This study compares component substitution (CS) and multiresolution analysis (MRA) pansharpening algorithms applied to high-resolution WorldView-4 imagery over the Indian Western Himalaya. The performance of these methods was evaluated using quantitative (i.e., visual assessment) and qualitative metrics (such as Relative Average Spectral Error (RASE), Root Mean Square Error (RMSE), Error Relative Global Dimensionless Synthesis (ERGAS), Bias, and the Fidelity-Deformation (FD) metric). The FD metric captures both spectral fidelity and spatial structure preservation by integrating localized and global error measures. The results indicated that MRA-based approaches (i.e., ATWT_M2, M3, and MTF_GLP) exhibit reduced spectral distortions, as reflected by lower Bias and RASE values, making them suitable for applications that demand high spectral fidelity. In contrast, CS-based approaches, such as HCS and BDSD, achieved lower ERGAS and RMSE values, suggesting improved spatial detail preservation. Overall, although pansharpened imagery may be advantageous for developing fine-resolution applications, the choice of the pansharpening algorithm should be made carefully, considering the specific application.

The focal point of this article is to study a functional equation of sum form involving four unknown mappings. We primarily concentrate on exploring all possible solutions of the equation. An attempt is made to delve into the intricacies of obtaining these solutions without assuming any regularity condition on the mappings. Our study further aims to examine the stability of these general solutions, thus shedding light on the behaviour of sum form functional equations under perturbations. The article concludes by reflecting upon the relevance of the general solutions in information theory and diversity index.

The most common cropping production system in South Asia, transplanted puddled rice followed by conventional-tillage wheat, is highly unsustainable, extremely energy-intensive, and emits a large amount of greenhouse gases. The practices used in conservation agriculture, including diversified cropping rotations, residue retention, zero-tillage direct-seeded rice, and zero-tillage wheat, can increase crop productivity while reducing energy use requirements and carbon footprints. Therefore, to promote a sustainable and energy-efficient conservation agriculture-based system with a less energy-intensive rice–wheat system, contrasting tillage and residue management scenarios were evaluated in this study. The treatments include triple cropping systems of zero-tillage direct-seeded rice (ZTDSR) during the rainy season, followed by zero-tillage rice–wheat–mungbean (ZTRWM) in winter, as well as zero-tillage rice–lentil–mungbean (ZTRLM), zero-tillage rice–chickpea–mungbean (ZTRCM), and zero-tillage rice–mungbean–mustard (ZTRMM) along with the conventional-tillage rice–wheat (CTRW) system. Zero-tillage systems exhibited significantly lower operational energy for irrigation (~40%), sowing (~26%), and land preparation (100%) compared to a conventional-tillage (CT) system. Compared to the conventional-tillage rice–wheat system, zero-tillage cropping systems achieved significantly higher system biomass yields. The zero-tillage system also increased wheat yields, resulting in a significant reduction in resources (fuel, fertilizer, and machinery) under zero-tillage (ZT) interventions. More than 60% of energy utilization came from crop residue, irrespective of the diverse cropping production systems. The maximum net energy returns, energy ratios, energy productivity, and energy intensity were recorded with the zero-tillage rice–wheat system. Zero-tillage production systems had significantly lower carbon footprints, higher carbon efficiency, and better carbon sustainability index than the conventional-tillage (CT) management system. Thus, it can be concluded that triple-zero-tillage production systems, along with residue management, yield lower net energy output, greenhouse gas emissions, and carbon footprints as compared to conventional-tillage-based systems.

