Background: A long-term experiment on legume crops, integrated with various rice-based cropping systems, was initiated in the 2019-20 agricultural year under the All India Coordinated Research Project. This experiment is being conducted at the research farm of the Department of Agronomy, Acharya Narendra Deva University of Agriculture and Technology (ANDUAT), Ayodhya, Uttar Pradesh, India. The present study focuses on legume crops and was carried out during the 2022 and 2023 growing seasons. 
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Methods: The present investigation was conducted with ten treatments: T1-rice-wheat-fallow; T2-rice-wheat-green gram; T3- rice-french bean-green gram; T4-rice-gram-cowpea; T5-rice-mustard-green gram; T6-rice-linseed-black gram; T7-rice-berseem-sudan grass; T8-rice-oat-maize+cowpea; T9-rice-cauliflower-okra; and T10-rice-potato-cowpea (vegetable). 
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Result: The rice-frenchbean-greengram cropping sequence consistently outperformed other sequences in terms of grain and straw yields, biological yield and soil microbial activity. The inclusion of legumes and their residues significantly enhanced both yields and soil health, leading to improved microbial populations and enzymatic activities, particularly dehydrogenase and urease.

Assessment of Legume Crops&#39; Impact on Soil Microbial Populations, Enzymatic Activities and Productivity under Rice-based Cropping Systems

<div style="text-align: justify;">Soil organic carbon is strongly affected by agricultural management practices. Cropping systems can influence the amount of carbon present in soil. Increase in SOC can be related with the choice of crops present in the cropping sequence as well as on the management practices followed. The present study was undertaken to quantify the changes in soil carbon stock under different cropping systems. Two major cropping systems i.e. pearlmillet-wheat and pearlmillet-mustard were selected in Mewat, Haryana while soybean-wheat cropping systems was identified in Dhar, Madhya Pradesh. Results showed that SOC of surface soil layer decreased from 0.42% to 0.39% in pearlmillet-mustard cropping system during the study period. But in soybean-wheat cropping system it increased from 1.14% to 1.24%. Legume based cropping system showed enhancement of surface soil carbon.&nbsp;</div>

Changes in soil carbon stock in Mewat and Dhar under cereal and legume based cropping systems&nbsp;

<div>Background: Increasing demands of food, depleting land, degrading land resources and changing climate are some of the important factors driving the cropping systems of any agro-ecological zone in the world. Cropping systems have been traditionally structured to maximize crop yields. Now, there is a strong need to design cropping systems which take into consideration the emerging social, economical and ecological or environmental concerns. Legumes can provide opportunities for increased productivity to be sustained because of their adaptability to various cropping patterns and ability to fix nitrogen. Legume-based crop rotation helps to conserve organic matter, maximise soil nitrogen, balance soil nutrients, maintain soil physical properties and break soil-borne disease cycles. Moreover, with increasing fertilizer prices and emphasis of the government for doubling the farmer&rsquo;s income by reducing cost of cultivation, the current trend is to explore the possibility of supplementing chemical fertilizer with the organic ones, more particularly organic manures and bio-fertilizer of microbial origin. The current study is aimed to increase small and marginal farmer&rsquo;s income and to reduce cost of cultivation through legume based vegetable cropping systems and to find out best farming practices. 
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Methods: Field experiments were conducted at Organic Farm Holta, Department of Organic Agriculture and Natural farming, CSK HPKV, Palampur (H.P.) during kharif and rabi seasons of 2018-19 and 2019-20 on silty clay loam soil to study the performance of different farming practices in legume based cropping system under mid- hills of H.P. Himalayas. The experiment was laid out under Split plot design with three replications comprising of three sequences in legume vegetable-based cropping systems i.e., &ldquo;Soybean-Onion&rdquo;, &ldquo;Okra-Pea&rdquo; and &ldquo;Mash-Garlic&rdquo; under different farming practices i.e., Organic farming, Natural farming, Inorganic and Integrated farming practices. For comparison of different crops soybean equivalent yield and pea equivalent yields were calculated in kharif and rabi seasons, respectively. 
 
