Yogita D. Gore, M.S.S. Nagaraju, Rajeev Srivastava and R.A. Nasre

Transcription

1 244 Agro-Informatics and Precision Agriculture Proceedings of 2012 AIPA (AIPA 2012, INDIA 2012) SPATIAL VARIABILITY OF SOIL PROPERTIES IN BASALTIC TERRAIN FOR PRECISION AGRICULTURE USING GEOSPATIAL TECHNIQUES: A CASE STUDY IN SAVLI VILLAGE OF WARDHA DISTRICT OF MAHARASHTRA Yogita D. Gore, M.S.S. Nagaraju, Rajeev Srivastava and R.A. Nasre National Bureau of Soil Survey and Land Use Planning, Amravati Road, Nagpur , Maharashtra ABSTRACT Spatial variability of soil physical properties (particle size, bulk density), hydraulic properties (moisture retention at 33 kpa and 1500 kpa), chemical properties (ph, organic carbon, cation exchange capacity), soil available macronutrients (N, P, K) and micronutrients (Fe, Mn, Cu, Zn) were quantified through semivariogram analysis and the respective surface maps were prepared through ordinary kriging. Sand, ph, OC has displayed strong spatial dependence whereas, silt, clay, bulk density, cation exchange capacity, moisture retention at 33 kpa and 1500 kpa, available N, P, K, Fe, Mn, Cu and Zn showed moderate spatial dependence. Bulk density, organic carbon, and available Zn were spatially correlated for a short range. Spherical model fits well with experimental semivariogram of sand, moisture retention, ph, organic carbon, cation exchange capacity, available N, P, K, Fe, Mn and Zn. Gaussian model was found best to fit the experimental semivariogram of clay, bulk density and available Cu. Cross validation of kriged map shows that spatial prediction of soil properties using semivariogram parameters is better than assuming mean of observed value for any unsampled location with reasonable accuracy for variable rate fertilizer application and irrigation management for increasing the farm productivity. Keywords: Spatial Variability, Soil Properties, Semivariogram Analysis, Kriging, Thematic Maps. 1. INTRODUCTION Soil variability is the outcome of many processes acting and interacting across a continuum of spatial and temporal scales and is inherently scale dependant (Trangmar et al., 1985). Knowledge of spatial variation of soil properties is important in precision farming and environmental modeling (Santra et al., 2008). The concept of management zones was evolved in response to large variability with the main purpose of efficient utilization of agricultural inputs with respect to spatial variation of soil properties. Therefore, understanding of spatial variation of soil properties is very essential for refining farm management practices, modeling at landscape level and assessing the impact of agriculture on environment. The application of parametric statistics is inadequate for analysis of spatially dependant variables because, they assume that measured observations are independent in spite of their distribution in space. Geostatistics provide a tool for improving sampling design by utilizing the spatial dependence of soil properties within a sampling region and useful to illustrate the spatial interrelationship of soil data which reduces error, biasness and increase the accuracy of data for interpolation (Oliver, 1987). The information on spatial variability of soil properties at village or watershed level, particularly, in soils of basaltic terrain is meager. Therefore, the present study has been planned to quantify the spatial variability of soils in Savli village of Wardha district of Maharashtra. 2. MATERIALS AND METHODS 2.1 Study Area Geographically, the Savli village is located between to E longitudes and 21 7 to 21 9 N latitudes in Karanja tehsil of Wardha district, Maharashtra. The total area of the village is 1158 ha. The study area falls in the SOI toposheet No. 55 K/12 and 55 K/8. The geology of the area is basalt. The climate of the area is sub-tropical dry subhumid with mean annual temperature of 33.5 C and mean annual precipitation of 903 mm. The area qualifies for ustic and hyperthermic soil moisture and temperature regimes, respectively. 2.2 Samping Design, Sample Collection and Analysis In the present study, a grid size of 400 by 400 m was established on georeferenced SOI toposheet as well as on cadastral map of the village. A total of 75 soil samples were collected at a depth of 0 20 cm covering the entire study area. Nearly 2.0 kg of representative soil sample from each grid was collected for laboratory studies. The soil samples collected during

