‘x é‘.u¢‘¢“u"r .. w -r.:~:‘s>x”-‘-“.:~;~‘Az.-FF x" .\ ~ 7': _‘. ’ ~ _.._V. 339‘ t ‘ _.:'.. 1'; u. -;3 J . "11:3 In, 0 I: *' s - r . . .A.~_~1.ka‘-?:2,fk . >3. - w gar u u‘“ , . . ,2 #3:»... ‘4' ' .' n ..m u ~ 1 L. _ ‘1 £3.93. U - , 4:: 2:3,..L, \ ‘- - :3! ‘. ".1“: - ~ . ‘ ,4 ‘r «:3 1 ' *1 -—~ 2-3.1: A v y. nze‘ » “9." .~ S H-rr. t;'_'.:‘.‘T."'-,. a. ,1 -. ..V F's-:1" '- 3:51;: ‘-,, A... ‘rfif' ‘ v." y‘ w‘ r .. . u I. .. .1.-. .1767: 5 ‘ 'tu, x MICHIGAN STAT HIHH l 3 1293 LIBRARIES illllllluuumuml 1044 5215 l! This is to certify that the thesis entitled PRELIMINARY DRIS NORMS IN HIGHBUSH BLUEBERRY (Vaccinium corymbosum L.) presented by Carmen Goni Altuna de Otero has been accepted towards fulfillment of the requirements for Master degree in Crop & Soil Sciences fiW/éflé' / Major professor Date _ApLiJ_22_,_J.9_9A_ 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution LIBRARY MIchlgan State Unlverslty PLACE II RETURN BOXto magma-mum mm. TO AVOID FINEs mum on or baton date duo. DATE DUE DATE DUE DATE DUE ’ < 9‘... I l CHI DI C 3 Cl MSU IOMWWWOMIW Wm: _fi __—fi__ _ _ —_ 5—_. PRELIMINARY DRIS NORMS IN HIGHBUSH BLUEBERRY (Vaccinium corymbosum L.) BY Carmen Gofii Altuna de Otero A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Crop and Soil Sciences 1994 Boyd G. Ellis, Advisor ABSTRACT PRELIMINARY DRIS NORMS IN HIGHBUSH BLUEBERRY (Vaccinium corymbosum L.) BY Carmen Gofii Altuna de Otero The development of the Diagnostic and Recommendation Integrated System (DRIS) was a major advantage in the utilization of foliar analysis as a diagnostic tool during the past decade. But, application of DRIS is complex; consequently, there are still some controversies about its application as a means of assessing mineral nutrition of fruit crops. The present study was conducted to develop DRIS norms from 1074 observations of previously published and unpublished data of leaf nutrient composition and yield in highbush blueberry (Vaccinium corymbosum L.), and to compare the DRIS diagnosis with standard values previously developed for the crop to determine if relative deficiencies or excesses associated with lower yields would have been detected routinely by DRIS analysis. Critical values were calculated for each nutrient and correlated with the DRIS indices. The first tentative set of DRIS norms developed and their diagnostic results have identified relative deficiencies of N, P, Ca, and Zn and relative excesses of K and. Mn for blueberries. The most and least relative limiting nutrient for both subpopulations were identified. DRIS and. the Sufficiency Range Value (SRV) were in agreement for detecting excesses of K and.Mn, but DRIS detected excesses for the other nutrients where the SRV considered others sufficient. DRIS appeared to be less sensitive than the Critical Value (CV) in detecting Mn, Fe, Zn and B deficiencies. The comparison among the different diagnostic criteria used (DRIS, SRV, and CV developed from the data base and reported) showed some divergences. DRIS indices for Cu, B, Zn and A1 for certain ranges of nutrient concentrations seems to be of little advantage to a ratio based diagnosis over the CV. Copyright by Carmen Gofii Altuna de Otero To Alvaro, my husband, for his love, care and support. ACKNOWLEDGMENTS I would like to express my sincere gratitude to Dr. Boyd G. Ellis, my major professor, whose high quality teaching and advise were always readily given and mostly for his friendship that he has generously given during my master's program. I also extend my appreciation and thanks to the members of my guidance committee, from the Crop and Soil Department, Drs. Joe T. Ritchie and Darryl D. Warncke, for their cooperation and assistance with the thesis, and from the Horticulture Department I give a special thanks to Dr. Eric J. Hanson for his interest, support and suggestions that made this work possible. I extend my sincerest thanks to Dr. James J. Hancock, Dr. Adam Dale, Dr. John R. Clark and Dr Irvin Widders for their generously sharing their data, both published and unpublished, to enlarge the data base. Finally, to the Instituto Nacional the Investigacion Agropecuaria from Uruguay for their financial support, and to all my friends whose work and example gave me a stimulus in my career. To my parents and most of all to my husband for his loving, patient and helping support. TABLE OF CONTENTS Page LIST OF TABLES....................................... ..X LIST OF FIGURES........................................XI LIST OF APPENDIX.....................................XIII INTRODUCTION............................................1 LITERATURE REVIEW.......................................6 1. Mineral Nutrition in Fruit Trees...................6 1.1. Nutrient Requirement.......................6 1.2. Root Growth Influence......................7 2. Methodologies to Access Nutrient Requirement.......9 2.1. 8°11AnaIYSj-SOOOOOOOOOOOOO00.0.0000000000009 2.2. Leaf AnaIYSiSOoooooooooooooooooooooooo000010 2.2.1. Expression Forms: advantages and disadvantages.0.0.00.0...OOOOOOOOOOOO0.0.011 2.2.2. Interpretation Forms......................14 a. Relationship between Leaf Composition and Yield.................................14 b. Critical Nutrient Concentration...........15 0. Balance Index.............................16 d. other Methods.............................17 3. Diagnostic and Recommendation Integrated system (DRIS)OOOOOOCOOOOOOOOOOOOOOCOOOOOOOOOOO0.0.18 3.1. Beaufils' Idea............................18 3.1.1. Causal Relationships......................19 3.2. DRIS Methodology Protocol.................21 3.3. DRIS Relationships between Yield and Indices...................................23 3.4. DRIS Advantages and Weaknesses. ..........26 3.5. Effect of Variety or Geographical Region on DRIS............................27 VII 3.8. 3.9. 3.9.1. 3.9.2. 3.9.3. 3.9.4. 3.9.5. Blueberry. 4.1. 4.2. 4.3. 4.4. 4.5. 4.5.1. a. b. C. 4.5.2. 4.5.3. 4.5.4. 4.5.5. 4.5.7. 4.5.8. Effect of Leaf Age and Position on DRIS Diagnosis.................................31 Ability of DRIS to Rank the Nutrient Requirements..............................35 DRIS Limitation and Proposed Modifications.............................40 DRIS Reports..............................45 Agronomic Crops...........................45 Pastures and Forage Crops.................45 Vegetable Crops...........................46 Forest Trees..............................46 Fruit Trees...............................47 OOOOOOCOCCOCOOOOOOOOO0.0.00.0000000000054 Potential Yields..........................55 Root System and Soil Characteristics......56 Soil Reaction.............................57 Leaf Sampling and Seasonal Variation......59 Plant Composition and Response to Nutrient Elements.........................60 Nitrogen..................................60 Ammonium vs Nitrate source................61 Critical Level............................63 Interactions..............................64 Phosphorus and Potassium..................64 Manganese.................................66 Aluminum..................................67 Calcium and Magnesium.....................68 Sulfur and Chloride.......................70 Boron, Copper and Zinc........ .......... ..71 HYPOTHESIS AND OBJECTIVES..............................73 MATERIALSANDMETHODS...’OO0000......0.00.00.00.000000074 RESULTS AND DISCUSSIONOO..OOOOOOOOOOOI0.00.0000...0.0.077 1. Characterization of the Highbush Blueberry PopulationOOOOOOOOO..OOOOOOOOOOOO0.000......O0.0.077 1.1. 1.2. 1.3. Yield values..............................77 Leaf Nutrient Concentrations..............78 Relationships between Yield and Nutrient Concentration........... ..... ....83 Development of Blueberry DRIS Norms...............91 2.1. 2.2. 2.3. Subdivision of the Population.............91 Ratios of Nutrient Elements...............94 DRIS Nutrient IndeXOOOOOOOOOOOOOOOOO0.0.0.97 VIII 2.4. 2.4.1. 2.4.2. 2.5. 2.6. Identification of unbalances.............103 The Nutrient Imbalance Index (NII).......107 Relative Order of Nutrient Limitations..............................109 Comparison among Approaches in Diagnosing Nutritional Limitations.......111 Some Limitations found in the DRIS.......112 3. Correlation between DRIS and the Critical Value..121 SMARY AND CONCLUSIONOOOOOOOOOOOO0.0.0.0000...0.0.0.0131 LITEMTURE CITEDOOOOOOOOOOOOOOOO..OOOOOOOOOOOOOOO0.0.0134 APPENDIXOOOO0....0.0.....OOOOOOOOOOOOO..OOOOOOOOOOOO0.157 IX LIST OF TABLES Table Page 1. Statistical characterization of yield and foliar nutrient concentrations of the Blueberry population...............................79 2. Blueberry nutritional characterization compared to the suggested Critical Nutrient Levels (Eek, 1981)00.00.0000...OOOOOOOOOOOOOOO0.0.00.0000084 3. Blueberry nutritional characterization compared to Michigan Standard values (Hancock and Hanson, 1886)00....0......OOOOOOOOOOOOOOOOOCOOOO00.84 4. Characterization of the high and low yielding Blueberry population...............................93 5. Mean, variance and coefficient of variation among ratios selectedOOOOOOOOOOOOOO0.0.0.000000000000000095 6. DRIS Norms for Highbush Blueberry (Vaccinium comosum L.)00......0........OOOOOOOOOOOOOOOOOO0.98 7. Effect of age of bushes for three high yielding subpopulations on the optimum of nutrient and nutrient ratios...‘00....OOOOOOOOOOOOOOOOOOOOOOO0.119 8. Blueberry DRIS related to age range for equal number of nutrient observation....................120 9. Comparison of Blueberry DRIS nutrients norms and Critical level estimated and reported in the literatureOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO00.122 10. Correlation between DRIS and Critical Value.......122 Figure Page 1. Frequency distribution of Blueberry yield..........80 2. Frequency distribution of mean yield with age Of plantSOOOOOOOOOOOOOOOOOIOOOOOOOOOOOOOOO0..0.81 3. Scatter diagram of leaf nutrient concentration with Blueberry yield. a) N % ; b) P% ; c) split data setp f,(plant response) ----- > Yield,; 2. Climatic conditions ----> f2(plant response) ----> Yield,; 3. Farming practices ----- > f3(plant response) ----- > Yield,; 4. Soil treatments + Soil properties = f,(soil response) etc; 5. Soil response + climatic conditions + farming practices = f,(plant response) etc. ; 6. Plant response (2 internal characters) ----- > Yield. From these relationships calibrations are progressively established on norms and refined from: a) the diagnosis of the resulting effects reflected in plant composition for a given situation; b) the diagnosis of the primary causes reflected mainly in soil composition and environmental, management factors and attempts for the observed situation; c) the dependence on the extent to which the observed situation can be explained and remedied; d) the quantities of the elements to be applied obtained from pre-established norms; and e) the new information recorded after corrections and the subsequent refined norms. 21 3.2 DRIS Methodology Protocol. Beaufils (1973a) , proposed the DRIS methodology following a step series. A. The first step is toldefine the characters to be improved and all factors that are suspected to affect them. B. The second step is to gather all reliable data available from field operations and experiments. C. The third step is to study the relationship between yield and so—called external characters (i.e. light, rainfall, etc) in four operations: 1) each character must be expressed in the greatest number of possible forms (parameters); 2) each character in a parameter form is plotted against yield; 3) the spread of data of the whole population is used to divide into a number of external character classes and the class which corresponds to the highest yield determined by Fisher's test; and 4) the same operation is repeated for every possible interaction factor and the subclass within which the highest yield is found. D. The fourth step is to study the relationships between yield and internal characters (i.e. nutrient content) where twelve operations are justified: 1) each internal plant character should be expressed in all possible forms; 2) the 22 whole population is divided into three subpopulations (high, medium and low yield) selected simultaneously on the basis of health, quality and yield; 3) the mean of each subpopulation is calculated for all expressions selected; 4) if necessary class interval limits between average and outstanding yielders are readjusted; 5) a x2 test is performed; 6) variance ratios between poor and average yield populations for all modes of expression are compared; 7) every parameter that shows a significant variance ratio between the two populations is retained and the coefficient of variation for the values belonging to normal yields is calculated; 8) from the parameters retained, two core (main) characters are selected for which variance ratios were found to be significant when simultaneously referred to moisture, and chain-diagnosis of the other characters is established by simultaneous reference to these central characters; 9) as alternative core characters, three internal plant characters are selected for which variance ratios were found to be significant when expressed to one other, and chain-diagnosis of the other characters is established by simultaneous reference to these alternate central characters (used when moisture data are not available); 10) the remaining parameters are retained from operation; and 11) the diagnosis indices reflecting the resulting interactions between all nutrient considered. Each internal character is calculated from a general formula: 23 R index (In) = [ f(R/S)- f(S/R)] /2 where 1. If R/S > X“, then f(R/S) 100 [ (R/S - 1) / xm ](k / CV) 2. If R/S < xms, then f(R/S) 100 [ (1- xms) / R/S ] (k / CV) R/S is the nutrient concentration ratio of R and S in the sample, )qm is the average of the nutrient concentration ratio of the high yielding population, k is an arbitrary number, usually 10, which is used to assure that function and index are whole numbers, and CV is the coefficient of variation of the nutrient concentration ratio from high yielding population. E. The sixth step is to prescribe and continually refine the most suitable remedy for each particular set of conditions. 3.3. DRIS Relationships between Yield and Indices. In the DRIS system maximum crop yields are attainable only when the values of important ratios approach an optimum value, which is approximately the mean value of the ratio in 24 a desirable or selected high-yielding population. Both high or low yields can be obtained at optimum R index, e.g. N index values; however, the standard deviation about this optimum value decreases with increasing yield. This indicates that the higher the yield, the smaller is the permissible deviation from the composition required. Therefore, the .known luxury consumption. would. not be a characteristic of high yielding plants (Beaufils, 1971). Since important ratios must approach their optimum values for high yields to be obtained, the variance of important ratios are smaller than in a low yielding population. This is the reason that the ratio of the variance between the high and low'yielding population is used to select the important.ratios (Beaufils, 1973a). The DRIS calibration system characterized by the above six functions in plant response allows one to classify yield factors in their order of limiting importance. For each nutrient it is possible to calculate a DRIS indexu This index is based on the mean deviation of each important ratio (where a nutrient element could be either in the numerator or in denominator) from its optimum value. An optimum DRIS index for any nutrient element is zero. The indices give an indication of the intensity with which the plant or soil required a particular nutrient. For example, during one diagnostic, the negative DRIS index indicates the most limiting element with respect to the other nutrients tested; 25 a zero index indicates an element in quantities statically associated with high yield and positive DRIS index indicates the most excessive nutrient with respect to the other nutrients tested. However, plant indices only indicate the nature and degree of balance of the nutrients. Therefore, it is not possible to establish what could be the demand of the plant at a given site; thus DRIS does not give an indication of the amount of a particular element which must be added (Beaufils, 1973a). In addition, it is possible to establish a general relationship between yield and plant internal characters that could be approximated by three separated regression equations. A positive regression coefficient will be described every time the studied character limited yield. A negative regression coefficient is found when the particular character is limiting yield because it is present in excess. A zero regression coefficient is founded when the parameter is not affecting yields (Beaufils, 1971). Beaufils (1973a), said that optimal and normal indices are not always completely coincident. Certain characters showed a steady inclination toward either positive or negative values; thus, Beaufils (1971), suggested that this is the main reason to divide the whole population into three subclasses (high, medium and low yields). The mathematical frequency distribution of the internal characters of the high population yield was used as a guide to indicate the direction towards 26 optimum yields . 3.4. DRIS Advantages and Weaknesses. Recently the DRIS approach has received considerable attention because of the multiple advantages that it offers over the other interpretation methods. According to Beaufils (1973a; 1975; 1976), the main advantage of the system is that it: 1) permits the use of a simple equation with all plant, soil, yield and quality factors; 2) classifies these factors in the order of their limiting importance on yield and quality; 3) permits one to assess relative nutritional imbalances or deficiencies or both; 4) permits one to measure deviation of certain nutrient ratios in the plant tissues from corresponding nutrient ratios previously establish as norms. By comparing the norms with sets of nutrient ratios it is possible to state the relative sufficiency or deficient of each element; 5) has unrestricted possibilities of application: different locations, age of tissue, and form of sampling; 6) has the possibility of observing, studying and remedying problems in the growing season as they occur; 7) increases the flexibility and rapidity in research work, saving time, and money; and 8) has unlimited possibility for in-depth investigation and enlarged application of results. Some of the most remarkable DRIS papers found in the 27 literature will be used to show its characteristics. 3.5. Effect of Variety or Geographical Region on DRIS. If the DRIS system norms are derived from a sufficiently large data base, it is possible to make DRIS diagnosis applicable irrespective of varietal or geographic regions (Beaufils, 1956, 1971, 1973a; Sumner, 1977a; Elwali and Gascho, 1984). Sumner (1977a), studied the relevant literature on maize (Zea mays L.) published throughout the worLd. He selected papers from Terman's works in the United States (Terman, 1974; Terman and Allen, 1974) and Kacar' work in Turkey (Kakar, 1974). Data from the selected papers in their original form together with the same data recalculated with the standard set of norms developed in South Africa were compared. The use of one set of norms was enough to explain the responses in terms of yield and plant composition to different rates of lime and N applications. Beaufils indices were able to show differences in P uptake and to explain yield increases caused by the use of different N carriers. According to the author, in all cases Beaufils' plant calibration indices have been superior to those criteria used by the different authors in diagnosing nutrient requirements and uptake for corn. Sumner emphasized the general applicability of the DRIS system which provided a means of comparing data for a particular crop grown 28 at various sites in the world. Payne et al., (1990) working with bahiagrass (Paspalum notatum), collected 857 pasture samples from ongoing field fertility trials in nine counties throughout central Florida. They analyzed tissue for N, P, K, Ca, Mg, Fe, Mn, Zn, and Cu. At the time of sampling forage yields were recorded» The DRIS norms developed were tested in an independent study in which bahiagrass was grown in greenhouse solution culture where known deficiencies were accurately diagnosed. The nutrients limiting bahiagrass forage yield in a fertility trial conducted under field conditions were also accurately identified by the DRIS norms. The authors concluded that DRIS can be successfully applied to bahiagrass grown under a wide range of conditions. ‘Use of this methodology can :readily and correctly identify nutrient deficiencies and DRIS methodology can be used in developing more accurate fertilization programs for pastures. Elwali and Gascho (1984), conducted a study to evaluate the effectiveness of soil testing and foliar analysis interpreted by the Critical Nutrient Level (CNL) approach and DRIS for sugarcane (Saccharum officinarumum) in eight fields representing the mayor sugarcane soils in south Florida. Foliar analysis interpreted by either method revealed a need for applying one or more micronutrient(s) to all fields, a practice which had not been previously been recommended in Florida. Application of N was also recommended for the first 29 time on Torry muck soil by foliar analysis using both methods. They found that the nutrient balance index (NBI) was the most appropriate parameter on which statistical analysis can be performed to evaluate the effect of treatments on nutrient balance. All of the interactions of field by method were not significant for all measured parameters. DRIS guided fertilization resulted in significantly better balance among nutrients than fertilization recommended by the other methods. Sugarcane and sugar yield were significantly higher for DRIS fertilization than for fertilization guided by the CNL method. Wortmann et al. , (1992) , working with dry bean (Phaseolus vulgaris L.), arrived at a slightly different conclusion. They estimated DRIS norms from a total of 1110 records collected from Colombia, Uganda and Rwanda, and tested these on farm trials conducted in Tanzania and Uganda. The efficiency of the DRIS in predicting responses to applied fertilizers was compared to prediction using CNL determined for beans (Howeler, 1983). The CNL were found to be too low to have a good predictive capacity in the test environments. The result showed that DRIS was a superior approach for interpreting foliar tissue analysis for beans. DRIS predictions were less affected by varying plant age than was the prediction with CNL. However, the East Africa DRIS norms generally differed from those estimated from the Colombia data. When DRIS norms estimated from the two sets of data were compared with paired t—test they found that they differed 30 for most nutrients. The authors concluded that the accuracy of the DRIS could be improved by having different sets of norms for different bean production environments. HacKay et al., (1987), arrived at a similar conclusion working with 1086 sets of yield and analytical data from 28 field experiments with potato (Solanum tuberosum) conducted over a period of 8 years on irrigated Boroll soils and 1260 data sets from.5jyears of experimentation on Spodosol soils in the temperate humid area of Nova Scotia in Canada. Canadian developed DRIS norms differed from those reported for potatoes in South Africa. DRIS nutrient indices computed from nutrient ratios established as normal on Spodosol areas were reasonably suitable for diagnosing nutrient deficiencies on the Boroll soils. Indices from Spodosol norms were usually indicative when deficiencies were severe, but N indices were often negative where there was not a response to further application of N. The pattern for P and K indices indicated the same trends utilizing Boroll or Spodosol norms. Indices based on the South African norms were unsuitable for assessing leaf data from the Boroll and Spodosol area. The authors noted a discrepancy in the low value for N/K and the high value for the RIP from the South African norms in relation to both of the Canadian regions. However, they noted that the ratios from soil and climatic areas widely different from Nova Scotia and southern. Alberta. were still of value in. predicting deficiencies in either area. 31 Working in wheat (Triticum vulgaris), Amundson et al., (1987) said that norms regionally derived and geographically restrict may enable the DRIS system to provide more accurate diagnoses than norms developed outside the local area and developed from other cultivars. Using the set of published DRIS norms developed in the Midwest, Amundson found that nutrient diagnosis calculated on irrigated winter wheat grown in Central Washington tended to over estimate N deficiencies and 'under estimate P ideficiencies at early heading and tillering, respectively, relative to parallel diagnose by DRIS based on a corresponding set of norms. Escano et al., (1981) suggested also that for corn (Zea mays L.) the use of locally calibrated norms may be more accurate in diagnosing nutrient deficiencies than norms developed from plants in other geographic regions. 3.6. Effect of Leaf Age and Position on DRIS Diagnosis Another advantage of the DRIS system is its ability to minimize the effect of leaf age on diagnosis, enabling one to sample over a wider range of tissue age than is permissible when diagnosing by the critical value approach. Sumner said that nutrient ratios in plant tissues remain constant throughout the growing season, and correct diagnosis using the DRIS procedure is possible regardless of the physiological age of plants (Sumner, 1977b, 1977d). 32 Sumner (1977c), worked with a survey of published and unpublished soybean (Glycine max) data with a total of 1245 sets of N, P and K leaf analysis and their corresponding yields data. The effect of stage of growth and position of the leaf sampled were also analyzed. He concluded that the developed DRIS norms were able to diagnose when N, P or K limited soybean production. DRIS indices diagnosed the follow order of nutrient requirement N > P > K with only two exceptions despite the fact that the N, P and K contents of the leaves varied more than twofold. At the same time, the DRIS system was able to predict nutrient imbalances even when the nutrient concentration in the plant were at or above the sufficiency level range. Sumner, using the published.set of data in corn (Zea mays L.), tested the hypothesis that DRIS was able to minimize the effect of the age of tissue and to detect nutrient requirement" He also analyzed.the effect of leaf position and leaf part sampled on the nutrient content. The results noted that.calculated.DRIS indices had the same‘order'of’requirement irrespective of the position of the leaf on the plant with only one exception in which the order of P and K were reversed. This fact was true for the whole leaf and blade with midrib and margin removed. But, for the margin and midrib alone, the order of nutrient requirement was different but consistent. The author pointed out that as long as one type of tissue was sampled, DRIS diagnoses were independent of 33 the position of the leaf in the plant. However, it was only valid to make a diagnosis on the basis of the same type of tissue as was used in establishing the norms. Using DRIS diagnostic the position of the leaf on the plant is not very critical, particularly if a leaf or outer three-quarters of the leaf from the middle of the plant is sampled (Sumner, (1977b) . Sumner (1977d) , in other work, using a total set of 1108 published and unpublished data on wheat (Triticum vulgaris) developed DRIS norms which were applicable irrespective of variety and age at which the leaf samples were taken. Irrespective of the time of sampling the nutrient requirement diagnosis was K > N > P. The reason of the ability of the DRIS system to make diagnosis at different stages of growth is due to the fact that when ratios are computed, the effect of dry matter component (which are accumulated with the age and caused a dilution of the nutrient content) is reduced. The same diagnosis was made irrespective of the variety used in all but one case. However, working with wheat (Triticum vulgaris) , Amundson et al., (1987), derived two sets of norms, one from tissue analysis of winter and spring wheat grown in Washington State, and the other from statistical analysis of the nutrient ratios from winter wheat grown in eastern Washington which indicated that each of six ratios (N/P, K/N, K/P, N/S, P/S, and K/S) exhibited significant sampling date/time dependence. Consequently, nutrient diagnosis provided by the DRIS approach 34 may not always be independent of the origin and age of plant. Hanson (1981), employed the DRIS system to evaluate N, P and K balance of soybean (Glycine max) using foliar analysis data from 3 experiments in Brazil where P was the responsive nutrient. DRIS was useful for calculating nutrient balance association with highest yields and the greatest nutrient imbalance at lower yield levels. This fact was true when samples were collected at the R-2 growth stage. The method was useful in the evaluation of phosphorous fertilizer materials. The author demonstrated that as the soybean plant aged the DRIS method could still predict the highest level of nutrient balance when highest yields were obtained. The greatest discrepancy occurred with samples collected later in plant. maturity ‘which suggested. that. plant age is still important, but less important than with the critical value method. A similar conclusion was reached by Beverly et al., (1984), who :noted that. DRIS diagnoses ‘were affected. by differences in type or age of tissue sampled for Valencia oranges. However, Sumner (1990), noted in a review about the use and application of plant analysis, that the form of expression used to calculate DRIS indices has an important effect in minimizing the effect of age in diagnosis. He pointed out that if the tissue under diagnosis was sampled at the usually accepted time, ratio forms for all nutrients would be 35 appropriate. 