TH £1815 . g,“ [ l .. I!!!MIN/INHIHIIUIWMI!!!{III/HIIll/IIIIIIHIIIIM 3 1293 10375 6619 f" *1 ilr"_ " any}. 9‘ This is to certify that the dissertation entitled INTERCROPPED TOMATO AND SNAP BEAN: A COMPUTER MODEL presented by Ian Bruce McLean has been accepted towards fulfillment of the requirements for Ph . D . degree in Horticulture /-'71 Major prof A /, Date August 7, 1981 f MS U is an Affirmative Action/Equal Opportunity Institution 0- 12771 RETURNING MATERIALS: 1V1£31_J Place in book drop to remove this checkout from w your record. FINES will be charged if book is returned after the date stamped below. W 77.77 INTERCROPPED TOMATO AND SNAP BEAN: A COMPUTER MODEL By Ian Bruce McLean A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Horticulture 1981 ABSTRACT INTERCROPPED TOMATO AND SNAP BEAN: A COMPUTER MODEL BY Ian Bruce McLean A dynamic simulation model of intercropped tomato (Lycopersicon esculentum cv. PikRed) and snap bean (Phaseolus vulgaris cv. Bush Blue Lake) is presented. Beans are planted in a matrix of hexagonal rows with a tomato transplanted centrally in each hexagon. Model inputs are real weather data and shadow lengths. Carbon fixation and respiration rates for each species are computed, and photosynthate is partitioned to plant components. Canopy growth and above-ground inter- and intra- specific competition is modeled. Tomato-tomato distance is varied from 0.8 to 1.2 m, and bean-bean distance from 0.05 to 0.15 m. Bean planting date is also varied. By repeated simulations the model predicts the combinations of these three variables which optimize individual and combined species yields. Field validation experiments confirmed the predicted optimum combinations of 0.8 m tomato-tomato and 0.05 m bean- bean distances with the earliest bean planting for maximum total yield. ACKNOWLEDGMENTS Deep appreciation is felt for my wife, Elly, and children James, Stephanie, Heather and Erica. Their unbounded support and patience have been essential for the successful completion of this degree. Dr. H. Paul Rasmussen served as my adviser for the M.S. and for all but the last six months of the Ph.D., and has my deep affection and gratitude. Dr. John F. Kelly graciously accompanied me through to completion as committee chairman. The interest and assistance of my committee was also appreciated, especially that of Dr. Brian Croft who contributed vitally to the generation of the dissertation problem. Bonna Davis provided invaluable assistance in typing the draft and final dissertation. Her kindness and long hours of work were critical for its timely completion. Financial support from Title XII funds and the People of the State of Michigan has enabled me to attend Michigan State University. I am deeply appreciative of this honor, and will strive to return some of this goodness to the communities in which I will live. ii TABLE OF LIST OF TABLES . . . . . . . . . . . LIST OF FIGURES O O O I O O O O O O O I. INTRODUCTION . . . . . . . . . . A. MULTIPLE CROPPING. . . . . . 1. 7. Definitions. . . . . . . a. Sequential Cropping. b. IntercrOpping. . . . CONTENTS Distribution and Characteristics Multiple Cropping Examples . . . . . Experimental Designs and Statistical Interpretation of Yield Data Recent Research Trends . Farming Systems Research . . PLANT COMPETITION. . . . . . l. 2. Plant Competition Research Methodology . . . . Future Research Needs. . THE SYSTEMS APPROACH AND MODELING... l. 2. 3. Definitions. . . . . . . Reasons for Agricultural Simulation. Crop Modeling Applications . . . . . a. Historical View. . . b. Examples of Plant Models . iii Page vi ix ll 12 12 l3 l4 l4 l4 TABLE OF CONTENTS - continued II. MATERIALS AND METHODS . . . . . A. B. C. D. E. SPECIES SELECTION . . . . . 1. 2. criteria. 0 o o o o o 0 Preliminary Field Experiment. INTERCROP PLANTING DESIGN . MODEL CONDITIONS. . . . . . 1. System Boundary . . . . Driving Variables . . . Initial Plant Description Variables for Testing . MODEL DESCRIPTION . . . . . DATA COLLECTION FOR THE MODEL overViEWO O O I O O O O in the Model. Main Program (Program DRIVER) . . Weather Data (Subroutine WEATHER) Evapotranspiration and Soil Water (Subroutine WATER). . . . . . . Photosynthesis and Respiration (Subroutine PHOTO). . Carbohydrate Partitioning (Subroutines BNPART and TOMPART). Bean and Tomato CanOpy Growth (Subroutine CANOPY) . . . . . . . Bean and Tomato Yield (Subroutine YIELD). Simulation Termination. iv Page l6 l6 16 16 I7 23 23 23 24 24 25 25 25 27 27 35 44 52 59 59 59 TABLE OF CONTENTS - continued Page F. FIELD VALIDATION. . . . . . . . . . . . . . . . . . . . 64 1. Spacing Experiment. . . . . . . . . . . . . . . . . 66 2. Planting Date Experiment. . . . . . . . . . . . . . 72 G. MODEL SIMULATIONS . . . . . . . . . . . . . . . . . . . 75 III. RESULTS AND DISCUSSION . . . . . . . . . . . . . . . . . . 76 A. DATA COLLECTION . . . . . . . . . . . . . . . . . . . . 76 1. Plant Growth Curves . . . . . . . . . . . . . . . . 76 2. Fruit Fresh Weight : Dry Weight Ratios. . . . . . . 76 3. Soil Water Content. . . . . . . . . . . . . . . . . 84 4. Preliminary Hexagon Trial . . . . . . . . . . . . . 84 B. SIMULATION RESULTS. . . . . . . . . . . . . . . . . . . 87 1. Spacing Simulation. . . . . . . . . . . . . . . . . 87 2. ‘Model Response to Weather Changes . . . . . . . . . 101 C. VALIDATION RESULTS AND SIMULATION COMPARISON. . . . . . 101 1. Spacing Experiment. . . . . . . . . . . . . . . . . 101 2. Planting Date Experiment. . . . . . . . . . . . . . 109 IV 0 MY AND CONCLUSIONS 0 O O O O O O O O C O O O O O C O O 126 APPENDICES APPENDIX A. Results of Preliminary Intercropping Experiment . . . . 128 B. Glossary of Modeling Terms and Flowchart Symbols. . . . 131 C. Weather Summaries for 1968-1970, 1980 . . . . . . . . . 134 D. Computer Model Printout . . . . . . . . . . . . . . . . 135 LIST OF REFERENCES 0 O O O O O O O C O O O O I O C O 0 O O O O O 183 LIST OF TABLES Table Page 1. Bean and Tomato Fruit Fresh Weight:Dry Weight Ratios. . . . . 83 2. Bean and Tomato Yield per Plant . . . . . . . . . . . . . . . 86 3. Component and Total Papulations at Each Planting Distance Combination. . . . . . . . . . . . . . . . . . . . 88 4. Component and Total Yields Simulated from 1970 Weather Data with Bean Planting Date = 1, Bean Harvest Date - 52. . . . . . . . . . . . . . . . . . . 9O 5. Component and Total Yields Simulated from 1970 Weather Data with Bean Planting Date 8 10, Bean Harvest Date - 62. . . . . . . . . . . . . . . . . . . 91 6. Component and Total Yields Simulated from 1970 Weather Data with Bean Planting Date - 20, Bean Harvest Date - 72. . . . . . . . . . . . . . . . . . . 92 7. Component and Total Yields Simulated from 1970 Weather Data with Bean Planting Date = 30, Bean Harvest Date - 82. . . . . . . . . . . . . . . . . . . 93 8. Component and Total Yields Simulated from 1969 Weather Data with Bean Planting Date 8 1, Bean Harvest Date 8 52. . . . . . . . . . . . . . . . . . . 94 9. Component and Total Yields Simulated from 1968 Weather Data with Bean Planting Date a 1, Bean Harvest Date = 52. . . . . . . . . . . . . . . . . . . 95 10. Component and Total Yields Simulated from 1968 Weather Data with Bean Planting Date - 10, Bean Harvest Date - 62. . . . . . . . . . . . . . . . . . . 95 11. Component and Total Yields Simulated from 1968 Weather Data with Bean Planting Date - 20, Bean Harvest Date - 72. . . . . . . . . . . . . . . . . . . 97 12. Component and Total Yields Simulated from 1968 Weather Data with Bean Planting Date = 30, Bean Harvest Date a 82. . . . . . . . . . . . . . . . . . . 98 vi LIST OF TABLES - continued Table 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. Bean and Tomato Fruit Yields over 3 Different Years' Weather Data . . . . . . . . . Analysis of Variance (ANOVA) of the Spacing Experiment Bean Yields. . . . . . . . . . . Spacing Experiment Bean Fruit Yields. . . . Analysis of Variance (ANOVA) of the Spacing Experiment Tomato Yields. . . . . . . Spacing Experiment Tomato Fruit Yields. . . . . Bean Fruit Yields per Plant and per Unit Area from Simulations and the 1980 Spacing Validation Experiment . . . . . . . . . . Tomato Fruit Yields per Plant and per Unit Area from Simulations and the 1980 Spacing Validation Experiment . . . . . . . . Total Yields per Unit Area from Simulations and the 1980 Spacing Validation Experiment. . . Analysis of Variance (ANOVA) of the Planting Date Experiment Bean Yields per Plant . . . . Analysis of Variance (ANOVA) of the Planting Date Experiment Bean Yields per Unit Area . . . Planting Date Experiment Bean Fruit Yields per Plant. Planting Date Experiment Bean Fruit Yields per Unit Area. . . . . . . Analysis of Variance (ANOVA) of the Planting Date Experiment Tomato Yields . . . . . . Tomato Fruit Yields per Plant in the Planting Date Experiment. . . . . . . . . . Tomato Fruit Yields per Unit Area in the Planting Date Experiment. . . . . . . . . . . Comparison of Average Yield for All Plant Spacing Combinations at each of 2 Bean Planting Dates . vii Page 99 . . . . 102 . . . 103 105 . . 106 o o 107 108 . . . . 110 111 112 113 , , , , .114 116 117 118 125 LIST OF TABLES - continued Table. Page APPENDICES 29. Tomato Yields for 1978 and 1979 from the Preliminary Intercropping Experiment. . . . . . . . . . . .129 30. Cabbage, Pea and Bean Yields for 1978 and 1979 from the Preliminary Intercropping Experiment . . . . . . .130 viii LIST OF FIGURES Data. . Figure l. Hexagonal Planting Design . . . . . . . . . . . 2. Systen Diagram of MULTICROP . . . . . . . . . . 3. System Diagram of Program DRIVER. . . . . . . . 4. System Diagram of Subroutine WEATHER. . . . . . 5. System Diagram of Subroutine WATER. . . . . . . 6. Evapotranspiration Function . . . . . . . . . . 7. Soil Profile of the Somewhat Poorly Drained Capac Loan. 8. System Diagram of Subroutine PHOTO. . . . . . . 9. Tomato Leaf Respiration Rate. . . . . . . . . . 10. Tomato Photosynthetic Rate. . . . . . . . . . . 11. Tomato Photosynthetic Rate Maturity Function. . 12. Shading of the Tomato CanOpy. . . . . . . . . . 13. The Fraction of Shadow Height S over CanOpy Height C Used to Partition Bean Ground Area . 14. System Diagram of Subroutine BNPART and TOMPART 15. Equations Used to Partition Carbohydrate Between Plant Parts . . . . . . . . . . . . . 16. System Design of Subroutine CANOPY. . . . . . . 17. Bean CanOpy Simulation Modes. . . . . . . . . . 18. Bean Canopy Overlap with Approximating Ellipses 19. System Diagram of Subroutine YIELD. . . . . . . 20. Planting Design for Bean and Tomato GrowthCurve 21. Plot Design for Preliminary Spacing Experiments 22. Schematic Representation for Spacing Experiment ix Page 19 22 26 28 30 32 34 36 37 38 41 43 45 48 51 54 56 58 6O 62 65 67 LIST OF FIGURES - continued Figure 23. Individual Main Plot Design for Spacing Experiment. . . . 24. Segment of Spacing Experiment Plot. . . . . . . . . . . . 25. Schematic Representation for Planting Date Experiment . . 26. Individual Main Plot Design for Planting Experiment . . . 27. Bean Leaf Dry Weights with Fitted Curve . . . . . . . . . 28. Bean Stem Dry Weights with Fitted Curve . . . . . . . . . 29. Bean Fruit Dry Weights with Fitted Curve. . . . . . . . . 30. Tomato Leaf Dry Weights with Fitted Curve . . . . . . . . 31. Tomato Stem Dry Weights with Fitted Curve . . . . . . . . 32. Tomato Fruit Dry Weights with Fitted Curve. . . . . . . . 33. Gravimetric Soil Water Content. . . . . . . . . . . . . . 34. Mean Tomato Yields per Plant in the Planting Date Experiment at Constant Tomato and Bean Distances Across Planting Dates . . . . . . . . . . . . . . . . . . 35. Mean Tomato Yields per Plant in the Planting Date Experiment at Constant Bean Spacing within Planting Date. 36. Mean Tomato Yields per Unit Area in the Planting Date Experiment at Constant Tomato and Bean Distances Across Planting Dates . . . . . . . . . . . . . . . . . . 37. Mean Tomato Yields per Unit Area in the Planting Date Experiment at Constant Bean Spacing within Planting Date. APPENDICES 38. Plot Design for One Replicate of Preliminary Intercropping Experiment. . . . . . . . . . . . . . . . . Page 69 71 73 74 77 78 79 80 81 82 85 119 120 121 122 128 INTRODUCTION Multiple cropping is practiced by millions of smallholders1 throughout tropical and subtropical Africa, Asia and Latin America. These farmers have been largely neglected by agricultural researchers aiming at increased food production. Concern has been primarily with the minority capable of incorporating segments of Western technology and achieving rapid gains dependent upon large inputs (49. 60)- Growing recognition of the widespread practice of multiple crepping throughout the tropics has led to increased research into its principles (124. 125). This dissertation is an extension of that process. Intercropping research has focused on two main areas: first, the relative yields and competitive strengths of component species grown as intercrops and as monocultures, and second, the environmental changes occurring within the intercropped planting (46). Classical plant competition research methods do not readily lend themselves to investigation of individual inputs into an ecosystem. A simulation model may be especially useful in testing effects of variables such as solar radiation and light utilization efficiency. Numerous simulations may be quickly and economically made while holding all but one variable constant. 1More than 90% of all tropical farms are of less than 5 ha in area (49). Diverse traditional farming systems exist on these farms. Smallholder refers to a member of this large group of traditional farmers on small land holdings. A simulation model of intercropped tomato (Lyc0persicon esculentum) and snap bean (Phaseolus vulgaris) is the basis for this investigation into the effects of environmental inputs and plant spacings. This is a unique approach to the study of intercropping. Plant models for crops in monoculture have been published (9,30, 41, 53 93), but there is no published model of systematically intercropped agronomic or horticultural crops. It is intended that this model of two intercropped vegetables will provide new insight into this complex ecosystem. The initial conceptualization of the model was performed in 1979 by a student group working on a class project for Systems Science 843 at Michigan State University (26). The model was proposed by this author. Crop growth simulation by this model failed after several days and no results were obtained. The model which forms the basis of this disser- tation is conceptually derived from this first attempt by the group. The valuable assistance rendered by that initial modeling effort is gratefully acknowledged. GENERAL LITERATURE REVIEW A. MULTIPLE CROPPING 1. Definitions Multiple cropping refers to the growing of mixed crops simul- taneously, sole crops in sequence, or mixed and sole crops sequentially, on a unit land area per farming year2 (6). Two major groupings of multiple cropping patterns exist (6): a. Sequential cropping. Two or more sole crops grow sequentially on the same field per year. No intercrop competition occurs, and the farmer manages one crOp at a time on that field. Double, triple, quadruple and ratoon cropping are practiced. b. Intercropping. Two or more crops grow simultane- ously on the same field during all or part of their growth period. Intercrop competition occurs during all or part of crop growth, and the farmer manages more than one crop at a time in that field. Mixed, row, strip and relay intercropping is practiced. 2. Distribution and Characteristics Multiple cropping is an important part of traditional farming systems throughout tropical and sub-tropical Africa, Asia and Latin 2The farming year is 12 months unless aridity restricts cropping to 24 month cycles. 