Modeling the Spatial Variability of Soil Nutrients - A Case from Soil Health Card Project, India

Ascertaining and mapping soil nutrient data is crucial for governments to maintain soil health on farmlands. As part of the soil health card project, a total of 329 geo-referenced soil samples were collected from Thaticherla village, Anantapur mandal, Andhra Pradesh, India. These samples were analyzed for various soil properties such as soil pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), available phosphorus (P), available potassium (K), available sulphur (S), DTPA extractable micronutrients (Fe, Mn, Zn, Cu), and hot water-soluble boron (B) at a depth of 0 to 15 cm. The results showed high variability (>35%) in coefficients of variation in Cu, EC, Zn, and B. The findings indicated positive correlation between Zn and Mn; N and OC; and OC and Zn. The data underwent logarithmic and Box-Cox transformations to achieve normalization. The ordinary kriging method was employed to analyze the spatial variability. The findings revealed that exponential model was appropriate for B, Fe, Mn, Zn, and OC; Gaussian for K; J-Bessel for N; K-Bessel for Cu, P, and S; stable for EC and rational quadratic for pH, respectively. The analysis showed a strong to weak spatial dependency. In the study area, the spatial variability maps exhibited deficiencies of 97%, 96% and 40% for N, OC and Zn, respectively. Therefore, it is urgent to apply suitable manures and fertilizers in the study area to address these issues. The study area exhibited significant variation in spatial patterns, emphasizing the importance of implementing field-specific plans for soil health and environmental management.

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Publication Details

Journal
International Journal of Environment and Geoinformatics
Published
2024-09-10
DOI
https://doi.org/10.30897/ijegeo.1465671
Primary Topic
Soil Geostatistics and Mapping
Type
article
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article

Modeling the Spatial Variability of Soil Nutrients - A Case from Soil Health Card Project, India

Ranga Rao Velamala, Pawan Kumar Pant
International Journal of Environment and Geoinformatics
Soil Geostatistics and Mapping
article

Modeling the Spatial Variability of Soil Nutrients - A Case from Soil Health Card Project, India

Ranga Rao Velamala, Pawan Kumar Pant
article en

Abstract

Ascertaining and mapping soil nutrient data is crucial for governments to maintain soil health on farmlands. As part of the soil health card project, a total of 329 geo-referenced soil samples were collected from Thaticherla village, Anantapur mandal, Andhra Pradesh, India. These samples were analyzed for various soil properties such as soil pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), available phosphorus (P), available potassium (K), available sulphur (S), DTPA extractable micronutrients (Fe, Mn, Zn, Cu), and hot water-soluble boron (B) at a depth of 0 to 15 cm. The results showed high variability (>35%) in coefficients of variation in Cu, EC, Zn, and B. The findings indicated positive correlation between Zn and Mn; N and OC; and OC and Zn. The data underwent logarithmic and Box-Cox transformations to achieve normalization. The ordinary kriging method was employed to analyze the spatial variability. The findings revealed that exponential model was appropriate for B, Fe, Mn, Zn, and OC; Gaussian for K; J-Bessel for N; K-Bessel for Cu, P, and S; stable for EC and rational quadratic for pH, respectively. The analysis showed a strong to weak spatial dependency. In the study area, the spatial variability maps exhibited deficiencies of 97%, 96% and 40% for N, OC and Zn, respectively. Therefore, it is urgent to apply suitable manures and fertilizers in the study area to address these issues. The study area exhibited significant variation in spatial patterns, emphasizing the importance of implementing field-specific plans for soil health and environmental management.

International Journal of Environment and GeoinformaticsVol. 11(3)
ICAR-National Bureau of Soil Survey and Land Use Planning (IN)
Zero hunger
Openalex Percentile: Top 86%
Soil Geostatistics and Mapping
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