Result: In kharif seasons okra crop attained highest soybean equivalent yield followed by soybean and mash crop. In rabi seasons garlic crop attained highest pea grain equivalent yield followed by onion and pea crop. During both kharif seasons organic farming practices attained highest yield and was at par with integrated farming practices. Significantly highest yield was attained from integrated farming practices and was at par with organic farming practices in both rabi seasons. Highest net returns, net returns per rupee attained was in inorganic farming system. Highest cost of cultivation was found in organic farming practices.</div>

​Performance of Different Farming Practices in Legume based Cropping System under Mid-hills of H.P. Himalayas

Background: For the enhancement of agricultural productivity, while ensuring sustainability, this study delves into the under-explored domain of monitoring legume crop health and growth. Traditional methods of crop assessment encounter limitations, prompting a push for innovation by integrating advanced remote sensing technologies and artificial intelligence (AI). The purpose is to revolutionize crop assessment techniques and overcome existing constraints. 
 
Methods: The data was collected using a combination of satellite imagery and ground-based sensors, resulting in a rich repository of multispectral and spatial information. By using the capabilities of AI, a robust model was developed to interpret the gathered data, offering a detailed insight into the health and growth dynamics of legume crops. The AI algorithms not only identify anomalies but also forecast future states, facilitating timely interventions and informed decision-making in agriculture. 
 
Result: The findings of this study signify a significant change in precision agriculture, where the synergy of remote sensing and AI optimizes resource allocation, minimizes environmental impact and maximizes crop yields. The research unlocks the potential to transform legume farming practices, promoting sustainability and ushering in an era of data-driven cultivation. The implications extend beyond the legume crop sector, influencing the broader agricultural landscape with the promise of more efficient and sustainable practices.

Remote Sensing and AI-based Monitoring of Legume Crop Health and Growth

<div>Background: An intensive cropping system involving legume and vegetable crops not only highly productive and profitable but should also be stable over time and maintains soil health in present scenario. So, keeping this point in view the present investigation was carried out to evaluate the productivity, profitability and sustainability of legume entailing different ultra high intensity rice based cropping system models under irrigated sub-tropics.&nbsp; 
 
Methods: The field experiment was conducted at Research Farm, SKUAST-Jammu, Main Campus, Chatha for two consecutive years from kharif 2019 to summer season of 2021. The experiment consisting of five legume entailing rice based cropping system models of cropping intensity varying from 300-600% viz. T1: Rice (Basmati-370) - Wheat (HD-3086) - Cowpea (Lobia Super-60) having 300% cropping intensity, T2: Rice (Basmati-564) - Potato (Kufri Badshah) - Wheat (Raj-3765) - Mixed fodder (Maize+Cowpea+Charri in 2:2:1 ratio) having 400% cropping intensity, T3: Rice (SJR-129) - Knol khol (G-40) - Potato (Kufri Sindhuri) - Green gram (IPM 02-3) having 400% cropping intensity, T4: Rice (Pusa-1121) - Radish (CR-45) - Green onion (Nasik Red) - French bean (Anupama) - Okra (Seli special) having 500% cropping intensity in relay cropping from fourth crop onwards and T5: Rice (IET-1410) - Fenugreek (JF-07) - Knol khol (G-40) - Green onion (Nasik Red) - Dry onion (Selection-1) - Black gram (Pant U-19) having 600% cropping intensity in relay cropping from third crop onwards was laid out in a randomized block design with four replications. 
 
Result: The results obtained under two years research study revealed that treatment T4 recorded significantly highest basmati-370 rice equivalent system productivity followed by treatment T5. Further, treatment T4 also recorded highest system cost of cultivation, system gross returns and system net returns. System soil fertility status in terms of available NPK after completion of second crop cycle was significantly affected. Among different cropping systems, significantly higher available nitrogen and phosphorus were recorded in treatment T5 system which was at par with treatment T4.</div>

Performance of Diversified Legume Entailing Ultra High Intensity Rice based Cropping System Models for Higher Productivity, Profitability and Sustainability

Background: The continuing adoption of the rice-wheat cropping system (RWCS) and indiscriminate use of inorganic fertiliser has led to a decrease in soil fertility, an increase in multiple micronutrient deficiencies, a decrease in the water table and excessive greenhouse gas emissions, especially in the Indo Gangetic Plains. Adoption of crop diversification strategies, including legumes in cropping systems with integrated use of organic and inorganic nutrient sources, could be a viable option for achieving higher fodder tonnage and sustainable crop production in aforesaid ecologies. 
 
