft * * * A it it i'iit C C C C T H I S LOOP W I L L S I M U L A T E THE H A RVEST SCHEDULE F I V E T I M E S I T S 7 A T R T S 3Y I N I T I A L I Z I N G THE FOLLOW I NOG V A R I A B L E S : T S M N I , T5MN2 THE TOTAL NUMBER OF T R I P S C A RR I E D OUT BY THE TRUCKS ON HANO DURI NG THE E N T I R E H A R V E S T / F A R M 00 LOOP iti't it it it it it it it it it V:it it it it it it it it C K D A Y . T T 7 F M ARE THE T O T A L NUMBER OF H A RV E S T C THE T O T A L F R U I T H A R V E S T E D / F A R M DAYS ANO 3 0 13 1 = 1 . 5 TSMNI= 0 . 0 TS MN2 = 0 . 0 KD A Y = 0 777FN-0.0 C TTTHW.7770 ARE THE TOT A L FRUIT WE I GH T ANO OOLLAR RETURN/ - A?. , - 288 T7THW=0.0 T 7 7 D = 0 .0 FARM=S I I E IF (NWISH.EQ.O)GO 70 IJ C it it* * & PR I N7 I MG THE RETURNS HEADI NGS PR I N 7 25 PRINT 35 P R I N 7 75 P R I N T =5 P R I N 7 =5 PRIM7 §50 P R I N T 75 17 C 0 N 7 ! NUE C ***** WRI7ING 7HE H E A D I N GS TO LFN 7 A P E I 0 W RI7E(10.25) WRI7E ( 1 0 . 3 5 ) WRI7E(10,75) WRI7E ( 1 0 . 5 5 ) WR I 7 E I O . 0 3 ) WR I T S ( I O . 9 5 O) WR I T S ( 1 0 , ?5) C 7H0UR IS THE TOT A L H A RVEST H O U R S / r ARM TH0UR=O . 0 c ** **** ** *** **** ** *** * C THIS C WI TH C LOOP W I L L THE F I R S T 00 00 LOOP S I M U L A T E 7'HE LOOP FOP. THE :: * * * * * * * * * * : : * * * * j': * * * * * * SCHEDULI NG T H A T COMING 10 DAYS STARTS 11* J = l , 1 0 T?J I F ( F A R M . E Q . 0 . 0 ) GO TO 9 3 1 F ( T . N E . I ) GO TO 9 0 C A L C U L A T I N G THE F R U I T NUMBER/ HA A V N I A - S S * F N I A (T) * R I A ( TYPE) AVN 1 0 » S 3 * F N I 3 ( t ) * R I 3 ( TYPE) AVN2»S5*FN2(T)*R2(TYPE) AVN3A=»SS* FN JA ( T) * R 3 A ( TYPE) AVN 3B=SS*FN3B(T) AR33(TYPE) A V n C - S 5 * F N 1 * (T) * RI * ( TYPE) FOR GAY I 7 A V N - A V N 1 A- MV N I B+A V N 2 * A VN 3A + A VN 33+AVN1* H W 1 A - S * I 2 . 7 * F N 1 A ( T) * R I A (TYPE) H W I 8 = S * 3 0 . o 7 * F N 1 3 ( T ) * R 1 3 ( TYPE) H W 2 = 5 " 7 3 * P ” F N 2 CT) * R 2 (TYPE) HW3A* S A I 1 2 . 0 * F N JA ( T) * R 3 A ( TYPE) H W 3 B - S * I 3 9 . 2 * F N 3 3 ( 7 ) * R 3 3 ( TYPE) HWu=S A I 9 3 • 2 * FN A ( T) * RA ( TYPE) THW-HW 1 A+HWI 3+HW2^HW'3 A^HW33tHWA HW0 S = O. 0 C HWOS IS THE WEI GH T OF OVERSIZE FRUIT PER HA WOS (T) = 0 . 0 TW ( T) - T H W / S T A R E A 'I 2 5 5 . O/TW(T) Z Z = “ W ( T) * I . 0 7 5 2 F F N N - F N 1 A ( T) - F N 1 3 ( 7 ) + F N 2 (T) - F N 3 A ( T) tFNSB ( T) - : NA (T) M A T * (FN 1 A - T) -rFN 13 ( T ) ) / FFNN GO TO 12 * *** - I h P H - l 1 - 1 - I H H -1 ~ l----------------O H « « 0 I 'l'X t'-lX l •«» o ‘i i -ii ■i» m -»»-n*n n j * —| — O £ C C » - l O ' i z z i r z z z , — ______. * o — m z N ------ - 5 i » C Z f v .>jv^j Kj — —---- ------------ H jc i - H II t o j - ii z x j u u c d p - ii t o t - o o o o o ^ m o — X | | U *» U 11 —I II K J- II II i - 1- J- > O fl U 2 rsH -IH n > i: i U 70 Hm H U £*> 1 X ^ H- C H — + w II 1> —VJl - i > —\ J l X X f f —O ' '.— • \tO U I OH + X ^ iC VJly-s c “ I NJ 1 + --. X £ VyJ i1 X c VyJ CO * r u C C a J -ix z m x iX J m X J X J * * o — Ini ^ H O — l - r n m w n r n z 2 2 2 y -m t j > u j }» i n o 1- ) J-1> X I X X y -.l - XJ 70 ;; t - z X) | j> Js* r n i> j* n i | ----------------1* I- j * i . » » n » - | S H Z < < 2 < > * w 4* X* ' 1 C —■ Otv>i z xjuj co x* UJV» '— *—r'v 2 -\J . . . . —It/llII C/l > c » r o r “ - | ->in -1 ••i n»- | o > - | ------| a £ • • ♦ • ZS. <--• • x* t* i—> '- * ! n -< 1 X) x» xj xj — o -I •ii X X 'X 'X |C I —, w '-* C £ X X. x- xZ :; o o n a i-o CCm m u* 1 XJXJ 1- 1J>i^j -I ■»l l/l I/) U II O — II II 1 , 0 ^- v^j VyJ ti — i> V -1*—' ai X ------X )U XJ ■ X ito •4 'X o £ io o n -I -»i X £ 1• f n — t /i O i-I x? • o o *o x •*" X X X X X X X r c r c s r c c O P o jo j ro — — t/> u a> i~ ii u j i > II v * U U i / t U N o» u> i n JI*i/i i n C sa r c ir w c c o ---V y J U » ^ - — i/> H UJ *> H OJ *» ___ ||1------l - l -1 - - I 4 w J. . ( . - I f —~ —— —• — c £ i : i: c c •it n f v i u i r o — —• >^-.D J J> 3 * X H . - — s -l------- - ! » * • » * .