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Utilization of the boundaryline approach in thedevelopment of soilnutrient norms for soybeanproductionG. K. Evanylo a & M. E. Sumner ba Eastern Shore Agricultural ExperimentStation , Virginia Polytechnic Institute andState University , Painter, Virginia, 23420b Department of Agronomy , University ofGeorgia , Athens, Georgia, 30602Published online: 11 Nov 2008.

To cite this article: G. K. Evanylo & M. E. Sumner (1987) Utilization of theboundary line approach in the development of soil nutrient norms for soybeanproduction , Communications in Soil Science and Plant Analysis, 18:12,1379-1401, DOI: 10.1080/00103628709367906

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COMMUN. IN SOIL SCI. PLANT ANAL., 18(12), 1379-1401 (1987)

UTILIZATION OF THE BOUNDARY LINE APPROACH IN THE DEVELOPMENT

OF SOIL NUTRIENT NORMS FOR SOYBEAN PRODUCTION1

G. K. Evanylo2

Eastern Shore Agricultural Experiment Station

Virginia Polytechnic I n s t i t u t e and State University

Painter, Virginia 23420

M. E. Sumner2

Department of Agronomy

University of Georgia

Athens, Georgia 30602

Key Words: P, K, Ca, Mg, pH, c r i t i c a l value, Diagnosis and

Recommendation Integrated System, DRIS, Giycine

max L., soil t e s t i n g .

ABSTRACT

Boundary Unes, which previously have been employed for

development of plant diagnostic norms for DRIS, were applied t o

so i l s for c r i t i c a l value Improvement. Soil t e s t values and

yields for soybeans (Glycine max L.) were collected for Alabama,

Georgia, and South Carolina experiment station plots and

farmers' production f i e l d s . The sampling population was divided

on the basis of surface soil texture and yield level in order t o

formulate soil c r i t i c a l value norms for both fine-textured

(clayey) and coarse-textured (sandy) Ultisols in the

1379

Copyright © 1987 by Marcel Dekker, Inc.

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1380 EVANYLO AND SUMNER

southeastern United States. Boundary Une approach (BLA) norms

for the fine- and coarse-textured soils were similar to those

norms employed by state-operated soil testing laboratories for

Piedmont and Coastal Plain s o i l s , respectively. Two Coastal

Plain soils having different surface soil textures» viz. clayey

versus sandy, gave c r i t i c a l values by the BLA corresponding to

the norms for fine- and coarse-textured s o i l s . Greater

precision in the development of soil c r i t i c a l values requires

greater mineralogical and textural delineation of so i l s , a

process for which the BLA is well-suited.

INTRODUCTION

Crop production has traditionally been based on the

utilization of residual soil nutrients. Crops continue to be

grown in large part on the basis of mining the soil for most

nutrients despite the Increase in f e r t i l i z e r usage since the end

of World War I I 3 . I t 1s essential that nutrients be supplied

whenever adequate quantities cannot be furnished by the s o i l .

This necessitates finding a method that will permit the

Identification of those elements determined to be limiting plant

growth.

A soil t e s t 1s a rapid chemical method for estimating the

nutrient-supplying power of a s o i l . A portion of the total

nutrient supply 1n the soil 1s measured from which the needs of

the soil may be determined before the crop is planted. To

employ such a measurement 1n predicting nutrient needs of crops,

the t e s t Is usually calibrated against nutrient-rate experiments

1n the field and 1n the greenhouse.

Soil testing, as 1t 1s most commonly used, refers t o

Interpretation, evaluation» and f e r t i l i z e r recommendations based

on the actual chemical analysis. Much progress has been made 1n

soil testing 1n measuring the available nutrients 1n s o i l s . The

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THE BOUNDARY LINE APPROACH 1381

major problem continues to be Interpreting the results 1n termsof fe r t i l i z e r needed. The degree of accuracy depends on severalfactors Including knowledge of the soil» the yield levelexpected» level of management, and weather. Traditionally» whena soil t e s t has been Initially developed 1n an area, criticallevels have usually been determined so that 1t can beascertained whether a particular nutrient should be added. Thecritical value concept 1s based on the assumption that certaincritical nutrient levels must be attained to obtain maximumyields and that those nutrients present 1n amounts less thancritical levels will be limiting yield.

