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Concept of soil fertility and soilproductivity: evaluation of agriculturalsites in the Czech RepublicVáclav Voltr aa Institute of Agricultural Economics and Information , Prague ,Czech RepublicPublished online: 12 Oct 2012.
To cite this article: Václav Voltr (2012) Concept of soil fertility and soil productivity: evaluation ofagricultural sites in the Czech Republic, Archives of Agronomy and Soil Science, 58:sup1, S243-S251
To link to this article: http://dx.doi.org/10.1080/03650340.2012.700511
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Concept of soil fertility and soil productivity: evaluation of agricultural
sites in the Czech Republic
Vaclav Voltr*
Institute of Agricultural Economics and Information, Prague, Czech Republic
(Received 3 June 2012; final version received 3 June 2012)
This paper describes the methods used to assess soil fertility and model soilproductivity for agricultural land in the Czech Republic. The assessment is basedon the obtained crop yields and the various factors that influence those yields.These factors incorporate the physical characteristics of the soil, the climate andthe applied agricultural practices. They have been quantified from monitoring*500 homogeneous plots of land covering a total area of 9200 ha since 2002. Soilproductivity has been modelled using crop yield and crop production functions. Alinear production function has been developed through factor analysis. Thepresented example details wheat production under average conditions for theCzech Republic. It is possible to use the crop production function to improve ourunderstanding of the soil system, as well as for better identification of soilcharacteristics for economic and environmental purposes.
Keywords: soil productivity; soil fertility; crop yield; wheat; crop productionfunction
Introduction
The assessment of soil fertility and productivity has become an enduring research topicacross the globe. The natural fertility of a soil is defined by the physical characteristicsof the soil together with the climate. By contrast, the productivity of the soil may bemodelled using these same physical characteristics, together with an assessment of thoseanthropogenic practices designed to enhance natural soil fertility. The most importantof these relate to the quality and volume of the inputs needed to produce additionalbiomass. However, other technological processes are also significant that lead tointensification. This paper investigates the various crop production functions in order tobetter define soil fertility and soil productivity within the Czech Republic.
Heady and Dillon (1961) described the application of crop production functionsas a fundamental approach to defining soil productivity, and this continues tounderpin many applied research activities. It was contended that productionfunctions represent a very important tool for analytical, descriptive and predictiveassertions. Karlen et al. (1997) noted that because soil quality cannot be measureddirectly, it serves as an umbrella concept for examining and integrating relationshipsand functions among the various biological, chemical and physical parameters that
*Email: [email protected] at the ‘International Conference on Soil Fertility and Soil Productivity’, 17–20March 2010, Humboldt Universitat, Berlin, Germany.
Archives of Agronomy and Soil Science
Vol. 58, No. S1, 2012, S243–S251
ISSN 0365-0340 print/ISSN 1476-3567 online
� 2012 Taylor & Francis
http://dx.doi.org/10.1080/03650340.2012.700511
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are measured and are important for sustainable agricultural and environmentalsystems. An international comparison of soil and climatic conditions, which focusedon soil texture in combination with other factors (i.e. depth, slope, drainage,salinization), has been undertaken in order to investigate soil fertility (Alterra &INRA 2005). It may also be possible to compare soil conditions by defining certaingroups of crops that are suitable for the specific soil type (Reinds and Van Lanen1992), but with similar unifying criteria, primarily clay particle content.
In the Czech Republic, soil quality has been evaluated on the basis of soil genesis,moisture content and, partly, texture. The system comprises 78 groups, the so-calledmain soil units (HPJ). This is then divided into 557 main soil–climatic units (HPKJ).These are further divided according to the land configuration, soil thickness andstoniness into 2199 soil–climatic units (BPEJ). The HPJ and HPKJ characteristicsenable the underlying physical properties of the soil and climate to be quantified andthe subsequent soil productivity modelling to be undertaken in relation to basic soilfertility factors. The BPEJ are defined mainly for the purpose of taxes and state aid.They are, however, also utilized to quantify environmental issues.
Methods
The method used to define the complex interactions that affect soil productivity isbased on an investigation of the physical characteristics of the soil and an assessmentof other influencing factors (e.g. climate) and agricultural practices. Dabbert (1994)demonstrated that the crop production function might result from a wide range ofpreparatory factors, defined by a primary equation (1):
Yt ¼ f ðWt; St; At; Zt; Pt; Lt; TÞ ð1Þ
where: Y ¼ crop yield, t ¼ monitoring period (years), W ¼ climatic variables,S ¼ type and condition of the soil, A ¼ tillage, Z ¼ plant nutrition, P ¼ level ofchemical protection of plants, L ¼ soil preparation and T ¼ technological progress.