2 Spatial Variability of Soil Properties in Basaltic Terrain for Precision Agriculture Using Geospatial Techniques 245 the field work were initially air dried in laboratory at room temperature, ground using wooden mortar and pestle, screened through 2 mm sieve, properly labeled and stored in polythene bags for laboratory analysis. Soil samples were analyzed for physical, hydraulic, chemical properties and soil fertility following the standard procedures (Jackson, 1967; Black et al., 1965). 2.3 Geostatistical Analysis of Soil Properties The theory of recognized variables (Matheron, 1971) was used to investigate the soil spatial variability. Spatial variability is expressed by semivariogram ˆγ (h) which measures the average dissimilarity between data separated by a vector h. (Journel and Huijbregts, 1978). It was computed as half of its average squared difference between the components of data pairs. ˆγ (h) = Sample semivariance. N(h) = Number of data pairs within a given class of distance and direction Z(x i ) = Value of the variable at the location x i Z(x i +h) = Value of the variable at a lag of h from the location x i The experimental semivariogram value for each soil property was computed using geostatistical analyst of ArcGIS software and plotted with lag distance on abscissa, ˆγ (h) on the ordinate. Lag increment was fixed as 400 m of sampling distance. The computed semivariogram values ˆγ (h) for corresponding lag (h) were fitted with the available theoretical semivariogram models using Root Mean Square Error (RMSE). Two commonly used semivariogram models namely spherical and Gaussian models were fitted for each soil property. Surface maps of basic soil properties were prepared using semivariogram parameters through ordinary kriging in geostatistical analyst of ArcGIS software. Accuracy of soil maps was evaluated through cross validation approach (Santra et al., 2008). 3. RESULTS 3.1 Descriptive Statistics of Soil Properties The descriptive statistics of soil properties are presented in Table 1. The mean values for sand, silt, clay content were 35.2, 32.6 and 32.1 per cent with a range of , 19.8 to 75.1 and 7.8 to 67.6, respectively. Clay had the Table 1: Descriptive Statistics of Soil Properties Soil Property Minimum Maximum Mean Standard Deviation CV (%) Skewness Kurtosis Soil Physical Properties Sand Silt Clay Bulk density Soil Hydraulic Properties Moisture retention ( 33kpa) Moisture retention ( 1500 kpa) Soil Chemical Properties ph Organic carbon CEC Soil Fertility Available N Available P Available K Available Fe Available Mn Available Cu Available Zn

3 246 Agro-Informatics and Precision Agriculture 2012 (AIPA 2012) largest variation (CV = 45 per cent) followed by sand (CV = 43 per cent) and silt (CV = 24 per cent). Average bulk density was recorded as 1.7 mg m 3 with range of 0.96 to 1.7 mg m 3. Bulk density was found to be least variable (CV = 23 per cent) among the soil physical properties. Silt and bulk density were not normally distributed with more skewness and kurtosis values. Moisture retention at 33 kpa and 1500 kpa varied from 18.7 to 48.3 per cent and 10 to 36.5 per cent with mean values of 31.4 and 21.4 per cent, respectively. The moisture retention of 33 kpa and 1500 kpa were found moderately variable with CV of 22 and 26 per cent, respectively. Moisture retention at 33 kpa was found non-normal with higher skewness and kurtosis. The ph varied from 6.1 to 8.6. Organic carbon varied from 0.18 to 2.86 per cent with a mean value of Cation exchange capacity (CEC) varied from 13.1 to 63.7 c mol(p + )kg 1 with a mean value of 28.6 cmol(p + )kg 1. The CEC was found to be highly variable (CV = 57 per cent) followed by organic carbon (CV = 47 per cent) while, ph was found least variable (CV = 10 per cent). Among the chemical properties, ph and organic carbon were not normally distributed with higher skewness (ph) and kurtosis (organic carbon). The available N, P, and K varied from to kg 1, 1.12 to 30.7 kg ha 1 and 70.3 to kg ha 1 with mean value of kg ha 1, 5.7 kg ha 1 and kg ha 1, respectively. The available micronutrients Fe, Mn, Cu, and Zn varied from 4.8 to 103.8, 3.5 to 131.4, 2.7 to 36.7 and 0.16 to 1.90 g kg 1 with mean values of 32.0, 42.5, 7.5, and 0.70 g kg 1. Among the macronutrients, available K was found to be highly variable (CV = 110 per cent) followed by available P (CV = 107 per cent). Available N was found to be moderately variable (CV = 27 per cent). All the micronutrients were highly variable with CV ranging from per cent. Among the soil fertility parameters, available N, P, Cu and Zn were found non-normal due to higher value of skewness and kurtosis. 