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This paper deploys the material and discursive politics of storage as a lens into agrarian change in rural India. Focusing on onions (Allium cepa), a price volatile crop and a kitchen staple, it maps out the contested meanings of and motivations for storage among growers, governments, and agri‐logistics companies. Specifically, this paper asks why storage is increasingly positioned as a key solution to the problem of price instability. This emphasis, I argue, represents a distinct regime of agrarian biopower that seeks to intervene into the physical properties of the bulb to extend its material life, and thereby, manage produce, prices, and populations. In doing so, however, fundamental issues of social inequality and guaranteed prices are bypassed. Moreover, innovations in storage technologies lay the grounds for the entry of corporate capital into agriculture. Ultimately, techno‐capital‐intensive biopolitical infrastructures of storage portend the restructuring of India's agrarian economy in favour of elite farmers and agri‐logistics firms by circumventing the issue of a fixed and fair price for producers and consumers. [ABSTRACT FROM AUTHOR]

The article proposes to advance the understanding of the multiple functions of agriculture by creating contextual prototypes of multifunctional farming systems. Topics discussed include concerns on productivity-driven agricultural policies in India, economic, social and environmental aspects associated with multifunctional agriculture, and potential for sustainable multifunctional agriculture in India.

This paper examines the impact of land ownership on farm-level investment and investment risk (variability) in Indian agriculture using nationally representative survey data. The study adopts a flexible moment approach with White's heteroscedasticity consistent standard error method. Results reveal that land ownership intensity significantly augments on-farm investment and reduces its variability. Other variables like family head's education, access to irrigation, technical advice, credit and nonfarm income have significant inducement effects on-farm investment. We also observe that farmers' education, age, irrigation, technical advice, MSP awareness and commercialization reduce farm investment risk. Findings have important policy implications for Indian land tenancy [ABSTRACT FROM AUTHOR]

This paper focuses on the trend of the credit supply and its impact on agricultural growth in the Indian scenario since the independence eve. After independence, India emerged as a centre of attraction for national and international policymakers and investors. With the diversified demographic structure, India was tied in one strong thread of economic activity which was agriculture. With time, the need for agriculture is getting higher no matter what number projects. In this pandemic scenario, this was the only sector which reported positive growth. Food was always on the list of basic necessities. So will be in future. Therefore, it is always a prioritised sector to consider while making the fiscal or monetary policies. Under monetary policies, credit supply is a major indicator to intervene the agricultural productivity and the accompanying factor of production. Henceforth, this paper examined the impact of agriculture and other factor of production like land, labour force participation in agriculture, and irrigation facilities on agricultural productivity. With our paper, important light was thrown upon the various factors like bank credit provided to the rural area, irrigation facilities, technological development, and capital formation and how does it affect the agriculture development in the Indian scenario. Study Found that Land, Labour, and Irrigation are the major factor which can increase the agriculture productivity. But the other Components like Bank credit, Technology, Capital and fertilizer usage must be look after to transform the picture of Indian agriculture. [ABSTRACT FROM AUTHOR]

Agriculture is the bone that plays important part in the frugality of India. India is an agrarian country and its frugality largely grounded upon crop product. It'll allow policy makers and growers to take effective marketing and storehouse way to prognosticate crop yields before in their crop. The design will allow growers to capture the yields of their crops before civilization in the field of husbandry and therefore help them make the necessary opinions. Preparation of such a system with a use and the machine learning algorithm an also be distributed. The results attained are granted access to the planter. and yet there are colorful styles or protocols for similar veritably data analytics in crop yield vatic nation, and were suitable to prognosticate agrarian productivity with guidance of all those algorithms. It utilizes a random forest algorithm. By probing similar problems and issues similar as rainfall, temperature, moisture, downfall, moisture, its overcome using random forest technique, acceptable results an inventions resolve the situation. In countries like India, indeed in the agrarian sector, as there are numerous types of adding profitable growth. In addition, the processing is useful of ratiocinating the product of crop yields. [ABSTRACT FROM AUTHOR]

