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DIVISION S-6-SOIL & WATER MANAGEMENT & CONSERVATION

Soil Organic Matter Content as a Function of Different Land Use History

Laboratory of Soil Science and Geology, Dep. of Environ. Sci., Wageningen Agricultural Univ., P.O. Box 37, 6700 AA Wageningen, The Netherlands

mirjam.pulleman{at}oio.beng.wau.nl

	ABSTRACT

TOP ABSTRACT INTRODUCTION Materials and methods Results Discussion and conclusions REFERENCES

 
A regional survey of management and crop type and soil organic matter (SOM) content was conducted in one soil series in the Netherlands (loamy, mixed, mesic, Fluventic Eutrudept). The objective was to determine the effects of land use history on SOM contents in a prime agricultural soil, using available soil survey information and statistical analyses. Soil organic matter content is a relatively stable, integrating soil characteristic that reflects long-term land use and is an important indicator of soil quality. The SOM contents and information about past land use were obtained from 45 fields. Land use history was expressed in terms of (i) tillage; (ii) crop type; and (iii) use of chemical fertilizers, (iv) manure, and (v) biocides, for six successive periods (63–31, 31–15, 15–7, 7–3, 3–1, and 1–0 yr before sampling). Only four land use types occurred: conventional-arable, conventional-grass, organic-arable, and organic-grass. The SOM contents ranged between 17 and 88 g kg^-1. Regression models of the actual SOM content as a function of crop type and management in the different periods showed that SOM contents were increased under long-term grass or, to a lesser extent, by organic farming, when compared with conventional-arable use. The regression model depends on the nature of land use history in any particular region and on the length of the selected periods, but it provides an easy method to predict SOM content as a function of management in a given soil series. The method can be an alternative to simulation modeling in situations where detailed data records from long-term field experiments are not available.

Abbreviations: SOM, soil organic matter

	INTRODUCTION

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TRADITIONALLY, ACTIVITIES OF SOIL SURVEY were focused on the production of soil maps and soil classification systems. Those activities are nearly completed and soil survey expertise is increasingly being used to study sustainable management systems (Bouma, 1988). Because maintenance and improvement of soil quality is critical to sustaining agricultural productivity (Reeves, 1997), the soil quality concept recently has received much attention. Karlen et al. (1997) defined soil quality as "the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation". In order to express soil quality, different indicators have been used. They can be static, using soil parameters such as bulk density, porosity, and SOM content (Lal and Kimble, 1997), or dynamic, using simulation modeling (Bouma and Droogers, 1998).

A given soil series cannot be considered to have a static set of characteristics. Land use influences soil properties and therefore soil functioning. In this respect, Droogers and Bouma (1997) distinguished between soil genoform and soil phenoform. The former has been defined as the genetically defined soil series, and the latter indicates differences in a certain genoform as a result of a different land use history. They found that different types of agricultural land use have resulted in different soil conditions within one soil series in marine clay deposits in the southwestern part of the Netherlands (a loamy, mixed, mesic, Fluventic Eutrudept; Soil Survey Staff, 1998). Based on significant differences in total SOM contents, bulk densities, and porosities within one genoform, three different phenoforms were distinguished, resulting from: (i) organic management, (ii) conventional management, and (iii) permanent grassland. Simulation modeling was used to translate differences in static soil properties into a dynamic quality indicator, which was expressed as the ratio between actual production and potential production (Bouma and Droogers, 1998).

Criteria to characterize phenoforms and soil quality are still poorly defined. Soil structure parameters and related soil physical conditions may strongly depend on short-term effects of management decisions. Therefore, we believe that parameters such as bulk density or porosity are not reliable criteria to distinguish effects of different long-term land use. However, organic matter is a relatively stable parameter that reflects the influence of management and crop type over periods of decades and is of essential importance for soil quality (Christensen and Johnston, 1997). When soils are properly managed, SOM contributes to a favorable soil structure, thus improving rooting patterns and soil aeration. Directly, and also indirectly via its beneficial effects on soil structure, organic matter enhances water retention and nutrient availability. Long-term studies have consistently shown the benefit of manure, adequate fertilization, and crop type on maintaining agronomic productivity by increasing C inputs into the soil (Reeves, 1997). Moreover, modern agriculture has contributed to elevation of the atmospheric CO₂ concentration by reducing C inputs and increasing C losses from the soil. An important objective of sustainable use of soil resources is therefore to increase the organic C pool in soils (Lal and Kimble, 1997; Paustian et al., 1997). Because of the crucial role of SOM in influencing many soil characteristics and processes that are important for soil functioning, we believe that SOM can be considered a suitable integrating soil parameter indicating soil quality within a given soil series.

