Study on the Method of Soil Productivity Assessment in Black Soil Region of Northeast China

Agricultural Sciences in China

April 2009

2009, 8(4): 472-481

Study on the Method of Soil Productivity Assessment in Black Soil Region of Northeast China DUAN Xing-wu, XIE Yun, FENG Yan-jie and YIN Shui-qing State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Geography, Beijing Normal University, Beijing 100875, P.R.China

Abstract The objective of this paper is to investigate a simple and practical method for soil productivity assessment in the black soil region of Northeast China. Firstly, eight kinds of physicochemical properties for each of 120 soil samples collected from 25 black soil profiles were analyzed using cluster and correlation analysis. Subsequently, parameter indices were calculated using physicochemical properties. Finally, a modified productivity index (MPI) model were developed and validated. The results showed that the suitable parameters for soil productivity assessment in black soil region of Northeast China were soil available water, soil pH, clay content, and organic matter content. Compared with original productivity index (PI) model, MPI model added clay content and organic matter content in parameters while omitted bulk density. Simulation results of original PI model and MPI model were compared using crop yield of land block where investigated soil profiles were located. MPI model was proven to perform better with a higher significant correlation with maize yield. The correlation equation between MPI and yield was: Y = 3.2002Ln(MPI) + 10.056, R2 = 0.7564. The results showed that MPI model was an effective and practical method to assess soil productivity in the research area. Key words: PI, MPI, soil productivity, black soil region of Northeast China

INTRODUCTION As it is well known, owe to large agricultural area and fertile soil, the black soil region of Northeast China is one of the most important crop production bases and plays a key role in food security in China. However, soil fertility keeps declining and soil productivity has been seriously affected by the interaction of natural erosion and artificial cultivation since reclamation began (Xin et al. 2002; Yan and Shang 2005). In order to protect the black soil resources and to ensure Chinese food security, it is very important to assess the soil productivity in this region through simple and effective method based on available data. The current quantita-

tive assessment methods included statistical analysis method, multi-factor expert scoring method, and model simulation method (Pierce and Lal 1994; Olson et al. 1994). The statistical analysis methods established the relationship between the physicochemical properties and crop yield through black-box processing. They were believed not to reflect the respective contributions of various soil physicochemical properties to soil productivity. Besides, there was no unified form in different area, which limited its extensive application. For the multi-factor expert scoring method, a plenty of soil productivity indicators were selected, and the weights of each indicator were determined according to the experts’ experiences. Although this method can be easily mastered, there exists large differences among

This paper is translated from its Chinese version in Scientia Agricultura Sinica. DUAN Xing-wu, Ph D candidate, E-mail: [email protected]; Correspondence XIE Yun, Professor, E-mail: [email protected]

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Study on the Method of Soil Productivity Assessment in Black Soil Region of Northeast China

different research results. The mechanism model is a mathematical model based on impacting process of soil physicochemical properties on crop productivity. The mechanism models, such as erosion productivity impact calculator (EPIC) (Williams et al. 1983) and nitrogen-tillage-residue-management (NTRM) (Shaffer et al. 1994), were believed to have clear physical meaning and good behavior. While, they were not easily to be manipulated and required a large number of parameter inputs, which restrict its application in areas where the manipulators were not skillful in complicated models and data were not satisfying (Pierce and Lal 1994). A relatively simple productivity index (PI) model was firstly developed by Neill (Lindstrom et al. 1992). In PI model, the soil was regarded as an environment for root growth and water depletion; the different contributions of soil in different depth to the root growth were considered and the impacts of soil physicochemical properties on crop yield were described. PI model had clearer theoretical foundation and physical meaning compared with statistical analysis methods and multifactor expert-scoring methods; meanwhile, it required fewer parameters which were easily obtainable compared with complicated mechanism models, so it was believed to be a useful and practical method. The PI model has been validated in many places (Larson et al. 1983; Lindstrom et al. 1992; Pierce et al. 1983, 1984a, 1984b; Gantzer et al. 1987; Gale 1991). It has been widely used by Thompson (1992), Schumacher et al. (1994), Uadawatta and Henderson (2003), Woolery et al. (2002), Grigal (2000), Yang (2003), Lobo et al. (2005), Garcia et al. (2000). However, there were few systematic introduction and validation studies in China. Current soil productivity assessment methods in this region were mainly simple classification or rough evaluation based on single soil indicator or multiple soil indicators (Shi et al. 1989). Although some complex mechanism models may increase the assessing accuracy (Wang et al. 2007), the large demand for data to input limited their generalization. So it is especially important to study on the assessment methods of soil productivity, which is not only suitable for the soil properties in the research region, but also ready for generalization. However, there were no validation researches of original PI model in the study area. Furthermore, to reduce the number of parameters, modification should be made