The level of food crop nutrition may be defined in the same manner usingEquation (2):
Xt ¼ fðWt�n; Ut�n; At�n; S; Pt�n; Lt�n; TÞ ð2Þ
where: X ¼ the level of nutrition; t–n ¼ the previous period t–n and variables;W ¼ climatic factors; U ¼ use of land for a particular crop; A ¼ tillage; S ¼variables describing the soil type, nature and condition of soil; P ¼ chemical plantprotection; L ¼ soil preparation; and T ¼ technological progress.
Equations (1) and (2) describe the complexity of soil–climatic and operationalconditions. All of the considered factors are presented in the database, but becauseof collinearity, the formulas could be reduced to the main intensification factors. Theplant nutrition and level of chemical protection are simply reflected by the dosage ofnitrogen.
Equation (1) is then used in simplified form by Equation (3)
Yt ¼ f Wt; St; Ntð Þ ð3Þ
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when
Wt ¼ f Wt; St; Ztð Þ
where: Wt ¼ weather factors, St ¼ soil factors and Zt ¼ dosage of nitrogen.Equation (3) is calculated through a sample survey of the actual crop yields obtainedunder the main soil–climatic conditions. The survey comprises *500 homogeneousplots of land covering a total area of 9200 ha. It represents the 65 main soil units(HPJ) and the 127 most common soil–climatic units (BPEJ). These cover three-quarters of the total area of arable land in the Czech Republic. Individual plots coveran area of at least 5 ha, with the main soil unit covering 480% of the total area ofthe plot (Voltr 2008). In the period 2002–2008, production-related data werecollected for each of the monitored plots of land.
The basic indicators for determining soil fertility and soil productivity are definedby the physical characteristics of the soil and the climate, supplemented withtechnological data related to crop production. The analysed soil characteristics are:topsoil and subsoil texture, pH, chemical composition, humus content, soilabsorption complex and soil moisture during the vegetation period. The analysedclimatic data relate to the average precipitation and soil temperature for a specificmonth at any given location, as collected by the Czech HydrometeorologicalInstitute. The analysed technological data relate to fertilizers, plant protection andtillage, as well as the penetrometric resistance of the soil. The general development ofthe dependence and crop production function is shown in Figure 1.
The linear model helps to determine the crop production function by defining anarea in which: (1) intensification cannot take place for environmental and economic
Figure 1. Development of the crop production function. Zones: (a) non-economicintensification factors; (b) non-economic level of land use, which may also occur due to theimposition of environmental restrictions on agricultural production; (c) optimal input level,based on the price of fertilizers and the final production derived from production models. Inthe linear model, the calculation considers linear dependence; (d) natural soil fertility withouttechnological input, e.g. production functions under similar conditions; (f) productionfunctions under the soil–climatic conditions investigated by the Crop Research Institute;(h) natural soil productivity with technological inputs; (E) point of economic level of inputunder specific economic conditions.
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reasons; (2) development can be estimated on the basis of comparison with crop yielddependence on nitrogen under experimental conditions at the Crop ResearchInstitute in Prague; and (3) the amount of fertilizers and plant protection productsapplied during monitoring was optimal.
The description of the crop production functions for the relationship betweenEquations (1) and (2) is based on the selection of significant variables that allow therobust characteristics to be defined. For this purpose, a set of individual andassociated variables based on factor analysis was constructed, taking into accountthe significance of the variables. Processing of crop production functions from 2002to 2008 monitoring is involved. The results of the production functions will beapplied to the system of frequently represented soil–climatic units (BPEJ) based onstandardized physical properties of the main soil units, the climatic factors, and theconfiguration of the site. The general procedure is given in Figure 2.
Results and discussion
The development of crop production functions demonstrates the possibility formodelling of soil productivity following Equation (3). The model was prepared withthe help of statistical analysis of the crop yields and crop production factors. Acomprehensive list of all the variables based on statistical relationships from theunderlying database requires the data to be prepared with regard to the degree ofcorrelation and significance. For the selected agricultural crops, the yields depend onindicators with a high degree of significance, but a lower correlation coefficient. A listof the most important correlation coefficients obtained and their impact on the yieldof selected crops is presented in Table 1.
The highest correlation coefficients in relation to yields are obtained for the levelsof exchangeable pH in topsoil and subsoil, soil textures with diameters of up to0.05 mm in topsoil and subsoil, soil depth, stoniness, the number of operationsinvolving the application of plant protection products and fertilizers, and to thepenetrometric resistance of soil (Voltr and Fronek 2009).
Another problem is the selection of appropriate indicators for the functionalrelationship. Linear functions for predicting the yield could be built on simple oraggregated variables. The large number of indicators considered in this study hasbeen combined using factor analysis for reasons of model stability. To date, theinstability of relationships between indicators has not made it possible to performstructural modelling.
Figure 2. Land evaluation procedure.