Critical values of nutrients for various crops have usuallybeen developed by conducting a series of experiments whereweather conditions, crop varieties, pest control methods»management practices» and soil types are Hkely to vary.Development of critical values from such diverse studies oftenInvolves subjective decisions by scientists since each study 1soften evaluated independently. Many of the calibrationsemployed by state-operated soil testing f a c i l i t i e s have been"borrowed", at least 1n part, from other states 1n theirregion. This 1s evidenced by the similarities 1n fe r t i l i z e rrecommendations made by Georgia 4, Alabama^, and Virginia6

for crops grown on soils having comparable physical and chemicalcharacteristics. Sharing of data 1n order to develop criticalvalues can be productive; however, since soils Included withinthe same category for soil testing purposes often vary 1nsurface texture and mineralogy?, greater delineations of soilclassification 1s desirable for greater precision 1n criticalvalue determination.

Inherent to calibration studies are several problems whichmake the critical values obtained of questionable worth. Manycalibration studies have been designed as single-factor,multi-level experiments8»9. Because each element 1n a single

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1382 EVANYLO AND SUMNER

factor experiment 1s considered 1n Isolation, synergistic and

antagonistic effects between nutrients are neglected and the

Importance of nutritional balance is ignored. In addition, the

study 1s Immediately biased toward a unique level of the

nutrient being investigated. This occurs because all other

elements are applied at constant basal levels which should

achieve the best nutrient balance with one specific rate of the

element to be varied10. When multi-factor, multi-level

(factorial) experiments are analyzed10'11'12'13'14, i t

becomes obvious that the optimum level of a specific f e r t i l i z e r

element is characteristic of the level of basal treatments.

Hence, i t 1s Imperative that calibration studies employ many

levels of as many factors as possible. Unfortunately, this

becomes a logistical problem.

Another problem with current approaches to developing

critical values was observed in the work of Reich et a l . 1 ^ .

The researchers correlated many crop, soil, and climatic

variables to soybean (Glvdne max L.) yield 1n two successive

seasons. Because of variability and unpredictability in

uncontrollable factors, great differences resulted in the

significant variables and their correlations from year to year.

The fruitlessness of attempting to Increase yield potential by

development of models based on regressions of soil nutrients was

evident.

Relatively l i t t l e correlation work has been conducted during

the past few years and, as a consequence, many calibrations are

not sufficiently precise for use with confidence at high yield

levels. Since current soil test norms have generally been based

on experimental data obtained at average yield levels, the

recommendations developed may lead to yields below their

potential. Work on soil t e s t calibration needs to be

continuously conducted in order to maintain and enhance the

confidence 1n these programs.

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THE BOUNDARY LINE APPROACH 1383

An approach whose adoption for the development of criticalvalues may overcome some of the obstacles previously discussedInvolved the use of a "boundary Une"16'17. With thisapproach» the performance of the best 1n a sample 1s employed asa standard against which to judge the remainder on theassumption that there are reasons other than chance whichaccount for the superior performance of part of the population.The Une defining the best performance in the population lies atthe edge of any body of data, hence the name "boundary Une"»and occurs wherever a cause-and-effect relationship existsbetween two variables. When 1t is allied to a knowledge of thecomponents of yield» the position of the boundary Une can beused to direct attention to aspects of growth most Hkely torespond to better management. I t 1s this boundary Une conceptupon which DRIS18»19»20, w nich has been employed to diagnosenutrient limitations 1n crops» has been based.

In the Boundary Line Approach (BLA)» relationships betweenyield and nutrients can be schematically represented (figure1). The figure 1s a scatter diagram of yield values versus anutrient soil test or pH level for all data accumulated. Thelines drawn around the scatter of data points represent boundaryUnes which confine all observations. The diagram 1s dividedInto two zones corresponding to cases (1) and (2). When thesoil t e s t value (hereafter referred to as X) has a valuerepresented by a (case 1), the relative yield could not exceed avalue a1» but could be lower. If 1n this case X was the mostlimiting nutrient, the addition of X would probably result in ayield response. When X has a value of b (case 2)» maximum yield1s possible but i t 1s not always obtained because some otherfactor might be limiting. Thus» when X has an optimal value»any yield can be obtained» but when X is too high or too low»maximum yields attainable will be determined by the boundaryUne. Conversely» to obtain a high yield» X can vary only over

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1384 EVANYLO AND SUMNER

Case 2

a bNutrient Parameter (X)