0n the other hand, if one samples earlier or later than the accepted time, one should use ratios for the nutrients which change in the same way with age and products for the nutrients which change in opposite directions. In the same paper he said that when Beverly's (1984) data were recalculated using the appropriate form of expression, DRIS diagnostic was appropriate. 3.7. Ability of DRIS System in Ranking Nutrient Requirement According to Beaufils (1973a),(1973b),(1975),(1976); Beaufils and Sumner, (1976); Sumner, (1981); Elwali et al., (1985); Payne et al. , (1990); Jones and Sinclair, (1991); Wortmann et a1, (1992) and many other authors who have worked with the DRIS system, Beaufils methodology has an advantage because it is able to qualitatively and quantitatively classify the nutrients required by the plant in order of their limiting importance on yield, which is not possible when the critical value approach is used. The effects of appropriate and inappropriate treatments can be distinguished. Beaufils (1973a), gave multiple examples where the proposed methodology was able to rank nutrient requirements of different crops and confirming accurate treatments. Meldal-Jhonsen et al. (1975), applied the DRIS system to soil calibration with potatoes (Solanum tuberosum) . The 36 developed norms were tested on independent soil data from a 3‘ N, P, K, lime factorial fertilizer experiment. The DRIS calibration system showed the soil response to differential fertilizer treatments and evaluated the nature and amount of nutrient applied. A low yield was observed when the sum of the indices was high. The reverse was not necessarily true. The author noted that the norms need to be derived separately for the different soils series. Clay percentage was used as a basis for deriving soil indices, and it separated soils according to their inherent fertility patterns. Sumner (1977c) , working with soybean (Glycine max) stated that the developed DRIS norms were able to detect K as the most limiting nutrient required for the crop. The order of nutrient requirement was K > N > P. When the developed norms were tested with literature trial data and appropriate treatments were used, the results were always an increase in yield. Vigier et al. (1989), working with the same crop, compared the results made from the sufficiency range method, the American DRIS norms, eastern Canadian DRIS norms and a modified version of the DRIS system (which used the whole population data rather than the high yield data). Data analyses were divided according to soybean cultivars in three trials under research where fertilizer effects were significant on some of the plant nutrient contents. The diagnosis made on all trials showed some differences among the 37 methodologies used. Nevertheless, the advantage of the DRIS diagnosis was quite apparent in all trials. The American DRIS was equal or superior to the sufficiency range in 5 out 6 sets of data. While the Quebec and the modified DRIS were equal or superior in only 3 out of 6 sets. The overall yield gained with the Quebec DRIS was 685 kg/ha compared to 402 for the American. DRIS and 537 for the ‘modified DRIS while the sufficiency range diagnosis gave 50 % less increase on yield when compared to the mean results of all DRIS diagnosis. Elwaly and Gascho (1984), noted for sugarcane that the DRIS approach was a better guide for fertilization than the soil testing method. The high cane and sugar yields obtained by DRIS guide fertilization were attributed to the better nutrient balance as revealed by DRIS indices late in the season. However, Caron et al. (1991), developed DRIS norms for greenhouse tomatoes (Lycopersicon esculentum L.) which were evaluated in nutrient solution trials with different rates of N concentration and salinity levels. The critical nutrient range (CNR) was also used as another diagnostic approach” iDry matter index was included in the modified DRIS (M-DRIS) approach to separate between excessive or deficient nutrients. Both DRIS and M-DRIS were found to be effective in managing N fertilization while CNR arrived at a wrong diagnosis. However, using the nutrient imbalance index as a critical level for intervention was not sufficient to handle salinity 38 problems. High salinity caused a constant increase in the NII, but, the calculated NII values did not correspond with the low yields obtained and were found ineffective as an indicator of any salinity effect on yield in both DRIS and M- DRIS. Jones and Sinclair (1991) , developed DRIS norms for white clover (Trifolium repens L.) in New Zealand. The developed N, S, P, K and Ca DRIS norms were used for testing, ranking and comparing 8 and P deficiencies and responses under field conditions. DRIS indices derived from N, S and P ratios and with a common set of norms had the same interpretation in highly contrasting environments (lowland and high country pasture areas). The inclusion of K, Ca and DM in the calculation of S and P index values made the S and P indices more negative in the high country data set, but did not alter the interpretation of the lowland data set. A uniform interpretation of the indices was impossible. DRIS indices using N, S and P only and with lowland norms identified S and P deficiencies in high country and lowland situations using a single criteria for nutrient adequacy (DRIS index 2 -2) . DRIS made a successful ranking of S and P deficiencies in order of severity. Evanylo et a1. (1987), used DRIS for identification and correction of nutrient limitation on soybean (Glycine max) production. They used a large data base with yield and soil analysis throughout the southeastern United States to develop 39 the norms. Diagnostic norms based on Mehlich 1 extractable P, K, Ca and Mg were developed for coarse and fine textured Ultisols. Differences in optimum nutrient ratios between soils were done with regional liming and fertilization practices based on the amount of clay in the plow layer. DRIS soil norms accurately diagnosed nutrient limitations at low yield levels, but were unable to correctely diagnosis at high yield levels. Soil nutrient balance was not critical for determining yields on the highly weathered soils of the area at levels less than 3000 kg soybean/ha except when the level of some nutrients were excessive. They concluded that differences in optimum nutrient ratios may be greater for regional cultural practices rather than for nutrient balance requirements. Beverly (1993) , researched the agreement of diagnosis between the DRIS and sufficiency range for wheat (Triticum vulgaris) , corn (Zea mays L.) , and alfalfa (Medicago sativa) . According to the proposed methodology of the author, for wheat both methods showed agreement but only DRIS predicted S deficiency satisfactorily. For corn, P and K diagnoses were acceptable; however, N and 8 showed marginally accurate diagnoses of yield response to fertilization. The DRIS approach was similar for P and K, and inferior for N and S. For alfalfa, the proposed methodology showed little agreement between the DRIS and sufficiency range diagnoses. The author concluded that neither the sufficiency range nor the DRIS 40 diagnoses were consistently superior or even acceptable for the crops researched. 3.8. DRIS Limitations and Proposed Modifications. Beaufils (1973a), noted that the amount of data recorded is the limitation of the proposed system. Even so, all the pioneering work done in rubber trees was achieved.without the help of electronic computers. Jones (1981), said that the complexity of the DRIS methodology has discouraged its use; nevertheless, many workers have found that the DRIS approach.was more accurate in diagnosing nutrient element deficiencies than conventional methodologies. To overcome part of the limitation, he proposed three modification to Beaufils methodology that have facilitated the successful use of DRIS in the Hawaiian sugar industry. The proposed modifications include the following: 1) a simplification in the logic and calculation of the intermediate DRIS functions. (He proposed the use of the simplified equation: f(R/S) = (R/S - st) / (SDm) ; 2) the use of the mean and variance significance in the selection of important parameters; and 3) a prediction of yield response to additional fertilizer whenever the DRIS index for a particular nutrient falls below zero. .Letzsch and Sumner (1983), developed a computer program, written in FORTRAN, for calculating nutrient indices for the 41 DRIS. The program is comprised of a main program and four subroutines (INDICE, CVR, KKN and RREV); the main program handles the imput/output. The following crops are covered: corn, soybean, sorghum, potatoes, wheat, rubber, sugarcane, sunflower and alfalfa. Although DRIS was considered an improvement over the CVM, it has a disadvantage in that each time it is used, it predicts that one or more nutrients are limiting. But, it does not necessary define all limiting nutrients. Therefore, when one limiting nutrient is corrected one or more nutrients may become limiting even though they were not originally detected as limiting. A team of researchers worked to compare the CVM and the DRIS in confirming P and K deficiencies on soybean (Glycine maX'L.) and to determine if including nutrient concentrations in DRIS can be useful in separating limiting from non-limiting nutrients. They introduced the following modification: a) nutrient concentrations were introduced as: (nutrient x 100/d.m.); b) dry matter was treated as a nutrient, and its index value calculated for each treatment; Whether N/D.M. > n/d.m. or N/D.M. < n/d.m. f(N/D.M.) = (N/D.M. - n/d.m.)10 x 1/S where N/D.M. = nutrient concentration in the sample tissue; n/d.m. = nutrient concentration of DRIS data base; and S = standard deviation of respective nutrient concentration. 42 A P-K-lime soil fertility trial was used to confirm the accuracy of nutrient deficiency diagnosis among the methods. DRIS was slightly better than the CVM in confirming K deficiencies and much better in detecting P deficiencies. The CVM failed to detect P deficiencies. DRIS was biased in favor in diagnosing K insufficient and against detecting P deficiencies. Each time DRIS was used it predicted at least one nutrient as limiting but, incorrect diagnoses were still made. When nutrient concentrations were included in the calculation of a dry matter index value, it helped to separate between limiting and non-limiting nutrients. But DRIS and the modified DRIS (M-DRIS) were biased against detecting P deficiencies. The authors concluded that further work is needed to determine if detection of P deficiencies can be improved by adjusting P in the M-DRIS data base (Hallmark et al., 1987). Beverly (1987a), using a factorial N, P, K experiment, with over 4000 samples of soybean (Glycine max L.) , made a comparison among alternative diagnostic methods in foliar analysis. The comparison involved the following: a) sufficiency range (SR); b) the traditional DRIS approach; c) DRIS, with only negative index values taken to indicate probability of response; d) DRIS incorporating dry matter (M-DRIS); e) DRIS used with logarithmic transformation (109([R1/[S] = ‘0 (109([R1/[31H3n (109([R1/[51Hsn 43 f) DRIS using the whole population with the simplified calculation of the "Jr Index” JR = x ; (109([R]))sn and g) the K2 value, similar to one proposed by Kenworthy, (1973) calculated by the equation: Kn =-=([B]-[2]x . [Rlsn All methods diagnosed the same most limiting nutrient. But, the SR method did not make the correct diagnoses on 5 week-old samples. The traditional DRIS were the least conservative, indicating only the order in which N, P and K would likely limit yield. The two methods based on concentration of nutrients rather than ratios were similar to the DRIS when the dry matter index was used. The KR method was almost identical to M-DRIS. The yields in the test data set increased with nearly every nutrient application and the least conservative DRIS showed the greatest yield advantage. No diagnostic method consistently identified the nutrient causing the greatest yield response as most limiting. The author concluded that the demonstration of fundamental equivalence and functional similarity of the ratio and concentration based diagnostic methods was a remarkable result of the investigation. The same author working with Valencia oranges (Citrus sinensis) found that a derivation and interpretation of the DRIS diagnoses could be simplified by the following: 1) using a logarithmic transformation of the nutrient ratio data; 2) 44 using population parameters rather than high yield subpopulation values; 3) using a simple index calculation "In" = (LIMA) ' L110!” law where: A = is the concentration of any nutrient. a = is the mean of the all population for the particular nutrient. a“ = is the standard deviation of the mean; and 4) incorporating the probability of yield response to a treatment (Beverly, 1987b). Knowing that the most accurate DRIS norms were ideally developed.with very large data bases, Walworth et al., (1988) compared norms for corn (Zea mays L.) developed from a set of 10 observations of field-grown corn with yields greater than 18 Mg/ha with norm generated from 8494 observations throughout the world. The P, K, Mg, S, Mn and B norms calculated from the two data bases were significantly different, while N, Ca, Fe and Cu were not. The norms developed from both data bases were tested using a factorial N3P’K3S3 greenhouse experiment. Those developed from the limited data base were slightly better in predicting yield increases than those from the larger data base. The use of a few extremely high yields in the data base was noted as an efficient, accurate, reasonable and inexpensive option for generating nutrient norms. 45 3.9. DRIS Reports The Physiological diagnosis, later called DRIS system, was first applied to rubber trees (Hevea brasiliensis) (Beaufils, 1957a), and then to agronomic crops. 3.9.1. Agronomic Crops. For field crops DRIS has been used on: corn (Zea mays L.) (Beaufils, 1974; Sumner, 1977a; Escano et al., 1981; Elwali et al., 1985; Walworth et al., 1988; Beverly, 1993), sugarcane (Saccharum officinarum) (Sumner, 1975; Beaufils and Sumner, 1977a; 1977b; Meyer, 1981; Jones and Bowen, 1981; Elwali and Gascho, 1983a; 1983b; 1984), soybean (Glycine max) (Sumner, 1977c; Beverly, 1979; 1986; Vigier et al., 1989), wheat (Triticum aestivum) (Sumner, 1977d; 1981; Amundson and Koehler, 1987), tobacco (Nicotiana tabacum) (Evanylo at al., 1988), sunflower (Helianthus annuus) (Grove and Sumner, 1982), sorghum (Sorghum bicolor) (Sumner et al., 1983) , and oat (Avena sativa) (Chojnacki, 1984). 3.9.2. Pasture and Forage Crops. The DRIS approach has been used for a few species of pasture and forage crops, for example, alfalfa (Medicago 46 sativa L.) (Erickson et al., 1982 and Walworth et al., 1986), bermudagrass (Cynodon dactylon) (Tarpley et al., 1985), bahiagrass (Paspalum notatum) (Snyder and Kretschmer Jr. 1988; Payne et al. , 1990) , and subterranean clover (Tritolium subterraneum) (Jones et al., 1986). 3.9.3. Vegetable Crops. For vegetable crops reports on application of DRIS exist for potato (Solanum tuberosum) (Meldal-Johnsen and Sumner, 1980; MacKay et al., 1987), celery (Apium graveolens) (Tremblay et al., 1990), lettuce (Lactuca sativa L.) (Sanchez et.al., 1991), and tomato (Lycopersicon esculentum) (Caron.and Parent, 1989; Caron et al., 1991). 3.9.4. Forest Trees. Application of DRIS to trees was first reported for rubber trees (Hevea brasiliensis) already mentioned (Beaufils, 1957a) followed by radiata pine (Pinus radiata) (Truman and Lambert, 1981), poplar (Populus spp.) (Leech and Kim, 1981 and Kim and Leech, 1986), eucalyptus (Encalyptus deglupta) (Yost et al., 1987), loblolly pine (Pinus taeda) (Needham et al., 1990), and fraser fir (Abies fraseri) (Hockma et.al., 1989 and Kopp and Burger, 1990). 47 3.9.4. Fruit Trees. The DRIS reports found for fruit crops were the following: pineapple (Ananus comosus) (Langenegger and Smith, 1978; Angeles et al., 1990), oranges (Citrus sinensis) (Beverly et al., 1984; Beverly, 1987b; Sumner, 1986), grape (Vitis vinifera) (Chelvan et al., 1984), sweet cherry (Prunus avium) (Davee et al., 1986; Righetti et al., 1988a, 1988b), peach (PrunuS"persica) (Sumner, 1986), hazelnut. (Corylus avellana) (Alkoshab et al., 1988; Righetti et al., 1988a, 1988b), apple (Malus domestica) (Fallahi and Righetti, 1984; Parent and, Granger, 1989; Szucs et al., 1990; Goh and Malakouti, 1992), pecan. (Carya .illinoensis) (Beverly' and Worley, 1991), walnut (Janglans regia) (Klein et al., 1991), and mango (Mangifera indica) (Shaffer and Larson, 1988). Angeles (1990), working with pineapple (Ananus comosus L.), stated that the derived DRIS norms were able to make correct diagnoses where critical values failed to make any diagnoses of deficiencies for N, P and K. The DRIS norms revealed nutrient deficiencies in the range normally considered to be sufficient. Increased precision was found in the evaluation of nutrient balance in the DRIS approach, which was ignored in the case of the critical values. Angeles.et.al. 1993, developed.DRIS norms for banana from 915 observations. Except for K and its ratios and products with other nutrients, the DRIS norms were very similar to the 48 average critical values. But, the validity of the DRIS norms and their superiority over the critical values in making correct diagnosis were partially confirmed and further field experiments are required. With Valencia oranges (Citrus sinensis L.) , where the sufficiency range method made a diagnosis, DRIS agreed with the diagnosis. It indicated not only the most limiting nutrient, but also the order in which factors would likely became limiting. DRIS diagnoses were found to be affected by differences in type or age of tissue sampled (Beverly, 1984). Sumner (1986), reported that despite the rather substantial differences between non-fruiting and fruiting nutrient levels of citrus leaves, DRIS makes essentially the same diagnosis of the order of nutrient requirement“ 'The DRIS approach reduces but does not eliminate the differences between leaf composition as affected by rootstocks. For sweet cherry (Prunus avium L.), Davee et al. (1986), stated that independent sufficient ranges were derived from commercial orchard data selecting a sufficiency range that produced the best agreement with DRIS evaluations. Trees with high nutrient imbalance indices were consistently low- yielding. In mulching treatments where unfavorable nutrient imbalances indexes were improved, yields were increased. The nutrient imbalance index was more strongly correlated with relative yield increase than any other mineral parameter. He said also that it ‘was possible to) develop useful DRIS 49 standards and DRIS-derived sufficiency ranges from survey data, even though conventional statistical approaches did not reveal strong relationships between mineral concentration and yield. Righetti et al. (1988a) , working with samples of hazelnut (Corylus avellana) and sweet cherry (Prunus avium) evaluated if biases in.DRIS formulas could prevent the detection of some deficiencies or excesses, using one and two equation calculation procedure. When they used one equation the data showed limits on maximum or minimum values that the index could obtain for Mn in hazelnut and Zn in sweet cherry but not for others elements. The sum of DRIS indices regardless of sign masked some relative deficiencies or excesses. When they used.two»equations the bias were not eliminated but were less. Regardless of the procedure utilized, the relationships between DRIS indices and their concentration varied with the elements -- some of them showed a clear dependence on the concentration of other elements -- which limits the use of the index in diagnostic. DRIS has served best as a supplement to sufficiency range, and DRIS has provided additional information when extreme imbalances exist. The same authors (1988b) in a complementary work, used the same crops and the lowest nutritional imbalance index calculated in the previous work as a new artificial data base. They determined ‘which nutritional values could be most accurate with the DRIS evaluations. When all but one element 50 concentration were at an ideal level and constant and one was changed, severe imbalances could be identified and related to the concentration of the given element. In addition to providing a ratio base diagnoses, DRIS norms were a supplement to sufficiency range when severe imbalances were detected and it provided a means of independent evaluation of current critical values. Szfics (1990), working with representative apple orchards (Malus domestica), in three consecutive years, found that the DRIS indexes related to lower yields in crops were correlated to oversupply of K and undersupply of P, while N status was neutral. Norms estimated by quadratic regression analysis for N, P and K showed a general oversupply of K and a relative undersupply of N and P, indicating that norms obtained by regression analysis reflect the extremes in nutrition-crop relations. In the case of apple trees with low values of nutritional imbalance index, these imbalances were not correlated with low yields. For apples (Malus domestica), Parent and.Granger (1989), derived DRIS norms from a 7-year fertilization trial on dwarfing rootstock with McIntosh. They stated that after the 6th year of planting, annual yield can be used instead of cumulative yields to generate DRIS norms. When leaf samples were collected at the appropriated sampling period, incorporating the dry matter index M-DRIS into the nutrient balance equation of orchard trees helped to separate limiting 51 from non-limiting nutrients and also integrated numerical information on nutrient concentration and nutrient ratios. These concentration and ratios were diagnosed independently or concomitantly with the sufficiency range approach and with the DRIS, respectively. In this case, M-DRIS was particularly useful when available critical values were not fully satisfactory. Goth and Malakouti ( 1992) , working with apples (Malus domestica) compared DRIS diagnoses from derived DRIS norms with mean sufficiency nutrient levels and published DRIS norms. Both calculated norms and indices from sufficiency range provided highly accurate ordering of the nutrient requirements of apples. High N and low Ca were identified as major nutrient problems. Calculated norms diagnosed Ca while published norms diagnosed P and Mg as limiting nutrients. For the experimental data conditions and the problems of the area the authors concluded that norms derived were better than those published. Fallahi and Righetti, (1984) worked with fruits and samples collected in August from apple (Malus domestica) orchards. They related the mineral composition to yield using the DRIS and critical value approaches. Fruit DRIS B, Zn, Mn, and Al correlated better with most storage quality factors than the original mg/kg values. Both methodologies were similar for fruit micronutrients. Leaf DRIS values correlated better with yield and storage problems than the original 52 values; however, DRIS indices were more strongly correlated with yield and quality factors than original leaf values when the methodologies used resulted in different diagnoses. Alkoshab et al. (1988), working with 624 mineral samples of hazelnuts (Corylus avellana,) compared the DRIS diagnoses and the sufficiency range approach. Both diagnoses methods agreed, specially if sufficiency range for some element were made more narrow. However, when the sum of DRIS indices were used, some nutrients were never identified by DRIS as a mayor relative deficiency or excess in any of the trees judged severely imbalanced. Nitrogen and Mg deficiency were not detected unless lower nutritional imbalance index thresholds were used. When they lowered the nutritional imbalance index threshold enough to detect N and Mg deficiencies, it was possible to detect not only N and Mg deficiencies but also imbalanced high yielding trees. The authors concluded that the DRIS did not detect all deficiencies or excesses. DRIS should be viewed as complementary to sufficiency range diagnoses that provided additional information when imbalances were detected. Beverly and Worley (1992), compiled more than 3000 data points for 11 elements and yield for pecan (Carya illinoinensis) . Mean and variation coefficient for each element and ratios corresponding to more than 58 kg/tree yield were established. The authors considered these values as the preliminary norms for the crop. 53 Shaffer and Larson (1988), used DRIS to identify mineral deficiencies associated with mango decline, a disorder of unknown etiology on mango (Mangifera indica L.) . Nutrient deficiencies associated with this problem were associated with the nutrition of the whole orchard and not with individual trees. The orchards with more decline were associated with the largest nutrient imbalance index. The most deficient nutrients in declining trees according with DRIS were Mn and Fe or a combination of both elements. The concentration of these elements'were.below'the critical value in two over three of the decline orchards. Magnesium concentration was higher in the declining orchards, and P had the most negative DRIS index. DRIS was a useful tool for detecting nutritional deficiencies associated with the disorder. Klein et al. (1991), using canopy photosynthetic photon flux (PP) density exposure as a primary external determinant of leaf mineral content characterized the spur leaf macroelement profile of walnut (JUnglans.regia), cvs "Harley" and ”Serr". Spur N, P, Ca, and Mg contents were linearly correlated with PP and specific leaf weight (SLW) when they were expressed on the basis of leaf area (A). Potassium content was linearly correlated with SLW on a percent of dry weight basis (W). All mineral ratios were calculated using all possible combinations of A and W and correlated with spur leaf SLW. All ratios based on weight per weight and area per area were not related to light exposure and SLW. Calculated 54 DRIS indices of gradually less exposed and less productive spurs showed a strong exponential increasingly positive imbalance for K or N and K in both cultivars. But, Ca and Mg revealed a reverse pattern. Phosphorus DRIS indices were opposite in sign in the cultivars as spur light exposure decreased. The nutritional imbalance index value of spur exposed to decreasing light intensities increased exponentially. The authors proposed a modification of the DRIS system that take into account the effect of light exposure of the leaf. Chelvan et al., (1984) used the DRIS methodology for examining nutrient imbalances and poor yields in Thompson Seedless vine grapes (yielding less than 15 kg/vine) receiving differents levels of N and KfiL Petioles were sampled at the bloom time. ‘Vines yielding more than 20 kg/plant were used to develop DRIS norms. ‘The P index was high for the low'yielding plants, while K index was low. Low K and high P indices were associated with low yields. Low K and N indices were counteracted by high Ca and Mg indices. The authors concluded that N, K and Mg indices at bloom time worked in.a pivotal way in determining the yield in Thompson Seedless grape. 