< < < < < < “ |n < Z Z 2 2 Z Z II Z Z i r u i u i r o -------—. U II II 0 3 * - H UJV -n t» j> i/» n U u> II u Z Z < U) l/> l/l (/) t/l C/» ------- Z ::• > > — “ri -• -¥» “• S* —> z m i z m i HH + r z z i o z z f --- — — — < - | OJ V - I OJ u ------- •£ 1 ^ — 4 . ------+ 4------------I H ------4 - 1 " • ' i p ' ' f 4— ' 4 4z 4-------------------------j>XJ'--------'XJ— • cd u» < f ::• to _____ Z . - .X j xj, - . xj xj —| ~ | IO -J u j WJ - 1— —• |- I | -< UJ J- -< UJ J-* ------- x— “O— '- ' '* '< ,1 1 - 1 - 1 1 0 - 1 -1 ^ 1 Z - ' < •< — ■< < O'U U O • t iZ J* im n imih -••ro | —— «—— - 1Z - |< 1 Z —OJ H -i-4 z :c C f 4 X j: co H < *o in c) "n -n — 0 2 2 " r o —^ -\.^ a > x o H __J> 4 -l-l -II " C l z z o PV aJ y-~CDH -1 .^ 0 1 -I ‘•I 71 — •»! It z z**»z z f U i —.Vyi — - .o ir c t * i> ° ° C 1 *U '-"rn it H• z -n o lo Z —U ' H ujvji c *"J II 11 u 2 0 f • .—."^4 H co r. | iri O Z * V>J !•J CD 1 -lo -l-l o - ' ------- • ;:• - | -O O O O *•• XJ c — -i Vi) .- .O O -I. . ------1 1 OO V-o t/1 .O .J f h o O '-'O -1 4 - O O f ♦ 4 UJ O 'O m o • - i i c* 1 1 4 ------O '— • II U O ''" Tl VJi z z f'j io -r» . „ Z -i-4 ... ■— - * O 11 i1 -i 1 II • • "O O z • ♦ —OJU» ojvj t o i ■ -i —' Z Z Ji-r-Ji-J •c fTi — Cl X -I u ii r. £ p .D O O — XJ.O -IX) — i/ic n -< — > . < OJ 5- UUJ___ -A - | U.— '— -*in H • | —' - | -< 4 » -< o OO o u *om • • mm i»»—* —O *— 4 1 • • •> z < V-OZ »* f o 4 —+ > r~c> H*-------- u r n - l — II u -'J 11 U . • o — —o jo j — O O OJoj — O ro • • • V O io O - • u ^ u i v f ♦ :; u > sj u io i ro O -n -o ---------- n 2 -n C £ 2 ■»• " i j t i 2 O O tr £ Z ^ - ,2 — «✓» AyiOJ - | — J> ,.v - |U ) > | LO.— - l-l \ t | - ,4 I 1 H - *n------Z 'm i —____ uj rt i> 4 -1 -1 + • * - r o o o z -1 — z c m o i* r* o c r~ X* -I — z a — O'. Co ::• ;;• »• z z — -UJ u> 1 H I 4 Z CJ OJ iw ji ZOJ — OJ y~- Z H — • ') * I 0 -1 *»• Z + v * II — p»'------’ • U • II II 2 " O "*i n | -Z • 2 2 . -v>4 U Vy» — - I u j '- »» t* IO .- . -I ■n —Z | VyjO CD♦ y-xO -|*U 1 n OZ ♦ VyJ Cj ^ VJl. -1 T« - •* z VyJ )y r> f 1 4 H H -I a coi * 1 a»y> o 14 OJO »• — • — 1f VyJZ u it n iZ Z z - l Vyl rJ | )»i H •» z v>j - - '4''i '4-''4‘' f *'-4--i--4‘- iO 'a -a a -a 'a -a v d 'O v o vj»vjvviivnvi»vjivj\vjtvjivj» c- c t - c- p ^ c t - 1 - t v i u j ^ * v > ^ » v / A ^ ^ . w w « . » t J N » f j iv > r a u 4 J i.j VJi t w w —0 ‘£>OJ- JO i l l t-\yjfO — Ov£)0>~ JUiJl Pv>j|0 —0»£>Oi"JO VJl t V-i to —Ovt> Ct»- J0 VJi pVyJl * --------- O'iJOJ--JO »Ji f-v^K* — OvO Cu-I O »ii t~Vs>l-J - -OvO CO-J Cli.J) t ‘V.*j I-.J—•OvfMtJ--4 0 *J I O O O O O O O O O fiO O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O ro oo \o v i c i * v m m ' v i N i l ‘ y i C y i n n ' y i n a v v s y y ' a v o n " nown (St? ‘ o i ) s i i an 3DNI1NC3 OS O l S d V l N i l 0 1 O N i 1 iBrt 0 S3IWVNAC l i n t ; Si I I N I be c i i ‘ n n n ‘ n ; i i ' c i ‘ n n i ‘ n a v i ‘ Se? i n i a d S 1I I N I be n C i ‘ WK1 ‘ *;N i l ‘ *iC ' W ‘ 5NAV ' S i 5 IN I t d S i C l ‘ S S n K l ' S £ N i l ' S S C S S n h " Si NAV‘ S ° 9 IN I be y £ c i ' y S n K i ‘ v £ N i i ' y S c ‘ y£nH'y£NAy'St? i n i t c z oi ' twHi' z ni i ' t o' tnu' z NAy' S?9 inibd e i c i ' s i n w i •s i N i l ‘ s i c ' E in n •e in a ¥ ‘S£9 in ia d y i c i ‘ y i n t i i ' v i N i l 1y i c 1 y i nn * v i NAy ‘ v s a y ' i y o n * n o w n ' S ? ° i n i a t OS 0 1 00 ( 0 * 03 ' H§ IMN) i I SDIWVNAG l i n t ; O N IlN lb d D l-rAVC»=AVO>! 