3 America. Millions of smallholders use complex cropping patterns developed through empirical experimentation (49, 60). The high cropping intensity achieved by multiple cropping is dependent upon, or associated with, several factors (11, 49, 60). a. Adequate water from precipitation and/or irrigation. b. A long growing season. c. Suitable soil conditions. Small farm size. e. High marketing costs. f. Spreading of peak labor demands. g. Low level of mechanization and high labor inputs. Several benefits are derived from multiple cropping. Most important is the potential for greater total crop yield due to optimum utilization of growth resources (11, 124). Instances of substantially higher yields from sequential and intercrop patterns have been reported (7, 23, 88, 91, 126). Associated with the possible yield advantage is "harvest insur- ance." If one crop fails due to climatic or other causes, the remaining crop(s) may partially compensate by producing at least some yield. Harwood gt 31.,(49) suggest that the failing crop will already have impaired the yield of any intercrop, and that harvest insurance is an an argument for crop diversification rather than multiple cropping. Nevertheless, it is commonly mentioned in the literature as a benefit (6). Multiple cropping may allow more effective use of family labor. Peak labor demands may be smoothed out and higher labor productivity obtained (60). Family labor may be a readily available input which multiple crOpping most effectively utilizes. Pest control in multiple cropping is especially complex. Weeds may be inhibited by the more competitive community and dense canOpy of intercropped species (39, 124). However, insect and disease control is not necessarily enhanced. Instances of better control have been reported (22, 74). But poorer control has also occurred, especially in uninterrupted sequential crepping of rice (74)- Integrated pest manage- ment in multiple crapping requires a planned diversity of control methods. Crop rotation, crop diversity, resistant genotypes, modified plant spatial arrangements, and judicious pesticide use where economically feasible are some control components (74. 99). 3. Multiple Cropping Examples Multiple cropping patterns frequently involve a main crop inter- cropped with, or followed sequentially by, a legume or vegetable. Examples of the former are maize (Egg EEZE): sorghum (Sorghum spp.) or millet (Panicum miliaceum) intercrOpped with bean (Phaseolus vulgaris), ground nut (Arachis hypogaea), pigeon pea (Cajanus cajan), cowpea (Vigna unguiculata) or soybean (Glycine max) (2,4,10,23,84,90,100). Examples of sequential crapping involving a cereal are rice (Oryza sativa) followed by rice and/or sweet potato (Ipomea batata), potato (Solanum tuberosum), maize (Zea mays), tobacco (Nicotiana tabacum), soybean (Glycine max), or vegetables (cucurbits, solonace- ous species, cole crops, legumes, etc.) (7). Wide diversity occurs in the number and types of crops which are combined, including numerous combinations not mentioned above. For example, variation in cultivar characteristics such as plant height and days to maturity may be exploited. Many patterns may even occur within a village. For example, about 60 different combinations were identi- fied in a single village in India (60). Multiple cropping systems are as diverse as the cultures and geographic areas from which they have arisen. 4. Experimental Designs and Statistical Analysis Compact experimental designs are required for intercropped plant population studies. The large number of crop spacing combinations quickly make experiments large. Several compact designs are used. a. Fan designs in which the plant position grid is formed by the intersection of concentric circles and the equally spaced radii of the circles. Various forms emphasize changing shape or area occupied at each grid point. Methods for statis- tical analysis of the results are available (14,87). Willey (125) noted that fan harvest areas are particularly small, and that results from the fan may not be typical of comparable situ- ations in conventional row designs. Therefore, he suggested a modified fan to allow row planting. Huxley, §£_§l,, (58) sug- gested methods for interpreting the yield/population curves derived from fan designs. b. A 2-way grid, non-systematic design which introduces ran- domization and permits conventional analysis of variance techniques (73). 5. Interpretation of Yield Data The analysis of intercrOpping experiments has been addressed by several authors (85,86,94,125,127). These analyses generally attempt separation of the competitive abilities of the intercrOpped species. The overall performance of intercropping patterns commonly has been expressed as Land Equivalent Ratio (LER). LER is defined as "the relative land area under sole crops that is required to produce the yields achieved in intercrOpping", given the same level of management in both situations (124). For example, an LER = 1.10 indicates that 10% more land under sole crops would be required to produce the intercropped yield. 6. Recent Research Trends Monoculture associated with advanced mechanization has predomi- nated in 20th century Western agriculture. This has led to the early rejection of multiple cropping by Western-trained agriculturists as primi- tive and inefficient. It was assumed that as modern, Western agricultural practices were explained to traditional farmers, there would be a natural adoption of these practices and multiple crOpping would decline (124). This assumption has succumbed to the continued, widespread practice of multiple cropping in spite of efforts to promote monoculture among sub- sistence farmers (89). Increasing recognition of multiple cropping has been reported in several reviews and books during the last decade (7,24,48,60,7l,68,69, 70,92,106,124,125). These include descriptions of multiple cropping methodology in various cultures and geographic areas. Recent research by agronomists has focused on population levels, genotype selection, pest management, nutrient and water uptake, and light interception as they influence yield (3,5,39,75,9l,99,110,122,126). 7. Farming Systems Research Cropping systems are enmeshed in the societies of which they form a part. Recent emphasis on this fact has led to the deveIOpment of 8 farming systems research (FSR). FSR seeks to "increase the productivity of the farming system in the context of the entire range of private and societal goals, given the constraints and potentials of the existing farming system" (38). Interdisciplinary FSR is currently a major com- ponent of programs at the International Rice Research Institute and the International Crops Research Institute for the Semi-Arid Tropics (66,134). FSR methodology is especially suited to the study of multiple cropping and other traditional farming methods (89,135). Its further incor- poration into smallholder research will reduce the incidence of proposed technological packages which are unsuited to the socio-economic milieu of the traditional farm and village (34,89). B. PLANT COMPETITION Plant competition is a vital component of agronomy, ecology, horticulture and weed science. It is central to research into inter- crapping principles. The classical plant competition definition is that of F. E. Clements in 1929, as cited by Hall (43). Competition is purely a physical process. With few exceptions, such as the crowding of tuberous plants when grown too closely, an actual struggle between competing plants never occurs. Competition arises from the reaction of one plant upon the physical factors about it and the effect of the modified factors upon its competitors. In the exact sense, two plants do not compete with each other as long as the water content, the nutrient material, the light and the heat are in excess of the needs of both. When the immediate supply of a single nec- essary factor falls below the combined demands of the plants competition begins. Interference by one plant upon another may include allelopathy. Rice (102) defined allelopathy as ...any direct or indirect harmful effect by one plant (including microorganisms) on another through the production of chemical compounds that escape into the environment. Interference is the sum of competition and allelopathy. Attention was focused on plant competition research methodology in the 19503 through early 19703. Several publications reviewed com- petition principles, research practices and mathematical models (13,28 29,46,47,82,107,128,129). Trenbath (118,119) reviewed plant inter- actions :hi mixed communities. A series of papers by Kira and associ- ates dealt with intraspecific competition and density-yield relation- ships of several crops (54,55,62,63,64,65,l33). 1. Plant Competition Research Methodology Mathematical models of varying complexity are used to quantify plant competition. a. Yigld. Total yield as the plant population is varied is used widely in agriculture to establish optimal planting rates. Associated with this is the quantitative analysis of plant growth and yield components (46,104). b. Environmental change from increased plant density. Light, water and nutrient level changes due to increased plant density have been studied extensively in agriculture (46). These factors are critical for crop growth and most commonly are affected by population increases. c. Plant weight. Three measures of plant weight are used (79). First, the coefficient of variation of plant weight 10 may increase with time at high densities. Second, the frequency distribution of individual weights in a plant population may change from "bell-shaped" to "L-shaped" during growth. Third, the correlation between a plant's weight and the weights of neigh- boring plants may indicate cooperative or com- petitive situations. ' d. Mixture diallel. The diallel design is used by geneticists and agronomists to compare the relative performance of a number of species or genotypes (86). Adaptations of this design to competition studies are available (45,86,101, 127). e. Replacement series and derived functions. The replacement series technique originated by de Wit (128) and expanded by de Wit g5 g1., and Hall (43), (132)18 widely used in plant competition research (124)- Two or more species are grown together in different prOportions and in monoculture. The two species' yields are represented on two Y axes, and their relative population prOportions on the inter- secting X axis. Lines join yield levels at component portions from 0 to 100% of the total papulation. Interpretation of the resultant diagram places the interspecific relationship in one of three broad categories: mutual cooperation, mutual inhibition, or compensation. 11 Functions developed by de Wit and associates assist in competition analysis (128). The relative reproductive rate of two or more annuals is the ratio of number of seeds sown to number of seeds harvested. The relative replacement rate is derived by resowing seeds of both species pre- viously harvested from monocultures and mixtures and is closely related to the relative repro- ductive rate. The relative crowding coefficient measures the activity of a species when crowding another species for space. f. Lotka-Volterra equations and Land Equivalent Ratio. A theoretical explanation of LER>’1 using the classi— cal Lotka-Volterra equations describing two interacting populations was reported (121). Vandermeer (121) showed that for LER to exceed 1 the mutual inter- ference of the two species must be sufficiently weak. He also showed that LER is mathematically identical to the competitive exclusion principle of theoretical ecology. 2. Future Research Needs Incorporation of the above principles into multiple cropping research must be expanded. Presently there are few instances of the use 12 of ecological principles and research methods in the applied multiple crapping literature. Willey (124,125) clearly outlined their potential applications. A fertile research area awaits workers capable of bridging the two fields. C. THE SYSTEMS APPROACH AND MODELING The reductionist approach to scientific research reduces phenomena to their basic parts. Independence of the parts is requisite. Reduc- tionism leads to the fragmentation and proliferation of disciplines which remain essentially isolated. The expansionist approach assumes that all entities and phenomena are parts of greater wholes. Interdependence of objects and events is declared. Holistic, interdisciplinary team research is integral to expan- sionism (27). The systems approach is formalized expansionism. 1. Definitions De Michelle (81) defined a system as ...an assemblage of objects united by some form of regular interaction or interdependence. It is an identifiable portion of the real world about which we can construct a boundary. Through this boundary we monitor or control all inputs and outputs. An ecosystem has living organisms as some of its objects (113). An agro-ecosystem adds stricter control over the ecosystem boundary and its inputs and outputs (78). A model of some sort is essential to systems study. The model is a "set of hypotheses about the system cast in the form of a set of mathematical equations" (81). Systems simulation is problem solving 13 using a dynamic model over time (8). Two types of models are identified (81). a. Descriptive (from observed responses). Responses are recalled from similar past circumstances. Curves are fitted to experimental data and used predictively. Nothing new is learned about the system. b. Mechanistic (from fundamental concepts). A set of equations describe hypotheses about events occurring within the system. New insights into system behavior are sought. Models may initially be descriptive, and become increasingly mechanistic through refinement. 2. Reasons for Agricultural Simulation Several benefits are derived from the modeling process itself, and from crop simulation(8,15,81,96). a. Insight may be gained into a system whose component interactions are too numerous and complex to be comprehended otherwise. b. Interdisciplinary coordination of research efforts may reveal and remedy information lacunae. New knowledge from this research may be stored and used in the model. c. New hypotheses may be generated and tested. d. New knowledge may be gained from the model since the system is more than its isolated parts. e. Agricultural management systems may be optimized. f. Agricultural problems may be examined which other- wise are intractable due to time, cost or disruption constraints. l4 3. Crop Modeling Applications a. Historical view. Pioneering work in crop modeling in the 19603 was by de Wit at Wageningen, Duncan at the Universities of Kentucky and Florida, and Stapleton at the University of Arizona(81,ll4) During the last decade plant modeling groups have been active at the Agricultural University at Wageningen (Netherlands), the Glasshouse Crops Research Institute (Littlehampton, England), Mississippi State University, Ohio Agricultural Research and Development Center, Purdue University and University of California (Berkeley). Several books and reviews specifically addressed to crop modeling have been published (8,25,31,77,115,131), Two journals, Agricultural Systems and Agra-Ecosystems, are devoted exclusively to extensive agricultural systems and modeling articles. A number of other journals publish plant models. b. Examples gf plant models. A model-building pre- requisite is the establishment of its Operational level. The model's goals largely establish its resolution. Plant models exist with levels ranging from enzyme kinetics and gas diffusion, to the whole plant (9,76). (l) (2) (3) 15 Plant physiological processes. Models exist for light interception, photo- synthesis, respiration, evapotranspiration and partitioning of carbohydrate (1,21,35, 50,56,76,108,ll6,120,l3l). No attempts to simulate plant hormone control have been published. Organ level. The growth of several plant organs has been modeled. These include leaves, roots and flowers (18,19,20,37). To date, no model has been published of fruit growth pg; gs, Whole plant level. Whole plant models for several species now exist. The first whole plant model was of cotton, and models of this species have con- tinued to be developed (41,114) Models of other economic species include alfalfa, apple, maize, ryegrass, soybean, sugar beet, tomato, and a management model for asparagus (9,30,32,53,57,7l,80,93,109,117). Modeling has been utilized heavily in pest management research and plant models have been required for plant-herbivore models. An example is the cotton-herbivore interaction (123). MATERIALS AND METHODS A. SPECIES SELECTION 1. Criteria Vegetables form an integral part of intercropping systems in the trapics (92). However, the bulk of intercropping research has been with agronomic crOps. Two vegetables were selected for this study to increase knowledge about vegetable intercrOpping. The fir3t criterion for species selection was wide adaptation throughout temperate to trOpical zones. Availability of a compre- hensive literature was required for both species. The two canOpies were to combine readily into a cropping pattern. Tomato (Lchpersicon esculentum) was selected to be intercropped with either pea (Pisum sativum), snap bean (Phaseolus vulgaris), or cabbage (Brassica oleracea var. capitata). 