4. Blueberry. Blueberries belong to the genus Vaccinium in the family Ericaceae. Four taxas of Cyanoccoccus are cultivated in North 55 America: vaccinium corymbosum L. (highbush), Vaccinium myrtilloides Mitch., Vaccinium angustifolium Ait. (lowbush), and vaccinium ashei Reade (rabbiteye) (Hancock and Draper, 1989). Highbush blueberries are cultivated in the northern latitudes of the United States, extending from the Atlantic Coast states to the Mississippi Valley and northward into Canada. The species tolerates cold temperatures and requires as much chilling as a peach for normal development of flower buds (Ryugo, 1988). 4.1. Potential Yield. Potential yields vary greatly among the types of blueberries, although climatic conditions and cultural practices are more important than genetic differences. Highbush plants can achieve yields of 25-30 t/ha, with 4.5 to 5.5 t/ha being the norm. Fruit size varies among blueberry types and is strongly influenced by environmental conditions. Highbush cultivars have the largest fruit, often reaching 3-4 g/berry. Most highbush cultivars fall in the range of 1-2 g/berry (Hancock and Draper, 1989) . Siefker and Hancock (1986), working with 9 highbush blueberry cultivars indicated that yield was more strongly determined by canes per bush and berries per cane than by berry weight. High numbers of berries per cane were associated with low berry weights in all cultivars. 56 4.2. Root System and Soil Characteristics. The blueberry is a shallow rooted plant that lacks root hairs (Holmes, 1960) . The fibrous roots of the blueberry require an open, porous soil for easy growth (Cain and Slate, 1953). According to (Kramer et al., 1941, Shutack et al., 1949 and Laycock, 1967) root distribution of blueberries are concentrated in the organic soil horizons of the surface applied mulches, with more specific localization occurring with the lower decomposing organic layers. Blasing (1985), indicated that poor soil aeration status is implicated in poor highbush blueberry growth. A greater part of the variation in rooting depth and pattern between Vaccinium species is related to the depth to the water table or fluctuating water table (Korcak, 1986). Organic mulches received early attention due to the physical benefits derived in field blueberry plantings. Organic mulches improved the water holding capacity, soil tilth, weed control and reduced temperature fluctuations (Shutak and Christopher, 1952). Cummings et.al. (1981), noted that organic mulches were more effective than S in acidifying soils. Fry and Savage (1968), said that chemically mulching maintained a more constant media pH. Townsend (1973a) and Korcak, (1986,1989), noted no consistent relationship between foliar Ca levels of highbush blueberry and Ca saturation percent over a range of soils. 57 Eck (1966), applied Ca, K and Mg to a H-bentonite clay in a ratio of 6:2:1 at 3 different percent saturation that induced 3 different pH levels. Highbush blueberry growth increased with increased saturation (50 % to 90 8) and that coincided with the pH of the media increasing from 3.3 to 5.8. But, when total cation levels ranged from 22.5 to 90 meg] 100g clay, growth was greater at low level. 1However, Korcak (1988), said that blueberries can grow over a fairly broad range of relative K, Mg and Ca saturation percentage and any of the three saturation percentage may not represent a critical level. 4.3. Soil Reaction. As early as 1910 it was established that blueberries required an acid growing medium (Coville, 1910). Johnston (1934), indicated that blueberries grew better at pH 4.4 than at pH 3.4, 5.5 or 6.8. Death of plant and marginal leaf scorch was observed at pH below 3.4 (Merril, 1939). Harmer (1944), changed the pH of an organic soil with addition of S and limestone and found that.a pH range between 4.0 to 5.2 was satisfactory and pointed out that a range of 4.5 to 4.8 as optimum. Cain (1952), indicated that the lack of ammoniacal N may be responsible for the appearance of Fe deficiency showing chlorosis on highbush blueberry plants growing at high pH. He 58 induced Fe chlorosis on plants on sand grown culture by changing equivalent amounts of N in the form of Ca(N03)2 for NH,NO3 in a nutrient solution where the pH was maintained below 5.5. Only when a complete substitution of NHJHL was made did the chlorosis appear. Holmes (1960), changing the acidity and P concentration in a nutrient solution achieved the best growth at pH between 4 and 5. When the pH was above this range, Fe chlorosis was more severe and plant growth poorer. Iron deficiency was more severe at high P levels. But, the Fe content of the older leaves didn't change with increasing pH: despite the Fe chlorosis development, while in young leaves the Fe content diminished. He noted that an increasing pH may affect Fe metabolism. However, it was possible to maintain the green color in plants grown at pH 7.0 with the use of Fe chelate (FeEDDHA) instead of inorganic salts. Norvell (1972), noted that at low pH (4.5) Fe sources, sulfate or EDTA (ethylenediaminetetraacetic acid) had no effect.on.dry'matter production, but.a high rate of Fe chelate restricted plant growth in peat or soil at pH 6.5. The growth limitation was due to a decrease in leaf Mn levels. As a result of the known characteristics of the FeEDTA complex which becomes unstable near pH 6.0 and the higher stability of the MnEDTA near neutrality. Arnold and Thompson (1982) , suggested that chlorosis of highbush blueberries grown at the upper pH limit (5.2) was a consequence of Fe inactivation by 59 excessive P. But, Brown and Draper, (1980) hypothesized that ther is an Fe efficient gene in blueberry. Iron efficient blueberries were found to be capable of lowering pH by a proton release from roots, but Fe inefficient plants did not change the solution pH. All the Fe efficient crosses contained less Ca than the Fe inefficient crosses, but neither of the two crosses released a reductant. 4.4. Leaf Sampling and Seasonal Variation. Leaf analysis is used to diagnose the blueberry nutritional status. IBailey et al. (1962), suggested that when several elements need to be studied, leaf samples should be taken just. prior to fruit. harvest. However, Ballinger (1966a), said that the time of leaf sampling and the selection of leaf samples should correspond to a period of relatively stable element concentration in the plant, and preferably a minimum concentration. He noted that for blueberry this occurred during the midseason, following fruit harvest. The midshoot leaves of lateral shoots immediately below the fruit cluster are representative of leaves that will reflect the stress of fruit bearing and be indicative of the nutritional status of the plant at its maximum need. Doughty et a1. (1981), measured the changes in element content of blueberry leaf during the growing season. He found that the N concentration decreases rapidly early in the 60 growing season and levels off to a steady state by mid-July to early August. The P concentration was also highest in young leaves, declining as the leaf matured and stabilizing by midseason, Changes inIK‘were rising and falling as the season progressed. The Ca, Mg and trace elements tended to increase slowly during the growing season. Similar trends were also found in different cultivars of highbush blueberry by others researchers (Bailey et al., 1962 ; Eaton and Meehan, 1971; Ballinger, 1966a; Chuntanaparb and Cummmings, 1980). 4.5. Plant Composition and Response to Nutrient Elements. Korcak (1986), noted in his review of the nutrition of blueberry and other calcifuges that one of the most surprising differences in the nutritional status of the Ericaceae compared to other plants is the general low concentration of essential elements required for adequate growth. With the possible exceptions of Fe, Cu and S, highbush blueberry requires lower levels of all nutrients than other fruit producing plants. 4.5.1. Nitrogen. As reported above, blueberry roots are concentrated in the more decomposed organic material (Kramer et al., 1941). 61 Ericaceous plants are higher in phenolic compounds, on the order of six to eight fold higher than corn (Dirr et al., 1972). It is known that several compounds including phenolics inhibited strongly the nitrification process (Rice and Pancholy, 1974). a. Ammonium vs Nitrate source. Rorinson (1980), using a nitrate free environment, reported that the combination of NH4-N and high soil Al was less toxic than high levels of NO3-N and high Al. Stribley and Read, (1974) researched the effect of mycorrhizal infection on the utilization of soil N other than NHfi'or NO; by Ericaceae. It was reported that mycorrhizae may aid not only in NH,+ uptake but also in the utilization of proteins and.peptides (Stribley and.Read, 1976; Bajwa and Read, 1985). Cain (1952) , said that NH4-N may be essential for normal blueberry growth. However, Ballinger et al. (1972), using a constant flow gravity drip system of nutrient culture in sand, reported that "Wolcott" blueberry grow equally well either with a No; or NH,+ source of N. Townsend (1967), demonstrated that shoot and root growth were distinctly favored by NH4-N and a combination of NO,’ and NH,-N as compared to a nutrient solution containing only NO,-N. The same author demonstrated N03 reductase activity in the leaves and roots of lowbush blueberry (Townsend, 1971) . 62 Dirr et al. (1972), reported the presence of NO, reductase in the leaves of highbush blueberry; therefore, to be able to assimilate NO3-N. No visual differences in growth between NO3-N and NH4-N in "Jersey" plants was found. All plants appeared healthy and vigorous without any symptoms of chlorosis or toxicity. The activity of the N03 reductase in the root of "Jersey" plants were similar to that reported by Townsend, (1971). Havill et a1. (1974) , reported low NO3 reductase activity in the Ericaceae; and Routley (1972), said that the activity level varies within the Ericaceae. Lee and Stewart (1978), noted the existence of a general correlation between the growth rate and the N03 reductase activity levels. They found that lower growth was correlated with low NO3 reductase activity. Eck (1988), said that blueberry appeared to utilize both NH,+ and NO,‘ forms of N. He said that at a pH below 5.0 blueberries respond equally well to both forms of N and above a soil pH 5.0 the NHfi'fOrm appears to be favored, mainly due to the NH,+ influence on the cation and/or organic acid or anion content of the leaf and on the Fe metabolism in the plant. Generally, among the different N sources, NH4-N has produced better growth than NO; for highbush blueberry (Cain, 1952; Herath and Eaton, 1968). But, Oertli (1963), noted the opposite if Fe was supplied in a chelated form. A similar 63 conclusion was reached by Doehlert and Shive, (1936) with the use of low P in sand cultures. Hanson and Retamales (1992) , reported for "Bluecrop" highbush blueberries that split applications of urea (half applied at budbreak, half at petal fall) resulted in 10% higher yields than the same amount of N applied at budbreak. They tested two controlled-release fertilizers which were as effective as urea as N sources. The dissolution rate of the fertilizers used did not affect yields or leaf N levels. b. Critical Level. Kramer and Schrader (1942), were the first to describe a N deficiency symptom in highbush blueberry. Eck (1977), has suggested that the critical level for N in highbush blueberry is 1.65 percent of the dry'matterx This level agreed.with the values observed by Ballinger and Kushman (1966), for optimal production of "Wolcott" cultivar. However, in a Michigan survey the blueberry yields were directly proportional to the N content of the leaves up to 2.1 percent of the dry weight basis (Ballinger, 1957). Townsend (1973b), reported that over a six year sampling period the average N content was 2.21 percent for treated plants compared to 1.88 percent for those not receiving N fertilizeru The application rates used.were 32 kg N/ha in the first year to 228 kg N/ha in the seventh year. 64 c. Interactions. Nitrogen fertilization has been reported to affect other essential element in the plant. Eck (1977), observed that N fertilization to 135 kg N/ha increased not only the leaf N’but also decreased leaf K. Leaf Ca and Mg were also decreased as the N rate increased over 68 kg N/ha. He reported also a strong biannual bearing pattern developed at all the levels of N application by a consistent change in leaf Ca level as the yield fluctuated from year to year. Townsend (1973b), also reported that increased N nutrition resulted in decreased leaf Ca levels in ”Blueray" plant in three of six years sampled. Leaf Ca levels correlated positively with yield more than other foliar nutrients (Cain and Eck, 1966). Cummings (1978), found that increasing N fertilization decreased Ca and Mg in the plant but a difference with Eck's results (Eck, 1977), was that the K concentration increased. He noted also that an increase in N fertilization increased the foliar concentration of Fe and decreased those of Cu and Zn. 4.5.2. Phosphorus and Potassium. All the descriptions of P deficiency in blueberry have been based on induced deficiency by nutrient cultures from which P was omitted. No P deficiency symptoms have been 65 reported in field planting of highbush and rabbiteye blueberry (Eck, 1988). Blueberry growth response to P fertilization has been reported on muck soils by Van de Geijn (1967), quoted by Eck (1988). Cummings et al. (1971), found that increasing the P fertilization in a low available P sand soil significantly increased the vegetative growth of young plants. The same author increased the available soil P with continuous application of superphosphate at rates of 25 to 50 kg P/ha and noted an increase in the P levels but a decreased in Cu and Mg levels in the leaf (Cummings, 1978). Ballinger, et.al. (1958), proposed leaf K conte t.of 0.53 percent on a dry weight basis as a standard for blueberry planting in the Michigan area, .A similar content was reported for North Carolina conditions where leaf K ranged from 0.42 to 0.91 in "Wolcott" cultivar, with a mean of 0.61 percent. For "Murphy" cultivar the range reported was 0.46 to 0.88 percent, with a mean of 0.58 percent. Eck (1983), found that the optimum leaf K concentration ranged between 0.45 to 0.55 percent. Eck (1977), noted that increasing N fertilization was associated with a decrease in the leaf K concentration in a field fertilizer experiment using bearing "Bluecrop" plants. Blueberry yields have been increased by K fertilization on different soil types. As early as 1920, it was reported that for a Berryland sand blueberry yields were doubled by the 66 application of 45 kg K/ha, as K280, made in split applications. In addition, Johnston (1934), reported the need of K for organic soils. Cummings (1978), said that the application of 47 kg/ha of K increased the crop yield; however, yield.did.not increase with further application to 94 kg K/ha. Eck (1983), working with the ”Bluecrop" cultivar on a Berryland soil, found that application of 40 kg K/ha was associated with maximum yields. Potassium sulfate was the most favored source of K for blueberry (Eck, 1988). Townsend (1973a), noted that the use of KCl treatments not only increased the winter injury but also reduced the fruit size. Potassium sulfate-magnesia used at a dose of 0.4 kg/plant reduce yield (Bailey et al., 1966). 4.5.3. Manganese. The Ericaceae represent a special group of plant in relation to Mn, with the exception of V. uliginosum (Popp, 1983). Values of Mn in excess of 2000 to 4000 mg/Kg have been noted for V. angustifolium; however, high tissue Mn levels have not been associated with toxicity symptoms (Hall et al., 1964; Townsend, 1969). The fact that excess Mn affects the tops ‘more severely’ than roots suggest. that. a. mechanism reducing upward translocation of Mn confers a degree of tolerance (McGrath.and.Rorinson, 1982). It is known also that Mn levels in roots tend to be lower than foliar levels (Foy et 67 al., 1978). But, a range of blueberry progenies grown on a number of soils tended to have either higher or similar root Mn levels compared to those of shoot levels (Korcak et al.,1982). There are few reports of Mn deficiency. Kender and Anastasia (1984), were the first to report Mn deficiency for lowbush blueberry. It was suspected by Ballinger et al. (1958), in field samples of highbush blueberry leaves with interveinal chlorosis on leaves containing 230 mg Mn/kg. However, Doughty et al. (1981), said that the normal range in blueberry leaves is 50 to 350 mg/kg, and deficiency symptoms appeared when the concentration dropped to 23 mg Mn/kg. He noted also that toxicity symptoms can be expected to develop at concentration above 450 mg/kg. High Mn uptake has been noted to reduce the activity of the N03 reductase, which is an indication that NH,-N is the preferred N source in soils of high Mn content (Jones and Menary, 1974). 4.5.4. Aluminum. Aluminum is considered to be the most growth limiting factor associated with soil pH levels of 5.0 or below (Adam, 1981). Peterson et al. (1987), acidified a sandy loam with elemental S or A12(SO,)3 with or without the addition of sawdust. They found that the growth of Rabbiteye blueberry was more restricted by A12(SO,)3 compared with elemental S. However, the application of sawdust overcame the restricted 68 growth. Hargrove (1986), noted that the growth of Ericaceae near neutral conditions may be influenced by Al, due to the influence of Al-organic matter complexes solubility in the pH range 5 to 7. Reich et al. (1982), reported that the Al levels in roots of "Jersey" highbush blueberries were not affected by soil porosity with or without mycorrhisal infection, but found that the root concentrations decreased with increased fertility level in sand cultures. Korcak et al. (1982) and Korcak, (1986), noted that the root Al levels of a range of blueberry progenies compared over a variety of soils had a trend similar to that reported in the commercial blueberry area of New Jersey, where the root Al levels were about 700 mg/kg from plants grown in sand culture and about 360 mg/kg for the control plants grown on Berryland soils. The same author reported the distribution Al pattern within blueberries. He said.that the tops had.a higher Al level than the roots (Korcak et al., 1982). However, Townsend (1969, 1971), found an opposite trend for the Al distribution. 4.5.5. Calcium and Magnesium. Loneragan and Snowball (1969) , reported that the Ca requirement of blueberry is low and that it agreed with the functional Ca requirement of some herbs and legumes. The foliar Ca levels of highbush blueberry has been shown not to be affected by a pH range between 3.4 to 6.0 (Herath and 69 Eaton, 1968). Ballinger et al. (1958), reported a standard Ca value of 0.74 % of dry matter by the analysis of fruiting shoot leaves of high yielding highbush blueberry plant grown under Michigan condition. Doughty et al. (1981) , reported 0.13 % as the Ca deficient level in the blueberry leaf. The standard range varied from a minimum of 0.40 % to a maximum of 0.80 %, where 1.0 % was noted as an excess value. But, blueberries grown in the coastal plain soils of the eastern United States tend to have Ca levels in the low part of the standard range (Cummmings et al., 1971). The same authors reported a foliar range of Ca concentration from 0.34 % when no lime was applied to 0.38 % when lime was applied to ”Wolcott" variety grown on Leon sand. Eck (1977), found that the Ca concentration in "Bluecrop" range from 0.30 % at high levels of N fertilization to 0.33 % at low levels of fertilization. Townsend (1967), found that Ca concentration in the blueberry leaf were significantly lower with an NHfl' rather than a NO3—N source. Bailey (1941) and Cain (1952), reported a lime induced chlorosis (Fe deficiency) in blueberry leaf developed when the pH of the medium was raised to about 5.5 by Ca applications. Cummings et al. (1971) and Cummings (1978), reported that the application of less than a metric ton of lime per hectare raised the pH to 4.5 and depressed growth. However, this dose did not effect fruit yield. Eck (1977), reported that when a high load of fruit is observed, leaf Ca concentration was 70 high. He said that this occurred when available Ca reserves were distributed throughout a smaller amount of secondary vegetative growth. The foliar Ca levels were more closely associated with blueberry yield than any of the other foliar nutrients (Townsend, 1973b). The Mg requirement for blueberries is also relatively low. The first reported field Mg deficiency in highbush blueberry was corrected by either MgSOg or dolomitic limestone. .A level below 0.20 % ‘was reported as deficient by Mikkelsen and Doehlert (1950), and Bailey and Drake (1954), reported deficiency symptoms at a value below 0.10 %. They said that variation in the Mg concentrations were influenced by the K content in the leaf. Any growth response to applied Mg was observed without the presence of deficiency symptoms (Bishop et al., 1971). 4.5.7. Sulfur and Chloride. Kramer and Schrader (1942), experimentally developed a S deficiency in "Cabot” blueberry. The foliar symptoms, although similar to those of N deficiency, affected only the young leaves. The S toxicity was found when the air SO2 concentration exceeded 7.0 mg SOz/kg and leaf defoliation was noted when the concentration in the air exceeded 15 mg sozlkg (Brennan et al., 1970) . Ballinger (1962), reported in.a North Carolina blueberry 71 survey that the S leaf content varied from 0.05 to 0.50 % of the dry matter with more than two-thirds of the plants sampled between 0.16 and 0.35 %. He also found that the S leaf content increased when the S in the nutrient solution was increased. Doughty et al. (1981) , suggested an optimum S leaf range between 0.125 to 0.20 % of the dry matter. Harmful effects of KCl on blueberry growth have been reported by Merrill (1944); Slate and Collinson, (1942) and Ballinger, (1962). Ballinger reported a decrease in the Ca uptake by plants when the nutrient solution had a S to Cl ratio of 4:1, while a high C1 to 804 ratio favored Ca uptake. The author noted. that lower Ca uptake was a desirable condition for blueberry plants. A high S to Cl ratio in fertilizer was favored the blueberry growth. Toxicity symptoms were associated with leaf Cl level of 0.89 percent. Leaf Cl contents in normal field plants of North Carolina ranged from 0.02 to 0.14 percent.of the dry matter (Ballinger, 1962). Townsend (1973b), reported significantly' reduced yields of ”Blueray" cultivar when the soil saturation extracts were between 1.68 to 2.28 mmhos/cm. 4.5.8. Boron, Copper and Zinc. Boron, Cu and Zn deficiencies were developed experimentally by Doughty et al. (1981) , who placed the 72 optimum B concentration in blueberry in the range between 30 and 70 mgB/kg, and the deficiency at 20 mgB/kg and toxicity levels about 200 mgB/kg respectively. The same authors reported that a level below'S mgCu/kg in leaf was associated with deficiency, 5 to 20 mgCu/kg was a sufficiency range and an excess level was 100 mgCu/kg. The deficiency concentration for Zn was established at 8 ngn/kg, the sufficiency range‘was 8 to 30 ngn/ g and the excess level was 80 ngn/kg (Doughty et al., 1981). HYPOTHESIS AND OBJECTIVES In the last few' years Beaufils' approach. has been successfully applied to some fruit crops. The fact that blueberry is a perennial crop, which allows for periodic tissue sampling and adjustment of fertilization practices to improve production, increases the desirability of developing Diagnostic and Recommendation Integrated System (DRIS) norms. The research hypothesis is: DRIS is superior to the Critical Value approach as a means of assessing the mineral nutrition of highbush blueberry (Vaccinium corymbosum L.) . The hypothesis will be tested in two stages. The first step is to develop the DRIS norms and the second is to test these norms in field experiments. The purpose of the present research is to achieve the first hypothesis stage. The objectives are to: a) compile a DRIS data base from existing published and unpublished data of leaf analysis and yields, b) develop DRIS foliar diagnostic norms for eleven nutrients (N, P, K, Ca, Mg, Fe, Mn, Zn, Cu, B, and Al), c) compare and correlate the preliminary DRIS norms with the critical value approach published in the literature and calculated from the data base used to develop the DRIS norms. 73 MATERIALS AND METHODS The DRIS technique consists of describing the nutrient ratios status of a high-yielding population, and then identifying variation from ratios in unknown samples. A data bank. comprising 1074 observations of leaf nutrient concentrations and yields of highbush blueberry (Vaccinium corymbosum L.) was developed as described previously by (Beaufils, 1973a; Sumner, 1977d). The data used for developing DRIS norms came from several different sources: the number between the rectangular brackets means the number of data points from each source. ([66] and [9] Ballinger, 1957; 1969; [30] Bailey, 1966; [2] Brown, 1988; [6] Cummings, 1978; [625] Dale, 1973; [15] and [4] Eck, 1977; 1985; [180] Widders et al., 1994; [11] Lareau, 1989; [15] Martin, 1983; [10] Naumman, 1985; [12] Popenoe, 1952; [15] Rosca, 1985; [8] Scibisz, 1990; [16] and [8] Townsend, 1973a; 1973b) . The observations were divided into high and low-yield subpopulations, using 5 kg berries/bush to separate the subpopulations. The yield used to divide between low- and high-yielding population was 31.5% of the highest yielding highbush blueberry. Thus, the high-yielding sub-population 74 75 reflected conditions that were considered desirable for the crop (Beaufils, 1973a). For the two sub-populations the :mean (x), standard deviation (SD) and variance (3’) were calculated for each nutrient concentration as well as for all ratios between nutrient concentrations. A variance ratio (szfor low-yield population/s2 for high yield-population) was calculated for each nutrient concentration ratio. Of the two ratios involving each pair of nutrients, the one with the larger variance ratio was selected. The mean and coefficient of variation (cv) values in the high-yield population for the selected ratios were used for calculating DRIS indices using the standard equation (Beaufils, 1973a). The nutrient with the most negative index was considered the most limiting or most relatively limiting. A nutritional imbalance index (NII) was calculated as a measure of balance among nutrients for each DRIS evaluation. It was obtained by adding the values of DRIS indices irrespective of sign (Beaufils, 1973a; Sumner, 1977e). The larger the NII, the greater the imbalance intensity among nutrients. The same data bank was used to compare DRIS diagnosis with the standard values.developed.by Ballinger et al., (1958) and Doughty et al., (1981) to determine if relative deficiencies or excesses associated with lower yielding bushes would have been detected routinely. Critical values for each 76 nutrient were calculated as was described by Ulrich and Hill, (1967) and both diagnostic results correlated. RESULTS and DISCUSSION 1. Characterization of the Highbush Blueberry Population. The blueberry data base show a wide range in yield values (0.01 to 15,9 kg berries/bush) and foliar nutrient concentrations. Statistical characterization of the data base shows not only high coefficient of variation for all parameters but also high differences in the number of points recorded for each nutrient (Table 1). Highbush blueberries ranged between 3 and 15 years old, with a big proportion of the points of the data base (81.9 %) from.Ontario and Michigan areas (Ballinger, 1957, 1969; Dale, 1993; Hancock and Hanson, 1993), as a consequence, results and conclusion may be most applicable to these regions (Appendix Table 1). 