3n N llN C 0 ?1 es o i co Avon"nown ( S i 9' o i ) s i i an AVON‘ NOWN S i s I N l t d I t 01 O D ( O ' O S ' H S i n N ) i l V3t V+WaVi =Wa Vi o i o o ( i ' o s ' (r) n ie v o iiiv ai shi aoi 91 a i i i n) it i o n ix o 3ko 3nNllN00 gn-Avor=AVON o ot 6«nown 3nNllN03 Ot 01 00 L I -AVOr=AVON 8 = N 0 WN £i o ioo ( e v i o * Avor) ii 01 Ot 6l 8i CO 1l-(-AV0r=AV0N Z=NOWN Octt 81 08 :S OitS o?:S ot iS Ovtr o£tS ottS 01 t S oots Oo 1s o s iS 011S 091S osiS 03 i s of tS oi 1 S3ABVK 00 ( Z l - 10 ' A V O r ) i l iC S1 V 0 3HI O N IlV O dn 0 011+0111=0111 wavi asa onv avc tsd a v n o o n v i o i 3h i s i ' o m ' o n v3av*oi=on y3av-.<*i0=50i vsaw eso-eioi ysay=.=y£o=v£ci vsav=.-zc=tci vsav- ve i o = e 101 vsav>;vio=vioi O t ; ; Oils ooiS OoCS O8 0 S *io+Bfo+y£o+to+Eio+vio=oi OSOr 0S 0 S v£nH.-;y£d=y£0 tnHytd=to sinH vsie!=eio Vi nHv Vl e =V1 0 (sonn-n nH)vid=oo OioS e£nH>.t8 £d=efo onoS OSOS otoS OIOS oocs n K ii- .n H iii- n K iii vsav»jnHi=nHii V3'dV:.: ,rnH=«rtHl vsayvECnH-eSnHi ysa v v y £ MH=v £ n Hi 0S6 n Cgf " oa S* o«6" 062 0 291 UP. i 7 1 [ 10 , =>35'i AVN I 3 , HW 1 3 , D 1 3 , T r N i 3 . 7HW ! 3 . TO i 3 WRI TE l O . b L j ) AVN2 , H W 2 . 0 2 . T F M 2 . T H W2 . TO 2 WRI TE 1 10 , b P 5 ) A V N j A , H W 3 A . Q 3 A , T F N 3 A . 7 H W 3 A . TD 3 A UR I TE ( 10 , b o ? ) A.VN 33 . HU'33 . 0 33 , TEN 33 . 7HW33 . TO >3 WRI TE ' 1 0 . 6 7 5 ) AVN i * . H W L . O L , 7 F N L . THWu , TOU WRI TE ’ 0 . 1 1 5 ) W R I T E \ I 0 . 6 o 5 ) T A V N . THW, T O . TTF N . TT HW, TTO 3MN2 = ( A R i A f ' Z Z ) / ( 2 2 0 0 0 . 0 * (S l-s-S2) ) C CALCULATING THE NUMBER WR I T E ( 1 0 . I 15) SMN I “ 2 . 0 :': SMN2 C C A L C U L A T I N G THE TOT A L OF TRUCK T R I P S / O A Y TRUCK TRIPS/FARM TSMNl=TSMNl+SMN! T3MN2-T3MN2+3MN2 98 IL iV* CONTI NUE JO AY = JOA Y—I CONTI NUE V: V: :'J* :Y3':V; V: Y :£N 0 00 LOO? IL i'::'"';3,:3,t*3,:;,; :';:'::::ii':*s’::,:3'::'::'::: J Q A Y = J O A Y - 10+1 C C T T T A V F N . T T T A V H W . T T T A V O , IS THE TOTAL A V E R A G E / H A / F A R M C OF F R U I T N U M B E R . F R U I T WEI GH T ANO F R U I T OOL - AR TTTAVFN-TTTFN/S IZE TTTAVHW-TTTHW/SIZE TTTAV0-TTT0/SI2E C PRINTING SUMMARY OF I F (NWI PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT 22 RETURNS SH . EO . 0 ) GO TO 22 a95.TTTFN.TTTHW.TTT0 7 0 5 , TT TA VFN .TT TA VH W, TTT AV O 95 £5 75 /IS 963 725 75 735 7^5 755 7o5 15 CONTI NUE C WRITING THE RETURNS TO LFN TAPEIO WR I T E ( 1 0 . a 9 5 ) T T T F N . T 7 T H W , T T T D WR I T E ( l 0 . 7 0 5 ) T T T A V F N . T T T A V H W , T T T A V O WRITE1 W R tTEH O .L,) WR I T E ( 1 0 , 7 5 ) WR I T E ( 1 0 , 7 15 WR I T E ( 1 0 , 9 6 5 WR I T E ( 1 0 . 7?R WRI TE WR I TE WRI TE WR I TE WR W RI ITE TE(10,765 WR I T E 101 103 I F ( S I . G T . O . ) GO TO 101 CAPN1-0. CAP' . I = 0 . GO TO 103 CONTI NUE C A P N 1= ( N R E N T i / 3 1 ) * T 3 M N 1 C A P L l - ' . L E A S c i / S i ) - T SMN I CONTI NUE I F ( S 2 . G T . 0 . ) GO TO 102 >10 a3' r»n n i-j O 1-0 <00 H u x > o c 2 : + x cmut o ii XX — 3> 3* T| m z H — 2 n tU ^ v a>i> :o 3 < p»* X - |i> j » C < o Jm U ZZOUi c rn X X X X X 3* 3> 3- 3* 3- 111-»-10 ci-n x s: m oo a x z COO u U U 50* O O CD U -< 0 —0 50 • in Uto . n U O* • C 2w O O m x r.-—on — 2 . Xm r;:vn —- H X H :;• **■-. i> - XC1H i>n»o -I n tu OH t/» z -I* x- :i:< :t 5>N» x t*"-* Xr~ H X —X H Ci 0 co o 50 :;o i.-C o z H* z c o l o i z c O cr-j» x z X :u - IX cm 5» 2 -1 X O -»»H 3- J- x»r is < ZO X IX' in (si* H I ZIM1> 05JH 3* *>»o X c arm zx rrn > tu tu X* m xz '*— * CDZ X) o < c pDmZ *u*» — r~ 1 mH o c- —X C * 53 3> X 505)3- o zo I — o - C O O —X ■oc ;;*o — j - xi j - ji-x j o |- x x 3- 3> J> CD5JO P l t/ lH ;o -10 J s-^ O n n o H i< j -p - X X — Lrt VJl •13* i*Oi x o • 50 X>l»»X u> 3* n —C 50 m 1/11“ X* J* 2 t» orn :x —t i» x — in 2 -IO m o Z i/t 3> -I z 00 m *n -I HH OO H —1 ZO ZO c c NI — II u -l-l A )A i C C DO C C (/test rJ — + + 00 c c z z mm 5»XI to — 1^ 4 -1 H tu to -l-i J-> JX X rj — 1 h Aj AJ 1*3 NI — 1 1 r r 3* J-» (U CD OO zozo 1J — 1 1 *I» •»* CC n im 0 O Z H — Z c m —I H - * in 3I5JC C c c n tm O O C fC C IJ t/n /t u u N» — ------ ■ II H • • ♦ * «• X* M w n -n z e e H - |( n m w jjr r d e m ­ o n c i cr m en N» — i* OJCD OO 50 50 to — II II r r fONJ (JO0 0 • > OO __ —. — . . — tor j ;J. OO 0*ON It* It* x x *■ —*—■ JJ* J!