Analysis of policy interventions in agriculture and ICT based mechanistic approach towards sustainability: an Indian perspective

Balkrishna, Acharya; Mittal, R.; Kumar, Ashwani; Singh, Deepti; Arya, Vedpriya

doi:10.18488/journal.1005/2020.10.1/1005.1.194.213

Cited by 10 publications

(10 citation statements)

References 2 publications

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“…Access to credit facilities: Availability of credit facilities at affordable terms and conditions is necessary for the farmers to purchase good quality agricultural inputs. In addition, financing options to upgrade farm infrastructure and start secondary agricultural activities should also be available to improve farm productivity and profitability (Balkrishna et al 2020).…”

Section: India's Farmers Conditionmentioning

confidence: 99%

“…Agricultural facilities, like warehousing, must be made accessible to the farmers at affordable prices, especially for perishable agricultural products (Aditya et al 2017, PIB 2021, Gulati et al 2021. Market linkages must be developed to leverage the demand for fresh and high quality agricultural products (Aditya et al 2017, GOI 2017, Balkrishna 2020.…”

Section: India's Farmers Conditionmentioning

confidence: 99%

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Indian Farmer and Government Initiatives: Policies, Gaps and Way Forward

2022

IJE

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In India, the government continues to implement various programs and policies yearned at increasing agricultural production by facilitating the sale of its products and providing tax relief to Indian farmers. However, most farmers cannot benefit from such plans/policies due to a lack of understanding and are prone to misinformation and scepticism. Farmers are well aware of the risks posed by climate variability and adverse weather conditions. They are unsure which crop to grow due to changes in soil structure caused by unpredictable weather conditions. These are just some of the problems facing Indian farmers. Extensive research has been waged on the importance of farmers to the nation, how they contribute to the national gross domestic product, why they commit suicide, and how beneficial efforts and programs affect them. In this review article, mainly discuss the government's agricultural and production policies, Indian farmers' conditions, government initiatives to alleviate the problem of farmers, advances in sustainable agriculture technology, policies and suggestions on the development of farmers.

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Section: India's Farmers Conditionmentioning

confidence: 99%

Section: India's Farmers Conditionmentioning

confidence: 99%

Indian Farmer and Government Initiatives: Policies, Gaps and Way Forward

2022

IJE

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“…The adaptability and availability of modern technology will enhance the efficiency and responsiveness of the modern-day PHSC (Glass and Callahan, 2015; Priyadarshi et al , 2019; Mariam and Nair, 2020). The provision and establishment of government policies for incentives and subsidies for crop irrigation, cultivation, harvesting, crop insurance and value-addition set-ups will enable and encourage the farmer to establish the level of vertical integration at the rural level (Narrod et al , 2009; Owczarczuk, 2013; Alizamir et al , 2019; Priyadarshi et al , 2019; Balkrishna et al , 2020; Saini et al , 2020). The adaptation of technology(i.e.…”

Section: Literature Surveymentioning

confidence: 99%

Analyzing the post-harvest supply chain enablers of vertical integration for rural employability and marketability

Priyadarshi

Routroy

Garg

2021

JBIM

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Purpose The purpose of this study is to analyze the post-harvest supply chain enablers (PHSCEs) for vertical integration to enhance rural employability, farmer profitability and rural produce marketability (i.e. market prospects) in the post-harvest supply chain (PHSC). The impact of vertical integration is also explored for various commercial produces. Design/methodology/approach A structural equation modeling (SEM) of PHSCEs for vertical integration was developed to enhance market prospects, rural employability and farmer profitability. The impact of business-to-business (B2B) and business-to-customer market prospects are explored in various dimensions for stakeholders such as farmers, manufacturers (processors), distributors and retailers. The fuzzy technique for order of preference by similarity to ideal solution (F-TOPSIS) was used to prioritize these PHSCEs to improve market prospects and rural employability. Findings The PHSCEs are clustered into three groups, namely, initiatives at the strategic frontier, initiatives at the tactical frontier and concerns for rural employability via vertical integration using exploratory factor analysis, confirmatory factor analysis and SEM to prove the null hypothesis. With F-TOPSIS results, the availability of warehousing was found to be the most crucial enabler when observing the PHSCEs from the initiatives’ perspective. The technology adaptability and availability, institute for training and research and information infrastructure and information visibility were found to be the key PHSCEs when observed from PHSC stakeholders’ perspectives. Research limitations/implications The implementation of this study will improve the rural produce marketability, rural employability, B2B marketing (i.e. effective distribution) and subsequent value chains with the practice of vertical integration for fresh produce at the rural level. Practical implications The outcomes of this study have a key role in developing the rural regions and improving rural livelihoods via value addition. The awareness of commercial cultivation and value addition in rural areas needs to be improved. This will help farmers to earn better revenues with improved market prospects in comparison to the revenues obtained from the cultivation of staple/conventional crops. Originality/value In an era of cold chains and food processing, this study aims to disseminate awareness about value addition for commercial and fresh produces at the rural level. The implication of this study will improve rural produce marketability, rural employability and farmer profitability at the rural level with the level of vertical integration.

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“…The data used for prediction includes meteorological data (temperature, rainfall, relative humidity), remote sensing data, and field statistics. Different data mining tools used for crop prediction include Artificial Neural Networks (ANN), Support Vector Machines (SVM) (Balkrishna et al 2020). A huge number of databases are used as input for crop prediction.…”

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confidence: 99%

Crop Production Prediction Models in Indian Agriculture: Possibilities and Challenges

2022

IJE

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Agriculture sector is an important contributor to Indian economy. Wheat yield has decreased from 3.53 kg ha (FY 2019) to 3.42 kg -1 ha (FY 2020). Crop production is affected by a lot of factors including climatological factors (temperature, precipitation), soil type and seed -1 quality. Crop production prediction is an important aspect for farmers, agro based industries and policy makers. Many technological tools based on data mining models are being developed to draw correlation agricultural datasets and crop yield. The prediction methodology involves learning the pattern of crop yield during a set of conditions based on the previous years' data and thus predicting the yield in the current set of environmental parameters. The present paper focuses on commonly used models for crop yield prediction in Indian agriculture including artificial neural network, bayesian belief network, support vector machine, decision tree regression analysis, random forest, least absolute shrinkage regression operator and elastic net. A comparison of their efficiency has been drawn based on the previous studies.

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Analysis of policy interventions in agriculture and ICT based mechanistic approach towards sustainability: an Indian perspective

Cited by 10 publications

References 2 publications

Indian Farmer and Government Initiatives: Policies, Gaps and Way Forward

Indian Farmer and Government Initiatives: Policies, Gaps and Way Forward

Analyzing the post-harvest supply chain enablers of vertical integration for rural employability and marketability

Crop Production Prediction Models in Indian Agriculture: Possibilities and Challenges

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