Assessing spatial variability of soil and drawing location-specific management zones for coastal saline soils in Ramanathapuram District, Tamil Nadu
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Abstract
The production of crops in saline and alkali-degraded areas is difficult due to the heterogeneous and spatial variation of soil fertility. First, their spatial variability was analyzed and maps of the spatial distribution were created using Geostatistical techniques. The fuzzy k-mean clustering analysis was then used to define Management zones in the coastal saline soils of Ramanathapuram district in Tamil Nadu. One hundred and fifty geo-referenced soil samples (30 cm depth) were taken and analyzed for pH, electrical conductivity (ECe) in the saturated paste extract (USSL method), organic carbon (OC) (Walkley-Black chromic acid wet oxidation method), calcium carbonate (CaCO3) (Rapid titration method) and available phosphorus and extractable micronutrients (Multinutrients extraction method), revealing significant variation in soil characteristics throughout the coastal saline soils of Ramanathapuram district. The most significant factors, which together accounted for four principal components and 69% of the overall variability, were pH, electrical conductivity (ECe), calcium Carbonate and available zinc. According to Geostatistical analysis, the Exponential (pH, OC (organic carbon), P, Fe, Mn and Zn) and Stable (ECe) was the best fit semivariogram ordinary kriging model with weak to moderate spatial dependence. Fuzzy k-mean clustering was also used to identify zone 1, zone 2 and zone 3. For every soil property, there was a significant difference between MZ1(zone 1), MZ2(zone 2) and MZ3(zone 3). These results also showed that cluster analysis gave farmers a chance to use location-specific nutrient management strategies by minimizing variability within the zone. The management zones can decrease agricultural inputs and environmental pollutants while increasing crop productivity.
Article Details
Article Details
Fuzzy clustering, Geostatistics, Kriging, Management zones, Principal component analysis
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