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Pigeon pea is the second most important grain legume in India, primarily grown under rainfed conditions. Any changes in agro-climatic conditions will have a profound influence on the productivity of pigeon pea (Cajanus cajan) yield and, as a result, the total pulse production of the country. In this context, weather-based crop yield prediction will enable farmers, decision-makers, and administrators in dealing with hardships. The current study examines the application of the stepwise linear regression method, supervised machine learning algorithms (support vector machines (SVM) and random forest (RF)), shrinkage regression approaches (least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)), and artificial neural network (ANN) model for pigeon pea yield prediction using long-term weather data. Among the approaches, ANN resulted in a higher coefficient of determination (R

Soil moisture is a key parameter in the hydrological cycle and plays a critical role in global climate. The capacity to forecast drought and floods, manage water resources, and make field-scale decisions depends on accurate and thorough information on soil moisture. In addition to the instrument-based field observation approaches, dynamic mapping of soil moisture has been made possible by satellite remote sensing technologies. Estimates of soil moisture at a global and regional scale from optical and thermal remote sensing have been explored, and considerable advancements have been made. However, these global soil moisture products have coarse spatial resolutions and are typically unsuitable for field-level hydrological and agricultural applications. In this regard, this chapter presents a comprehensive review of the latest downscaling methods to improve the coarse-spatial and temporal resolution of soil moisture products. The main approaches discussed in the chapter include active passive fusion, optical/thermal based, topography based, and data assimilation methods. The physical background, current status, advantages and limitations associated with each downscaling approach has been thoroughly examined. Each of these optical/thermal, microwave-based methods for soil moisture estimation involves intricate derivation at different spatiotemporal scales, which can be combined using recent advances in machine learning.

Identifying sustainable crop production systems that improve yield from existing farmland while improving resource use efficiency is critical to meet the growing demands of the increasing human population and diminishing natural resources. Considering the increasing cost associated with inorganic fertilizer, integrated nutrient management using both organic and inorganic sources is important. Therefore, optimizing nutrient management practices that increase yield, improve soil quality, build up soil organic carbon storage, and maintain energy balance can help achieve sustainability in farming systems. In this regard, different nutrient management practices under the rice–rice (Oryza sativa L.) cropping system were evaluated using five different criterions, namely, (i) crop yield response, (ii) soil quality, (iii) soil carbon stock, (iv) energy efficiency, and (v) profitability under lowland situations in the Goa state situated in the western coast of India. We tested six nutrient management treatments, namely, inorganic fertilizers, organic (farmyard manure), rice straw, and their combinations over three years from 2016 to 2019. The results revealed that integrated nutrient management improved soil carbon stock, microbial biomass carbon, and soil fertility more than the other treatments. The integrated use of farmyard manure and chemical fertilizer showed significantly higher crop yield (9.86 v/s 9.41 Mg ha−1), microbial biomass carbon (354 v/s 233.7 mg kg−1 soil), soil carbon stock (36.65 v/s 25.5 Mg C ha−1), energy efficiency (23.8 v/s 22.3), and net return (1776 v/s 1508 USD) than those associated with chemical fertilizer alone. We conclude that the application of chemical fertilizers/organic sources alone may not be sustainable for the rice–rice cropping system in the Goa state of India; the focus should be on integrated nutrient management systems.

Cashew is an important cash crop which is ecologically sensitive, making it vulnerable to climate change. So, the present study compares the performance of stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), elastic net, and artificial neural network (ANN) individually against the ANN model combined with SLR, LASSO, elastic net, and principal components analysis (PCA) for prediction of cashew yield based on weather parameters. The model performances were evaluated using three approaches: (1) Taylor plot; (2) statistical metrics like coefficient of determination (R

Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with Analytical Hierarchy Process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF) and support vector machine (AHP-SVM) model. The model performances were assessed through area under the receiver operating characteristic (AUC). AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977) and AHP-GBM (0.975). The study revealed that AHP combined with RF could significantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The findings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.