As a follow up of the work by Droogers and Bouma (1997), a broad regional survey of agricultural land use and SOM contents was carried out covering land that was occupied by the same soil series in the southwestern part of the Netherlands. The existing 1:50000 soil survey of the area was used to select 45 fields, which were studied to derive a quantitative relation between SOM content and management and cropping history. Additionally, we wished to characterize, in terms of SOM content, the range of different phenoforms developed in this soil series under different management and crop types. Soil management information was obtained from farmers. Total SOM contents of the fields were measured, and observations on soil structure, including soil degradation features, were made. Statistical procedures were used to evaluate the effect of management and crop type on total SOM content.

	Materials and methods

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Sampling and Data Collection
Fields with different agricultural land use histories were selected after consultations with farmers. This soil series is considered to be prime agricultural land and covers 100000 ha in the Netherlands, where it is classified as an Mn25a according to the Dutch soil map 1:50000 (Pleijter et al., 1994). Clay content of the soil characteristically ranges from 17.5 to 25%, without abrupt texture changes along the profile. The survey was carried out in two stages and the results of two successive studies were combined. A first study was carried out in the summer of 1996, when information about management and cropping history and SOM contents of 15 different fields was collected. In March 1998 the survey was continued and 30 additional fields were studied. In both stages of the survey the same procedures were followed. For each field, four locations were selected across the field. First it was checked whether soil characteristics at each of the locations matched the classification criteria of the selected soil series, and soil texture was validated by field grading. If positive, at each location a 100-cm³ sample for SOM content was taken at an average depth of 15 cm. The samples were bulked to form one composite soil sample and dried. Coarse plant remnants were picked out and CaCO₃ was removed by adding excess HCl. Total organic C was measured by element analyzer. Conversion to total organic matter was made, assuming a factor of two for the ratio of total organic matter to organic C (Nelson and Sommers, 1982).

From each field, a simple, semiquantitative description of past and current land use was obtained from interviews with the farmers. Different land use types were distinguished on the basis of five main factors, all related to activities affecting the organic matter content of the soil. Those five factors were divided into two groups: (i) crop type and (ii) management (i.e., tillage, use of chemical fertilizers, use of organic manure, and use of biocides).

Because the interviews had to be easy to fill out and interpret, the only answer to be provided was "yes or mostly" or "no or hardly ever". The only distinction made in crop type was the one between arable crops and grass. Knowledge about and the effect of past land use practices on current SOM content were assumed to decrease with time. Therefore, land use factors were expressed in terms of periods, increasing in length according to a square function. A period of 63 yr was covered, divided into six successive periods: 63 to 31 yr (Period I), 31 to 15 yr (Period II), 15 to 7 yr (Period III), 7 to 3 yr (Period IV), 3 to 1 yr (Period V), and 1 to 0 yr (Period VI) before sampling. When management factors or crop type had changed within a certain period, the dominant practice was considered to represent the whole period.

Statistical Analysis
To consider whether total SOM content is significantly different (P < 0.05) for the different agricultural land use types found in this soil series and for the different periods, a nested ANOVA procedure (SPSS, 1997) was applied. For every period, the effects of crop type on the one hand, and of the four management factors on the other, were tested separately and together, respectively. When crop type, or a certain combination of cultivation measures, in a certain period had a significant effect on SOM content, it was incorporated into a linear multiple regression equation that estimates the actual SOM content as a function of either crop type, management type, or both, in the different periods. Factors of significant influence were added stepwise. The factor and period having the lowest probability of the F value (i.e., lowest probability that the F value was not significant) was incorporated into the regression. The ANOVA procedure was repeated for the remaining factors until no more factor–period combinations of significant influence could be added.

To validate the multiple regression, the procedure was repeated four times, while randomly excluding five fields from the input data. The regression equation thus obtained was used to estimate the SOM content of the excluded fields. The estimated SOM contents were compared with measured SOM contents using a paired t test, and standard deviations of the four regression models were calculated (Draper and Smith, 1998).

	Results

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Land Use Types and Soil Organic Matter Content
The five land use factors that were considered yield 32 (2⁵) possible theoretical combinations; however, practically, only four combinations occurred, and the number of agricultural land use types could be reduced to four: conventional-arable, conventional-grass, organic-arable, and organic-grass (Table 1) . Management and cropping history of the individual fields, separated into the six different periods, and current SOM contents are presented in Fig. 1 . The 45 fields were sorted according to SOM content, ranging from 17 g kg^-1 at the left side to 88 g kg^-1 at the right side of the figure. In the left part of the figure, where fields with relatively low SOM contents are listed, conventional-arable land use is dominant. The right part, where fields with highest SOM contents are shown, is dominated by grassland, whereas organically managed fields are in the central part. Not all combinations of management and crop type occurred in all periods. Organic management followed by conventional management did not exist, and old permanent grasslands that have been managed organically were not found.

View this table: [in this window] [in a new window]   Table 1 Management types based on five factors.

View larger version (66K): [in this window] [in a new window]   Fig. 1 Management and dominant crop type of the individual fields for the six successive periods, and current soil organic matter contents. Labels represent field codes for the fields that were used for validation

Analysing the effect of crop type (arable or grass) showed that crop type in Period I and V has significantly affected SOM content. Stepwise regression resulted in the following regression model:

(1)

where SOM is in grams per kilogram and C_I and C_V are the crop type in Periods I and V, respectively (1 for grass, 0 when arable).