* Z Z OO r .i: -I H U lU 1 z z ro — 5 5 Z Z NJ — 1 1 n o i> 3 ~v *u z z to — 1 1 n o i-1 * *\J*U r * i_ ro — — hi 3> 53 1 VO CD — 5UXI 5 5 N» — U It -IH 00 NJ — It* It* OO • • OO O ,— — X X X II Z -< z z 00 f t z z NJ — HH 5)5 3 HH X* J* X X to ­ ll « OO . 1 00 —- — IT4T it* it* Z Z OO C. t l z z N —It* It* HH O O ItC C itz z mm ^ z u z u y> ro— 0 II II X HH — -------z o o m ro — ;u It* .1- H OO ♦ • O OO O VOvOC/t ro ro -J Z Z OO c c OO z z f 0- — ro — HH 51 'Ai C C OO C C (S3 (SI N» — U II Z Z ZO zo in n i z z H -l 1 0— I!* ItOO v i> *0*0 z z NJ — It* It to — ro — OO OO OO 0000000 H X m 01>J"OOt*V Z*U*UZ *D‘U -lr z Hh p z —toNJ —OM Ivi O O (St -1 0 *n Z || H Z UU C - C —O O rorzrno- • i n 50 > in l/tZ X~ P i-I I j i j O c in H 0 'X “ IH O IL /I -I zo c n >: Te Tc z 2 U N OO It* HH A JA ) NJ — *——' 1 + 0*1 3- 3*u-o z z NJ — O O 1^ 00 00 1 * 1*u *u r~ r~ mm 3- 3-* tnt/1 m rn gtJxO ‘O 'O'iJ •() 0 «0 '(J 0 ' 0 'U ‘*0 'CJ -O'O »0 *(J,'U'‘0 ^0 ''0 *0 ‘-0 '-0 -*0 '(l'U 'U -O •O'-ONO-'O'd'O 0 ‘(J •U -D'O -O' \./J^wK>JUKyJV/HjvlAiJVC>l£j OJOJOJCVJQjQjOjOOOJQJ - 1-*-4 *0 '0 •0*'0 -0 ' 0 -O'O *IIVII CO- IO »it Pv>»IJ —•O v u c o -J cl »J1 C u tM — • O U tlO - lll^ J l fV*»U - CaOOD- 10 **Ji Pvo u —Ovfi ClN-lO »Xt I vo U —OvOOn*-! CTtJl P*V/J u —OVOCCr-iG *J» | v * U -—CHi)(XMO i j i ( V*»|o O lO O t O O O O O O O O O O O O O O O O O O O O O O O G O O O G O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O ro VO ro 293 7 390 7L00 7L10 TOTCOS- TOTHAP. - TOT. RU I + T 0 T R U 2 A V M A C Q S = T 0 T C 0 3 , ' S i ZE 7 0 R 7 M » T 7 7 0 - ” 07C33 A V R T N = T O R T N / S I ZZ PR INTING rftftft* 7H E MA CHI NE J*~>0 7 “ 3C f -uQ C0S7 7-50 7 LoO C 9 I F ( N W I S H . E Q . O ) G O 7 0 92 P R I N 7 7Z5 P R I N 7 7 3 5 , OWNER I , 7 R 7 A X I , RM I . L A 3 0 R I . F U E L I . 7RUCUS I . CL' : . T O T R U i PRINT 7 9 5 P R I N T 7 o 5 . OWNER 2 , T R T A X 2 . R M 2 . L A 3 0 P . 2 , SU E L 2 . TRUCUS2 . CL2 . TOTRL' 2 1 2 3 ^ 5 3 7 3 9 1 2 3 ^ 5 3 7 3 9 1 2 3 ^ 5 0 7 8 9 1 2 3 t‘ f o 7 3 ° I 2 3 i* 5 3 7 3 9 1 2 3 - 5 3 7 3 9 7 PRINT PR I NT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PRINT PR I NT PRINT PRINT C WRITING 92 THE MACHI NE WRI 1 WRI TE WR I 1 i WR I TE COST ANO RETURN 7500 7510 7520 ?!§§ 7 §30 7590 7=00 76 10 7 = 20 m i n$ i O 00 765c 7700 SUMMARY 77 10 7720 775 7 3 5 ) OWNER! ,, T R T A LA3CR AXXI1 ., RMI ., L A 3 C R II . FFUEL U E L I , TRUC 795) .CL2 7 3 5 ) C W N E R 2 . T R T A X 2 . P M 2 . L A B O R 2 . F U E L 2 . 7 RP.UCUS2 UC 30 3 7 HAOWN. HATAX. i WARM, H A L A 3 0 R . H A F U E L . T HHI ,L . TQTHAR WR I TE 7l3o 7 L 90 U)° I?-*u 3 0 5 , H A O W N . H A T A X . H A R M . H A L A 3 0 R . HA F U E L . 7 H L . TOTHAR 315 3 2 5 . TOTCOS 75 105 11 5 335 3 £5 955 355 3 o 5 . T T T F N . ‘ T T H W . T T T O . T O T C O S . 70RTN 8 7 5 . T T T A V F N . T T T A V H W . T T T A V O . AVMACOS, AVRTN 385.K 0AY 75 CONTI 7 L 70 TOTCOS ■’"OT.RU 1 TQTRU2 / 1-u 7750 7 7 3O 77~0 7730 719° 7800 WRI TE 7 | 10 7320 WR I TE $ 8 W R ITE WRI TE WR I 1 =, WR I TE WR I 1 c C i 13 Y* CHECK I N . T F N . TT T H W . T T T O . T O "COS . TORTN T T T A V F N . T T T A V H W. T T T 1V O , A V M A C O S , A V R T N XOAY FOR ANOTHER C ONTI NUE PRINT 399 REAO*.MAKE I F ( M A K E . E Q . I ) GO TO P R I N T 75 P R I N T 1000 P R I N T Li *5 P R I N T 75 STOP 99 RUN 99 CON T I N U E PR INT =05 P R I N T L 55 3 RI NT 5 2 5 . N I A ( ! ) , F N I 3 ( 1 ) . F N 2 { 1) . F M 3 A 7 ! ) , F N 3 3 { I ) , F N L ( l ) PRINT 565 PR I NT 5 3 5 , 3 I 2 E . MON. O A Y , NY E A R . M l ( I ) , N 2) . N I (3) . N I {'■*) . - N I ( 5 ) , N I ib) . N 1( 7 ) , N I ( 3) . N I (9) . Ml (10) PRINT U7; P R I N T 5 L 5 . N O W N I . L E A S E i . N R E N T I . T 1CI PR I NT 5 5 5 . TR i . OLI PR I NT 5 o 5 , NOWN2 , L E A S E 2 , N R E N T 2 , 7 1 C2 PRINT 5 5 5 .T R 2 .0 L 2 PRINT 535 PR I NT 5 7 5 1 A . ? I 3 , P 2 . P 3 A . P 3 3 . P L >!?