1.1 Yield Values. Blueberry yields should increase until the plants are 6 to 8 years old. An average mature bush should produce 2.5 to 3.5 kg' of berries, although. higher‘ yields are jpossible (Hoffman, 1978). The yield frequency polygon shows that the distribution is positively skewed and that approximately 60 % 77 78 of the samples are concentrated below the mean value of the population 3.15 kg/bush (Figure 1). Although there is a tendency to increase yields with age and the yields in the data base follow the expected age pattern, exceptionally very high yields were found on some research trials. The very high yields were results of not only more controlled conditions but also yield record found for the crop that bias the average yield per age of bush (Figure 2). Generally, a very good yield for most blueberry growers is considered to be near the 5 kg/bush in mature bushes. Thirty one percent of the yields in the data base where higher than 5 kg berries/bush. Another reference for comparison is the average yield of mature blueberries 2.4 kg/bush in Michigan (Hanson, 1993). 1.2 Nutrient Concentrations. Unequal number of data points were available for the different nutrients. Macronutrients with the exception of P have more data points than micronutrients. The dissimilar number of points is a consequence of the different information available from each source (Table 1). A comparison of the total number of samples which have a nutrient available with the suggested critical values found in the literature (Eck, 1988) , shows that P has the greatest number of samples in the deficiency range (55.7%) in contrast to Mg with a high percentage (90.6%) in the high range. The 79 Table 1. Statistical characterization of yield and foliar nutrient concentrations of the Blueberry population. Range of Mean Standard CV % n observation deviation Yield 0.01 - 15.9 3.15 2.58 81.9 1074 (kg/bush) N % 0.42 - 3.8 2.09 0.41 19.6 1040 P % 0.05 0.8 0.12 0.06 48.0 122 K 4 0.10 1.8 0.57 0.18 31.6 1055 Ca % 0.10 1.3 0.51 0.16 30.9 1041 Mg % 0.14 0.5 0.19 0.06 30.2 1056 Mn mg/kg 16 3500 369 324 87.9 748 Fe mg/kg 7 44 77 41 53.3 736 Zn mg/kg 4 86 15 9 63.8 391 Al mg/kg 38 309 109 34 81.6 190 Cu mg/kg 1 79 7 9 125.7 299 B mg/kg 10 121 35 16 46.1 247 80 \\’ I I\ I\H\I\H\”\H\H\H\ I\\I\\\\\\ u.n.u.n.u.u.u.u. \ \ \ \ \ \ \ \ \I\I\I\I\”\H\I\H\I\I\I\”\ \ \ \ \ \I\ \ \I\ \I\ \I\ \ I I I I I I I I I IIIIIII I \ \ \ \ \ \ \ \ \ \ \ \ § \ \ \ I I‘I I I I I I I I I I I \ \ \ \I\I\ \ s \ s s \ s s \I I I I I I I I I I I I I I I I I Isl‘IsIsI\I\I\I\I\I\I\I\I\I\I\ I\I\I\I\I\I\I\I\I\I\I\ \I\I\ \ \ s \ s s \ s s s s s \I\ s \I\ I\I\I\I\I\I\ I\I\I\I\I\I\ \ \ \ \ \ \ I I I I I I I I \ \ \ s \ \ \ \ s \ \Hs \ \ x \ \ \ I\I\ \ \ \I\I\I\ \I\ s \I\ sI\I\ \I\ \I\I I I\ \I\H\H\I\I\I\Hs \H\H\ \I\ \ \H\I\H\I\ \I\I\ \ \ s \ s \I\ \ \I\I\I\I\ \ \ \ I\I\I I\I\I \ \ I I I I I I I I I\I I\I\I\I\I\I I§I\I\I\I\l \ \ \ \ \ I\ \I‘I‘ ‘I\ \I\ IsI\I\ \ I\ \ ..u . \ I\I I‘I \ I \ \ \ \ \ \ \ \ \I\ ‘I‘ \ \ \ \ \ \ \I\ \ \ \ \ \ \ \ \ \ \ ”\H\“\I\H\”\H\“\”\I\”\H\H\”\”\I\I‘H‘”\H\H\H\H\”\I\I\H\I\H\H\I\”\I\H\H\”\I‘“\”\”\H\H\”\“\H\”\H\U\U\H\H\I I I\ \ \ \ \I\ \ \ \ \ \I‘ \ \ \ \ \I\I\ \ \ \ \ \ \ \I\I\ \I\ \ \ \ \I\ \ \ \I\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ‘I\ \ \ \ \ \ \HsusIsnsH\usH\H\I\H\H~H\H\H\H\I\H\u\”\H\H\H\“\H\n\”\H\H\H\Hs”susnsH\”s”s”\H\H\”\H\H\H\H\H\H§I\I\I\H \ \ \Is \ s \ \ \Is \ s \ \ s \I\ s \ \ \ s \ s t s s s \ \ \ s s \ s s \ \ s \ \ \ s \ s s \ s s s V '.' ' ’ ' ' ' I.' ' ' I ' ’ ' ’ v ' ' '.' ' ' I ' ' ' ’ I.' '.I I ' ' ' ' I ' ' ' ' ' ' ' '.'v'v 2 .m .m Damage... 150-" 100“ c “25 6:5 Yield (kg berries/bush) (15 Frequency distribution of Blueberry Yield (kg berries/bush). Figure 1. 81 m 6" g I \\s\ q \'\'\'\ x ”’ ‘\\\ V III \’\’\’\ \I\’\’\ \\\\ .— \’\’s’\ III \\\\ 1 \’\’\’\ \\\ e- I I II \\\\ \\\ III IIII \\\\ \\\ III IIII ‘\\\ \\ III IIII \\\\ \\ III III I I I I ‘I‘I‘I‘ I II I‘I‘I‘I m \ \ \’\ ’\’\’\’ \’\’\’\ \’\’\’\ ’\ \’\ .1 \\ \ss \\\\ \\\\ \\\ III IIII III III III \\\\ \\\ \\\\ \\\\ \\\ I IIII III II III \\\\ \\\ \\\\ \\\\ \\\ III IIII III III IIII \\\\ \\\ \\\\ \\\\ \\1 III III! III III IIII \\\\ \\\ \\\\ \\\\ \\\ III IIII III II III \\\\ \\ \\\\ \\\\ ‘\\ III IIII III IIII III III \\\\ \\\ \\\\ \ss \\\\ s\\ III IIII III IIII II I I \\\\ \\\ \\\\ ‘1‘ \\\\ \\\ III IIII III IIII II IIII \‘\\ \\\ \\\\ \\\ \\\\ \\ III IIII III IIII III III \\\\ \\\ \\\\ N \ \\\\ \\\ IIII III IIII III IIII III III \\\ \\\ \\\\ \\\ \\\\ \ \ \\\\ \ \ III [III III IIII III IIII III III \\\ \\\ \\\\ \\\ \\\\ \\\ \\\\ \\\ III III III III IIII III III III III ‘I‘I‘I I‘I‘I‘ ‘I‘I‘I ‘I‘I‘I‘ I‘I‘I‘I ‘I‘I‘I‘ I I‘I‘I ‘I‘I‘I‘ I‘I‘I‘ ‘\\ \\\ \\\ \\\\ \\\ \\\\ \\\ \\\\ \\\ q III III IIII III IIII III IIII III III \\\ \\\ \\\ \\\‘ \\\ \\\\ \\\ \‘\\ \\\ VIII III IIII III IIII III IIII III IIII V‘I\’\I ’\’ \’ \ ‘I \’ ‘I ‘I \' \’\ ’\’ \’ ‘I \I\I \’ \ I‘I \’ \’ \’ \’\’\ ’\’ \’ ‘I \\\ \\\ \\\ \\\\ \\\ \\\\ \\\ \\\\ \\\ III III IIII III III! III IIII III III \\\ \\\ \\\ \\\\ \\\ \\\\ \\\ \\\\ \\\ III III IIII III IIII III IIII III III sss s\\ \\\ \sss \\\ \\\\ \\\ \\\\ \\\ III III III III IIII III IIII III III \1\ \\\ \\\ \\\\ \\\ \\\\ \\\ \\\\ \\\ I III IIII III IIII III IIII III IIII \\\ \\\ \\\\ \\\ \\\\ \\\ \\\\ \\\ :1. \ 3 4 5 6 ‘7 8 £9 10 11 Age of bushes Figure 2. Frequency distribution of (kg berries/bush) with Age of Plant. \\\ III I‘I‘I‘ I‘I‘I‘ \’\\ \\\ III \\\ ’\'\'\ III \\\ I‘I‘I‘ III \\\ III I‘I‘I‘ \\\ ’\'\I\ III \\\ III \\\ III \\\ III \\\ III ’\’\’\ I‘I‘I ’\’\’\ \\\ \\\ III III \\\ \\\ III III \\\ \\\ III III \\\ \\\ III III \\\ \\\ III III \\\ \\\ I‘I‘I‘ ‘I‘I‘I III III \\\ \\\ III III \\\ \\\ III III \\\ \\\ III III \\\ \\‘ III III \\ \\\ \\\ II III III \\\\ \\\ \\\ \\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ \\\\ I III II III \\\\ \\‘ \\\ \\\\ I I III III III \\\\ ‘\\ \\\ \\\\ III III III III \\‘\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\‘ \\\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \\ \ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ \\\\ III I I II III \\\\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ \\\\ III III II II \\\\ \\\ \\\ \‘\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \\\\ \\\ \\\ ‘\\\ \' \’ \I \ l \’ \’ \ \l \’ \’ '\l " \t \I \’ \ ’ \’ \’ \ \’\’ \' ’\’\’ \ III III III III \\\ \\\ \\\ \\\ III III III III \\\\ \\\ \\\ \\\\ III III III III \’ \’ \’\ I‘I‘I‘ \’\’ \’ I‘I‘I‘ \\\\ \\\ \\\ \\ .1 N .1 CO .1 .fi .1 01 Mean Yield 82 surprising high percentage of samples with P in the deficientrange contrast with the known assumption that "no P deficiency symptoms have been reported in field plantings of highbush or rabbiteye blueberry" (Eck,1988) , and also that given a high percentage of samples with high level of Mg it would.have been anticipated.that.K levels would.have been low. But, more than 60% of samples had K and Ca in the normal range. Nitrogen levels in samples were uniformly distributed among the different ranges of reference. Micronutrients ShOW’ different patterns in range of distribution. Copper and B had approximately the same number of samples below and above the normal range. Iron had two- thirds of the samples in the normal range and the other one- third in the deficiency range. But, Mn and Zn contrasted with Fe by having a high proportion of samples between normal range and high range values (Table 2). Critical values have certain limitations when they are developed for some elements under one set of conditions and then are applied to another or when the stage of plant development and leaf sample specified differ from those to be compared. Diagnosis of a deficiency may not be possible under these conditions (Walworth and Sumner, 1988). To partially avoid this scenario the same data base was compared with standards developed for Michigan conditions. The new comparison.shows that the percentage of samples in the range of deficiency decrease drastically for P, N and Ca. For 83 practical purposes the same diagnosis was obtained for K and Hg. But, an increase was found in the percentage of samples in the low range for all the microelements (Table 3). The effect of time of leaf sampling was not considered because I assumed that the type and.time of tissue sampled*was standardized. 1.3 Relationship between Yield and Nutrient Concentrations. Usually the relationship between leaf composition and yield under unrestricted conditions reported in the literature showed a wide range of nutrient concentrations in low yielding plants and this range narrows as the yield increases until with very high yielding plants this range is very narrow, in other words a triangle shape is obtained, with an optimum nutrient closely related to the vertice range. The highest yield class is surrounded by two zones of limitation, one of insufficiency and the other of excess. This is the most common pattern. Another pattern occurs when the highest yield class is at the extremity of one zone of limitation. The shape reflects the idea of balanced composition, thus unless a plant has a balanced composition it can not reach a high yield; however, if it has a balanced composition a high yield may still not be obtained because the yield could be limited by other factors (Beaufils, 1973a). 84 Table 2. Blueberry nutritional characterization compared to the suggesteded Critical Nutrient Levels (Bck, 1981). Percentage of samples in range Nutrient Deficient Low Normal High Excess N t 19.7 5.2 26.7 33.8 14.6 P t 55.7 13.0 30.6 0.7 - K t 1.5 4.5 65.1 17.3 11.6 Ca 4 0.2 22.9 73.3 2.7 0.9 Hg 8 0.3 4.4 4.5 90.6 0.2 Hn mg/kg 1.2 11.0 42.1 13.9 31.8 2n mg/kg 10.5 - 84.9 4.3 0.3 11 mg/kg 11.1. 11.1. N.A. N.A. N.A. Cu mg/kg 51.9 - 44.8 3.3 - B mg/kg 17.8 27.9 52.3 2.0 - Sources: Ballinger et al., (1958), Doughty et al., (1981) N.A.- not available. Table 3. Blueberry nutritional characterization using Michigan Standard Values (Hancock and Hanson, 1986). Nutrient Standard 8 Below Standard N t 1.65 13.85 P t 0.10 34.80 x t 0.35 6.07 Ca 5 0.34 12.68 Hg 5 0.12 4.64 Hn mg/kg 168 30.70 Fe mg/kg 150 95.80 2:: ”95:9 i2 :2-32 mg 9 . B mg/kg 49 82.60 Modified from A. L. Kenworthy. Research report 379. Plantings. Nutrient trend in Michigan Fruit Michigan State Agr. Exp. Sta. 85 Regardless of the number of points available, the scatterdiagram of the relationships between yield and leaf composition for each nutrient shows two different patterns (Figures 3 to 6). Nitrogen and Ca are rather close to the expected most common pattern. They show a wide dispersion of the element concentrations in the low yields and a narrower range in the higher yields. They have an "optimum" in the average of the range of sufficiency used for highbush blueberry (near 2 % for N and 0.6 % for Ca). Potassium and Mn are good examples of the second pattern. Even.though.there are different number of points for K and Mn, both show a rapid increase in yield with increasing leaf content followed by a slower rate of decline in yield with excess (higher leaf composition). The optimum range for K and Mn is close to the lower limit of the sufficiency range (0.25 % for K and appoximately 40 to 50 ppm for Mn). Both elements have low linear correlation with yield (r = -0.45 and r = 0.37“”). Eck (1983), states that the interpretation of foliar K levels in terms of yield response is complicated due to the strong influence of fruit load on leaf K. Hancock and Nelson (1988), found a significant correlation between leaf K and yield in only one of the three years studied. In several other studies, no association was observed between leaf K and yield even though foliar K values ranged widely (Ballinger and Goldston, 1967; Bailey et al., 1966). Results from state-to-state and year-to-year may be variable because 18 1M 12" 104 ‘4 “61.0ng 1o 14¢ 121 b 1m 01 8'1 YIELD (k9 W503") 0‘ . -' fin - r g t v I—1 0 0.1 0.2 0.3 0.4 0.5 0.8 0.7 0.8 PHOSPHORUS % 18 . - 14q I I 12" 101 .4 81 YIELDaxgberfleslbt-h) C’ 055 as Figure 3. Scatter diagram of Leaf Nutrient Concentration with Blueberry Yield. a) N% ; b) Pt ; c) split data set P < 0.3 t . 87 18 1 ll 14‘ = 12* E a a 5 E V Y I 1 4 1.8 1.8 18 I I .- 14- I I I 121 b E I a E 1.4 CALCIWfi 18 H II- 14'! r I I 2. 121 ~ - - i u» u ' - ' c u é . u 0 0.1 0.2 0.3 0.4 0.5 0.8 MAGNESIW % Figure 4. Scatter diagram of Leaf Nutrient Concentration with Blueberry Yield. a) Rt 3 b) Cat 3 c) Mgt . 88 é a I 5. ; I . so am» sun E b O a 'II'_. a I g : I I. I " 'l 0 so 100 150 260 253 360 350 460 450 moupmn 1o 144 12‘ E 10' C I ..l - 5 34 - I a . 44 ' I - I 2+ I I o. W V U fl r I c» 10 20 so so so oo 70 so so ZINCppm Figure 5. Scatter diagram of Leaf Nutrient Concentration with Blueberry Yield. a) Mn mg/kg; b) Fe mg/kg; c) Zn mg/kg. 89 .2" $313 IPIhI 850 a&: 7 mm mm uh ALUMINUM ppm mummy...» gioéa COPPER ppm C 0 u e .m V... ‘_ I .w I I I If I I “I: fiw t. a". use a e- flfnwneuee o e.4 .- Rum... ‘0 2 :- 2 m. .u m L. co 13 d 4 ‘ 1 ‘ .215 9: 3m; BORON ppm Scatter diagram of Leaf Nutrient Concentration with Blueberry b) Cu mglkg; e) B mglkg- H) Al mg/kg; Figure 6. Yield. 90 of differences in overall nutrient availability and in leaf tofruit ratios (Hancock and Nelson, 1988). Low levels of manganese seems to be associated with high yield on highbuSh blueberry. This observation agrees with data that plants of V. corymbosum usually contain lower Mn levels than other plants (Korcak et al., 1982). The Mn critical deficiency levels are surprisingly narrow among plant species, cultivar and environmental conditions, in contrast to the wide range commonly found for Mn toxicity (Marschner, 1986) . From the scatter of points and the variability of nutrient concentration of the data base it appears that a high proportion of samples have some degree of limitation, probably by excesses. But, most ericaceous plants are Mn accumulators and do not exhibit.Mn toxicity symptoms at foliar levels up to 15,000 09.9" (Miller, 1987). The pattern for Fe, Zn and Cu are similar to the patterns of K and Mn, even though they have few points. Copper in contrast to Mn has a narrow range between deficiency and toxicity levels (Marschner, 1986), whidh could explain not only the shape of the scatter of points but also the distribution of points observed. Copper availability has a strong relationship with soil organic matter content which may make it difficult to explain relationships between yield and Cu leaf content in a large data base with different organic matter content of soils. Phosphorus levels are very concentrated for a wide range 91 of yields and very low P concentration appears to be optimal for blueberry production. High yields are obtained at a P concentration less than the limit suggested by the literature, which can explain the high percentage of blueberry samples diagnosed in the low ranges (Figure 3b and 3c). The pattern for Mg (Figure 4c) is similar to K and Mn but reversed. This might suggest an interaction between Mg and K exist in the data base confirming prior literature (Bailey and Drake, 1954). Magnesium, in contrast, has an optimum yield associated with Mg levels above the normal sufficiency range near 0.3 %. There is a low positive correlation between Mg content and yield (r = 0.40 ). Boron and Al have few points and could be similar to the shape of N and Ca. 2. Development of Blueberry DRIS Norm. 2.1 Subdivision of the Population. The blueberry' population. ‘was> divided in 'three subpopulations according to their yields. The C subpopulation with yield values between 15 and 10 kg of berries/bush; the B subpopulation between 10 and 5 kg/ bush; and the A subpopulation with yield values less than 5 kg/bush. Since the C subpopulation has only 1.76% of the points of the data base, the C + B subpopulation was used as the high population for the purpose of the development of the DRIS norm. Thus, 92 the criteria for dividing the population in two subpopulations was a yield value of 5 kg/bush. The result was a low yielding population with yields below the 5 Kg of berries/bush (A population) with 857 points (79.8 % of the total data base), and a high yielding population (B population) with yields greater than or equal to 5 kg/bush (20.2 % of the total data base) (Table 4). Yield greater than 5 kg berries/bush is still considered a high yield for field conditions. According to Walworth et al. (1988) , who compared corn nutritional diagnosis from DRIS norms derived from a set of 10 observations, with another generated by 8494 points, stated that the use of extremely high yield observations was not only accurate but an inexpensive means of generating plant tissue nutrient optima. The C subpopulation should be considered as a valuable set of points due to the record yields contained in that subset and the validity of the values tested in the future. The A and B populations have differences in the number of points for each elements; however, the difference is less than the total population before analyzed. Both populations have a different number of observations for N, K, Mg and Mn mean leaf concentration. With the exception of Mn both populations (high and low yielding) have a similar average of microelement concentrations. High yields seem to be associated with low Mn concentration (229 ppm vs 423 ppm for the A population), which agrees with the pattern of the relationship between yield and 93 Table 4. Characterization of the high and low Blueberry population. Mean Standard CV1 n Deviation Bl Population (high yield) Yield 7.29 2.17 29.7 217 (kg/bush) N t 1.93 0.27 14.2 211 P t 0.11 0.03 26.2 139 x t 0.44 0.11 22.1 212 Ca 1 0.52 0.14 27.4 214 Mg 8 0.23 0.05 25.4 217 Mn mg/kg 229 219.20 95.6 213 Fe mg/kg 71 43.70 61.4 208 2n mg/kg 14 9.20 65.2 171 Al mg/kg 114 33.30 29.5 104 Cu mg/kg 6 5.35 90.0 137 B mg/kg 35 15.05 42.7 137 A Population (low yield) Yield 2.01 1.30 61.9 857 (kg/bush) N t 2.13 0.43 20.1 829 P t 0.12 0.06 55.5 283 X t 0.60 0.18 30.4 843 Ca 1 0.51 0.16 31.7 827 Mg 8 0.18 0.05 29.7 839 Mn mg/kg 423 341.78 80.6 535 Fe mg/kg 79 39.74 50.1 528 2n mg/kg 16 10.08 62.1 220 A1 mg/kg 102 34.41 33.5 86 Cu mg/kg 9 11.64 134.5 162 B mg/kg 35 17.35 50.0 110 m-m+cmmam 94 nutrient concentration shown in Figure 5a. The low yielding population could have been limited by a Mn accumulation. 2.2 Ratios of Nutrient Elements. A total of 110 nutrient ratios were calculated that included all possible combinations among N, P, K, Ca, Mg, Mn, Fe, Al, Zn, Cu and B. The DRIS approach relies on the use of nutrient ratios (Beaufils, 1973a) to avoid the problem of changing nutrient concentration with leaf age. 0n the other hand, nutrient products were used with elements like Ca and Mg, which tend to increase with the age of tissue, to reduce the problem generated with variation in sampling time (Beaufils, 1973a; Sumner,1988). Finally, the biggest ratio of variance between the low over the high yielding populations was used as a selection criteria for discrimination among ratios (Beaufils, 1973a). The results of the selected criteria are shown in (Table 5). It was noted before that not all samples had the same number of nutrient analysis available; as a consequence, the number of points per ratio was dissimilar. However, all ratios were used as a way of assuring symmetrical DRIS indices with the sum equal zero (Beaufils, 1973a). Nutrients that varied greatly in concentration were usually in the numerator. This was the case generally for Mn and Cu and probably could have some effect on DRIS diagnosis. 95 Table 5. Mean, variance and coefficient of variation among nutrient ratios selected. Nutrient A population 8 Population Sfl/Sfl ratio (low yielding) (high yielding) x s2 cvo x s2 cvs N/Fe 0.036 9.9x104 86.98 0.044 0.0016 91.15 0.61 N/Zn 0.157 0.0049 44.75 0.163 0.0035 36.74 1.38 N/Al 0.019 8.3x104 45.60 0.016 2.1x105 28.09 3.89 N/B 0.064 0.0010 50.92 0.059 0.0005 39.62 1.98 N*Ca 1.091 0.199 40.90 1.011 0.105 32.09 1.90 N*Mg 0.392 0.021 37.53 0.440 0.013 6.79 1.56 P/N 0. 064 0.0006 39.19 0.063 0.0001 21.26 3.48 P/K 0.256 0.0146 47.03 0.284 0.0023 16.97 6.29 P/Fe 0.002 1.3x10J 124.30 0.003 1.5x105 110.47 0.86 P/Al 0.001 13(10‘7 38.59 0.001 5x10‘ 25.33 2.93 P/B 0.003 4.3x10‘ 53.69 0.004 1.5x10‘ 34.52 2.76 P*Ca 0.057 0.002 83.91 0.062 0.001 51.12 2.26 P*Mq 0.025 0.001 133.40 0.029 1.6:!!10‘4 45.83 6.05 K/N 0.283 0.0060 27.40 0.230 0.0018 18.52 3.32 K/Fe 0.009 7.9):10‘5 94.30 0.010 0.0001 103.59 0.67 K/Zn 0.036 0.0002 44.98 0.036 0.0001 33.31 1.96 R/Al 0.004 3.3x10‘ 40.13 0.003 6.6x101 23.82 5.00 R/B 0.015 6.6):10'5 52.01 0.013 1.8x105 33.62 3.55 K*Ca 0.307 0.021 47.44 0.233 0.008 38.75 2. 61 K*Mg 0.109 0.002 43.47 0.101 0.001 31.70 2. 20 Ca/Al 0.004 1.7x10‘ 29.71 0.004 6.7x101 19.17 2. 64 Ca/B 0.017 0.0001 60.39 0.016 2.8x10’ 32.38 3. 99 Mg*Ca 0.096 0.002 50.02 0.123 0.002 43.54 0.80 Mg/Al 0.002 1.1x10‘ 46.37 0.002 2.9x107 24.54 3.89 Mg/B 0.008 1.91:10‘5 54.55 0.008 7.3x10‘ 33.64 2.64 Mn/N 192.3 22857.1 78.60 109.9 9532.93 88.83 2.40 Mn/P 3162.3 17419386 131.90 826.9 346317. 71.16 5129 Mn/K 739.8 340331.8 78.85 466.6 167401.8 87.67 2.03 Mn/Fe 6.150 28.912 87.42 4. 493 20.717 101.30 1.40 Mn*Ca 225.3 50413 99.64 122.8 14323 97.45 3.52 Mn*Mg 77. 70 5775.16 97.79 48.07 1802 88.30 3.20 Mn/Al 0.806 0.667 101.20 0.615 0.4823 78. 47 2.87 Mn/Cu 58.57 6787.06 140.60 30.89 1636.26 130. 94 4.15 Mn/B 2.759 7.1527 96.92 2.618 2.0808 55.09 3.44 Fe*Ca 39.50 516.0 57.49 34.81 423.7 59.12 1.22 Fe*Mg 14.63 68.12 56.41 16. 52 145.3 73.00 0.47 Fe/Al 0.945 0.5405 77.77 0.773 0.2261 61.53 2.39 Pe/B 2.439 4.7696 89.51 2. 267 3.0670 77.24 1.56 2n]? 108.3 1733.31 38.40 105.52 1190.56 32.70 1.46 2n/Mn 0.127 0.011 82.79 0.145 0.0069 57.29 1.62 Zn*Ca 9.432 47.80 73.29 8.261 47.32 83.27 1.01 2n*Mg 3.405 4.512 62.37 3.384 5.269 67.82 0.86 2n/Fe 0.448 0.3486 131.72 0.428 0.2904 125.76 1.20 2n/B 0.426 0.1075 76.85 0.378 0.0295 45.39 3.64 Al/Zn 12.55 29.591 43.31 12.69 17.150 32.63 1.73 Table 5. Cufll Cu/P Cu/R Cu/Fe Cu*Ca Cu*Mg Cu/Zn Cu/Al 00/3 B/Al (cont'd) 5.131 53.128 80.06 1026.9 19.87 671.38 0.282 0.2140 5.571 89.20 1.663 3.321 0.554 0.0766 0.041 0.0003 0.256 0.0701 0.313 0.0085 96 142.03 131.15 130.39 163.95 169.50 109.50 49.96 46.33 103.20 29.59 3.355 50.18 14.40 0.269 3.693 1.589 0.483 0.035 0.182 0.269 8.8777 1423.6 123.93 0.1886 21.98 2.515 0.1596 0.0007 0.0203 0.0051 88.81 75.18 77.28 161.41 126.90 99.78 82.55 74.48 78.34 26.69 5.98 7.75 5.42 1.13 4.06 1.32 0.48 0.54 3.45 1.65 97 Righetty (1988a), found that the high variability of Zn.and Mn in sweet cherry has a hidden effect in the detection of other deficiencies. A DRIS system requires as a norm the mean and the coefficient of variation of each of the selected ratios that belong to the high yielding population. As a consequence those values were used as the first tentative set of DRIS norms for highbush blueberry (Table 6). Further calculation of intermediate functions and DRIS indices will make possible a nutritional diagnosis. These preliminary DRIS norms need to be‘calibrated in future field trials and refined in successive diagnostics. 2.3 DRIS nutrient index. Nutrient functions were calculated according to Beaufils DRIS protocol, and used for the final calculation of nutrient indices which were related to the respective yield values (Appendix Table 2). The scatter diagram of DRIS indices and yield show divergences with those of nutrient concentrations and yield (Figures 3 to 10). DRIS indices vary more for some elements than for others. It may be that DRIS indices calculated from intermediate functions are not independent of the variability of their components. The nutrient content for some elements varies more than for others (Table 1). Many factors can affect the 98 Table 6. DRIS NORM for highbush Blueberry (Vaccinium corymbosum L.). Form of Mean CV% Form of Mean CV8 Expression Expression N/Fe 0.044 91.15 N/Zn 0.163 36.74 N/Al 0.016 28.09 N/B 0.059 39.62 N*Ca 1.011 32.09 N*Mg 0.440 26.79 P/N 0.063 21.26 P/K 0.284 16.97 P/Fe 0.004 110.47 P/Al 0.001 25.33 P/B 0.004 34.52 P*Ca 0.062 51.12 P*Mg 0.029 45.83 K/N 0.230 18.52 K/Fe 0.010 103.59 K/Zn 0. 036 33. 31 K/Al 0.003 23.82 NIB 0. 013 33. 62 K*Ca 0.233 38.75 K*Mg 0.101 31. 70 Ca/Al 0.004 19.17 Ca/B 0.016 32.38 Mg*Ca 0.123 43.54 Mg/Al 0.002 24.54 Mg/B 0.008 33.64 Mn/N 109.9 88.83 Mn/P 826.9 71.16 Mn/K 466.7 87.67 Mn/Fe 4.493 101. 30 Mn*Ca 122.8 97.45 Mn*Mg 48.07 88. 30 Mn/Al 0.615 78.47 Mn/Cu 30. 89 130. 94 Mn/B 2.618 55.09 Fe*Ca 34. 81 59.12 Fe*Mg 16.52 73.00 Fe/Al 0.773 61.53 Fe/B 2.267 77.24 Zn/P 105.5 32.70 Zn/Mn 0.145 57. 29 Zn*Ca 8.261 83.27 Zn*Mg 3. 384 67. 82 Zn/Fe 0.428 125.76 Zn/B 0.378 45. 39 Al/Zn 12.69 32. 63 Cu/N 3. 355 88. 81 Cu/P 50.19 75.18 Cu/K 14. 41 77. 28 Cu/Fe 0. 269 161. 41 Cu*Ca 3. 693 126. 90 Cu*Mg 1.589 99. 78 Cu/Zn 0.483 88.55 Cu/Al 0.035 74.48 Cu/B 78.34 B/Al 0.269 26.69 0.182 a A 40 so 16 14+ 12« b magnum) § 8-1 8% M 2‘ 04 4 18 .. 14‘ ‘1 u YEU)«gbIfiunmd0 e 9 3 3.3 D 5"» it: .t p 0 ‘ . ." a. A a“ .7131; t‘a ‘ ‘7 ’ ‘ s? 'v. 'v 5 M“ A A 3‘ V fix _. A t 5 A ‘ t ' I.‘ v ‘ v 2* “ ‘ ““1""? 3:)”. 1w" 3“ IA ‘ v .1. ‘ . ) . 0! ‘ ‘ i ‘1 ‘5' -{4'.' fr. 5“ ‘ ‘ -50 -40 & ~20 -10 0 10 20 30 40 80 POT ASSIUM INDEX Figure 7. Relationship between DRIS Index and Blueberry yield. 8) In 3 b) It i C) Ix - .100 ‘4 10 0 MAGNESSIUM INDEX 10 1+ Relationship between DRIS Index and Blueberry yield. c) 1“,. Figure 8. 9’10.) YELDM ? 9' ‘P ? YIELD“ 101 18 14- 12‘ 12‘ 10- 60 ALUMINUM INDEX Figure 9. Relationship between DRIS index and Blueberry yield. 8) 19.3 b) 17. I c)IM. 18 - ‘A 4 A 14‘ M . A ~12‘ a: a 10- ‘. . ‘ a “A. I‘ a a- ? . 4H. “ g 4“ A A‘:‘ “A‘ ‘a ‘ a a 5‘ a. ‘A‘ . . fitug ‘ ‘a‘f ‘AI’A‘A‘A ‘ 41 A Afflu‘ ‘ 3“A ‘ ‘ .Aa“ ‘ a ‘a 2 I‘fig.’ ‘~‘ ‘ ‘ 1 ,m ‘ A ‘ ‘ c ‘ A‘ In) “ -50 10 450 -éo 40 0 10 2'0 3'0 4‘0 50 OOPPERINDEX 16 - A “ 14‘ f IA -124 b 5 A a 10" ‘ A . Aa‘ ‘ 4 a ‘aa :QA‘ek At...‘ ‘ o ‘ ‘{“A‘ A . é a‘ ‘3 ‘ * : a 3 3 ‘ ‘5 "I A. I A eAA ‘ E . ‘ ‘ A: ‘1 a .a t ‘a ‘ 5‘Aa 4a. ‘ t ‘as S ‘ 2‘ ‘ ‘ ‘4‘ :A t ‘A‘: a c a a ‘ ‘ ~60 40 -éo 6 20 40 so BOHONINDEX Figure 10. Relationship between DRIS Index and Blueberry yield. a) Io, ; b) IB . 103 possible variability of a nutrient element in a large population; plant genetic; plant age; soil and management" It was noted, as an example, that Mn.is the only nutrient element that shows consistent soil and progeny differences for leaves, roots and shoots in highbush blueberry (Korcack, 1989). DRIS indices from nutrients with a wide nutrient concentration variation have a wider range of values than indices from elements that are less ‘variable and. the differences in concentration variation between elements also affects the ratio selection (Righetti, 1988a). 2.4 Identification of Unbalances. The nature of the DRIS system is to always make a relative diagnosis (Beaufils, 1973a) . Since DRIS indices are symmetrical (their sum equal zero) there are no problems in identification of unbalances when few elements are included in the DRIS data base, but when more elements are included in the system, indices with large variation can mask the detection of a deficiency or excess of an index with a narrow range (Righetty, 1988a). DRIS always mades a relative diagnosis, but not an absolute measurement of the nutrient status. DRIS identifies the order of limitation, even if all the nutrients are present in a normal range of concentrations. A low index means for that particular nutrient compared with the other nutrients present in the same sample, that it is lower than 104 the same nutrient compared to those in the reference population. From the scatter diagram of each DRIS index and yield it is possible to say that DRIS has identified relative deficiencies of N and P, relative excesses of K and not only deficiency' but also excesses for' Mn and Fe. Relative deficiencies and relative excesses were detected also on Sweet Cherry by Davee et al., (1986). A DRIS index could be considered sufficient if it falls within 1.33 units of zero for indices calculated from N, P and K concentrations (Beaufils, 1973a). Using this criteria for delimiting relative excess and deficiency the diagnosis change a little (Figure 11). Seven percent or more of the total samples show deficiency not only for N and P but also for Mn, Fe and Cu. Similar percentages were found for excess in K, Ca, Fe and Zn. Iron and Al have similar percentage in both zones of limitations. Nitrogen and P indices have a high and wide dispersion of points in the deficiency area for the low yielding plants--an indication that they are limiting factors for blueberry production. Ballinger, (1969) reported that the N status of the plant appears to play a major role in the production of blueberries. High application of N was associated with low berry acidity and a high soluble solids to acid ratio. Too little N was associated with slow ripening and to much N with small berries. The level of N affected positively the number of leaves per plant, the ripening date 105 is . u Egg/QC wigz e Zgé/EF . n 2.2M . g V////////////_. M ._ . I J a 2% c gag/gin Eli/ZN 10') q q _ - u _ — 4 — 9876543210 manila ".0 Emommm [/ Relative deficiency - Relative excess Sdn; er08 ua.1..r . 