2. Preliminary Field Experiment Preliminary experiments were carried out at the Michigan State University Horticulture Research Center in 1978 and 1979 to assist intercrop species selection. On May 2, 1978, cabbage plants (cv. Market Victor) were transplanted .46 m apart, and peas (cv. Lincoln) direct-seeded .05 m apart, in 1.5 m rows which were 7.6 m long. Bush snap beans (cv. Bush Blue Lake) were direct-seeded .05 m apart in similar rows on May 29. On the same day, low lying determinate hybrid tomato plants (cv. PikRed) were transplanted .76 m apart in rows 16 l7 exactly centered between the cabbage, bean and pea rows. A tomato row was also planted without any intercrOp. Monocultures of each intercropped species were also grown at the end of the plot in .76 m rows at the same density within the row as above. The treatments were replicated four times. Prior to planting, 64 kg/ha of 5-20—20 fertilizer was added to the Capac loam soil, and the field was sprayed with Treflan. The tomatoes were side-dressed on July 19 with 9 kg/ha of N. The experiment was repeated an essentially the same dates in 1979. Harvest was performed by hand, and the cabbage, bean and pea plants were removed from the field after harvest. The results of this preliminary experiment are presented in Appendix A. The three intercropped species affected the tomato yields dif- ferently, due partly to their different planting dates and growth stages at planting (transplant or seed). These differences influenced their relative competitive effect on the tomato plants. The beans had the least deleterious effect on tomato yields in 1978. The 1979 results were less conclusive due to a latent infestation of quackgrass (AgroPyron repens). Due to these results and the excellent literature available for Phaseolus vulgaris, this species was selected to be inter- crOpped with tomato. Bean cv. 'Bush Blue Lake' was especially suit- able due to its short harvest period. B. INTERCROP PLANTING DESIGN Hexagonal bean rows with single tomato plants located centrally in the hexagon form the planting design (Figure 1). Each hexagon side with its adjacent tomato plants may be considered a segment of a straight row. 18 Figure l. Hexagonal planting design with bean rows placed between equidistant tomato plants. 19 O €33 6 tomato plant $1? \/\/\/ / $ \ 20 Hexagon symmetry allows simplification of the model. All tomato plants are equidistant and effectively separated by the dense bean canopy. They are assumed to be identical and non-competing with each other. This assumption is maintained after bean plant removal following bean harvest. The tomato plants are sufficiently separated to have little competitive effect. All tomato plants in the field are, there- fore, represented by one plant in the model. All bean plants are similarly located with respect to the tomato plants. The bean plant at position 3 in Figure 2 is approximately 15% further from the tomato plants than that at position p, Also, it is adjacent to three tomato plants rather than two. However, it is assumed that bean plants located at position b_are representative of all bean plants in the field. The hexagons form convenient experimental units and pack perfectly in a matrix. The tomato plant yield is entirely allocated to its enclosing hexagon. The associated bean yield is divided equally between the two hexagons whose boundary it forms. The model allows for a range of tomato-tomato distances. This distance automatically sets the hexagon width, circumference and area. The bean-bean distance within the row may also be adjusted. These two parameters (tomato-tomato and bean-bean distances) define the component and total plant papulations. Hexagonal planting patterns were used previously in plant com? petition research (47,63,81). Single plants located at hexagon corners were surrounded by various mixtures of their own and other species. Hexagonal planting patterns are recommended in the biodynamic/French intensive organic farming technique (59). 21 Figure 2. System diagram of MULTICROP 22 rune-.63 Ecuaao coaumwfiuufi .:0Humuaafiooum meamzH AHom A .88 H 882M182 _l .88 A 1n— 2232 38 g1 :03 - , fl d ...u _ n . _ . _ u . 82288.85 _ 822282.822 :28 3429288822 " “ 223.22.588.12 25m 9 _ _ = Fllll llllllllll L — a c 2388 a a an: . a 2855 5.2.2 :— 8828 .l I... llllllllll ll— _ . _ 2.22 3.88.28 _ u 2.22 388.28. 2.822 6822 82 " 2o8<28m2<282<>m _ 19822 82 2.822 . a — _ _ _ .. . _ . _ L pea: .mHSumquEou .mm .ooaumfivmu nu-mw_\m «r—f‘ mmmfi Figure 4. System diagram of Subroutine WEATHER 29 Figure 5. System diagram of Subroutine WATER 30 C 3 Establish COMMON Adjust weather inputs to correct units Compute atmospheric and canopy diffusion resistances Compute evapotranspiration rate Compute change in soil water content Compute new soil volume occupied by roots Increment cumulative values for evapotranspiration, irrigation and rain Compute soil water potential Compute leaf water potential Z Store current values in arrays/ [ C .0.) 31 sections of this subroutine are adapted from subroutine WATER in SOYMOD/OARDC (82). This model is a dynamic simulation of soybean (Glycine max) growth, deveIOpment and seed yield. Modifications have been made to adapt the logic to the requirements and variable of MULTICROP. However, the section remains essentially the same as sub- routine WATER in SOYMOD/OARDC. The evapotranspiration rate is cOmputed using a modified Penman equation after Monteith (85) (Figure 6). Atmospheric diffusion resistance is assumed to be a function of wind velocity above the canOpy (130). ATMDRES = 8.28 / WINDSPD where: WINDSPD = wind speed m s"1 The canopy diffusion resistance utilizes stomatal diffusion resistance and leaf area index (LAI). Stomatal diffusion resistance is assumed to be the same for both craps. It is computed by a function derived from bean (Phaseolus vulgaris) data (105), and is solely dependent upon temperature. Canopy diffusion resistance is then computed from stomatal diffusion resistance divided by the total leaf area per unit ground area (2 LAI) (12). The LAI used is for tomato both prior to and after bean growth. During bean growth the LAI used is for bean because of the relative spatial dominance Of the bean canOpy. The evapotranspiration function assumes a closed canOpy and no soil surface evaporation. This is a shortcoming Of this subroutine, particularly early in the season and in the absence of the bean canopy. 32 .Ammv Luamocoz “muum coauocsm oowumuwomcmuuoom>m .o ouswam use ousmmoua uoam> Hmsuom u mzmem<> woo unsumuanOo ucouuso um unannoua uoam> menopaumm n H U ousumummEou u mime . HIm NIEO woo zaocmo Ono wo moo man up oompwoca cofiumfiomu O>Huom zHHmowomcuchmOuona u mo n zeom<>m e x Aesop x somsooo. . NHAo.v u v x .ova + Amm 33 Following computation of evapotranspiration, the soil water content is updated. Inputs are rain and irrigation, and the output is evapotranspiration. NO irrigation is involved in the simulation but the capacity to use it is there. The model does not allow for surface runoff or losses to the water table. The soil volume modeled is a cone directly under a bean or tomato plant selected according to the same criteria as was LAI. The cone depth is set at .3 m which was the maximum effective depth Of the root systems on the Capac loam. A heavy clay layer prevented deeper root penetration (Figure 7) (112). The cone radius was assumed to be the tomato radius, or the bean radius in the direction perpendicular to the row. Soil water potential is computed from soil water content and bulk density. SWPOT a 42.61 x e (-15.27 x SWC/SOILBD) where: SOILBD a soil bulk density g cm‘3 SWC 3 volumetric soil water content SWPOT - soil water potential bar This function is for Wooster silt loam and is derived from data by Brady g£_§l, (16). It is an approximation for the Capac loam. Leaf water potential is computed from soil water potential. LFWPOTL - 2.61 x e (.161 x SWPOT) where: LFWPOTL = leaf water potential bar This function is for soybean (Glycine max) and is an approxi- mation for the two crOps modeled here. 34 O. m Ap very dark grayish brown loam .23 m .28 m B & A light olive brown loam B21t brown loam .38 m B22tg grayish brown clay loam .71 m B23t brown loam .81 m Cg grayish brown loam 1.52 m Figure 7. Soil profile of the somewhat poorly drained Capac loam. 35 The final step in WATER is to store variables for printing. The 1200 hr and 2400 hr values are placed into arrays through COMMON and are later printed as a block. All following subroutines act identically. 5. Photosynthesis and Respiration (Subroutine PHOTO) This subroutine computes bean and tomato plant net photo- synthetic rates in full sun and 60% shade. Photosynthate produced in grams of glucose for each species is the subroutine output (Figure 8). Dark respiration rates for the canOpies Of both species are calculated according to the method of Acock g£_§1,, (1) (Figure 9). The function is used identically for both species. Different respiration rates arise because of their different LAI. The equation integrates respiration over the entire leaf area Of the canOpy. A deficiency of this model is that the rate is unaffected by diurnal light changes. However, the average light flux density at the top of the canOpy over the most recent 7 days indicates recent photosynthetic history and is used to modify respiration rate. Stem, root and fruit respiration is assumed to be 1/3 of leaf respiration. Acock g£_§1,, found this to be a fair approximation (1). The total plant respiration is adjusted for temperature with Q10 = 2, and Optimum temperature set at 25 C (53). Photosynthetic rates for both species are calculated according to the same Acock, 35 31, model (1). The canOpy photosynthetic rate function is shown in Figure 10. The function is the same for both species. 36 c 3 Establish COMMON Compute leaf and structural respiration rates <:Compute photosynthetic rates Compute shaded photosynthetic rates :7 <7 Compute bean height and shadow length Compute tomato ground area in shade Compute bean canopy height in shade Compute photosynthate produced in full sun and shade portions l/IStore current values in arrays 7 ( Stop > Figure 8. System diagram of Subroutine PHOTO 37 .Awmma ..MMZMM xooo< amommv xmocmo OH053 Ono um>o emumquucH mums :oHumuHOOOO wmoa OumEOH oomumooo n >9 ucmumsoo u x9 DOOHOHLLOOO cosmmfiEmcmuu wmoa OumEou u mzoua Ono wcfiuav mooomo osu LO QOO ocu um uaoomwow zufimomv xaam ucwfia owmuo>m u wumzm>< AAH0mzm>< x rev + Amz< x why + Amzo twomuwouow Oumu OHuo;u:>moOoco OumEOe .oH ouswfim Hun Noe we monogamous coauausaaoa Osman Lama oumsoo I NHOHeae uGoaoawmooo :OHmmAEmomuu mama OumaoO u mzm0uonm OumEou cu ucoaumsnom u onH nommoa n moHHoua Ono wofiusv hooomo man we do» OLD um ucovfiooa aufimcmp xsam uanH owmuo>m n wumzm>< “muoc3 Hfim< x wummzm x NHAHHDH x me n th< x 49v + A>ummzm x NHAHHDH x Amz0. Add 25% to longterm storage Add balance to shortterm storage shortterm storage yes ).l x longterm storage [ Add 10% of longterm to shortterm storage Compute plant component demands for photosynthate Compute component increases Update component dry weights [/fStore current values in arrays/27 C Stop ) 49 The partitioning of carbohydrate to plant components is a major problem in plant modeling. Two general approaches have been taken. First, functions have been fitted to dry weight data accumulated over the growing season. This descriptive approach reveals nothing about the mechanism of partitioning and is somewhat season-specific. An example is the perennial ryegrass model of Sheehy, ggngl., (109). This approach is also used in MULTICROP. Regression equations of dry weight data are used in this subroutine to partition both bean and tomato photosynthate. The second approach is to attempt descriptions of carbohydrate flow rates between plant parts. Holt, g£_§l,, (53) used this approach in an alfalfa model, but scaled the maximum rates back with "maturity factors" which are again descriptive. A more complex model devoted entirely to dry matter distribution in sugar beet uses a primarily mechanistic approach, with partitioning rates controlled by the internal water supply and internal photosynthate supply (35). The most complex attempt to describe partitioning mechanistically is that of Thornley (117). The descriptive approach was used in this model due to its relative simplicity and reasonable accuracy. Further expansion and refinement of the model would benefit from inclusion of a mechanistic partitioning section. The component demand functions for carbohydrate used in parti- tioning are regressions fitted to field data (Figure 15). An exception is that for roots. The root : shoot ratio is an extremely complicated ‘relationship to which an entire model has been devoted (l8). 50 Figure 15. Equations used to partition carbohydrate between plant parts. 51 Bean demand functions: stem demand . 10(-.84 + .0466 DAY) fruit demand - 156.3 - 8.73 DAY + .122 DAY2 if DAY<37, fruit demand = 0. root demand if DAY<20, root demand - .16 (stem + leaf + fruit demands) 2149 root demand 8 .08 (stem + leaf + demands) Tomato demand functions leaf demand - .882 + .308 DAY + .0269 DAY2 stem demand . 1.17 + .274 DAY + .0161 DAY2 fruit demand - 47.3 - 5.10 DAY + .138 DAY2 if DAY40, root demand 8 .08 (stem + leaf + fruit demands) where: DAY 2 days after commencement Of the simulation 52 A simple method based on the data Of Richards, 25 gl., (103) and Gulmon, g; 31., (40) is used in this model. Root:shoot ratios ranging from .16 in young plants to .08 in Older plants are used. No attempt to modify these ratios in response to environmental factors is made in the model. The component demands are balanced so that their total is l. The carbohydrate available is allocated according to this balance, and reduced by 25% before being added to the component cumulative dry weights. Data by Penning de Vries, g; 21., (97) indicate a conversion efficiency of 75% in producing plant structure from glucose, assuming an adequate supply of NH3 and minerals. This value is also used else— where (53). The outputs of these subroutines are cumulative values of leaf, stem, root and fruit dry weight for bean and tomato plants. 