FIG 1. General relationship between soil nutrient

parameter and yield (Sumner, 1978)21

a narrow range, whereas at low yields almost any X value 1spossible. The BLA may be utilized to develop soil criticalvalues 1n the same manner that 1t has been employed to developcrop tissue norms» I.e. the mean of the h1gh-y1eld1ng populationor some similar variant. Several characteristics of the BLAthat make this approach attractive for the purpose of developingsoil t e s t norms are:1. Existing data from a wide range of locations may be employed

1n developing norms. In addition to time-consuming fieldstudies» data from growers1 production fields may be

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THE BOUNDARY LINE APPROACH 1385

obtained by soil testing laboratories for Incorporation Intothe data bank. Hence» a producer can theoreticallycontribute to the supply of data required for calibratednorms. The data should be easily obtained by cooperatingfarmers» since many send their soil samples to soil testinglaboratories for analysis and recommendations.

2. Norms may be developed for diverse soil types orassociations and» as the size of the data bank Increases»more precise recommendations may be made for specific soilclasses.

3. As new data becomes available» the system can becontinuously updated so that soil nutrient norms may keeppace with higher yields due to better varieties» improvingcultural practices» and new technologies.

4. Remedial recommendations can be developed based oncalibration of soil response to applied nutrients.

The objectives of this study were:1. to compile a data bank of soil t e s t pH» P, K, Ca> and Mg

values for soybeans from which preliminary norms for thesoil t e s t variables may be developed from the high yieldingpopulations employing the BLA.

2. to compare and contrast the BLA norms with those employed bya state soil testing service for similar soils.

3. to ascertain how two different soil series 1n the same soiltesting region might respond to the Identical soil testing»diagnosis, and recommendation.

MATERIALS AND METHODS

To accomplish the previously outlined objectives» survey and

calibration methods similar t o those Implemented previously were

employed16'18.

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1386 EVANYLO AND SUMNER

Su rvey

The survey technique Involved the selection of a largenumber of sites representative of full season soybeans 1nsoutheastern United States. The locations Included bothproduction fields and state experiment station plots 1n Georgia,Alabama» and South Carolina. Soil t e s t values, crop yield, soilcharacteristics, cultivar» Irrigation, and nature and amount offer t i l i z e r applied were obtained for each sit e and stored 1n adata bank.

Soil t e s t values consisted of soll pH (1:1 soil to H2O)and of MehUch I (0.05 M HC1 + 0.025 M H2S04) extractable P,K, Ca, and Mg22. soil tests for Alabama, Georgia, and SouthCarolina Experiment Station studies were carried out 1n theirrespective states by the above method, while soils obtained fromproduction fields were analyzed at the Georgia Soil Testing andPlant Analysis Laboratory 1n Athens, Georgia» 1n the Identicalmanner. Existing data from field experiments and other sourceswhich were derived by the same methods were also added to thedata bank. Researchers and locations where data were generatedare summarized 1n Table 1.Calibration

The population of observations were divided on the basis ofsoil type. Fine- and coarse-textured soil classes weredesignated where "fine" described those soils whose plough layerparticle size distribution defined them as clays, s i l t s , andloams, except for sandy loams. "Coarse" soils Included sandsand sandy loams. Classification was based upon surface soiltexture corresponding to the plough layer textures of theIndividual soil series classified 1n Soil Taxonomy23. Soilseries and types were obtained from existing soil mappingpublications and from the researchers whose experiments wereutilized for the data bank development. Scatter diagrams forthe fine and coarse soils were constructed 1n order to study theyield nutrient relationships (figures 2,3)

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THE BOUNDARY LINE APPROACH 1387

Table 1.

Sources of Data Used 1n Derivation of Soil Diagnostic

Norms for Soybeans

Source Location Type of study Soil type(S)+

Boerma, H. R. Georgia Survey C.P.» P.M.V.

Parker, M. B. Georgia Fertility expt. C.P.

Woodruff, J. R. South Carolina Fertility expt. C.P., P.M.V.

Sumner, M. E. Georgia Fertility expt. P.M.V.

Cope, J. T. Alabama Fertility expt. C.P., P.M.V.

+C.P. and P.M.V. refer to soils which are, or would be,classified as Coastal Plain and Piedmont, Mountain and Valley,respectively, by the Georgia Cooperative Extension Soil Testingand Plant Analysis Laboratory, Athens, GA.