Methods: A field experiment was conducted during the kharif, rabi and summer seasons of 2018-19 and 2019-20 in a split-plot design comprising four cropping systems (CS) i.e., rice-wheat, rice-berseem, pearl millet-oats-moong bean and maize-cowpea-wheat in main plots; and four nutrient management (NM) practices i.e., 100% RDF (recommended dose of fertiliser), 100% RDF + cow urine (foliar spray), 100% RDF + PGPR (seed treatments) and 75% RDF + cow urine + PGPR were assigned in sub-plots with three replications of each treatment.&nbsp; 
 
Result: Investigations revealed that the significantly highest system productivity, net returns and higher nutrient availability were obtained under the rice-berseem cropping system with 100% RDF + PGPR treatment. The magnitude of increment in system productivity of the rice&ndash;berseem cropping system was 35.06, 57.87 and 87.29% over rice-wheat, maize-cowpea-wheat and pearl millet-oats-moong bean cropping system, respectively. The legume-based cropping system has strengthened and sustained crop productivity and profitability. The results of the present work confirmed that the adoption of legume-based crop diversification has significantly alleviated the crop yield of dairy-based farming systems.

Productivity and Profitability of Diversified Legume-based Cropping Systems in the Indo-gangetic Plains of India as Influenced by Nutrient Management

<div>Background: Conservation agriculture (CA), an agricultural production system with optimum inputs, high returns and sustainability while conserving environment is primarily required for command areas and rainfed uplands. CA helps to improve and conserve soil health through crop rotation, mulching, minimum field traffic and mechanical soil disturbance etc and conserve water to achieve economically and ecologically sustainable crop production. 
 
Methods: The field experiment was conducted for two years during 2019-21 to evaluate the influence of conservation agricultural practices on the system productivity, production efficiency and energy use under legume based cropping system in a command area. Treatments comprised of four cropping systems as Groundnut - foxtail millet (C1), Groundnut - barnyard millet (C2), Daincha - foxtail millet (C3) and Daincha - barn yard millet (C4) in main plots and foliar application of organics, 3% panchagavya, 1% PPFM and 0.1% humic acid formed subplots. 
 
Result: System productivity in terms of Groundnut equivalent yield (GEY) was significantly higher (8395 kg/ha) in the Groundnut - Barnyard millet cropping system with foliar application of PPFM 1% in CA system than that of conventional method. The production efficiency was maximum in Groundnut - barnyard millet system (34.41 kg/ha/day) and Groundnut - foxtail millet recorded the highest energy use efficiency (6.8%) which shows that maximum energy was effectively utilized under the system. Daincha - foxtail millet system had highest energy productivity of 0.91 kg M/J. Thus, the conservation tillage based Groundnut - barnyard millet system recorded more system productivity, highest resource use efficiency (both production and land use efficiency) and the highest energy use efficiency.</div>

​Conservation Agriculture Practices: Impact on Productivity, Energy Utilisation and Profitability of Legume-based Cropping System

Background: The purpose of this project is to evaluate the prospective use of drone-based remote sensing for assessing legume crop development and predicting their yields. Traditional agricultural procedures often fall short in delivering timely and accurate monitoring, necessitating the adoption of innovative techniques. 
 
Methods: The study considers vegetative indicators such as NDVI, GNDVI and canopy cover to track the growth of three legume crops-peanut, soybean and common bean. Machine learning models, including random forest, support vector machines and multiple linear regression, were developed to predict agricultural production using remote sensing data. Statistical analysis was performed to verify the trustworthiness of vegetation indicators against ground-truth measurements. 
 
Result: The models achieved high accuracy, with R&sup2; values reaching up to 0.92. Statistical analysis confirmed strong relationships between vegetation indicators and ground-truth data. Among the studied crops, soybeans exhibited the highest growth vigor and yield. The study demonstrates that integrating machine learning with drone photography can enhance precision agriculture, making it more scalable and sustainable. Future research is recommended to explore different crop varieties and environmental conditions to further optimize the application of these technologies.