A study has been conducted on frequency of western disturbances (WDs) and variability of precipitation over J&K for the period 1980 to 2019. The frequency of WD’s from November to April during study period was determined and results showed that total 600 WD’s cases were observed during the period 1980-2019. It has been observed that on an average during the month of November and December, 2 moderate to severe WD’s cases were observed and 3 cases during January, February, March and April respectively. Trends of WD and Precipitation over different months were analyzed using Innovative trend analysis, Mekendal test and linear regression analysis. The analysis showed that WD frequencies showed decreasing significant trend (p

During 2018 an intensive study was conducted to determine the viruses associated with cucurbitaceous crops in nine agroclimatic zones of the state of Uttar Pradesh, India. Total of 563 samples collected and analysed across 14 different cucurbitaceous crops. The results showed the dominance of Begomovirus (93%) followed by Potyvirus (46%), cucumber green mottle mosaic virus (CGMMV-39%), Polerovirus (9%), cucumber mosaic virus (CMV-2%) and Orthotospovirus (2%). Nearly 65% of samples were co-infected with more than one virus. Additionally, host range expansion of CMV, CGMMV and polerovirus was also observed on cucurbit crops. A new potyvirus species, zucchini tigre mosaic virus, earlier not documented from India has also been identified on five crops during the study. Risk map generated using ArcGIS for virus disease incidence predicted the virus severity in unexplored areas. The distribution pattern of different cucurbit viruses throughout Uttar Pradesh will help identify the hot spots for viruses and will facilitate to devise efficient and eco-friendly integrated management strategies for the mitigation of viruses infecting cucurbit crops. Molecular diversity and evolutionary relationship of the virus isolates infecting cucurbits in Uttar Pradesh with previously reported strains were understood from the phylogenetic analysis. Diverse virus infections observed in the Eastern Plain zone, Central zone and North-Eastern Plain zone indicate an alarming situation for the cultivation of cucurbits in the foreseeable future.

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest.

Eutrophication level in lakes and reservoirs depends on both internal and external phosphorus (P) load. Characterization of sediment P fractionation and identifying the P pollution sources are important for assessing the bio-availability of P and the dominant P source, for effectively controlling the water pollution. For determining the availability and sources of sediment P and eutrophication status, spatio-temporal variation in different P fractionation of sediment of hyper-eutrophic Krishnagiri reservoir, Tamil Nadu, India, was investigated. Sediment average total P (TP) content ranged from 4.62 to 5.64 g/kg. Main phosphorus form was the inorganic P (IP), and it makes up to 73.4–87.7% of TP. Among the different P fraction, viz. calcium bound (Ca-P), iron bound (Fe–P), aluminium bound (Al-P), exchangeable (Ex-P) and Organic-P (Org-P), Ca-P was the dominating fraction in both IP and TP. Trend of IP fraction was as follows: Ca-P > Fe–P > Al-P > Ex-P in pre-monsoon season, Fe–P > Ca-P > Al-P > Ex-P in monsoon and Ca-P > Al-P > Fe–P > Ex-P in post-monsoon. Overall the trend was as follows Ca-P > Fe–P > Al-P > Org-P > Ex-P. Bio-available-P (BAP) fractions ranged from 35.2 to 64.0% of TP, indicating its comparative higher value. Pearson’s correlation matrix revealed that there was strong correlation among the different P fractions. Factor analysis indicates that different fractions of P were the dominating factor than the other sediment parameters. The observed variation in sediment P fractionation indicate the differences in source and characterization of P which is very helpful for implementation of effective management practices in controlling pollution that arises due to phosphorus in this hyper-eutrophic reservoir.

Spatio-temporal trends of rainfall during the last 119 years (1901 to 2019) in different meteorological sub-divisions of India were analyzed using gridded rainfall data. Innovative trend analysis (ITA) was performed for detecting the trends in seasonal and annual rainfall. The results of long-term trends and their magnitudes obtained from the ITA method were compared with traditional Mann-Kendall (M-K), modified Mann-Kendall (mM-K), and linear regression analysis (LRA) methods. Significant trends in seasonal and annual rainfall were detected in almost all the sub-divisions of India. The monsoon and annual rainfall showed increasing trends in most sub-divisions of the peninsular and northwest India, whereas decreasing trends were observed in the central northeast part of the country. Winter rainfall showed decreasing trends in most of the sub-divisions of the country. Results of M-K/mM-K and LRA concurred with the trends detected by ITA. However, the ITA is more sensitive in detecting hidden trends missed out by the traditional M-K/mM-K and LRA tests. This study will provide scientific reference for assessment and proactive mitigation of climate change impacts on water resources to manage the risk of climate variability.

Biological invasions are a worldwide threat to the aquatic systems and have the potential to homogenize entire foodwebs and shift species abundance distributions to more skewed ones. Invasion impacts include effects on the foodweb structure and ecosystem functioning leading to a loss in native fish biodiversity and commercially important fisheries in many open water systems. The impacts of invasives are generally devastating as they spread in the foodweb with each species potentially interacting with multiple species. The foodweb modeling studies conducted in different inland aquatic ecosystems show that not all exotics cause a detrimental effect on the resilience of an ecosystem. Information on the foodweb structure and ecosystem properties is a prerequisite for formulating management protocols for conserving biodiversity, enhancement programmes and sustaining fisheries. The present paper reviews the impacts of invasive fishes in Indian aquatic ecosystems in the context of a changing foodweb scenario due to exotic fish species invasions. The information generated here could be applied for future research on similar ecosystems for deducing management actions.