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based on the soil properties and the relationships between different physicochemical properties of the study area (Gale et al. 1991; Uadawatta and Henderson 2003). Soil data from the Second National Soil Investigation in China (The National Soil Survey Office 1995) were collected and 25 black soil profiles (in Chinese genetic classification) distributed in our research region (Fig.1), as well as crop yield, were investigated. In order to modify the model, parameters were selected through correlation and cluster analysis. Feasibility of these parameters was assessed according to existing researches. Validation of this model was done using the crop yield data where soil profiles located. The research may provide a feasible way to assess soil productivity of the black soil and may be meaningful for the black soil resources protection.

MATERIALS AND METHODS Overview of the research region The black soil region of Northeast China was mainly divided into three different parts: Hulunbeier region mainly with chernozem soil, Songnen region with black soil and Sanjiang region with meadow soil (Liu et al. 2008). The research area is located in a gentle hilly region in Songnen, which is a transition zone from the east of Great Khingan to the west of Lesser KhinganChangpai (43°N-50°N, 126°E-128°E). This gentle hilly region is narrow in shape of long arch with an area of about 9.4 million ha, occupying 45% of Songnen black soil region. The climate is characterized by humid and sub-humid monsoon of temperate zone with annual mean temperature from -5.7 to 4.1°C and annual rainfall from 400 to 700 mm. The winter is relatively long and cold comparing with the other areas in China. The summer is hot and moist. The hot and rainy season, together with the fertile soil, is very favorable for agricultural production (Zhou et al. 1957). This region is rich in production of wheat, soybean, corn, and other summer crops. This area belongs to black cultivated land type which is one of seven types of cultivated land region in China (Ministry of Agriculture of People’s Republic of China 1996). It plays a very important role in the grain production in Northeast China. However,

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due to the interaction of natural erosion and cultivation, black soil has been eroded and its productivity has been decreasing (Xin et al. 2002; Yan et al. 2005).

Investigation of soil and yield data According to the second national soil survey results (National Soil Survey Office 1995; The Heilongjiang Province Soil Survey Office 1990), 25 black soil species were chosen, which belong to 3 kinds of black soil sub-types: Black Soil, Meadow Black Soil, and Albic Black Soil (in Chinese genetic classification). These soil species evenly distributed in the study area from south to north (Fig.1). An on-site investigation on typical soil profiles was carried out in June-September 2007 based on geophysical location and profile layer infor-

mation recorded in Soil Species of China (The National Soil Survey Office 1995). GPS and topographic map were used to locate typical profiles. Soil profiles were sampled following the soil survey standards (Wang and Zhang 1983). 120 soil samples were collected in different layers of genetic horizon according to soil species records and field judgment. Crop yield data of land block where soil profile located were collected simultaneously. According to National Soil Analysis Standards (Liu 1996), physicochemical properties of 120 samples were determined in laboratory, including organic matter (OM), total nitrogen content (TN), available phosphorus content (OP), total potassium content (TK), particle composition, soil bulk density (BD), field capacity (FC), permanent wilting point (PWP), and pH value. OM

Kilometers

Fig. 1 Spatial distribution of the soil profiles selected.