Rice-fallow areas, widespread in rainfed rice-growing regions of South Asia, remain uncultivated during the post-rainy (winter) season due to multiple challenges, including inadequate irrigation infrastructure, cultivation of long-duration rice varieties, and soil moisture imbalances. South Asia has approximately 22.3 million hectares of rice-fallow land, with India contributing the largest share (88.3%). Eastern Indian states, which account for 82% of India’s rice-fallow area, presents significant opportunities for cropping intensification. However, several constraints—such as biotic (pest and disease), abiotic stresses (temperature extremes, drought, etc.), rapid soil moisture depletion, and disturbances from free-grazing livestock-hinder efforts to cultivate a second crop, perpetuating poverty among the small and marginal farmers. Introducing stress-tolerant rabi crops, particularly pulses (chickpea, lentil, lathyrus, field pea) and oilseeds (mustard, toria, safflower, linseed), offers a promising solution to enhance system productivity and improve the farmers’ livelihoods. Policymakers have recently increased the public investment in rice-fallows intensification, yet fragmented and ad-hoc initiatives often fail to deliver sustainable outcomes due to complex and multidimensional challenges involved. This study critically examines the key issues affecting rice-fallow lands and provides strategic recommendations to convert these underutilized areas into the productive cropping systems during winter and spring. Additionally, it reviews Central and State Government programs related to rice-fallow management, emphasizing the need for research to align with ongoing policy initiatives for maximum impact. The findings of this study offers a valuable insights for the policymakers, planners, and stakeholders, highlighting the potential of pulses and oilseeds to enhance the food security, reduce poverty, and promote sustainable, climate-resilient agricultural production systems in the region.

India's agricultural landscape is largely dominated by marginal and small farmers, who constitute approximately 67% and 18% of the total farming community respectively. This translates to a staggering 92 million marginal farmers with less than 0.40 hectares of land and 24 million small farmers with an average landholding of 1.42 hectares. Despite their higher productivity compared to large farmers, these smallholders face significant resource constraints and market challenges. While the formation of Farmers Producer Organisations (FPOs) has partially addressed these issues, newly established FPOs continue to encounter significant sustainability challenges. These include limited volumes, low capital, low member engagement and a subsidy-oriented rather than market-oriented approach. The present article identifies critical challenges such as limited capital, low member engagement and a predominant subsidy orientation. Currently, over 8875 FPOs are registered nationwide, but only 16-30% are sustainable. A significant issue is the inability of the majority of FPOs to raise more sizable amount of paid-up capital, highlighting the urgent need for a robust grassroots organisational ecosystem. This study proposes an eight-component ecosystem model for FPO sustainability, encompassing market, policy, infrastructure, services, inputs, HRD, finance and innovation. This model aims to create a comprehensive support structure for FPOs, facilitating better market access, financial resources, member participation and innovative practices. The implementation of this model, along with suggestive measures for strengthening FPOs, is crucial for their longterm viability in India's evolving agricultural sector.

Abstract Snow depth (SD) exhibits high spatiotemporal heterogeneity in Western Himalaya (WH), and its knowledge is essential for applications related to water resources, disaster management, climate, etc. However, due to inclement weather and rugged topographical conditions, only a sparse network of SD monitoring stations exists in WH. Spaceborne passive microwave (PMW) remote sensing data sets provides valuable information about SD; however, only a limited PMW SD studies that cover subregions of WH are available. Different machine learning (ML) methods viz. support vector machine, random forest, and Extremely Randomized Trees (ERT) were tested for estimating SD. Based on our preliminary assessment of these ML approaches, the current study utilizes ERT approach to estimate daily SD over the entire WH region. The ERT SD model is developed using PMW brightness temperature data sets from Advanced Microwave Scanning Radiometer‐2 (AMSR‐2), snow cover duration (SCD), and other auxiliary parameters (i.e., location, elevation, vegetation cover, etc.) during the winter period between 2012–2013 and 2019–2020. The data between 2012–2013 and 2017–2018 is used for training the model, whereas the data between 2018–2019 and 2019–2020 is used for testing the model. The results demonstrate: (a) The ERT SD model has shown improved SD estimates compared to the available PMW remote sensing‐based operational SD products and empirical PMW SD models. (b) In general, with an increase in SD, the mean absolute error of SD retrievals has increased in all SD products/models. (c) Unlike the operational AMSR2 SD product, and Northern Hemisphere Machine Learning SD product, the ERT SD model retrievals have shown better consistency with MODIS snow cover. (d) The developed model has shown a wider range in SD retrievals as compared to other products considered in this study.