3.2 Semivariogram of Soil Properties Semivariogram parameters (range, nugget, partial sill, sill and nugget/sill ratio) of soil properties are presented in Table 2. Among the two different theoretical models tested, the spherical model was found best fit for sand and silt whereas, Gaussian model was found as the best fit for clay and bulk density. For particle size distribution, the range varied from 773 m (silt) to 4000 m (clay). Bulk density was spatially correlated for a short range (773 m). Out of the total variation (sill), nugget component was 11.0, 61.0 and 37.2 per cent for sand, silt and clay, respectively. Nugget component was found to be 80 per cent of total variation in bulk density. Soil Property Table 2: Semivariogram Parameters of Soil Properties Semivariogram Model Range (m) Nugget (C 0 ) Partial Sill (C) Sill (C 0 +C) Nugget/Sill Ratio (%) Soil Physical Properties Sand Spherical ln (Silt) Spherical Clay Gaussian ln (Bulk density) Gaussian Soil Hydraulic Properties ln (Moisture retention, 33 kpa) Spherical Moisture retention ( 1500 kpa) Spherical Soil Chemical Properties ln (ph) Spherical ln (Organic carbon ) Spherical CEC Spherical Soil Fertility ln (Available N) Spherical ln (Available P) Spherical Available K Spherical Available Fe Spherical Available Mn Spherical ln (Available Cu) Gaussian ln (Available Zn) Spherical

4 Spatial Variability of Soil Properties in Basaltic Terrain for Precision Agriculture Using Geospatial Techniques 247 Spherical model was found best fit for moisture retention at 33 kpa and 1500 kpa. Highest range was observed for moisture retention at 1500 kpa (4000 m) and the lowest range was observed for moisture retention at 33 kpa (3800 m). Out of the total variation (sill), nugget component for moisture retention at 33 kpa and moisture retention at 33 kpa were 34.2 per cent and 44.0 per cent, respectively. Among the two different theoretical models tested, the spherical model was found best fit for ph, OC and CEC. The range varied from 773 m to 4000 m. Highest range was observed for ph (4000 m) and the lowest range was observed for OC (773 m). Out of the total variation (sill), nugget component was 11.4, 17.0 and 52.5 per cent for ph, OC and CEC, respectively. Spherical model was found best fit for all the fertility parameters studied except for available Cu wherein Gaussian model was found best fit. Among the macronutrients, highest range was observed for available N (3496 m) followed by available P (2686 m) and available K (2230 m). Out of the total variation (sill), nugget component for available N, available P and available K were 33.5 per cent, 62.8 per cent and 44.8 per cent, respectively. The semivariogram parameters for micronutrients indicated that available Fe and available Cu have the highest range followed by available Mn. Available Zn was spatially correlated for a short range (879 m). Out of the total variation (sill), nugget component for available Cu was highest (48.3 per cent) followed by available Mn (46.4 per cent), available Zn (36.7 per cent) and available Fe (24.7 per cent). 3.3 Kriged Maps of Soil Properties Spatial variability maps of available N, P, K and Zn are presented in Figure 1. Spatial map of available N shows that it has areas under very low (<140 kg ha 1 ) in north west part to low ( kg ha 1 ) in central and southern part of the village. Spatial map of available P shows that it is very low (<7 kg ha 1 ) in majority of the area. Spatial map of available K shows that it has areas with low ( kg ha 1 ), medium ( kg ha 1 ) and moderate ( kg ha 1 ) available K. Spatial map of available Zn shows that majority area is under low (<0.5 mg kg 1 ) followed by marginal ( mg kg 1 ) and adequate ( mg kg 1 ) available Zn. (a) (b) (c) Fig. 1: Kriged Maps of a) Available N b) Available P c) Available K d) Available Zn Retention (d)