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Study on the Method of Soil Productivity Assessment in Black Soil Region of Northeast China

Variables in this model are all dimensionless from 0 to 1, reflecting the relative suitability degree of the parameters for crop growth. The greater parameter value represents higher suitability for crop growth. The model takes into account not only the impacts of physicochemical properties of different soil on the soil productivity, but also contributions of soil layer depth on the crop growth. Generally the surface soil is most important to crop growth and the importance decreases with the soil depth. For some special cases, the deeper layer soil doesn’t act on the crop growth at all. There are two key steps for modifying PI model. The first step is to select appropriate parameters which not only represent physicochemical properties in the research region, but also have a major important effect on crop growth. The second is to establish a standardized index for determining the suitability of model parameters for crop growth and to make sure the parameters are independent each other. The model should reflect the quantitative relationship between the physicochemical properties of the soil and crop growth. Eight soil elements which significantly affect the soil productivity (Wan et al. 2002) and represent the characteristics of black soil were selected as alternative model parameters: OM (%), TN (%), OP (%), TK (%), AWC (% in volume), BD (g cm-3), pH value, as well as clay content (%). The main feature of the black soil was thought to be rich in OM content. However, due to the combined action of long-term natural erosion (Yan et al. 2005) and cultivation (Xin et al. 2002), the OM content has significantly decreased (Yang et al. 2004). The current fertilizer in this region is mainly for nitrogen and phosphorus supplement, which has little compensating effect for OM (Lin et al. 1994). The OM content has become an important factor affecting soil productivity in the region (Wan and Li 2002). So OM was chosen as an assessment indicator. Considering that the black soil has high clay content, which reflects the effects of the soil particle composition on soil productivity (Xiong and Li 1987), the clay content was chosen as one of assessment indicators. Furthermore, considering the

was measured by potassium dichromate oxidation-external heating method, TN by semi-micro Kjeldahl method; OP by Olsen method; TK by hydrofluoric acid-perchloric acid boiling-flame photometry; particle composition by pipette method in which particle division by international system (sand: 2-0.02 mm; silt: 0.020.002 mm; clay: < 0.002 mm); BD by cutting ring method; FC by field measurement of cutting ring method (Wang and Zhang 1983); PWP by pressure membrane method in which humidity content under 1.5 MPa pressure was taken as permanent wilting point; and pH value was measured by potential method (the soil water ratio is 2.5:1). Available water capacity (AWC) equals to FC minus PWP.

Introduction of PI model and its modification The PI model was firstly developed by Neill and then improved by Kiniry (Lindstrom et al. 1992) and Pierce et al. (1983). The model improved by Pierce was widely applied with the following basic form: n

PI

¦ (A uC i

i

u D i u WFi )

(1)

i 1

Where, PI is a productivity index, which is dimensionless varying from 0 to 1; the greater value show the higher soil productive level; i = 1, 2, ....; n stands for different soil layer; Ai is the suitability index of available soil water content in the ith layer; Ci is the suitability index of soil bulk density in the ith layer; Di is the suitability index of the soil pH in the ith layer; WFi is the weight of the ith layer. Pierce (1983) suggested 100 cm as a suitable thickness for the corn growth and divided it into 10 layers. The weights of different soil layers were determined according to the utilization function of soil moisture by corn in different-layer under ideal conditions. The formula is as follows: WF

(2)

0.35  0.152 lg(depth  depth 2  6.45 )

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Where, depth stands for soil depth within the profile. Integrations are performed on the calculation results of formulation (2), then the weight of the ith layer is obtained by normalizing the total value to 1.0 (Table 1). Table 1 WFi value for each soil layer Layers (i) WF i

1

2

3

4

5

6

7

8

9

10

0.314

0.196

0.143

0.108

0.082

0.061

0.044

0.03

0.017

0.005

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importance of soil physicochemical properties on crop growth, the universality of indicator application and the data availability, several other indicators were also chosen, including BD, AWC, pH value, TN, OP, and TK. The 8 indicators of the 120 soil samples were analyzed through R-cluster analysis and correlation analysis. Several widely applied and independent indicators reflecting soil properties were finally determined. Then, values of model parameters were calculated based on former researches. In order to assess the effect of the model modification, PI values of the original model and modified model calculated based on the investigated physicochemical properties of the soil samples were compared and regressions between PI value and corn yield were also done.

