Leveraging cluster and PCA analysis to uncover key soil and environmental drivers for groundnut cultivation in the Kharif and Rabi seasons
DOI:
https://doi.org/10.14719/pst.4810Keywords:
groundnut, Kharif, Rabi, soil moisture, satellite, sensor, meteorology factorsAbstract
The cultivation of groundnut, which is crucial for its protein-rich kernels and edible oil, is highly sensitive to variations in soil moisture, particularly under rainfed conditions. The objective of the present study is to improve the accuracy of soil moisture monitoring by using principal component analysis (PCA) and clustering to analyze data from sensor and satellite sources. In addition to the use of satellite images from SMAP, ERA5 and Sentinel 1A in addition to in situ sensor data, this study was carried out at the Oil Seed Research Station in Tindivanam. Important factors, such as soil moisture, potential evaporation (PET) and volumetric water content (VWC) were examined at various crop stages. According to PCA, VWC at different depths and soil moisture data clustered closely during the Kharif season, indicating substantial relationships. A significant loading on the first component (PC1) explained 51.26 % of the variance. The significance of soil moisture and PET was highlighted by cluster analysis, which revealed four major clusters with strong intracluster relationships. On the other hand, PCA for the Rabi season revealed that ERA5-SM, WS and ST were crucial, with PC1 accounting for 67.53 % of the variation. Three clusters were found in the cluster analysis for Rabi, highlighting the significance of ST and WS in crop development. A study of the seasons revealed that during Kharif, soil moisture and evaporation were crucial, whereas during Rabi, soil temperature and wind speed had greater impacts. This emphasizes how vital it is to apply season-appropriate agronomic techniques to maximize crop productivity and resource efficiency.
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