Since the launch of the Green Revolution (GR) in Indian agriculture in the 1960s, Indian food production has successfully become self-sufficient but this has also led to increasing soil nitrogen (N) surpluses and various negative environmental impacts, such as NH3 emissions. Using the IMAGE Global Nutrient Model, this study explores the development of food production, soil N surpluses and associated NH3 emissions in India during the GR; the use of subnational data for compiling spatially explicit maps of N inputs (N fertilizers, manure N, biological N fixation, atmospheric deposition) and outputs (crop harvest, grazing) was compared with results using country-scale data. The results show that in the period 1960–2010 food production growth was dramatic (374%), particularly in the region of the GR states (Punjab, Haryana and western Uttar Pradesh). This production increase was primarily based on spectacular increases in crop yields and N inputs. However, due to slowly changing nutrient use efficiency, N surpluses and associated NH3 emissions increased rapidly, with hotspots especially in the GR states. Maps using data at subnational scale yield a better representation of spatial heterogeneities of the soil N surpluses, emissions and environmental impacts than maps based on country data. This is beneficial for effect calculations, as the location of negative environmental side effects strongly depends on the location of soil N and P surpluses. [ABSTRACT FROM AUTHOR]

In the last six decades, the consumption of reactive nitrogen (Nr) in the form of fertilizer in India has been growing rapidly, whilst the nitrogen use efficiency (NUE) of cropping systems has been decreasing. These trends have led to increasing environmental losses of Nr, threatening the quality of air, soils, and fresh waters, and thereby endangering climate-stability, ecosystems, and human-health. Since it has been suggested that the fertilizer consumption of India may double by 2050, there is an urgent need for scientific research to support better nitrogen management in Indian agriculture. In order to share knowledge and to develop a joint vision, experts from the UK and India came together for a conference and workshop on “Challenges and Opportunities for Agricultural Nitrogen Science in India.” The meeting concluded with three core messages: (1) Soil stewardship is essential and legumes need to be planted in rotation with cereals to increase nitrogen fixation in areas of limited Nr availability. Synthetic symbioses and plastidic nitrogen fixation are possibly disruptive technologies, but their potential and implications must be considered. (2) Genetic diversity of crops and new technologies need to be shared and exploited to reduce N losses and support productive, sustainable agriculture livelihoods. (3) The use of leaf color sensing shows great potential to reduce nitrogen fertilizer use (by 10–15%). This, together with the usage of urease inhibitors in neem-coated urea, and better management of manure, urine, and crop residues, could result in a 20–25% improvement in NUE of India by 2030.

The Indian agricultural sector is the world's largest producer of pulses, milk, and jute, and the second-largest producer of rice, wheat, vegetables, fruit, and cotton. Climate change threatens food security worldwide and has severely impacted the Indian agricultural sector. As a result of the unpredictable climate and low profits, economic uncertainty has forced men to seek employment in non-farming sectors. Under the deepening crisis, the farming landscape has transformed into a female-majority workforce. To identify pragmatic solutions to the deepening agrarian crisis, we employed a qualitative triangulation approach to our investigation of the feminisation of the farming sector, which was conducted in 2020. Our analyses of Indian media narratives, archive stories, responses to an online story completion task, and in-depth expert interviews show that, despite their developing role and increasing contribution, women remain marginalised and discriminated against in a culturally male-dominant sector. Supported by a long-term field ethnographic experience, we used Installation Theory to identify three domains in which policymakers can intervene to promote the interconnected issues of social sustainability, gender equality, and sustainable economic development in this sector. [ABSTRACT FROM AUTHOR]