7. Bean and Tomato Canopy Growth (Subroutine CANOPY) This ‘model converts leaf dry matter to leaf area and simulates bean and tomato canOpy growth. Four different modes of bean canOpy exapansion are modeled (Figure 16). Leaf dry matter is converted to leaf area by a regression Of ground area on leaf dry weight. Both functions are derived from growth data collected in 1980. BLFAREA .0241 BDRYLF -.0062 TLFAREA I .0243 TDRYLF -.0143 where: BDRYLF - bean leaf dry weight g BLFAREA = bean leaf area m2 TDRYLF = tomato leaf dry weight g TLFAREA 8 tomato leaf area m2 Figure 16. System design of Subroutine CANOPY 54 l Start > Establish COMMON If tomato-bean overlap at maximum yes Compute tomato ground area Compute tomato leaf area, LAI If bean radiZS‘B“~a. yes at maximum Compute bean ground area If bean-bean overlap yes at maximum If bean radius A at maximum s MODE 1 MODE 2 A MODE 3 ]\ MODE 4 ‘//Store current values in arrays 7 55 Bean and tomato canopy ground areas are also functions of their respective leaf dry weights. The functions are regressions derived from 1980 field data. 10 {-1.79 + .779 log BDRYLF) 10 (-l.62 + .685 log TDRYLF) BGRAREA - TGRAREA where: BGRAREA - bean canopy ground area m2 TGRAREA = tomato canOpy ground area m2 Tomato canopy ground area is assumed to be circular. Its radius is limited by either an absolute limit when no bean canOpy is contacted, or a maximum tomato-bean overlap of .1 m. Tomato LAI is computed from leaf and ground areas. Bean canOpy ground area changes shape during the season. Its maximum radius in the direction perpendicular to the row is a function of hexagon size, derived from 1980 field data. MBNRADB - .327 + .632 log TTDIST where: MBNRADB 8 maximum bean radius perpendicular to the bean row m TTDIST I tomato-tomato distance (hexagon width) m Bean canopy growth simulation is in 4 modes (Figure 17). a. Mode 1. Initial bean canOpy growth is assumed to be circular. The canOpy radii parallel to the row (BNRADA) and perpen- dicular to the row (BNRADB) are equal. The radius is derived from the ground area. NO bean-bean overlap occurs. b. Mode 2 Early overlap of neighboring bean canopies occurs in .mmtos cowumfisawm >romo :mom a moo: m mac: VOA VOA 56 /\ A. ’VV '00 cos zaocmo OumEOu wcfiommfiuo>o mmHuo>o zoom amHHO>o comm .ea adamam N moo: H one: O O O 57 Mode 2. Circular canOpy expansion continues until a maximum overlap of .05 m occurs. The duration Of the simulation in Modes 1 and 2 is highly dependent upon bean-bean distance. The overlap of neighboring bean canopies adds to LAI Of the single plant modeled. The model simulates this by computing the overlap area which is approximated as an ellipse (Figure 18). Dry leaf weight of the overlap area contributed by the neighboring plant is computed and added to the dry leaf weight of the modeled plant. Leaf distri- bution in the canopy is not evenly spread over the plant ground area. The LAI at the canopy edge is less than in its center. Therefore, the LAI of the neighboring plant added in the overlap is reduced by two-thirds. This method of increasing LAI due to the overlap is also followed in Modes 3 and 4. c. Mode 3. Upon expansion of BNRADA to its defined limit, the canOpy changes shape to an ellipse. Further advance in BGRAREA is in the BNRADB direction. d. Mode 4. Elliptical growth continues until the maximum tomato- bean overlap is just exceeded. In Mode 4, BGRAREA expan- sion has terminated and BDRYLF is added to the ground area occupied. Outputs of this subroutine are LAI and ground area. 58 Bean canopy Overlap area Elliptical approximation of overlap area Figure 18. Bean canopy overlap with approximating ellipses. 59 8. Bean and Tomato Yield (Subroutine YIELD) Bean and tomato fruit yields are computed in this subroutine. Plant populations are computed and various yield descriptions given (Figure 19). Fresh fruit is computed from fruit dry weight using regression functions derived from 1980 field data. TFRFRT - (50.5 + 18.9 TDRYFR) /1000. BFRFRT .014 BDRYFR where: BDRYFR = bean fruit dry weight g BFRFRT 8 bean fruit fresh weight kg TDRYFR - tomato fruit dry weight g TFRFRT a tomato fruit fresh weight kg Hexagon area, circumference and number per hectare are computed. This allows computation of bean and tomato pOpulations. Yield for each species and their sum is computed per hexagon, 2 m and hectare. These are the subroutine outputs. 9. Simulation Termination Upon completion Of the required number Of iterations, the program prints variable values as initially requested. The Operator is asked if further simulations are intended. If not, the program immedi- ately terminates. A positive response causes a return to an early point in DRIVER and initialization of variable values. The Operator may then run the program again. E. DATA COLLECTION FOR THE MODEL 1. Plant Growth Curves Growth curves were required for both species to partition carbohydrates in the model. Plants were grown as non-competing 60 C 3 Establish COMMON Compute fruit fresh weights per plant Compute hexagon area and plant populations Compute individual species yield per hexagon m-2 hectare Compute total intercropped yield per hexagon m—2 hectare Z/IStore current values in arraysl/z Figure 19. System diagram of Subroutine YIELD 61 individuals and harvested at regular intervals throughout the 1980 season. Tomato transplants (cv. PikRed) were started in VSP Peat Lite Mix in cell packs on April 28, 1980. After hardening-Off, they were transplanted into the field on May 25. The Capac loam soil was fer- tilized with 170 kg P 170 kg K20 and 113 kg N per hectare, and 205’ sprayed with .85 kg per hectare Treflan 1 week prior to planting. A 250 ml portion of 12:48:8 starter solution and .078% Chlordane was poured in each hole before putting in the transplant. The plants were not staked. Cultivation following transplanting was by hoe. A hexagonal planting design was used, which placed the plants lrnfrom each neighbor (Figure 20). Interplant competition was vir- tually absent at this distance. A total Of 100 plants were planted. Bean seeds (cv. Bush Blue Lake) were planted in the field on June 5. The field conditions were as above with the exception of the starter solution and Chlordane. Three seeds were planted in each of 100 positions in a .8 m hexagonal grid (Figure 20). The germinated seedlings were thinned to 1 plant per position. Entire plants of both species were harvested at 4 day intervals throughout the growing season, commencing June 23. Five randomly selected plants Of each species were measured for height and canopy diameter in 2 directions, at each harvest date. The plants were cut off at ground level, placed immediately in individually sealed plastic bags, and transported to a 2 C refrigerator. Leaf area was measured in a Lycor leaf area meter for each bean plant for the first 4 weeks. Leaves were removed from the petioles and their areas were measured. The plant parts were then dried and 62 Figure 20. Planting design for bean and tomato growth curve data. 63 weighed. After 4 weeks the plants were large and this Operation became prohibitively time-consuming. Tomato leaf area was measured similarly. After 2 weeks the number of plants measured was reduced to 1 due to the canopy size. All harvested plants were oven-dried at 70 C in a forced-air oven. Space in this oven was unavailable during the latter half of the season. During this time plants were stored at -5 C and dried as above when space became available. With the exception of those plants dissected for leaf area measurement, the plants were dried intact and separated into leaf, stem and fruit components prior to weighing. Curves were fitted to the data and plots made on the MINITAB interactive statistical analysis system (98). 2. Fruit Fresh Weight : Dry Weight Ratios Three 1.5 kg fresh samples of been fruits were dried to Obtain a fruit fresh weight : dry weight ratio. These samples con- tained a range of fruit sizes typical of those found in harvests from the validation experiments described later. Three samples each of 10 tomato fruit were dried to Obtain their fresh weight : dry weight ratio. Each sample was composed of a different fruit ripeness stage ranging from mature-green to firm-ripe. 3. Soil Water Content Gravimetric soil water measurements were taken on June 24 (one day after model commencement) and July 16. A .1 m diameter hand auger was used to take soil samples from each .1 m layer to a depth of 1.5 m. The samples were taken from 5 different positions across the field on each date. 64 Samples were removed from the auger and immediately enclosed in a sealed plastic bag. Their gravimetric water content was deter- mined by oven drying approximately 250 g samples at 70 C. 4. Preliminary Hexagon Trial A preliminary experiment was performed during the summer of 1979 to determine suitable hexagon sizes and bean spacings for the model. Three plots were planted with tomato plants placed hexagonally and encircled by hexagonal bean rows (Figure 21). Each harvested tomato plant and its associated bean hexagon was completely surrounded by guard hexagons. Each of the three plots was identical except for the hexagon width, which was .8, 1., or 1.2 m per plot. Within each plot, the harvested hexagons were planted with beans .05, .1, or .15 m apart within the row. Three hexagons were randomly allocated to each bean planting distance, giving a total Of 9 harvested hexagons per plot. Soil type and field preparation were as above. The beans were hand-harvested twice, and the plants were removed from the field after the second harvest. The tomatoes were hand-harvested 5 times. These planting distances were found to be suitable for the simulation. Reasons for this are discussed in the Results and Dis- cussion section. The hexagon dimensions and bean populations were retained for use in the 1980 validation experiment. F. FIELD VALIDATION Two field validation experiments were performed in 1980. Bean and tomato plants were grown in hexagons with various plant Populations and bean planting dates. 65 O = harvested tomato plant and associated bean hexagon C) a guard tomato plant and associated bean guard row -- = bean row Figurezfl.. Plot design for preliminary spacing experiments. in"; 1110. V'- ‘s .\" ‘V 8.. 66 1. Spacing Experiment A replicated spacing experiment was conducted to test the hypothesis that hexagon size and bean population had no effect on individual species yields. The design was a split plot in a 3 x 3 factorial randomized block design. The main plots were hexagon widths of .8, l. and 1.2 m. Since hexagons of differing widths cannot combine into a single matrix, whole plots were assigned to each main plot level (Figure 22). Split plots were 3 bean-bean planting distances at .05, .10 and .15 m. Three subsamples Of each subplot were planted (Figure 22). All main plots were of an identical design (Figure 23). Nine hexagons with bean and tomato for harvesting formed the center of each plot. At one end were 17 tomato plants without encircling bean hexagons. Three of these were harvested. At the Opposite end were bean hexagons without tomato. Each harvested hexagon of this group had a different bean spacing. A difficulty with the hexagonal design is the large number of guard hexagons required. This was minimized by bisecting the hexagon side which linked neighboring hexagons with different bean planting distances (Figure 24). Any error introduced was uniformly applied across the plot and assumed to be negligible. Field preparation and planting details were as stated above. Hand cultivation and harvesting were practiced. This and all other experiments received 3 overhead irrigations. A total of .065 m of water was applied. Beans were harvested and weighed on August 5 and 12. All the bean plants in the harvested hexagons and the guard rows were .2 a.“ 3: .4 s be 67 l replicate hexagon width - 1.2 m split plot individual hexagons .8m 3 subsamples Bean spacings €29 .05 m (:> .10 m €29 .15 m Figure 22. Schematic representation for spacing experiment. Main plot 68 Figure 23. Individual main plot design for spacing experiment. O O O O 0/0 0 O O O harvested tomato 0 O o O 0 without bean subsample with 3 O 0' bean-bean distancess guard bean and tomato plants ‘ O/ harvested bean and tomato plants harvested bean without tomato 0 \ 70 Figure 24: Segment Of spacing experiment plot showing interlocking of hexagons planted to different bean—bean distances in the row. beans planted .05 m apart beans planted .10 m apart beans planted .15 m apart 71 72 removed from the field during the second harvest. Care was taken during the harvests to minimize spatial dislocation of the two canopies. Heights and widths Of the two species' canopies were measured prior to the first harvest. Tomatoes were harvested at the 'breaker' stage on September 4. A second harvest on September 10 removed all remaining fruit regardless of size and ripeness. Reported yields are totals of all fruit from both harvests. Analysis of variance and Duncan's multiple range test were performed on the yield data (115). 2. Planting Date Experiment A replicated planting date experiment was conducted to test the hypothesis that varying the bean planting date would have no effect on individual species yield. The design was a split-split plot in a 3 x 3 x 3 factorial randomized block design (Figures 25,26). The main plots were hexagon widths Of .8, l. and 1.2 m. Split plots were bean-bean planting distances of .05, .l and .15 in. The split-split plots were 3 planting dates. All the tomato trans- plants were set on May 25. The first been planting was on June 5, with the others following on July 9 and August 8. Harvests of the first beans planted were on August 5 and 12. The second planting was harvested on September 5 and 11. Bean plants in the third planting date section were frozen and no yield data were collected. Statistical analysis was performed as for the spacing experiment. 73 fl 1 replicate main plot planting dates .8m (split-split plot) bean distance (split plot) a?» planting date l (:> planting date 2 m1) planting date 3 Figure 25. Schematic representation for planting date experiment. Split split plot with 3 planting dates 74 split plot with 1 bean-bean planting distance (3 harvested bean and l tomato plants I 1 0| 0 _ , o o .O Figure 26. Individual main plot design for planting date experiment. 75 G. MODEL SIMULATIONS Simulations were performed to ascertain the model's response to planting distances of both species. Tomato planting distance (hexagon size) was held constant at .8 m while bean-bean distance was varied between .05 and .15 m. The tomato distance was then increased to l. and 1.2 m and the process was repeated. This series Of simulations was repeated with the bean planting date moved later into the season. Simulations were run with planting distances in all combinations of .8, l. and 1.2 m between tomato plants and .05, .10 and .15 m between bean plants. The third series of simulations was to repeat the first series with weather data from two other years. A total Of 3 years' weather data were used. Finally, tomato-tomato distance was increased to 5 m and bean- bean distance to 2 m. This simulation was equivalent tO isolated tomato and bean plants. RESULTS AND DISCUSSION A. DATA COLLECTION 1. Plant Growth Curves The bean and tomato growth curve data appear with their fitted curves in Figures 27-32. The data show increasing leaf and stem dry weight up to the final harvest. This is because the plants were still vegetatively expanding. The tomato samples were more variable than were the bean samples. This resulted in lower R2 values for all the tomato parts. No outstanding reason for the greater tomato variability was apparent. The logarithmic bean leaf and stem growth functions reflected the short bean growing season. The longer growing tomato was adequately described by a quadratic equation. The fitted curves were incorporated into subroutines BNPART and TOMPART. Functions relating leaf area to leaf dry weight were derived from the leaf area and leaf dry weight data. Similar functions were derived for predicting canopy ground area from leaf dry weight. These functions appear in subroutine CANOPY. 2. Fruit Fresh Weight : Dry Weight Ratios Bean and tomato fruit fresh and dry weights, and their ratios, appear in Table 1. Due to the larger number of tomato data points, a function could be fitted to them rather than a simple ratio. The 76 LEAF DRY WEIGHT (G) 77 TIME (DAY) * = one datum point ’ - number of data at that position. Fitted curve: with: R2 - .95, adjusted for 79 d.f. Figure 27: Bean leaf dry weights with fitted curve. STEM DRY WEIGHT (G) 78 Fitted curve: Y = 10 (- TIME (DAY) .840 + .0466X) with: R2 = .96, adjusted for 79 d.f. Figure 28: Bean stem dry weights with fitted curve. FRUIT DRY WEIGHT (G) 79 TIME (DAY) Fitted curve: Y = 156.3 - 8.73x + .122x2 with: R2 = .97, adjusted for 34 d.f. Figure 29: Bean fruit dry weights with fitted curve. LEAF DRY WEIGHT (G) 80 TIME (DAY) Fitted curve: Y = .882 + .308x = .0269):2 with: R2 = .87, adjusted for 55 d.f. Figure 30: Tomato leaf dry weights with fitted curve. STEM DRY WEIGHT (G) 81 *** + --------- + --------- + --------- + ————————— + --------- + o. Is. 30. 4s. 60. 5. TIME (DAY) Fitted curve: Y = 1.17 = .274x + own2 with: R2 = .85, adjusted for 55 d.f. Figure 31: Tomato stem dry weight with fitted curve. FRUIT DRY WEIGHT (G) 82 400.+ 300.+ X 200.+ lOO.