Modeling the spatial variability and uncertainty of soil fertility parameters is crucial for sustainable agriculture but remains a challenge due to complex interactions between soil properties. Traditional models often assess individual parameters, such as pH or nitrogen (N), without considering their combined influence and uncertainty. This study develops a fuzzy logic and geoinformatics-based approach to simultaneously assess multiple soil fertility parameters. The model integrates 80 fuzzy rules to evaluate macro- and micronutrients, incorporating 250 soil samples analyzed using the PUSA Soil Test and Fertilizer Recommendation Meter (STFR). Experimental results showed soil fertility parameter ranges: pH (7.46–8.26), ECe (0.267–0.807 dS m⁻¹), organic carbon (0.24–0.56%), N (85.56–146.32 kg ha⁻¹), P (21.99–34.28 kg ha⁻¹), K (116.41–156.16 kg ha⁻¹), S (5.60–20.86 mg kg⁻¹), Fe (1.065–5.095 mg kg⁻¹), Mn (2.058–2.637 mg kg⁻¹), Zn (0.748–1.105 mg kg⁻¹), B (0.372–0.530 mg kg⁻¹), and Cu (0.230–0.788 mg kg⁻¹). The fuzzy model-derived fertility scores ranged from 41.55 to 52.60, with pH, organic carbon, nitrogen, phosphorus, potassium, and iron as critical parameters influencing fertility. Geostatistical kriging interpolation estimated fertility values at unsampled locations, generating a continuous, high-resolution soil fertility map for precision agriculture. Validation with crop yield data ranked suitability as: Pearl millet (0.919) > Mustard (0.890) > Wheat (0.863) > Barley (0.861). Multi-criteria decision analysis confirmed pearl millet as the most suitable crop based on fertility and yield potential. The study categorizes soil into low and moderate fertility zones across Jhunjhunu, Rajasthan, ensuring a systematic assessment for optimal nutrient management. By integrating fuzzy logic with GIS-based spatial modeling, this study enhances soil fertility classification, site-specific nutrient recommendations, and sustainable crop planning, reinforcing the role of fuzzy-GIS frameworks in precision agriculture.

Spatially explicit soil fertility diagnosis is still difficult in data sparse paddy cultivated areas because single soil test indicators cannot separate soil chemical condition, primary nutrient supply, micronutrient balance and yield relevance. This study developed a soil-test driven, domain-balanced Soil Fertility Index (SFI) framework using 64 georeferenced long-term paddy-field samples from Hanumakonda district, Telangana, India. Ten laboratory indicators (pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, zinc, iron, copper, and manganese) were transformed into agronomic scores and aggregated into Chemical Condition, Macronutrient, Micronutrient, and Overall Soil Fertility indices. Regularized regression models were used to approximate the derived fertility-index targets from the original soil-test variables and to identify the conditional contribution of individual indicators within each index formulation. Ridge, Lasso and ElasticNet regression models were evaluated using five-fold cross-validation within the training dataset ( n = 51) and independent test-set evaluation ( n = 13). Among the evaluated models, Lasso provided the best prediction of Overall SFI (R ² = 0.594, RMSE = 0.033, RPD = 1.63), whereas ElasticNet performed best for the Micronutrient Index (R ² = 0.683, RPD = 1.85) and showed stable prediction of the Macronutrient Index (R ² = 0.577, RPD = 1.60) Relative zone mapping showed that low to moderate priority classes dominated the predicted overall SFI surface, while the Micronutrient Index produced the sharpest local management contrast. The predicted fertility surfaces were mapped across approximately 140,387 ha within the empirical point-covered sampling domain using a 30 m spatial analysis grid. Independent yield validation supported the agronomic relevance of the framework. Available nitrogen and organic carbon were strongly correlated with yield. Yield validation showed that predicted MNI relative zones significantly separated productivity classes, with mean yield increasing from 22.52 to 26.81 q acre ⁻¹ (Kruskal-Wallis H = 17.52, p < 0.001), whereas Overall SFI, MiNI and SFI 5core zones did not show significant yield separation. The proposed framework provides an interpretable and reproducible decision-support approach for prioritizing soil testing and site-specific nutrient management in data-sparse paddy landscapes.

