Dr. T. ANJAIAHKONGA BHUVANA2024-09-232024-09-232024-03-11https://krishikosh.egranth.ac.in/handle/1/5810214803Soil quality is the integrated effect of management on most soil properties that determine agricultural crop productivity and sustainability. The present investigation was undertaken for assessing the soil quality of high (HPRS), medium (MPRS) and low (LPRS) productive rice soils of Mahabubnagar district to establish the Minimum Data Set (MDS) of soil quality indicators and discriminate the variation in productivity areas. A total of 225 samples, comprising 75 from each category (HPRS, MPRS, LPRS), were meticulously collected before transplanting for rabi season, 2022-2023 using stratified random sampling method. Twenty-four soil parameters encompassing physical [soil texture (sand, silt, clay %), BD, MWHC], chemical [(pH, EC, SOC, TC, available N, P2O5, K2O, S, Cu, Mn, Fe, Zn, exchangeable cations (Caex, Mgex, Naex and Kex)], and biological (DHA, ACP, ALP) parameters were determined. For biological analysis, fresh soil samples were collected. The Karl-Pearson correlation analysis unveiled significant correlations among the soil properties at both the 0.05 and 0.01 significance levels. Further scrutiny through one-way ANOVA highlighted the significance of soil properties (P ≤ 0.05) namely clay content, BD, MWHC, EC, SOC, avail. N, S, Zn, Caex, Mgex, Naex, Kex, DHA, ALP as the distinguishing soil characteristics among the three rice productivity soils. The Tukey’s Honestly Significant Different test indicated that under LPRS; BD, EC and Naex were significantly higher than HPRS; whereas MWHC, SOC, avail. N, Zn, Kex, DHA, ALP were significantly lower than HPRS and clay content, Avail. S, Caex, Mgex were significantly lower than HPRS and MPRS. Remarkably, HPRS showed markedly superior soil characteristics like lower BD, EC, and Naex coupled with higher clay content, MWHC, SOC, available N, S, Zn, exchangeable cations and improved DHA and ALP activity than those of LPRS. The fourteen attributes having significant differences underwent principal component analysis (PCA), revealing that the first five PCs (eigen value ≥1) accounted for 72.40 % of the total variance. The resulting MDS included SOC in PC1, Caex in PC2, EC in PC3, Naex in PC4 and Avail. Zn in PC5. The respective weighing factors for PCs are 0.445, 0.224, 0.124, 0.107 and 0.100 for PC5. The contribution of retained MDS variables followed the order: SOC (44.5 %) > Caex (22.4 %) > EC (12.4 %) > Naex (10.7 %) > Avail. Zn (10.0 %). Discriminant analysis identified clay content, BD, avail. S, avail. Zn, Caex, Mgex, Naex and ALP as the discriminating soil quality indicators between HPRS, MPRS, and LPRS, particularly highlighting distinctive features of LPRS. After scoring and weighting for the retained MDS indicators, soil quality index (SQI) was calculated that showed varying mean ± SD for HPRS (0.61 ± 0.10), MPRS (0.56 ± 0.10), and LPRS (0.48 ± 0.08). A significant correlation was observed between SQI and farmer’s rice yields of the three rice productivity soils. The relation between MDS (independent variable) and rice yields (dependent variable) tested using step-wise regression analysis inferred that all the five MDS significantly correlated the yield (R2 = 0.76) and of them; SOC, Caex, Avail. Zn exhibited positive correlations, while EC and Naex displayed negative correlations with the rice yields and explained 76 % variation. The remaining 24 % variation is explained by the soil properties which were dropped and not considered in the study. The findings underscore the critical role of soil quality assessment in shaping sustainable land use management and enhancing grain yield, particularly addressing the constraints associated with low SOC, Caex, Avail. Zn, and elevated EC and Naex in LPRS.EnglishThesis