1dt08 ) anazoolfiu vai 24. (eat... 1 a as... . _ 3d78 flax-J33 mldhOAL X n B E a WbI ox ))k skMieh 4 mm... ., . $4.! 4 m “19;. .mneinmJZ Gang—18+— fine-.11 e r5 +_3 Bea—9 . . 4 ..hth13. I e S n 3 m .m)a h Dumalk emu mij .1 fx 9 tr e REM; n eeWIJEl Rf BB2: rcR+_+-1 .o)D 11 1 n 9.. 1 oh.20 exams . rtaexmm Wai III evr ireo))) ngfcfi 106 and the fruit to leaf ratio and negatively the fruit size and the acidity. Low level of P was associated with few leaves, a high fruit to leaf ratio and low berry weight. Potassium index shows a wide scatter of points in the excess area which may be due to a negative interaction with the N content of the low yielding population or to the strong influence of crop load on leaf K, and also to differences in nutrient availability among locations“ IEck (1987), found that increasing yield created an important K sink. 0n the other hand, Hancock and Nelson (1988), reported that there exist a certain ambiguity about K sufficiency range. They said that leaf K optimum is closer to 0.43 % of dry matter than the 0.53 % reported. If there is no relationship between leaf K and yield (Ballinger and Kushman, 1966; Eck, 1983), the use of leaf levels at harvest may not be a good indicator for generating recommendations. Calcium levels in foliage are known to have the best relationship with blueberry yield (Townsend, 1973b). In a study made to evaluate the adaptability of different progenies from essentially pure highbush to interspecific hybrids containing varying amounts of evergreen, lowbush, black highbush and rabbiteye over four typical soils for the crop (Berryland sand, Galestown sandy clay loam, Manor clay loam and a Pope sandy clay loam) planting with or without the addition of peat moss, it was found that foliar Ca varied among soils. For the Berryland soil and peat, most amended 107 soils showed elevated leaf Ca levels. The positive effect of the peat may be due to the relative high Ca of the material compared to the K content (Korcack, 1989) . Most of the samples analyzed in this data base came from similar soils. However, Cummings (1978) , noted a negative effect of high K on the foliar content of Ca. Elevated Zn foliar levels were also reported as a consequence of K applications (Cummings et al., 1971). 2.4.1 The Nutrient Imbalance Index (NII) Beaufils (1973b), said that another technique to determine imbalances is to only consider the relative deficiencies or excesses when ‘the sum. of DRIS indices, irrespective of sign (NII), is excessive (Appendix Table 2). The scatter of points of the NII and yield for the whole population shows that there was no relationship between NII and yield (Figure 12). However the general pattern usually found in other works is also present (Davee et al., 1986; Szucs, 1990; Klein et al., 1991). As a consequence, when there is not a relationship between both variables, it was assumed that samples which fall in a range greater than the mean of all the NII plus 1.33 times the standard deviation are considered extremely unbalanced (Figure 11) . Both populations have points far from this limit. The high yielding population has 12.4 8 of its samples and the low yielding 8.63 t in 108 E? In 3 .D \ CD .2 E CD or v D _l !1_-' >- + + + +34+++ + I I I 300 400 500 600 NUTRIENT IMBALANCE INDEX I High yield + Low yield Figure 12. Relationship between DRIS Nutrient Imbalance Index (NII) and Yield for the High and Low Yielding Population. + Low yielding, A Population, (x < 5 kg berries/bush). - High yielding, B Population, (x 2 5 kg berries/bush. 109 unbalanced conditions. There is no reference in the literature which supports the difference found in percentage of unbalances between. both. populations of’ high and low yielding plants. However, two factors might induce such variability: the mix of plant age, and the number of nutrient analyses available per sample in each of the subpopulations. But, low yields were always associated when the sum of DRIS indices was very high. A similar conclusion was arrived at by Meldal-Johnsen et al., (1975). 2.4.2 Relative Order of Nutrient Limitation. One of the advantages noted for the DRIS approach was its ability to rank the nutrients in their order of relative limitations. When DRIS indices were placed in increasing order for all samples, limiting elements with both relative deficiencies or excesses were identified (Appendix Table 2). When only the most and least relative limiting nutrient were analyzed a close similitude with the nutrients before noted as unbalanced were found. From the total of samples that belong to the high yielding population the relative most limiting (deficient) nutrients were Mn, Fe and Cu and the relative least limiting (excess) were Mn, Fe and Ca. In contrast the most limiting nutrient for the low population were N, P, Ca and Zn and the relative least limiting nutrient were K, Mn and B (Figures 13 and 14). 110 N P KCaMgMnFeZnAICuB [I Hows-wins - Lemming J Figure 13. Relative most limiting nutrient (percentage of the Blueberry population with the lowest DRIS index for all the DRIS indices availables per sample). PERCENT OF POPULATION to on \ N P KCaMgMnFeZn Al CUB [I'lrmmthhgllllowthhg Figure 14. Relative least limiting nutrient (percentage of the Blueberry population with the biggest DRIS index for all the DRIS indices available per sample. 111 Finally, considering the whole information that the DRIS could bring in detecting nutritional limitations it is clear that the whole population can be characterized by the presence of deficiencies in N, P, Ca and Cu and excesses in K and Mn. Future calibration of the DRIS norms in field trials would bring more information about the accuracy of detection limitations and in the possible masking effect that some nutrients could have. 2.5 Comparison of Different Approaches in Diagnosing Nutritional Limitations. Diagnosis made by traditional methodologies, Sufficiency Range Value (SRV) and Critical Level (CL), for the whole population have also diagnosed a high proportion of samples with N, P and Ca and micronutrients like Fe, Mn, Zn, Cu, and B as limiting nutrients (Table 2 and 3). There appears to be aiclose agreement between.the SRV and DRIS in detecting a high proportion of samples with excess of both K and Mn. The SRV has detected 28.4 % of samples in the higher ranges for K and DRIS has detected 24 %. Similarly for Mn the SRV has detected 45.7 % and DRIS 40.9 %. With the other nutrients DRIS seems to indicate excess where the SRV consider the nutrient as sufficient with two exception -- N and Mg where only DRIS detect a very small percentage as relatively in excess. 112 DRIS is in a total agreement with the percentage of samples below the Michigan Standard for K but, again DRIS detected deficiencies for N, Ca, Mg and Cu where SRV and CV standards usually placed samples in the normality range. However, the DRIS approach seems to be less sensitive in detecting deficiencies of Mn, Fe, Zn and B. This data is in agreement.with the general concept that DRIS reveals nutrient deficiencies in the range normally considered to be sufficient (Angeles at al., 1990), and with.the possible bias effect that some nutrients with a wide concentration variation have in the ratio selection and in masking limitations (Righetty, 1988a) . 2.6 Some limitations found in the DRIS. Usually the few applications of DRIS found for fruit crops have used independent sets of data from field experiments with a narrow range of plant age and uniform number of nutrients per sample to make comparison with other diagnostic systems like the SRV and or the CL (Angeles, 1990; Beverly et al., 1984; Goh and Malakouti, 1992; Righetti et al. ,1988b) . As mentioned before, two possible factors could have caused the relative unbalances found between the low and high yielding population--the plant age and the number of nutrient analysis available per sample. The effect of the number of nutrients analysed available per sample shows a trend of increasing the average of NII 113 (Figure 15). The discrimination of possible samples in a data base using the criteria of the number of nutrients analysed goes against one of the principles of DRIS, the indiscriminate use of any sample (Beaufils, 1973a), but it could simplified calculations. Plant age is a known factor related with fruit productivity'(Leopold, 1964;‘Westwood, 1988; Kozlowski, 1991). However, the productivity is also related to the plant density, the total uptake of nutrients, the importance of an adequate root system, the demand for nutrients at any time, and the extent of the internal remobilization of nutrients (Atkinson, 1986). Most of these important factors were not included in this preliminary data base, but an approximation of the effect of plant age was examined. A simple way to observe the effect of plant age on the nutrient balance index was to split data among age range of bushes and to observe the distribution of NII versus yield. The general pattern observed for the whole population is still present; however, when a range between 0 to 100 of NII was analyzed a difference among the average of NII and plant age appears (Figures 16 to 18). As plant age range increased the NII is displaced to the higher limit. This suggests a new hypothesis that DRIS could be dependent on plant age. To overcame some of the possible factors that. may contribute to the observed pattern, the population was 114 V\\\\\\\\\\\\\\\\\\\\. 10 11 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\x 9 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\. _ a Number of nutrient \\\\\\\\\\\\\\\\\\_ 7 V\\\\\\\\\\\\\\\\\\\\ 6 \\\\\\\\\\\\\\\\\\\\\\\\\\\x i \\\\\\\\\\\\\\\\\\\ 4 \\\\\\\\\\\\\\\\\\\\k 3 as“; :2 3822 - High \\\\\\‘ Low between the average Nutrient Relationship 15. Imbalance Index (NII) for the high and low yielding population and the number of nutrient availables per sample. Figure 115 CD YIELD (kg berries/bush) a I I - I 300 400 500 000 NUTRIENT IMBALANCE INDEX I 3-5 years I 51 . I I . a 4- .8 5 a- b O é 9 2‘ 9 >- 1-1 G 01'0 203040506070 8080100 NUTRIENT IMBALANCE INDEX I 3-5 years Figure 16. Relationship between the Nutrient Imbalance Index for the plant age 3 to 5 years old and Yield. a) scatter diagram for the whole NII range; b) scatter diagram for NII between 1 to 100. 116 5 3 3 E ' - a I o 3' ' ' ' _UI_ k‘ I - . >' f ‘ I II -' I I .. I.- ' I .I 100 180 260 280 300 NUTRIENT IMBALANCE INDEX I 6-11year8 YIELD (kg berries/bush) ‘P I I 4'0 50 60 7'0 NUTRIENT IMBALANCE INDEX I 6-11yoars Figure 17. Relationship between the Nutrient Imbalance Index for the plant age 6 to 11 years old and Yield. a) Scatter diagram for the whole NII range; b) Scatter diagram for NII between 1 to 100. YIELD (kg berries/bush) ? 6.. I' I I 4. I II- . I 2. A I "o 50 100 150 200 250 300 an NUflmENTIMBALANCEINDEX I 1245yems 16 - II. - . 14‘ I : I I t; 12- I I g 10‘ I ' , I I I E - ‘ ' ' 8. I I ‘- g I I H II. II: o 6" - -'- . II “—11 I. -I-- I‘. I F. 4‘ ~ .- t 'I I I I - I ' 2" - - I .- ' ' I ‘I' In". J- "0 1'0 2'0 ab 30 5'0 6'0 7'0 80 9'0 100 NUfiRENTIMBALANCEINDEX I 12-15years Figure 18. Relationship between The Nutrient Imbalance Index for the plant age 12 to 15 years old and Yield. NII range; a) Scatter diagram for the whole b) Scatter diagram for NII between 1 to 100. 118 analyzed by plant age range and only samples which have equal number of nutrients were included (Table 7). A clear difference of the effect of plant age among nutrient optimum and nutrient ratios appears. The nitrogen mean decreases as plants age. This confirms studies where heavy crop loads can decrease the N content of leaf (Ballinger and Goldston, 1967; 1969) . The potassium content decreases with age, as was mention before since increasing yield creates an important K sink (Eck, 1983), and the expected increases in M9 as plant developed was observed (Parent and Granger, 1989). There are controversial reports about the independence of the DRIS diagnostic on the tissue age (Sumner, 1977a, 1977b, 1977d; Admunson, 1987; Beverly et al., 1984; Hanson, 1981). The effect of plant age was shown only in the significant year to year variation that DRIS norms has in the report of Parent and Granger (1989). They derived DRIS norm for apple trees on dwarfing rootstocks from a seven years fertilization trial and conclude that, although the cumulative yields were used to define the norms, the relationships between cumulative and annual yields and DRIS parameters gave similar DRIS norms after the 5th and the 6th years of plantation due to the strong influence that tree development stage has on leaf nutrient composition. The wide range of plant age used in this work allowed the derivation of DRIS norms for the ranges noted before (Table 8) 119 Table 7. Effect of age of bushes for three high yielding subpopulations on the optimum of nutrients and nutrient ratios. Range age of bushes 3-5 6-11 12-15 Mean‘ Std Mean Std Mean Std Yiold 4.13 C 0.51 7.78 B 0.67 13.16 A 1.76 (kg/bush) N 8 2.25 A 0.26 2.22 AB 0.14 1.99 B 0.13 P 8 - - - - 0.13 0.03 K 8 0.63 A 0.11 0.51 AB 0.09 0.40 B 0.07 Ca 8 0.55 ab 0.12 0.49 b 0.14 0.60 a 0.07 Mg 8 0.19 BC 0.03 0.18 C 0.04 0.31 A 0.04 N/P - - - - 16.29 2.18 P/N - - — - 0.06 0.01 N]! 3.70 B 0.76 4.44 AB 0.57 5.14 A 0.67 KIN 0.28 A 0.05 0.23 BC 0.04 0.20 C 0.03 N/Ca 4.42 A 1.55 4.91 AB 1.45 3.35 B 0.40 Ca/N 0.25 AB 0.06 0.22 B 0.07 0.30 A 0.04 N/Hg 12.29 A 3.01 12.64 A 2.75 6.60 B 0.96 Mg/N 0.09 B 0.02 0.08 B 0.02 0.16 A 0.03 p/x - - - - 0.32 0.02 P/Ca - - - - 0.21 0.03 Ga]? - - - - 4.88 0.63 P/Mg - - - - 0.41 0.08 Mg/P - - - - 2.50 0.41 K/Ca 1.24 A 0.51 1.15 A 0.47 0.66 B 0.08 Ca/K 0.91 C 0.27 1.00 BC 0.36 1.54 A 0.18 K/Mg 3.41 A 0.95 2.98 A 1.11 1.31 B 0.28 89]! 0.31 C 0.08 0.37 BC 0.12 0.79 A 0.14 Ce/Mq 2.94 A 0.70 2.75 AB 0.85 1.98 B 0.24 Mg/Ca 0.37 B 0.14 0.40 AB 0.12 0.51 A 0.06 N*Ca 1.23 NS 0.30 1.09 NS 0.32 1.20 NS 0.18 P*Ce - - - - 0.08 0.03 K*Cl 0.34 A 0.09 0.25 BC 0.06 0.24 C 0.07 Mg*Ca 0.10 BC 0.03 0.09 C 0.03 0.19 A 0.05 N*Hq 0.42 BC 0.07 0.41 C 0.10 0.61 A 0.09 P*Mg - - - - 0.04 0.01 K*Mg 0.12 a 0.02 0.09 b 0.02 0.12 a 0.04 ' Means followed by different capital letters are significantly different at (P - 001) by Duncan's Multiple Range Test. Means followed by different minor letters are significantly different at (P I 005) by Duncan's Multiple Range Test. MS - not significantly difference. .120 Table 8. Blueberry DRIS NORM related to age range for equal number of nutrient per observation. Age Range 3-5 6-11 12-15 Form of Mean CV8 Mean CV8 Mean CV8 Expr. N/P - - - - 16.29 13.40 K/N 0.28 14.88 0.23 15.33 0.20 15.66 R]? - - - - 3.17 6.83 N*Ca - - 1.09 29.66 1.20 15.09 Mg/N 0.09 22.31 0.08 22.71 - - N*Mg - - - - 0.61 14.42 P*Ca - - - - 0.08 33.29 P*Mg - - - - 0.04 36.16 R*Ca - — - - 0.24 30.80 R*Mg 0.12 20.13 0.09 16.71 0.12 30.72 Ca*Mg 0.10 29.22 0.09 37.01 0.19 26.28 n 402 315 287 121 Although there are only few nutrient ratios for comparison and there were not always coincidence with the selection of the important ratios with the previous derived DRIS norms, it appears that there:is a close similarity of the DRIS norms derived from the whole data base with those in the range of 12 to 15 years old blueberries. However, discrepancies between norms from the whole data base and those of young plants seems to be greater. Future analysis will be required.to~determine if plant age will affect the DRIS norms. It seems reasonable that a more accurate diagnosis could be made if DRIS norms are derived for very young plants before stabilization of crop yield is found. 3. Correlation between DRIS and the Critical Value. The use of the CV as a diagnostic tool has been applied extensively since the concept was developed by Ulrich and Hill (1967) ; however, little information about correlation with DRIS is available in the literature (Needham et al., 1990). The possible reasons for the lack of this information is that most researchers are interested in the development of DRIS norms and in the evaluation of the impact of its multiple nutrient composition in diagnosis and fertilizer recommendations. There is information available about CV levels for the different nutrient elements on highbush blueberry (Eck, 1988; Table 9. 122 Level estimated and reported in the literature. Comparison of Blueberry DRIS nutrients and Critical DRIS CRITICAL LEVEL Nutrient Norm Optimum Estimated‘ Reported from data Eck MI N 8 1.93 1.92 1.96 1.80 1.65 P t 0.11 0.12 0.11 0.12 0.10 x 4 0.44 0.43 0.34 0.35 0.35 Ca 8 0.52 0.45 0.63 0.40 0.34 Mg 8 0.23 0.23 0.32 0.12 0.12 Mn mg/kg 229 452 83 50 168 Fe mg/kg 71 127 56 60 150 2n mg/kg 14 13 11 8 20 Cu mg/kg 6 13 3 5 15 B mg/kg 35 33 33 30 49 1 Estimated as described by Ulrich and Hill,(1967). Table 10. Comparison between DRIS and Critical Value. Coefficient of determination (RH DRIS Critical value‘ Correlation Estimated Reported range, Literature. N t - I" n.s. n.s. n.s. P t - I, 0.64** 0.53** 0.70** K ‘ - Ix 0044** nose ne.e C‘ ‘ - IC. 0062** 0e49** 0e46** “g ‘ _ Iu‘ nose ne'e he's “n Mlkg - I“- Oe64** 0e47** 0e61** Fe mg/kg - I“ 0.61** 0.89** 0.48** 2n mg/kg - Ia, 0.75** 0.54** 0.52** Cu m/kg — I“ 0e73** 0e69** 0e79** 8 mg/kg - I. 0.74** 0.73** 0.71** n.s.s Not significant. ** I P > 0.01. 11 Values between the estimated optimum DRIS index and DRIS index. 1% Values between the suggested by Eck, (1988) and the DRIS index. 123 Kenworthy, 1979; Ballinger et al., 1969; Hancock and Hanson, 1986) however, these reference values need to be used for similar conditions to those under which they were established to get accurate diagnosis“ It‘was noted by Bates (1971), that many factors can affect the critical concentrations of a tissue (age, cultivar, interactions among nutrients, fraction of nutrient to be measured, environment); however, any factor that reduces yield has the potential of interacting in the growth response and consequently affecting the CV (Walworth and Sumner, 1988). It was mentioned before that the DRIS data base was a pool of samples from‘different cultivars, plant age, nutrient interactions, soil conditions and crop managements; thus it may be expected that.more than one factor at a time could have influenced in the productivity and in the possible evaluation made by a CV approach. DRIS diagnosis makes a relative diagnosis, taking into account all internal and external factors that affect yield. As a consequence, the two approaches are conceptually different. Therefore, to make a correlation between both methodologies requires some adjustment. I agree with the criteria guiven by Needham (1990), in his work for loblolly pine. A foliar nutrient optima was defined by averaging the concentrations of each respective nutrient element of all high yielding bushes that exhibit simultaneously optimum DRIS index (mean plus/minus 1.33 standard deviation) as a new tentative 124 critical level (Table 9). There is agreement in the N nutrient concentrations among all diagnostic approaches with the exception of the Michigan standard value; however, a total agreement was found for the P values. The K values derived from DRIS are equal to the last critical value 0.43 % K reported for Michigan condition (Hancock and Nelson, 1988). The optimum Ca value is close to that reported by Eck, (1988). Magnesium and Mn show values with the poorest agreement among diagnostic criteria. The Fe DRIS optimum value is close to the Michigan value; however, those values which belong to the DRIS norm, or estimated from the data base and that reported for Eck are closer. zinc and B values developed from the same data base are close to those reported by Eck, (1988). Developed DRIS optimum values and the Michigan values for Cu tend to agree; but, the Cu DRIS norm and the calculated critical value are in concordance with those reported by Eck, (1988). Except for the agreement found for P values, it appears that the differences among values are probably due to the lack of uniformity among data and methodologies used to establish those values. The interpretation of these results would require calibration with further trials, thus under more uniform conditions. To aid in visualization of the possible relationship between each. DRIS index .and. the nutrient concentration, 125 scatter diagrams were constructed (Figures 19 to 22). The relationship between DRIS indices and its respective nutrient concentrations vary with the elements. With the exception of N and Mg all nutrients show some degree of association. All nutrients with the exception of Mg and Ca are positively associated and all micronutrients could be described by a linear relation” DRIS indices for Cu above 10 ppm, B above 20 ppm, Al above 50 ppm, and Zn in all the range studied seem to be so dependent on their concentrations, that it may be of little advantage to use a ratio based diagnosis over a CV approach. The lack association for N and Mg, in contrast, shows that DRIS indices could be dependent on others nutrients or others factors. The Mn scatter and to some degree that of P show an exponential form. The coefficient of correlations calculated between DRIS index and the nutrient concentration, the DRIS index and the defined foliar nutrient optima and the DRIS index with the normal range suggested for highbush blueberry (Table 10) show that with the exception of N and Mg noted before, all nutrients have medium to high correlation (r > 0.6). The coefficient of determination of these relationships were highly significant. The degree of association of the estimated critical value and DRIS indices show variations among nutrients and a decrease in the relationships was observed for Mn and an increased for Fe. This could indicate that variation in Mn concentration affected the ratio 126 0.35. b 91005050102» 0 23106080100 POTASSIUMINDEX Figure 19. Relationship between DRIS Index and leaf nutrient concentration. a) N; b) P; c) K. 127 1 .2‘ 1* ‘ 0.84 A 0.6d CALM“ 0.4* 0.2‘ 910080-6010-20' 100 0.6 0.5- 0.3‘ MAGPESlUM 96 0.2‘ 0.1 ‘ .8'0 .60 10 .50 6 2'0 4'0 60 80100 menssuumuoex 8 1 .Fjarggggggm AA {om-30.206204108060100 MANGANESEINDEX '8‘ Figure 20. Relationship between DRIS Index and leaf nutrient c=C>ncentration. a) Ca; b) Mg; c) Mn. 2 N 5 Z 2 a 1* Figure 21 . concentrat ion . 128 80‘ 60‘ ‘? 40" as 20 fi r r 4‘- r r . . ~11” -80 -60 40 -20 0 20 40 w 80 IRONINDEX 90 a 80" ‘ a 70‘ a 60" a a 50‘ a 40‘ 30- 20" 10" a c 1 60 60 80 100 Z1NCINDEX 356 300- ‘ 250‘ A 200« ‘ a 150‘ ‘ ‘ A M A 100- at 50. ‘ ‘ ‘ As a C v so .40 -20 0 2'0 70 8'0 80 ALUMINUM INDEX Relationship between DRIS Index anf a) Fe; b) Zn; c) Al. leaf nutrient 129 cawtnnmI 315553838 5' A “A A 91730 -50 0 5'0 160 150 cmmmnmma BOHONppm Figure 22. Relationship between DRIS Index and leaf nutrient concentration. a) Cu; b) B. 130 selection and consequently masked some deficiencies. Of course there is no way to prove this until DRIS norms can be calibrated in field trials. However, since correlations were calculated for a different number of observations, it must be pointed out that higher correlations are required for significances when fewer number are included. SUMMARY AND CONCLUSIONS A data base of 1074 observations of highbush blueberry ranging from 3 to 15 years old containing yield and foliar nutrient concentrations was used for developing the Diagnostic and Recommendation Integrated System (DRIS) norms. The criterion for dividing the population in two subpopulations was a yield value equal of or greater than 5 kg berries/bush. A total of 110 nutrient ratios were calculated among all possible combination of nutrient (N, P, K, Ca, Mg, Mn, Fe, A1, Zn, Cu and B) elements. The largest ratio of variance between the low over the high yielding population of all possible ratios was used as a selection criteria for discrimination among ratios. The resulting selected ratios of the high yielding population were used as the first tentative set of DRIS Norms for highbush blueberries. Analysis of the whole population in the data set and of specific subpopulations were made utilizing the tentative set of DRIS Norms. These analysis show that: 1. The number of nutrient analysis available per sample, nutrient concentration variations and the age of plants have been identified as possible factors that can influence DRIS diagnosis; 131 132 2. DRIS diagnosis has identified relative deficiencies of N, P, Ca and Zn and relative excesses of K and Mn for the blueberries; 3. DRIS diagnosis has identified N, Ca and Zn as the most limiting (deficient) nutrients for the low yielding population, and Mn, Fe and Cu for the high yielding population; 4. DRIS diagnosis has identified K, Mn and B as the least limiting nutrient for the low yielding population, and Mn, Fe and Ca for the high yielding population; 5. DRIS and the Sufficiency Range Value (SRV) were in agreement for the detection of K and Mn excesses. DRIS appears to indicate excess for the other nutrients where the SRV considers them to be in the sufficient range; 6. DRIS and the Michigan Critical Value (M-CV) had total agreement for K diagnosis. DRIS detected deficiencies for N, Ca, Mg and Cu where the CV placed samples in the normal nutrient range; 7. DRIS appeared to be less sensitive than CV in detecting Mn, Fe, Zn and B deficiencies; 8. The comparison among the diagnostic criteria (DRIS Norms; DRIS Optimum; and CV estimated and reported from the literature) show some divergences, but a total agreement was found for the P'valuesw The lack of uniformity among data and methodologies used have probably affected the results; 10. The correlations found between DRIS indices and leaf 133 nutrient concentrations were medium (r 2 0.6) to high (r 2 0.8) with the exception for those of N and Mg; 11. Correlation coefficients similar to those relating DRIS indices and leaf nutrient concentration were also found for DRIS indices and each CV estimated and reported from the literature; 12. DRIS indices for Cu above 10 mg/kg, B above 20 mg/kg, Al above 50 mg/kg and Zn in all the ranges studied seemed to be so dependent on their nutrient concentration, that there may be little advantage of a ratio based diagnosis over the CV approach; 13. DRIS norms developed in this work are biased for the high proportion of samples that belong to the.Great Lakes area and conclusions and the DRIS norms developed from this work have to be taken as preliminary and tentative and should be tested in future field trials. LITERATURE CITED Adams, F. 1981. Nutritional imbalances and constraints to plant growth on acid soils. J. Plant Nutr. 4:81-87. Alkoshab, O., T. L. Righetti and A. R. Dixon. 1988. Evaluation of DRIS for judging the nutritional status of hazelnuts. J. Amer. Soc. Hort. Sci. 113:643-647. Amundson, R. L. and F. E. Koehler. 1987. Utilization of DRIS for diagnosis of nutrient deficiencies in winter wheat. Angeles, D. E., M. E. Summer and N. W. Barbour. 1990. 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Blueberry DRIS Data Base YIELD Kalbush 4.32 5.54 9.52 1.92 5.88 2.01 7.27 2.18 8.34 3.75 7.08 3.37 2.13 2.04 8.93 5.73 11.09 10.38 8.53 7.55 1 1.05 4.50 1.50 0.45 2.72 %N 96? 96K 960s Sula --.._..-_....-_- * --_-_....-- 2.18 2.41 2.20 2.18 1.99 0.19 0.18 0.20 0.15 0.20 0.14 0.14 0.14 0.13 0.14 0.18 0.15 0.21 0.15 0.17 0.10 0.15 0.13 0.19 0.14 0.20 0.14 0.19 0.12 0.13 0.11 0.18 0.15 0.18 0.18 0.19 0.14 0.18 0.15 0.11 0.11 0.15 0.13 0.13 0.15 0.13 0.15 0.15 0.15 0.15 0.13 0.17 0.15 0.18 0.13 0.12 0.10 0.15 0.11 0.15 0.12 0.17 0.14 0.15 0.17 0.18 0.12 0.20 0.13 0.17 0.84 0.55 0.78 0.81 0.88 0.52 0.57 0.29 0.20 0.37 0.20 0.28 0.19 0.25 0.23 0.30 0.24 158 Fe Zn Al Cu B Meme ------------ Ina/kg —-—------- 1 78 323 283 325 289 31 7 1 18 140 84 74 129 242 94 182 1 88 1 72 18 17 32 18 18 10 20 14 20 14 18 71 Belngu.1957 46 lengemm'fl 67 Salingemm'fl 47 Balinget.1957 67 8a|IIngen1957 35 BaIIInger.1957 46 Belingec.1957 33 Befllngem957 33 BellnguJ9S7 27 Ballinger.1957 60 Ballinger.1957 61 BalIInget.1957 67 Balflnget.1957 52 BaIIIngem957 121 Bellinger.1957 64 8allinget.1957 46 BalIInget.1957 31 BeIInget.1957 69 Ballinger,1957 42 Belingst.1957 44 Bellinget.1957 27 BeIInger.1957 22 Ballngu.1957 14 Bellinger.1957 66 Bsflngu.1957 20 Balinger.1957 25 Belllnget.1957 17 Belinget.1957 35 Ballingen1957 22 Ballingen1957 36 Bellingem957 22 leInger.1957 26 8sIIinget.1957 22 BeIInQIJOW 45 Ballnget.1957 16 Belinget.1957 53 Bellnget.1957 20 Belinget.1957 39 8ellinger.1957 27 Ballinget.1957 46 Belinget.1957 52 Ballinget.1957 45 Salinget.1957 32 Bellogen1957 35 Bellingsn1957 29 Bellinget.1957 70 leIInger.1957 47 Belinget.1957 66 Ballnget.1967 43 BaIInger.1967 67 Megan1957 33 Belinger.1957 10 Balinget.1957 65 Balingu.1957 62 BallInger.1957 35 Bellinger.1957 55 Ballinget.1957 49 Ballinget.1957 46 Balinger.1967 45 Ballinger.1957 41 Belinget.1957 32 Ballingem957 61 Belingu.1957 41 leInger.1957 56 Balm.1957 TabIe 1. Cont'd. 15 9 YIELD 16M 96F 96K 960a 96149 Mn Fe Zn AI Cu B fletennce Kg/bush ----- ------- 96 ----—--—- ------------ 109/k9 --—------- 7.00 1.80 0.17 0.44 0.45 0.34 154 10 22 20 48 “0967,1957 1.15 1.43 0.