Reducing dependence on mineral fertilizers requires integrated nutrient management (INM) strategies that sustain maize productivity while improving soil functioning. A field experiment evaluated nine combinations of mineral NPK, farmyard manure, vermicompost, and Azotobacter-Bacillus inoculation in a randomized block design with three replications. The 75% NPK treatments integrated with either FYM or vermicompost and inoculation formed the leading treatment group. T7 (75% NPK + 5 t vermicompost ha⁻¹ + Azotobacter–Bacillus) recorded the highest observed grain yield (40.8 q ha⁻¹), stover yield (62.5 q ha⁻¹), and total N, P, and K uptake (121.0, 31.3, and 92.2 kg ha⁻¹, respectively), but these responses did not differ significantly from T6. Microbial biomass carbon and dehydrogenase activity were also highest under T7 (112 mg kg⁻¹ and 4.4 µg TPF g⁻¹ h⁻¹, respectively) and remained statistically comparable with T6. Available soil K was the only variable across nutrient uptake, soil fertility, micronutrient availability, and biological activity that differed significantly between T7 and T6. Relative to sole 100% NPK, T7 increased microbial biomass carbon by 32.9% and dehydrogenase activity by 51.7%. T7 generated gross returns of ₹99,340 ha⁻¹ (US$1,263.79 ha⁻¹), net returns of ₹59,722 ha⁻¹ (US$759.78 ha⁻¹), and economic efficiency of ₹567.66 ha⁻¹ day⁻¹ (US$7.22 ha⁻¹ day⁻¹). Principal component analysis associated the 75% NPK-based treatments with greater water-holding capacity, soil organic carbon, nutrient availability, and microbial activity. Thus, the complete 75% NPK-based organic-biological treatment packages provided effective fertilizer-saving options under the tested conditions.

Target-yield fertilizer prescriptions are usually validated through crop yield, but their link with rhizosphere biological functioning remains less understood in Alfisol based pulse systems. This short-term field study evaluated whether FYM adjusted STCR-IPNS could improve greengram productivity, nutrient uptake, enzyme activity, microbial response, and PCA derived soil quality index (SQI). Greengram cv. CO 8 was grown in a 12 treatment randomized block design comparing STCR-NPK and FYM adjusted STCR-IPNS at 0.8, 1.0, and 1.2 t ha^− 1 yield targets with vermicompost, FYM, RDF, RDF + FYM, farmer practice, and control treatments. STCR-IPNS at the 1.2 t ha^− 1 target produced the highest crop response, with plant height, LAI, and SPAD values of 56.63 cm, 3.98, and 50.76, respectively. The treatment achieved 105.8% of the target yield and recorded higher response ratio, benefit: cost ratio, crude protein content, and crude protein yield than the other nutrient management options. Harvest stage N, P and K uptake peaked at 56.01, 16.96 and 41.43 kg ha^− 1, respectively. FYM adjusted STCR-IPNS also improved microbial biomass, selected nutrient cycling enzyme activities, and PCA derived SQI. These results indicate that FYM adjusted STCR-IPNS can support greengram productivity while strengthening biological soil quality indicators under the tested Alfisol condition.

Phosphorus (P) - zinc (Zn) interactions regulate nutrient acquisition, physiological function, productivity, and seed nutritional quality. However, their integrated effects in blackgram remain insufficiently characterized. A single-season field experiment was conducted with blackgram cv. VBN 8 on a Zn-deficient Typic Haplustalf using a 4 × 3 factorial randomized block design (FRBD) with three replications. All plots received a uniform basal application of 25 kg ZnSO₄ ha⁻¹. Treatments comprised four P₂O₅ rates (0, 25, 50, and 75 kg ha⁻¹) and three supplemental foliar Zn-EDTA concentrations (0, 0.5, and 1.0%), applied at 30 and 45 days after sowing. Soil biochemical properties, plant nutrient concentration and uptake, carbonic anhydrase activity, growth, yield, seed Zn concentration, phytic acid concentration, and phytic acid molar ratio were evaluated. Factorial ANOVA revealed significant P × Zn-EDTA interactions for seed and haulm yields. P₂Zn₁ produced the highest numerical seed yield of 9.48 q ha⁻¹, compared with 5.47 q ha⁻¹ under P₀Zn₀, representing a significant increase of 73.3%. However, its yield was statistically comparable with P₂Zn₂ at 9.27 q ha⁻¹. P₂Zn₁ also recorded the highest seed Zn concentration of 52.75 mg kg⁻¹ and a favourable phytic acid molar ratio of 22.92. Under the tested single-site and single-season conditions, 50 kg P₂O₅ ha⁻¹ combined with 0.5% supplemental foliar Zn-EDTA delivered the most favourable integrated productivity and seed-quality response. The phytic acid molar ratio was interpreted as an indirect indicator of potential seed Zn bioavailability rather than a direct measure of Zn absorption. These findings provide a strong scientific basis for multi-season and multi-environment validation before broader agronomic recommendation.