Each soil class was then subdivided Into high- and low-yielding populations by employing 3000 kg«ha~l as thedividing Une. This division allowed the h1gh-y1eld1ngpopulation to represent the upper portion of the Industry whiles t i l l containing enough observations from which to formulatenormsl8'19. The fine- and coarse-textured soils wererepresented by 1500 and 1000 observations, respectively. Of thetotal samples, 230 of the fine-textured and 500 of thecoarse-textured samples met the criterion for the high yieldingsubpopulation. The mean» standard deviation, and variance foreach nutrient variable within each subpopulation were thencomputed. The variables for which significant variance ratiosbetween the low and high yielding subpopulations were obtained,as determined by F te s t , were expected to discriminate between

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1388EVANYLO AND SUMNER

i i f io

(kg/ha)

• • • •

i

0 25 SO 7S 100 12S ISO IIS 200 225 ¡SO 0 2S SO 7S 100 IIS ISO 175 100 US

" (ko/ht) '(ko/h.)

FIG. 2. Soybean yield versus soil K» P» Ca, andMg on coarse-textured soils.

high- and low-yield subpopulations. The mean values of thenutrient variables for the high yielding populations comprisedthe calibrated norms for fine- and coarse-textured soils andrepresented "optimum" soil nutrient concentrations.

Nutrient norms derived from the two soil textural classeswere contrasted. Comparisons were also made between establishedsoil t e s t norms and those derived from the BLA. Finally, twosoil series having different surface textures, but both located

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THE BOUNDARY LINE APPROACH 1389

Τ ΙΙ1.0

(kg/hi)

isoo

3500

500 1000 ISOO

(k«/ha)

2000 J50C 0 SO

FIG. 2 (continued)

100 ISO 200 2S0 300 3S0 MOO

"G (kg/ht)

within the same soil testing region, I.e. soils whose f e r t i l i z e r

recommendations are based upon equal soil responses to nutrient

application and depletion, were Investigated with regard to soil

requirements of soybeans.

RESULTS AND DISCUSSION

Scatter diagrams for the relationships between soybean

yields and soil test nutrient concentrations are shown 1n

figures 2 and 3. Nutrient concentrations have been presented 1n

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1390 EVANYLO AND SUMNER

100 200 300 <IOO SOO GOO 0 $ 0 100 ISO 2 0 0 2S0 300 3S0 « 0 0

FIG. 3. Soybean yield versus soil Κ, Ρ, Ca, and

Mg on f1ne-textured soils.

units of weight per volume (kg per ha to a depth of 15 cm) as

normally expressed by soil testing laboratories. Only Ρ gave

distributions for which the scatter of data points of the high

yielding population was greatly less than that of the

Iow-y1eld1ng population. This 1s evident from the variance

ratio data 1n Table 2.

For the coarse soils, only Ρ gave a significant variance

ratio, whereas for the fine soils, significant variance ratios

were obtained for Ρ, Κ, and pH. Population size differences 1n

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THE BOUNDARY LINE APPROACH 1391

1000

0 1000 2000 3000 «000 5000 6000 7000 0 100 200 300 100 S00 600 700 tOO 900

CD (ko/h«) ne (kg/ht)

FIG. 3 (continued)

the data banks may have accounted for the variability 1n number

of nutrient variables attaining significant variance ratios.

Calcium and Mg were not significant 1n either soil class. This

1s likely due to the wide ranges of soil Ca and Mg permissible

for high yields and 1s reflected 1n traditional soil testing

laboratory requirements» which usually Indicate no upper limits

for soil Ca and Mg concentrations^'^. I t appears that the

levels of Ca and Mg which may antagonistically Inhibit uptake of

other elements and each other are extremely high and occur

rarely. This 1s not the case for variables such as pH, P» or Κ

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Table 2.

Nutrient Variable Means, Standard Deviations, and Variance Ratios of Low and High Yielding

Soybean Populations for Coarse- and F1ne-Textured Soils.

Var iab le 1

pH

Ρ

Κ

Ca

Mg

Coarse-Textured Soils

Yield <3000 kg/ha

N2= 500

Mean S.D.

6.08 0.40

105 53.50

105 34.2

821 310.2

81 44.2

Yield >3000 kg/ha

Ν

Mean

6.15

101

109

838

84

= 500

S.D.