+ TIME (DAY) Fitted curve: Y = 47.3 - 5.10x + .138X2 with: R2 = .89, adjusted for 43 d.f. Figure 32: Tomato fruit dry weight with fitted curve. 83 Table 1. Bean and Tomato Fruit Fresh Weight:Dry Weight Ratios Fresh Weight Dry Weight Bean Sample (kg) (g) Ratio 1 1.22 87.55 .0139 2 1.79 123.75 .0145 3 1.76 129.50 .0136 Ave. Fruit Ave. Fruit Tomato Fruit Color Fresh Weight Dry Weight Ratio (kg) (3) Mature Green .1667 7.69 .0216 1/2 Red .2162 7.74 .0279 Firm Ripe .2753 11.42 .0241 84 functions used in the model appear in Materials and Methods on page 16. 3. Soil Water Content The gravimetric soil water content remained quite high by July 16, 1980. A diminution was apparent (Figure 33), but the upper .3 m retained 10-12% water. The WATER subroutine is not functioning cor- rectly in the present version of the model. It fails to increment soil moisture accurately. It appeared that soil moisture deficits had little impact on the validation experiment. This model deficiency probably did not significantly affect the simulated yields. 4. Preliminary Hexagon Trial Field space limitations prevented replication of this experi- ment. NO statistical analysis Of the data was performed. However, the experiment provided valuable information regarding the plant spacings selected (Table 2). Bean yield per plant more than doubled as bean~bean distance changed from .05 to .15 m. A slight response was observed with increased hexagon size and a constant bean-bean distance. Tomato yield was less consistent. Yields in all the .8 m hexagons and .05 m bean rows were smaller than those from the other spacings, which were indistinguishable. Visual Observations confirmed that canopy density, and there- fore, plant competition, varied noticeably across the plots. From these data and observations it was decided to retain the 9 spacing combinations used in the trial. 85 oomucoo amumz Hfiom OfiHDOEH>muo unmoooo neon: OHuuoaH>muu «H ma NH HH OH 1 I moon mafiamamm cocoon oumo wowaoamm umuam .mm muswfim A ca 933133 (deep mgT° uses) 86 .082 .088 .110 1.89 3.07 2.95 Table 2. Bean and Tomato Yield per Plant (kg) Bean distance (m) .05 .10 .15 Tomato .8 .047 .084 .114 distance (m) 1.0 .047 .092 .125 1.2 .054 .117 .159 .049 .098 .133 Bean yield per plant (kg) Bean distance (m) .05 .10 .15 Tomato .8 1.09 2.77 1.81 distance (m) 1.0 2.63 2.95 3.63 1.2 2.00 3.36 3.49 1.91 3.03 2.98 Tomato yield per plant (kg) 87 B. SIMULATION RESULTS Component and total pOpulations varied widely across the 9 possible spacing combinations (Table 3). Total population per hectare at .8 m tomato—tomato and .05 m bean-bean distances was 4.5 times that at 1.2 m and .15 m respectively. The component and total population variation profoundly influenced yield per unit area. 1. Spacing Simulation Component and total fruit yields appear in Tables 4-12. Simulation results from 9 planting distance combinations, and 4 bean starting dates, appear in Tables 4-7 (weather data from 1970) and Tables 9-12 (weather data from 1968). Results from a single series of 9 simulations with only the earliest bean starting date, and 1969 weather data, appear in Table 8. Results from 3 simulations with 5. m tomato- tomato distance and 2. m bean-bean distance using 1968-1970 weather data are in Table 13. The following discussion centers primarily on Tables 4—7, with 1970 weather data. The same trends are apparent in the 1968 results (Tables 9-12). a. Bean Fruit Yield. When tomato-tomato distance was held constant, bean yield per plant doubled as bean planting distance increased from .05 m to .15 m. This ratio was constant over the 4 been starting dates. When bean-bean distance was held constant, bean yield per plant increased approximately 50% as tomato-tomato distance increased from .8 m to 1.2 m. Maximum bean Tomato distance (m) Tomato distance (m) Table 3. BEAN POPULATIONS Per Hexagon 88 Component and Total Populations at Each Planting Distance Combination Bean distance (m) .05 .10 .15 .8 55 28 18 1.0 69 35 23 1.5 83 42 28 TOMATO POPULATIONS Per Hectare Bean distance (m) .05 .10 .15 .8 18043 18043 18043 1.0 11547 11547 11547 1.5 8019 8019 8019 Tomato distance (m) Tomato distance (m) Per Hectare Bean distance (m) .05 .10 .15 .8 500072 250036 166691 1.0 400058 200029 133353 1.5 333381 166691 111127 TOTAL POPULATIONS Per Hectare Bean distance (m) .05 .10 .15 .8 518115 268079 184733 1.0 411605 211576 144900 1.5 341400 174710 119146 89 Table 4. Component and Total Yields Simulated from 1970 Weather Data with Bean Planting Date - 1, Bean Harvest Date I 52. BEAN FRUIT WEIGHT (kg) Bean distance (m) E Q) 005 310 .15 8 g .8 .044 .066 .088 ...g '2’ 1.0 .055 .082 .109 It.) 2 112 .064 .097 .128 IS TOMATO FRUIT WEIGHT (kg) 1; Bean distance (m) Q) 005 010 015 9 g .8 1.878 2.001 2.064 .H 21.0 1.968 2.084 2.156 1.1 g 1.2 2.069 2.188 2.283 E-4 90 BEAN YIELD/UNIT AREA (kg m’z) Bean distance (m) g; .05 .10 .15 3 g .8 2.213 1.655 1.471 U) 8 1.0 2.196 1.649 1.456 0 § .1.2 2.127 1.612 1.419 O H TOMATO YIELD/UNIT AREA (kg m'z) Bean distance (m) g u .05 .10 .15 8 g .8 3.388 3.610 3.724 °H ': 1.0 2.273 2.407 2.490 1.; E 1.2 1.659 1.755 1.831 a COMBINED YIELD/UNIT AREA (kg m'Z) ’E‘ Bean distance (m) u .05 .10 .15 8 g ,8 5.601 5.265 5.194 n-l ': jug 4.469 4.056 3.746 H g jflz 3.786 3.366 3.250 [-1 Table 5. 91 Component and Total Yields Simulated from 1970 Weather Data with BEANPLANT - 10, HARVEST - 62 BEAN FRUIT WEIGHT (kg) Tomato distance (m) Bean distance (m) 1.0 1.2 .05 .10 .15 .043 .064 .085 .053 .080 .106 .062 .092 .125 TOMATO FRUIT WEIGHT (kg) Tomato distance (m) Bean distance (m) 1.0 1.2 .05 .10 .15 1.684 1.762 1.816 1.883 1.994 2.087 1.996 2.169 2.247 BEAN YIELD/UNIT AREA (kg m'z) Bean distance (m) .05 .15 .15 .8 2.127 1.607 1.422 1.0 2.107 1.595 1.408 1.2 2.055 1.539 1.389 Tomato distance (m) TOMATO YIELD/UNIT AREA (kg m'z) Bean distance (m) .05 .10 .15 .8 3.038 3.179 3.277 1.0 2.174 2.303 2.410 1.2 1.601 1.739 1.801 Tomato distance (m) COMBINED YIELD/UNIT AREA (kg m‘z) Bean distance (m) .05 .10 .15 .8 5.165 4.785 4.699 1.0 4.280 3.898 3.818 1.2 3.655 3.278 3.190 Tomato distance (m) 92 Table 6. Component and Total Yields Simulated from 1970 Weather Data with BEANPLANT - 20, HARVEST - 72 BEAN FRUIT WEIGHT (kg) BEAN YIELD/UNIT AREA (kg m-Z) A Bean distance (m) A Bean distance (m) E E " .05 .10 .15 " .05 .10 .15 3 , 8 g .8 .037 .056 .074 E .8 1.858 1.392 1.234 (D U) 23 1.0 .046 .069 .092 S 1.0 1.841 1.383 1.222 O 0 g 1.2 .053 .080 .107 g 1.2 1.783 1.333 1.191 O O H H TOMATO FRUIT WEIGHT (kg) TOMATO YIELD/UNIT AREA (kg m-Z) ABean distance (111) A Bean distance (m) E E " .05 .10 .15 " .05 .10 .15 8 8 § .8 1.518 1.577 1.669 g .8 2.738 2.846 3.011 U) U) 43 1.0 1.829 1.908 1.980 13 1.0 2.112 2.204 2.286 O 0 g 1.2 2.126 2.256 2.315 g 1.2 1.705 1.809 1.856 O O E-4 E-0 COMBINED YIELD/UNIT AREA (kg m-Z) 3% Bean distance (m) § .05 .10 .15 g .8 4.596 4.237 4.246 1; 1.0 3.952 3.587 3.508 E 1.2 3.488 3.142 3.047 Table 7. 93 Component and Total Yields Simulated from 1970 Weather Data with BEANPLANT I 30, HARVEST 8 82 BEAN FRUIT WEIGHT (kg) Bean distance (m) E .05 .10 .15 8 g .8 .028 .042 .055 0) 33 1.0 .034 .052 .069 O ‘3 1.2 .040 .060 .080 g E-4 TOMATO FRUIT WEIGHT (kg) Bean distance (m) E " .05 .10 .15 8 g .8 1.460 1.863 2.007 (D 36' 1.0 1.706 1.974 2.171 0 ‘§ 1.2 2.115 2.228 2.365 O [-4 BEAN YIELD/UNIT AREA (kg m'z) Tomato distance (m) Bean distance (m) 1.0 1.2 .05 .10 .15 1.391 1.045 .922 1.374 1.035 .916 1.333 1.005 .885 TOMATO YIELD/UNIT AREA (kg m'z) Tomato distance (m) Bean distance (m) 1.0 1.2 .05 .10 .15 2.635 3.361 3.621 1.970 2.279 2.507 1.696 1.787 1.896 COMBINED YIELD/UNIT AREA (kg m-z) Tomato distance (m) Bean distance (m) 1.0 1.2 .05 .10 .15 4.205 4.406 4.543 3.345 3.314 3.423 3.030 2.791 2.781 Table 8. BEAN FRUIT WEIGHT (kg) Tomato distance (2) Bean distance (m) o on H O O H O N .05 .10 .15 .042 .062 .084 .052 .078 .104 .061 .091 .121 TOMATO FRUIT WEIGHT (kg) Tomato distance (m) Bean distance (m) o 00 H O O H O N .05 .10 .15 2.091 2.228 2.303 2.177 2.305 2.393 2.270 2.404 2.512 94 Component and Total Yields Simulated from 1969 Weather Data with BEANPLANT - l, HARVEST = 52 BEAN YIELD/UNIT AREA (kg m‘z) Tomato distance (m) Bean distance (m) H . O O O (D H N .05 .10 .15 2.093 1.562 1.395 2.075 1.552 1.381 2.021 1.520 1.342 TOMATO YIELD/UNIT AREA (kg m‘z) Tomato distance (m) Bean distance (m) o (D H I O H O N .05 .10 .15 3.773 4.021 4.154 2.514 2.661 2.763 1.820 1.928 2.014 COMBINED YIELD/UNIT AREA (kg m‘z) Tomato distance (m) Bean distance (m) H O O O (I) H O N .05 .10 .15 5.866 5.583 5.549 4.589 4.213 4.145 3.841 3.448 3.356 Table 9. 95 Component and Total Yields Simulated from 1968 Weather Data with BEANPLANT ' l, HARVEST = 52 BEAN FRUIT WEIGHT (kg) Bean distance (m) E 8 .05 .10 .15 a 3 .8 .032 .047 .063 U) ,4 'U 1.0 .039 .058 .079 o 1g 1.2 .046 .069 .092 is TOMATO FRUIT WEIGHT (kg) ,‘ Bean distance (m) e m .05 .10 .15 (a) 3 .8 2.227 2.381 2.469 U) 'H ’U 1.0 2.327 2.480 2.573 o H g 1.2 2.444 2.587 2.696 o [-1 BEAN YIELD/UNIT AREA (kg m‘z) Tomato distance (m) Bean distance (m) 1.0 1.2 .05 .10 .15 1.585 1.187 1.046 1.560 1.170 1.053 1.526 1.146 1.018 TOMATO YIELD/UNIT AREA (kg m'Q) Tomato distance (m) Bean distance (m) 1.0 1.2 1.0 .05 .10 .15 4.017 4.296 4.455 2.687 2.864 2.971 1.960 2.074 2.162 COMBINED YIELD/UNIT AREA (kg m'z) Bean distance (m) .05 .10 .15 5.603 5.483 5.502 4.247 4.034 4.024 3.486 3.221 3.180 Tomato distance (m) 1.2 96 Table 10. Component and Total Yields Simulated from 1968 Weather Data with BEANPLANT 8 10, HARVEST - 62 BEAN FRUIT WEIGHT (kg) BEAN YIELD/UNIT AREA (kg m72) Bean distance (m) Bean distance (m) E ’8 m .05 .10 .15 m .05 .10 .15 8 E 3 .8 .039 .059 .079 :3 .8 1.955 1.468 1.310 "3 01 fl -H 1’ 1.0 .049 .073 .098 '9 110 1.942 1.460 1.302 0 o U E 1.2 .057 .086 .114 t$1.2 1.892 1.427 1.270 2‘3 .2 TOMATO FRUIT WEIGHT (kg) TOMATO YIELD/UNIT AREA (kg m‘z) 1; Bean distance (m) ,\ Bean distance (m) .5 £5 Q) 005 010 .15 Q) 005 .10 015 8 8 3 .8 1.144 1.199 1.216 ‘3 .8 2.064 2.164 2.194 m U) 7" H '2 1.0 1.284 1.362 1.402 '9 1,0 1.483 1.573 1.619 O u u g 1.2 1.369 1.481 1.554 g 1.2 1.098 1.188 1.246 O 9 Ed COMBINED YIELD/UNIT AREA (kg m'z) Bean distance (m) H o o O “3 Tomato distance (m) H N .05 .10 .15 4.019 3.632 3.504 3.424 3.033 2.921 2.990 2.615 2.516 97 Table 11. Component and Total Yields Simulated from 1968 Weather Data with BEANPLANT = 20, HARVEST = 72 BEAN FRUIT WEIGHT (kg) BEAN YIELD/UNIT AREA (kg m‘z) f\ Bean distance (m) f‘ Bean distance (m) E S E .05 .10 .15 E .05 .10 .15 § .8 .023 .034 .045 § .8 1.142 .855 .757 53 1.0 .028 .042 .057 4'”: 1.0 1.136 .849 .755 g 1.2 .033 .050 .066 g 1.2 I) 1.105 .828 .733 E-' E-‘ TOMATO FRUIT WEIGHT (kg) TOMATO YIELD/UNIT AREA (kg m'z) 3% Bean distance (m) 1; Bean distance (m) g .05 .10 .15 E .05 .10 .15 +3 .8 1.084 1.126 1.217 E .8 1.956 2.031 2.195 E 1.0 1.313 1.389 1.422 § 1.0 1.516 1.604 1.642 :3: 1.2 1.521 1.627 1.681 ‘3 1.2 1.219 1.304 1.348 B 8 COMBINED YIELD/UNIT AREA (kg m'z) Bean distance (m) 3 «g .05 .10 .15 g; .8 3.098 2.886 2.952 E 1.0 2.652 2.452 2.396 g 1.2 2.324 2.132 2.081 E4 Table 12. 98 Component and Total Yields Simulated from 1968 Weather Data with BEANPLANT ' 30, HARVEST - 82 BEAN FRUIT WEIGHT (kg) Bean distance (m) 3 8 .05 .10 .15 :1 § .8 .015 .022 .030 0H '2 1.0 .018 .028 .037 U S 1.2 .021 .032 .043 a TOMATO FRUIT WEIGHT (kg) 1; Bean distance (m) o .05 .10 .15 8 g .8 1.276 1.611 1.721 '1-4 '2 110 1.440 1.806 1.929 H g 1.2 1.680 1.853 2.074 E-‘ BEAN YIELD/UNIT AREA (kg m‘2> 1.0 1.2 Tomato distance (m) Bean distance (m) .05 .10 .15 .743 .553 .492 .733 .550 .488 .712 .537 .475 TOMATO YIELD/UNIT AREA (kg m'z) 1.0 1.2 Tomato distance (m) Bean distance (m) .05 .10 .15 2.303 2.908 3.106 1.663 2.085 2.228 1.307 1.486 1.663 COMBINED YIELD/UNIT AREA (kg m'z) Bean distance (m) 1.0 1.2 Tomato distance (m) .05 .10 .15 3.046 3.461 3.598 2.396 2.635 2.716 2.059 2.023 2.138 99 Table 13. Bean and Tomato Fruit Yields over 3 Different Years' Weather Data. Planting Distances Were 5 m Tomato-Tomato and 2 m Bean-Bean Year Bean Yield Per Plant (kg) Tomato Yield Per Plant (kg) 1968 .578 2.803 1969 .675 2.984 1970 .723 3.742 100 yield per plant was at 1.2 m and .15 m tomato and bean spacings. Bean yield per hexagon is confounded with the rapidly increasing bean pOpulation as hexagon width changes from .8 m to 1.2 m. Yields per hexagon are not reported here. Rather, yields per m2 are reported because this relation- ship simultaneously accounts for changes in both pOpulation and area. Yield per hexagon deals only with population changes. Maximum bean yield per 1112 occurred at .8 m and .05 m tomato and bean planting distances. This was exactly Opposite to the individual plant optimal spacing. Individual plant yield maxima peaked where intra- and inter- specific competition was at a minimum. Yield per unit area peaked where the competition was at a maximum. b. Tomato Fruit Yield. Maximum individual plant yield occurred with the most distant plant spacings. The relative yield increase when moving from .8 m and .15 m to 1.2 m and .15 m tomato and bean spacings was less than the relative bean yield increase over the same changes in planting distance. Maxi- mum tomato yield per m2 was at .8 m and .15 m tomato and bean distances. The bean canOpy was not as tall with the .15 m spacing as with the .05 m spacing. Diminished shading from the .15 m spacing canOpy allowed greater yield. 101 2. Model Response to Weather Changes The model responded to differences in weather data. Yields generated using weather data from 1969 and 1968 appear in Tables 8 and 9-12, respectively. A comparison using the earliest bean commence- ment date is obtained by comparing Tables 4, 5 and 9. The two species responded differently to the weather changes. Bean yields in descending order were from 1970, 1969 to 1968. Tomato yields in descending order were from 1968, 1969 to 1970, which was the opposite direction to the bean. The tomato pattern was not the same when the plants were simulated as free-standing individuals (Table 13). In this case, the tomato yields paralleled the bean yields in descending order over 1970, 1969 to 1968. This reversal is largely due to the dominance of the bean canopy in the model caused by the bean shading effects. When the bean grew relatively better, tomato growth was diminished. C. VALIDATION RESULTS AND SIMULATION COMPARISON l. Spacing Experiment. Bean yields per plant differed significantly (1% level) as a result of changes in either bean-bean or tomato-tomato distances. Bean yield per unit area differed significantly (1% level) only in response to change in bean-bean spacing (Table 14). The mean separations of yield per unit area revealed no difference due to changes in tomato- tomato distance. However, there was a trend toward increased yield with .8 m hexagons (Table 15). 