§ 7 3 to 7880 7390 7900 7910 7920 7930 7 9 C0 7§5° 79=0 79;0 7 3 S0 7=90 3ooo 3010 3020 3030 8 0 C0 3090 30b0 3 0 70 3080 3090 3 ’ 60 31 10 3120 3130 3 I -0 3 ' 50 3 I =0 3170 3 130 3190 r ! 0 S ! S . ? f ? « ! ^ 2 ? ? 0 0 . ? f: . ? ? 0 2 ? 9 0 0 S 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 O O O O O O O O O O O Q O O O O O O O O O O O O O O O O O O O O O O O O O O r i ' J in ,f 11* < n o > o ' • n ^ j u v o f ^ O ' O — iM iA ^ tr» o J - c o m o — N * a i ir> o r - c o m o — * " • < tr> D t - n j O ' O - n r o ,4 tr * o r- c o 0 \ o — N f v a ir» D f s t J O ' O - r jr ^ v J i f > i > r 'r o 0 ' 0 r Ir i <~l< IrJ»'lrlr J r |r j f 'v ^ r ^ n r O f ^ r o n ^ f ' V J -4 -Jf _i -J -J — "Itr\tf\mt/Vf>t CVCMf\t fMf|O v 0 '-i)vO',£?\O'D'‘.O’-O'X) '"•t'-.f'-f '"•C'-t 'COCOCOCOCfJCOCOOOCOCQ(J\CTWI\C!\(J\Q\0 \(^(!JyJ'0 (0ni i CO I r—i vO I ij — UJ rn —. CM X rt, III -J * a' •* X z ;* o z u or r. 1—1—»—h7 7 7 7 —— — — o: a: crcr a n era. —' rO Z u. * r < o c n o — oi r>j r>|r'l«o«oro rI | — z - z r»o o • - I I I I l~- I Oi CO u_ O OOOOOO l - l - l - l * 1- 1- X o — X ;t o o o o o o O O U O i —rv| z 3 I - -J z o u j a: - a: — *o a < x tu 'jC iu 3 z — 1z o O m ro Z - 1—1- •>: —.O h z z o tn iZ --------O '-' o n r o t u j —o u a a a ' z o + - —m vrs^t 3 'O X O »mn UNO. uj m _oi—.r~~.uj —m u j Oi— r n \u j >- r o Z 7 —I--K O 7 —H * O f-Z Q IZ —< OOfuJO 3 — I - i - i - i- * x m *1- *o 3 r o a ro h Z Z Z Z O N h 1-201- o t r o . 'o r o r u j — o u a . o . n . a . c t h «J uo.(to 2 ------- tU + Z —t OftTuJO cn i co » o inf— m - ~1 i —I I o z CO I r—i vO I m t ro < oi I i- 2 Zrt o i^ or o z -J r*l *— r> — I ftr-I C"—' Ul — >- Z z • z u. • H ro | OJ I oco .— -• z — o z r. . m a: o tr\ z — -z >- - — i in » -1 I ro I '-'O i —r\J Z 1L • < o \- vO __'— • Z — -z <1 vO v/> o 00 CJV—' 3<7iZX z -l-h -so l-z o z Z — > x m o * I-* o :: o :t c o lr -.< \o r. met -4 o ro»i_ •ii •.0 rj tj ol CO M in m »o -4 m in 295 -* TOTAL AREA RETURNS ***) 9 0 '0 a 0 20 C 9 95 75 35 95 105 115 315 325 335 3^5 355 3o5 375 37o 38 5 395 1*05 LI 3 L25 L35 LL5 L;5 Lo 5 L 75 L3R L95 505 525 535 5L5 555 5o5 575 535 595 o05 123^50739 12 3 ^ * 5 6 7 3 ? I 2 3 L 5 5 7 3 9 FORMAT { 3 0H MONTH 0Av ▼.' AREA! ( KG/AREA) ( S / A R E A ! ) 12 3 L 5 S 7 S9 (ha) I 23“ 5o739 HA) I 2 3 l = c 7 3 9 12 ( KG/ HA; (3 HA) 3G3C ^ 90-0 30=0 30o0 f o r m a t ( 8 o h -------------------------------------------------------------------------------------------------------------------------------- 9 0 7 0 ▼ " ............ ................ J 9 0 SC FORMAT ( 2 p X , 2 7 H * * H A RV E S T I N G RETURNS * *•*) =030 3100 FORMAT ( 2 S X . 2 7 H * * HARVESTING COST **) 9'. 10 9120 FORMAT ( 2 I X , 37H * * SUMMARY OF MANAGEMENT STRATEGY * * ) 9130 F O R M A T ( 2 I X , 3 7 H .... ...................................................- ......................................................................................... f U o 9 ' =0 FORMAT ( * ENTER THE NUMSER OF F R U I T OF GRADES I A . i 3 , 2 . 3A , 33 , U FOR A = ! 7 Q ▼9 • 3 5 0 . M. ( 1 0 0 3 0 . F T . ) SAMPL I NG PLOT-1.-) 9 1 30 F O R M A T ( * ENTER THE FARM S I Z E IN HA , ANO ENTER TOOAYS D A T E : Y E A R . M 0 N T 9 I 9 0 -H.OAY*) 9200 9210 FORMAT ( A ENTER THE T R A F F I C A B I L I T Y FORECAST FOR THE NEXT 10 OAY 3E3220 ▼QUENCE : l = T R A F F I CABL E . 