0.42

39.7

32.5

326.5

48.9

Variance

ratio

0.91

1.82**

1.11

0.90

0.82

F1ne-Textured

Yield <3000 kg/ha

Ν =

Mean

6.19

74

191

1075

148

1270

S.D.

0.49

61.40

69.9

464.8

72.9

Soils

Yield >3000 kg/ha

Ν

Mean

6.26

59

185

1347

247

= 230

S.D.

0.40

29.2

60.0

793.9

138.9

Variance

ratio

1.52**

4.42**

1.36**

0.34

0.28

lp, K, Ca, Mg norms expressed 1n units of Kg/ha s o i l .

?N = Number of observations.

**S1gn1f1cant at Ρ <0.01.

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THE BOUNDARY LINE APPROACH 1393

which have been observed to cause nutritional problems or

antagonisms at commonly elevated Ievels25»26,27,28,29#

The differences 1n "optimum" nutrient availability between

the coarse and fine soils are functions of the amounts of clay

1n each class. The greater presence of Al and Mn and higher

buffering capacity of the fine-textured s o i l s4 have likely

contributed the higher mean pH observed for the h1gh-y1eld1ng

population of the fine-textured soils than the coarse-textured

soils. The coarse soils» having lower buffering capacities than

fine soils, generally do not require as large additions of

limestone4'^. For this reason, and since dolom1t1c limestone

1s often the liming material of choice, required levels of Ca

and Mg are higher 1n the fine soils. In addition, the higher

cation exchange capacities of the clayier fine soils results 1n

higher levels of K, Ca, and Mg. Extractable Ρ levels required

for high yields are lower 1n fine than coarse soils due to the

greater ability of the colloidal fraction to bind Ρ tightly 1n

the fine soils^O. Apparently soybeans can obtain a

considerable fraction of the tightly bound Ρ 1η fine soils to

meet their crop requirements^.

Wider ranges for the nutrient variables were observed 1n the

BLA norms than 1n the Georgia Cooperative Extension Service

(GCES) norms (Table 3). The GCES ranges4 for Ρ and Κ were

those where application of Ρ or Κ would be expected to e l i c i t a

yield response of less than ten percent. The ranges for Ca and

Mg were those values at which no Ca or Mg would be recommended.

The wider ranges of norms for the BLA than for the GCES may

permit greater yield responses to the applied nutrients. The

BLA ranges should theoretically narrow as higher norm-generating

yield levels are employed. This appears true for all of the

fine soil norms (figure 3), but only for Ρ on the

coarse-textured soil (figure 2). Only the BLA Κ norms did not

fall within the GCES recommended range. This may Indicate that

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LO

Table 3.

Comparison of Soil Nutrient Requirements for Soybeans by Soil Testing Laboratory

and BLA Data Bank Norms.

Nutrient

Variable

pH

Ρ

Κ

Ca

Mg

Data Bank High Y1eld1na PoDulation Parameters

Means

Coarse

6.15

101

109

838

84

Fine

6.26

59

185

1347

247

Ranae

Coarse

4.8-7.1

25-210

49-251

298-2464

11-392

Fine

4.4-7.0

kg/ha

20-128

58-370

298-6429

15-806

GA. Coop. Ext.

C.P.

5.9-6.5

68-112

192-308

>224

>68

(Plank. 1978)4

Range

P.M.V.

5.9-6.5

46-84

281-448

>448

>136

M«SA

NYL

O

>

Ö

SS

M

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THE BOUNDARY LINE APPROACH 1395

GCES recommended levels of Κ may be higher than actually

required by soybeans 1f all other conditions are optimal.

BLA-der1ved norms for two soil series which comprised a

portion of the data bank» Tifton and Greenville» are presented

1n Table 4. Although these soils vary 1n surface soil texture»

both are classified by the GCES as Coastal Plain soils for the

purpose of making soil diagnoses and fe r t i l i z e r

recommendations.~ Because of the great number of h1gh-y1eld1ng

observations obtained on these two soils, the yield level from

which to formulate norms was raised from 3000 to 3360

kg-ha"1.

The differences between h1gh-y1eld1ng nutrient concentration

means for the two soils 1s as striking as the differences 1n

norms between the fine- and coarse-textured soils. This 1s not

surprising since the Greenville soil is a clayey» kaolinitie»

thermic, Rhodic Paleudult while the Tifton 1s a fine-loamy,

siliceous, thermic PHnthic Paleudult. Further delineation of

GCES coastal plain region soils would separate these soils and

result 1n more precise calibrations. As 1n the BLA norms, the

Ca:Mg ratio for the fine-textured soil (5.0) was about half the

value of the CatMg ratio of the coarse-textured soil (11.3).