Cereal crop (rice, wheat, maize etc.) residues mainly include the aboveground parts of plants that remain in an agricultural field after crop has been harvested. Nutritional value, organic matter content, and porous nature of cereal crop residues make them suitable for use on agricultural land to improve the soil physical, chemical, and biological properties. Application of crop residue into the soil needs proper strategies, which could sustain crop production as well as soil health. In this comprehensive review, efforts have been made to compile data on studies conducted the impact of residues on various soil health properties, current management practices and residue removing issues for a better understanding of soil health improvement strategies through cereal crop residue retention or incorporation. Further, the article discussed the relationship between cereal residue retention/incorporation and soil health properties by considering 113 peer-reviewed studies. We evaluated site-specific advantages and disadvantages involved on residue retention/incorporation. In addition, we found out the suitability of residue incorporation vs. retention, in respect to soil health improvement. This review found that residue retention or incorporation can positively influence soil physical, chemical, and biological characteristics associated with soil health. Significant influences of abiotic factors on residue management were observed in different locations. To our knowledge, this comprehensive review is the first one to present global view of current scenario of cereal crop residue management, their influence on soil health improvement, and residue removing issues with citing worldwide evidences.

Glacier retreat represents a highly sensitive indicator of climate change and global warming. Therefore, timely mapping and monitoring of glacier dynamics is strategic for water budget forecasting and sustainable management of water resources. In this study, Landsat satellite images of 2000 and 2015 have been used to estimate area extent variations in 29 glaciers of the Bhagirathi basin, Garhwali Himalayas. ASTER DEM has been used for extraction of glacier terrain features, such as elevation, slope, area, etc. It is observed from the analysis that Bhagirathi sub-basin has a maximum glaciated area of 35

Spatiotemporal snow depth (SD) mapping in the Indian Western Himalayan (WH) region is essential in many applications pertaining to hydrology, natural disaster management, climate, etc. In situ techniques for SD measurement are not sufficient to represent the high spatiotemporal variability in SD in the WH region. Currently, low-frequency passive microwave (PMW) remote-sensing-based algorithms are extensively used to monitor SD at regional and global scales. However, fewer PMW SD estimation studies have been carried out for the WH region to date, which are mainly confined to small subregions of the WH region. In addition, the majority of the available PMW SD models for WH locations are developed using limited data and fewer parameters and therefore cannot be implemented for the entire region. Further, these models have not taken the auxiliary parameters such as location, topography, and snow cover duration (SCD) into consideration and have poor accuracy (particularly in deep snow) and coarse spatial resolution. Considering the high spatiotemporal variability in snow depth characteristics across the WH region, region-wise multifactor models are developed for the first time to estimate SD at a high spatial resolution of 500 m × 500 m for three different WH zones, i.e., Lower Himalayan Zone (LHZ), Middle Himalayan Zone (MHZ), and Upper Himalayan Zone (UHZ). Multifrequency brightness temperature (TB) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), SCD data, terrain parameters (i.e., elevation, slope, and ruggedness), and geolocation for the winter period (October to March) during 2012–2013 to 2016–2017 are used for developing the SD models for dry snow conditions. Different regression approaches (i.e., linear, logarithmic, reciprocal, and power) are used to develop snow depth models, which are evaluated further to find if any of these models can address the heterogeneous association between SD observations and PMW TB. From the results, it is observed from the analysis that the power regression SD model has improved accuracy in all WH zones with the low root mean square error (RMSE) in the MHZ (i.e., 27.21 cm) compared to the LHZ (32.87 cm) and the UHZ (42.81 cm). The spatial distribution of model-derived SD is highly affected by SCD, terrain parameters, and geolocation parameters and has better SD estimates compared to regional and global products in all zones. Overall results indicate that the proposed multifactor SD models have achieved higher accuracy in deep snowpack (i.e., SD >25 cm) of the WH region compared to previously developed SD models.