3 —NQNTRAFF I C A B L E 1’*) 9230 92L0 FORMAT ( * ENTER THE NUMSER ANO TYPE OF OWNED H A RV E S T E R S. I = W I L O E , 2 - 9 2 5 0 + C U K E . 3 - M S U , E N T E R THE NUMSER OF LEASED H A R V E S T E R S 1-) 92bO 9270 FORMAT ( * ENTER THE NUMBER OF LEAS E D , OWNED, ANO RENTEO TRUCKS OF C A P 9 2 3 0 ▼AC I T Y ( I I , 0 0 0 KG) . ANO CAP AC I T Y ( 2 2 . 0 0 0 KG) R E S P E C T I V E L Y * ) 9290 F O RMA T <* ENTER THE I N I T I A L COST OF C A P A C I T Y I T R U C K . C A P A C i T Y 2 T R U 9 3 0 C +CK.ANO HARVESTER*) 3310 FORMAT ( * ENTER THE TRA NS PORT AT I ON RATE FOR A CUSTOM RENT TRUCK * ) 9 320 9330 F O RMA T ( * I N S / 2 2 . 5 KG . ANO THE COST OF H I R I N G A TRUCK D R I V E R * , = 3=0 + * I N S / T R I P FOP. TRUCK S I Z E 1&2 R E S P E C T I V E L Y * ! 93=0 FORMAT ( * ENTER THE D I S TA NCE ( I N KM ) TO ANO FROM THE PROCESS IMG = L 5 ; cO ▼ANT ANO YOUR F A R M* ) 5 370 FORMAT( * ENTER THE P R I C E STRUCTURE ( I N S / K G ) FOR THE S I X P I C K L I N G 0 5 3 3 0 ▼UCUMSERS GR A OE S* ) 5390 PACO p a 10 F O RMA T ( * 0 0 YOU WI SH TO HAVE THE OUTPUT SHOWN ON THE S C R E E N : l - Y E S 5 - 2 0 ▼. O - N O * ) P L 30 F O R M A T ( * ENTER THE RUN N UM3 E R* ) PLuO PL =0 F O R M A T ( * I S THE A 8 0 V E I N F ORMAT I ON C O R R E C T , I - Y E S , 0 = N O * ) 9 “ oO F O R M A T ! * WELCOME TO C U C H A R V * , / ) 9L7Q F O RMA T { I 2 X , * ME C H A N I C A L CUCUM8E.R H A RV E S T I N G MOO EL I N M I C H I G A N = L 3 c + .............* , / ) 9LP0 9500 FORMAT ( 2 X , * SECTION 1 : * ) 9=10 FORMAT ( 2 X , * SECTION 2 : * 9=120 FORMAT ? 2 X . * SECTION 3 : * 95)0 FQRMAT(2X.* SECTION I * : * ) 9 =^0 FORMAT(2X, * SECTION 5 : * j 9?50 FORMAT(2X, * SECTION a : * ) P50O PR 70 F O R M A T ( 2 X . * N U M B E R OF F R U I T / G R A O E : 1 A = * , r L . 0 , * , I 3 = * , F L . 0 , * , 2 = * . 9930 ▼F L . 0 , * , 3 A » * , F L . 0 , * , 3 3 = * , F L . 0 , * , L = * , F L . 0 . / ) 5 = =0 9 0 OO F 0 R M A T ( 2 X , * F A P . M S I Z E = * , F L . O . * ( H A ) . TOOAYS DATE : * , I 2 , I X . I 2 , 9610 +1 X , r 5 . 0 , I X , *TP,AF F I CA3 I L I TY SEQ: * . 10 I I , / ) 9S20 FO RMA T( 2X ,* CAP AC ITY I "RUCKS: O W N - * , F 3 . 0 , * , L E A S E - * . F 3 . 3 , P6 3 O + * , R E N T a * , F 3 . 0 , * , OWNED I N I T I A L GOST « * , F6 . 3 , * S * ) §6*0 F O R M A T ( 2 0 X , * T R A N S P Q R T R A T E - * . F 3 . 2 . * S / 2 2 . 5' KG. DR I VER H I R I N G C O S T - * , 9350 ▼FL . I , * S / T R I p * . / ) 960O F 0 R M A T ( 2 X . * C A P A C I T Y 2 TRUCKS: O W N - * , F 3 . 0 . * . L E A S E = * . F 3 . 0 . 36 7 0 + * , R E N T - * , F 3 . 0 . * , OWNED I N I T I A L COST - * , F b . 0 , * 5 * ) 9630 SojO F 0 R M A T ( 2 X . * P R I C E STRUCTURE ( S / G R A D E ) : I A : * , F 3 . 2 . * , I 3 : * , F 3 . 2 , 97CC ▼ * . 2 : * , F 3 . 2 . * , 3 A : * . F 3 . 2 , * . 3a : * . F 3 . 2 . * . L : * . F 3 . 2 , / ) 97 10 F O R M A T ! * ENTER YOUR P L A NT P OP U L A T I ON DENS I " v : I = L I G H T D E N S i T v ' L O . 0 = 7 2 0 - 0 0 P L T / H A ) , 2 - ME D I UM D E N S I T Y ( 3 0 . 0 0 0 P L T / H A ) , 3 - H t A V Y DE.NS I TY ( ! I 0 = 7 30 ▼.000 P L T /H A )* ) p ; lo = 7=0 - O R M A T ( 2 X , ' ' HARVESTERS : ~ Y ? S - * . I I . * . O W N E D - * . F 3 . 3 , * , L E A S E D - * . 3 'iO ▼F 3 . 0 , * , I N I T I A L COST/ OWNED H A R V E S T E R - 5 * , F o . 3 , / ) =770 9730 3790 F O R M A T ( 2 X . * P 0 P U L A T l O N D E N S I T Y - * , I 1, =300 + * . 0 I STANCE TO PROCESSI NG P L A N T - * , FL . 