Land degradation is becoming a serious problem in the west coast region of India where one of the world's eight biodiversity hotspots, the ‘Western Ghats’, is present. Poor land management practices and high rainfall have led to increasing problems associated with land degradation. A long-term (13-year) experiment was done to evaluate the impact of soil and water conservation measures on soil carbon sequestration and soil quality at three different depths under cashew nut cultivation on a 19% slope. Five soil and water conservation measures - continuous contour trenches, staggered contour trenches, half-moon terraces, semi-elliptical trenches, and graded trenches all with vegetative barriers of Stylosanthes scabra and Vetiveria zizanoides and control were evaluated for their influence on soil properties, carbon sequestration, and soil quality under cashews. The soil and water conservation measures improved significantly the soil organic carbon, soil organic carbon stock, carbon sequestration rate and microbial activity compared to the control condition (without any measures). Among the measures tested, continuous contour trenches with vegetative barriers outperformed the others with respect to soil organic carbon stock, sequestration rate, and microbial activity. The lower metabolic quotient with the measures compared to the control indicated alleviation of environmental stress on microbes. Using principal component analysis and a correlation matrix, a minimum dataset was identified as the soil available nitrogen, bulk density, basal soil respiration, soil pH, acid phosphatase activity, and soil available boron and these were the most important soil properties controlling the soil quality. Four soil quality indices using two summation methods (additive and weighted) and two scoring methods (linear and non-linear) were developed using the minimum dataset. A linear weighted soil quality index was able to statistically differentiate the effect of soil and water conservation measures from that of the control. The highest value of the soil quality index of 0.98 was achieved with continuous contour trenches with a vegetative barrier. The results of the study indicate that soil and water conservation measures for cashews are a potential strategy to improve the soil carbon sequestration and soil quality along with improving crop productivity and reducing the erosion losses.

Rugose spiraling whitefly (RSW), Aleurodicus rugioperculatus Martin, is a highly polyphagous invasive pest native to Central America. The occurrence of A. rugioperculatus in the Oriental region was first reported from Pollachi, Tamil Nadu, India in 2017. This pest is widely distributed in India, causing severe economic damage to coconut and other horticultural crops. It is a recent invasion in India and information on its potential distribution is lacking. Thus, in the present study we used the latest Coupled Model Intercomparison Project phase 6 (CMIP6) dataset through Maximum Entropy species distribution modelling (version 3.4.1, MaxEnt) to determine the potential distribution of RSW in present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 126 and SSP585 emission scenarios. The performance of the model was evaluated using the area under the curve (AUC), true skill statistics (TSS) and the continuous Boyce index (CBI).The MaxEnt model performed well and predicted the potential distribution of A. rugioperculatus with high-accuracy AUC values of 0.991 and 0.989, TSS values of 0.891 and 0.842, and CBI values of 0.972 and 0.934 for training and testing, respectively. Jackknife analysis revealed that A. rugioperculatus distribution was mostly influenced by temperature-based bioclimatic variables contributing 62.1% of the suitability, with precipitation variables contributing the remainder. The most important bioclimatic variables for RSW distribution were annual mean temperature (Bio 1, 28.9%) followed by mean diurnal range (Bio 2, 19.5%) and annual precipitation (Bio 12, 19.1). Potential suitable areas for RSW establishment were mostly found in all coastal and southern states of India. A. rugioperculatus prefers a warm and humid climate, indicating that the tropics, subtropics and temperate regions are ideal for its spread and invasion. Our results highlighted that the suitable habitat area for A. rugioperculatus is predicted to increase and highest probability of invasion and spread in 2050 and 2070 under future climate change scenarios of SSP126 and SSP585 compared to present climatic conditions.This is the first study to use the latest CMIP6 models and it predicts the potential distribution of RSW in India under present and future climate change scenarios. The implementation of strict domestic quarantine measures may prevent the spread and damage of RSW to noncoastal regions of India. The results of the current study should help in timely monitoring and surveillance of RSW and to formulate integrated pest management strategies at the national level to restrict its spread, invasion and damage to new areas. © 2022 Society of Chemical Industry.