RESULTS Results of model modification The R-cluster analysis results of 8 soil physical and chemical indicators showed that: Between the rescaled distance of 12-18, 8 elements could be divided into 6 classes (Fig.2): OM and TN as the first class, TK as the second class, OP as the third class, BD and AWC as the fourth class, clay content as the fifth class, and pH value as the sixth class. The first three classes represented indicators of soil nutrients, the fourth represented soil moisture, the fifth represented soil particle composition, and the sixth represented soil pH value. The classification had clear physical meaning.

DUAN Xing-wu et al.

The first three classes represented indicators of soil nutrients. In the first class, OM has a significant impact on productivity in research area, and its supplementary effect in the current fertilizer application is very weak, so OM was chosen as one of the model parameters. TN content was not chosen because of its strong correlation with OM (Fig.3). It is found in the investigation that, chemical fertilizers were put into land block for the growing season (the average amount of fertilizer is more than 200 kg ha-1, and N:P2O5:K2O is about 1:1.3:0.3). Therefore, the current amount of nutrients, such as N, P contained in the soil, cannot truly represent the amount absorbed by crops in the growing season. Since artificial fertilization cannot be ruled out, it was supposed that the nitrogen and phosphorus nutrients in the soil were fully supplemented in the research region and therefore, N and P were not necessary to be included in the model. Although the total potassium belongs to a separate class, it was not considered in the model because the potassium is ample in the study area (Xiong and Li 1987), and potassium nutrients can also be supplemented by increasing investment. BD and AWC belonged to the same class. As the correlation analysis proved a significant negative correlation between them (Fig.4), it was reasonable to select only one of them. The great role of AWC for crop yield had been commonly recognized (Ritchie 1981), meanwhile, the impact of soil erosion on soil productivity can be represented by the loss of AWC (Larson et al. 1985; Williams et al. 1981), so soil AWC is chosen to represent this class. Soil clay content and pH indicators were chosen respectively. Altogether, the parameters of the model included OM, AWC, clay content, and pH value. The modified PI model (MPI) was as follows: MPI

n

¦ (A u D i

i 1

Fig. 2 Dendrogram of cluster analysis for soil physicochemical parameters.

i

u Oi u CLi u WFi )

(3)

Where MPI stands for the modified soil productivity index; Oi stands for the suitability value of OM in the ith layer; CLi stands for the suitability value of clay content in the ith layer, and other terms have the same meaning as in formula (1). The suitability value of soil available water content was calculated by the original PI model (Pierce et al. 1983) method:

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Study on the Method of Soil Productivity Assessment in Black Soil Region of Northeast China

Fig. 3 Correlation analysis of soil OM and TN.

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Fig. 4 Correlation analysis between BD and AWC.

(4)

Where AWCi stands for the available water content in the ith layer (% in volume), and other terms have the same meaning as in formula (1). The suitability value pH was calculated by the original PI model method (Pierce et al. 1983):

range, the contribution of organic matter to the soil productivity is increasing with its content; above this certain critical point, the soil productivity is no longer affected by the organic matter content. In the second national soil survey, 4% was regarded as a critical point to discriminate the levels of OM content (The National Soil Survey Office 1998). In this research, 4% was used as a critical point for the impact of organic matter content on crop growth to define the value of organic matter: (6)

(5)

Where pHi stands for the pH value in the ith layer, and other terms have the same meaning as in formula (1). The impact of OM on soil productivity was general represented by “the more the better” type of standard scoring functions (Wan et al. 2001). Within a certain