102 Table 14. Analysis of Variance (ANOVA) of the Spacing Experiment Bean Yields ANOVA OF BEAN YIELD (WEIGHT PER PLANT) Source of variation gf. Mean square ‘E Replication 2 .00015 .579 Tomato spacing 2 .00717 27.145** Error a 4 .00026 Bean spacing 2 .0244 81.078** Tomato spacing x Bean spacing 4 .00008 .262 Error b 12 .00030 ANOVA OF BEAN YIELD (WEIGHT PER UNIT AREA) Source of variation g§_ Mean square ‘E Replication 2 .062 .540 Tomato spacing 2 .261 2.284 Error a 4 .114 Bean spacing 2 .709 l7.873** Tomato spacing x Bean spacing 4 .079 1.991 Error b 12 .040 103 Table 15. Spacing Experiment Bean Fruit Yields with Averages Separated by the Duncan's Multiple Range Test WEIGHT PER PLANT (kg) Bean distance (cm) 5 10 15 Tomato 80 .042 .074 .099 .072c distance 100 .057 .091 .12 .089b (cm) 120 .069 .11 .13 .103a .056c .092b .116a WEIGHT PER UNIT AREA (kg m‘z) Bean distance (cm) 5 10 15 Tomato 80 1.68 1.64 1.51 l.6la distance (cm) 100 1.86 1.52 1.34 1.57a 120 1.58 1.41 1.29 1.43a 1.71a 1.52b 1.38c 104 Tomato yields per plant differed significantly (1% level) as a result of changes in either bean-bean or tomato-tomato distances. Tomato yields per unit area differed significantly (5% level) only in response to changes in bean-bean spacing (Table 16). The mean separa- tions confirmed the lack of difference in yield per unit area over different tomato-tomato distances (Table 17). Comparisons of the simulation and validation yields appear in Tables 18-20. Simulated bean yields per plant from 1970 weather data closely paralleled validation yields at each spacing combination. A Chi2 test to assess the goodness of fit of the two data sets was significant at the 1% level. The goodness of fit of simulation and validation bean yields per unit area was not significant. This was contributed to by errors in bean populations in the field. Cold, wet weather during bean ger— mination, followed by warm, dry weather and soil surface crusting, led to uneven germination across the plots. Some plots did not reach their specified population levels. The validation did support the prediction of maximum bean yield at the .05 m bean-bean distance (Table 15). Simulation tomato yields per plant followed the same direction as the validation, but were not significant (Table 19). Tomato yields per plant doubled from minimum to maximum levels in the field, but increased only 18% in the simulation. However, the range of simulated yields was similar to the validation results. Simulation and validation yields per unit area for tomato showed similar trends,but again were not significant (Table 19). As bean-bean distance increased, tomato yields also increased. However, 105 Table 16. Analysis of Variance (ANOVA) of the Spacing Experiment Tomato Yields ANOVA OF TOMATO YIELD (WEIGHT PER PLANT) Source of Variation g§_ Mean square ‘5 Replication 2 1.030 1.296 Tomato spacing 2 21.797 27.425** Error a 4 .795 Bean spacing 2 2.651 8.163** Tomato x Bean spacing 4 .410 1.262 Error b 12 .325 ANOVA OF TOMATO YIELD (WEIGHT PER UNIT AREA) Source of Variation gf_ Mean square ‘E Replication 2 .823 .828 Tomato spacing 2 2.482 2.497 Error a 4 .994 Bean spacing 2 3.407 5.533* Tomato x Bean spacing 4 .348 .566 Error b 12 .616 Table 17. Tomato distance (cm) Tomato distance ( cm) 106 Spacing Experiment Tomato Fruit Yields with Averages Separated by the Duncan's Multiple Range Test WEIGHT PER PLANT (kg) Bean distance (cm) 5 10 15 80 1.55 1.90 1.82 100 1.79 2.27 2.62 120 3.05 3.89 3.53 2.12b 2.69a 2.66a WEIGHT PER UNIT AREA (kg m‘z) Bean distance (cm) 5 10 15 80 2.80 3.43 3.28 100 2.07 2.62 3.02 120 2.45 3.12 2.83 2.44b 3.06a 3.04a 1.76b 2.23b 3.49a 3.17a 2.57a 2.80a 107 Table 18. Bean Fruit Yields per Plant and per Unit Area from Simulations and the 1980 Spacing Validation Experiment SIMULATION (1970 weather) VALIDATION (1980) Fruit weight/plant (kg) Fruit weight/plant (kg) ,\ Bean distance (m) Bean distance (m) E A e, a 0 .05 .10 .15 T’ .05 .10 .15 8 8 3 .8 .044 .066 .088 g .8 .042 .074 .099 co 1.: oa m '° 1.0 .055 .082 .109 33 1.0 .057 .091 .12 o u o g 1.2 .064 .097 .128 g 1.2 .069 .11 .13 a 5 Fruit weight/unit area (kg m-z) Fruit weight/unit area (kg m'z) Bean distance (m) Bean distance (m) 19‘ E a .05 .10 .15 m .05 .10 .15 8 8 B .8 2.213 1.655 1.471 3 .8 1.68 1.64 1.51 U) a: fi -H '° 1.0 2.196 1.649 1.456 ‘2 1.0 1.86 1.52 1.34 O u U E 1.2 2.127 1.612 1.419 g 1.2 1.58 1.41 1.29 O O H 'r-t PP; ..< 3),“ AEV 7L 3 3: Cu 16.——. vi 3 a 2:: i. E «Ev 3.12—1‘2: T 3 u .251. 108 Table 19. Tomato Fruit Yields per Plant and per Unit Area from Simulations and the 1980 Spacing Validation Experiment SIMULATION (1970 weather) VALIDATION (1980) Fruit weight/plant (kg) Fruit weight/plant (kg) ,\ Bean distance (m) f\ Bean distance (m) E E m .05 .10 .15 m .05 .10 .15 8 8 3 .8 1.878 2.001 2.064 3 .8 1.55 1.90 1.82 (0 U} "-4 "-4 '0 1.0 1.968 2.084 2.156 'U 1.0 1.79 2.27 2.62 o o H 21.2 2.069 2.188 2.283 E 1.2 3.05 3.89 3.53 a 8 Fruit weight/unit area (kg m'z) Fruit weight/unit area (kg m'z) ,\ Bean distance (m) ,\ Bean distance (m) S E m .05 .10 .15 w .05 .10 .15 8 8 3 .8 3.388 3.610 3.724 3 .8 2.80 3.43 3.28 U) U} ca ~H '9 1.0 2.273 2.407 2.490 'U 1.0 2.07 2.62 3.02 o o E 142 1.659 1.755 1.831 g 1.2 2.45 3.12 2.83 o o B H 109 variability in the field reduced the clarity of this comparison as it did with the bean. In this case only one plant was involved in each hexagon, so the specified pOpulation was always met. The variability was, therefore, entirely in plant growth. Total yields of both species per unit area appear in Table 20. The simulation predicted a maximum total yield at .8 m and .05 m tomato and bean distances. This was due to the overriding influence of the bean yields. The validation results were too random to be useful. However, given the results for each species previously presented, it seems reasonable to expect that maximum yields in this system would come from .8 m and .05 m tomato and bean distances, out of the spacing combinations tested. 2. Planting Date Experiment Bean yields per plant and per unit area differed significantly (1% level) as a result of changes in either bean-bean distance or planting date (Tables 21, 22). Tomato-tomato distance was significant in neither case, as in the spacing experiment (Table 14). The early bean planting produced significantly higher yields than did the later planting (Tables 23, 24). Tomato yields per plant and per unit area differed significantly (5% level) as a result of changes in planting date. Yields per unit area also differed significantly (1% level) as a result of changes in tomato- tomato distance. Tomato x bean distances and planting date x bean distance interactions were both significant at the 1% level in the yields per plant and per unit area (Table 25). 110 Table 20. Total Yields per Unit Area from Simulations and the 1980 Spacing Validation Experiment SIMULATION (1970 Weather) Fruit Weight/Unit Area (kg m“2) Bean distance (m) .05 .10 .15 .8 5.165 4.785 4.699 1.0 4.280 3.898 3.818 1.2 3.655 3.278 3.190 Tomato distance (m) VALIDATION (1980) Fruit Weight/Unit Area (kg m'z) Tomato distance (m) 1.0 'Bean distance (m) .05 .10 .15 4.48 5.07 4.79 3.93 4.14 4.36 4.03 4.53 4.12 1.2 111 Table 21. Analysis of Variance (ANOVA) of the Planting Date Experiment Bean Yields per Plant. ANOVA OF BEAN YIELD (WEIGHT PER PLANT) Source of Variation d§_ Mean Square ‘F Replication 2 .00056 .348 Tomato 2 .00349 2.188 Error 3 4 .00159 Bean 2 .01807 38.184** Tomato x Bean 4 .00049 1.033 Error b 12 .00047 Planting 1 .02160 35.453** Planting x Tomato 2 .00060 .985 Planting x Bean 2 .00136 2.225 Planting x Tomato x Bean 4 .00021 .337 Error c 18 .00061 112 Table 22. Analysis of Variance (ANOVA) of the Planting Date Experiment Bean Yields per Unit Area ANOVA OF BEAN YIELD (WEIGHT PER UNIT AREA) Source of Variation df_ Mean Square .E Replication 2 .028 .101 Tomato 2 .042 .151 Error a 4 .278 Bean 2 1.361 13.858** Tomato x Bean 4 .111 1.131 Error b 12 .098 Planting 1 1.245 13.299** Planting x Tomato 2 .206 2.198 Planting x Bean 2 .158 1.691 Planting x Tomato x Bean 4 .106 1.135 Error c 18 .094 113 Table 23. Planting Date Experiment Bean Fruit Yields per Plant with Averages Separated by the Duncan's Multiple Range Test. Tomato distance (m) Tomato distance (m) 1.0 1.2 1.0 1.2 WEIGHT PER PLANT (kg) Bean distance (m) .05 .10 .15 .077 .13 .13 .070 .12 .16 .087 .12 .18 Bean distance (m) .05 .10 .15 .043 .053 .080 .063 .073 .11 .067 .093 .13 .068c .098b .132a Date .1l9a Date .086a 2 .099a .0796 .113a 114 Table 24. Planting Date Experiment Bean Fruit Yields per Unit Area with Averages Separated by the Duncan's Multiple Range Test. WEIGHT PER UNIT AREA (kg m‘z) ,\ Bean distance (m) {E .05 .10 .15 Date E .8 2.47 2.09 1.28 l 33 1.0 2.04 1.70 1.65 1;§9a g 1.2 1.86 1.57 1.51 E53 ’\ Bean distance (m) {E .05 .10 .15 Date E .8 1.73 1.22 1.30 1.68a 2 :3 1.0 1.79 1.33 1.48 1.673 IL42b g 1.2 1.85 1.39 1.36 1.59a o E5 1.96a 1.55b 1.43b 115 Table 25. Analysis of Variance (ANOVA) of the Planting Date Experiment Tomato Yields. Bean 8 beanrbean distance Plant - bean planting date Tomato - tomato-tomato distance 116 ANOVA OF TOMATO YIELD (WEIGHT PER PLANT) Source of Variation d§_ Mean square .E Replication 2 1.872 .899 Tomato 2 1.982 .951 Error a 4 g 2.083 Bean 2 .326 .961 Tomato x Bean 4 3.550 10.452** Error b 12 [.340 Planting 2 3.119 3.508* Planting x Tomato 4 .433 .487 Planting x Bean 4 3.741 4.208** Planting x Tomato x Bean 8 .406 .457 Error c 36 ANOVA OF TOMATO YIELD (WEIGHT PER UNIT AREA) Source of Variation d2. Mean square ‘3 Replication 2 2.576 1.211 Tomato 2 40.960 19.256** Error a 4 2.127 Bean 2 .602 1.408 Tomato x Bean 4 3.370 7.888** Error b 12 .427 Planting 2 6.188 4.299* Planting x Tomato 4 2.050 1.424 Planting x Bean 4 6.175 4.290** Planting x Tomato x Bean 8 .983 .683 Error c 36 117 Table 26. Tomato Fruit Yields per Plant in the Planting Date Experiment, with Averages Separated by the Duncan's Multiple Range Test* Plant Date 3 Bean distance (m) 1 g .05 .10 .15 :3 .8 1.54aAY 1.31aAY 1.76aAy “U 1.0 1.70aAY 1.43an 1.18an 3 1.2 1.70aAXY 1.01aAY 2.31an 8 6* Meanl 1.65 1.25 1.75 Plant Date 5Bean distance (m) 2 g _ .05 .10 .15 § .8 1.56aAY 2.35an 2.78an g 1.0 1.59aAY 2.03an 1.09an 3 1.2 .94aAY 2.44an 3.15an E aMeanl 1.36 2.27 2.34 Plant Date EgBean distance (m) 3 g .05 .10 .15 g .8 3.45an 2.61an 1.92aAXY E 1.0 3.44an 1.51an .59be 2 1.5 2.21an 1.49aAXY 2.75an U 1 gueanl 3.03 1.87 1.75 E-i MEAN YIELD AT CONSTANT TOMATO AND BEAN DISTANCE ACROSS PLANTING DATE {éBean distance (m) § .05 .10 .15 E .8 2.18 2.09 2.15 'U 1.0 2.24 1.66 .95 8 1.2 1.62 1.65 2.74 E 0 [-¢ *A, B, C - bean distance a, b, c - tomato distance X, Y = planting date lMean yield at constant bean spacing within planting date. 118 Table 27. Tomato Fruit Yields per Unit Area in the Planting Date Experiment, with Averages Separated by the Duncan's Multiple Range Test* Plant date :gBean distance(m) l g .05 .10 .15 g .8 2.78aAY 2.37aAY 3.17aAY z; 1.0 1.97aAY 1.6SaAX 1.36aAX 3 1.2 1.47any .81aAX 1.86aAX m §Meanl 2.07 1. 61 2 .13 Plant date {EBean distance(m) 2 § .8 2.81bAY 4.25abAX 5.01an ‘5 1.0 1.83aABY 2.35aBX 1.25aBX '° 1.2 .75aBY 1.96aBX 2.53aBX o 'éMeanl 1.80 2.85 2.93 0 Plant date ::Bean distance(m) 3 {E .05 .10 .15 g .8 6.19aAX 4.70abAX 3.47bAY m g 1.0 5.82aAX 1.74bBX .68bBX :3 1.2 2.21aBX 1.20aBX 2.21aABX o ‘§Mean1 4.74 2.55 2.12 o [-4 MEAN YIELD AT CONSTANT TOMATO AND BEAN DISTANCE ACROSS PLANTING DATE ngean distance(m) *A, B, C a bean distance a, b, c - tomato distance X, Y - planting date 3 .05 .10 .15 § .8 3.93 3.77 3.88 .g 1.0 3.21 1.91 1.10 '2 1.2 1.48 1.32 2.20 U (U E O 9' 1-Mean yield at constant bean spacing within planting date. 119 3. 2 + ‘I ’63 65 ’U H OJ °H >11.» 0. . A .05 .1 .15 Bean-bean distance (m) I 8 .8 m tomato-tomato distance . 8 l. m H H ll 8 1.2 m Figure 34. Tomato yields per plant in the planting date experiment. Data are means of yields at constant tomato and bean distances across planting dates. Refer Table 26. 120 (kg) Yield 1 2 3' Planting date I - .05 m bean-bean distance ...lm II N 4‘,- .15 m Figure 35. Tomato yields per plant in the planting date experiment. Data are means of yields at constant bean spacing within planting date. Refer Table 26. Figure 36. 121 fl 3. P ’63 :‘5 2. p -o H m H >4 1. b 0. A A .05 .l .15 Bean-bean distance (m) I = .8 m tomato-tomato distance |} = 1. m " A - 1.2m H H Tomato yields per unit area in the planting date experiment. Data are means of yields at constant tomato and bean distances across planting dates. Refer Table 27. 122 0. * 1 2 3 Planting Date I - .05 m bean-bean distance 0 ‘ 3 015m H H .1 m N H Figure 37. Tomato yields per unit area in the planting date experiment. Data are means of yields at constant bean spacing within planting date. Refer Table 27. 123 Tomato yields per plant and per unit area are shown in Tables 26 and 27. Means across planting dates with tomato and bean distances held constant, and across tomato distance with planting date and bean distance held constant, are also shown. These means assist interpre- tation of the interactions, and are presented graphically in Figures 34-37. The tomato x bean planting distances interaction from the tomato yield per plant ANOVA is depicted graphically in Figure 34. Yields were constant with .8 m tomato-tomato distance as bean-bean distance increased. However, with l. and 1.2 m tomato-tomato distance the yields decreased and increased respectively as bean-bean distance increased. Similar results occurred in yield per unit area (Figure 36). The bean planting date x bean-bean distance interaction from the tomato yield per unit area is depicted graphically in Figure 35. Yield per plant increased with the third planting date when bean-bean distance was held constant at .05 m (Figure 35). No such increase occurred with the two larger bean-bean distances. Similar trends occurred with this interaction in tomato yield per unit area (Figure 37). The reasons for these interactions are not clear. No tomato x bean distance interaction occurred in the spacing experiment. Its presence in the planting date experiment may have been an aberration unique to that season. Further field experiments would be required to establish its importance. A partial explanation of the tomato yield increase in the .05 m bean-bean distance hexagons in planting date 3 is possible. The beans were killed by frost damage in this final planting. Also, the intense bean competition did not occur until late in the season, and was “a; Avis 124 diminished by tomato shading of the bean canopy. The reduction in bean competition may have had a relatively greater effect with the .05 m bean-bean than with the lower competition from the wider bean spacings. A comparison of simulation and validation bean yields across 2 planting dates appears in Table 28. Yield reductions in weight per plant as planting date was retarded were 37% and 33% in the simulation and validation yields respectively. The yield reductions in weight per unit area as planting date was retarded were 37% and 17% in simulation and validation yields respectively. Comparisons of simulation and validation yields for tomato are not made here because of the interactions. These results reconfirm that the canopy shading section of the model has captured a significant part of the real system. Without that section, neither the changes in planting distances nor bean planting dates would have caused the simulation yield changes reported. 125 Table 28. Comparison of Average Yield for All Plant Spacing Combinations at each of 2 Bean Planting Dates. Simulated Yields Were for 1970 Weather Data, and BEANPLANT - 1 or 30 Average Yield for All Planting Combinations I - Planting Weight/Plant (kg) Weight/Unit Area (kg m 2) Date Simulation Validation Simulation Validation 1 .081 .119 1.76 1.80 30 .051 .080 1.10 1.49 126 SUMMARY AND CONCLUSIONS The Optimum plant spacings,cfi?those combinations tested, for maximizing bean and total yields were .05 m bean-bean and .8 m tomato- tomato distances. Maximum tomato yields were at .15 m bean-bean and .8 m tomato-tomato distances. These maximum yields occurred with the earliest bean planting date. It has not been shown whether closer spacings would produce greater yields. However, it is expected that the above spacings are close to the ideal for maximum yields. The model performs well in its present form. Simulation and validation bean yields were significant at the 1% level. Although all other results were not significant, the simulation results were of the same order of magnitude and moved in similar directions to the validation yields. Further work with the model is required for increased accuracy. The soil water subroutine is not functioning correctly and needs reworking. No attempt has been made to model root competition for water and nutrients. This area is vital to overall competition. Tomato shading of the beans should be incorporated into the photosynthesis subroutine. This is especially important for later bean plantings which are shaded by a relatively large tomato plant. Presently the model uses the bean plant nearest to the tomato. A more representative bean plant would be the plant located equidistant 127 between the nearest and furthest bean plants. Potential tests and uses of the model include modification of some existing constants, rates, spacings, etc. Photosynthetic rates could be reduced by a known amount, and yields observed to determine if the yield reduction was equivalent. Different canopy shapes and types could be incorporated, such as caged tomatoes or pole beans. The planting design could be converted to rows, or even to a monoculture. The program has numerous comments throughout to enable another person to quickly comprehend it. It is hoped that others will take this current version and improve and modify it for new purposes. APPENDICES APPENDIX A 7.6 m Figure 38. 