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.

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 nonlinear), 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 < 0.01) with EC, respectively. Thus, it is clear that the SQ considerably reduces with an increase in soil salinity. The performance of the MDS-based SQIs was better than the TDS to discriminate different soil salinity classes. The agreement between the linear and non-linear scoring method of SQI had a linear relationship with a coefficient of determination (R 2) of 0.91-0.96. Thus, assessing the SQ of saltaffected acid soils using MDS, linear scoring, and weighted approach of the soil quality indexing could save the time and cost involved.

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Salt stress is a major abiotic stress affecting the productivity of rice crop worldwide. This study screened a set of 71 rice genotypes collected from coastal parts of Goa and Karnataka states in India for seedling stage salinity tolerance to identify genotypes alternative to standard salt-tolerant donors Pokkali, FL478, etc. Phenotyping for seedling stage tolerance was carried out under micro plots at an induced salinity of 12 dS/m. For haplotyping, 14 Saltol QTL linked simple sequence repeat (SSR) markers on chromosome 1 were utilized. On the basis of the response to salt stress, 10 genotypes were found tolerant, 16 moderately tolerant, 29 sensitive and 15 as highly sensitive. Among the SSR markers, marker RM10871 was the most polymorphic with a polymorphic information content of 0.90, exhibiting 14 different alleles followed by RM10793 (0.84) and RM3412 (0.80) with 8 and 7 alleles, respectively. These markers also exhibited high values for the effective number of alleles (Ne) and gene diversity (I). The haplotype analysis revealed that the allelic constitution of the Saltol region of 10 tolerant genotypes from our study varied in comparison to the reference tolerant check FL478. Further, the haplotype of three tolerant genotypes, namely, Goa Dhan 2, Panvel 1 and Goa wild rice (GWR) 005 appears to be completely different from the FL478 haplotype indicating tolerance in these genotypes is controlled by genomic region other than Saltol. These three genotypes with probable novel regions for seedling stage salt tolerance can be considered for enhancing salinity tolerance of rice cultivars.