5 248 Agro-Informatics and Precision Agriculture 2012 (AIPA 2012) 3.4 Cross Validation of Soil Properties Evaluation parameters of kriged map of soil properties is presented in Table 3. The Mean Absolute Error (MAE) was found to be zero for sand, silt, clay and bulk density. Highest Mean Squared Error (MSE) was observed for clay followed by sand. The goodness of fit (G) values were positive and highest G value was observed for sand followed by clay. The G values for silt and bulk density were negative. MSE values were lower for moisture retention at 1500 kpa compared to moisture retention at 33 kpa. Higher G value was observed for moisture retention at 33 kpa compared to moisture retention at 1500 kpa. Higher MSE value was observed for cation exchange capacity and lower MSE values were observed for organic carbon and ph. Higher G value was observed for ph compared to cation exchange capacity and organic carbon. Higher MSE values were observed for available K, followed by available N, available Mn and available Fe and lower MSE values were observed for available Zn, available Cu and available P. Higher G values were observed for available N followed by available Fe, available Mn and available K compared to available P, available Zn and available Cu. Table 3: Evaluation Parameters of Kriged Map of Soil Properties Soil Property MAE MSE G Soil Physical Properties Sand ln (Silt) Clay ln (Bulk density) Soil Hydraulic Properties ln (Moisture retention, 33 kpa) Moisture retention ( 1500 kpa) Soil Chemical Properties ln (ph) ln (Organic carbon ) CEC Soil Fertility ln (Available N) ln (Available P) Available K Available Fe Available Mn ln (Available Cu) ln (Available Zn) DISCUSSION The soil properties displayed differences in their spatial dependence as determined by their semivariograms. Sand content has displayed strong spatial dependence, silt and clay moderate spatial dependence and bulk density weak spatial dependence in terms of ratio of nugget to total semivariance (nugget variance) expressed as percentage (Cambardella et al., 1994). Bulk density showed largest nugget variance which indicates the sum of micro-scale variation and error (Webster and Oliver, 1990) and better described through sampling with fine grids (Santra et al., 2008). Silt content and bulk density has shorter range compared to sand and clay. Samples separated by distances closer than the range are related spatially and those separated by distances greater than the range are not spatially related. Trangmar et al. (1985) reported that semivariogram ranges depend on the scale of observation and the spatial interaction of soil processes affecting each property. Moderate spatial dependence was observed for soil moisture retention at 33 kpa and 1500 kpa and spatially correlated with sand and clay content. Sharma et al. (2011) from a 50 by 50 m grid sampling, observed a range of 355 m and 297 m for soil moisture retention at 33 kpa and 1500 kpa, respectively. The soil chemical properties namely ph and organic carbon displayed strong spatial dependence whereas, cation exchange capacity showed moderate

6 Spatial Variability of Soil Properties in Basaltic Terrain for Precision Agriculture Using Geospatial Techniques 249 spatial dependence (Cambardella et al. 1994). The results also corroborated with Cambardella and Karlen (1999) and Abu and Malgwi (2011). Organic carbon was spatially correlated for a shorter lag distance. Available macronutrients (N, P, K) and micronutrients (Fe, Mn, Cu, Zn) showed moderate spatial dependence. Reported ranges of spatial dependence were 53.1 m sampled at 25 by 25 m (Sharmishta-Pal et al. 2010) to 8.9 km sampled at 2 by 2 km (Reza et al., 2010) for available N; 25.8 m sampled at 50 m interval (Abu and Malgwi 2011) to 47.7 m sampled at 25 by 25 m (Sharmishta-Pal et al., 2010) for available P and 60 m sampled at average interval of 20 m (Geypens et al., 1999) to 6.9 km sampled at 2 by 2 km (Reza et al., 2010) for available K. Among the micronutrients, available Zn was spatially correlated for a shorter distance and at a distance less than the range, measured soil property of two samples become more alike with decreasing distance between them (Eltaib et al., 2002). Spatial dependence of DTPA extractable Zn, Fe, Cu and Mn were also reported by Nayak et al. (2006). 