The use and dissemination of agricultural technology has sparked widespread attention across disciplines since it is seen as a solution to food shortages, low productivity, income, and yields in farming, particularly in developing nations such as India. As of mid-2021, there are approximately 226 AgriTech startups in Bangalore, Karnataka, out of 700 in India. The purpose of this study is to examine the factors (age, educational level, economic status, farmer category) that influence the adoption of AgriTech startups services in Karnataka's rural Bangalore district. This study was conducted in Byrasandra and Byadarahalli in Namangala taluk of Rural Bangalore among farmers who had adopted AgriTech and who had not adopted agritech services provided by agritech startups. A descriptive analysis such as chi square and cross tab was used to examine the objectives using a random sample of 100 farmers. From the result it was evident that farmer's age, educational level, economic status and farmer category plays a significant role in the rural Bangalore uptake of AgriTech given by AgriTech startups. The study recommends the future studies on adoption of AgriTech in India to widen the range of variables used by researchers. [ABSTRACT FROM AUTHOR]

Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate. [ABSTRACT FROM AUTHOR]

Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. HHaJathrough Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate.

Agriculture in India is unique, in terms of topography climate and size of landholdings being very small. Agriculture has been in focus for both positive and negative reasons. Despite liberalization to a more marketoriented economy in 1991, the agriculture sector continues to have policy distortions. The issues related to agriculture are complex in nature which require a balance between the market and the state. This needs addressing major long-term challenges from food security to natural resource sustainability, especially soil and water. The objective of the paper is to highlight some of the problems of Indian agriculture and policy response of the government. It also traces the trajectory of the farmers' agitation against the farm laws. After the repeal of farm laws, the question of reform in agriculture is paramount. The paper proposes a road map for agri-reforms. It is argued that more unified framework of analysis is needed for an effective agricultural policy. This paper may be helpful in the field of public policy particularly relating to agriculture. [ABSTRACT FROM AUTHOR]

An attempt has been made in this study to find out whether the farmer households are increasingly becoming labour households in India by utilising state-wise SAS data of three-time points: 2002–2003, 2012–2013 and 2018–2019. Four different approaches have been followed to test this assertion. The results from approach I which compared the wage income with the crop cultivation income of the farmer households in absolute term show that the percentage of states having wage income more than cultivation income has increased from 33 percent in 2002–2003 to 75 percent in 2018–2019. Approach II of the analysis, which used the definition of the agricultural labour households of Rural Labour Enquiry, shows that the number of states earning wage income more than 50 percent in the total annual income of farmer households has increased substantially from 22 percent in 2002–2003 to 46 percent in 2018–2019. The results of approach III wherein the growth rate of wage income and crop income has been compared show that the number of states having a growth rate over 2.50 percent in wage income has increased considerably from about 33 percent in period I (2002–2003 to 2012–2013) to about 83 percent in period II (2012–2013 to 2018–2019). Except for Punjab, which registered negative growth in wage income, almost all the agriculturally advanced states have moved from the lower growth bracket in period I to over 2.50 percent growth rate in period II. The analysis of approach IV which has employed regression analysis to find out the impact of wage income vis-à-vis the crop income on the total annual income of the farmer households shows that the coefficients of wage income are higher than that of the crop income in all the three-time points. [ABSTRACT FROM AUTHOR]

Agricultural information delivery is the process of delivering research-based information to farmers. Currently, there is low access to agricultural information among Indian farmers, which has resulted in gaps in crop production practices. We present a review of literature about antecedents of agricultural information delivery in India, prominent methods of delivering agricultural information, current access to agricultural information, and issues in Indian agriculture due to low access to agricultural information. The review of literature is followed by an edited transcript from a panel discussion on the topic. The article highlights the complexity involved in delivering information to farmers and some recent developments in the field.

Ensuring Food Security through balanced and efficient use of plant nutrients is the prime objective of the Fertiliser Association of India (FAI), New Delhi. With a view to promote healthy competition and reward outstanding work, the FAI has instituted number of annual awards and awards in collaboration with International Institutions. Out of which, to create incentive for high quality research amongst scientists in India in the field of "Plant Nutrition", based on their published research work during the past 10 years preceding the year of the award will be given as award for excellence for the best work done in the field of Plant Nutrition. This has been awarded for the year 2021 to Dr. N. B. Prakash during FAI annual seminar 2021 held at New Delhi, India. Dr. N. B. Prakash isrecipient of this award representingUniversity of Agricultural Sciences (UAS), Bangalore, India. He received this award for his ground-breaking research work on silicon Indian Agriculture. [ABSTRACT FROM AUTHOR]