0 , * , SCREEN O P T I O N - * , =3)0 ______________ _ _______ O O O O O O O G O O O O O O O Q O O O O O O O O O O O O O O O O O O O O Q O O O O O O O O O O O O O O O O O O O Q O O O O O O O O O O O O O O O O O O O O O — N t o j i n i f ' O O O ^ O ' - r i r o j i r « o r - < t > ( h o - r | f n j i r v f l r x n O ' O - r |r*^4 -i r> D r> o ] c n o - N m - i m o r - r o m o — r*j »*"\ i UvOr'OD r *roj »-CO 0>O*“ r>l ^ 4 ■I * 0 <'CO O 'O O O O O O O O O O — — M N N fMr'I M N N N r j r'^rnr^r'M^r or^ r^ i^ rr.,-j 4 4 4 4 J 4 4 J J UMr>UMf^fM/Vr»ir»in -t n t^n o vr? (5 " 2 ? l5 Cn!D a > 0 ' 0 > 0 ' 0 ' 0 > t ,' 2 ) 0 o O o O O O o o O O O O O O O O O G O O O O O O O O O O O O O O O O O „ O _ O, O O n t n m in r n c n c n c n c n c n e n c n m c n o v —• ■ ---------.— — —_ _ _ _ _ -----_ _ _ . _. _ _ _ _ _ _ _O, O . O _O. 0 O O , O „ ,O O _ _ _ i ,O O O . O O O ( ~) O O . O 0 • as en ti. i«. . X X — • to r-. 'CH. u - . CO It. . X “ it. . X l- .n 2u_ 0 o z X in in * <43 x —co ♦«.. ■n » •U. X - X o vO •n x - n r tcr> — -'tl_ — n 2 0 rr • — o * *ro X XU. — .— - vO x X in OO <00 <4CO cr • o x cr u. it. 11. 11. — it.— » 1 + o x «rro Xu- cr 1n o » 1 * 1 •• 1— 1 in * o x u .— 1- UJ • -OOV»l X K l-im iltK K X K U con in Ol d O'- o r i — r'l-j: x: U — ------------rl-;: -4 \ rl - '- 'O I— • I—I—I—-»»- O I - I - I-1 - b- vO er x 1-0 I - • i u a cr a< K I I -J 1 < iD 2 O 3o I I I |- - u x n tjj u. x o — 1.1 1— t: o r-co in in — ru m TOCO ro ro 1 _j < t C -J I ' - 'O f * l h tr I 11. I * I I I K. 1-2 *in XX 0 i. m 2 0 — 1u UJ cr cr 0 0 IU *«t T. I- • tr -t X 0 0 0 2 0 a o n :o t i X 2 :: 0 2 — in O >- n •t • I-:; x rr OUJ 2 in z o 2 UJ O 0 o r* 2 * O -c r — 0 c r itj D i/l O > a O > -2 2 n < 0 3 »n Dui o>— x tr — UJ w x l-i- IU in - —x I0 — IU iu.^ i / > :: X X OO 1•tr 0 0 o o o t r o x —— t«ltiJ 1 —X X *-!1- O - cr ZD O >X 2 r-l - - x < c ro a tn c r a .t r tr e r a X _» O u. in in in ro ro 0 en en cr 0 it. X 20 2 U IU J-T X X -—1- - I X in I- — —2 I U. s* •*: • —h - o m c t ’u.r-" o z V n_ — » en - • n n . '- 'j r x ^ t x — I-:: *I O• O'**. •- ‘X a z a • < ru u i o. _t ru _ ^"O r |_ '- 'O I- - > 0 ro • Xu- 1 < 2* r r u ju — ncl—1 *_ • l u ^ . i o n . u z " u r i n o x J T r o z — 2 -t — CO X I- x 1 l- u i u . — o •- r o | ro r*l^. O t l » x < — - x m —\ z l-l- — _ 4 z. u. 1nor. •co :: o UJ I — •QC r*-. i». • X — o> r- -z luxni lO —TOO * rl o 0 . r o O um — 0 x * 0 0 0 ru. • X l /) r i o O in - —in :: vO ti. • X O CM \O u . X I- . . CTi cr 1 1.0 • \D Ii. « X z 0 ro O 'X o or • r;' o ro O ro V- . tr 0 N O 2 -4 in 926. 52. 18. 7. 8. 237. 807. 38552033103?. 1596. 78. 266. 691. . 261*. §361*. 86. 121 . 21*2. 81*. 3A. 37- 109337 2b. 361. 1230. 3202. 1220. 382. 295 • 131. 9562. 11* 1* 9 . 601 * . 17790. 73S2. 736 6689. 1026. TOTAL/FARM: AVERAGE/HA: 103. HARVESTI NG COST LABOR FUELLUBRI­ CANTS 10868. ** COST OF RENTED EOUI PMENT (S) REPAIR £ M A 1N T ENANCE 70965. 1087. 7097- COST OF OWNED QUI PMENT (S1 OWNER TAX£ SHIP INSU­ RANCE 938. TOTAL TOTAL TYPE OF E QU 1PMENT 25- I 85°5 s 18 7 COST OF LEASEO EQU 1PMENT ?S) TOTAL COST CAP ( 1 ) TRUCK: 552. 81*. 1* 2 0 . 510. 109. 0. 0. 1675- CAP (2) TR U C K : 736. 112. 560. 510. 57- 0. 0. 1971*. 0. 2029. HA R V E S T E R : 7 8 0 ,. 360. 120 651* - 115 TOTAL (S) ** FOLLOWI NG' THE ABOVE FRUIT H XI OOO HARVEST F R U I T WT TOTAL/FARM 1026. A V E R A GE / H A 103. TOTAL SUMMARY OF MANAGEMENT HARVEST DA Y S : 16 ftAREA* (HA) fc.5 5673. AA SCHEDULE: (KG) DOLLAR RETURN DOLLAR COST 70965. 108 68 . 5678 . 7097. 1087. 568. NET RETURN (S) 5190. 519- 2 HA RV E S TI NG ■’• HARVEST D A T E * MONTH OAY STRATEGY *GRADE * 1A IS 2 3A RETURNS * * AVERAGE RETURNS * * (#/HA) ( KG/HA) (S/HA) XI OOO 12. 508 . 3123. 2033- •a?: Ill: * * * TOTAL AREA RETURNS * * * (rf/AREA) (KG/AREA) (S/AREA) XI OOO 671*. 131 . 82 . 2562. 11* 091 * . 9171*. 223. 8i*o. 2537. 1 193- 322 TOTAL 17 6. 10. 193/- 77. 10b. 8805. 120L. IA IB 3-7 10. 5 j* • 3A ** 1.8 - * E0. 11: S'S- 1* 9 5 2 8 . 6095- 8. 16. 100. 1*. 3. 57. 289. 19. S3 aB 1?: 1L . J5S8: 1931. ?1 9 !5 . G 3fc- 6775. 2k 1. IS: 60. 158. 18825. 181*3. 