5. CONCLUSIONS Spatial variability of soil physical, hydraulic, chemical, and soil fertility was quantified through semivariogram analysis and interpolated through ordinary kriging. Spherical model was found as the best fit for sand and silt whereas, Gaussian model was found as the best fit for clay and bulk density. Bulk density was spatially correlated for a short range and nugget component was 80 per cent of total variation. Spherical model was found as the best fit for moisture retention at 33 kpa and 1500 kpa and for ph, OC and CEC. The soil chemical properties ph and CEC were spatially correlated for longer distance than organic carbon. Spherical model was found as the best fit for all the soil fertility parameters except for available Cu wherein Gaussian model was found as the best fit. Available Zn was spatially correlated for a short range. Spatial maps of soil properties shows that areas with higher clay content displayed low sand content and areas with higher moisture retention at 33 kpa and 1500 kpa correspond to higher clay content. Spatial maps of soil available N, P, K and Zn indicated deficient areas. Cross validation of kriged maps shows spatial prediction of soil properties with reasonable accuracy except silt and bulk density. REFERENCES Abu, S.T. and Malgwi, W.B., 2011, Spatial variability of soil physico-chemical properties in Kadawa irrigation project in Sudan Savanna agroecology of Nigeria. International Journal of Agricultural Research, 6, Black, C.A., Evans, D.D., White, J.L., Ensmingel, L.E. and Clark, F.E. (Eds.), 1965, Methods of Soil Analysis, Part I. American Society of Agronomy, No. 9, Madison, Wisconsin. Cambardella, C.A. and Carlen, D.L., 1999, Spatial analysis of soil fertility parameters. Precision Agriculture, 1, Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F. and Konopka, A.E., 1994, Field scale variability of soil properties in Central Iowa soils. Soil Science Society of America Journal, 58, Eltaib, S.M., Amin, M.S.M., Hanfi, M.M., Sharif, A.R.M. and Wayayok, A., 2002, Spatial variability of N, P and K in rice field in Sawah Sempadan, Malaysia. Songklanakarin Journal of Science and Technology, 24, Geypens, M., Vanongeval, L., Vogels, N. and Meykens, J., 1999, Spatial variability of agricultural soil fertility parameters in a gleic podzol of Belgium. Precision Agriculture, 1, Jackson, M.L., 1967, Soil chemical analysis, Prentice Hall India Pvt. Ltd., New Delhi. Journel, A.G., Hujibregts, C.J., 1978, Mining Geostatistics. Academic Press, London, UK. Matheron, G., 1971, The theory of regionalized variables and its applications. Les Cahiers di Centre de Morphologie Mathematique, No. 5, Centre de Geostatistique, Fontainebleau. Nayak, A.K., Khan, U., Sharma, D.K., Mishra, V.K., Verma, C.L., Singh, R. and Singh, G., 2009, Spatial variability of soil physicchemical properties under Prosopis juliflora and Terminalia arjun in sodic soils of Indo-Gangetic plains. Journal of the Indian Society of Soil Science, 57, Oliver, M.A., 1987, Geostatistics and its application to soil science. Soil Use and Management, 3, Oliver, M.A., 1987, Geostatistics and its application to soil science. Soil use and management 3, Reza, S.K., Sarkar, D., Baruah, U. and Das, T.H., 2010, Evaluation and comparison of ordinary kriging and inverse distance weighing methods for prediction of spatial variability of some chemical parameters of Dhalai district, Tripura. Agropedology, 20, Santra, P., Chopra, U.K. and Chakraborty, D., 2008, Spatial variability of soil properties and its application in predicting surface map of hydraulic parameters in an agricultural farm. Current Science, 95, Sharma, P., Shukla, M.K. and Mexal, J.G., 2011, Spatial variability of soil properties in agricultural fields of Southern New Mexico. Soil Science, 176, Sharmistha-Pal., Panwar, P. and Bhatt, V.K., 2010, Evaluation of spatial variability of soil properties based on geostatistical analysis: A case study in Lower Shivaliks. Indian Journal of Soil Conservation, 38, Trangmar, B.B., Yost, R.S. and Uehara, G., 1985, Application of geostatistics to spatial studies of soil properties. Advances in Agronomy, 38, Webster, R. and Oliver, M.A., 1990, Statistical methods in soil and land resources survey. Oxford University Press, New York (316 pp).