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Table1.
Thecorrelationandsignificance
betweentheyieldsofcertain
cropsandthemostim
portantfactors
aswellasindicators
ofsoilfertilityandsoil
productivity.
Correlationcoeffi
cient
Significance
Factor
Wheat
Spring
barley
Winter
rape
Sugar
beet
Wheat
Spring
barley
Winter
rape
Sugar
beet
Tillagesowing
0.451
70.227
70.022
0.078
0.000
0.093
0.834
0.642
Yearofharvest
0.214
0.070
0.250
0.244
0.000
0.170
0.000
0.001
Tem
perature
inApril
0.344
0.273
0.339
0.054
0.000
0.000
0.000
0.481
Supply
ofsoilmoisture
inApril
0.216
0.180
0.122
0.263
0.000
0.000
0.020
0.001
Tem
perature
inSeptember
0.168
0.064
0.345
70.100
0.000
0.229
0.000
0.240
Supply
ofsoilmoisture
inJune
0.158
0.049
0.200
0.234
0.000
0.332
0.000
0.002
Tem
perature
inAugust
0.125
0.241
70.226
70.170
0.000
0.000
0.000
0.027
Supply
ofsoilmoisture
inMay
0.177
0.120
0.138
0.245
0.000
0.018
0.008
0.001
Rainfallin
May
70.116
70.192
0.037
0.022
0.000
0.000
0.483
0.779
Rainfallin
April
70.101
70.108
0.040
70.002
0.001
0.033
0.447
0.983
Precipitationin
July
70.087
70.046
70.091
0.032
0.005
0.363
0.083
0.676
Supply
ofsoilmoisture
inJuly
0.076
70.014
0.182
0.271
0.013
0.780
0.000
0.000
Tem
perature
inMay
70.002
0.112
70.301
70.195
0.947
0.027
0.000
0.011
Tem
perature
inJune
70.002
0.077
70.302
70.174
0.961
0.130
0.000
0.022
%ofparticles0.05mm
intopsoil
0.466
0.447
0.210
0.219
0.000
0.000
0.010
0.031
%ofparticlesupto
2mm
intopsoil
70.440
70.385
70.262
70.295
0.000
0.000
0.002
0.003
%ofparticles0.05mm
insubsoil
0.431
0.345
0.183
0.285
0.000
0.000
0.022
0.005
%ofparticlesupto
2mm
insubsoil
70.417
70.341
70.269
70.150
0.000
0.000
0.001
0.143
%ofparticlesto
0.01mm
intopsoil
0.313
0.343
0.339
70.123
0.000
0.000
0.000
0.214
%ofparticles0.01mm
inthesubsoil
0.291
0.297
0.265
70.134
0.000
0.000
0.001
0.177
%ofparticlesto
0.001mm
intopsoil
0.286
0.377
0.201
70.154
0.000
0.000
0.012
0.133
%ofparticles0.001mm
insubsoil
0.238
0.347
0.138
70.189
0.000
0.000
0.086
0.065
Degreeoftopsoilsorptionsaturation
0.227
0.274
0.214
70.007
0.000
0.004
0.013
0.960
Thecontentofexchangeable
basesin
thetopsoilsorptioncomplex
0.189
0.169
0.227
0.066
0.001
0.080
0.008
0.643
pH
accordingto
agrochem
icalsoiltesting
0.325
0.300
0.138
0.132
0.000
0.000
0.010
0.089
(continued)
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Table
1.
(Continued).
Correlationcoeffi
cient
Significance
Factor
Wheat
Spring
barley
Winter
rape
Sugar
beet
Wheat
Spring
barley
Winter
rape
Sugar
beet
Depth
ofsoil
0.373
0.376
0.120
0.215
0.000
0.000
0.022
0.005
Stoninessofsoil
0.346
0.316
0.135
0.246
0.000
0.000
0.010
0.001
Number
ofoperationsfortheprotectionofplants
0.396
0.227
0.133
0.080
0.000
0.000
0.011
0.300
Thetotaldose
ofmineralnitrogen
0.233
70.019
0.085
0.000
0.000
0.721
0.105
0.997
P2O
5contentbyAZP
0.136
0.093
0.185
70.001
0.000
0.076
0.001
0.994
Thecontentofhumus
70.117
70.037
0.121
70.032
0.020
0.649
0.132
0.755
Penetrometer
resistance
at38cm
70.485
0.002
Penetrometer
resistance
upto
72cm
70.421
0.009
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Soil productivity correlates with intensification parameters. Construction of thesoil production function depends on the levels of nitrogen, provided that the otherproduction factors retain an optimal ratio to nitrogen. Soil productivity is thenexpressed as the efficiency of nitrogen production under the specific soil–climatic conditions, representing soil fertility. An example of the composition andsize of coefficients of the linear production function for winter wheat is shown inTable 2.