When the occurrence of management type and crop type were combined in the nested ANOVA test, taking into account all periods, crop type in Period V and in Period I, and management type in Period IV came out as significant factors. This resulted in the following regression model:

(2)

where M_IV is the management type in Period IV (1 for organic, 0 when conventional).

The latter regression model, with the highest adjusted R², indicates that grass increased SOM to a large extent, but also that organic farming had a significant positive effect on total SOM content. The intercept of 20.7 can be considered as the average minimum organic matter content for this soil series that is reached when management has been conventional arable since at least 63 yr before sampling.

Validation of the Regression Model
Validation of the regression procedure by repeating the regression analysis four times while randomly excluding five fields from the input data resulted in the following four regression equations.

(3)

(4)

(5)

(6)

Because there were no changes in crop type between Periods I and II, except for one field (Fig. 1), Eq. [3] to [6] are almost similar to the original equation (Eq. [2]). The SOM contents estimated for the excluded fields of each of the regression models and standard deviations are given in Table 2 . For most fields, SOM contents could be predicted reasonably well by the regression equations, although in three cases estimated SOM contents deviated more than 20% from measured values (Fields GE and MH in Eq. [4] and SG1 in Eq. [6]).

	Discussion and conclusions

TOP ABSTRACT INTRODUCTION Materials and methods Results Discussion and conclusions REFERENCES

Validation of the proposed regression analysis showed that, in general, SOM content could be predicted reasonably well, given the limited accuracy of input data. For Field MH, SOM content was severely underestimated by the regression equation (Table 2; Eq. [4]), which may be explained by the management history of the field. The change from arable to grassland occurred in the second half of Period I, so that the whole period was classified as arable. For GE (Table 2; Eq. [4]) and SG1 (Table 2; Eq. [6]) estimations were more than 20% higher than measured SOM contents, which may be explained by the severe compaction features that were observed in those fields. Crop production and the incorporation of organic matter into the soil by soil fauna may have been restricted because of the compacted top layer. In addition, a higher bulk density will give a lower gravimetric SOM content when organic matter inputs and losses remain equally high.

In general, the use of gravimetric rather than volumetric SOM content has affected the outcome of the regression analysis. Volumetric SOM contents of the different fields will cover a smaller range of values because fields with small SOM inputs generally have higher bulk densities. As a consequence, the absolute values of the factors in the regression equations would have been smaller when volumetric SOM contents had been used. For long-term grasslands, average bulk densities of the topsoil were 1.4 g cm^-3. For organic and conventional cultivated fields, mean bulk densities were 1.5 and 1.6 g cm^-3, respectively (E. Meijles, 1996, unpublished data), but bulk densities were strongly dependent on the time since the field had been plowed.

The regression analysis showed that both grassland and organic management increased SOM content and that the effect of crop type was dominant. Grassland was especially beneficial when occurring for an extended period of time. This is in line with the results from long-term experiments in Denmark and England that revealed the slowness with which SOM levels change under temperate conditions in response to changes in land use (Christensen and Johnston, 1997).

Estimation of organic matter levels in soil and evaluation of the effects of crop type and management on SOM dynamics also have been attempted by means of simulation modeling of SOM turnover as a function of land use (Parton and Rasmussen, 1994; Jenkinson et al., 1994). However, adequate calibration and validation of those models require rather detailed data records from long-term field experiments, which are usually not available. Here, use of soil survey information offers an alternative. A wide variety of "field experiments" is already there, waiting to be sampled and analyzed. Regression analysis provides a relatively easy procedure to derive a useful model to describe or predict SOM content as a function of land use history. Given the limitations that are inherent to broad surveys (i.e. not many parameters can be measured), SOM contents seem to be a useful quality indicator for broad surveys within a given soil series.

	ACKNOWLEDGMENTS

 
We wish to thank B. van Lagen for the laboratory measurements, A.G. Jongmans for his help in the field, and A. Stein for his advice concerning the statistical analyses and for his comments on the manuscript. This study was supported by the Netherlands Earth and Life Sciences Foundation (ALW) with financial aid from the Netherlands Organization for Scientific Research (NWO).

Received for publication March 22, 1999.

	REFERENCES

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• Bouma J., Droogers P. A procedure to derive land quality indicators for sustainable agricultural production. Geoderma 1998;85:103-110.
• Christensen B., Johnston A.E. Soil organic matter and soil quality-lessons learned from long-term experiments at Askov and Rothamsted. In: Gregorich E.G., Carter M.R., eds. Soil quality for crop production and ecosystem health. Developments in Soil Science 25. Amsterdam: Elsevier, 1997:399-430.
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• Karlen D.J., Mausbach M.J., Doran J.W., Cline R.G., Harris R.F., Schuman G.E. Soil quality: A concept, definition, and framework for evaluation. Soil Sci. Soc. Am. J. 1997;61:4-10.
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