Ratios of 8:1 have been suggested as the widest desirable Ca:Mg

ratio 1n Georgia s o i l s4. The data from the Tifton and

Greenville soils appear to support a narrower Ca:Mg external

requirement for soybeans grown on clayey than on sandy Ultisols.

Leaves sampled at midbloom from the Greenville and Tifton

soil soybeans were analyzed for nutrient composition. The

nutritional status of the h1gh-y1eld1ng plants were Investigated

(Table 5) by employing DRIS tissue norms developed for soybeans

32. Although the Greenville soil contained higher mean soil

Ca levels than the Tifton soil and the BLA coarse soil norms,

the soybeans grown on the Greenville soil were most limited by

Ca and least limited by Mg according to DRIS soybean norms. The

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Table 4

Comparison of BLA-derived Soil Norms of Two Coastal Plain Soils Which Vary In Surface Texture.

Nutrient

Variable

pH

Ρ

Κ

Ca

Mg

Yield

Ν

Mean

6.10

87

99

806

76

<3360 kg/ha

i"+ 1003 Var.

0.184

244

298

46648

997

Tifton Soll Serles

Yield >3360 kg/ha

Ν -

Mean

6.10

94

99

811

72

666

Var.

0.252

356

320

55928

925

Variance

ratio

0.73

0.68

0.94

0.83

1.08

Sufficiency2

range

4.8-7.1

54-175

52-251

298-1998

11-296

Yield <3360 kg/ha

Ν

Mean

6.26

45

220

1245

222

- 85

Var.

0.158

271

1328

62088

6335

G r e e n v i l l e Soil

Yield £3360 kg/ha

Ν -

Mean

6.23

49

199

1158

233

394

Var.

0.138

419

1599

29205

3810

Series

Variance

ratio

1.15

0.65

0.83

2.13··1.66··

Sufficiency2

range

5.5-7.0

20-128

94-360

580-2097

72-538

IN » Number of observations.

^Sufficiency range • range of values of each nutrient variable In high yielding population.

^Heans and sufficiency ranges for P» K» Ca« and Mg are In units of Kg«ha~l.

»•Significant at Ρ <0.01.

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THE BOUNDARY LINE APPROACH 1397

Table 5

Comparison of Leaf Norms from H1gh-y1eld1ng Populations

of Various Soil Types

Variable1

Ν

Ρ

Κ

Ca

Mg

Ν/Ρ

Ν/Κ

N/Ca

Mg/N

Ρ/Κ

P/Ca

P/Mg

K/Ca

Mg/K

Mg/Ca

Order of

Tifton

Soll

Mean

47.0

3.3

18.9

10.7

2.6

14.2

2.52

4.58

0.0556

.179

.324

1.31

1.86

0.140

0.248

requirement: Mg

Κ >

Serles

Range

35-58

2.6-5.0

12.9-25.0

6.3-15.3

1.8-3.9

> Ν >

Ρ = Ca

Greenville

Soll

Mean

54.1

3.2

21.1

9.7

3.8

17.3

2.62

5.71

0.07

0.151

0.336

0.849

2.27

0.184

0.398

Ca

Κ >

Series

Range

41-65

2.4-4.4

14.2-27.7

6.2-13.9

2.7-5.5

> Ρ >

Ν > Mg

Soybean

Data Bank

Mean

15.2

2.63

5.00

0.0684

0.172

0.331

1.04

2.01

0.175

0.322

P» K» Ca» and Mg expressed 1n units of g/kg s o i l .

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1398 EVANYLO AND SUMNER

external nutrient requirements for the Coastal Plain Greenville

soil conform more closely with those of Piedmont» Mountain, and

Valley s o i l s . Magnesium was most limiting and Ca was least

limiting 1n the Tifton soil-grown soybeans. Perhaps the soil

Ca:Mg ratios were partly responsible, as the actual soil

requirements were adequate.

Support has been mounting for local calibration of DRIS

tissue norms. I t appears that soil norms may also attain

greater precision employing local calibrations. The BLA which

has provided a basis for the development of. DRIS tissue norms,

1s a promising tool for formulating soil norms.

