Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R² ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R² ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R² ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

The effect of medium-term (5 years) application of organic and inorganic sources of nutrients (as mineral or inorganic fertilizers) on soil organic carbon (SOC), SOC stock, carbon (C) build-up rate, microbial and enzyme activities in flooded rice soils was tested in west coast of India. Compared to the application of vermicompost, glyricidia (Glyricidia maculate) (fresh) and eupatorium (Chromolaena adenophorum) (fresh) and dhaincha (Sesbania rostrata) (fresh), the application of farmyard manure (FYM) and combined application of paddy straw (dry) and water hyacinth (PsWh) (fresh) improved the SOC content significantly (p < 0.05). The lowest (p < 0.05) SOC content (0.81%) was observed in untreated control. The highest (p < 0.05) SOC stock (23.7 Mg C ha⁻¹) was observed in FYM-treated plots followed by recommended dose of mineral fertilizer (RDF) (23.2 Mg C ha⁻¹) and it was lowest (16.5 Mg C ha⁻¹) in untreated control. Soil microbial biomass carbon (C_mb) (246 µg g⁻¹ soil) and C_mb/SOC (1.92%) were highest (p < 0.05) in FYM-treated plot. The highest (p < 0.05) value of metabolic quotient (qCO₂) was recorded under RDF (19.7 µg CO₂-C g⁻¹ C_mb h⁻¹) and untreated control (19.6 µg CO₂-C g⁻¹ C_mb h⁻¹). Application of organic and inorganic sources of nutrients impacted soil enzyme activities significantly (p < 0.05) with FYM causing highest dehydrogenase (20.5 µg TPF g⁻¹ day⁻¹), phosphatase (659 µg PNP g⁻¹ h⁻¹) and urease (0.29 µg urea g⁻¹ h⁻¹) activities. Application of organic source of nutrients especially FYM improved the microbial and enzyme activities in flooded and transplanted rice soils. Although the grain yield was higher with the application of RDF, but the use of FYM as an organic agricultural practice is more useful when efforts are intended to conserve more SOC and improved microbial activity.