Assessing Legume Crop Growth and Yield Prediction using Drone-based Remote Sensing

Background: Enhancing productivity while conserving soil and resources is a pre-requisite for achieving sustainable intensification of agro-ecosystems. Diversification by integrating legumes with millets for such high-intensity systems not only offers a potential of improving soil fertility by fixing nitrogen and residue addition but overall enhances proper balance in nutrient cycling, ensures efficient use of land enhancing year-round productivity under irrigated conditions. 
 
Methods: Field experiment was conducted at Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu under irrigated conditions to evaluate high intensity legume-millet-millet cropping sequences. The study consisted of twelve cropping sequences laid down in a randomized block design with three replications, covering a complete annual cycle from rabi to kharif. Legume crop (Pea) was grown as common rabi crop across all treatments, followed by three millets foxtail millet, proso millet and pearl millet in the summer season and four millets finger millet, barnyard millet, little millet and kodo millet during kharif. 
 
Result: Productivity of pea remained statistically unaffected during both years. During the summer season, cropping sequences involving pearl millet recorded significantly higher yields followed by foxtail millet. While among the kharif crops, finger millet outperformed other millet crops in sequence in terms of productivity. Overall, cropping systems integrating foxtail or pearl millet during summer and finger millet during kharif achieved the significantly higher system productivity (14,533.92 and 14,582.74 kg ha-1&nbsp;in the first and second year, respectively) and profitability in terms of benefit : cost ratio of 2.62 and 2.38. Hence, the inclusion of pea-foxtail/pearl millet-finger millet proved to be most productive and economical viable option for irrigated conditions. 
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Performance of Diversified Legume-based High-intensity Cropping Sequence on Productivity and Profitability

<div>Background: Rainwater harvesting is commonly practiced in areas, where the rainfall is insufficient for crop growing. Due to the intermittent nature of run-off events, it is necessary to store the maximum possible amount of rainwater during the rainy season so that it may be used as irrigation to enhance the crop productivity and farm income under soybean based cropping system. 
 
Methods: A study was carried out during 2018-2019 in Indore district of Malwa region. Rainwater harvesting tanks at on station (42&times;21&times;2.4m) and on farm (15&times;11&times;4m) were constructed for irrigation water availability. Provision of water harvesting tank increased the irrigation water availability (1781m3 and 630m3 respectively) and stored water was managed through various irrigation systems viz. rain gun, drip and flood. 
 
Result: It was resulted that the provision of water harvesting tanks enhanced the crop productivity and farm income under soybean based cropping system. Availability of irrigation encouraged the farmers to diversify the cropping pattern (soybean-chickpea, soybean -wheat). It is also clear from the study that even with smaller storage tank and through conjunctive use of ground (1164.2m3) and surface water (596m3), multiple crops (Soybean, potato, sweet corn, chickpea, onion, garlic etc.) can be grown. Soybean-Chickpea cropping system at station gave the net return of 70976 Rs/ha with B: C ratio of 3.15. Soybean-Wheat cropping system at farm gave the net return of 119000 Rs/ha with B:C ratio of 3.38.&nbsp;</div>

Assessment of Legume Crops' Impact on Soil Microbial Populations, Enzymatic Activities and Productivity under Rice-based Cropping Systems

Changes in soil carbon stock in Mewat and Dhar under cereal and legume based cropping systems

Performance of Different Farming Practices in Legume based Cropping System under Mid-hills of H.P. Himalayas

Remote Sensing and AI-based Monitoring of Legume Crop Health and Growth

Performance of Diversified Legume Entailing Ultra High Intensity Rice based Cropping System Models for Higher Productivity, Profitability and Sustainability

Productivity and Profitability of Diversified Legume-based Cropping Systems in the Indo-gangetic Plains of India as Influenced by Nutrient Management

Conservation Agriculture Practices: Impact on Productivity, Energy Utilisation and Profitability of Legume-based Cropping System

Assessing Legume Crop Growth and Yield Prediction using Drone-based Remote Sensing

Performance of Diversified Legume-based High-intensity Cropping Sequence on Productivity and Profitability

Crop Productivity Enhancement under Soybean Based Cropping System through Harvested Rain Water in Malwa Region