275. .25: 127- >'«* 12131. H A RV E S T I N G LABOR FUELS LUBRICANTS 32951 • 323&. 11* 7 6 5 . 121313. 11*77. COST ** COST OF RENTED EQUIP MENT^) OWNER TAXS REPAIR SHIP INSUS RANCE MAI N T ENANCE i? i |8 : 101* 0 . 1303. 270. 1*22 . 55?5: 10026. 271 • COST OF OWNED EQU 1 PMENT (S) TYPE OF E QU I P ME NT 13737 I9bo 326 5^3 1631 • AVERAGE/HA: 161 1*25 2608 L0&9 59 ■ TOTAL/FARM: 1*§7 1289 15123 2R. 13255. TOTAL 350. 5 UJ - - > 35. 87. 138. IA • IB 287. 3 8 8 3 ^* - 11*82 3 3670 19. 1*I . 201 . IA?. TOTAL 18 im 10S9 3i: 3B 25 , t t: 11 . 2 8 795- COST OF L EASED EQUI PMENT (S) TOTAL COST CAP ( 1) TRUCK: 552. 8L. U20. 510. 186. 0. 0. 17 5 2 . CAP ( 2) TRUCK: 736. 112. 560. 510. 97- 0. 0. 2015. 0. 2215. HARVESTER : 780 . 120. 360. 11*3 - 812. TO TALIS): ** FOLLOWI NG THE ABOVE HARVEST FRUIT # XI OOO TOTAL/FARM 1271. A V E R A GE / H A 127. TOTAL SUMMARY OF FRUIT HARVEST DA YS : 3 WT MANAGEMENT STRATEGY =' 5981. : SCHEDULE: (KG) DOLLAR RETURN DOLLAR COST 121313. 1L765. 5981 • 12131. 11*77- 5 98 • NET RETURN (S) 8 7 S 1* . 878. 323 *:': •-•HARVEST D A T E * MONTH D* Y 8 17 * area* (HA) 3-7 HARVESTING * g r a d E* 2. 1 1. 57. TOTAL CO 00 3.2 19 69 129. 13376 1568. A30 8 5263 >Z93 6330 2 1: 66A. 68A. 1A3. 217. 18518 a 2 3 5 0 £7 13 33 ' . . . . . . 3 0. 2 . It: l [3 si'. 50 2207 5A?5 2692 10858 155. 23535 AO 103 s i . 2076. 2 1 A0 . LLQ . 1 7A1 . A61 5791A 5 AA0 . 1. 16. 397 . 705. 2,15. 3^2. 1 c cA lSS 0 1 173 17 13 0 =957 12 1=5 8*80 3A232 1077. 1681. A88 7 A3 5 8 5300. 182028. 16579- 1A 2 8 1A3 . it it 13203. HARVESTING C OS T OF OWNED EQUI PMENT (S) T Y P E OF E Q U I P ME N T OWNER T AXS REPAIR SHIP INSUS RANCE MAI N T ENANCE LABOR *o7 • 68 2 A6 ' >532 16*59 5607 20A8A TOTAL/FARM: AVERAGE/HA: 6 ^ 2003 17 = 5 . 5333 • $ IA IB 2 3A 95 1283 15570 13578 C0 5 1 I3&76 A9757 22 H 7. TOTAL A2 212 1 21 * 8 : 131. I 6 8 3. 1 IA. 3i : 19. IA IB 2 TOTAL * * * T C ' A L AREA RETURNS * * * (KG/AREA) ( S / A R E ; : ( # / A R E A) X 1000 3n I* 18 S 3650 1089 3676 * H ** * * AVERAGE RETURNS * * (#/HA) (KG/HA) ( S /H A) X 1000 IA IB 2 3A 3.1 RETURNS 675- A. 7 1 ^ . I658. COST ** COST OF RENTED E Q U I P ME N T ( S ) C O S T OF LEASEO E Q U 1PMENT ( S) T otal COST FUELS LUBRICANTS CAP ( 1 ) TRUCK: 552. 8A. A20. 510. 279 0. 0. CAP ( 2) TRUCK: 736. 112. 5oO. 510. 11*5 0. 0. 2063. 0. 2 A3 9 . HARVESTER : 780 120. 360. 177. 1002. TOTALS) : ** SUMMARY O F MANAGEMENT S T R A T E G Y ** 18 A5 . 6 3 A6 . 324 FOLLOWING THE ABOVE FR U IT XIO O O HARVEST # FR U IT SCHEDULE: WT (KG) DOLLAR RETURN TOTAL/FARM I A* 2 3 . 182028. 16 5 7 5 • AVE RA GE / HA IL3. 18203. 1653. TOT A L HARVEST DA Y S : AAREAA (HA) CO CO 3-1 COST NET 6 3 L6 . RE TU R N 'S ) 10233- 635. 1023. 3 AA AHAP.VEST OATE a MONTH DAY DOLLAR AGRAOE 1A IB 2 3A 1 H ARVESTI NG RETURNS AA AVERAGE RETURNS a * ( * * / HA) (KG/HA) (S/HA) XI OOO TOTAL AAA TOTAL AREA RETURNS a a a ( # / AREA) ( KG/AREA) (S/AREA) X 1000 il. 1. 26. 00L . 1 3 lll \ J ': 5263. 13. 33- UU: 6SU. 1U3. 217. LO 103 !llll: 5610. 2 0 U9 5 . 2077. 21U1 . uug. 679. 1u s . 18501. 1736. U57 57891. 5 U3 1 . 0. . aa 1U. 2U6. 81. ■ 8 19 2.7 0 . 0 . IA IB 2 3A ?S TOT A L 8 20 2 . It IA IB 2 3A TOT A L 8 21 1.8 10 9 . 215. M l: 3U2. ,21 7285. 29360. 235U7- 1668. U 15 63716. U5 1 5 . 0. 0 0 0. 0. to: Ill]': 0. 1. 211. 5UL. illl: ,3 $ : 2?l: 2610. 38020. . 6s0i9:. 192 157. 28565- 1U 3 6 . 37U 68133. 3 5 U5 • 0 0 0. 1. 1065. 0. 0. 192. 63 U. L26. 1020. UQ. 19. 35 • 220? I 5U552692 . 10858. 153. 0. 0. 16. 37. 0. 108: lB 8. 2U. 21 . 1 0 U. 2989. 20673. TOT A L 153. 333U7- 2?|I: 356. A* 153. 2 U9 0 9 . HARVESTING i! 1U uu L876. s?39-**. 1132. ViV. 186 m : 1276. 282 59352. 2271 • 1527 2U9091. 15762. TOTAL/FARM: AVERAGE/HA: 1. 13. 1075 1919. 0. 0. IA IB 2 3A 2. uo. 0 1 81 132 ?°- U 0. 1576. COST AA TYPE OF EQUI PMENT - COST OF OWNED OUI PMENT