Environmental Earth Sciences

Not AvailableThe 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 < 0.01) and the highest values of correlation coefficient in the SQINLW (r = 0.67?0.74, p < 0.01). The urease enzyme activity (35.1?66.6%) and EC (10.1?40.6%) contributed the most to the SQINLW and thus emphasizes the importance of these properties while assessing the soil quality of salt-affected soils. The soil quality indexing approach (non-linear scoring and weighting of a minimum dataset) identified in the study could reduce cost and save time and be a good guide for growers, land managers, extension specialists and policy or decision-makers for its utilization

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

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

Ecosystem Network Analysis in a Smallholder Integrated Crop–Livestock System for Coastal Lowland Situation in Tropical Humid Conditions of India

The integrated crop&ndash;livestock system (ICLS) is a farming strategy that helps to sustain agrobiodiversity, ecosystem services, and restores environmental sustainability. Furthermore, ICLS provides food and nutritional security to the small and marginal farmers in developing nations. In this context a mass-balanced ecosystem model was constructed for a smallholder ICLS along the Indian west coast to analyze the agro-ecological performance in terms of sustainability, resource use, nutrient balance and recycling. Thirteen functional groups were defined in the ICLS model with trophic levels ranging from 1.00 (detritus and benthic nitrogen fixers) to 3.00 (poultry and ruminants). The total system throughput index was estimated to be 1134.9 kg N ha&minus;1 year&minus;1 of which 60% was from consumption, 15% from exports, 10% from respiration, and the remaining 15% eventually flowing into detritus. The gross efficiency of the ecosystem was estimated to 0.3, which indicated higher growth rates and low maintenance energy costs. The higher food self-sufficiency ration of 7.4 indicated the integration of crop&ndash;livestock as an imperative system to meet the food and nutritional requirement of the farm family. The indices such as system overhead (60%), Finn&rsquo;s cycling index (16.6) and mean path length (3.5) denoted that the ICLS is a small, resource-efficient, stable, maturing and sustainable ecosystem in terms of the ecosystem principles and recycling. The present model will serve as the first model on the ICLS from the humid tropics and will help in the evaluation of the other agro-ecological systems using the Ecopath modelling approach. In conclusion, farm intensification through crop and animal diversification has the highest impact on farm productivity, food self-sufficiency and resource-use-efficiency of the smallholder&rsquo;s livelihood security

Ecosystem Network Analysis in a Smallholder Integrated Crop–Livestock System for Coastal Lowland Situation in Tropical Humid Conditions of India

Not AvailableThe integrated crop–livestock system (ICLS) is a farming strategy that helps to sustain agrobiodiversity, ecosystem services, and restores environmental sustainability. Furthermore, ICLS provides food and nutritional security to the small and marginal farmers in developing nations. In this context a mass-balanced ecosystem model was constructed for a smallholder ICLS along the Indian west coast to analyze the agro-ecological performance in terms of sustainability, resource use, nutrient balance and recycling. Thirteen functional groups were defined in the ICLS model with trophic levels ranging from 1.00 (detritus and benthic nitrogen fixers) to 3.00 (poultry and ruminants). The total system throughput index was estimated to be 1134.9 kg N ha−1 year−1 of which 60% was from consumption, 15% from exports, 10% from respiration, and the remaining 15% eventually flowing into detritus. The gross efficiency of the ecosystem was estimated to 0.3, which indicated higher growth rates and low maintenance energy costs. The higher food self-sufficiency ration of 7.4 indicated the integration of crop–livestock as an imperative system to meet the food and nutritional requirement of the farm family. The indices such as system overhead (60%), Finn’s cycling index (16.6) and mean path length (3.5) denoted that the ICLS is a small, resource-efficient, stable, maturing and sustainable ecosystem in terms of the ecosystem principles and recycling. The present model will serve as the first model on the ICLS from the humid tropics and will help in the evaluation of the other agro-ecological systems using the Ecopath modelling approach. In conclusion, farm intensification through crop and animal diversification has the highest impact on farm productivity, food self-sufficiency and resource-use-efficiency of the smallholder’s livelihood security