In recent years, energy consumption and greenhouse gas (GHG) emissions are of major concern across the globe In this context, using data envelopment analysis, a study was done to determine the energy usage pattern and efficiency of rice farmers in the coastal state. The results showed that rice production systems had energy use efficiency, energy productivity, net energy, and human energy profitability of 2.40, 0.16 MJ/kg, 15728 MJ/ha, and 42.8, respectively. 5 and 21 farmers were judged to be efficient based on technical and pure technical efficiency, respectively. The mean scale efficiency of inefficient farmers was 0.68 which indicated the scope for refining agricultural practices to input use. Nitrogen, farmyard manure, and seeds have a positive impact on crop yield, whereas labor and diesel harm both crop yield and energy, according to the econometric model. The main non-renewable inputs contributing to GHG emissions are nitrogen fertiliser (72.1 kg CO2 eq./ha), fuel (68.5 kg CO2 eq./ha), and machinery (68.9 kg CO2 eq./ha). Indirect (81.7%) and non-renewable (73.8%) energy consumption was found to be higher than direct (18.3%) and renewable energy consumption (26.2%). Our findings suggested that farmers in this region should use conservation tillage and better crop management strategies to save energy and minimize GHG emissions.

Soil quality (SQ) under any agriculture system is greatly influenced by source of nutrients, type of soil, crop, and their management practices. In this regard, a field experiment was conducted und...

The research aims to propose the new ensemble models by combining the machine learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k-nearest neighbour (KNN), boosted...

Freshwater has a significant role in determining the ecological environment, public health and socio-economic development. This study analyzed the spatio-temporal variation in surface water quality of the tropical reservoir Mettur, India, to determine whether its water is suitable for human consumption and aquatic ecosystem. Thus, 21 water quality parameters were monitored seasonally (pre-monsoon, monsoon and post-monsoon) at nine sampling sites covering riverine, transitional and lacustrine zones of the reservoir during 2018. The study revealed spatial variation, which was due to input from industrial and domestic wastewater in the lacustrine zone of the reservoir. However, temporal variability often exceeded spatial variability. The factor analysis indicates that basin geology, anthropogenic stress and agricultural applications simultaneously act vital roles in deciding the reservoir water quality. Based on similarity of water quality parameters, cluster analysis grouped all the nine sampling sites into three clusters of high, moderate and less polluted sites. Correlation analysis also indicated that multiple sources influence the reservoir water quality during all the seasons. Based on BIS and WHO values, the calculated water quality index (134–158) indicates that water of the reservoir is not fit for human consumption, while the trophic state of this reservoir is moderately-eutrophic to eutrophic. However, with precaution, this aquatic body seems to be suitable for aquatic life and fish growth. The overall water quality deterioration of this reservoir indicates a strong need for development of a regular monitoring system for implementation of an effective management system to safeguard human and environmental health.

Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur (Rcal2 = 1, Rval2= 1 and RMSEcal = 0.002, RMSEval = 0.003) and Surguja (Rcal2 = 1, Rval2= 0.99 and RMSEcal = 0.004, RMSEval = 0.214) districts as compared to the other models, whereas for Bastar, ELNET (Rcal2 = 90, Rval2= 0.48) and LASSO (Rcal2 = 93, Rval2= 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R2 = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.

The unintended impact of natural summer fire on soil is complicated and rather less studied than its above-ground impact. Recognising the impact of a fire on silvopastoral soils and their resilience can aid in improving the management of silvopastoral systems. We studied the immediate (after 1 week (W)) and short-term (after 3 months (M)) recovery of different soil biological and chemical properties after the natural fire, with specific emphasis on phosphorus (P) dynamics. Soil samples were collected from four different layers (0-15, 15-30, 30-45, and 45-60 cm) of Morus alba, Leucaena leucocephala, and Ficus infectoria based silvopastoral systems. In the 0-15 cm soil layer, soil organic carbon (SOC) declined by ∼37, 42, and 30% after the fire in Morus-, Leucaena-, and Ficus-based systems, respectively within 1W of fire. However, after 3M of fire, Morus and Leucaena regained ∼6 and 11.5% SOC as compared to their status after 1W in the 0-15 cm soil layer. After 1W of the fire, soil nitrogen (N), sulfur (S), and potassium availability declined significantly at 0-15 cm soil layer in all systems. Iron and manganese availability improved significantly after 1W of the fire. Saloid bound P and aluminium bound P declined significantly immediately after the fire, increasing availability in all systems. However, calcium bound P did not change significantly after the fire. Dehydrogenase and alkaline phosphatase activity declined significantly after the fire, however, phenol oxidase and peroxidase activity were unaltered. Resiliencies of these soil properties were significantly impacted by soil depth and time. Path analysis indicated microbial activity and cationic micronutrients majorly governed the resilience of soil P fractions and P availability. Pasture yield was not significantly improved after the fire, so natural summer fire must be prevented to avoid loss of SOC, N, and S.