Where Oi stands for the suitability value of OM content in the ith layer and OMi stands for the OM content in the ith layer (%). The impact of soil clay content on soil productivity was generally showed by “an optimum range” type of standard scoring functions (Wan et al. 2001). It is suitable for crop growth within a certain range beyond which crop growth is restricted. A range of 20-40% was considered to be the optimum range for plant growth (Wan et al. 2001), and suitability value of clay content was defined accordingly:

(7)

Where CLi stands for the suitability value of clay

content in the ith layer, clay is the clay content in the

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DUAN Xing-wu et al.

ith layer (%).

Comparison of PI and MPI calculation results The result of 25 soil species by original PI model varied from 0.298 to 0.994 with an average 0.726, while by MPI it was 0.206-0.874 with an average 0.524. The minimum value is 30.9% smaller than that of original PI model and the maximum value is 12.1% lower. Averagely, there is a 27.8% reduction than the original PI model (Table 2). For original PI result, there were 2 soil species less than 0.4, 12 soil species between 0.4 and 0.8, and 11 soil species more than 0.8 (Table 2). Most soil species have a higher PI value in the original model, which is inconvenient for distinguishing the productivity among different soil species. However, for MPI result, 5 soil species were less than 0.4, 17 species between 0.4 and 0.8, and 11 species more than 0.8 (Table 2), low-, medium- and high-level productivities are normally distributed, which benefits to distinguish the productivity among different soil species. The scatter plot of PI and MPI value (Fig.5) clearly showed that 96% soil species have higher PI values than MPI values.

The correlation analysis between productivity indices (PI and MPI value) and corn yield per unit (Fig.6) showed that the corn yield corresponded better with MPI results than PI results. Both of MPI and PI can accurately reflect the productivity level of low-yield species (corn yield per unit is lower than 6 t ha-1 ) and high-yield species (corn yield per unit is higher than 8.5 t ha-1). However, for the mid-yield species (corn yield per unit is 6.5-8.5 t ha-1 ), the PI values were obviously higher and didn’t reflect the actual productivity level. For example, the soil species in profile number “11”, “1”, “3”, “2”, “4”, and “12” produced higher PI value, but the actual productivity level reflected by corn yield was not higher correspondingly. According to the suitability value of these profiles (Table 3), although higher suitability values of available water content ( A), bulk density index ( C), and pH ( D) result in higher PI values, the suitability value of organic matter ( O) is obviously low, which restricted the productivity of the soil species. The introduction of organic matter and clay content parameters in the MPI model decreased the productivity index of above kinds of soil species and made MPI more accurate to assess the

Table 2 Productivity indices of PI and MPI for 25 soil local type in the research area < 0.4 PI MPI

0.4-0.8

> 0.8

Number

%

Number

%

Number

%

2 5

8 20

12 17

48 68

11 3

44 12

Fig. 5 Scatter diagram of soil productivity index calculated by the two models for the 25 soil local types.

Min

Max

Mean

Std.

0.298 0.206

0.994 0.874

0.726 0.524

0.198 0.178

actual productivity level of these soil species. However, for some soil species, both models had poor performance. For example, both PI and MPI values of profile “18” were high, but the actual production was not high, which suggested that other factors which are not included in model might influence the crop yield. So the models should be further studied and improved in the future. Regression of MPI values with yield and that of PI also showed that (Fig.7), both productivity indices had significant correlation with maize yield, but MPI performed better than original PI fitting using logarithm regression. The determination coefficient of regression for PI value and yield was 0.5385, while

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Study on the Method of Soil Productivity Assessment in Black Soil Region of Northeast China

for MPI it was 0.7564. When the productivity index was low, the simulation resulted for yield per unit by both models were good. As the productivity index increasing, the accuracy of PI model decreased while the MPI kept its good performance. In addition, the

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increasing trend in the productivity index value was not obvious when the yield was over 9 t ha -1. The determination coefficient of the regression equation showed that, 75.64% of the crop yield can be explained by MPI and the remaining 24.36% of the re-

Fig. 6 Productivity index change along with maize yield per unit. Table 3 Suitability indices of model parameters for some soil profiles for example Profile id 11 1 3 2 4 12 *

A*

C*

9.16 7.17 7.37 10.00 10.00 9.98

9.36 9.43 9.16 9.85 10 9.98

D* 9.85 9.76 9.04 9.37 9.71 8.47

have the same meaning as these in formula (3).