128 rt '0 n 'O n rt rt :3 _.—o——o-—o— n —o—o_o—o— n [ .5 m Plot design for one replicate of preliminary intercropping experiment. Where: b 8 bean row c cabbage row n - no intercrop between tomato rows p = pea row t - tomato guard row t' = tomato harvested row t' n t b t' b t 129 Table 29. Tomato Yields for 1978 and 1979 from the Preliminary Intercropping Experiment TOMATO YIELD Intercropped Year Yield Percent of species (tons/acre) control control 1978 33.2 1979 24.8 cabbage 1978 18.6 56 1979 13.4 54 pea 1978 20.7 62 1979 19.3 78 bean 1978 27.2 82 1979 17.2 69 130 Table 30. Cabbage, Pea and Bean Yields for 1978 and 1979 from the Preliminary Intercropping Experiment CABBAGE YIELDS Situation Yield Percent of (tons/acre) control 1978 1979 1978 1979 intercropped 11.1 9.5 116 133 control 9.6 7.1 PEA YIELDS Situation Yield Percent of (tons/acre) control 1978 1979 1978 1979 intercropped 2.6 2.3 124 164 control 2.1 1.4 BEAN YIELDS Situation Yield Percent of (tons/acre) control 1978 1979 1978 1979 intercrOpped 3.7 4.4 73 85 control 5.1 5.2 APPENDIX B Glossary of modeling state variables rate variables driving variables output variables system boundary 8X0 8 enou S 131 APPENDIX B terms. elements describing the components of the system; levels or amounts of material. Examples are leaf dry weight, photosynthate produced in one hour, and leaf area index. rates of transfer of material between state variables per unit time. forces external to but acting upon the system. Examples are solar radiation, temperature, rainfall and relative humidity. states or rates produced by the model and which are the objectives of the system. the boundary between the system and its environment. outside the system boundary. 132 Flowcharting Symbols (36,72) Beginning or end of Any input or output an algorithm operation Arithmetic calculation Beginning of series or state variable of operations to be performed repetitively; a D0 loop Comparison or Jump (used to direct decision making to another flowchart segment) Rate variable APPENDIX C 133 Climatic data for 1968, 1969, 1970 and-1980 compiled from Local Climato- logical Data, National Climatic Center, and from data provided by the Agricultural Weather Service, Michigan State University. 134 .mqmm .Omom .o .0 .mo .Hmm .qu .man .coe .com .Nq .o .o .o owoa .mmom .mmam .o .q .qu .wmq .Hmo .omo .mHm .Hmm .NNH .o .o .0 Quad .mamm .cmom .o .o .mma .omm .mco .qmc .OH< .NNN .mm .m .o .o moma .ommm .cmem .0 .HH .ooa .omq .mqo .omo .mom .qma .mw .0m .0 .o mama mzma mmuwmo 3.58: cm on: 0.0H o.m~ oo.m oa.o No.m NN.m om.q om.m om.m qw.~ Hq.~ q¢.H mm.o o~.o owofi m.oH mN.Nm wc.~ mw.N mm.m Ho.q mm.~ nm.m No.m mm.~ mH.m o~.~ oc.o Hm.o ohma N.mH mo.mm mm.o mm.N cm.N N©.H ma.o aw.< mo.q m¢.¢ No.q oo.H N~.o Hm.H mowa w.om mm.mm Nc.~ o~.m om.H mn.~ m¢.~ mo.m ¢¢.N o~.q oo.~ oq.H cm.H oe.a mead Ach cofiumuqafiomum c.mo m.mq n.m~ n.0m N.m< w.Ho m.Hn m.- m.mc m.mm o.mq m.m~ c.mH N.N~ owma w.qc m.oc w.m~ H.¢m w.Hm m.ao ~.wc ~.o~ m.mo o.wm n.0q H.m~ o.m~ q.oa Omma m.mo o.mc w.m~ N.om o.om N.¢© w.- H.Hn a.Hc m.om N.nq c.om q.m~ m.HN mead ¢.qo m.oc m.m~ n.5m m.Hm “.mc m.mc N.o~ n.0c N.mm m.w< m.om N.HN H.0N moma Amv wuaumumaEmH ammum>< .uammlhmz Hmocc< .omo .>oz .uuo uamm .m:< hash mash hm: .ua< .umz .nmm .cmh APPENDIX D i O 135 PROGRAM DRIVER (INFUT=6$OCUTPUTOTAPE10=/5001TAPE206TAPE30' PTAPE61=OUTPUT) 100 110 120 130 *ifititiiiitttttitifitfitftfifititiiifittfiiiiitt*Qtiitfitttttittttitti'iiitifiitho D....i.‘D......Oiiittfilbbbtiifibibiibi...’¢.§§.§§i‘ifltit. ATMDRES AVENRGY BA BB BBDIST BBMOVLP BBOVLP BCHOOMD BCPOOL BCSTORE BCUHPHD BDRYFR BDRYLF BDRYRT BDRYST BEXCOEF BFRDHO BFRFRT BFRINCR BGRAREA BLAI BLFAREA BLFDMD BLFINCR BLFRESP BNFLAG BNHIGHT BNPLANT BNRADA BNRADB BNSHADE BOVAREA BOVFRCT BPHSATE BPNRATE BPOPHEC BPOPHEX BRESP BRFRDHO BRLFDHD BRRTDHD BRSTOHD DEFINITION OF CONSTANTS AND VARIABLES ATMOSPHERIC DIFFUSION RESISTANCE S CMPP-l AVERAGE LIGHT FLUX DENSITY INCIDENT AT TOP OF CANOPY DURING PREVIOUS HEEK U HPP-Z CONSTANT CONSTANT BEAN-BEAN PLANTING DISTANCE M MAXIMUM ALLDVABLE OVERLAP OF BEAN-BEAN CANOPIES M OVERLAP OF THE BEAN-BEAN CANOPIES M BEAN CARBOHYDRATE DEMAND FOR LONGTERM STORAGE G BEAN SHORTTERM CARBOHYDRATE STORAGE G BEAN LONGTERM CARBOHYDRATE STORAGE G BEAN CUMULATIVE PHOTOSYNTHESIS PER DAY MG C02 BEAN FRUIT DRY WEIGHT G BEAN CANOPY LEAF DRY HEIGHT G BEAN ROOT DRY HEIGHT G DEAN STEM DRY WEIGHT G BEAN CANOPY EXTINCTION COEFFICIENT BEAN FRUIT DEMAND FUNCTION FDR PARTITIONING CARBOHYDRATE G BEAN FRESH FRUIT HEIGHT KG' BEAN FRUIT DRY MATTER INCREASE G AREA UNDER THE BEAN CANOPY MPP2 BEAN LEAF AREA INDEX BEAN CANOPY LEAF AREA M442 BEAN LEAF DEMAND FUNCTION FOR PARTITIONING CARBOHYDRATE G BEAN LEAF DRY MATTER INCREASE G BEAN LEAF DARK RESPIRATION RATE PER UNIT GROUND AREA MG c02 HPP-Z SPP-l FLAG To DECLARE THAT BEAN-BEAN GVERLAP HAS REACHED THE MAXIMUM ALLONABLE DISTANCE HEIGHT OF THE BEAN CANOPY M BEAN PLANTING DATE IN MODEL DAYS BEAN CANOPY RADIUS IN THE DIRECTION OF THE ROU M BEAN RADIUS IN THE DIRECTION PERPENDICULAR TO THE ROU M HORIZONTAL LENGTH OF THE BEAN CANOPY SHADOH M SUM OF OVERLAP GROUND AREA ON BOTH SIDES 0F BEAN CANOPY HPP2 BEAN-BEAN DVERLAP AREA AS A FRACTION 0F BEAN GROUND AREA BEAN NET PMOTOSYNTHATE EXPRESSED As GLUCOSE G BEAN NET PHOTOSYNTHESIS RATE PER UNIT GROUND AREA MG C02 HPP-2 SPP-l BEAN POPULATION PER HECTARE BEAN POPULATION PER HEXAGON BEAN TOTAL DARK RESPIRATION RATE PER UNIT GROUND AREA I MG C02 MPP-z SPP-l BEAN RELATIVE FRUIT DEMAND FDR PARTITIONING 0F CARBOHYDRATE ' ' BEAN RELATIVE LEAF DEMAND FOR PARTITIONING OF CARBOHYDRATE BEAN RELATIVE ROOT DEMAND FOR PARTITIONING 0F CARBOHYDRATE BEAN RELATIVE STEM DEMAND FOR PARTITIONING OF P150 P160 P170 P180 P190 P200 P210 P220 P230 P240 P250 P260 P270 P280 P290 P300 P310 P320 P330 P340 P350 P360 P370 P380 P390 P400 P410 P420 P430 P440 P450 P460 P470 P480 P490 P500 P510 P520 P530 P540 P550 P560, P570 P580 P590 P600 P610 P620 P630 P640 P650 P660 P670 P680 P690 P700 IIOOQIQODIDQDOD.OOOOOQOODQDQOIOQIQOOQ'OfifitfifiiifififitOOOOQQQIDDIPI BRTDMC BRTINCR BSTDMD BSTINCR BSTRESP BSUMDMD BTRANS BTRLCTE BUTILIZ BX BY BYLDHEC BYLDHEX BYLDHZ CANDRES COUNTER C02 CUMENDY CUMEVAP CUHIRGN CUMRAIN DAY DELTA DSHC DVOL EBDRYLF ECOUNT ENERGY ENSTORE EVAPOTN GAMMA HARVEST HBBDIST HEXAREA HEXCIRC HEXPHEC HTTDIST I IBNDAY IDAY IHR IHONTH IRRIGTN JDAY JMONTH KOUNTER LFHPOTL MBNRADB MODE 136 CARBOHYDRATE BEAN ROOT DEMAND FUNCTION CARBOHYDRATE G - BEAN ROOT DRY MATTER INCREASE G BEAN STEM DEMAND FUNCTION FOR PARTITIONING CARBOHYDRATE G BEAN STEM DRY MATTER INCREASE G BEAN STRUCTURE DARK RESPIRATION RATE PER UNIT GROUND AREA (ROOT. STEM AND FRUIT) MG CO2 MPP-Z SPP-l BEAN SUM OF THE COMPONENT PARTITIONING DEMANDS FOR CARBOHYDRATE G BEAN LEAF TRANSMISSION COEFFICIENT BEAN CARBOHYDRATE REMOVED FROM SHORTTERM STORAGE FOR TRANSLOCATION TO THE VARIOUS COMPONENTS G BEAN LEAF LIGHT UTILIZATION EFFICIENCY MG C02 J**-1 CONSTANT CCNSTANT BEAN YIELD PER HECTARE BEAN YIELD PER HEXAGON BEAN YIELD PER UNIT AREA KG MPP-Z CANOPY DIFFUSION RESISTANCE S CMPP-l COUNT OF NUMBER OF ITERATIONS DURING A PARTICULAR DAY C02 CONCENTRATION MG C02 MPP-S FOR PARTITIONING KG HECTAREPP-l KG HEXAGON..-1 CUMULATIVE ENERGY OVER THE DAY H MPP-2 CUMULATIVE EVAPOTRANSPIRATICN M CUMULATIVE IRRIGATION M CUMULATIVE PRECIPITATION M DAYS AFTER COMMENCEHENT OF THE SIMULATION (FIRST DAY IS DEFINED AS DAY 1) DAY CHANGE OF SATURATION VAPOR PRESSURE HITH TEMPERATURE MB CHANGE IN SOIL MOISTURE IN A GIVEN VOLUME 0F SOIL M HRPP-l CHANGE IN SOIL VOLUME OCCUPIED BY THE ROOT SYSTEM MPPS EFFECTIVE BEAN LEAF DRY HEIGHT DUE TO BEAN-BEAN OVERLAP G COUNT OF EVENT HHEN ENERGY EXCEEDS .01 0 SET TO ZERO EACH DAY HHEN COUNTER = U. USED TO COMPUTE 81 IN PHOTO. LIGHT FLUX DENSITY INCIDENT AT TOP OF CANOPY .H MPP-Z STORED VALUE OF ENERGY FOR THAT ITERATION H MPP-Z ACTUAL EVAPOTRANSPIRATION RATE M HRPP-l CONSTANT MB BEAN HARVEST DATE IN MODEL DAYS HALF THE BEANPBEAN PLANTING DISTANCE M HEXAGON AREA HPP2 HEXAGON CIRCUMFERENCE M NUMBER OF HEXAGONS PER HECTARE HALF THE TOMATO-TOMATO PLANTING DISTANCE M TOTAL NUMBER OF ITERATIONS IN THE RUN NUMBER OF BEAN DAYS AFTER BNPLANT VARIANT 0F DAY USED IN THE HEATHER SUBROUTINE SAME AS COUNTER. VARIANT OF MONTH USED IN THE HEATHER SUBROUTINE IRRIGATION M VARIANT OF DAY USED IN THE HEATHER SUBROUTINE VARIANT OF MONTH USED IN THE HEATHER SUBROUTINE SPECIAL VERSION OF COUNTER HHICH RUNS FROM 1 TO 8 AND IS USED IN CALLING SHADE LEAF HATER POTENTIAL BAR MAXIMUM ALLOHABLE BEAN CANOPY RADIUS PERPENDICULAR TO THE ROH M TYPE OF BEAN GROHTH P710 P720 P730 P740 P750 P760 P770 P780 P790 P800 P810 P820 P830 P840 P850 P860 P870 P880 P890 P900 P910 P920 P930 P940 P950 P960 P970 P980 P990 P1000 P1010 P1020 P1030 P1040 P1050 P1060 P1070 P1080 P1090 P1100 P1110 P1120 P1130 P1140 P1150 P1160 P1170 P1180 P1190 P1200 P1210 P1220 P1230 P1240 P1250 P1260 P1270 P1280 P1290 P1300 P1310 i' I1 ‘1 N' II I> It It 1‘ I> 1* I> ID I' I. I. I? II l> l> I1 l> It I‘ II N? II II N’ l- N’ it I' Ir IP l> I? l1 I’ I’ IP IT IP IP I’ It O’ l' 1* I’ l* i? I' I? I' I! l' ‘P lb 1' I. MONTH PAR POOLCHK RAIN RATIOE RATIOM RATIOP RATIOT RELHUM RHMSAVE RTDEPTH S SHADE SOILBD STPNRTE SHC SHPOT SYLDHEC SYLDHEX SYLDM2 TA TB TBMOVLP TBOVLP TCHODMD TCPOOL TCSTORE TCUMPHD TDAY TDIAM TDRYFR TDRYLF TDRYRT TDRYST TEMP TEXCOEF TFRDMD TFRINCR TFRFRT TGRAREA TGRSHDE TLAI TLFAREA TLFDMD TLFINCR TLFRESP TMPSAVE II II II II II II II II II II II II II II II II II II II II II II II II 137 MONTH OF THE YEAR PHOTOSYNTHETICALLY ACTIVE RADIATION CANOPY ERG CMPP-Z SPP-I MINIMUM CARBOHYDRATE LEVEL ALLOHED IN SHORTTERM STORAGE PRECIPITATION M LESSER VALUE OF RATIOP AND RATIOT. USED TO SCALE DOHN PHOTOSYNTHETIC RATE ADJUSTMENT TO TOMATO PHOTOSYNTHETIC RATE DUE TO AGE OF THE CANOPY ADJUSTMENT TO PHOTOSYNTHETIC RATE DUE TO LEAF HATER POTENTIAL DIFFERENCES FROM THE OPTIMUM ADJUSTMENT TO PHOTOSYNTHETIC RATE DUE TO TEMPERATURE DIFFERENCES FROM THE OPTIMUM RELATIVE HUMIDITY INCIDENT AT TOP OF 'STORED VALUE OF RELHUM TO ALLOH FOR MISSING VALUES IN THE HEATHER DATA ROOT SYSTEM DEPTH M AVERAGE ENERGY RECEIVED DURING A 3 HOUR PERIOD FOR ONE OF THE PREVIOUS 7 DAYS RATIO OF AN OBJECT"S SHADOH LENGTH TO THE HEIGHT OF THE OBJECT SOIL BULK DENSITY G CMPP-S SHADED TOMATO NET PHOTOSYNTHESIS RATE PER UNIT GROUND AREA MG C02 MPP-Z SPP-l VOLUMETRIC SOIL HATER CONTENT SCIL HATER POTENTIAL BAR SUM OF THE YIELDS OF THE THO SPECIES PER HECTARE KG SUM OF THE YIELDS OF THE THO SPECIES PER HEXAGON KG SUM OF THE YIELDS OF THE THO SPECIES PER UNIT AREA KG CONSTANT CONSTANT MAXIMUM ALLOHABLE OVERLAP OF TOMATO-BEAN CANOPIES M OVERLAP OF THE TOMATO-BEAN CANCPIES M TOMATO CARBOHYDRATE DEMAND FOR LONGTERM STORAGE G TOMATO SHORTTERM CARBOHYDRATE STORAGE G TOMATO LONGTERM CARBOHYDRATE STORAGE G TOMATO CUMULATIVE PHOTOSYNTHESIS PER DAY MG C02 CONVERSION OF DAY TO REAL FOR RATIOM CALCULATION TOMATO CANOPY DIAMETER M TOMATO FRUIT DRY HEIGHT G TOMATO CANOPY LEAF DRY HEIGHT G TOMATO ROOT DRY HEIGHT G TOMATO STEM DRY HEIGHT G DRY BULB TEMPERATURE C TOMATO CANOPY EXTINCTION COEFFICIENT TOMATO FRUIT DEMAND FUNCTION FOR PARTITIONING CARBOHYDRATE ’G TOMATO FRUIT DRY MATTER INCREASE G TOMATO FRESH FRUIT HEIGHT KG AREA UNDER THE TOMATO CANOPY M**2 GROUND AREA OF THE TOMATO CANOPY SHADED BY THE BEAN CANOPY MPP2 TOMATO LEAF AREA INDEX TOMATO CANOPY LEAF AREA MPPZ TOMATO LEAF DEMAND FUNCTION FOR PARTITIONING CARBOHYDRATE G TOMATO LEAF DRY MATTER INCREASE G TOMATO LEAF DARK RESPIRATION RATE PER UNIT GROUND AREA MG C02 MPP-Z S‘*'1 STORED VALUE OF TEMP TO ALLOH FOR MISSING VALUES IN THE HEATHER DATA P1320 P1330 P1340 P1350 P1360 P1370 P1380 P1390 P1400 P1410 P1420 P1430 P1440 P1450 P1460 P1470 P1480 P1490 P1500 P1510 P1520 P1530 P1540 P1550 P1560 P1570 P1580 P1590 P1600 P1610 P1620 P1630 P1640 P1650 P1660 P1670 P1680 P1690 P1700 P1710 P1720 P1730 P1740 P1750 P1760 P1770 P1780 P1790 P1800 P1810 P1820 P1830 P1840 P1850 P1860 P1870 P1880 P1890 P1900 P1910 P1920 .‘INQNOOII#.*§NIOQQO§t.9*Qfififitfiiitfi‘filbtfiiiitfi. TOTLPOP TPHSATE TPNRATE TPOPLTN TRAD TRESP TRFRDMD TRLFDMD TRRTDMD TRSTDMD TRTDMD TRTINCR TSHADE TSTDMD TSTINCR TSTRESP TSUMDHD TTDIST TTRANS TTRLCTE TUTILIZ TX TY TYLDHEC TYLDHEX TYLDM2 VAPSAT VAPTEMP VOLSI VOLSZ HINDCON HINDSPD -HNDSAVE 138 TOTAL PLANT POPULATION PER HECTARE TOMATO NET PHOTOSYNTHATE EXPRESSED AS GLUCOSE G TOMATO NET PHOTOSYNTHESIS RATE PER UNIT GROUND AREA MG C02 MPP-Z SPP-l TOMATO TOMATO TOMATO POPULATION PER HECTARE CANOPY RADIUS M TOTAL DARK RESPIRATION RATE PER UNIT GROUND AREA MG C02 MPP-Z S**-1 TOMATO RELATIVE FRUIT DEMAND FOR PARTITIONING OF CARBOHYDRATE TOMATO RELATIVE LEAF DEMAND FOR PARTITIONING OF CARBOHYDRATE TOMATO RELATIVE ROOT DEMAND FOR PARTITIONING OF CARBOHYDRATE TOMATO RELATIVE STEM DEMAND FOR PARTITIONING OF CARBOHYDRATE TOMATO ROOT DEMAND FUNCTION FDR PARTITIONING CARBOHYDRATE G TOMATO ROOT DRY MATTER INCREASE G HORIZONTAL ENCROACHMENT DISTANCE OF THE BEAN ONTO THE TOMATO GROUND AREA M TOMATO STEM DEMAND FUNCTION FOR PARTITIONING CARBOHYDRATE G TOMATO STEM DRY MATTER INCREASE G TOMATO STRUCTURE DARK RESPIRATION RATE PER UNIT GROUND AREA (ROOT. STEM AND FRUIT) MG CO2 MPP-Z SPP-l TOMATO SUM OF THE COMPONENT PARTITIONING DEMANDS FOR CARBOHYDRATE G TOMATO- ~TOMATO PLANTING DISTANCE M TOMATO LEAF TRANSMISSION COEFFICIENT TOMATO CARBOHYDRATE REMOVED FROM SHORTTERM STORAGE FOR TRANSLOCATION TO THE VARIOUS COMPONENTS G TOMATO LEAF LIGHT UTILIZATION EFFICIENCY CONSTANT CONSTANT TOMATO YIELD PER HECTARE TOMATO YIELD PER HEXAGON KG HEXAGONPP-I TOMATO YIELD PER UNIT AREA KG MPP-2 SATURATED VAPOR PRESSURE AT CURRENT TEMPERATURE VAPOR PRESSURE AT CURRENT TEMPERATURE BAR SOIL VOLUME OCCUPIED BY ROOT SYSTEM MPP3 NEH VALUE OF VOLSl CONSTANT HIND SPEED M SPP-I STORED VALUE OF HINDSPD T0 ALLOH FOR MISSING VALUES IN THE HEATHER DATA SHADOH MG C02 JPP-l KG HECTAREP*-1 BAR P1930 P1940 P1950 P1960 P1970 P1980 P1990 P2000 P2010 P2020 P2030 P2040 P2050 P2060 P2070 P2080 P2090 P2100 P2110 P2120 P2130 P2140 P2150 P2160 P2170 P2180 P2190 P2200 P2210 P2220 P2230 P2240 P2250 P2260 P2270 P2280 P2290 P2300 P2310 P2320 P2330 P2340 P2350 P2360 P2370 P2380 P2390 tittittttttittittttttttttttt*titttit!tiittttttttttitttttttttttttttttttitZQOD * '0‘ Q..$.. ESTABLISH REAL AND INTEGER VARIABLES INTEGER COUNTERQDAYPPRINTYPQXCOUNTRPXDAYQXIQXMONTHPXDATEQXJMONTHQ PXJDATEPXIMONTHPXIDATEPDATEPBNPLANTPHARVESTPXBNPLANPXHARVES REAL MBNRADBPLFHPOTL ESTABLISH ALL COMMON BLOCKS. TRANSFER OF VARIABLE VALUES BETHEEN SUBROUTINES. ALL OTHER COMMON BLOCKS ARE USED FOR STORING VARIABLE VALUES FOR PRINTING. 'MAIN' IS THE PRIMARY COMMON BLOCK FOR 2410 2420 2430 2440 2450 2460 2470 2480 2490 2500 2510 2520 2530 lit 0. t 139 COMMON IMAIN/ BBDISTPBDRYFRPBDRYLF9BDRYRTPBDRYSTPBGRAREAPBLAIv PBNRADAPBNRADBPBPHSATEPCOUNTERPENERGY(24)oHTTDISTcRAINo PRELHUMIZA).TDRYFR.TDRYLF.TDRYRTPTDRYSTPTEMP(24IPTGRAREAQTLAIP OTPHSATEgTRADoTTDIST.HINDSPD(24IgI)SHADE(10198)oIHRgDAYoLFHPOTL. PHBBDISTQMONTHPDATEPIMONTHPIDATEPJMONTHPJDATEPBNPLANTPHARVEST 2540 2550 2560 2570 2580 2590 COMMON lHATER/XCANDREIIBO)9XCUMEVA¢180IPXCUMIRGCIBDI.XCUMRAI(180Iv2600 PXOELTA(180)9XDSHC(180)7XDVOL(180)PXEVAPOTI180)PXLFHPOTCIBOIP PXRTDEPTI180)PXSHC(180IPXSHPOT(180IPXVOL51(180)PXVAPSAT(180)P PXVAPTEMIIBO) COMMON lPHOTO/XDAYIIBO)PXSHADE<180IPXCOUNTRIIBOIPXAVENGY(180IP *XBCUMPH(180IoXBLFRSPtlBO)PXBNHIGH(180IPXBNSHDEI180).XBPHSTE(180I9 QXBPNRTE(IBOIPXBRESP(180IoXENERGY(180)cXSl(180)oXSZIlBO)9X831180I9 PXS4(180)PXSEIIBOIPXSGIIBOIPXS7‘180IPXSTPNRT(180)PXTCUMPH(180I9 PXTGRSHD(183IPXTLFRSP<180IPXTPHSTE(IBOIPXTPNRTE(180IPXTRESP(180)9 PXTSHADE(180)9XRAIN(180)PXRELHUMIISOIoXTEMPIIBOI.XHINDSPIICO). +XI(180IPXMONTH(180)PXDATE(180)9XJMONTHI180IPXJDATEIIBOI9 PXIMONTH<18079XIDATE(180)PXBNPLAN(IBOIPXHARVESIIBOI COMMON ITOMPART/XPOOLCH(100I9XTCHODM(180I4 .XTCPO0L(180)IXTCSTORIIBO)OXTDRYFRIIBOIOXTDRYRTIIBOI‘XTDRYLF‘150)9 PXTDRYST(J80).XTFRDMD(180IPXTFRINCIIBOIPXTLFDMD(180).XTLFINCI180). +XTPHSAT(180I.XTRFRDM(180)oXTRLFCMIIBOIPXTRRTDMIIBOIPXTRSTDM(180IP oXTRTDMD(180)PXTRTINC(180)PXTSTDMD(180)PXTSTINCIIBGIPXTSUMDM(180I9 PXTTRLCTIIBO) COMMON lBNPART/XPOOLCKIIBOIPXBCHODM(1BOIPXBCPOOL(180I. PXBCSTOR(180I9XBDRYFR(180IPXBDRYRTI180I9XBDRYST(180IPXBDRYLFI180). *XBFRDMD(180I9X8FRINC¢180I9XBLFDMO(180)oXBLFINC(180}PXBPHSATIIOOIP +XBRFRDM(180)gXBRLFDMIIBOIoXBRRTDM(180).XBRSTDM(180I9XBRTDMDI180I9 PXBRTINCIIBOIPXBSTDMDIlBOIQXBSTINCC180)oXBSUMDMIlBOIoXBTRLCTI180) COMMON ICANOPY/IMODEIIBOIoXBNRADAI180)PXBNRADB(1801QXBGRARA(180IP PXEBDRYL(180)PXBLFARA(180)PXBLAI(180)PXBBOVLPCIBOIP PXBOVARA‘IBO)PXBOVFRC(180I9XBNFLAG(180)PXTRAD(180IQXTGRARA(180)9 *XTLFARAIISOIPXTLAICIBOIPXTBOVLP(180) 2610 2620 2630 2640 2650 2650 2670 2680 2690 2700 2710 2720 2730 2740 2750 2760 2770 2780 2790 2800 2810 2820 2830 2840 2850 2860 2870 2880 2890 2900 2910 COMMON IYIELD/XBFRFRT(180)9X8PCPHC‘IBOIPXBPOPHX(180IPXBYLDHCI180)92920 PXBYLDHX(180IPXBYLDM2(180)cXHEXARE¢180IQXHEXCIRIIBOIPXHEXPHEIIBOI9 +XSYLDHC(180IPXSYLDHX(180IPXSYLCM2(18039XTFRFRT(180)P +XTOTLPO(180IPXTPOPLTIIBOIPXTYLDHCC180I9XTYLDHXI180IPXTYLDM2(180), PXBBDIST(180)QXHTTDISKIBOI . READ IN SHADE DATA DATAIISHADEIMPNIPN=IPBIPM=1410II .009505010590.9079202’709009009505'1.510490792029700009 .00950501.5004007920297090.90005.59105,040.7'2021709009 .000505910500‘007120207090000095059105.cqfio79202'70900' 90005059105909907920297090090005059105905.079202970'0.9 *0695.591.5000907920297oooo00.15.59I.50.Rg.792.297.00./ DATACISHADEIMQNIQN=IPBIQM=IIQZOII P0..5.5.1.5..4..7.2.2.7..o..0..5.5.1.5..4..7.2.2.7..0.. 