“ 0.41 0.31 0.1 1 8680,1988 1.10 1.47 0.09 0.43 0.33 0.12 Beley.1988 0.97 1.48 0.09 0.43 0.31 0.11 Defley.1988 0.90 1.59 0.10 0.45 0.31 0.12 Beley.1988 0.90 1.88 0.1 1 0.45 0.31 0.11 Bailey. 1988 1.05 1.77 0.11 0.44 0.32 0.11 Belley.1966 1.28 1.82 0.09 0.41 0.28 0.15 Beley.1988 0.97 1.95 0.09 0.42 0.27 0.15 ”v.19“ 0.98 2.00 0.10 0.42 0.24 0.18 BeIley.1966 0.85 1.87 0.08 0.44 0.22 0.14 Beley,1988 0.79 1.81 0.09 0.44 0.22 0.15 8680,1988 0.59 1.78 0.09 0.42 0.20 0.14 Beley.1988 1.” 1.85 0.08 0.47 0.24 0.11 Belley.1966 1.39 1.71 0.09 0.48 0.24 0.11 Beley.1988 0.91 1.72 0.08 0.50 0.20 0.10 Beley.1988 1 .07 1.48 0.09 0.42 0.31 0.11 Beley,1988 1.17 1.44 0.09 0.42 0.31 0.11 Belley,1966 0.99 1.45 0.09 0.43 0.32 0.12 BeIIey.1966 0.88 1.87 0.11 0.42 0.31 0.10 Belley.1966 1.01 1.89 0.11 0.45 0.33 0.12 BaIIey.1966 0.98 1.87 0.11 0.47 0.30 0.12 “”3988 1.05 1.88 0.09 0.35 0.25 0.14 BUIey,1988 1.1 1 1.98 0.09 0.44 0.28 0.18 8e11ey.1966 1.05 1.92 0.09 0.47 0.25 0.18 WA” 0.74 1.71 0.08 0.38 0.22 0.12 WA“ 0.78 1.80 0.“ 0.48 0.22 0.15 ”$1988 0.75 1.73 0.08 0.48 0.20 0.15 BeIley.1988 1.37 1.74 0.“ 0.37 0.23 0.10 BeIIey,1988 1 .27 1.85 0.08 0.50 0.23 0.11 Daley.1988 0.97 1.89 0.08 0.58 0.22 0.11 DeIIey.1988 3.70 1.71 0.48 0.20 285 ”0.1983 394 1.94 0.48 017 290 ”6180,1983 480 1.98 047 017 338 “80.1983 440 2.14 048 017 348 ”0.1983 3 88 2.01 0.50 0 17 388 “0.1983 8 03 1.98 0.57 0 22 253 “80.1983 10 59 1.94 0.58 0 21 348 ”110.1983 10 75 1 98 0.80 0 20 372 “6010.1” 8 73 1 99 0.81 0 20 489 “6010,1983 9 98 2 08 0.81 0.18 478 "8110,1983 3 98 0.89 0.30 584 “6150,1983 4 71 0.85 0.30 590 “0110,1983 4 70 0.71 0.30 881 “6010,1983 3.85 0.81 0.29 803 “8110,1983 4.34 0.81 0.28 831 ”0.1983 4 30 1.91 0.11 0 45 0.38 0 11 275 55 84 Lam-moss 2.80 2 07 0.12 0.51 0 53 0 13 388 57 112 Lereeu.1”9 5 50 1 91 0.10 0.44 0 49 0 12 327 52 78 meanness 3 80 1 85 0 10 0.49 0 39 0 13 147 53 75 111’“qu 5 00 1 92 0 10 0.45 0 48 0 13 280 54 79 “7660,19“ 3 50 1 92 0 12 0.45 0 43 012 288 54 92 117660.19” 4 20 1 97 0 10 0.48 0 43 012 318 58 98 Lueeu,1989 3 80 1 94 010 0.48 0 45 013 283 54 88 1.3.0.1989 4 00 1 85 0 11 0.44 0 48 0 12 270 54 87 Lereeufim 4 30 1 93 0 18 0.45 0 43 0 12 273 53 84 Lereeu.19“ 4 00 2 03 0.11 0.48 0 43 0 13 309 58 91 Lleeu.1989 584 1 59 0.05 042 053 0 19 207 45 11 2 58 876970.138 8.38 1.48 0.08 0.“ 0.58 0 19 189 38 11 2 58 876970.1988 1 .47 1 .89 0.12 0.54 0.29 0.28 5618,1977 1.40 1.94 0.12 0.53 0.27 0.28 561:.1977 1.33 2.08 0.12 0.53 0.27 0.25 5611,1977 2.13 1.84 0.11 0.53 0.44 0.30 5611,1977 2.28 1.78 0.11 0.51 0.44 0.30 5611,1977 2.48 1.83 0.11 0.47 0.43 0.29 5611,1977 1.33 1.80 0.18 0.51 0.32 0.19 5618,1977 1.80 1.70 0.18 0.50 0.27 0.17 5611,1977 961’ *K 960s 96119 .—-—-- -_——_ - _ % ..---- _ _ - Table 1. Cont'd. YIELD *N 1(9le 1.35 1.85 3.80 1.55 4.05 1.88 4.35 1.87 3.50 1.55 3.20 1.83 2.80 1 .88 4.75 5.“ 8.20 5.91 2.08 2.35 2.07 1.92 2.40 1.98 1.78 1.80 1.90 2.10 1.81 1.81 2.78 2.04 2.57 1.92 2.83 1.” 1.91 1.“ 1.50 2.18 1.82 2.38 2.13 2.10 2.14 2.18 1.84 2.37 0.31 2.82 0.41 2.” 0.40 2.88 0.30 2.78 0.24 2.80 0.20 1.83 0.38 2.49 0.24 2.89 0.22 2.88 2.77 1.29 3.87 1.38 2.58 1.41 3.30 1.45 2.83 1.51 2.58 1.14 3.43 1.38 3.33 1.35 2.83 1.41 2.00 1.47 0.13 1.35 0.14 1.35 0.19 1.32 1.13 1.88 0.14 1.47 0.85 1.40 0.18 1.50 1.35 1.50 0.27 2.12 0.18 2.20 0.40 2.30 0.39 1.81 0.84 2.08 0.51 2.17 0.82 2.17 0.“ 1.92 1.32 2.21 1.12 2.29 1.08 2.34 1.47 2.01 1.10 1.97 0.“ 2M 0. 1 8 0.07 0.08 0.15 160 --------- -—— 109/k9 - --—------ Fe 111 Zn Al Cu Refacnce 501:. 1977 Eek.1977 Eek. 1977 56K1977 Eek. 1977 56K1977 Eek.1977 Eck.1965 Eck.1965 Eck.1965 Eck.1965 Reece.1965 Roses. 1965 Rosca.1965 Roses. 1965 Roscs.1965 Reece, 1965 Reece. 1965 Boecs,1965 Rosca.1965 Roeca,1966 Reece. 1965 Rosca.1965 Fleece. 1965 Rosca.1965 Boscs.1965 Bellnget.1969 Salinger. 1969 Bellinget,1969 Bellnger.1969 Selling“, 1 969 Salinger. 1969 Ballinger,1969 Bellnget.1969 ”new. 1969 Neumsn.1965 Naumsn. 1965 Neumsn.1965 Naumsn,1965 Neuman.1965 Neuman. 1965 Nauman. 1965 Nauman.1965 Nauman. 1965 Nemnsn. 1965 mm. 1990 ”161,19” 3691182. 1990 “182.1“ “5182,1990 905182.199!) 80.1162, 1990 6091181. 1990 Townsed.1973 Townsed.1973 Townsed.1973 Townsed.1973 Townsed.1973 Townsed.1973 Townsed. 1973 Townsed.1973 Townsed.1973 Townsed. 1973 Townsed.1973 Townsed.1973 Townsed.1973 Tosnsed.1973 *P %K 166a 96149 ----- ---..- _ - * -__-..- - _. - Table 1. Cont’d. YIELD 96M Kg/bush 0.99 2.15 1.31 1.73 2.53 1.35 2.53 1.47 2.17 1.55 2.16 2.56 2.46 2.33 2.49 2.41 2.41 2.40 0.12 2.30 0.51 2.03 0.41 2.14 0.61 1.91 0.74 2.44 1.26 2.12 0.40 2.07 1.07 1.66 0.35 1.50 0.49 1.57 0.27 1.50 0.26 1.65 0.44 2.06 0.37 1.27 0.59 1.53 0.50 1.43 0.46 1.65 0.31 1.56 0.16 2.04 0.71 1.64 0.44 1.54 0.54 1.79 0.31 2.03 0.42 1.57 0.15 2.46 0.70 1.55 0.21 1.76 1.03 1.51 2.07 1.65 0.99 1.56 0.64 1.73 1.14 1.72 0.72 1.52 1.27 1.54 1.39 1.52 1.06 1.60 0.53 1.52 0.77 1.44 1.07 1.74 1.14 1.46 0.66 1.52 0.66 1.44 1.59 1.51 0.47 1.76 2.10 1.49 0.65 1.50 2.76 1.64 2.50 1.79 2.55 1.54 3.16 1.69 3.36 1.71 2.50 1.60 2.63 1.46 2.49 1.56 0.12 0.11 0.“ 0.11 0.12 0.88 0.54 0.34 0.31 0.” 0.15 0.14 0.18 0.18 0.17 0.17 0.18 0.18 0.19 0.17 0.18 161 ----—-----—- Ina/kg ---—-—---- 410 227 Fe 102 78 44 47 50 118 51 Zn A1 18 13 12 Cu OO‘JUIVO ON .000000101000NOGOOOOMOGM&.0110001..0#00§b§& Mence Townsed. 1973 Towneed.1973 CUInInIng.1976 Cummlng.1976 Cumming.1976 Cumming.1976 Cumming.1976 c ummlng . 1 976 Cummlng.1976 Cumming.1976 Cumming.1976 CUInInIng.1976 Cumming,1976 Townsend. 1 973 Townsend.1973 Townsend, 1973 Towneend, 1973 Townsend. 1973 Towneend.1973 Townsend. 1973 Townsend. 1973 Clerk.1993 Clerk.1993 “P %K 1608 96119 --........ ---..- - - * ---..-.. - - .. mm 1. Cont'd. YIELD 9m Kg/bueh 2.49 1.56 8.88 1.71 3.74 1.74 2.87 1.66 8.88 1.47 7.58 1.62 6.39 1.61 8.87 1.69 7.55 1.47 7.78 1.75 7.49 1.62 8.85 1.47 7.19 1.75 8.87 1.62 7.41 1.40 8.87 1.75 6.72 1.66 5.93 1.54 8.78 1.66 8.08 1.66 8.80 1.66 8.88 1.62 5.61 1.66 8.28 1.61 4.40 1.62 802 1.54 3.62 1.66 7.55 1.62 8.50 1.66 5.88 1.62 8.48 1.33 5.71 1.66 8.58 1.66 5.85 1.33 8.08 1.66 5.88 1.54 4.77 1.47 5.77 1.47 5.64 1.66 6.14 1.82 8.88 0.88 6.02 1.40 9.11 1.61 8.54 1.54 6.14 1.61 8.88 1.62 8.80 1.75 7.88 1.66 8.08 1.62 8.70 1.66 6.61 1.62 13.36 1.62 14.71 1.96 13.93 2.08 13.25 2.17 13.32 1.69 13.77 1.69 14.56 1.62 15.66 1.96 14.59 1.96 14.75 2.03 13.90 1.62 14.47 1.96 4.88 1.66 5.88 1.61 5.28 2.03 8.82 1.47 0.11 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.88 0.78 0.78 0.72 0.15 0.12 0.14 0.14 0.24 162 .Fe Cu --—--------- 109/kg --------- 98388888388 83888238283888 Zn AI 27 149 10 145 11 126 11 139 11 151 9 135 10 145 25 151 14 217 10 204 11 152 6 104 8 118 9 120 9 117 7 113 6 107 9 113 9 105 9 116 10 121 10 110 9 115 6 150 9 157 6 156 6 160 9 163 9 164 6 159 6 169 9 165 9 164 6 147 9 165 6 96 7 96 7 102 7 62 6 91 7 97 6 106 7 101 7 97 6 116 9 111 6 102 12 110 11 105 11 107 10 107 13 119 9 103 10 105 10 115 10 114 10 117 10 112 11 109 12 123 10 127 7 126 7 115 NV NO 8% a. ONNV“OO.OOOOOU§§NO”NONNNNONMd-bdddddMM-b-Od500°00-00bnddddddddddd 42 Hancock. 1963 :33Hencock1963 :EZancock1963 :33Hancock1963 41 Hencock1963 :91Hsncock1963 34 Hencock.1963 26 Hancock.1963 281HIDCOOKJ983 :K3H8n808k4963 IMJHencockJ963 25len808kJ963 24 H8n808k1963 34 Hencock.1963 :NJHen808k1963 25 Hancock.1963 26Iwencookd963 33 Hancock.1963 27 Hancock.1963 26 Hancock,1963 31 Hancock,1963 34 Hencock1963 :N1H8n808k1963 43 Hancock.1963 46 Hancock,1963 45 Hencock.1963 43 Hancock.1963 43 Hancock.1983 IMBHsncock1963 47 Hancock,1963 55 Hancock.1963 49 Hencock.1963 54lHencockJ963 1R1H8n808k1963 49 Hancock.1963 40 Hancock.1963 35 Hancock.1963 :91Hencock1963 36 Hancock.1963 39 Hancock.1963 45 Hancock,1963 33 Hencock.1963 :MlHencockJ963 34 Hancock.1963 37 11871808181963 41 Hencock.1963 40 Hancock.1963 31 Hancock.1963 27 Hancock.1963 31 Hancock,1963 :31Hencock1963 37 Hancock,1963 29 Hancock.1963 36 Han808k1963 36 Hancock.1963 36 Hsn808k1963 37 Hancock,1963 :N1H8n808k1963 33 Hancock.1963 27 Hancock.1964 25lfluwmckn964 17 Hsn888k1964 14 Hancock1964 9111’ 16K .._..-- ----- _ _ * ------ .. .. - T811141 1. Cont'd. YIELD 9611 XII/bush 5.94 1.47 4.64 1.66 4.29 1.69 4.54 1.62 4.42 1.62 4.91 1.66 5.24 1.47 4.15 1.75 6.95 2.17 6.19 2.03 7.31 2.03 7.61 1.66 6.99 2.03 7.79 2.03 6.75 1.96 7.16 1 .54 6.07 1.66 6.73 1.66 7.16 1.62 6.16 1.46 3.76 1.61 3.14 1.69 2.69 2.03 3.63 1.75 2.67 1.40 3.66 1.61 4.15 1.66 4.79 1.62 4.01 1.66 3.72 1.61 4.25 2.10 4.42 1.54 4.49 2.03 5.57 2.03 4.95 2.17 4.92 1.62 5.01 2.10 4.43 1.62 4.56 1.62 6.16 2.03 4.61 1.69 5.41 1.75 5.56 1.61 5.16 1.75 6.53 1.75 5.56 1.61 6.61 1.66 6.55 1.62 6.66 1.69 5.91 1.66 7.23 2.03 6.66 1.62 6.64 1.96 5.66 1.69 5.02 1.66 6.56 1.69 2.25 1.75 2.64 2.03 2.96 1.62 2.42 1.62 2.46 1.66 1.27 1.75 0.65 1.62 0.90 1.62 1.74 1.62 1.32 1.75 2.29 1.75 0.07 0.“ 0.10 0.10 0.“ 0.10 0.“ 0.10 0.10 0.1 1 0.10 0.10 0.1 1 0.“ 0.“ 1608 96149 0.44 0.16 0.51 0.21 0.43 0.19 0.46 0.19 0.35 0.16 0.44 0.20 0.41 0.16 0.46 0.22 0.46 0.23 0.43 0.25 0.34 0.20 0.43 0.25 0.36 0.22 0.46 0.23 0.47 0.23 0.47 0.25 0.40 0.26 0.43 0.25 0.37 0.20 0.44 0.23 0.51 0.22 0.51 0.22 0.46 0.21 0.47 0.22 0.46 0.21 0.54 0.23 0.55 0.25 0.55 0.23 0.65 0.25 0.60 0.26 0.56 0.24 0.63 0.26 0.30 0.17 0.33 0.17 0.26 0.15 0.33 0.19 0.31 0.17 0.34 0.17 0.36 0.16 0.36 0.19 0.30 0.16 0.33 0.19 0.31 0.16 0.35 0.19 0.46 0.19 0.35 0.17 0.39 0.17 0.44 0.20 0.40 0.16 0.36 0.15 0.41 0.16 0.39 0.16 0.37 0.16 0.39 0.16 0.42 0.17 0.44 0.19 0.34 0.19 0.39 0.20 0.36 0.19 0.40 0.23 0.34 0.20 0.45 0.23 0.35 0.16 0.37 0.20 0.30 0.19 0.42 0.23 0.36 0.22 163 F8 Zn Cu 6818787108 --—----—---— lug/k9 ---------- 28388383883836288838323388 ‘ d“ 823828 883233.3388883388388288388888 33281188 1":28888832338333888:33383233333398383888288832338388328328882388388 A1 3 143 13 127 13 123 13 123 10 123 17 103 11 123 13 123 3 35 10 74 3 32 3 77 3 32 3 33 3 30 3 33 3 111 3 30 7 35 11 34 3 143 3 152 3 155 3 137 3 140 3 147 11 137 10 143 11 153 10 141 3 142 12 145 3 77 7 37 3 34 3 73 3 73 3 33 3 100 3 33 3 33 10 33 3 33 3 105 14 107 12 32 12 33 11 33 12 33 1O 37 10 30 3 30 10 100 10 105 10 33 11 33 11 101 10 100 10 111 3 37 3 33 3 104 10 73 10 34 10 32 10 3O 10 36 b‘01..OOGO&ONGNGOOO‘ICOONGNONNQfibO“b.050.00&&.0..bfiU..G.UO§5§NONGONO° 15 1187100010964 26 1187100011.1964 10 1187100010964 30 1187100010964 24 1187100010964 25 1187100010964 23 1187100010964 39 1187100010964 22 1187100010964 22 1187100010964 19 1187100018. 1964 21 1187100010964 21 1187100010964 22 1187100010964 20 1187100010964 22 H8800010964 16 1187100010964 19 118n00010964 19 1187100010964 20 1187100010964 56 1187100010964 56 1187100010964 61 1187100010964 49 1187100010964 57 1187100010964 60 1187100010964 65 1187100010964 63 1187100010964 69 1187100010964 10 H8710001t. 1964 60 1187100010964 64 1187100010964 26 1187100011. 1964 26 1187100010964 26 1187100010964 25 1187100010964 30 1187100010964 30 118n00010964 34 118n00010964 30 1187100010964 27 1187100010964 29 1187100010964 29 1187100010964 41 H8800010964 29 1187100010964 27 1187100010964 26 1187100010964 25 1187100010964 23 1187100010964 29 1187100010964 26 1187100010964 32 1187100010964 26 1187100010964 27 1187100010964 27 1187100010964 35 1187100010964 24 1187100010965 26 1187100010965 26 1187100010965 19 1187100010965 19 1187100010965 31 1187100010965 22 118n00010965 23 1187100010965 16 1187100010965 20 1187100010965 17 1181100010965 T8b18 1. Cod'd. YIELD Kglbu811 16M 1 .13 1 .37 2.03 2.32 2. 14 2. 13 2.24 2.33 2. 13 2.73 SP %K 1608 16119 ----- -..-..- - - * --.._-- - .. - 0.23 0.23 0.23 0.21 0.27 0.21 164 F8 271 Al Cu 11818787108 ------—---—-- mm ——-------- 47 33 110 388338388883 ‘ 33 31 73 d d .8 GOOODO0000°00‘40“"N00‘1‘40OONOOOONNOOINCCN.NCOONOVVN-fi .8 01 .8 GAOOOUGGUGGG&OHOO&§.§«5‘OGONNUMGGONNM‘Guao¢00¢0000 29 1187100010965 19 1187100010965 23 1187100010965 17 1187100010965 17 1187100010965 15 1187100010965 23 1187100010965 22 1187100010965 16 1187100010965 20 1187100010965 21 1187100010965 16 1187100010965 25 118n00010965 47 1187100010965 46 1187100010965 43 1187100010965 39 1187100010965 39 1187100010965 46 1187100010965 42 1187100010965 49 1187100010965 52 1187100010965 43 1187100010965 44 1187100010965 51 1187100010965 19 1187100010965 24 1187100010965 16 1187100010965 19 1187100010965 21 1187100010965 22 1187100010965 22 1187100010965 19 1187100010965 20 1187100010965 24 1187100010965 22 1187100010965 26 1187100010965 19 1187100010965 15 1187100010965 19 1187100010965 16 1187100010965 14 1187100010965 21 1187100010965 16 1187100010965 20 1187100010965 17 1187100010965 17 1187100010965 19 1187100010965 23 1187100010965 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0818,1962 0d8,1962 0818,1962 0818,1962 1851. 1. 087178. 165 YIELD 1611 161’ 96K 1608 96119 Mn F8 Zn Al Cu 6 11818787108 1(9/1111811 ----- ~—----- 15 -—-----—- --- -—------— 789/179 ---------- 0.04 1.50 0.30 0.33 0.17 “16.1332 0.43 1.33 0.34 0.43 0.13 “6.1332 1.07 1.73 0.30 0.43 0.13 “16.1332 0.03 1.34 0.71 0.34 0.17 “6.1332 0.34 1.73 0.57 0.73 0.20 “16.1“2 0.23 1.31 0.75 0.27 0.15 0616.1332 0.41 1.41 0.34 0.34 0.14 “16.1332 1.70 1.31 0.33 0.43 0.17 “6.1332 0.72 1.43 0.53 0.” 0.14 M.1332 1.27 2.14 0.33 0.40 0.13 “16.1332 0.55 1.31 0.37 0.44 0.13 “16.1332 1.52 1.31 0.30 0.44 0.13 “16.1332 0.” 1.57 0.37 0.44 0.13 0616.1332 0.30 1.37 0.33 0.33 0.13 “16.1332 0.31 2.11 1.03 0.37 0.17 “6.1332 2.34 1.73 0.37 0.40 0.13 “6.1332 3.03 1.30 0.31 0.51 0.17 0616,1332 0.41 2.30 1.“ 0.57 0.14 “6.1332 1.23 2.13 0.73 0.33 0.13 “6.1332 0.30 2.23 0.33 0.37 0.13 0616,1332 0.37 2.00 1.03 0.37 0.17 0616,1332 1.04 1.33 0.73 0.43 0.13 WA” 1.13 1.35 0.70 0.73 0.21 0616,1332 2.00 1.31 0.75 0.43 0.13 “16.1332 5.05 1 .34 0.53 0.31 0.20 “6.1332 0.33 1.53 0.34 0.43 0.17 0616,1332 0.32 2.17 1.03 0.33 0.17 “6.1332 1.33 2.04 0.75 0.52 0.17 “16.1332 2.53 1.73 0.35 0.53 0.20 “6.1332 3.31 1.33 0.53 0.32 0.21 M.1332 1.70 1.37 0.70 0.53 0.13 “6.1332 2.32 1.34 0.35 0.53 0.20 “6,1“2 1.33 1.35 0.37 0.53 0.21 ”.1332 3.33 1.33 0.73 0.33 0.13 “6.1332 2.32 1.31 0.73 0.55 0.13 M.1332 2.20 1.73 0.73 0.52 0.13 “16.1332 0.13 1.37 1.03 0.33 0.15 “6.1332 1.13 2.00 0.34 0.33 0.14 “6.1332 0.33 1.31 0.33 0.37 0.13 “6.1332 2.30 2.17 0.31 0.54 0.13 “6.1332 3.11 1.33 0.53 0.30 0.13 “6.1332 1.32 1.” 0.74 0.41 0.15 0616.1332 1.00 1.37 0.33 0.33 0.13 (”6.1332 1.33 1.73 0.74 0.52 0.13 “6.1332 2.57 1.33 0.54 0.44 0.13 “16.132 1.34 1.51 0.54 0.53 0.17 “16.1332 0.53 1.43 0.53 0.43 0.13 “6.1332 1.30 1.32 0.31 0.50 0.13 “6.1332 0.04 1.43 0.33 0.42 0.17 “16.1332 0.33 1.41 0.31 0.41 0.13 “6.1332 1.35 1.30 0.31 0.47 0.17 “16.1332 0.55 1.30 0.35 0.33 0.13 0616.13” 1.35 2.77 0.35 0.34 0.14 ”.1333 2.53 2.43 0.77 0.44 0.13 0616.133 1.33 2.33 0.73 0.44 0.13 M.1333 1.33 2.73 0.35 0.50 0.17 R16.1333 2.1 1 2.30 0.33 0.52 0.15 “16.1333 1.33 2.37 0.73 0.50 0.14 “6.1333 1.34 2.33 0.34 0.32 0.14 “6.1333 0.50 3.17 1.03 0.50 0.13 “16.1333 0.33 2.43 0.30 0.32 0.24 “6.1333 2.75 2.31 0.30 0.30 0.13 0616,1333 1.73 2.43 0.33 0.53 0.17 “16.1333 2.52 2.45 0.33 0.50 0.13 “6.1333 3. 13 2.37 0.37 0.30 0.17 0616,1333 3.73 2.32 0.33 0.53 0.17 “6.1333 2.33 2.33 0.30 0.43 0.14 “6.1333 T411141. 0071111. 155 YIELD 96M 96? 96K 9608 96119 Mn F8 Zn N 00 0 11818787108 K071111411 --—-- ----- — - 18 --—--- - - - - — - —--——- - - - 77191189 - -----— - - - 0.56 8.01 0.62 0.42 0.20 0414,1888 1.95 2.88 0.82 0.76 0.18 0818,1963 0.66 1.88 0.80 0.88 0.18 0414,1888 8.88 2.88 0.88 0.82 0.17 0414,1888 2.56 218 0.88 0.80 0.18 0414,1888 1.82 2.60 0.78 0.54 0.15 0414,1888 2.14 2.80 0.64 0.44 0.14 0414,1888 0.14 2.66 1.07 0.88 0.21 0818,1963 0.70 0.88 0.66 0.17 0414,1888 1.15 2.50 0.78 0.80 0.18 0414,1888 1.76 2.52 0.63 0.66 0.20 0818,1963 1.18 2.88 0.82 0.72 0.17 0818,1963 1.82 2.72 0.70 0.70 0.17 0414,1888 1.05 250 0.70 0.80 0.15 0414,1888 1.58 282 0.92 0.78 0.25 0414,1888 1.74 2.71 0.88 0.56 0.15 0818,1963 1.82 2.87 0.78 0.66 0.17 0414,1888 2.17 2.40 0.56 0.82 0.22 0414,1888 8.58 2.88 0.80 0.88 0.18 0414,1888 1.03 2.22 0.87 0.88 0.23 0414,1888 1.85 1.64 0.47 0.66 0.22 0414,1888 2.24 2.26 0.50 0.72 0.20 0414,1888 0.58 2.08 0.56 0.58 0.21 0414,1888 1.77 2.87 0.82 0.58 0.18 0414,1888 1.62 2.78 0.64 0.56 0.14 0414,1888 0.78 0.63 0.52 0.21 0414,1888 1.20 2.84 0.71 0.58 0.21 0414,1888 1.82 2.29 0.51 0.82 0.15 0414,1888 0.48 2.59 1.00 0.80 0.18 0818,1963 0.21 2.75 0.55 1.08 0.24 0818,1963 0.69 2.39 0.58 1.14 0.20 0414,1888 1.29 8.00 0.78 1.02 0.26 0414,1888 0.88 1.94 1.14 0.54 0.22 0414,1888 1.01 2.22 0.88 0.46 0.17 0818,1963 2.58 2.05 0.76 0.48 0.18 0818,1963 1.82 2.19 0.58 0.44 0.16 0414,1888 2.88 1.84 0.58 0.52 0.18 0414,1888 2.05 2.28 0.85 0.58 0.20 0414,1888 1.88 2.34 0.77 0.48 0.18 0414,1888 2.80 2.82 0.88 0.56 0.17 0818,1963 1.16 2.57 0.77 0.44 0.16 0414,1888 2.70 2.28 0.88 0.52 0.22 0414,1888 0.70 2.37 0.70 0.52 0.18 0818,1963 0.80 2.28 0.80 0.54 0.18 0414,1888 1.85 2.57 0.61 0.44 0.16 0414,1888 1.97 2.21 0.78 0.50 0.14 0414,1888 0.71 2.84 0.96 0.46 0.22 0414,1888 1.35 2.72 0.78 0.40 0.16 0414,1888 1.07 2.48 0.80 0.52 0.22 0414,1888 2.64 2.28 0.85 0.50 0.18 0818,1963 1.00 2.79 0.87 0.58 0.28 0414,1888 2.08 2.50 0.78 0.60 0.18 0414,1888 2.66 2.88 0.88 0.42 0.14 0414,1888 8.85 2.71 0.65 0.80 0.21 0414,1888 8.17 2.88 0.88 0.58 0.17 0414,1888 2.74 2.44 0.88 0.80 0.16 0414,1888 0.88 1.82 1.08 0.80 0.17 0414,1888 0.44 2.82 0.87 0.80 0.17 0818,1963 1.22 2.28 1.07 0.64 0.15 0414,1888 1.17 2.84 0.93 0.50 0.18 0818,1963 0.82 2.33 0.90 0.42 0.18 0414,1888 0.72 0.42 0.41 0.24 0.10 0414,1888 0.76 2.46 0.81 0.50 0.17 0414,1888 0.87 2.52 0.74 0.46 0.18 0414,1888 1.87 2.84 0.87 0.80 0.17 0414,1888 0.81 2.79 0 88 0.74 0.15 0184,1888 2.40 2.42 0.31 0.53 0.15 0616,1333 T4014 1. 00711'0. 167 YIELD 9611 961’ %K 9608 96119 In F8 Zn Al 00 0 11d878n08 K971117811 ----- -—----- 96 --------- ---------—-- 709/179 -------—-- 2.41 2.32 0.32 0.54 0.13 “16.1333 2.13 2.20 0.53 0.43 0.13 0616.1333 4.23 2.01 0.53 0.32 0. 15 “6.1333 0.23 2.35 0.34 0.52 0. 13 W6. 1333 1.35 2.33 0.31 0.34 0.13 “6,1“3 3.00 2.52 0.70 0.43 0.13 0616. 1333 0.35 2.51 0.32 0.43 0.17 0616,1333 2.73 2.33 0.77 0.34 0.23 “6.1333 4.03 2.34 0.37 0.72 0.21 0616,1333 3.33 2.40 0.33 0.33 0.20 0616,1333 3.31 2.24 0.33 0.33 0.22 “16.1333 4.03 2.57 0.72 0.50 0.13 0616, 1333 0.33 2.50 0.75 0.53 0.14 N6, 1333 2.22 2.53 0.30 0.53 0.17 0616. 1333 2.03 2.03 0.53 0.44 0.14 0616,1333 1.30 2.53 0.35 0.34 0. 13 0616,1333 1.33 3.43 0.31 0.43 0. 13 0616,1333 0.30 2.33 0.73 0.44 0.17 M.1333 0.03 2.30 0.73 0.43 0.13 0616,1333 2.53 2.03 0.77 0.43 0.17 0616,1333 2.71 2.45 0.34 0.53 0.13 “16.1333 2.30 2.23 0.33 0.52 0.13 0616,1333 3.23 2.23 0.33 0.44 0.14 M, 1333 2.30 2.41 0.32 0.43 0.15 0616,1333 2.53 2.30 0.33 0.73 0.23 0616, 1 333 0.37 2.37 0.70 0.44 0.21 0616, 1 333 1.21 2.24 0.35 0.30 0.17 “16.1333 1.31 2.33 0.33 0.33 0.13 0616,1333 0.30 2.20 0.33 0.33 0.15 0616,1333 1.73 2.30 0.34 0.33 0. 15 0616,1333 1.30 2.04 0.33 0.33 0.13 0616,1333 1.31 2.31 0.33 0.53 0. 13 0616,1333 2.23 2.72 0.33 0.30 0.13 “16.1333 4.13 2.33 0.73 0.54 0.13 0616,1333 4.13 2.33 0.37 0.54 0.17 “16.1333 1.34 2.35 0.33 0.42 0.20 0616,1333 2.35 2.33 0.35 0.42 0.17 “6.1333 1.35 2.37 0.32 0.33 0.13 0616,1333 1.74 2.43 0.32 0.33 0.13 “16.1333 2.13 2.45 0.33 0.33 0.13 0616,1333 2.37 2.31 0.31 0.43 0.13 0616, 1333 3.37 2.50 0.53 0.50 0.17 0616,1333 1.33 2.72 0.72 0.43 0.15 “16.1333 3.13 2.37 0.73 0.72 0.20 0616, 1333 1.42 2.03 0.57 0.33 0.13 M,1333 3.21 2.43 0.53 0.70 0.21 0616,1333 2.11 2.04 0.52 0.53 0.20 0616,1333 4.03 2.07 0.54 0.34 0.22 0616, 1333 2.31 2.23 0.57 0.30 0.21 0616,1333 1 .33 2.00 0.33 0.70 0.23 0616,1333 2.53 1.30 0.33 0.33 0.24 0616,1333 3.37 2.42 0.32 0.30 0.13 “16.1333 2.33 2.31 0.34 0.32 0. 13 0616,1333 2.“ 2.52 0.35 0.54 0.13 “6.1333 0.“ 2.34 0.71 0.34 0.23 “6.1333 1.30 2.13 0.33 0.70 0.23 “6.1333 0.“ 2.44 0.72 0.52 0.21 “16,133 1.“ 2.27 0.33 0.43 0.13 0616,1333 1.03 2.13 0.72 0.30 0.24 “16.1333 1.33 2.52 0.34 0.42 0.13 330 1 12 0616,1334 1.73 2.52 0.34 0.43 0.13 504 34 “16.1334 1.44 2.57 0.77 0.43 0.15 333 32 “16.1334 1.37 2.37 0.73 0.54 0.13 332 124 0616, 1334 2.34 2.23 0.55 0.53 0.17 333 53 W6, 1334 2.57 1.31 0.50 0.53 0.13 502 112 0616,1334 1 17 2 43 0.71 0.54 0.13 333 33 W6,1334 0:57 2.42 0.54 0.82 0.18 782 52 0414,1884 T4514 1. Gourd. 153 YIELD 9611 96? 96K 9608 96119 1171 F8 Zn N 017 B R81878n08 K971117411 ----- ----- - - 11. ------ --- -- - -----— - - - 019/179 - ---—-- - - - 0.29 257 0.78 0.52 0.18 770 88 0414.1884 2.72 2.67 0.64 0.54 0.18 696 112 0414.1884 8.1 1 2.51 0.60 0.46 0.18 788 104 0414,1884 8.81 2.14 0.88 0.48 0.22 254 88 0414.1884 8.28 2.46 0.85 0.74 0.22 760 82 0414.1884 4.88 2.88 0.88 0.54 0.18 888 82 0414.1884 2.78 2.28 0.48 0.52 0.15 580 84 0474,1884 0.88 2.36 0.58 0.58 0.18 488 100 0414.1884 228 282 0.82 0.80 0.15 828 66 0414.1884 1.11 2.51 0.84 0.58 0.18 580 80 0414.1884 8.80 288 0.88 0.58 0.14 700 60 0414.1884 5.10 252 0.82 0.88 0.24 762 78 0414,1884 2.01 2.28 0.62 0.48 0.12 818 88 0414.1884 1.74 2.77 0.81 0.48 0.18 882 78 0414.1884 0.04 214 0.55 0.84 0.15 184 88 0414.1884 0 88 2 88 0.78 0.52 0 18 818 88 0414.1884 1.65 2 11 0.64 0.52 0.18 544 80 , 0414.1884 1.99 1.80 0.58 0.42 0.18 188 88 0414.1884 1 78 2 52 0.56 0 60 0 25 408 124 0414.1884 1.22 2.41 0.70 0.52 0.14 450 104 0474,1884 0.78 287 0.46 0.78 0.36 250 108 0414.1884 1 70 2 08 0.58 0 40 0.14 228 84 0414.1884 1 84 2 41 0.42 0 82 0.16 884 84 0414.1884 2.69 2.17 0.55 0.74 0.25 452 88 0414.1884 8.88 225 0.58 0.54 0.18 510 88 0414.1884 1.12 2.08 0.47 0.54 0.35 272 88 0414.1884 0.87 1.81 0.44 0.56 0.18 144 66 0414.1884 1 58 2 28 0.58 0 60 0 18 888 100 0414.1884 0 21 240 0.72 0 48 0 15 888 84 0414.1884 1 80 2 28 0.50 0 54 0 17 822 82 0414.1884 0 48 2.69 0.62 0 44 o 14 852 100 0414.1884 0 06 8 14 0.80 0 82 0 17 124 0474,1884 1 06 2 48 0.77 o 48 018 880 104 0414.1884 1 84 2 44 0.73 0 40 012 442 80 0414.1884 0 44 2 88 0.77 0 54 0 14 758 88 0414,1884 0 06 1 05 0.85 0 84 o 18 488 120 0414,1884 0 87 2 14 0.59 0 48 0 13 480 84 0414.1884 0.44 2.28 0.52 0.82 0.81 420 82 0474,1884 1.04 8.07 0.93 0.46 0.18 1108 _ 118 0414.1884 282 2.69 0.80 0.58 0.17 610 80 0414.1884 1.72 2.62 0.75 0.44 0.18 888 108 0414.1884 8.41 2.57 0.58 0.44 0.17 744 80 0414.1884 2.26 2.26 0.56 0.52 0.88 518 104 0414.1884 2.27 2.14 0.80 0.44 0.88 854 100 0414.1884 2.80 2.81 0.58 0.52 0.17 572 60 0414,1884 1.87 2.71 0.65 0.52 0.18 1840 82 0414.1884 2.51 2.19 0.52 0.54 0.27 474 60 0474,1884 0.88 2.17 0.88 0.80 0.17 822 112 0414.1884 1.18 2.82 0.88 0.62 0.18 8000 100 0414.1884 2.58 8.15 0.88 0.82 0.16 780 72 0414.1884 2 48 2 58 0.88 o 42 0 11 516 88 0414.1884 1 04 2 54 0.88 0.48 0 12 478 88 0414.1884 1 17 2 71 0.71 0.36 0 12 722 72 0414.1884 1 01 8 00 0.63 0 42 0 17 484 78 0414.1884 8 18 2 78 0.88 0 48 0 18 850 84 0414.1884 0 78 2 88 0.74 0.44 o 12 460 80 0474,1884 1 88 296 088 0.54 0 17 1102 84 0414.1884 5 10 2 11 0.58 o 58 0 18 1014 82 0414.1884 4 18 2 28 0.57 0.58 0 21 542 88 0474,1884 8 88 2 45 0.58 0.54 0 25 584 100 0414.1884 8 47 2 58 0.58 0 84 0 12 202 84 0414.1884 0 13 2 82 0.75 0 82 0 28 510 88 0414,1884 0 58 2 58 0.88 0.48 0 12 470 104 0414.1884 1 17 8 08 0.84 0.40 0 27 578 10 0474,1884 1 4o 2 82 0.72 0.54 0 15 484 66 0414.1884 0.41 8 18 0.96 0.40 0 17 542 82 0474,1884 0.87 2 58 0.67 0.58 0 22 1880 104 0474,1884 T4614 1. 057175. 159 YIELD 9611 96F 96K 9608 9639 3n F8 Zn A1 011 6 11818787108 K971111471 —---- ----- -- 11. ------ -- - - -- -----— - - — 71191119 - ----—- - - - 0.45 248 0.85 0.58 0.18 484 88 0414,1884 0.88 8.85 1.17 0.84 0.28 1500 88 0474.1884 0.84 2.51 0.71 0.50 0.28 854 144 0414.1884 0.50 3.29 1.18 0.44 0.15 1042 84 0414.1884 0.87 8.75 1.85 1.02 0.17 1076 120 0414.1884 1.57 288 0.88 0.48 0.18 588 78 0414.1884 2.58 2.28 0.84 0.70 0.16 488 52 0414.1884 1.62 1.81 0.51 0.46 0.18 414 80 0414,1884 5 55 2 87 0.53 0 60 0.16 822 88 0414,1884 0 47 2.64 0.82 0 82 0.21 1088 120 004.1884 0 80 2 88 0.60 0 88 0.16 492 124 0414.1884 278 2 52 0.75 0 50 0.17 844 88 0414.1884 1 84 2 81 0.88 0.80 0.28 820 0414,1884 1 80 1 88 0.58 0.84 0 12 316 116 0414,1884 8 08 2 26 0.58 0.54 0 17 428 60 0414,1884 2 87 2 80 0.88 0 66 0 17 482 126 0414.1884 8 81 2 18 0.48 0 52 0 18 888 88 0414.1884 5 18 2 58 0.75 0 48 0 12 808 84 0414.1884 2 80 2 30 0.58 0 78 0 17 854 84 0414.1884 208 2 14 0.88 0 82 0 08 214 40 0414,1884 208 2 82 0.65 0 80 0 18 702 82 0414.1884 2 46 2 70 0.78 0 80 0 15 810 88 0414.1884 1.88 2.57 0.88 0.82 0.13 418 84 0414,1884 1.28 2.42 0.71 0.50 0.18 412 84 0414.1884 008 820 1.77 080 088 8500 124 0414.1884 2 78 2 40 0.84 0 82 0 18 552 72 0474.1884 4 18 1 99 0.87 0 80 0 28 482 84 0414.1884 2 88 2 54 0.88 0 48 0 15 444 112 0414.1884 8 85 252 0.75 0 54 0 18 888 88 0414.1884 0 45 250 0.88 0 56 0 25 544 88 0414.1884 2 88 2 46 0.88 0 48 0 14 480 84 0414,1884 0 48 8 45 1.82 1 02 0 18 418 128 0474,1884 1 28 2 78 0.88 0 54 0 18 758 100 0414,1884 1 84 247 0.82 0.88 0 21 272 88 0474.1884 1 18 2 82 0.74 0.40 0 14 656 84 0414,1884 1 81 2 72 0 90 0.88 o 14 636 66 0414.1884 1 77 2 58 0.75 0.44 0 20 470 140 0414.1884 2 04 2 48 0.88 o 50 0 15 720 78 0474,1884 2 88 277 0.76 0 82 0 18 1284 84 0414.1884 8 84 2 84 0.87 0 74 0 18 2214 82 0414.1884 8 75 2 81 0.85 0 42 0 14 880 84 0414.1884 2 22 2 88 0.62 0.88 0 15 888 82 0414.1884 1 48 2 87 0.78 0.84 0 17 428 104 0414.1884 1 88 2 88 0.80 0 50 0 11 360 84 0474.1884 1 58 2 48 0.85 0.52 011 184 446 0414.1884 1 84 2 41 0.85 0.70 0 18 888 172 0474.1884 2.63 2 30 0.71 0 8o 0 20 888 124 0414.1884 2 87 2 58 0.57 o 78 017 406 82 0414.1884 210 2 88 0.50 0 70 0 82 418 120 0414.1884 8 25 2 81 0.85 0 48 0 15 488 84 0474.1884 8.88 282 0.46 0.54 0 18 540 54 0414.1884 2.15 2.78 0.82 0.84 0.40 556 100 0474.1884 8.22 248 0.88 0.52 0.16 578 78 0414.1884 0.85 2.87 0.70 0.60 0.17 880 124 0414.1884 1.51 2.57 0.88 0.58 0.17 414 104 0414.1884 0.41 2.58 0.81 0.84 0.17 282 84 0414.1884 254 225 0.70 0.88 0.21 874 88 0474.1884 0.28 2.87 0.84 0.84 0.18 458 144 0414.1884 1.88 2.88 0.84 0.54 0.18 878 78 0414.1884 0.81 2.54 0.88 0.80 0.27 726 104 0414.1884 0.08 2.88 0.65 0.48 0.18 718 84 0414.1884 0.07 2.27 0.74 0.87 0.10 142 78 0414.1884 1.88 2.88 0.77 0.36 0.17 1084 80 0474,1884 0.78 275 0.80 0.50 0.12 422 84 0414.1884 2.91 2.29 0.66 0.80 0.13 502 100 0414,1885 4.81 2.88 0.88 0.48 0 12 250 88 0414,1885 3.54 2.31 0.73 0.31 0.14 450 34 “16.1335 T411141. Com’d. 170 YIELD 1414 96F 95K 9608 96% Mn F8 Zn Al Cu 8 8808781158 119/511411 ----- ----- -- 14 ------ - -- --- ----—- —- - lug/kg - -—---- - ~- 1.58 2.15 0.78 0.41 0.08 284 84 0414,1885 1.88 2.11 0.88 0.40 0.15 258 100 0414,1885 4.05 2.25 0.78 0.80 0.18 410 124 0414,1885 8.88 2.80 0.85 0.88 0.15 882 58 0414,1885 0.55 2.48 0.55 0.42 0.15 822 82 0414,1885 0.87 2.45 0.75 0.45 0.18 288 75 0414,1885 5 08 1 84 0.87 0 48 0 18 520 50 0414,1885 884 248 0.58 048 015 808 100 0414,1885 8 81 215 0.85 0 87 o 10 882 88 0414,1885 588 2 28 0.88 o 41 0 18 508 112 0414,1885 802 285 0.50 048 0 17 520 100 0414,1855 8 27 2 25 0.58 0 88 0 15 582 88 0414,1885 1 25 2 50 0.58 0.41 0 25 528 120 0414,1885 5 18 2 84 0 51 0.81 o 22 514 115 0414,1885 2 45 0.58 0.42 0 18 548 100 0414,1885 5 21 2 81 0.52 0.40 0 21 575 82 0414,1885 4 50 0 52 0.48 0.57 o 11 224 80 0414,1885 1 74 2 15 0.88 0.48 o 08 875 854 0414,1855 887 228 0.54 081 012 452 85 0414,1885 001 288 0.78 088 014 508 44 0414,1885 045 240 0.58 050 0 15 480 120 0414,1885 1 88 2 24 0.58 0 81 0 17 472 118 0414,1885 088 224 0.87 044 008 155 50 0414,1885 1 45 2 80 0.54 0 82 0 18 448 108 0414,1885 4 77 2 07 0.48 0 41 0 18 884 72 0414,1885 1 48 2 48 0.55 0 88 0 18 572 124 0414,1885 0 88 2 88 0.75 0.25 0 15 544 88 0414,1885 2 82 2 51 0.75 0.88 0.15 780 82 0414,1885 5 08 2 88 0.50 0 41 0.25 552 85 0414,1885 4 08 2 20 0.45 0 21 0.18 400 84 0414,1885 748 228 0.45 052 0.28 580 112 0414,1885 1 47 2 28 0.47 0.20 0.27 544 140 0414,1885 1 18 2 47 0.52 0.25 0.15 814 140 0414,1885 4 85 2 84 0.40 o 48 0.21 58 100 0414,1885 0 50 0.82 0.27 1000 104 0414,1885 2 88 2 45 0.57 0.40 0.10 475 88 0414,1885 0 81 2 58 0.52 0 45 0.14 884 104 0414,1885 1 48 2 50 0.55 0 88 0.15 440 182 0414,1885 2 55 2 28 0.48 0 88 0.18 550 82 0414,1885 1 24 2 48 0.58 0 42 0 18 872 50 0414,1885 1 85 2 42 0.54 0 87 o 17 540 84 0414,1885 258 282 0.47 084 018 850 852 0414,1885 o 01 2 14 0.85 0 48 014 284 85 0414,1885 208 205 0.58 025 017 880 72 0414,1855 4 20 2 72 0.58 0 48 0 11 280 72 0414,1885 8 85 2 00 0.44 0 25 0 18 418 82 068.1905 4 80 2.11 0.44 0 81 017 444 72 0414,1885 1 75 2 80 0.52 0 85 o 12 284 84 0414,1885 4 58 2 01 0.45 018 0 15 478 75 0414,1885 7 14 2 05 0.51 0 85 0 17 510 124 0414,1885 2 50 2 07 0.48 0 28 0 18 552 124 