For the purposes of defining soil fertility, the main indicators identified by factoranalysis were prepared using the main components method. This comprised twofactors of soil moisture, four factors of soil texture and five factors of weather.Through statistical analysis by linear regression, only certain indicators were chosen:two of soil texture, representing the clay and loam components, both factors of soilmoisture and four of the weather. Stoniness was also used as a main indicator.
The nonlinear dependence on nitrogen in the proposed functional relationship isconsidered in the logarithmic form, which allows further optimization of theproduction factors when calculating the economic efficiency presented in Figure 1.The production given at the base of Table 2 is valid for the monitored area identifiedby the linear elements given in Table 2. A summary of the linear production functionaccording to Equation (3) is given by Equation (4):
Yt ¼Xn
i¼1Fi � Bi ð4Þ
where: Fi ¼ linear members of equation inclusive constant and Bi ¼ linear co-efficients for the linear members used in an unstandardized form. For the purposes ofdefining the yield dependence on mineral nitrogen, the dosage can be given by aparabolic function that may then be included in the statistical calculation of linearregression in the form of a linear and quadratic linearized member. Use of alinearized quadratic member for the efficiency of nitrogen in the statistical evaluation
Table 2. Linear model of soil productivity for winter wheat incorporating the main soilindicators.
Linear members of equation
Unstandardizedcoefficients
Standardizedcoefficients
SignificanceFi Bi SE Beta t SE
(Constant) 71.580 1.730 70.913 0.362Natural logarithm (ln) of
mineral N level1.655 0.348 0.314 4.755 0.000
Soil moisture factor 1 0.401 0.131 0.260 3.051 0.003Soil moisture factor 2 70.258 0.094 70.179 72.750 0.007Soil clay component factor 0.266 0.082 0.187 3.240 0.001Soil loam component factor 0.496 0.091 0.356 5.475 0.000Weather factor 1 70.449 0.099 70.338 74.552 0.000Weather factor 2 70.444 0.122 70.279 73.627 0.000Weather factor 3 0.503 0.087 0.381 5.792 0.000Weather factor 5 70.208 0.086 70.154 72.429 0.016Soil stoniness 70.390 0.160 70.149 72.442 0.016
Note: SE, standard error.
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functions, when there are more explanatory variables in the statistical relationshipaccording to the Equation (4), depends on a balance of used variables. In someunbalanced solutions, when more variables are chosen, a quadratic linearizedmember is not significant enough.
On the basis of factor analysis, the obtained production function ( Equation 4) issufficiently significant in relation to the Czech Republic. The determination index ofthe regression function for the prediction of yield of winter wheat is 45%(R2 ¼ 0.450).
The results of the obtained model, calculated in SPSS (v. 17), are valid for alldata obtained during the project monitoring period. The factors representing soilfertility indicators were incorporated into the production function using the forwardmethod (Efroymson 1960). The forward method gives more stable results than thebackward method. The source values of physical properties of the soils, climate andtechnologies have been evaluated for the main soil–climatic units used in the sampleplots. The results presented in Table 2 show that soil fertility has a greater impact onwinter wheat yield than the intensification factor given by the nitrogen dosage. Boththe soil loam component factor and weather factor three, which representstemperature, have greater impacts than the nitrogen dosage.
As shown in Table 1, some of the variables are of different importance for the yieldof specific crops. For example, the importance of temperature or soil grain size is notthe same for wheat as it is for winter rape yields. It may be possible to model linearfunctions using factors of soil fertility to obtain a complex function of soilproductivity in the dataset. However, problems arise when attempting to specifythe soil–climatic categorization functions for different plants. A specification ofproduction functions for soil climatic units (BPEJ) according to main crops andspecification of the main soil–climatic conditions will be part of the final projectresults.
Conclusions
This paper has described the methods used to define crop production functions in theCzech Republic. It reflects a fundamental step towards determining the relationshipthat exists between natural soil fertility and anthropogenically enhanced soilproductivity. The latter is largely explained by nitrogen input. This provides astatistical assessment of the relationships that exist between the crop yield and themain production parameters. The results presented here demonstrate that it ispossible to establish the crop production functions using statistical data. The BPEJsystem of soil–climatic units ensures that crop production functions are based on thephysical properties of the soil and climate. It also better constrains the economics ofproduction. The crop production functions should be set according to the specificcrops typical for the chosen BPEJ.
Acknowledgements
This paper was written within the framework of Project QH72257 (Evaluation of AgriculturalLand Resources with Respect to the Protection of the Environment: 2007–2011).
Note
1. Presentation at the International Conference on Soil Fertility and Soil Productivity,March 17–20, 2010, Humboldt Universitat, Berlin, Germany.
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