This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region.

Snow covered crevasses in the Siachen glacier (Karakoram Range) cause great danger during glacier movement, and the knowledge of their spatial distribution is important for the safe travel. In the present study, crevasses in the Siachen glacier have been detected and further categorized as permanent open/hidden and seasonal open/hidden using a combination of optical (Landsat-8 and Sentinel-2) and microwave (ALOS-2) satellite data. The study is carried out for the year 2018. Initially, the locations of crevasses are manually marked using ALOS-2 data and further, their categorization in open and hidden is done using Sentinel-2 data. Apart from manual marking, the band ratio method is applied on Landsat-8 data to detect the permanent open crevasses in an automatic manner. Gray Level Co-occurrence Matrix (GLCM) technique has also been successfully attempted for the automatic classification of the crevasse and non-crevasse zones. The open crevasse locations using band ratioing are compared with manually marked and a good correlation is found having an accuracy of ~ 93%.  A total of 140 crevasse zones have been found within the study area, with 32% as permanent open, 29% as permanent hidden, and 39% as seasonal open/hidden. Manual digitization is done for estimating crevasse dimension (length and width) as this is important input required before the start of any movement. The study reveals that the highly varying nature of crevasses in terms of changes from hidden to open in lower altitude regions of the glacier is mainly observed between July and September. The present methodology of crevasse detection and categorization leads to crevasse information system, which will be used in future to monitor the opening and closing of crevasses in the Siachen glacier on regular basis.

Supraglacial debris influences the glacier-climate relationship by altering the ablation patterns of the debris-covered glaciers. The intricate surface morphology of debris-covered glaciers renders difficulties in their assessment through satellite remote sensing alone. In this context, unmanned aerial vehicles (UAVs) have the advantage of providing ultra-high-resolution maps of the debris-covered glacier surfaces, thus, aid in inferring the complexity in their behavior. Considering the limitations and to help elucidate the morpho-dynamic evolution of the debris-covered glacier surface, an in-depth investigation is conducted on the Panchi Nala-A glacier (area: 3 km2; debris percentage: 60

This work presents a metamaterial absorber (MMA) for X- and Ku-bands with a metallic resonating patch on top and a ground plane separated by substrate FR-4 with a thickness of $0.053~\lambda $ at the lowest resonance frequency. The proposed MMA demonstrates perfect absorption of 99.42, 98.48, 98.92, and 99.34 % at 9.948, 13.26, 14.92, and 15.80 GHz, respectively at normal incidence. The proposed MMA demonstrates perfect absorption for a polarization and incident angle over a wide range of angles up to 45°. To understand the fundamental EM behavior of the metamaterial structure, equivalent circuit analysis was carried out, and the circuit outputs accorded with the simulation results. This article also compares various machine learning (ML) methods for optimizing the design and predictive modeling of MMAs, such as decision trees, K-nearest neighbors, random forests, extra trees (ET), bagging, LightGBM, XGBoost, hist gradient boosting, cat boost, and gradient boosting regressors. The primary objective is to assess the usefulness of each regressor technique in estimating the performance of MMAs using multiple tests ranging from TC-40 to TC-80 and performance metrics such as adjusted R-squared score, MSE, RMSE, and MAE, in which the ET regressor excels. Simulation results suggest that ML-based techniques can save simulation resources and time while still being an efficient tool for predicting absorber behavior at intermediate and subsequent frequencies.