0414,1885 5 04 1 80 0.45 0 82 0 17 824 82 0414,1885 178 218 0.55 010 o 20 512 88 0414,1885 2 28 2 25 0.47 0 82 0 21 542 88 0414,1885 817 2 28 0.57 0 42 0 14 850 100 0414,1885 804 210 0.50 087 0 15 802 84 0414,1885 202 188 0.50 088 011 854 180 0414,1885 2 77 188 0.45 0 25 018 455 100 0414,1885 1 44 2 54 0.77 0.25 0 10 280 84 0414,1885 5 55 2 27 0.48 0.44 0.22 870 108 0414,1855 1 84 2 72 0.72 0 82 0.18 540 80 0414,1885 7 25 2.08 0.52 0 40 0.14 888 112 0414,1885 4 85 2.04 0 47 0414,1885 4 55 2 44 0.50 0 25 0.80 700 104 0414,1885 5 24 2 08 0.48 0.28 0.18 580 40 0414,1885 0 88 0.87 0.47 0.17 582 54 0414,1885 1 85 0.87 0.81 0.14 840 72 0414,1885 T4514 1. oom'd. 171 YIELD 9611 961’ 96K 9608 96149 Mn F8 Zn Al Cu 0 mm Kglbmh ----- ------- 96 --------- -—-— ----—--—--- mg/kg - -—--—-----— 1.00 3.00 0.55 0.47 0.10 020 50 “8.1005 3.01 2.00 0.50 0.20 0.10 300 50 1 0818,1005 0.00 2.21 0.00 0.23 0.11 304 40 0818,1005 0.37 2.20 0.70 0.22 0.10 200 00 “10.1005 0.10 1.07 0.73 0.37 0.00 122 04 0810,1005 1.20 2.00 0.04 0.00 0.14 172 120 0818,1005 0.00 2.20 0.04 0.22 0.14 540 100 “18,1005 0.32 2.44 0.04 0.27 0.13 210 120 0818,1005 2.11 2.37 0.52 0.41 0.20 500 00 0818,1005 3.07 1.07 0.40 0.27 0.10 320 02 0818,1005 2.52 1.04 0.00 0.50 0.11 120 54 0818,1005 4.20 1.04 0.55 0.17 0.12 210 120 0818,1005 0.50 2.20 0.01 0.43 0.10 320 00 0818,1005 1.00 1.04 0.40 0.32 0.15 410 02 0818,1005 3.00 2.05 0.40 0.30 0.10 352 104 0818,1005 3.13 1.30 0.50 0.30 0.20 422 04 “10.1005 3.70 2.00 0.40 0.25 0.14 230 00 0818,1005 7.00 2.13 0.40 0.50 0.10 270 02 0818, 1005 4.70 2.17 0.47 0.34 0.15 300 44 0818,1005 0.71 2.21 0.05 0.40 0.22 700 140 0818,1005 7.00 2.32 0.77 0.31 0.12 202 00 0818,1005 1.30 2.45 0.53 0.45 0.20 000 124 0818,1005 3.00 2.21 0.02 0.50 0.21 700 104 W0,1005 4.30 1.04 0.05 0.35 0.00 154 104 “10,1005 1.04 2.25 0.02 0.31 0.10 404 72 0810,1005 0.22 1.00 0.03 0.55 0.00 122 00 0810,1005 0.10 2.10 0.70 0.37 0.10 310 124 0818,1005 4.37 2.24 0.00 0.30 0.13 010 120 “0.1005 4.70 1.00 0.57 0.33 0.15 300 100 0818,1005 5.04 2.00 0.05 0.31 0.27 050 120 0818,1005 0.20 2.10 0.40 0.45 0.15 052 100 0810,1005 3.70 2.23 0.03 0.54 0.10 300 00 0818,1005 2.10 1.70 0.57 0.20 0.12 200 04 0818,1005 1.10 2.11 0.73 0.35 0.15 304 124 0818,1005 1.14 2.07 0.71 0.31 0.14 000 120 0818,1005 1.70 1.73 0.07 0.40 0.11 200 00 0818,1005 0.00 0.01 0.25 0.20 200 00 0818,1005 2.70 2.20 0.54 0.30 0.10 304 00 “0,1005 4.52 2.32 0.55 0.40 0.21 044 144 “0,1005 4.24 2.17 0.50 0.20 0.20 442 122 0818,1005 5.04 2.10 0.57 0.20 0.22 304 02 “10.1005 0.40 1.00 0.00 0.30 0.10 400 100 0810,1005 5.00 2.13 0.40 0.20 0.10 510 120 0810,1005 3.20 2.30 0.07 0.30 0.15 334 100 0818,1005 2.02 2.20 0.70 0.20 0.22 070 00 0818,1005 2.70 2.40 0.02 0.20 0.23 300 100 0810,1005 2.04 2.21 0.47 0.27 0.20 404 120 0810,1005 0.07 2.15 0.40 0.33 0.10 500 02 0818,1005 5.07 2.24 0.50 0.33 0.10 240 100 0818,1005 3.00 2.17 0.40 0.44 0.21 720 100 0818,1005 4.05 1.02 0.50 0.31 0.23 510 122 0818,1005 5 m 2 27 0 40 0.40 0 24 702 00 “10.1005 5 02 2 30 0.50 0.35 0.22 342 70 0818,1005 4 31 2 15 0.47 0 33 0.20 504 100 M,1005 7 00 2 10 0.43 0 47 0.25 500 124 “0.1005 4 10 2 03 0.47 0 30 0.25 220 00 “8,1005 3.00 2 15 0.42 0.45 0.25 402 110 “0.1005 3 10 2 20 0 52 0.24 0 24 274 04 “10.1005 0 57 2 10 0 40 0.27 0 15 300 100 0810,1005 3 70 2 12 0 03 0.21 0 20 174 124 0810,1005 7 00 2 20 0 47 0.42 0 24 402 00 0818,1005 1 07 2 03 0 04 0.20 0 23 550 140 “10,1005 1 72 2 20 0 50 0.34 0 20 302 124 0818,1005 5.30 2.44 0.50 0.50 0.24 400 144 0818,1005 1.04 2.10 0.03 0.34 0.10 270 52 10 0818,1000 5.42 2.00 0.47 0.07 0 23 320 52 14 0818,1000 3.33 2.20 0.54 0.40 0.10 310 50 10 0810,1000 T8018 1. Cont’d. 172 YIELD 96" 96P 96K 9608 96119 Mn F8 Zn Al Cu 0 Mon“ K911111411 ----- ----- - - 14 ------ - - - - - - ------ - - — 11191119 - «nu - - - 2.88 2.27 0.57 0.50 0.21 888 48 12 0414,1888 8.80 0.58 0.58 0.18 812 72 18 0414,1888 4.47 0.58 0.58 0.20 418 55 28 0414,1885 8.54 2.27 0.50 0.55 0.10 840 80 14 0414,1888 0.88 2.55 0.57 0.51 0.27 350 78 10 0414,1885 0.80 2.28 0.87 0.52 0.17 222 84 18 0414,1888 4.00 2.27 0.55 0.87 0.15 584 72 18 0414,1888 8.82 1.87 0.44 0.87 0.15 524 00 18 0414,1855 5.25 2.25 0.51 0.87 0.28 488 54 28 0414,1885 8.58 2.14 0.52 0.58 0.21 428 58 24 0414,1888 8.08 2.82 0.58 0.54 0.17 500 88 12 0818,1000 5.11 2.27 0.58 0.75 0.20 410 88 22 0414,1888 1.52 2.25 0.48 0.58 0.28 844 84 14 0414,1888 8.84 2.27 0.50 0.71 0.18 878 78 28 0414,1885 1.72 0.54 0.52 0.22 455 80 18 0414,1888 8.81 2.14 0.58 0.51 0.15 580 50 14 0818,1000 5.78 2.04 0.48 0.84 0.18 414 58 18 0414,1888 4.12 1.82 0.44 0.51 0.18 852 75 22 0414,1888 4.70 2.23 0.55 0.52 0.17 414 50 22 0414,1885 0.12 2.80 0.54 0.55 0.22 414 58 15 0414,1885 1.22 2.08 0.50 0.55 0.28 275 55 82 0414,1888 8.81 2.28 0.45 0.54 0.18 452 84 15 0414,1885 2.12 2.88 0.58 0.58 0.28 885 55 18 0414,1888 2.00 2.11 0.50 0.48 0.21 878 52 18 0414,1858 4.52 2.27 0.44 0.50 0.15 410 84 22 0414.18“ 1.52 2.25 0.45 0.84 0.22 310 50 22 0414,1888 2.08 2.41 0.47 0.58 0.21 712 55 18 0414,1885 8.50 2.42 0.48 0.75 0.21 405 82 50 0414,1858 8.48 2.12 0.45 0.48 0.20 218 48 10 0414,1888 4.15 2.10 0.43 0.88 0.28 850 54 15 0414,1858 5.85 2.02 0.84 0.58 0.28 248 85 14 0414,1888 1.10 2.18 0.47 0.78 0.20 404 54 14 0818,1000 1.80 2.30 0.45 0.88 0.28 540 52 18 0414,1888 8.14 2.24 0.41 0.75 0.21 825 54 20 0414,1885 1.27 2.31 0.55 0.58 0.23 070 178 25 0414,1888 4.88 2.05 0.47 0.58 0.17 275 50 15 0414,1888 8.48 2.28 0.45 0.55 0.17 284 52 72 0414,1885 0.82 2.11 0.50 0.57 0.17 485 48 12 0414.18“ 1.81 2.40 0.47 0.70 0.25 504 52 18 0818,1000 8.54 2.08 0.48 0.72 0.28 514 50 22 0414,1888 2.08 0.55 0.50 0.21 285 48 18 0414,1888 0.08 2.58 0.58 0.55 0.28 302 52 28 0414,1885 1.00 2.58 0.54 0.58 0.21 280 50 24 0414,1858 1.70 2.18 0.45 0.82 0.20 825 54 15 0414,1888 1.88 2.22 0.84 0.55 0.22 424 55 12 0414,1885 8.88 2.28 0.48 0.50 0.18 845 50 18 0414,1888 5.20 2.18 0.42 0.58 0.23 302 44 8 0414,1888 4.18 2.02 0.48 0.47 0.18 272 50 24 0414,1888 5.88 0.45 0.52 0.20 875 44 12 0414,1858 8.28 2.18 0.45 0.54 0.21 802 55 10 0414,1885 5.27 2.18 0.47 0.58 0.15 408 88 20 0414,1885 7.01 2.84 0.45 0.88 0.18 874 40 12 0414,1858 2.57 2.82 0.47 0.54 0.28 850 40 25 0414,1888 5.28 1.57 0.80 0.54 0.17 218 88 52 0414,1888 1.55 2.22 0.51 0.55 0.25 828 50 12 0818,1000 1.52 2.55 0.88 0.58 0.24 280 48 28 0414,1885 4.24 2.51 0.44 0.57 0.20 340 48 18 0414,1888 5.58 2.04 0.47 0.50 0.17 480 84 20 0414,1885 2.88 2.04 0.47 0.55 0.14 500 85 15 0414,1885 8.55 2.77 0.44 0.55 0.18 520 50 20 0414,1888 1.88 2.14 0.55 0.47 0.18 450 52 28 0414,1885 4.48 2.24 0.48 0.58 0.80 180 58 18 0414,1888 1.88 2.17 0.54 0.50 0.14 320 58 20 0414,1885 4.27 2.18 0.48 0.57 0.23 240 54 30 0414,1888 8.28 2.02 0.45 0.58 0.15 528 88 12 0414,1858 5.78 2.01 0.44 0.55 0.20 310 48 10 0414,1855 0.20 2.02 0.48 0.58 0.15 234 58 88 0414,1888 T411141. 0511M. 17 3 YIELD 96M 96P 96K 9608 96119 Mn F8 Zn Al Cu 0 1181878n88 119/b11411 ----- ----— - - 11 -—-—-— - - - - - - ------ - - - 11191119 - -—---- - - — 8.85 201 0.48 0.57 0.21 500 50 15 0818,1000 0.78 2.15 0.58 0.47 0.12 808 40 12 0414,1888 0.58 2.32 0.50 0.57 0.18 875 78 25 0414,1858 1.82 2.03 0.48 0.58 0.18 858 50 12 0414,1858 2.81 2.11 0.54 0.00 0.21 450 52 28 0414,1888 1.50 2.28 0.80 0.37 0.17 848 00 14 0414,1888 0.28 2.20 0.85 0.88 0.15 804 84 12 0414,1855 0.87 2.48 0.77 0.41 0.14 488 50 14 0414,1888 1.88 2.04 0.55 0.47 0.17 880 52 10 0414,1885 1.17 1.88 0.53 0.70 0.23 844 54 8 0414,1885 1.00 2.20 0.85 0.87 0.15 474 18 54 0414,1885 2.05 225 0.58 0.50 0.15 800 84 12 0414,1885 4.07 2.15 0.48 0.58 0.20 522 50 20 0414,1855 8.27 2.25 0.55 0.57 0.20 200 54 12 0414,1888 2.28 2.08 0.51 0.50 0.18 480 54 82 0414,1885 7.58 2.04 0.45 0.78 0.10 402 54 85 0414,1888 1.00 2.48 0.52 0.77 0.21 750 80 24 0414,1858 1.44 2.82 0.47 0.52 0.15 210 72 24 0414,1888 5.88 2.02 0.44 0.78 0.18 500 84 14 0414,1858 2.81 1.78 0.45 0.85 0.18 220 72 15 0414,1888 1.57 1.88 0.50 0.58 0.15 808 50 14 0414,1888 5.84 0.48 0.71 0.20 584 58 18 0414,1888 5.17 1.85 0.48 0.70 0.17 312 88 15 0414,1888 5.45 2.07 0.55 0.45 0.18 300 58 14 0414,1888 5.74 2.80 0.53 0.55 0.18 470 40 20 0414,1888 2.10 2.57 0.50 0.52 0.20 422 52 20 0414,1858 2.42 2.27 0.57 0.50 0.18 200 52 10 0414,1885 8.24 2.08 0.47 0.57 0.18 254 44 14 0414,1858 4.48 0.50 0.45 0.17 800 72 22 0414,1885 8.78 2.04 0.47 0.54 0.18 842 55 12 0414,1885 0.08 2.08 0.85 0.52 0.15 248 55 22 0414,1885 4.84 2.10 0.53 0.58 0.17 548 50 18 0414,1885 5.85 2.28 0.52 0.52 0.18 480 80 82 0414,1888 4.50 2.18 0.57 0.58 0.15 844 72 18 0414,1885 5.88 2.12 0.47 0.55 0.15 482 50 10 0414,1888 8.28 2.22 0.58 0.45 0.17 284 50 25 0414,1888 2.82 2.11 0.54 0.50 0.13 880 55 18 0414,1858 2.44 2.48 0.54 0.53 0.22 300 58 8 0414,1885 2.87 2.11 0.53 0.51 0.15 430 50 12 0414,1858 2.77 2.51 0.52 0.47 0.25 320 50 18 0414,1885 1.88 2.15 0.48 0.54 0.28 485 84 82 0414,1858 2.04 2.58 0.58 0.85 0.21 404 54 12 0414,1855 2.58 2.42 0.52 0.55 0.28 858 58 20 0414,1888 2.54 2.88 0.58 0.50 0.22 885 75 20 0414,1888 8.18 2.25 0.48 0.48 0.18 158 55 28 0414,1858 5.74 2.24 0.40 0.52 0.18 250 50 12 0414,1855 5.50 0.47 0.48 0.17 282 88 15 0414,1888 4.05 2.20 0.48 0.58 0.18 302 80 82 0414,1885 8.48 2.17 0.42 0.55 0.15 278 88 28 0414,1885 2.20 2.18 0.45 0.48 0.18 255 52 18 0414,1885 2.18 2.50 0.48 0.42 0.18 284 48 12 0414,1858 2.54 2.47 0.45 0.57 0.25 485 80 12 0818,1000 5.15 2.37 0.45 0.45 0.17 808 50 15 0414,1888 5.20 2.45 0.58 0.57 0.21 282 84 12 0414,1885 8.88 2.88 0.48 0.40 0.18 818 52 14 0414,1888 4.82 2.45 0.48 0.84 0.23 514 28 18 0414,1888 4.78 2.44 0.44 0.50 0.10 885 40 18 0414,1858 5.51 2.50 0.47 0.58 0.20 350 58 20 0414,1888 3.07 2.12 0.48 0.75 0.17 242 54 14 0818,1000 5.04 2.15 0.88 0.84 0.18 208 48 85 0414,1888 8.71 0.48 0.70 0.10 250 48 18 0414,1885 8.10 1.80 0.42 0.53 0.10 225 54 74 0414,1855 2.78 225 0.47 0.70 0.21 242 48. 48 0414,1885 5.84 2.44 0.48 0.70 0.10 550 80 28 0414,1888 4.88 2.17 0.44 0.82 0.21 825 50 14 0414,1885 4.75 2.25 0.58 0.75 0.28 800 48 18 0414,1885 1.50 2.05 0.52 0.70 0.28 848 50 12 0414,1885 T8bl0 1. Cont'd. YIELD K9Ibu8h 9611 9111’ 96K 9608 96119 _._......._. _.___._ _ _ 9‘ _._._._-.. .. - .. 2.44 2. 12 2.30 2.73 0.55 0.71 0.01 0.74 0.10 0.14 0.20 0.10 174 --- --------- 11191119 — ---—-- - -— F8 00 04 52 Zn Al 10 30 10 10 Cu R818t8nc8 0810,1000 0818,1000 0810.1000 “18,1000 T8018 2. 01118087731 DRIS. “an IDkflth 1 “m1 8 21M” 8 8 1471 7 1 mm 5 2'MH 5 8 14.71 7 4'mw 5 5'mw 8 51m" 8 7 mm: 8 8 mm 5 8 an 7 w'nu 5 H mm 5 12 13.25 11 m‘nm 5 14 11.05 -1 15 10.75 -8 15 10.58 -2 n'mu 1 “1mm 1 m mm 2 m 2% -4 21 8.52 -1 a 2m 5 28 8.87 -8 a 2n 4 a 2m -0 a 8m 5 a 2m -5 a 5m 8 m 0% 17 m 5» -8 m 8m 2 a mu 5 a mu -7 a 8m 7 a 8n -4 a 8m 8 87 8.70 10 a 5m -1 a 8M -8 w 89 41 n ma -1 a 8M 1 a 2n -8 « 8m 45 a 8m -4 a 8m 1 a 8m -8 a 8M -1 u 5% 5 w an —8 51 821 -4 a 8m 15 fl 5M 8 u an 2 a 8m 1 a 8m 7 a 8m 2 a 5m 4 a 5m 0 w 8m 0 m 8m -5 a 1m -4 a 1M 5 a In 2 a 7m 8 a In 5 p 0.530 -10 C8 149 1 0 2 1 0 3 1 0 2 1 0 3 2 1 3 2 3 3 4 2 0 4 0 4 3 2 2 1 0 2 -4 -3 -12 -13 -4 -1 -3 -1 —0 —0 5 5 -3 -2 -0 -2 --13 -13 10 11 -3 -3 10 0 —4 -0 12 0 0 -4 14 7 13 10 -17 —15 13 4 14 0 -0 0 11 5 -7 -2 -7 -3 15 10 0 1 -5 -5 10 -4 4 0 10 0 -5 -14 14 5 -25 2 7 7 2 4 -0 -2 14 0 1 -0 2 -7 10 10 0 4 13 5 7 4 20 13 11 7 14 7 -7 2 -2 -0 13 0 -3 -0 15 10 12 0 10 12 7 0 -7 -2 -10 -10 -11 175 F8 1 §OOONONO§GOOGOGO 1 .8 N -20 -30 -20 -10 40 -10 -30 55 -30 «00% M33053 24 7 Zn -14 -3 --23 -7 12 -10 -5 -5 -23 10 -10 -w 4 4 -m 4 -10 -2 -1 -4 -4 -2 -3 -4 -2 -3 -3 -5 -4 -4 -3 -3 -1 -3 -10 -4 -7 -3 -4 A -0 11 -11 -5 -11 -5 -5 -m -m -8 4 a -n -n -10 -u -0 -0 -2 -53 ODOGO‘N‘NGOO‘IGO‘IO -4 15 -u -8 -13 —w -5 a 332:: 338388883882 RELATIVE ORDER OF NUTRIENT REQUREMENTS Mn>20>l >N >P >M9>C8>F8>B >Cu>N Cu>25>K >M0>Al >P >M9>C8 >Fe>B >N Mn>Cu>AI >0 >K >20>M5>P >C8>N >138 Mn>20>K >N >P >M9>C8>Fe>3 >Cu>N Cu>20>l >Mn>AI >l’ >M9>C8>F8>B >N M000>N >0 >K >20>M5>P >C8>N >I'8 Mn>Cu>Zn>AI >K >P >M3>C8>F8>B >N Al >Mn>20>P >K >Cu>M5>C8 >N >F8>B Cu>Mn>AI >K >20>P >0 >C0>MPP¢ >N Cu>Mn>AI >K >Zn>0 >549? >C8>P8 >N Mn>Cu>K >Zn>P >AI >0 >M3>C8>F8 >14 M8>Cu>20>x >N >8 >849? >C8>N >118 Mn>Cu>AI >K >0 >P >25>M9>C8>N >F8 01>le >Al >2 >Mg>C8>20>0 >N >I'8 M5>C11>25>A1 >2 >M9>P >0 >C8>Pc >N F5 >M0>C8>M9>P >14 >K >Cu>0 >28 F8 >MpCu>N >Cu>Mn>B >K >21»? 8 >P >F8 >20>K >Al >C8>N >M9>Mn B >P >F5 >25>t >AI>C8>N >MpM0 Pe>C8>Mg>K >N >P >20>Cu>Mn>0 Fe>B >M0>K >P >N >20>C8>M5>Cu F8>C8>M9>P >N >K >Cu>B >Mn>20 B >P >178 >Zn>K >Al >C8>N >M5>M0 P8 >M3>C8>K >N >Mn>Cu>P >3 >20 M0>Cu>0 >K >Al >20>P >N >Pe >M9>C8 Pc>C8>Mg>N >Mn>K >P >Cu>0 >20 Mn>Cu>Zn>N >K >P >N >Mg>0 >C8>F8 P5 >M3>C8>20>N >K >M5>C0>P >0 M>ZO>P >K >AI >M3>N >C8>B >F8 K >N >M3>C8 >178 >140 Mn>B >25>K >AI >C0>P >M5>N >C8>F8 Mn>20>AI >0 >Cu>K >P >M9>C8 >N >P8 F8>C8>M3>N >K >M8>P >Zn>Cu>B Mn>25>Cu>P >AI >N >1 >Mg>B >C8>F8 Mn>Cu>Zn>AI >0 >K >P >N >178 >M9>C8 K >N >C8>Zn>Pe >Mg>M0 Mn>Cu>Zn>Al >P >K >M3>N >C8>B >P¢ C8>N >MpM0 Pe>C8>M8>M9>B >K >N >Cu>25>P M0>B >C11>20>P >AI >K >98 >11 >M‘)CI Mn>20>P >K >AI >N >M9>C8>Cu>0 >78 N >C8 >Mg>K >08 >140 N >K >M3>M8>P8>C8 F8>B >M0>N >C0>C8>P >Mg>25>K Mn>Cu>20>Al >P >K >N >M3>C8>B >P8 F8 >M5>N >C8>M0>C11>P >K >20>0 M0>N >20>P >AI >K >Mg>0 >C8>C0>P5 Pe>C8>P >14 >0 >M3>K >Mn>20>Cu Cu>N >Mn>20>l >0 >C8>A1 >M5>P >P8 C0>N >K >C8>M0>P >M3>AI >Fe>0 >25 M0>Fe >K >0 >M3>P >N >C8>Cu>211 M0>Cu>20>P >Al >K >14 >Mg>B >C8>F8 K >N >M5>C8>F8 >540 Mp! >N >F8>C8>M0 M0>AI >3 >K >Cu>20>P >M9>N >C8>Fe M0>Cu>20>AI >P >2 >M3>N >C8>B >P8 M0>Cu>20>P >Al >1 >14 >M9>C8>0 >178 Mn>B >K >P >Cu>20>N >N'>F¢>C0>N M90 >C0>Al >20>K >P >11 >08 >M9>C8 Mn>Cu>Zn>P >0 >N >N >M3>K >C8>Pc Mn>B >20>Cu>P >N >AI >K >M3>C8>F8 20>C8>K >N >P8 >M9>M0 C4>M3>N >501 Mn>20>Cu>N >P >A1 >K >Mg>B >C8>Pe K >M5>I~I >C8>M5>P8 Mn>B >Cu>Al >20>K >N >P >98 >MpC4 M0>Cu>0 >20>K >Al >N >P >Pe >M3>C8 M0>AI >K >3 >Cu>25>P >N >MpC8 >P8 Cu>20>Mn>K >N >M3>F8>C8>P >0 >AI 745142. 0511511. 11151.0 10 1191511411 11 P 57 7.78 15 ~3 58 7.75 7 8 88 7.78 7 0 70 7.71 5 5 71 7.58 12 8 72 7.57 12 5 78 7.00 ~11 74 7.58 ~2 75 7.50 ~8 78 7.55 8 ~5 77 7.55 —8 8 78 7.55 ~4 1 78 7.53 4 5 80 7.48 5 8 81 7.48 o 82 7.41 -5 8 88 7.88 8 ~1 84 7.31 12 -1 85 728 ~18 88 727 0 ~3 87 728 8 -2 88 720 2 2 88 7.18 2 5 80 7.18 1 1 81 7.10 8 -4 82 7.14 ~12 88 7.05 ~0 11 84 7.08 5 1o 85 7.01 5 88 7.00 ~5 8 87 7.00 -1 88 8.87 ~8 88 0.07 5 5 100 0.00 2 18 101 0.01 -1 4 102 8.88 4 8 108 8.87 8 4 104 8.87 8 -5 105 5.88 8 ~8 108 0.04 4 2 107 5.84 ~0 108 8.81 ~0 1 108 5.78 -7 -1 110 5.78 1 ~8 111 5.75 18 ~2 112 8.74 ~2 118 0.73 10 1 114 5.72 0 -8 115 8.71 ~11 115 8.85 -8 8 117 8.84 118 5.80 4 ~5 118 5.58 1 ~8 120 5.58 5 -4 121 855 2 ~1 122 0.53 ~4 128 0.53 -2 8 124 0.50 2 -7 125 5.48 ~14 128 5.48 ~8 ~0 127 5.88 ~4 ~28 128 8.85 -8 128 5.82 2 ~10 180 0.31 ~8 181 0.20 ~12 182 527 ~8 188 525 ~5 184 825 ~8 ~11 185 0.24 ~13 K ~11 ~12 ~10 08 M9 14 11 14 0 14 0 17 10 10 11 17 12 10 0 ~5 0 ~12 ~1 0 4 2 5 3 1 0 0 5 5 ~0 ~14 4 5 13 5 21 11 2 ~4 ~4 -4 15 12 2 ~1 4 4 12 0 10 10 7 ~4 ~13 0 0 ~12 ~0 4 10 ~5 1 -7 0 ~0 4 0 ~0 ~3 ~0 11 13 0 3 4 0 7 15 12 15 10 ~13 ~2 15 11 ~11 ~0 3 3 13 10 ~3 0 10 13 5 4 ~0 ~12 7 5 ~11 ~2 0 0 3 2 11 0 11 7 ~7 ~2 7 5 3 3 5 ~0 2 2 5 10 ~0 ~1 17 17 ~0 4 ~1 3 3 ~5 ~1 3 7 3 20 2 176 ZnNCuB Mn F8 ~30 21 ~3 ~23 0 ~4 ~23 0 ~4 ~27 0 ~3 ~30 0 ~2 ~32 11 1 ~7 ~0 1 7 0 1 23 ~1 20 ~10 10 0 1 ~5 ~35 ~0 3 0 ~3 3 11 ~2 21 10 1 0 3 ~40 50 ~15 ~41 20 ~4 20 4 ~7 ~0 0 ~20 ~4 ~2 ~3 ~30 7 ~0 4 0 ~20 23 ~3 ~0 ~2 ~12 0 0 ~0 ~25 ~22 ~4 ~43 12 17 ~5 ~13 ~1 ~54 12 10 4 11 3 2 10 1 2 ~22 11 3 ~42 0 ~27 2 2 1 7 ~3 ~30 55 ~0 ~20 ~1 ~0 ~20 ~2 ~4 10 3 5 ~27 ~1 3 ~11 ~11 5 ~20 52 ~0 ~31 17 ~0 17 ~0 ~2 ~47 24 ~4 ~33 54 ~12 10 12 ~2 11 33 20 1 ~11 ~30 01 ~10 ~3 20 ~0 ~25 ~4 ~0 ~24 ~1 ~2 12 ~2 5 ~21 ~1 4 1 20 ~0 7 4 ~3 10 ~10 7 ~2 ~1 20 ~0 ~10 ~14 2 ~0 23 ~2 ~10 ~1 ~2 25 20 0 25 0 ~21 ~51 00 ~10 13 ~10 ~5 ~3 ~4 1 ~10 ~10 ~0 ~3 ~0 ~1 -0 ~0 ~2 ~0 ~21 1 ~0 ~10 15 ~01 10 12 ~55 13 21 11 13 ~50 0 12 ~57 7 14 ~54 13 ~3 ~20 0 ~5 ~4 ~0 ~1 7 -0 3 4 10 12 37 ~50 ~5 ~1 ~4 ~5 2 ~3 -0 42 0 11 23 23 10 0 ~50 0 0 ~2 17 -1 ~0 ~1 ~4 31 ~50 5 ~0 ~20 ~2 ~3 3 0 ~0 ~1 ~0 ~3 3 1 14 20 0 ~15 0 ~3 1 ~7 ~0 ~7 ~0 2 ~0 ~7 13 ~00 ~4 —0 ~27 1 17 ~03 10 -2 5 0 ~10 20 ~4 ~3 3 ~0 15 ~02 10 15 ~22 14 ~23 32 14 0 ~10 1 ~1 -2 121 70 120 110 10 157 117 122 117 110 127 72 120 73 135 123 130 117 117 117 52 70 150 01 RELATIVE ORDER OF NUTRIENT REQUREMENTO M9Al >0 >2 >9 >C11>20>M3>C8>N >98 M93 >C11>28>AI >9 >2 >N >Mg>98>C8 M93 >20>C0>AI >9 >2 >N >M9>98>C8 Mn>C11>B >20>Al >2 >9 >N >98 >M9>C8 M90 >Cu>Al >25>9 >2 >98 >M3>N >C8 M90 >c11>2 >20>AI >9 >98 >M3>N >C8 N >M99¢ >Mg>2 >08 C8>2 >N >Mplh>98 C8>2 >N >M‘>F0 >11h>28 Cu>20>9 >M9M9>2 >N >C8>Al >0 >98 Cu>N >2 >25>C8>M9>9 >98 >M9AI >0 98 >20>M9N >9 >M9>C8>B >2 >01 Cu>20>M9N >9 >C8>M9>B >2 >98 >A1 Cu>20>M92 >C8>N >M5>9 >0 >98 >AI MpC8>2 >N >98 >040 Cu>N >M92 >20>C8>M9>9 >98>0 >Al M9Cu>20>Al >2 >9 >N >M9>B >C8>98 M90 >Al >20>Cu>9 >2 >M§>N >98>C8 N >M9>2 >C8>98 >140 M9M3>C8 >9 >Cu>98 >N >2 >0 >781 M9AI >2 >98 >9 >28>Cu>0 >N >M9>C8 98 >M9Mp2 >C8>9 >N >25>C0>0 Cu>B >2 >M9N >C8>98 >M9>9 >28>A1 M90 >2 >C0>25>Al >9 >N >M9>C4>98 20>M90 >9 >Cu>98 >A1 >2 >N >M9>G8 N >2 >Mg>C4>Mn>98 98 >28>C8>M9N >M92 >0 >9 >01 98 >MpM92 >C8>N >9 >Cu>281>0 20>C8>98 >2 >MpN >040 98 >N >M9>2 >M99 >C8>20>0 >Cu M92 >N >C8>98 >M0 2 >N >M9>98>C8>M0 Cu>Zn>M9C8>9 >N >0 >MpAI >2 >98 95>C8>M9>0 >2 >M9N >Cu>28>9 98>C8>B >N >M99 >20>2 >M9>Ou M9Al >0 >Cu>25>98 >9 >N >2 >M9>C8 Cu>25>M9N >C8>MpP >2 >0 >98 >Al M9Cu>25>9 >0 >A1 >2 >N >M3>C8>98 M920>AI >9 >2 >98>C0>N >0 >M9>C8 M920>98 >C11>A1 >0 >9 >N >2 >M9>C8 C8>2 >M9>N >98 >Za>Mn M9AI >2 >98 >N >9 >0 >Za>Cu>M9>Ca 98>C8>M9N >Mg>9 >2 >25>C11>0 M9Cu>9 >Zn>0 >Al >N >2 >C8>M9>98 M90 >20>2 >Al >9 >Cu>Mg>N >C8>98 98>C8>20>2 >N >M9>11h M9Cu>0 >20>2 >AI >9 >91 >M9>C8>98 M920>9 >Cu>B >N >AI >Mp2 >C8>98 MpN >C8>2 >98 >hh C11>N >0 >M99 >M9>C8>2 >98 >AI >28 C8 >20 >2 >M9>98 >540 M9Cu>20>9 >Al >0 >2 >N >M9>C8>98 Cu>20>M99 >N >M9>C8 >2 >AI >0 >98 M998 >9 >AI >2 >20>N >C11>M3>B >C8 M9Al >2 >0 >20>98 >9 >11 >M3>C8>Ou C8>N >2 >Mp98 >Za>lh M9A1 >N >98>B >2 >9 >Cu>20>M9>C8 Cu>9 >20>M9N >M3>C8>2 >AI >0 >98 N >Mg>2 >98>C8>11h Cu>Zn>N >9 >M92 >M9>C8>B >AI >98 9 >C11>N >98 >28>2 >C8>M9M9>0 281>C8>2 >N >M9>98 >Mn B >M99 >2 >20>98 >N >Cu>Al >M9>C8 Zn>N >2 >98>C8>Mplb 2 >9! >98>C8>M9M9>20 N >2 >M3>98>C8>MI 20>2 >N >C8>MpP8 >140 M99 >Zn>N >0 >Cu>2 >AI >Mg>Ca>98 98 >14 >2 >M‘>MDCO T011182. 051155. 177 YIELD ID 119le N P K CI Mg Mn F8 211 N 011 0 NII RELATIVE ORDER OF NUTRIENT REQUREMEN'IB 130 020 1 ~1 3 Mp2 137 5.10 13 -0 4 21 13 ~44 9 -3 -1 ~21 0 130 M9Cu>Zn>N>P >2 >0 >98>N >Mg>€h 130 0.10 2 -1 -0 17 12 ~34 20 5 -4 -3 -5 110 M92 >0 >N>Ou>9 >N >Zn>Mg>C8>98 1a 0.10 ~12 7 ~0 ~0 ~4 ~2 ~10 11 10 13 90 98 >N >C8>2 >M¢>M99 >Zn>0 >01 140 0.04 ~13 ~11 0 ~4 23 ~1 50 N >2 >Mg>98 >C8>M| 141 0.04 ~21 ~39 ~13 ~3 -0 -0 09 170 2 >91 >08>98 >M9M3>Zn 142 0.03 2 -0 5 1 2 5 23 ~12 17 ~02 27 104 Cu>Zo>9 >C8>N >M92 >Mn>Al >98 >0 143 0.02 ~4 ~9 ~0 5 3 ~30 00 ~0 ~2 ~17 5 147 M9Cu>9 >Zn>N >Al >2 >Mg>0 >C8>98 144 5.90 ~2 ~12 ~1 0 0 ~11 1 ~3 1O 4 ~3 03 9 >M928>0 >N >2 >98>C11>C8>M¢>A| 145 5.90 -0 1 0 ~7 0 0 29 N >M3>C8 >2 >98 >|h 140 5.“ 0 ~0 4 4 3 -0 25 ~10 10 ~01 19 155 Cu>Zn>9 >M9M3>C8 >2 >N >AI >0 >98 147 5.90 ~2 ~4 ~9 2 24 2 ~13 50 Zn>C8>2 >N >98 >Mg>11h 140 5.95 5 ~9 ~1 4 0 ~5 -2 32 2 >98 >Zn>C8>MpN >541: 149 5.95 3 ~3 5 Mp2 150 5.94 ~3 ~15 5 14 14 -7 1 ~10 24 4 ~10 121 0 >h>9 >M9N >98>C11>2 >MpC8>AI 151 5.93 ~2 ~0 3 3 3 ~35 52 ~7 1 ~15 5 132 M9Cu>9 >Zn>N >Al >2 >C8>M¢>0 >98 152 5.91 ~1 ~3 5 10 4 ~17 ~9 ~2 0 2 4 03 M998>9 >Zn>N >Al >Cu>Mg>0 >2 >08 153 5.91 -5 0 0 1 17 -4 ~30 9 1 4 77 98 >N >M99 >Ou>C8 >0 >2 >Zn>Mg 154 5.91 5 ~5 10 Mp2 155 5.00 ~2 ~3 0 7 22 0 ~24 59 2192 >N >98>Ca>Mg>M8 150 5.00 ~3 ~1 2 ~10 4 2 ~33 10 31 5 100 98>C8>N >9 >M92 >M90 >Zn>C11 157 5.00 4 ~4 ~0 14 11 ~20 ~2 ~4 3 5 2 70 M99 >28 >98 >2 >0 >Al >N >Cu>MpG8 150 5.04 ~9 ~2 15 ~10 3 3 43 04991 >2 >98 >M9C8 159 5.03 ~7 ~0 ~5 2 13 5 ~1 30 N >2 >C8>221>M3>98 >M8 100 5.01 ~4 7 7 ~5 -9 -7 ~21 9 9 14 94 98 >M3>M9C8 >N >9 >2 >011>Zn>0 101 5.77 ~3 ~5 0 5 4 1 21 -9 13 ~50 25 151 Cu>Zn>9 >N >M9M3>C8 >2 >Al >98 >0 102 5.70 -4 ~3 ~0 2 14 2 ~3 34 C8>N >2 >h>Mg>98 >518 103 5.75 0 5 14 9 10 ~13 ~51 15 13 ~10 140 98 >M90 >N >9 >C8>C11>2 >219)“ 104 5.74 2 0 ~3 4 0 1 ~10 27 Zn>C8>2 >98 >N >Mpth 105 5.73 ~0 ~17 0 2 0 1 ~45 2 10 33 130 98 >9 >N >M9C8 >Zn>2 >M5>Cu>0 100 5.73 1 ~1 1 4 14 ~2 ~17 30 Z1998 >2 >N >C8>M;>Mn 107 5.71 ~1 ~9 ~1 2 1 -5 21 ~0 14 ~25 12 100 Cu>9 >Zn>M92 >N >M5>C8 >0 >AI >98 100 5.07 ~5 ~5 10 ~4 ~3 0 35 2 >N >M3>M998 >C8 109 5.00 -2 0 4 -9 ~0 0 ~43 13 0 22 124 98>C5>M5>N >2 >9 >Gu>M9211>0 170 5.05 ~12 ~10 ~0 -0 ~11 ~3 11 ~14 11 12 13 94 Zn>N >9 >2 >M9M3>C8 >98 >Al >090 171 5.05 ~2 -5 -0 -5 4 0 25 2 >M3>N >C8 >lh>98 172 5.05 ~0 ~7 ~10 -3 12 3 13 54 08>2 >N >M3>98 >M92: 173 5.04 ~0 ~45 9 5 9 12 2 ~2 ~25 34 144 9 >Cu>Zn>N >98>C8>Mp2 >000 174 5.04 2 ~2 3 2 2 -1 20 ~0 10 ~02 21 144 Cu>Zn>9 >M9N >C8>Mg>2 >A1 >0 >98 175 5.03 ~1 ~0 0 3 3 0 14 ~5 10 ~57 23 137 Cu>9 >Zn>N >M9Mg>C8 >2 >98 >Al >0 170 5.02 ~2 ~4 7 ~0 3 1 23 Mp2 >N >98 >M99 177 5.01 3 ~7 ~2 0 0 ~51 07 ~7 ~1 ~15 -1 100 M9Q1>Zu>9 >2 >Al >0 >N >M3>G8>98 170 5.59 ~5 2 1 7 1 ~7 23 Zn>2 >98 >Mg>C8 >548 179 5.50 ~0 ~2 17 25 13 ~45 9 -5 2 ~22 0 140 M9Cu>Zn>9 >N >Al >0 >98 >M92 >C8 100 5.57 11 3 0 24 15 ~53 0 ~14 ~3 ~3 4 140 M9211>C11>Al >9 >0 >98 >2 >14 >Mg>C8 101 5.50 ~3 ~1 0 17 9 ~20 ~1 2 ~4 7 1 73 M9Al >N >98 >9 >2 >0 >Zn>Cu>Mg>C8 102 5.55 -1 ~3 ~23 ~3 20 4 0O C8>2 >MpN >98 >991 103 5.54 1 ~2 0 ~1 ~0 13 ~32 ~3 17 7 70 98 >Zn>9 >C8>Mp2 >N >0 >M9011 104 5.51 3 ~0 ~7 ~1 9 2 0 20 C8>2 >Mpln>98 >N >Mn 105 5.50 ~3 ~14 0 9 11 19 ~3 ~19 79 N >9 >98 >N >2 >C8>Mg>5h 100 5.40 ~5 1 3 0 11 1 ~11 33 28>N >Mg>2 >98>C8>Mn 107 5.43 ~0 0 1 3 ~0 12 ~27 19 10 ~23 110 98 >0 >N >M92 >C8>9 >M9Cu>Za 100 5.42 1 0 ~0 ~0 12 ~0 -7 20 Za>C8>98 >M92 >N >59: 1” 5.41 7 ~O 0 27 15 ~73 15 0 2 ~9 0 100 M90199 >Zn>Al >N >0 >2 >98 >Mg>C8 190 5.39 ~2 2 2 10 ~5 ~10 40 Zn>98 >2 >C8>Mg>Mn 191 5.30 ~0 ~0 ~7 ~10 9 15 47 M)‘ >N >|h>98 192 5.30 ~3 ~7 ~0 ~10 24 3 53 Mp2 >C8>N >98 >548 193 5.27 ~5 ~7 ~0 3 11 7 ~1 43 C8>2 >N >211>M5>98 >591 194 5.20 ~29 ~3 ~5 ~0 ~3 1 41 02 N >C8>2 >M9Mg>98 >241 195 5.20 ~2 ~4 ~11 -5 13 1 0 42 C8>Mg>2 >N >98 >h>hh 190 5.20 10 ~10 ~3 14 12 ~9 0 ~14 10 0 ~14 110 9 >Zn>0 >M92 >98>C11>N >Mg>C8>AI 197 5.24 ~7 ~0 5 11 0 ~21 7 ~1 0 2 ~5 02 M99 >N >0 >Zo>Cu>2 >98 >M3>Al >C8 190 5.20 2 10 ~5 ~3 9 2 ~15 44 Zn>C8>MpF8 >N >9h>2 1“ 5.20 0 ~1 ~1 2 25 ~2 ~20 04 21998 >2 >C8>MpN >M1 2” 5.10 ~0 ~2 0 ~13 11 0 47 M3>N >2 >98 >C8>0h 201 5.17 ~5 ~2 ~0 4 0 7 ~5 39 C8>N >Zn>2 >Mg>98 >591 202 5.10 1 ~7 4 2 7 ~1 ~5 20 2 >Zn >98 >N >MpC8 >091 203 5.10 2 ~3 0 17 9 ~30 5 ~0 2 ~11 10 117 M9Cu>Zn>9 >Al >91 >98 >2 >Mg>C8 >0 204 5.13 ~5 0 1 3 ~0 ~4 19 N >98 >M9C8 >Mp2 TM 2. Md. YIELD ID RM N P 205 5.11 ~1 200 5.10 ~11 207 5.10 1 200 5.00 ~2 209 5.00 ~11 210 5.05 ~13 211 5.04 ~11 212 5.04 ~17 213 5.03 ~17 214 5.02 3 ~4 215 5.01 9 3 210 5.00 ~2 ~12 217 5.00 ~7 2 210 4.” ~2 ~0 219 4.95 10 -1 220 4.95 ~0 221 4.92 9 5 222 4.91 ~2 ~5 223 4.09 ~4 224 4.05 ~13 225 4.79 ~1 ~12 220 4.79 ~5 227 4.79 ~14 220 4.79 ~4 4 229 4.70 3 2” 4.77 ~2 -5 231 4.77 ~10 232 4.70 5 233 4.75 234 4.71 235 4.70 ~5 230 4.70 237 4.“ ~3 ~0 230 4.09 -3 239 4.00 ~19 240 4.05 ~3 241 4.04 ~2 ~9 242 4.02 -5 243 4.01 7 ~2 244 4.00 ~7 245 4.50 10 ~3 240 4.50 0 -2 247 4.57 ~2 4 240 4.54 ~0 ~0 249 4.52 -0 250 4.50 ~0 3 251 4.50 ~07 252 4.50 ~5 253 4.49 10 2 254 4.40 255 4.40 4 250 4.47 257 4.43 5 2 250 4.42 ~7 ~11 259 4.42 ~0 ~11 2” 4.40 ~4 -7 201 4.40 ~4 202 4.39 ~21 203 4.39 4 204 4.30 ~0 205 4.37 ~14 200 4.30 14 -1 207 4.35 13 ~1 200 4.35 0 ~10 209 4.35 10 270 4.34 ~7 271 4.34 272 4.32 1 11 273 4.32 11 ~1 ~18 ~7 ~10 8 ~17 ~11 18 -4 14 10 22 4 ~0 ~9 ~0 10 12 ~5 3 ~11 ~10 2 ~3 ~10 ~2 ~23 ~1 0 ~1 ~10 ~17 -9 ~5 4 ~12 14 17 ~4 5 ~5 10 9 14 9 ~0 2 ~4 ~5 -1 5 ~2 ~2 ~0 7 13 10 ~5 0 ~3 ~3 ~14 13713 Mg Mn F8 ~1 12 2 2 ~6 31 2 ~11 23 1 ~12 16 4 ~7 6 9 3 ~1 2 4 ~21 14 7 3 13 ~6 14 ~26 ~4 ~1 ~2 11 ~52 6 ~7 ~10 9 12 ~2 ~17 1 13 ~37 10 17 4 ~31 1 13 ~47 6 ~12 0 16 ~66 6 ~3 ~7 6 ~16 4 12 ~1 3 5 5 -1 ~9 1o 9 ~1 -4 6 -7 7 3 6 ~11 -2 3 4 ~7 0 ~44 10 ~1 6 ~2 ~0 3 1 21 ~5 16 ~5 26 ~2 2 14 ~7 ~6 ~0 -9 26 0 10 ~2 1 ~10 33 5 ~13 3 ~0 6 1 20 1 ~6 ~10 0 ~4 ~21 16 5 4 ~7 4 2 5 2 10 3 1 15 ~44 6 -9 4 ~1 4 16 ~77 50 ~20 ~27 12 ~54 10 ~9 1 0 ~12 ~26 16 4 ~10 3 2 6 ~9 14 13 4 6 ~37 ~1 49 3 13 3 6 2 ~4 13 ~53 6 ~9 ~6 ~1 4 1 0 -6 ~1 4 ~11 ~4 10 ~3 5 13 ~47 5 ~9 ~3 ~1 ~6 2 ~1 5 7 ~16 2 ~5 11 1 ~31 60 ~7 2 ~2 4 10 ~15 6 2 12 2 ~7 ~5 23 1 ~3 9 7 15 ~76 52 ~16 ~14 10 ~59 46 ~22 ~10 1 4 ~32 16 0 27 ~2 ~10 ~11 26 ~4 2 ~42 3 2 31 ~13 ~14 -5 10 ~12 ~2 ~14 ~4 11 10 ~4 -0 ~10 ZnAIOuB 20 27 ~2 ~14 ~1 31 oiséafizazzzzzzss ‘ ‘ 383355888 142 RELATIVE ORDER OF NUTRIENT REQUIREMENTS C8>MpN >2 >98 >721>M11 N >C8>2 >Mg>F8 >M8 C8>Mp98 >N >2 >Ib Mp2 >N >C5>98 >M8 2 >N >M3>M998>C8 N >C8>Mp2 N >2 >M5>M998>C8 MpN >C8>98 >502 N >98 >M92 >C8>Ih M92 >9 >98 >Al >211>N >0 >Cu>Mpcn M9Al >28>Cu>9 >0 >98 >N >M‘>K >C8 AI >9 >98 >N >2 >C8>Mg>kh 98 >N >C8>Mp9 >2 >Cu>211>0 >991 98>C8>0 >N >9 >28>M92 >Cu>Mg M9C11>28>9 >Al >0 >98 >N >M92 >C8 N >2 M9Al >20>Cu>0 >9 >98 >N >2 >Mg>C8 M99 >2 >0 >N >Al >C11>98 >M‘>C9 >211 N >2 >C8 >211 >M3>M998 N >M92 >98 >C8>MI Cu>9 >211>M92 >N >M3>C8>98 >Al >0 98 >2 >N )M‘>MDCI N >2 >Mg>M998 >C8 98 >M3>C8>N >2 >9 >M9Cu>2a>0 C8>98 >Mg>211>N >2 >Mn Cu>9 >221>N >C8>M9Mg>2 >Al >98 >0 N >2 >Mg>98 >C8>M8 98 >2 >28>C8>MpN >Mn NPK C8>M3>Mn N >C8>98 >2 >Mg>Za >Mn Cu>Mg>Mn M99 >N >0 >2 >Za>98 >C11>M5>Al >C8 C8>20>N >2 >98 >MpMn N >2 >“PFO >M9C8 Mp2 >N >98 >C8>MII 9 >M92 >0 >N >28>M3>98 >C8>AI >Cu 2 >N >C8>Zn>Mg>98 >M8 M9Cu>28>9 >Al >0 >98 >N >2 >Mg>C8 N >Mg>M9C8 M9Al >Zn>Cu>9 >0 >M92 >N >C8>98 M92199 >Cu>Al >2 >N >98 >0 >M9C8 98 >M9N >C8>Mp2 >9 >0 >C11>211 M99 >2 >N >Cu>0 >218>98 >Mg>Ca>Al M3>C8 >N >2 >98 >Mn 98 >0 >N >211>9 >M8>NDCI>K >011 N >M9C8 >98 >2 >M; N >Zn>C8 >2 >98 >MpMu M928>Al >Cu>9 >0 >98 >2 >N >M‘>CI 2 >Mg>Zn>98 >C8>M8 M3>C8>M9241>2 >98 >N 2 >M3>C8 >98 >28>Mn M9211>Cu>N >9 >98 >N >0 >2 >M‘>CI 9 >N >M92 >28>C8>M3>Cu>98 >N >0 M99 >Zn>0 >Cu>N >98 >2 >M9N >C8 M9Cu>218>9 >N >2 >0 >M9N >C8>98 N >M5>C8 >591 N >Mn>2 >98 >M‘>CI C8>28>2 >MPFO >N >Mo C8>N >Mg>2 >98 >Mn N >2 >M998 >C8>Mn M9211>Al >0 >Cu>9 >2 >N >M3>C8>98 M928>Al >C11>9 >0 >2 >Mg>N >C8>98 9 >N >M92 >C8 M92 >M3>C8 >N >98 28>N >C8>2 >98 >Mg>hh C8>M5>M8 98 >M3>C8>2 >N >M928>C11>9 >0 28>C8>98 >2 >MpN >591 Tanzanw wan m mmu1n 274 481 ~5 275 4.31 -8 276 4.30 ~7 2n 4m ~5 276 4.30 ~7 278 4.30 25 2m 4m 48 m11u8 w an 4» 7 288 427 ~8 2“ 4m ~3 265 427 24 266 425 ~28 m71u5 8 an mu 5 288 424 ~7 an 4m ~8 2m mm ~2 an 4» ~4 2m 4w ~3 an mm ~7 2» 4w ~8 2m 4m ~8 m7.u5 2 2m 4w ~4 2m 4w ~2 mm mm ~8 851 4J3 ~2 an 4m ~7 an 4m 40 8m 4w ~4 mm mm 1 8m 4m 4 3M 4m 48 an 4m 0 8m 4w -7 810 4.05 4 811 4.01 4 812 4.00 ~2 313 4.00 ~5 8“ 8a 45 8m 8m 40 8m 8m 8" 8M 4o 8“ 8m 40 319 3.96 ~7 8m 8m ~5 821 881 8 322 3.91 ~4 8a 8a ~7 8a 8a ~2 8a 8a ~7 an 8n 2 8m 8» 8m 8w ~8 an 8» 18 an 8” 2 8m 8” ~2 8a 8a ~0 8m 8w ~2 3M 8» ~8 8a 8» ~2 83 an 8 8” 8n ~2 an 8n ~5 8” 8n ~5 8m 8m 41 8“ 8m 44 8& 8n ~8 p ~11 ~4 -1 ~12 ~17 -0 ~7 ~10 ~13 ~12 -9 ~11 -0 ~12 ~10 ~24 ~11 ~14 ~11 K C8 1119 ~7 7 ~11 4 1 3 ~4 9 7 ~2 14 6 ~11 15 ~3 19 33 20 2 5 15 12 52 27 o 12 9 11 ~5 1 ~10 ~4 ~3 25 27 16 ~10 52 ~10 ~3 ~O ~1 ~7 ~3 o ~2 6 ~15 6 ~5 5 ~2 3 5 ~0 9 7 0 ~6 ~5 11 ~6 ~5 ~2 6 ~5 ~7 2 o ~4 ~7 ~1 ~7 ~1 ~1 ~1 4 2 19 ~7 ~6 21 ~11 ~0 ~7 ~4 2 ~14 29 ~9 ~6 ~6 ~2 6 ~6 ~1 16 ~16 ~4 6 9 ~10 12 ~5 ~7 ~11 ~3 ~1 10 10 ~1 ~2 -3 ~2 1 6 5 ~1 6 10 24 ~6 ~3 23 ~11 ~2 ~19 ~7 -0 21 3 ~6 22 —3 ~5 6 ~3 4 0 15 ~14 ~4 11 ~7 ~0 ~6 ~2 -7 ~10 ~1 ~6 2 ~12 ~6 3 ~1 ~3 ~15 ~10 6 ~9 ~5 12 36 20 ~4 5 2 5 3 1 ~5 5 3 14 10 2 7 14 10 2 9 6 9 ~11 ~5 ~1 0 3 4 ~3 0 3 2 1 ~10 27 ~2 0 27 ~11 26 ~14 ~3 10 -3 10 ~110 ~29 ~11 ~00 ~10 ~14 84.. ~10 ~12 F8 ~0 -1 -21 -4 13759 ZnAlCuB ~9 ~12 10 ~11 ~10 -2 10 ~21 ~13 ~13 ~17 ~11 ~88 4 28 ~18 2 14 8 ~56 ~11 5 ~7 ~7 8 4 1 2 8 ~8 42 ~12 ~13 ~12 4 ~18 7 -7 o ~8 ~14 ~18 6 ~7 27 7 30 ~2 .8 ON“ 24 ‘588§§§§:8858 8833288838 83883883883832 863888888838: 88 42 RELATIVE ORDER OF NUTRIENT REOUREMENTS Mp2 >N >98>C8>0h N >M9C8 >98 >Mg>2 Al >N >98 >2 >9 >M‘>C9)ull 9 >AI >N >98 >2 >M5>M9C8 2 >N >Mg>98 >M M9AI >28>0 >Cu>9 >2 >M3>N >C8>98 M998 >N >9 >2 >C8>0 >28>Mg>08 M998 >AI >25>C11>0 >9 >2 >N >M90 0 >M99 >2 >98>011>28>N >H‘)C9>~ N >C8>M¢>2 2 >C8>N >Mg>M998 >28 M9Al >Cu>28>9 >0 >M9N >2 >C8>98 N >M92 >M998 >C8 9 >28>C11>M92 >M3>C8>98 >A| >N >0 2 >98 >C8>28 >M9N >691 MpN >2 >M9C8>98 N >C8>Mp2 N >98 >M92 >CI)H‘ 9 >Al >N >98 >2 >M5>C8 >M8 C8>M3>N >2 >98 >091 C8>N >MPPO >502 M;>N >2 >M998>C8 2 >N >98 >M9C8 >M928 C8>2 >28 >M3>N >98 >M8 9 >2 >M928>N >C11>M5>C8>N >98 >0 M99 >N >2 >AI >Mpou>08>98 >28>0 C8>N >Mg>2 Ca>M5>N >2 N >2 >C8>Mp98 >28 >M8 2 >N >MPF¢ >M90- C8>2 >28>N >M‘>PC >591 C8>MpN >2 C8>M3>N >2 N >MpM92 >98 >C8 M¢>C8>N >2 2 >N >C8 >M3>Mn>98 >211 9 >M9N >C8>2 9 >Cu>28>N >M9C8 >2 >M998 >Al >0 Al >9 >98 >N >2 >M‘>Cl >Mn Al >9 >N >98>2 >C8>MpM8 N >C8>Mp2 C8>N >M92 CI>MPMI 98 >N >2 >M;>M9C8 N >2 >98 >M5>M9C8 N >2 >W >98 N >Mg>M908 C8>MpN >2 C8>N >Mp2 2 >M9N >C8>98 >96: 2 >M3>N >C8>M998 C3>M‘>N >2 >98 >M8 M)" >2 C8>M3>M11 C8>N >M998 >2 >661 M928>Al >Cu>9 >0 >2 >N >M5>C8>98 M9Cu>Zn>9 >2 >AI >N >M90 >C8>98 9 >28>M9Cu>N >M‘>CI >98 >2 >AI >0 M99 >C11>28>2 >N >N >0 >MpC8>98 9 >N >Mg>C8 >2 AI >9 >N >98>M8>2 >Mg>08 9 >N >98 >N >2 >Mg>C8 >691 C8>M‘>N >2 28>N >2 >98 >C8>Mg>hh N >C8>MpM92 >98 Zn>9 >M8>C11>N >M3>C8>2 >98 >AI >0 N >2 >MII>MPFG >C8 N >M9Mp2 >98 >C- C8>N >M92 1.515 2. 