O*

CL

2.94 3.40 2.76 2.62 4.20 4.37

10.00 9.96 10.00 10.00 9.85 10.00

ACD

ADO

8.45 6.70 6.40 9.23 9.71 8.45

2.69 2.57 2.16 2.54 4.11 3.73

ADOCL 2.69 2.56 2.16 2.54 4.06 3.73

PI

MPI

Yield (t ha-1)

0.89 0.74 0.83 0.97 0.99 0.90

0.35 0.45 0.37 0.40 0.48 0.46

6.25 7.5 7.5 8 8 8

, sum of 1-10 layers.

siduals may be attributed to other factors like climate, farming management, etc. Therefore, MPI was better than PI for assessing soil productivity in the research area.

DISCUSSION

Fig. 7 Regression analyses between per unit maize yield and productivity index calculated by the two models.

The Northeast black soil region is one of major food production bases in China. It is imperative to develop a practical and accuracy method to assess soil productivity for development of precision agriculture and sustainable land use. The soil productivity was generally assessed using statistical analysis or expert-scoring

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method in the research area at present. These methods rely on empirical knowledge too much which not suitable to objectively evaluate the actual productivity of the black soil. Some other crop models with high precision were difficult to generalize due to data availability. In MPI model, 1 m depth of top soil was chosen as assessment object, and 4 model parameters were selected based on soil properties and the relationship among soil elements in the research region. Actual crop yield was used to verify the validation of MPI model. The MPI can explain 75.64% of the crop yield based on available soil elements. Compared with other models, MPI model was simple, feasible, and performed well. The required soil data inputs in model can be obtained from current Chinese soil database. It not only provided a good method for assessing the soil productivity in the research region, but also had potential to be applied as a soil module in crop models.

the soil productivity among different soil species than PI model. (3) Both MPI and PI values are significantly correlated with corn yield, while MPI performed better than PI. A regression equation was built to predict corn yield for normal years in the research region: Y=3.2002 Ln(MPI)+10.056. To sum up, MPI is a simple and effective method to assess soil productivity in the research area. Besides, the model parameters can be calculated using data available in the soil data base which made it potential to be used for assessing soil productivity in the research area.

Acknowledgements This work was supported by the National Natural Science Foundation of China (40671111).

References Gale M R, Grigal D F, Harding R B. 1991. Soil productivity

CONCLUSION

index: predictions of site quality for White Spruce plantations. Soil Science Society of American Journal, 55, 1701-1708.

Establishing an appropriate and accurate soil productivity assessment model is very important. It is beneficial to land use planning and land resource protection. 25 black soil species distributing in black soil region of Northeast China were sampled in field investigation and their physicochemical properties were determined. Grain yield data of corresponding land block were also collected in field study. The productivity index model was modified and its validation was verified. The main conclusions are as follows: (1) The cluster and correlation analysis showed that, the soil parameters which fit to assess the soil productivity in study area are the soil available water content, pH value, clay content, and organic matter content. The modified soil productivity index model in the region is, MPI

n

¦ ( A u D u O u CL uWF ) i

i

i

i

i

(3)

i 1

Compared with original PI model, two more parameters, organic matter content and clay content, were included while bulk density was omitted. (2) The PI value of 25 soil species was 0.298-0.994 with average 0.726, and the MPI value was 0.206-0.874 with average 0.524. MPI model can better distinguish

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