90.05.511.59.41.792.297.90.10.05.591.59.40.7o2.297.00.9 00.95.591.59050.792.267.90.90.95.591.51.49.812.599.90.9 00095.5.1.5..49.812.599.v0.90.95.501.54.RQ.892.599.90.! 90.95059105909908420509.90.90.95.591.50.40.892.549.000/ DATA((SHADE(M9NI9N=198I9M=2193OII 2930 2940 2950 2960 2970 2980 2990 3000 3010 3020 3030 3040 3050 3060 3070 3080 3090 3100 3110 3120 3130 3140 I ’1’. I 140 ‘00'50591059049080205990900900'5059105'049089205'90.00Q 3150 00.15.59105.049.892.5'9990090.05.591.50.4..802o599o00o9 3160 *00050511.50049.8v20599oQ0.90.95.511.59o4’0812o5q9o90.9 3170 .009505’105,04,08920599o90-9009505.105900'081205'90’00' 3180 *00950511059041089205990.00000050591.59.49.80205990QDo/ 3190 DATA((SHAOE(M9N)9N=10879H=31940)/ 3200 '90.090!1070.59.9'20290.900$0099001.790500992020000009 3210 *0099011.79050.902.2000Q00v0o190.107.059.992o210090-9 3220 *0099091.70-59099202100000,0019.91.79050.992.2v009009 3230 .000909107.059.992.290.900,00'909107!05909'202'000009 3240 *0oo9oo1o71¢59¢992o290¢'0oo0o99o91o79059.992.290.100, 3250 DATA((SHADE(HQN)9N=10879M=41950)/ - 3260 *0099001.7005909920290000.90099091o7v059.992.290.900, 3270 .00090'107905009920290090090.'90'1079050.992.2000’00’ 3280 90.990Q1o7oo59091202'0090¢.0.900.209.6o10920900900q 3290 *0090002090601002090010090.4001209069109209009000 3300 .0090092090601.920900900900000920.06.10920900’00/ 3310 DATA((SHACE(MQN)9N=198)9M=519600/ 3320 *00'00920006910020'0090090090012090691092-9009009 3330 .00900'209.601092090000090090092090691092.9009000 3340 +0.10o020906Q1c120000g0.90.90.92.006Q1o92o9009000 3350 *00900020006!10Q20900!00$00$00920006010920900900, 3360 40.90.920.06010.2.90090o900$0.92o0o6910v2o90o'09/ 3370 DATA((SHADE(HQN)QN=148)9M=61g70)/ 3380 *009009209089101.39590090o00090.92.0o89101930590090o1 3390 O009009209080101!305Q00900'(10'009209.8910193059009009 3400 *009000209089101930590.90.90.'00Q26008910193059009000 1 3410 O0090092.9.89101930500090090090.920008010103059009009 3420 .00000920903910193359000000009001200.801019305900900, 3430 DATA((SHADE(HVN34N=108)9M=71980)/ 3440 *00'00Q200089101'305'0090.900900§200089101930590000.9 3450 *00900'20'080101930590.'0.100900'209089101930590090.‘ 3460 4‘0090092000891019305900'00,0090.9209101102950900'00O 3470 .00000120910010295010090.90.90¢.20910910205.’00900' 3080 .00900920910910295090.'00'000009200109102150900900] 3490 DATA((SHACE(MON)!N=118’9M=81990)/ 3500 *00100'2091091.2'500009009009009200100102950000’009 3510 40.900920910910295..0000.00.90.92.11.91.295.000v009 3520 .00000'20010910295000000000000002.01.0102950900900' 3530 00ov00v209104102'5o90.90.90.10o92001001.295.90.9009 3540 #0010092o91.010295.00.90.90.90.02.91.01.295o000900/ 3550 DATA((SHADE(H9N)QN=198)0M=919101)/ 3560 90010093091.502098000090090.9009300105020980'000009 3570 "900100130910592018090¢90.00.90.03.91.512o380'0090o1 3580 90.000930010592098.900'00000000930£1059201809009009 3590 *001000300105020080900900.0090013001.502.980.001000 3600 90.900930010592098000010.9009000309105920380900000! 3610 .000000300105'2098090000., 3620 3630 CALL DISCON‘6LDUTPUT) 3640 REUIND 61 3650 CALL CONNEC‘6LOUTPUT) 3660 3670 t t t t t t a t a t t t t t i t t t t t t t t t t t t t t t t i t t t 3680 3690 RETURN TO THIS POINT IF A FURTHER RUN IS REQUIRED 3700 3710 1000 CONTINUE 3720 REHIND 10 3730 REUIND 20 3740 REUIND 30 3750 I .93.? 141 INITIALIZE ALL NECESSARY VARIABLES AND CONSTANTS. BBDIST=.05 BNPLANT=1 COUNTER=0 HARVEST=52 PRINTYP=1 TTDIST=o8 MBNRADB=o327 + .632*AL0610(TTDIST) CONFIRM INITIAL VALUES OF VARIABLES WHICH MAY BE CHANGED. ASK FOR ANY 101 102 103 104 105 106 107 108 109 110 111 .112 113 114 115 116 117 118 119 120 121 122 NEH VALUES. PRINT 101 FORMAT(*0*.5X.*THESE ARE THE INITIAL VALUES*) PRINT 102. TTDIST FORMAT(*0*.10X.*TOMATO-TOMATO DISTANCE IN METERS'.F10.2) PRINT 103. BBDIST FORMAT(*0*.10X.*BEAN-BEAN DISTANCE IN METERS*.F14.2) PRINT 104. BNPLANT FORMAT(*0*.10X.*BEAN PLANTING DATE IN MODEL DAYS*.I10) PRINT 105. HARVEST FORMAT(*0*.10X.*8EAN HARVEST DATE IN MODEL DAYS*.I11) PRINT 106. PRINTYP FORMAT(*0*.10X.*PRINT TYPE ( 1 3 DAILY AT 1200 AND 2400 HR. 2 PRINT 107 FORMAT(*0*.5X.*DO YOU UANT TO MAKE ANY CHANGES. YES OR NO?*) READ 108. 11 FORMAT(A2) IF(Il-E0o2HNO) GO TO 129 PRINT 109 FORMAT(*0*.5X.*CHANGE TOMATO-TOMATO DISTANCE. YES OR NO?*) READ 110. 12 FORMAT(A2) IF(12.E0.2HNO) GO TO 112 PRINT 111 FORMAT(*0*.10X.*TYPE NEH VALUE OF TTDIST ( IN METERS )*) READ'. TTDIST ' PRINT 113 FORMAT(*0*.5X.*CHANGE BEAN-BEAN DISTANCE. YES OR NO?*) READ 114. I3 ’ FORMAT(A2) IF(I3o E0. 2HNO) GO TO 116 PRINT 115 FORMAT(*0*.10X.*TYPE NEH VALUE OF BBDIST ( IN METERS )*) READ*.BBDIST PRINT 117 FORMAT(*0*.5X.‘CHANGE BEAN PLANTING DATE. YES 0R ND?*) READ 118. I4 FORMAT¢A2) IF(I4.E0.2HNO) GO TO 120 PRINT 119 FDRMAT(*0*.10X.*TYPE NEH VALUE OF BNPLANT ( IN MODEL DAYS )0) READ*. BNPLANT PRINT 121 FORMAT(*0*.5X.*CHANGE BEAN HARVEST DATE. YES OR NO?*) READ 122. 15 FORMAT(A2) 3760 3770 3780 3790 3800 3810 3820 3830 3840 3850 3860 3870 3880 3890 3900 3910 3920 3930 3940 3950 3960 3970 3980 3990 4000 4010 = DA4020 OILY AT 2400 HR. 3 = FINAL VALUES ONLY. 4 = FINAL YIELD ONLY )*.I3)4030 4040 4050 4060 4070 4080 4090 4100 4110 4120 4130 4140 4150 4160 4170 4180 4190 4200 4210 4220 4230 4240 4250 4260 4270 4280 4290 4300 4310 4320 4330 4340 4350 4360 142 IF¢IS.EC.2HNO) GO TO 124 4370 PRINT 123 4380 123 FORMAT(OO*.10X.*TYPE NEH VALUE OF BEAN HARVEST ( IN MODEL DAYS 1*)4390 READ*.HARVEST 4400 124 PRINT 125 4410 125 FORMAT(*0*.5X.*CHANGE PRINT TYPE. YES OR NO?*) 4420 READ 126. I6 4430 126 FORMAT(A2) 4440 IF(I6.EG.2HNO) GO TO 129 4450 PRINT 127 4460 127 FORMAT(*0*.10X.*TYPE NEH VALUE OF PRINTYPt) 4470 READ*.PRINTYP ' ‘ 4480 t 4490 129 HBBDIST=BBDISTI2o 4500 HTTDIST=TTDIST/2. 4510 t . 4520 PRINT 130 4530 130 FORMAT(t0*.*++++++++4+++++++++*+ PROGRAM EXECUTION COMMENCES ¢++4540 +++++++++++...+..+.) . 4550 4560 THE FOLLOUING DO LOOP CONTROLS THE SIMULATION. VARIABLE VALUES ARE 4570 STORED EACH 12 HOURS AND PRINTED IN ONE BLOCK AFTER THE RUN IS 4580 COMPLETED. 4590 4600 it*tttttittttttttttttttitttttttttittt*tititttitititttfittittl'ttttfitt*itt*4610 * *4620 94630 COMMENCE DO LOOP 4640 4650 1:0 4660 t ' 4670 DO 100 K=1.81 4680 t 4690 DAY=K 4700 t 4710 CALL HEATHER 4720 t 4730 00 200 IHR=1.24 4740 t 4750 COUNTER=IHR 4760 t 4770 IFCCOUNTER.E0.12.0R.COUNTER.E0.24) I=I¢1 4780 t 4790 CALL HATER 4800 * 4810 CALL PHOTO 4820 ' 4830 CALL BNPART 4840 i 4850 CALL TOMPART 4860 t 4870 CALL CANOPY 4880 . 4890 CALL YIELD 4900 t A 4910 200 CONTINUE 4920 100 CONTINUE 4930 t *4940 t .4950 fittiiitttAtttt*ifitttttttttitttttttfittitttittttttttititOtttttttttiittAGOQQQGO . 4970 5.4.. it! t 6' .40406044443344 1234 1235 t * THI PRI 900 150 21 22 701 702 23 24 25 143 4980 * PRINT HOURLY. DAILY 0R SEASONAL VALUES AS REQUESTED BY PRINTYP 4990 ' 5000 PRINT*.'CONTINUE OUTPUT 0N DECHRITER??' 5010 READ 1234.1ANS 5020 FORMAT(A1) 5030 IF(IANS.EG.1HY) GO TO 1235 5040 CALL DISCON (6LOUTPUT) 5050 CONTINUE 5060 IFIPRINTYP.E0.1) GO TO 900 5070 IFIPRINTYP.EO.2) GO TO 910 5080 IF(PRINTYP.EC.3) GO TO 920 5090 IF(PRINTYP.E0.4) GO TO 925 5100 5110 5120 t t a t a t t t t a t t t i t t t i t t t a a . t t t t . t t t t 5130 5140 5150 ...a................. 5160 t t 5170 t 12 HOURLY PRINT * 5180 . . 5190 tittttitttttittttittt 5200 5210 S SECTION PRINTS VARIABLE VALUES EVERY 1200 HR AND 2400 HR. 5220 5230 NT FOR SUBROUTINE PHOTO 5240 5250 PRINT 150 5260 FORMAT¢¢112 HOURLY PRINT FOR SUBROUTINE PHOTOt) 5270 PRINT 21 5280 FORMAT(*-DAY I COUNTER ENERGY TEMP RAIN RELHUM UINDSPD MONTH DAT5290 0E JMONTH JDATE IMONTH IDATE I BNPLANT HARVEST.) 5300 PRINT 22.(XDAY(I).XI(I).XCCUNTR(I).XENERGY¢I).XTEMP(I)sXRAIN(I). 5310 +XRELHUM(I).XUINOSP(I).XNONTHtl).XDATE(I).XJMONTH(I).XJDATE(I)9 5320 #XIMONTHII).XTDATE(I).XI(I).XBNPLAN(I).XHARVES(I).I=1.162.161) 5330 FORMAT(I3.2X.I3.1X.I4.4X9F5.1.2X.F4.1.1X.F4.2.1X.F5.2.1X.F7.1.2X. 5340 914.2X.I3.4X.I4.1X.I4.2X.14.3X.I4.3X.18.4X.I2.6X.12) ‘ 5350 PRINT 701 5360 FORMAT(*1 I DAY CANORES DELTA EVAPOTN VOLSI DVOL 0500 5370 9 $00 RTDEPTH SUPOT LFHPOTL CUMIRGN CUHRAIN CUMEVAP VAPSAT V5380 OAPTEHP*) 5390 PRINT 702.(XI(I).XDAY(I).XCANORE(I).XDELTA(I).XEVAPOT(I).XVOLSI(I)5400 ..XDVOLII).XDSUC(I). XSVCCI).XRTDEPT(I).XSUPOT(I). XLFUPOT(I). 5410 OXCUMIRG(I).XCUHRAI(I).XCUHEVA(I).XVAPSAT(I).XVAPTEM(I).I=1.162) ~5420 FORHAT(1X.I3.1X.IS.1X.F7.2.1X.F7.3.1X.F7.3.1X.F7.3.1X.F7.3.1X. 5430 +F7.3.1X.F7.3.1X.F7.3.1X.F7.3.1X.F7.3.1X.F7.2.1X.F7.2.1X.F7.2.1X. 5440 0F7.3.1X.F7.2) 5450 PRINT 23 5460 FORHAT¢¢1 I DAY ENERGY AVENRGY $1 82 S3 $4 $5 5470 0 S6 S7 TLFRESP TPNRATE TPHSATE TCUMPHDt) 5480 PRINT 24.(XI(I).XDAY(I).XENERGY(IT.XAVENGY(I).XSI(I).XS2(I). 5490 .XS3(I).XS4(I).X85(I).XS6(I).X57(I).XTLFRSPII).XTPNRTE(I). 5500 +XTPHSTE(I).XTCUMPH(I).I:1.162) 5510 FORMAT!1X.I3.1X.I3.1X.F6.2.1X.F7.2.1X.F6.2.1X.F6.2.1X.F6.2.1X. 5520 *F6o2.1X.F6.2.1X.F6.2.1X.F6.2.1X.F7.4.1X.F7.4.1X1F7.4.1X.F7.4) 5530 PRINT 25 5540 FORMAT(91 1 DAY 8LFRESP BPNRATE BPHSATE BCUNPHD BNHIGHT BNSHADE 5550 +TSHADE TGRSHDE STPNRTE TPNRTE SHADE TRESP BRESPt) 5560 PRINT 26.(XI(I).XDAY(I).XBLFRSPII).XBPNRTE(I).XBPHSTE(I). 5570 +XBCUMPHtI).XBNHIGH(I).X8NSHDE(I).XT$HADE(I).XTGRSHD(I). 5580 144 *XSTPNRTCI).XTPNRTEII)9XSHADE(I).XTRESP(I).XBRESP(I).1:1.162) 5590 26 FORMAT(1X.I3.1X.I3.1X.F7.4.1X.F7.4.1X.F7.4.1X.F7.4.1X.F7.3.1X. 5600 4F7o3.1X.F6.3.1X.F7.5.1X.F7.4.1X.F7.4.1X.F4.2.1X.F5.3.1X.F5.3)' 5610 4 Y 5620 * PRINT FOR SUBROUTINE BNPART 5630 * 5640 PRINT 151 5650 151 FORMAT(*112 HOURLY PRINT FOR SUDROUTINE BNPART*) 5660 PRINT 27 ' 5670 27 FORMAT(*- I DAY COUNTER BPHSATE BCSTORE POOLCHK DCHODMD BCPOOL 8T5680 ORLCTE BFRDHO BLFDMD BRTDMD DSTDND BRFRDMD DRLFDMD BRRTDMD BRSTCHD 5690 *BSUMDMO*) ' 5700 PRINT 28.(XIII).XDAY(I).XCOUNTR(I).X8PHSAT(I).XBCSTOR(I).XPOOLCK(I5710 4)9X8CHODM(IIOXBCPOOLCIIIXBTRLCTCIIOXBFRDMD(I).X8LFDND(I79X8RTDMD(15720 +79XBSTDMDII).XBRFRDM(I)OX8RLFDM(I)9X8RRTDMII).XBRSTDM(I).XBSUHDH(15730 44.131.162) 5740 28 FORMATIIX.I3.1X.I2.3X.12.4X.F7.4.1X.F7.4.1X.F7.4.1X.F7.4.1X. 5750 4F6.3.1X.F7.4.1X.F6.2.1X.F6.2.1X.F6.3.1X.F6.2.1X.F7.4.1X. 5760 ‘F70991XQF704'1X'F7.491XQF703) - 5770 PRINT 29 5780 29 FDRHAT(*1 I DAY COUNTER BFRINCR BLFINCR BRTINCR BSTINCR BDRYFR 805790 +RYLF BDRYRT BDRYST*) 5800 PRINT 30.(XI(I)9XDAY‘I).XCOUNTR(I)9X8FRINC(I).X8LFINC(I’.XDRTINC(I5810 OIQXBSTINC(I).XBDRYFRII).X8DRYLF(I).XEDRYRT(I).XEDRYST(I). 5820 4‘13]..162) 5830 30 FORMATCIX.I3.1X.I2.3X.12.4X9F7o4QIX1F7o4.1X.F7.4.1X.F7.4.1X. 5840 4F6.2.1X.F6.2.1X.F6.2.1X.F6.21 . 5850 * 5860 * PRINT FOR SUBROUTINE TOMPART 5870 * 5880 PRINT 152 5890 152 FDRMAT(*112 HOURLY PRINT FOR SUBROUTINE TOMPART*) 5900 PRINT 31 5910 31 FORMAT(*- I DAY COUNTER TPHSATE TCSTORE PDOLCHK TCHODMD TCPOOL TT5920 *RLCTE TFRDMD TLFDMD TRTDMD TSTDMD TRFRDMD TRLFDHD TRRTDMD TRSTDHD 5930 *TSUMDMD*) 5940 PRINT 329(XI(I).XDAY(I).XCDUNTR(I’.XTPHSAT(I).XTCSTOR(I).XPOOLCH(15950 O’DXTCHODM‘I,IXTCPOOLII)OXTTRLCT(I)OXTFRDMD(I)OXTLFDHDIIIQXTRTDNDII596D 0).XTSTDMD(I).XTRFRDM(I)OXTRLFDM(I).XTRRTDH‘I).XTRSTDM(I).XTSUMCM(1597O 419131.162) 5980 32 FORMAT(1X.I3.1X.I2.3X.I2.4X.F7.4.1X.F7.4.1X.F7.4.1X.F7.4.1X. 5990 4F6.3.1X.P7.4.1X.F6.2.1X.F6.2.1XOF6.3.1X.F6.291X.F7.4.1X. 6000 9F7.4.1X.F7.4.1X.F7.4.1X.F7.37 A 6010 PRINT 33 6020 33 FORMAT(*I I DAY COUNTER TFRINCR TLFINCR TRTINCR TSTINCR TDRYFR TD6030 *RYLF TDRYRT TDRYST*) A 6040 PRINT 34.(XICIIQXDAYII).XCDUNTR(I).XTFRINCCI).XTLFINC(I).XTRTINC(16050 OIQXTSTINCII).XTDRYFR(I)QXTDRYLF(I)QXTDRYRTCI’QXTDRYST(I). 6060 9I=101627 6070 34 FORMATI1XQI3.1X.12.3X.I2.4X.F7.4.1X.F7.4.1X.F7.4.1X. 6080 4F7.4.1X.F6.291X.F6.2.1XOF6o201X9F6o2) 6090 9 6100 P PRINT FOR SUBROUTINE CANOPY 6110 * 6120 PRINT 153 6130 153 FORMAT(*112 HOURLY PRINT FOR SUBROUTINE CANOPY*) 6140 PRINT 35 6150 35 FDRMAT(*- I DAY COUNTER MODE BNRADA BNRADB BGRAREA BDRYLF E80RYLF6160 * DLFAREA DLAI BDOVLP BOVAREA BOVFRCT BNFLAG*) 6170 PRINT 36.(XI(I).XDAY(I).XCOUNTR(I).IMODE(I).XBNRADA(I).XBNRADB(I).6180 .XBGRARACI).XBDRYLF(I).XEBDRYLII).XBLFARA(I).XBLAI(I).XBBOVLP(I). 6190 145 +X80VARAII79XBOVFRCII)!XBNFLAG(I70I=10162) 36 FORMATIIXOISOIXQ1294XOIZOSX'IIQZXOFGOSQIXQFGOSQ1XQF7OSOIXQ ‘FGOSQIXQFTODQ1X'F7QSQIXQFTOSQIXQF603’IXOFTQSQIXQF7OSQIX$F6027. PRINT 37 37 FORMATI'I I TRAD TGRAREA TDRYLF TLFAREA TLAI TBOVLPf) PRINT 389(XI‘I)1XTRAD(13QXTGRARAII)VXTDRYLF(IIVXTLFARA(I79 *XTLAIII)9XTEOVLP(I’QI319162) 38 FORMAT(1XQI3vF4o291X1F7o391X0F6o101XvF7o391XsF4o191X9F6o3) t * PRINT FOR SUBROUTINE YIELD t PRINT 154 154 FORMAT(*112 HOURLY PRINT FOR SUBROUTINE YIELDt) PRINT 39 6200 6210 6220 6230 6240 6250 6260 6270 6280 6290 6300 6310 6320 6330 39 FORMAT(*- I DAY COUNTER HTTDIST BBDIST HEXAREA HEXPHEC HEXCIRC TP6340 +OPLTN EPOPHEX BPOPHEC TOTLPOP*) PRINT 409(XI(I)9XDAY(I)9XCOUNTRSI)oXHTTDISCI)9XBBDIST(I39 +XHEXARE¢ITQXHEXPHE(I79XHEXCIR(I).XTPOPLT(I)oXBPOPHX(I)oXBPOPHC(I)9 +XTOTLPO‘I’9I310162'161) 40 FORMAT‘IX'ISQIXQIZ93X,IQQGXQF492'3XQF402'3X9F5o393X. +F60091X9F7o201X9F7o091X9F700v1X0F7o091X9F7o0) PRINT 41 6350 6360 6370 6380 6390 6400 6410 41 FORMAT(*1 I DAY COUNTER BFRFRT BYLDHEX BYLDNZ BYLDHEC TFRFRT TYL06420 OHEX TYLDH2 TYLDHEC SYLDHEX SYLDH2 SYLDHEC*) PRINT 429(XI(I)9XDAY(I)cXCOUNTR(I)sXBFRFRT(I)OXBYLDHX(I)9 +XBYLDM2(I)9X8YLDHC(I)9XTFRFRT(I).XTYLDHX(I)9XTYLDM2(I)9 +XTYLDHC(I)QXSYLDHX(I)9XSYLDN2(I)QXSYLDHCCI)9I=19162) tiflittfitifiiififittt THIS SECTION PRINTS VARIABLE VALUES ONCE PER DAY AT 2400 HR. PRINT FOR SUBROUTINE PHOTO 42 FORMAT‘IXOI391X9I203X9I994XsF6o3!1X9F7o391X9F60391X1F70091X9 *F6o301X1F7o391X9F6.391X9F70001X9F70391X9F6o311X1F700) t 60 TO 930 a t t t t t t t t i t t t t t t a t i a t t t c t t t t t t t t t t t a t t c t T t t t t a t t t t t t t t t t t t t t t t t t t t t t t t f t t t t t i t t t tacitttttttaatt*t * t t * 9 DAILY PRINT * t t i t O t t t t 910 PRINT 155 155 FORMATIOIDAILY PRINT FOR SUBROUTINE PHOTO.) PRINT #3 6430 6440 6450 6460 6470 6480 6490 6500 6510 6520 6530 6540 6550 6560 6570 6580 6590 6600 6610 6620 6630 6600 6650 6660 6670 6680 6690 6700 6710 43 FORMATIfi-DAY I COUNTER ENERGY TEHP RAIN RELHUH UINDSPD MONTH DAT6720 4”E JHONTH JDATE IMONTH IDATE I BNPLANT HARVESTt) PRINT 44v¢XDAY(I)QXI(I’vXCOUNTR(I)9XENERGY(I):XTEHP(I)gXRAIN(I)o ¢XRELHUH(I)vXHIND$P(I)9XHONTH(I):XOATE(I)¢XJHDNTH(I)oXJDATE(I)9 +XIHONTH(I).XIDATE(I)oXI(I)oXBNPLAN(I)9XHARVES(I)gI=2016291601 44 FORMAT(I392XQI391X9I494XoF5aly2XgF4.1c1X9F4.2o1X9F5.291XvF7.1v2X, *IQQZXQI3Q4XQI401X'1992X91493X01493X91894X0I2,6X9I2) PRINT 703 703 FORHAT(*1 I DAY CANORES DELTA EVAPOTN VOLSI DVOL DSHC 6730 6740 6750 6760 6770 6780 6790 6800 146 0 SEC RTDEPTH SUPOT LFUPOTL CUHIRGN CUHRAIN CUMEVAP VAPSAT V6810 *APTEHP*) 6820 PRINT 7041¢XI(1)QXDAY(I)QXCANDRE(I)QXDELTA(I)QXEVAPOT(1)0XVOL51(I)6830 OgXDVOL(I)9XOSHC(I)QXSUC(I)QXRTOEPT(I’vXSUPOT(I)QXLFHPOT(I)9 6840 *XCUMIRG‘I)1XCUHRAI(1)9XCUWEVA(I)9XVAPSAT=8$UMDMD 20930 XBTRLCT(I)=ETRLCTE 20940 XPDOLCK(I)=PO0LCHK 20950 t 20960 520 IF(DAY.LE.HARVEST) GO TO 999 20970 4 20980 XBCHODM(I)=0. 20990 XBCPO0L(11=ECPO0L 21000 XBCSTOR(1)=BCSTORE 21010 XBDRYFR€I1=BDRYFR 21020 XBDRYLF(I)=BDRYLF 21030 XBDRYRT(I)=EDRYRT 21040 XBDRYST(I)=BDRYST 21050 XBFRDHD(I)=0. 21060 XBFRINC(I)=0. 21070 XBLFDMDIII=0. 21080 XBLFINCCI)=0. 21090 XBPHSAT(I)=0. 21100 XBRFRDMIII=BRFRDMD 21110 X8RLFDH=8RSTDMD 21140 X8RTDND