0511511. YIELD 10 11M 11 343 3.75 ~5 344 8.75 ~3 345 8.74 ~23 848 8.78 ~4 847 8.72 8 346 8.71 848 8.70 ~8 850 8.88 ~3 851 8.88 ~8 852 3.69 ~2 353 3.66 4 354 8.88 10 355 8.58 ~4 858 8.88 4 357 3.67 ~88 858 8.88 1 858 8.88 1 880 8.85 7 881 8.85 8 852 3.63 ~85 858 8.51 ~2 884 8.81 ~2 885 8.80 ~8 885 8.80 367 8.80 4 858 3.59 14 888 8.58 ~4 870 3.56 20 871 3.54 ~2 372 8.54 ~13 373 8.54 ~2 874 3.53 4 875 3.50 ~88 376 8.50 ~5 877 8.48 4 878 8.48 ~5 379 8.47 o 880 3.47 4 881 8.48 ~18 882 8.48 ~87 888 8.42 22 884 3.41 4 885 3.39 ~o 888 3.37 2 887 3.37 ~3 888 3.36 o 888 3.36 ~12 880 888 ~20 881 3.34 ~2 392 3.34 ~11 888 3.33 5 884 3.33 ~2 885 3.33 ~41 888 3.31 ~5 397 3.31 ~15 888 3.30 ~40 888 3.30 ~7 400 3.29 ~25 401 3.27 ~18 402 827 2 408 3.26 ~0 404 828 ~8 405 828 ~11 405 825 ~7 407 3.24 4 408 3.23 4 408 822 ~5 410 821 10 411 3.21 4 p 1 ~27 ~11 ~17 10 ~1 ~4 ~7 37 ~13 ~23 ~12 K ~2 ~1 ~11 ~15 ~55 ~5 ~7 3 5 12 08 Mg 2 ~3 2 2 ~22 6 ~16 —7 10 9 ~4 5 4 1 2 ~1 10 ~4 ~5 ~2 1 1 ~11 0 ~6 ~3 2 ~1 42 6 3 4 ~6 ~10 ~9 -0 ~17 ~12 ~26 7 ~0 3 ~3 0 ~21 ~7 ~4 ~1 ~1 1 32 19 12 7 40 21 -4 ~0 12 ~6 ~11 ~3 ~12 ~2 57 ~16 9 7 3 1 ~3 3 3 17 14 1 ~6 ~3 37 16 29 17 ~2 ~6 ~3 1 ~9 ~2 ~4 ~5 ~10 ~1 24 ~4 27 11 ~12 ~1 ~41 ~16 ~14 ~3 2 ~11 40 12 ~1 ~5 12 4 53 10 ~6 ~1 5 5 -6 26 ~7 1 ~23 ~9 3 0 5 ~2 ~2 ~1 1 7 ~4 2 ~6 ~3 ~14 ~2 12 30 Mn F8 14 ~6 ~16 ~6 o 6 6 ~2 2 ~3 ~25 3 -3 17 ~2 -9 3 ~10 6 10 4 6 9 ~3 13 6 19 ~1 6 ~0 17 5 7 6 6 2 4 ~22 ~67 52 ~25 ~50 ~69 54 10 4 4 ~1 20 o 12 ~4 1 ~3 2 5 ~9 ~43 ~7 1 ~0 ~6 ~97 63 17 ~1 9 ~11 7 ~35 5 o 6 3 79 ~3 6 ~2 ~1 6 ~0 0 15 ~1 ~42 ~20 6 3 23 4 ~O —1 7 1 6 ~4 13 1 11 ~1 ~25 ~41 180 ZnAlOuB 12 11 01 ~5 ~2 25 ~12 ~0 ~17 -7 22 ~9 00 10 ~0 ~52 12 0 9 ~0 27 112 7 ~12 ~31 ~3 11 17 ~3 ~14 ~11 ~3 ~9 17 ~12 ~49 ~10 4 13 20 44 2 19 ~0 121 25 74 103 275 41 47 27 121 17 45 130 31 123 107 RELATIVE ORDER OF NUTRIENT REQURENENTO 98 >N >Mg>2 >C8>|h M90 >98 >N >2 >9 >Mg>C8>28>Cu 9 >N >C8>2 >Mg>011 C8>MpN >2 0 >Cu>M928>9 >N >98 >2 >M‘>CI >AI 2 >C8>98 >28>Mp|h N >M¢>M9C8 98 >M9N >Mg>C8>9 >2 >0 >28>C11 N >MpF8 >2 >M9O8 2 >C8>98 >M9N >Mn 28>9 >M9Cu>N >2 >M9C8>98 >Al >0 C8>lh>2 >Mg>28 >98 >N C8>N >Mg>2 >98 >M8 N >Mpc8>Mn N >9 >2 >Mg>08 28>2 >98 >N >C8>Mg>|h M3>C8>2 >N >98 >69: 2 >C8>28 >98 >M3>N >M8 C8>Mg>N >2 9 >N >C8>2 >Mpcu 2 >N >C8>98 >MpM8 28>2 >C8>N >Mg>F8 >M8 2 >C8 >N >Mg>98 >hh>28 28>C8>Mp2 >98 >991 98 >0 >C8>N >M92 >M9011>9 >721 M9Al >28>Cu>9 >0 >2 >N >M3>C8>98 98 >M9N >0 >2 >Mg>9 >C8>Cu>28 WZD>N >C8>0 >9 >2 >N >M9C8>98 28>C8>N >Mg>2 >98>Mo N >Mg>98 >M92 >C8 C8>2 >Mg>N >98 >28>|h C8>M3>N >2 N >M99 >2 >C8 Al >9 >N >98 >2 >M3>C8>M8 2 >98 >N >M9Mg>C8 >28 2 >N >C8>M9MpP8 >28 98 >0 >M9N >C8>9 >2 >Zn>MpCu M92 >N >Mp98 >C8 2 >N >C8>98 >Mg>M928 N >9 >2 >MpCa M9.“ >C11>28>0 >9 >M3>N >2 >C8>98 Mp2 >N >C8>98 >M8 2 >C8>28>N >98 >Mplln C8>M3>N >2 98 >M9C8 >N >9 >2 >M928>Cu>0 CI>MPN >2 2 >N >M9N >M90 2 >N >M3>C8 >9 C8>2 >N >Mg>98 >28>N: C8>M3>N >2 >98 >948 C8>M5>N >2 28>N >98 >Mg>2 >C8>M8 N >9 >2 >M¢>C8 N >Mplh>ca >2 >98 N >2 >M998 >H'PCI h>N >9 >2 >Mg>C8 C8>N >Mg>h >2 >9h N >C8>Mp2 M998 >N >9 >2 >C8>0 >28>M3>Cu 28>C8>Mg>N >98 >2 >Mn C8>M3>N >98 >2 >M8 N >2 >98 >M9M§>CI >28 N >M3>C8 >2 N >C8>Mg>98 >2 >110: 28>98 >2 >N >C8>1191>Mg 28>C8>2 >98 >M3>N >M8 C8>N >Mg>98 >2 >M8 C8>M3>2 >N 98 >M90 >28>N >9 >2 >C8>C11>Mg ‘mmzau8 “an m mmu1u 412 820 ~7 «8:mo-m «4 M6~m 415 8.18 ~5 415 3J6 ~4 417 3J7 7 418 3J7 ~9 419 3J7 ~8 4m 8m 2 «1.M4 0 4a 8“ 41 4m 8“ 8 424 3.13 4 4% 8m ~1 4a 8a 40 427 8.11 ~5 428 3.11 ~7 428 8.10 ~28 4w 8w ~7 «1:m8~w 49 8M 2 4a 8a ~4 4M 8m 18 4M 8M ~1 4a 8“ ~8 487 8.01 ~10 4m 8w ~8 4a 2a ~8 4m 2a -8 44 an ~2 4a 2a ~8 «8:u4~n 4“ 2m ~4 4« an 41 «8 an 1 4a 2a ~8 448 2.91 ~14 4m 2m 40 «o:u0~m 4m 2m ~4 4a 2a ~4 4a 2a 1 4a 2a ~8 455 2.67 ~5 4“ 2n 8 4n 2» ~3 4a 2a 5 4a 2“ 21 4» an ~2 4m 2M ~8 4a 2a ~2 4a 2a 41 «4zu8-u 4“ 2n ~4 4a 2a 41 487 281 ~8 4m 2m ~4 4» 2n 7 470 2.78 4 471 2.76 ~5 4n an 48 473 2.77 ~48 4n 2n 5 475 2.78 ~2 475 2.78 ~5 4n 2m 17 478 2.78 ~20 4n 2n 7 4» 2n ~o p 37 ~13 ~9 15 ~7 ~15 ~10 ~4 ~11 ~17 2 ~2 ~49 ~5 ~2 12 22 27 ~0 ~7 12 4 10 ~81 12 ~4 13 ~0 ~12 -1 ~7 12 ~11 11 12 -2 -4 12 18 ~2 ~12 ~14 ~9 13 ~54 10 10 4 57 19 ~7 23 ~7 ~19 ~10 ~11 -9 17 ~5 ~5 ~7 ~0 ~8 4 ~11 4 ~51 1o 15 ~21 ~12 ~23 47 ~0 ~0 ~10 ~10 ~10 ~1 ~11 ~2 19 ~10 ~0 Mg Mn ~3 0 ~21 12 ~10 ~1 ~6 23 13 ~50 ~1 2 ~3 ~4 ~1 ~7 ~6 1 9 0 ~5 ~2 ~3 7 ~5 16 2 -4 ~5 ~2 3 2 6 5 ~1 6 19 ~66 6 3 —2 6 ~6 3 ~5 ~4 4 5 6 3 -9 ~6 4 ~1 ~13 40 ~14 14 ~4 13 7 10 ~5 5 -6 3 ~2 12 ~9 ~1 13 ~3 5 16 ~11 6 ~3 12 4 ~11 26 ~96 1 6 1 ~4 3 12 6 14 ~1 ~1 ~3 13 3 16 ~11 ~12 3 ~3 0 ~2 6 11 ~7 6 -4 12 2 12 19 ~9 ~1 F6 ~7 ~0 ~4 181 ZnAIOuB ~11 ~11 70 13 ~5 ~12 20 24 44 ~10 ~5 ~20 10 39 ~7 ~10 -0 9 ~0 23 14 15 ~10 ~3 0 31 ~70 10 51 0 ~0 ~1 ~3 ~0 0 ~10 ~10 ~1 20 -0 -0 115 130 42 175 3821283838 0” NO §88288885585 147 19 RELATIVE ORDER OF NUTRIENT REOLIREMENTS N >MpM8>2 >98>C8 N >M99 >2 >C8 2 >N >Mg>C8 >9 MpN >2 >M998 >C8 Mg>C8>N >2 >98>M8 M9211>Cu>0 >Al >9 >N >2 >Mg>C8>98 N >M92 >M908 >98 C8>N >M92 C8>M3>N >2 9 >28>Cu>M9Mg>N >C8>2 >98 >Al >0 N >C8>Mp2 C8>2 >Mp98 >N >28>Ih 2 >M9N >98>M5>C8>28 CI>M3>N >2 N >MPFC >2 >M9C8 N >M9C8 >2 >98 >Mn N >C0>m>‘ 2 >N >C8>M9Mg>98 >28 98 >N >C8>Mp2 >9 >M928>011>0 N >CI>M‘>‘ C8>28>2 >N >98 >M9M; C8>N >M92 >98 >548 M951 >28>9 >C8>0 >2 >N >M3>C8>98 0 >2 >98 >N >M9C8>M3>9 5125501. 98 >2 >C8>N >9 >MPMII>CU>ZO>B N >98 >M92 >M9C8 MpN >C8>2 N >C8>M3>M998 >2 N >2 >98 >Mg>Mn>C8 M99 >28>N >0 >Cu>Al >Mg>98>C8>2 2 >M5>N >M998>C8 N >M9C8 >2 c5>Mg>98 >N >2 >M8 M¢>N >98 >2 >C8>M8 C8>Mp2 >98 >N >lb N >28>98 >2 >C8>Mg>M8 N >M92 >98 >M9C8 C8>N >M92 N >C8>Mg>2 2 >C8>N >Mp98 >591 C8>MpN >2 28>98 >Mpc8 >N >2 >Mn N >M92 >C8 28>N >98 >2 >CI>MPMI C8>Mp2 >N >98>Mn 2 >98 >N >Mg>C8>Mn M9C11>9 >28>N >0 >98 >M3>N >2 >C8 M928>Al >Cu>9 >0 >2 >N >M3>C8>98 98>C8>N >2 >143)” N >M9C8 >2 C8>M5>N >M92 >98 N >9 >2 >M3>C8 N >9 >2 >“‘>C0 98>C8>9 >N >M90 >28>2 >M¢>Cu N >C8>N')‘ C8>N >98 >M92 >28>M8 9 >Al >N >98 >2 )C3>M‘>“II C8>M3>N >2 MpN >2 >M998>C8 C8 >2 >N >98 >M3>M928 N >2 >Mg>98 >M90- N >9 >2 >M3>Cg MPZII)‘ >C8>98 >N >091 C8>M3>N >98 >2 >Nh 2 >N >C8>M¢>98>9h 9 >2 >N 2 >N >C8>Mp9 C8>MpN >2 C8>MpN >2 Til. 2. Cal'd. YIELD ID RM N 401 2.73 ~0 402 2.73 3 403 2.72 ~5 404 2.72 ~5 405 2.71 2 400 2.70 ~7 407 2.70 ~2 400 2.70 ~5 409 2.70 ~0 400 2.09 1 491 2.09 0 492 2.00 ~ 14 403 2.07 ~12 404 2.07 ~0 4“ 2.07 ~27 400 2.00 ~40 497 2.00 3 490 2.05 2 4“ 2.03 0 500 2.03 ~1 501 2.03 7 502 2.03 ~37 503 2.02 7 504 2.02 ~15 505 2.01 3 500 2.00 ~ 14 507 2.00 ~32 500 2.50 9 5“ 2.50 ~ 10 510 2.50 51 1 2.50 ~11 512 2.50 ~40 513 2.50 ~15 514 2.50 3 515 2.50 ~39 510 2.57 ~19 517 2.57 0 510 2.57 ~13 519 2.50 ~15 520 2.50 ~ 15 521 2.50 ~2 522 2.55 ~23 523 2.54 ~0 524 2.54 7 525 2.54 -1 520 2.54 4 527 2.53 ~10 520 2.53 4 529 2.53 ~10 5“ 2.53 ~7 531 2.52 ~12 532 2.52 ~7 533 2.51 ~0 534 2.50 ~19 535 2.50 -1 1 5a 2.49 537 2.49 ~29 530 2.49 ~40 539 2.40 ~1 540 2.40 541 2.40 ~5 542 2.40 -1 543 2.40 544 2.44 7 545 2.43 ~12 540 2.43 ~7 547 2.42 7 540 2.42 1 549 2.41 ~11 ~12 ~21 32 42 ~37 ~5 ~0 K ~5 ~0 ~17 21 ~11 ~7 19 10 12 I I -8I I g I .18 08 Mg ~7 ~6 20 12 ~15 ~9 ~7 ~2 ~7 ~4 ~6 ~2 ~6 ~6 5 ~4 ~6 ~2 0 ~2 ~17 ~6 ~o ~13 3 1 ~2 ~3 ~16 6 6 16 ~2 0 16 11 ~1 ~7 ~12 ~4 ~17 9 26 15 ~5 ~2 ~6 -3 14 ~0 61 ~23 ~21 ~6 4 ~4 6 ~5 4 37 10 ~1 ~4 ~12 ~10 46 10 9 4 -6 2 ~0 ~5 ~5 0 ~12 -0 21 14 ~21 ~10 ~4 ~4 ~6 ~4 19 13 13 10 ~16 ~9 10 10 ~12 ~2 7 15 ~7 ~9 ~6 ~9 25 14 34 13 16 3 ~13 9 ~36 ~2 5 ~5 5 ~16 ~5 17 6 ~1 ~9 ~1 ~1 4 15 6 2 15 9 3 6 12 ~44 ~15 ~0 10 -4 -9 ~42 10 10 12 21 10 ~51 10 11 10 ~10 15 ~17 ~10 11132 FOZnAIGJB 2 8842 4 ~82 5 8 ~21 25 742 8 51 5 ~10 ~7 1o 2 ~5 82 ~51 8241 8 2 ~0 1o 4 2 1 ~15 8 ~2 88 ~5 4 1 -2 ~2 7 -4 4 2 8 ~5 2 4-28 ~42 ~5 ~5 5 ~8 4 ~18 -2 ~11 4 8 48 8 14 1045 28 8 ~7 25 88 ~8 ~3 ~8 81 ~5 ~10 ~10 2 ~14 ~8 2 1 2 88 133 2 ~51 ~11 ~10 82 44 4 ~9 ~6 8 154 124 d d 888883818 d 58885=§88 558§§ d d d 88889233858 8828888528855588; 142 177 105 207 12 100 21 109 90 10 RELATIVE ORDER OF NUTRIENT REOLIREMENTS C8>M3>N >98 >2 >691 M928>C11>0 >9 >Al >N >2 >Mg>C8>98 C8>MpN >98 >2 >991 98 >M99 >C8>N >Mg>Cu>28>2 >0 C8>M5>N >2 C8>N >M92 “2%)" >2 N >M92 >98>M9€8 98 >M9C8>N >2 >Mg>9 >C8>0 >28 Cu>28>9 >M9Mg>Ca>N >2 >98 >Al >0 C8>M3>N >2 >98 >M1 2 >N >Mg>M9C8>98 N >28>M99 >C8>Mg>C8>2 >98 >Al >0 98 >2 >Mg>c8>N $9928 9 >2 >N >C8>Mg>011 N >C8>2 >11. 2 >28>C8>Mg>98 >N >591 M9Al >28>0 >C11>9 >2 >N >MpC8>98 9 >N >2 M3>C8>N >2 C8>MpN >2 N >9 >2 >C8>Mpcu M92190 >Cu>Al >9 >N >Mg>2 >C8>98 N >C8>Mg>2 C8>Mg>N >2 N >2 >NPFC>M N >M92 >9 >C8 C8>M3>N >2 N >M‘>C0>‘ 9 >011>Mg N >C8>Mg>2 N >9 >2 >Mg>C8 N >Mg>C8>2 C3>MPZII>F0 >N >2 >M8 N >9 >2 >Mg>08 N >Mg>2 >C8 9 >2 >N N >C8>2 >Mg>98>hh N >Mg>C8>2 N >C8>Mg>2 C8>98 >N >Mg>2 >918 28>2 >N >M.)C0>P C8>Mg>N >98 >2 >991 28>M3>C8 >2 >98 >N >991 C8>Mg>28>N >2 >98>M11 M9AI >28>2 >C11>9 >0 >N >MpC8>98 2 >N >98>C8>211>MpC8 CI>M‘>F¢ >N >2 >Mn N >2 >98>C11>28>M3>C8 C8>N >M92 M8>N >98>C8>2 >94; MpC8>N >2 M5>C8>N >2 >98 >M8 2 >N >M¢>Ca>9 2 >N >M5>C8>9 2 >M3>C8 N >9 >2 >CI>W N >9 >C8>2 >M¢>Ou Mg>9 >N >C8>2 9 >011>Mg C8>MpN >98 >2 >M8 M9Cu>0 >28>N >9 >N >98 >M3>C8>2 Mg>C8>2 >98 >991 zn>C9>MPFC >2 >N >991 98 >M92 >N >28>9 >C8>Mg>011>0 N >98 >2 >MpC8>lh M9Cu>0 >9 >AI >28>98 >N >2 >Mg>C8 28>98 >N >M>2 >94; 2 >Cll T“ 2. Oonl'd. YIELD ID RM N 550 2.41 551 2.41 -3 552 2.40 503 2.40 31 554 2.40 ~0 555 2.37 -2 550 2.37 ~3 557 2.30 ~0 550 2.35 ~5 559 2.33 -7 500 2.33 501 2.32 ~ 10 502 2.31 ~0 503 2.31 ~12 504 2.30 ~5 505 2.30 ~ 13 500 2.30 ~0 507 2.30 ~3 500 2.29 0 0“ 2.29 0 570 2.29 3 571 2.20 ~0 572 2.20 ~ 10 573 2.27 ~1 574 2.20 -5 575 2.20 1 570 2.20 ~4 577 2.25 ~2 570 2.24 10 579 2.24 0 500 2.22 ~3 501 2.22 ~1 502 2.20 ~1 503 2.20 ~19 504 2.19 ~1 505 2.19 ~3 500 2.10 507 2.10 ~21 500 2.10 5 509 2.10 11 590 2.10 7 591 2.17 3 592 2.17 ~0 593 2.10 ~0 594 2.15 10 595 2.14 5 5“ 2.14 13 597 2.14 ~5 590 2.13 ~9 5“ 2.13 31 000 2.13 ~0 ”1 2.12 10 002 2.11 ~1 003 2.1 1 ~3 004 2.11 -7 005 2.10 1 000 2.10 7 007 2.10 ~14 000 2.“ ~1 m 2.“ 1 1 010 ' 2.00 ~2 01 1 2.00 ~15 012 2.07 0 013 2.07 ~9 014 2.00 ~ 14 015 2.00 10 010 2.00 017 2.05 ~0 010 2.05 -2 P ~49 4 ~15 10 ~02 ~0 K I .81 -8 C... I 33Nbifib'b-LV8080NOC 1.8.8 I I 000000‘4‘ ”I .18 8”.“0... -2 4 17 10 ~15 22 12 ~2 31 ~2 ~0 10 14 ~3 11 ~$ 13 08 Mg Mn 19 ~1 ~3 ~1 ~5 1 ~17 ~7 ~26 ~1 9 ~0 ~1 4 ~3 ~13 1 9 20 22 13 3 ~9 15 ~3 5 2 ~7 ~2 ~6 ~2 ~2 ~6 ~3 ~16 0 19 2 ~2 20 14 ~56 ~14 ~5 17 6 ~27 ~10 ~1 13 ~6 ~2 12 ~3 ~16 5 9 ~6 11 ~0 ~17 13 4 ~6 13 5 ~15 21 14 ~55 ~12 1 ~10 ~6 ~19 ~4 14 3 3 4 ~2 ~2 1 ~2 ~20 ~6 12 ' 23 2 ~3 6 1 6 24 16 ~62 25 15 ~50 ~13 ~7 17 11 ~4 ~3 —5 ~23 ~33 16 25 17 ~67 0 ~6 ~4 ~7 ~11 4 ~6 ~14 ~12 29 ~4 0 1 ~6 12 ~2 ~1 ~3 ~3 12 ~16 ~13 9 ~3 7 29 ~10 ~3 17 11 ~36 ~17 ~9 15 42 41 ~7 ~16 11 51 19 ~6 3 ~2 0 6 6 4 13 ~6 ~4 F8 ~12 ~0 8 (I ONO-AN. 0 d ~3 ~25 183 Zn -1 11 ~2 4 ~13 ~3 ~11 ~1 10 ~3 ~11 A1 011 0 8 18 8 ~15 ~7 ~8 ~5 4 ~14 4 ~2 4 ~14 -8 1 2 ~15 842 ~5 ~9 ~15 ~5 18 ~5 ~15 o ~8 8 28 ~6 ~11 ~10 4 ~10 28—81 J 833383218838 588588853 0” ‘40 49 107 105 57 111 104 25 110 124 70 10 31 21 27 104 20 147 100 10 149 140 25 RELATIVE ORDER OF NUTRIENT REOLIREMENT 9 2 >M¢>C8 C8>N >M¢>2 2 >C8 9 >2 >N C8>N >M92 M5>N >C8>2 C8>N >Mg>2 >Mn>98 N >M9C8 >98 >2 >09: M3>N >C8>2 98 >N >2 >28>C8>Mg>hh 2 >M'>CI N >Mg>F8 >2 >C8>M8 N >08>2 >28>M998 >111; N >C8>M998 >2 >Mg>9 >0 >h>Cn N >Ca>MpK N >C8>Mg>2 C8>98 >2 >N >Mg>Mn N >M9C8 >2 M9Al >Cu>0 >28>2 >9 >N >Mg>c8>98 C8>MpN >2 M90 >Al >Cu>9 >28>N >98 >M5>C8>2 C8>N >Mp2 >98 >69: N >2 >C8 >M3>98 >M928 M908 >N >M8>2 >98 2 >M5>N >98 >C8>Mn Mpca >N >2 >98 >M8 Mp9 >N >C8>2 M99 >0 >28>Cu>N >Al >M‘>F¢ >2 >08 M9Al >Cu>28>9 >0 >2 >N >M3>C8>98 C8>Mg>2 >N C8>M5>N >2 C8>MpN >98 >2 >Mn 2 >28>98 >N >M3>C8 >Mn N >C8>Mp2 MpN >C8>2 C8>MpN >2 9 >C8>M; N >M92 >98 >M9C8 28>98 >2 >M9N >08>M8 M9Al >0 >28>9 >2 >C11>N >Mg>C8>98 M90 >28>C11>Al >9 >N >M92 >C8>98 C8>M3>N >2 2 >N >C11>28>98 >“‘>CI 98 >0 >M99 >C8>MpN >2 >28>011 M3>C8 >98 >N >M92 M9N >0 >28>C11>9 >N >2 >MpC8>98 9 >2 >N MpN >C8>2 98 >M9N >M9C8>2 >Cu>Zo>9 >0 9 >N >2 N >M99 >C8>2 C8>Mp28>98 >2 >N >Mn C8>N >M92 M92 >N >C8>98 >M8 N >C8>Mp2 98 >C0>MPZJ>N >2 >M8 C8>Mp2 >N >M998 9 >N >Cu>C8>2 >M5>98 >918 C8>M3>N >2 M9Al >Zn>Cu>2 >9 >0 >M9N >C8>98 C8>M3>N >98 >2 >09: 2 >98 >N >MDM'>CI 9 >2 >N 9 >C8>C11>N >2 >98 >Mg>lb N >MPFO >2 >M90- 9 >2 >N 28>98 >C8>Mg>2 >M8 0 >98>211>N >M3>C8>9 >2 >M9011 C8>M3>N >2 Twuzanu “an m mmu1n 818 205 4 an an ~5 «1 an ~7 an an 7 «M 2m 2 8m 2m 41 «M 2m 42 888 201 ~9 827 201 ~8 5a 201-m an 2w ~4 880 200 ~23 8m 19 48 an L» ~5 8” mm ~8 «n ma ~2 5a 1a 40 «8‘u7~u on La ~6 8a 1” 15 8a 1a ~4 8m 1m ~5 «1 um «2'u5 5 ua-m4-n 6“ LN 40 8“ La 44 «8 “3 4 80 mm ~5 M6 u2 4 8m 1m 5 an Lu ~4 851 181 4 852 1.91 ~5 um 1m 8 854 1.91 ~10 an mm 8 an 1» ~8 «7 mo 7 an mu ~8 an L» 48 «o‘uo—m «1‘u8-u an 1” 42 an Lu 4 6M 1M ~9 an Lu ~7 an L“ ~5 «7‘u4 a an t» ~8 an 1» ~8 870 1.62 4 n1'u2 a 8n 101-m 5m 1m 41 874 1.61 14 875 1.80 ~24 an L» ~1 8n 111-m ”O‘HO 8 fl9‘u9~fl 8» Ln 1 «1'n8~m an 1n ~1 “3‘U6 a an 1n ~8 6“ 1n 7 an 1n -8 an 1n 8 P ~3 ~17 ~35 ~10 ~45 K 4 ~0 ~0 ~1 ~5 ~0 ~2 ~2 21 10 19 15 10 ~7 ~10 ~0 10 ~2 24 11 19 10 Ca Mg Mn ~3 4 9 4 2 2 ~5 ~3 15 ~6 ~2 17 ~19 ~3 35 9 3 10 1 -1 3 4 0 6 7 17 46 10 2 ~2 11 2 ~11 9 5 ~10 1 ~5 ~1 ~7 ~14 ~3 ~2 ~3 ~1 ~1 ~6 ~10 11 ~4 ~5 12 5 ~5 9 ~2 ~10 14 ~6 ~0 ~26 ~3 4 7 ~1 ~12 ~3 ~2 ~2 -5 ~17 ~12 27 ~1 2 15 ~17 -3 4 ~3 ~12 ~6 26 ~11 ~17 ~16 ~10 1 ~6 16 ~17 10 2 ~10 ~5 22 15 ~1 5 ~2 ~1 ~5 ~3 5 ~2 ~1 ~4 ~12 ~5 ~3 11 ~20 ~10 24 4 ~9 ~1 ~3 1 1 13 ~3 3 -5 4 15 9 ~1 3 4 7 26 ~3 ~16 ~5 3 3 ~23 ~6 26 76 ~27 5 ~13 2 10 6 13 ~5 ~6 ~3 6 ~10 ~15 ~4 2 1 ~6 ~13 ~6 184 FOZnAIOuB '1 ~14 ~50 11 ~2 2 ~17 ~3 ~15 017 ~w 4 19 ~1 ~1 4 4 -7 ~3 9 ~11 14 17 10 10 ~12 10 19 dd d 8888822288 88888882388888088 19 58859825858855555: d 3833 88888888 RELATIVE ORDER OF NUTRIENT REOUREMENTS 28>C8>N >98 >2 >Mg>Mn 98 >2 >N >Mn>Mg>C8>9 >28>C11>0 N >C0>MPF9 >2 >lh 28>C8>Mp2 >98 >N >M8 28>C8 >2 >Mg>98 >N >991 N >2 >M90 N >2 >M9M3>C8 >98 N >2 >M998 >C8>M‘ 98 >9 >N >2 >0 >28>C8>Mg>011>Mn N >9 >2 >M9C8 N >98 >28 >2 >M3>C8 >9h N >Mg>08 >2 N >M998 >M92 >C8 N >M3>C8>2 N >M90. >2 C8 >M5>N >2 N >M3>C8 >2 N >M'>C0 >2 Mp2 >C8>N >98 >M92!) 2 >98 >M‘>C0 >M9N 2 >M9N >98 >C8>lb MpN >C8>2 M5>98 >2 >M9C8 C8>M3>N >2 N >C8>Mg>2 MpN >C8>2 N >C8>Mp2 M3>C8 >N >2 C8>M3>N >98 >2 >Ih 98 >28>9 >C8>N >Mg>2 >0 >M9011 CI>N‘>N >2 N >Mp2 >C8 C8>28>Mp2 >98 >N >991 M3>N >C8>2 C8>MpN >2 N >Mg>98 >C8>2 >M8 9 >N >2 C8>N >2 >M9M998 9 >2 >N C8>MpN >98 >2 >I1h N >2 >HPFO >M908 N >C9>M‘>‘ N >C3>m>‘ N >M5>M998 >C8>2 W)" >2 N >Co>Mg>98 >2 >99: C8>MpN >2 >98 >M8 MpN >98 >M9C8>2 9 >2 >N N >C8>98 >MpM92 N >98 >Mg>2 >M9C8 28>C8>N >2 >98 >MpM8 9 >2 >N N >M9C8 >2 N >98 >2 >C8>M¢>M8 9 >N >2 N >2 >M99 >C8 C8>MpN >2 N W>2 C8>Mg>221>2 >98 >N >119: N >M92 >98 >M9C8 C8>28>2 >N >M;>P8 >001! N >M998 >C8>2 >61. C8>MpN >2 9 >N >2 MpN >C8>2 C8>Mg>N >2 Cu>Zn>9 >M9N >Mg>C8>2 >98 >N >0 C8>MpN >2 Ti)“ 2. 0511511. YIELD ID I‘M N 000 1 .77 ~0 009 1 .70 ~3 U0 1 .75 ~4 001 1 .74 0 002 1 .74 -9 093 1 .74 ~0 094 1 .74 ~1 095 1 .74 ~29 0“ 1 .73 3 N7 1 .72 ~3 m 1 .72 ~0 000 1 .72 700 1 .71 ~24 701 1 .70 ~2 702 1 .70 ~23 703 1 .70 -1 1 704 1.70 ~0 705 1 .00 ~1 700 1 .07 1 707 1 .07 ~1 1 700 1 .07 ~7 709 1 .07 ~0 710 1 .07 1 1 71 1 1 .00 ~10 712 1 .00 - 15 713 1 .05 ~10 714 1 .05 ~1 1 715 1.04 ~4 710 1 .04 2 717 1 .04 1 710 1 .04 ~4 719 1 .04 ~14 720 1 .03 15 721 1 .03 ~0 722 1 .02 ~3 723 1.02 ~2 724 1.02 ~1 725 1 .02 1 720 1 .00 ~2 727 1 .00 4 720 1.00 ~10 729 1.00 ~0 730 1 .59 ~10 731 1 .59 ~15 732 1 .59 1 733 1 .57 ~4 734 1 .50 12 735 1 .55 2 730 1.53 ~2 737 1 .52 1 730 1 .52 3 7“ 1 .52 ~27 740 1 .51 ~2 741 1 .50 ~0 742 1.50 ~4 743 1 .50 1 744 1.50 30 745 1 .49 13 740 1 .40 2 747 1.40 ~0 740 1 .47 -3 740 1 .47 ~9 750 1.47 ~ 10 751 1 .45 ~9 752 1 .44 ~0 753 1 .44 ~ 13 754 1 .44 ~1 750 1 .43 ~5 750 1 .42 ~4 P ~7 ~0 13 ~72 ~10 ~0 10 17 -0 -3 -1 10 ~10 ~0 ~7 ~0 ~5 ~2 13 17 ~4 ~12 ~13 ~10 ~15 12 19 ~3 ~7 ~7 ~12 -7 -3 ~11 10 ~17 ~15 ~1 ~35 ~0 ~0 ~0 ~w ~8 n a ~4 ~5 ~10 ~5 ~11 ~3 ~5 ~10 ~11 ~0 -4 ~13 ~u ~10 ~3 ~2 ~7 ~5 ~7 ~3 ~2 ~1 ~1 ~0 ~20 ~0 ~5 ~0 -2 ~19 11 10 17 fiNV ~13 ~20 ~4 w 12 14 12 ~0 10 Fo 12 11 ~0 10 10 185 ZnAlCuB ~4 ~10 ~22 ~11 -0 ~13 ~13 10 ~9 ~12 ~13 ~10 ~3 13 12 ~17 14 13 10 -2 ~13 ~23 ~12 ~25 ~15 ~7 ~25 10 10 10 17 9 ~24 120 85558588358§§8858 d 8838838883 d dd‘d 82883883083 d N 0 8888228838883888 RELATIVE ORDER OF NUTRIENT REOUREMENTO MpN >C8>M92 >98 MpN >C8>2 >98 >991 98 >N >M92 >MpC8 M90 >9 >28>N >Cu>N >98 >Mg>08>2 M3>N >C8>2 N >C8>98 >Mg>2 >Mn C8 >M9N >2 N >C8>2 >M9Mg>98 C8>Mg>2 >N >|h>98 Mp2 >N >M90 >98 upc. >N >2 >98 >99: 28>C8 >M92 >98 >59: N >M5>C8 >2 Cu>Zn>M99 >N >2 >Al >Mpc8 >98 >0 N >Mpc8 >2 N >C8>Mp2 N >M998 >2 >M3>C8 MpC8>N >2 Cu>28>M99 >2 >N >M‘>C0 >98 >Al >0 Cu>28>N >M99 >2 >Mg>C8 >98 >A| >0 28>N >C8>98 >M5>2 >lb MpN >2 >M998 >C8 M9Al >28>C11>0 >9 >2 >N >MpC8>98 N >M998 >2 >MPC| N >C8>Mp2 N >Mg>2 >C8 MpN >C8>2 Cu>M928>9 >N >Mg>Al >2 >C8>98 >0 2 >C8>MpN >98 >M8 C8>MpN >2 28>N >98 >Mg>2 >M9C8 N >98 >2 >MpM9C8 Mp2 >C8>N Cu>Zn>M99 >N >M5>C8>2 >Al >98 >0 C0>M‘>N >2 C8 >N >Mg>2 C8 >M9N >2 C8>2 >MPFO >N >28>M| 2 >C8>N >Mg>M998 C8>Mp2 >98 >N >M928 N >M92 >C8 28>C11>M99 >N >M3>C8>2 >0 >Al >98 M9N >C8>2 >MpF8 9 >N >C8>C11>2 >N‘>FC >M8 Ou>28>9 >M9N >2 >M3>C8>98 >N >0 N >98 >2 >M3>C8 >991 C8>M3>N >2 28>M¢>C8 >98 >2 >N >M11 C8>N >M92 >|h>98 MpCa >98 >N >M92 >28 28>C8>Mp2 >N >98 >Mu N >Mg>08 >2 C8>M.>N >2 >M8>98 0 >98>C8>MpN >28>I1h>C11>9 >2 28>N >98 >M92 >C8>M8 28>C8>M5>N >98 >2 >M8 9 >N >2 28>C8>2 >98 >MpM9N C8>MpN >M998 >2 MpN >2 >C8>M998 9 >C8>N >2 >98 >MpMn N >M99 >2 >08 Mp2 >N >98 >M908 N >M92 >98 >M9C8 N >Mg>C8 >98 >2 >M8 N >MpM998 >2 >C8 2 >08>M9N >98 >M928 Mp2 >N >C8>98 >648 C8>N >M92 186 YIELD IDRMNPKOOManFOZnNOUB iiiifi 3 N N §§§§§§§§§§8§ -3 -7 -5 -9 -5 -2 -7 -10 -5 -4 -2 -5 -13 -o -4 —4 -4 -m -15 -3 -5 -3 -13 -37 -15 -7 -40 -23 d d -15 -5 -0 51 -15 -51 I gadOOGOUdO-ADO .2 . 23 —11 5 -13 -7 -23 -5 -11 13 -15 -9 37 25 -15 -0 -9 10 -1 -7 -3 -11 -7 -u -o -m -12 -5 -9 -2 -3 -1 -9 -3 -9 -2 -3 -1 10 -7 --13 -1 -10 -5 -13 -5 11 -12 -5 -1 -4 4 -7 -o 4 -3 -5 -9 -5 -7 -5 —3 -7 -12 14 -12 -10 15 -19 5 -2 11 -5 5 15 4 19 -1 -15 27 7 5 5 47 11 19 -2 ~23 22 0 55 -4 24 -4 -7 19 -49 -14 11 -10 5 -5 15 4 13 3 5 -3 -2 -20 4 0 25 1 2 5 14 2 15 3 5 2 33 21 -2 -5 3 -5 20 12 -11 11 10 9 24 -7 5 5 3 -1 15 17 -2 -2 9 -12 2 0 2 7 33 --92 13 -5 20 5 -25 7 —13 13 -14 22 -25 10 -12 -3 -5 -3 -1 15 15 -7 858 51 115 asazszaa 51 117 147 51 175 105 288288 ‘ 88388888883388282 -3 8388a RELATIVE ORDER OF NUTRIENT REOUREMENTS Cu>Mn>Zn>P >M92 >N >Ca>Al >3 >Po Zn>cu>Mn>P >N >M>2 >Al >3 >Po N >Ca>Po >M92 >lh WP >N >2 >03 P >N >2 >M90 N >P¢ >2 >Mg>Cn >Zn>hh N >M92 >Ca>Pc >lh Zn>Ca>M3>N >Pe >2 >lh WK >N >Ca>Po >55: N >M92 >Mn>Po >Ca P >Ca>N >2 >Cu>Mg>Pc >Mn Zn>N >80 >Mg>Ca >2 >|h Ca>M¢>N >2 >Pc >55! Ca>M3>N >2 Ca>Mg>N >Po >2 >113: P >2 >N >upcu MpN >Fc >Cn>2 >12: Zn>Cu>Mn>N >P >Mg>Cn>2 >3 >Pc >A| Zn>Mn>Cu>P >N >M92 >Ca>N >l'c >3 Cu>MpN >2 MpN >Co>2 N >2 >Mg>P >Ca N >M90 >2 Ca>N >M92 N >MpP >2 N >P¢ >M92 >lh>Cn Ca>Mg>N >Pe >2 >Mn Ca>Mch >N >2 >Ho P >M9N >2 >Cu N >M92 >P >Co N >Pc >Zn>Cs >2 >M9N: Mn>Al >3 >P >Cu>Za>Po >N >2 >M3>Ca Ca>Mg>N >2 P >Ca>N >M3>2 >Pc >51: P >N >Ca>Hn>Pc >2 >M' P >N >2 >M90 Ca>Ng>N >2 Ca>Mg>N >2 P >Ca>N >M3>Pc >2 >Hn Ca>N >M92 Ca>MpN >Pc >2 >M: UPC: >N >Po >Mn>2 Ca>HpN >2 >Zn>Pc >Mn N >M3>Ca >2 P >Zn>Mn>Cu>Al >N >3 >Pe >M92 >Ca P >N >2 >M90. P >Ca>N >2 >Cu>P¢ >M9N: Ca>Mn>MpN >Po >2 Mp2 >Cn>N >Pe >MI P >N >2 >ug>C. MpPo >N >2 >Cu>Hn N >Cg>ug>2 >Pc >Hn Ca>N >M92 N >2 >M90 >Po >Nn>Zn Ca>N >Mg>2 Co>Mg>N >2 N mph >2 >Cn>Mn Co>N >M92 Ca>N >M92 N >M90 >2 MpN >2 >Mn>Po >Ca Mn>AI >P >Cu>Zn>3 >Pc >N >N3>Ca>2 N >Mg>lln>2 >C:>Po MpN >Cu>2 N >P >2 >M90 Pe >Mg>N >Ca>2 >59: N >P¢ >M5>2 >Ca>lh Zn>Ca>N >Mg>Pc >2 >MI Ca>M5>N >2 'mmzauu “an IDkahN an L” -o u7 mo-m no ua-m nu mm —o an mu 5 M1 ma—m cu L“ 42 us mq-u “4 m4 4 no ma-a as La -3 ”1 m2 2 ca t" -4 ca tfl -7 no mo-a «1 mo 4 M2 mo-w an LM -3 on La -2 5“ LN 41 on mm 40 on mm -o uo'm1-a MO‘MW-fl uo‘mo—u «1‘mo-u nu mm -a no um 4 on 1m -9 an 1% 49 wo'mn-a on 1m -5 on mm -o an mm -2 no mu 1 «1 mm 47 on 1.01 -20 as: 1.01 -27 004 191 -7 005 1.01 -1 on 1m -2 an 1m 5 on mm 41 0a 1m 43 no 030 -o 071 one -27 an an -9 on an —7 an an -a n51». 2 an on -o on on —29 an 037 -20 In an -7 no on? -o u1N >Mg>Po >2 >511: Ca>MpN >Pc >2 >30: N >P >2 >M5>Ca Ca>M5>N >2 Ca>2 >N >515 N >M92 >Pc >Mn>03 P >2 >Ca>N >Cu>Mg>Pc >|b P >N >Ca>Cu>2 >M5>Po >14!) 1': >3 >P >Mn>Zn>N >Ca>Mg>Cu>2 N >M92 >P P >N >M9Ca>Po >2 >14: “9c. >2 >N >Mn>Pc Po >Ca>N >2 >Mg>|h P >N >2 >14an N >P >2 >MpCs Ca>2n>Mp2 >Po >N >Mn P >N >Ca>2 >31ch >Mn N >M5>Ca >Mn>Pc >2 P >M3>Ca>N >Pe >2 >Ih P >Cu>N >2 >C1>M3>P¢ >531 N >P >Mn>Ca >Pe >2 >Mg W>N >2 N >M5>Ca>2 N >P >2 M P. >N >Hg>2 >Mn>Ca>Zn P >N >Ca>Cu>Mg>2 >Pc>lh P >2 >N >M9C- C0>M5>N >2 P >N >2 >M90 N >P >2 >M90- N >NpCa>2 N >Pc >2 >MpCa>Mn Ca>Ng>N >Pc >2 >|b Ca>Mg>N >2 Ca>Mg>N >2 P >Ca>Cu>N >2 >Mg>Pc >lh N >P >2 >MpCa N >Cu>M5>2 N >M;>Cn>2 MpCa>Pc >N >M92 Ca>Mg>Zn>N >2 >Pc >351 M3>CI>N >2 N >Ca>2 >P¢ W241 N >M3>Cn>2 MpN >90 >2 >Mn>Ca N >P >2 >MpC. P >Ca>N >Ou>2 >M3>Pc>ih P >N >Pc>Ca>2 WM; P >N >H‘>C| >P¢ >2 >59: Ca>M3>N >2 P >2 >N >M¢>Ca N >P >2 >Ng>Ca P >N ”(p2 >Ca P >2 >N >ug>Ca Zn>N >Fe >Mg>Cn >2 >51!) N >2 >Pc MAO P >N >Mg>cu >2 >Po >Mn Co>N >Mp2 Mg>Co>N >2 N >Nn>Pc >Ca>2 >21; N >P >2 >MpCI MpN >Co>2 H3>Cu>Pc >2 >30: P >N >2 >Mg>Ca N >P >2 >N3>Ca N >P >2 >M3>Ca Mn>Al >Cu>3 >P >Zn>Po >N >2 >14an CI>NPN >21ch >2 Mpl'c >Mn>2 >6: Tanzann wan IDkahN as am 43 on mm 40 an an 14 uo1uI-u an an 44 am an -3 cm an -a 902 027 -7 m on -22 am an 41 an an -5 an an 40 am an 40 mucus-m an an 4 am an a on an 40 on an 213 024 -7 914 on -2 on an 42 914 on -2 217 021 1 on on o 919 031 41 am an n11uo-m an an 45 am an 11 924 0.70 -o 2% an an an 41 an an 45 on an 40 on an -3 cm an 44 «11ns—m on an -o am an 44 on an -7 m 0.72 -27o on an 47 u71n2-n m 0.71 41 am am -a ow mm on an 42 u21no-m on an 43 on an s on an 47 on an -7 947 on 10 on an 40 on a» 42 an a» 43 on an -o uzcua-a on an -o 954 0.41 -211 cu am 44 on an 41 on mm -5 “aqua-m an as -a an an 4 941 057 -4 an as -2 on an -2 P -49 -19 -34 -2 -32 -19 -17 -27 -37 -15 -32 -7 ~19 -27 K 5 15 15 -14 31 19 -7 10 34 14 -4 -20 31 10 -0 13 15 -5 43 30 13 -12 14 -5 -4 Ca M9 M11 13 -5 1 -7 -40 4 34 19 4 2 -1 -3 -3 -7 3 14 -9 3 -3 -3 3 53 -7 -3 7 -12 -4 13 -3 10 23 52 5 19 11 -25 -14 -5 2 —7 5 35 -10 -15 1 3 21 -1 4 20 -1 -12 2 -0 3 —23 —5 -10 -17 20 1 7 11 -2 -24 27 -3 -4 47 1 -19 -12 1 2 -1 5 -4 -0 -3 -15 —10 2 35 —5 2 1 -3 -11 4 47 O 50 -0 -4 -2 13 59 12 3 11 3 137 102 21 5 -12 2 40 -7 2 141 -5 -5 -19 1 3 11 4O -2 -2 -14 -3 -O -3 13 35 -3 1 2 1 -1 -33 -13 30 0 -11 -9 -4 24 31 -11 3 0 -13 4 2 15 5 -12 7 -2 -3 24 -13 5 —1 -5 1 53 1 —2 13 25 -3 -15 -3 5 22 4 -1 1 -5 -2 F0 -1 15 -2 -15 -1 10 1£38 ZnAlCuB -a a -7 -1 -7 -4 -11 -17 -1o -16 —1o -11 -a —24 -14 -2 -2 —1o -3 d 238388338888823832888838385388882828 d d d 114 117 105 15 17 RELATIVE ORDER OF NUTRIENT REOLIREMENTS N >Mg>2 >C- N >M3>Ca >2 Ca>MpN >2 N >P >2 >M90- N >MpCu >2 MpN >Ca>2 149C: >N >2 HvN >2 >C2>Po>ll¢ N >Mg>Ca >2 P >N >0u>Ca>2 >Mch>lb C0>N >M92 >Fc >Zn>Nn Ca>N >Mg>2 >30 >MI P >N >Ca>0u>2 >Pc >HpMn P >N >2 >H3>Ca Mn>Al >Cu>3 >P >Zn>Po >N >2 >M3>Ca Ca>Mg>Mn>N >2 >P¢ P >N >Cu>Ca>2 >Mg>Po >Mn Cn>Mg>2 P >N >Pe>Ca>2 >M9N: Zn>Pc >N >C2>2 >MpMn N >M5>Ca >2 2 >N >Hg>Ca >Mn>Po C2>Mg>N >2 M3>C2>N >Pe >2 >lb P >N >C|>2 >Pc >Nplln WM“ >1b N >Ca>lu>2 P >N >2 >M5>C2 Ca>ug>hh>2 >N >Pc N >80 >Mg>Ca >2 >35: M3>Ca>2 MpN >C2>2 P >N >Ca>Cu>Mp2 >Po >143) N >Pc >Ca >Mn>Mg>2 “9C. >N >Pc >M92 P >N >2 >Ms>CI P >N >M92 >Ca P >Ca>MpN >Po >2 >33: P >2 >N >M5>Ca Zn>Pe >N >Cn>2 >M9N; N >2 >M3>Ca N >Mg>2 >Ca P >N >C2>Cu>2 >M9N: >141: P >2 >Cu>N >Pc>Ca>Mplb Ca>Mg>N >2 C2>Mg>2 P >N >Cu>2 >P¢>C2>M3>Mn N >M3>Ca >2 Ca>N >Mg>2 Zn>Ca>Mch >N >2 >141: N >Pc >Mp2 >Mn>Cn N >fln>P¢ >M5>Ca >2 Ca>Mg>P¢ >N >Mu>2 N >upc. >2 N >Mg>P >2 P >N >Cu>Cu>2 >Pc >Mg>lln Ca>N >Mp2 N >M90 >2 P >N >P¢ >M92 >C2>Nh N >M3>Ca >2 N >Po >Mn>Ng>Zn >Ca >2 N >14ch >2 >Mn>C2 NpN >Zn>Cu>Mn>Po >2 P >N >2 >Man P >Cu>N >Ca>2 >Po >Mp|h MpC:>N >2 Po >Ca>2 >N >Mp|h 2 >N WWP0>C2 Ca>N >Np2 T“ 2. Oom’d. YIELD ID RM N “4 0.55 -32 955 0.55 ~42 9“ 0.55 -23 957 0.54 -5 955 0.54 -4 9“ 0.53 - 10 970 0.53 -10 971 0.51 -5 972 0.51 -5 973 0.50 -5 974 0.50 1 975 0.50 -5 975 0.50 -7 977 0.49 9 975 0.49 -34 979 0.49 -9 000 0.45 -13 “1 0.47 -3 “2 0.47 -22 953 0.45 -5 954 0.45 3 955 0.45 1 955 0.45 ~21 957 0.45 -3 “5 0.45 -7 9“ 0.44 -10 no 0.44 -1 “1 0.44 -12 ”2 0.44 10 ”3 0.44 4 “4 0.43 -5 “5 0.42 - 17 can 0.41 -44 “7 0.41 -3 on. 0.41 -31 no 0.41 5 1000 0.41 0 1M1 0.41 -7 1002 0.41 1 1 1003 0.40 -5 1004 0.40 0 1005 0.40 -11 1005 0.39 -32 1007 0.39 -25 10“ 0.39 - 10 1000 0.35 11 1010 0.35 -1 1011 0.35 -2 1012 0.37 - 15 1013 0.37 - 15 1014 0.37 -5 1015 0.35 -30 1010 0.35 -2 1017 0.30 -27 1015 0.35 -0 1019 0.34 -3 1020 0.33 -47 1021 0.33 -1 1022 0.32 -10 1023 0.31 - 12 1M4 0.31 -10 1025 0.31 13 1025 0.30 -5 1027 0.30 ~20 1025 0.29 -7 1 WI 0.25 -14 1N0 0.25 -7 1031 0.27 -10 1032 0.27 -9 -31 -4 -25 -17 -21 -22 —u -n -50 -n -19 -a -19 -23 -« -w 104 -37 -41 -25 K 14 24 d I 22842381215021.1130 -10 -2 10 15 -1 34 35 -3 Ca Mg 12 3 15 4 1 -7 3 12 -7 9 -2 -2 -15 9 5 1 2 2 0 12 -14 ~10 1 -3 -11 -14 —53 -9 3 4 4 19 2 10 -24 -17 3 -1 -3 -3 3 12 --11 -11 1 -14 -7 -2 -5 -2 11 37 3 10 -9 —2 -42 -3 -11 -11 -15 -11 17 22 24 5 -3 -11 -20 -9 -19 1 10 43 3 3 -32 -35 3 0 -33 -53 10 5 13 -4 —14 -15 4 3 -10 —13 --33 -30 -7 -1 3 3 33 -7 -23 --21 3 3 -3 -3 -33 -—1 -2 3 -3 -11 11 2 -19 -15 19 -5 -4 3 11 19 --27 -13 —54 -59 5 -9 -9 -3 9 3 10 1 -2 5 3 2 189 3111 F0 Zn Al Cu 3 29 1 -3 -11-23 27 -0 15 4 3 37 13 -5 13 1 10 11 30 1 -14 1 5 20 —4 4 12 .33 0 -11 90 -5 -17 29 3 44 4 -11 10 1 13 -34 19 20 —52 12 5 3 3 4 7 29 2 —32 57 3 -11 13 0 9 3 33-10 ——13 3 0 2 3 -34 -33 33 21 -13 11 3 7 7 7 4 1 11 44 -1 9 3 37 4 —19 -5 -4 39 2 o 2 35 2 -9 2 14 -9 7 30 5 ~13 31 -7 -21 17 O 43 -3 -13 3 -1 -17 55 3 —12 10 9 33888888 101 97 70 152 119 179 3% a RELATIVE ORDER OF NUTRIENT REOLIREMENTs P N >Mg>Ca >2 N >M3>Ca >2 N >Mg>Ca >2 >Cu >N >2 >P¢ >Ca>313>3ln Fe >Mn>Ca>N >P >Cu>2 >Mg>3 >20 P B >Pe>P 'U Fe >Mn> B N >31¢>C| >Pc >34n>2 >C2>N >Cu>2 >M5>Pc >313 P >N >31ch >2 >Cn>3h P >N >Ca>31g>2 >Mn>Po >Cu>N >2 >C2>Pc >3u>lln Mg>Ca >N >2 N >Mg>31n>2 >Ca>Pc M3>C2>N >Pc >2 >343 Ca>31p3ln>N >Po >2 N >M5>Ca >2 >Cu>N >50 >2 >Ca>Mg>Mn >Cu>N >2 >Pc>Ca>Mg>Mn Ca>MpN >Pc >2 >341: >N >Cu>Mg>Pe>Ca>2 >39: N >M3>Ca >Pc >2 >Mn >N >C2>M3>2 >Mn>Za>Cu Mg>Ca>N >2 >Pc >341: N >M5>Ca >2 Ca>N >Mg>2 >Pc >311: N >Ca>3lg>2 >31n>P¢ >Cu>N >2 >Po >Ca>3ln>3lg >Cu>2 >N >P¢>Co>31g>3lo N >C2>M3>Pc >2 >331: Cu>ug>N >2 319C; >2 >N >39 >341: Co>3lg>N >2 >N >Cu>Po >2 >Co>3lg>3h N >Mg>2 >C| 349C. >N >Pe >Nn>2 N >Cn>313>2 Ca>Mg>3ln>N >94 >2 >2 >N >P >Ca>Cu>Zn>Mg P >N >2 >C2>M3>Po >341: M3>Ca>N >2 >P P >N >M3>Ca >31n>P¢ >2 M5>Ca>2 >N >P P >N >2 >17. >Mg>Nn>Ca N >M5>Ca >2 N >M3>Ca >2 P >N >Mn>Ca >2 >M3>Pc 2 >M¢>Co >N >P Ca>Mch >N >2 >311: Ca>2 >N >Mg>Pe >341: >Cu>N >2 >P¢ >Mg>C2 >341: N >Mg>Mn>Pc >2 >Ca Ca>MpN >Po >2 >313 N >M5>Ca >2 Ca>Mg>N >Mn>Pe >2 C2>N >Mg>2 >Cu>N >Ca>2 >Pe >MpMn 349C. >N >M92 >Pc N >M3>Ca >2 C2>M3>N >2 N >Hn>Mp2 >Pc >Ca >Cu>N >2 >C2>P¢ >319“: >2 >Cu>N >Pc >C2>M3>Mn Ca>3lg>2 >N >P M3>C2>N >2 >P N >M3>Ca >2 C2>N >Mg>Pc >2 >311: >Cu>N >Fc >M3>2 >Ca>3h Za>N >Pc >Mg>2 >Mn>Co >Cu>N >Ca>2 >M3>Po >391 P >N >M92 >C3>Pc >31: T“ 2. Oom‘d. YIELD ID kglbth P 0.24 0.24 0.23 0.23 0.23 0.22 0.22 0.21 0.21 0.21 0.20 0.19 0.15 0.15 0.15 0.10 0.15 0.15 -20 159 2 70 -42 --13 -15 -11 -35 -00 37 -5 -25 -13 -25 -22 1o 41 —31 -3a -2 —2o -7 -55 21 -3 -23 -12 K Ca Mg 49 -53 -132 -25 -13 -29 13 31 -7 3 -23 -2 13 -3 -2 13 -33 --47 17 3 20 -11 9 27 24 -43 -3 3 -1 -1 -29 4 -7 3 20 3 13 13 3 3 -0 -11 9 0 34 7 -3 21 -9 11 12 0 3 32 41 ~23 ~17 -34 70 12 -17 —13 —155 200 4 2 2 13 -7 -7 4 11 2 24 22 -4 11 -21 -9 20 —3 3 13 -3 -2 3 15 7 35 —92 -92 9 -9 -11 -2 -3 -3 30 -0 -7 20 11 3 3 -4 3 37 13 -3 23 -3 4 10 2 -1 5 2 3 7 7 -1 Mn Fe 7 13 -13 —3 54 -7 4 -3 -21 3 11 9 57 2 49 -13 11 4 7 11 13 1 -2 7 10 7 13 13 1 -2 -13 —0 135 1 14 -1 4 -0 -5 2 -—1 -1 12 -14 190 ZnAl -11 10 Cu -15 -17 RELATIVE ORDER OF NUTRIENT REQUIREMENTS Mg>Ca>N >2 >P Mp2 >Ca>N >P N >3u>2 >Cn Ca>3IpN >2 >31ch N >Ca>up2 Mg>Ca>N >2 >P MDN >Pc >Ca>2 >31; P >Cu>N >2 >Pc>Ca>ug>3h Ca>34¢>2 >N N >Po >C- >Mg>3ln>2 2 >313>N >Ca>P N >2 >3u>P 3h>N >34 >2 >Mpc. P >N >Mg>Ca >2 >Pc>3ln P >Cu>N >2 >3u>Pc>Ca>3ln N >343>Cn >2 N >M5>P >2 P >Cu>Pc >N >Mg>2 >Ca>3ln N >P >2 >34; Ca>3le >2 2 >N >P >31; Mp0 >Pc >N >3h>2 2 >N >P >31. P >N >M3>C2 >2 >3ln>Pe Zn>Ca>MpN >Pe >M92 N >Mn>34g>2 >34 >Ca N >Mg>Ca >2 Ca>MpN >Pc >31n>2 N >Ca >M3>Mn>Po >2 N >Ca >Po >Mg>Mn>Zn >2 Mn>N >Pc >2 >M‘>C. Ca>MpN >Pe >2 >311: 345>C2>N >Pc >2 >31: Ca>Mg>2 >Pc >N >Mn>Zn N >313>Ca >2 N >345>Ca >2 Mn>Ca>N >Pc >2 >34. N >M§>Ca >2 N >C2>345>2 N >M3>Ca >2 N >Mn>Pe >C|>M;>2 Po >N >Mp2 >Ca>3ln ‘Wu'flmuizmjuflm@333?