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Soil salinity and acidity are some of the major causes of land degradation and have a negative impact on agricultural productivity. Assessing soil quality (SQ) of soils affected by soil salinity and acidity is required for their sustainable utilization for agricultural production. The aim of the present study was to evaluate the SQ of the salt-affected acid soils of the Indian West Coastal region using the additive and weighted soil quality indices (SQIs). The SQIs were developed using a total dataset (TDS) and a minimum dataset (MDS). The TDS comprised of 15 different soil properties as electrical conductivity (EC), pH, bulk density, soil available nitrogen (N), phosphorus (P), potassium (K), sulfur (S), boron (B), iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and exchangeable calcium (Ca), magnesium (Mg), and sodium (Na) measured on 300 soil samples (depth 0–0.15 m). Based on principal component analysis and correlation analysis, an MDS with soil properties like soil pH, EC, Na, Cu, Mn, and BD was formed. Using two approaches (additive and weighted), two datasets (TDS and MDS), and two scoring methods (linear and non-linear), eight SQIs were developed. The MDS-based linear weighted and non-linear weighted SQI found suitable to evaluate SQ of salt-affected acid soils and SQI had a significant and negative correlation of − 0.83 and − 0.70 (p

The coastal ecosystem is one of the most fragile ecosystems to climate change. Soil salinization in these ecosystems due to climate change-induced sea-level rise could be a major threat and constraint to agricultural production. Thus, assessing the soil quality of these soils using a suitable indexing method can help to decide the countermeasures for their sustainable utilization. The present study aimed to evaluate the soil quality of the salt-affected soils in the coastal region of India using different soil quality indexing methods. The soil quality indices (SQIs) were developed using two scoring methods: linear and non-linear of the minimum dataset and weighted approach. Based on electrical conductivity (EC1:2.5, EC in 1:2.5 soil to water ratio), the soils were categorized into five classes as non-saline, slightly-saline, moderately-saline, strongly-saline, and very strongly-saline. The soil salinity impacted the soil's physical, chemical, and biological properties significantly. Using principal component analysis and correlation, a minimum dataset comprising of eight soil properties namely basal soil respiration, urease enzyme activity, EC, soil available copper, zinc, boron, iron and soil pH was identified. The overall performance of the weighted SQIs developed using non-linear scoring was better than linear scoring. The weighted SQI developed using non-linear scoring (SQINLW) revealed that the class non-saline had the highest soil quality values and the very strongly saline the lowest. The SQINLW correlated strongly with the EC1:2.5 (r = 0.96; p moderately saline = slightly saline > strongly saline > very strongly saline and thus, the SQINLW could be used as an effective tool to assess the soil quality of salt-affected soils of the coastal region. The correlation analysis between the SQIs and grain yield for different salinity classes revealed significant (p

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.