Agriculture contributes approximately 28 percent of India's GDP, and agriculture employs approximately 65 percent of the country's labor force. India is the world's second-largest agricultural crop producer. Agriculture is not only an important part of the expanding economy, but it is also necessary for our survival. The technological contribution could assist the farmer in increasing his yield. The selection of each crop is critical in the planning of agricultural production. The selection of crops will be influenced by a variety of factors, including market price, production rate, and the policies of the various government departments. Numerous changes are required in the agricultural field in order to improve the overall performance of our Indian economy. By using machine learning techniques that are easily applied to the farming sector we can improve agriculture. Along with all of the advancements in farming machinery and technology, the availability of useful and accurate information about a variety of topics plays an important role in the success of the industry. It is a difficult task to predict agricultural output since it depends on a number of variables, such as irrigation, ultraviolet (UV), insect killers, stimulants & the quantity of land enclosed in that specific area. It is proposed in this article that two distinct Machine Learning (ML) methods be used to evaluate the yields of the crops. The two algorithms, SVR and Linear Regression, have been well suited to validate the variable parameters of the continuous variable estimate with 185 acquired data points. [ABSTRACT FROM AUTHOR]

This paper reviews the recent trends in agricultural investments (both public and private) and tries to find structural breaks in the trends over the period of 1960- 2017. Comparing the growth performance of investments and farm output (GDPAg and production) in various sub-periods based on breakpoints in both investment series the study finds that the recent agricultural stagnation spawns from a low capital formation in Indian agriculture, especially low public investment. This has been further strengthened by the regression results where both public and private investments along with fertilizer consumption, HYV seeds, terms of trade, and weather pattern signifi- cantly affect the agricultural output. Therefore, the policy implication of the study calls for an immediate arrest of the declining trend of public investment in order to stimulate more private investment. This may break the shackles of growth stagnation in Indian agriculture. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology in the agricultural sector can reduce carbon emissions from agrarian activities and revitalize the whole industry. The Indian agricultural sector has become costly, time‐consuming, and outdated owing to the global warming process. The deployment of AI technology in Indian agriculture is still a distant dream, given the peculiarities like small‐size farms, traditional farming methods, lack of credit, storage facilities, and the decision maker's risk‐seeking attitude. AI solutions need to be delivered at the farmers' doorstep in their local language, with proper training, input support, and in collective/cooperative manner for realizing sustainable and green agriculture. [ABSTRACT FROM AUTHOR]

In the last six decades, the consumption of reactive nitrogen (Nr) in the form of fertilizer in India has been growing rapidly, whilst the nitrogen use efficiency (NUE) of cropping systems has been decreasing. These trends have led to increasing environmental losses of Nr, threatening the quality of air, soils, and fresh waters, and thereby endangering climate-stability, ecosystems, and human-health. Since it has been suggested that the fertilizer consumption of India may double by 2050, there is an urgent need for scientific research to support better nitrogen management in Indian agriculture. In order to share knowledge and to develop a joint vision, experts from the UK and India came together for a conference and workshop on “Challenges and Opportunities for Agricultural Nitrogen Science in India.” The meeting concluded with three core messages: (1) Soil stewardship is essential and legumes need to be planted in rotation with cereals to increase nitrogen fixation in areas of limited Nr availability. Synthetic symbioses and plastidic nitrogen fixation are possibly disruptive technologies, but their potential and implications must be considered. (2) Genetic diversity of crops and new technologies need to be shared and exploited to reduce N losses and support productive, sustainable agriculture livelihoods. (3) The use of leaf color sensing shows great potential to reduce nitrogen fertilizer use (by 10–15%). This, together with the usage of urease inhibitors in neem-coated urea, and better management of manure, urine, and crop residues, could result in a 20–25% improvement in NUE of India by 2030.