REFERENCES

1. Contribution of the Agronomy Department, Un1ver1sty ofGeorgia, Athens, GA 30602.

2. Former Graduate Research Assistant and Professor,respectively.

3. Cummings, R. W.» and R. N. Gleason. 1971. The role off e r t i l i z e r 1n agricultural development. P. 1-25. In R. A.Olson et a l . (ed.) Fertilizer Technology and Use. Soil Sci.Soc. Am., Inc., Madison, WI.

4. Plank, C. O. 1984. Lime and f e r t i l i z e r recommendationsbased on soil t e s t s for all crops grown 1n Georgia (codebook). Georgia Cooperative Extension Service, College ofAgriculture, Univ. of Georgia, Athens, GA 30605.

5. Cope, J. T., C. E. Evans, and H. C. Williams. 1981. Soilt e s t f e r t i l i z e r recommendations for Alabama crops. AlabamaAgricultural Experiment Station. Circular No. 251.

6. Donohue, S. J., and G. W. Hawkins. 1979. Guide to computerprogrammed soil t e s t recommendations 1n Virginia. VPI & SUExtension Publication No. 834.

7. Perkins, H. F., and M. E. Shaffer. 1976. Soil associationsand land use potential of Georgia s o i l s . Georgia Agric.Expt. Station.

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THE BOUNDARY LINE APPROACH 1399

8. McLean, E. O. 1977. Fertilizer and lime recommendationsbased on soil t e s t s : good» but could they be better?Commun. Soil Sci. Plant Anal. 8(6):441-464.

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10. Sumner, M. E. 1987. Field experimentation: Changing tomeet current and future needs. In J. R. Brown (Ed.) SoilTesting: Sampling, Correlation, Calibration, andInterpretation. SSSA Spec. Pub. 21 (In press).

11. Beaufils, E. R. 1956. Recherches d'une exploitationrationnelle de l'Hevea d'apres un diagnostic physiologiquerepasant sur l'analyse minerale de diverses parties de laplante. Analyse des Plantes et Problemes des fumuresminerales. I.R.H.O. Paris. p. 360-376.

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18. Beaufils, E. R. 1973. Diagnosis and recommendationintegrated system (DRIS) Univ. of Natal Soil Sci. Bull. 1.130 p.

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1*00 EVANYLO AND SUMNER

19. BeaufUs, E. R. and M. E. Sumner. 1976. Application of theDRIS approach 1n calibrating soil and plant yield factors 1nsugarcane. Proc. South African Sug. Tech. Assoc.50:188-125.

20. Sumner» M. E. and E. R. BeaufHs. 1975. Diagnosis of theΝ Ρ Κ requirement of sugarcane Irrespective of plant age andseason using BeaufUs system (DRIS) - Preliminaryobservations. Proc. South African Sug. Tech. Assoc. 49:137.

21. Sumner» Μ. Ε. 1978. Interpretation of nutrient ratios 1nplant tissue. Commun. Soil Sel. Plant Anal. 9:335-435.

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23. Soil Survey Staff. 1975. Soil Taxonomy. A basic system ofsoil classification for making and Interpreting soilsurveys. Agrie. Hand. No. 436» USDA. U.S. GovernmentPrinting Office, Washington» DC.

24. Simson, C. R.» R. B. Carey» and M. E. Sumner. 1979. Effectof varying Ca:Mg ratios on yield and composition of corn(Zea mavs) and alfalfa (Medicago sativa). Commun. Soil Sel.Plant Anal. 10:153-162.

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26. Howell, R. W. 1964. Influences of nutrient balance onresponse of sensitive soybean varieties to high phosphorus.Agron.J. 56:233-234.

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30. Bohn, H. L.» B. L. McNeal» and G. A. O'connor. 1985. SoilChemistry. John WHey and Sons» Inc. U.S.A.

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THE BOUNDARY LINE APPROACH 1401

31. Fox. R. L. 1981. External phosphorus requirements ofcrops. P. 223-239. In R. H. Dowdy et a l . (ed.) Chemistryin the soil environment. Spec. Pub. 44. Am. Soc. ofAgron., Madison, WI.

32. Beverly» R. B., M. E. Sumner, W. S. Letzsch» and C. O.Plank. 1986. Foliar diagnosis of soybean by DRIS. Commun.Soil Sci. Plant Anal. 17(3):237-256.

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