The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. The Indian Western Himalaya ecosystem (between the tree line and the snow line) is an underappreciated component. Yet, knowledge of vegetation distribution, rates of change, and vegetation interactions with snow-hydroclimatic elements is lacking. The purpose of this study is to investigate the linkage between the spatiotemporal variability of vegetation (i.e., greenness and forest) and related snow-hydroclimatic parameters (i.e., snow cover, land surface temperature, Tropical Rainfall Measuring Mission (TRMM), and Evapotranspiration (ET)) in Himachal Pradesh (HP) Basins (i.e., Beas, Chandra, and Bhaga). Spatiotemporal variability in forest and grassland has been estimated from MODIS land cover product (MCD12Q1) using Google Earth Engine (GEE) for the last 19 years (2001–2019). A significant inter- and intra-annual variation in the forest, grassland, and snow-hydroclimatic factors have been observed during the data period in HP basins (i.e., Beas, Chandra, and Bhaga basin). The analysis demonstrates a significant decrease in the forest cover (214 ha/yr.) at the Beas basin; however, a significant increase in grassland cover is noted at the Beas basin (459 ha/yr.), Chandra (176.9 ha/yr.), and Bhaga basin (9.1 ha/yr.) during the data period. Spatiotemporal forest cover loss and gain in the Beas basin have been observed at ~7504 ha (6.6%) and 1819 ha (1.6%), respectively, from 2001 to 2019. However, loss and gain in grassland cover were observed in 3297 ha (2.9%) and 10,688 ha (9.4%) in the Beas basin, 1453 ha (0.59%) and 3941 ha (1.6%) in the Chandra basin, and 1185 ha (0.92%) and 773 ha (0.60%) in the Bhaga basin, respectively. Further, a strong negative correlation (r = −0.65) has been observed between forest cover and evapotranspiration (ET). However, a strong positive correlation (r = 0.99) has been recorded between grassland cover and ET as compared to other factors. The main outcome of this study in terms of spatiotemporal loss and gain in forest and grassland shows that in the Bhaga basin, very little gain and loss have been observed as compared to the Chandra and Beas basins. The present study findings may provide important aid in the protection and advancement of the knowledge gap of the natural environment and the management of water resources in the HP Basin and other high-mountain regions of the Himalayas. For the first time, this study provides a thorough examination of the spatiotemporal variability of forest and grassland and their interactions with snow-hydroclimatic factors using GEE for Western Himalaya.

This article presents a machine learning-based multiband metamaterial absorber (MMA) for terahertz applica-tions that considerably reduces simulation time while ensuring accuracy. The designed MMA has absorption peaks at 2.93, 3.34, 3.88, 4.30, and 5.43 THz with absorption coefficients of 99.35, 87.12, 99.70, 99.70, and 99.71%, respectively. The proposed ultrathin and compact structure has a thickness of 0.031 & lambda; and a periodic dimension of 0.199 & lambda; corresponding to the lowest absorption frequency. Furthermore, the effect of changing the geometrical features of the MMA is investigated to realize the absorption phenomenon. The absorption co-efficients at intermediate frequencies with substrate thickness, periodic dimension, and angles are predicted using extreme randomized tree (ERT) model. Regression models are assessed using an adjusted R2 score as an evaluation metric utilizing various values of nmin and test cases; TC-30, TC-40, and TC-50. The adjusted R2 score approaches 1 for a lower value of nmin, indicating that absorption values can be predicted with a high degree of accuracy. The regression analysis results suggest that the resource requirements can be reduced by 70% using the proposed ERT-based absorber model. The proposed highly efficient multi-band MMA design with machine learning behavior prediction capability can be used for sensing applications.

In this study, 29 glaciers of the Bhagirathi basin, Garhwali Himalayas, have been monitored using remote sensing (RS) satellite images for more than a decade. Bhagirathi basin has sub-basins, namely Bhagirathi, Bhilangana, Pilang, Jahnvi, Jalandhari, and Kaldi. The glaciers area > 5 km2 has been considered apart from a few small glaciers to estimate glaciers retreat and advances. Landsat satellite images of 2000 and 2015 have been used to estimate areal extent change in glaciers. ASTER DEM has been used for extraction of glaciers terrain features such as elevation, slope, area, etc. It is observed from the analysis that Bhagirathi sub-basin has a maximum glaciated area of ~ 35% and Pilang has the least with ~ 3.2%, whereas Kaldi sub-basin has no glacier. In this region, out of 29 glaciers, 25 glaciers have shown retreat while 4 glaciers have shown advancement and resulting the total glacier area loss of ~ 0.5%, while the retreat rate varies from ~ 0.06 m/yr to ~ 19.4 m/yr. Dokarni glacier has maximum retreat rate (~ 19.4 m/yr), whereas, Dehigad has maximum advancing rate (~ 10.1 m/yr). Glaciers retreat and advance have also been analysed based upon terrain parameters and observed that north and south aspect orientations have shown retreat, whereas the area change is highly correlated with glacier length. The study covers more than 65% of the total glaciated area and as per our knowledge based on the existing literature; this is one of the initial exhaustive studies to cover the highest number of glaciers in all sub-basins of the Bhagirathi basin.

Flooding is a recurrent phenomenon in South Asian countries during the monsoon season. In the state of Bihar in eastern India, 55

In this paper we employ SIR model to study the Covid-19 data of South Africa for a chosen period. This model is solved using three numerical methods, namely, Differential Transform Method (DTM), Multistage Differential Transform Method (MsDTM), Repeated Multistage Differential Transform Method (RMsDTM) to obtain approximations of the number of susceptible, active infected and recovered in South Africa for 60 days starting from June 1, 2021. The proximity of the solution of the RMsDTM to the actual data in comparison to solutions using the other two methods was observed. MsDTM is an improvement over DTM as it uses updated values of the variables as new initial conditions at each iteration of the method. RMsDTM, in which the values of parameters are also changed at suitable intervals of time, besides using updated values of variables is a further improvement over both these methods.

There is an urgent need for identification of eco-friendly and cleaner production systems that are more productive, profitable, efficiently use energy/water/carbon input and are environmentally safer. Under that context, a long-term experiment was conducted during 2019–21 at the farmers’ fields of Krishi Vigyan Kendra (KVK), Gaya, Bihar. The main objective of the study was to evaluate the productivity of diverse cropping systems for irrigated and rainfed conditions. Nine cropping system, viz. transplanted puddled rice (TPR)–wheat (conventional-till)-fallow (farmers practices) [CS1],TPR-wheat(zero-till)-mung (ZT) [CS2], Conventional-till direct seeded rice (CTDSR)-mustard (ZT)-mung (ZT) [CS3], ZTDSR-lentil (ZT)-fallow [CS4], Maize (CT)-lentil (ZT)-mung (ZT) [CS5], Bajra (CT)- lentil (ZT)-mung (ZT) [CS6], Bajra (CT)-wheat (ZT)-mung (ZT) [CS7], TPR-chickpea (ZT)-fallow [CS8] and TPR-maize (CT)-fallow [CS9] were used for the present study. Maximum system productivity was recorded with maize (CT)-lentil (ZT)-mung (ZT) (13.2 t/ha), which was 46, 3.9, 13.8, 94.7, 22.2, 15.8, 39.5, 11.9% higher compared to CS1, CS2, CS3, CS4, CS6, CS7, CS8 and CS9, respectively. Net returns (`211677/ha) and Benefit cost (B:C) ratio (3.59) were recorded maximum with maize (CT)-lentil (ZT)-mung (ZT). Land use efficiency was the maximum with TPR-wheat (ZT)-mung (ZT) (92.6%). Carbohydrate equivalent yield was also maximum with TPR-wheat (ZT)-mung (ZT). Diversification of rice-wheat system with millets i.e. Bajra (CT)-lentil (ZT)-mung (ZT)/Bajra (CT)-wheat (ZT)- mung (ZT) improves the system productivity by 19.5–26.1% compared to TPR-wheat (CT)-fallow. Thus, the present study could be important to identify an alternate cropping systems for enhancing the overall system productivity and profitability sustainably through adoption of environment-friendly technologies.