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Does Socio-Economic Dynamics Influence Crop Yield Variability?


Affiliations
1 Maricopa County Cooperative Extension, University of Arizona, Phoenix, AZ 85040, United States
2 Krishi Vigyan Kendra, Sitamarhi 843 320, India
3 Department of Environmental Science, University of Arizona, Tucson, AZ 85721, United States
4 Uttar Banga Krishi Viswavidyalaya, Coochbehar, West Bengal 736 165, India
5 Plants, Soils and Climate Department, Utah State University, Logan, UT 84321, United States
 

The present study examines the current research on how socio-economic factors affect the decision-making process and adoption of agricultural technologies by farmers in crop production scheduling. It reviews existing literature to identify gaps in knowledge and determine the most relevant factors influencing crop production in Northwest India from 2016 to 2021. The study finds that socio-economic factors, such as education, age, awareness and financial limitations, significantly impact farmers’ decision-making when it comes to crop planning. Additionally, societal issues like politics and religion also influence crop output. The study suggests that government policies and subsidies can help improve farmers’ livelihoods, and effective communication from agricultural scientists can encourage the adoption of affordable and environmentally friendly production technologies. However, the study emphasizes the need for more primary data to address socio-economic constraints in intervetion efforts.

Keywords

Agricultural Technologies, Crop Production, Farmers, Policies and Subsidies, Socio-Economic Factors.
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  • Roy, P. and Bhattacharyya, S., Doubling farmers’ income: its necessity and possibilities in Indian context. Indian J. Agric. Sci., 2020, 90(9), 3–9.
  • Roy, P. and Kaur, M., Status and problems of paddy straw management in West Bengal. Int. J. Agric. Environ. Eng., 2015, 1, 44–48.
  • Ramakrishnan, P. S., Shifting agriculture and sustainable development: an inter-disciplinary study from north-eastern India. Parthenon Publishing Group, Paris, France, 1992.
  • Giller, K. E., Rowe, E. C., De Ridder, N. and Keulen, V. H., Resource use dynamics and interactions in the tropics; scaling up in space and time. Agric. Syst., 2006, 88, 8–27.
  • Stoorvogel, J. J., Antle, J. M., Crissman, C. C. and Bowen, W., The tradeoff analysis model: integrated bio-physical and economic modeling of agricultural production systems. Agric. Syst., 2004, 80, 43–66.
  • Mittal, S., Gandhi, S. and Tripathi, G., Socio economic impact of mobile phones on Indian agriculture. ICRIER Working Paper No. 246, International Council for Research on International Economic Relations, New Delhi, 2010.
  • Norman, D. W., Simmons, E. B. and Hays, H. M., Farming Systems in the Nigerian Savanna: Research and Strategies for Development, Westview Press, Boulder, USA, 1982, p. 275.
  • Chavas, J. P., Structural change in agricultural production: economics, technology and policy. In Handbook of Agricultural Economics (eds Gardner, B. and Rausser, G.), Elsevier Science, Amsterdam, The Netherlands, 2001.
  • Paul, C. M., Nehring, R., Banker, D. and Somwaru, A., Scale economies and efficiency in US agriculture: are traditional farms history? J. Prod. Anal., 2004, 22, 185–205.
  • Dimitri, C., Effland, A. and Conklin, N., The 20th century transformation of US agriculture and farm policy. In Economic Information Bulletin 3, Economic Research Service of the USDA, Washington DC, USA, 2005; No. 1476-2016-120949.
  • Hoppe, R. A., Korb, P., O’Donoghue, E. J. and Banker, D. E., Structure and finances of US farms: family farm report, 2007 edition. In Economic Information Bulletin 24, Economic Research Service of the USDA, Washington DC, USA, 2007.
  • Williams, S. K. T. and Williams, C. E., The relationship of farmers characteristic to the sources of information on improved farm practices in western states of Nigeria. Bull. Rural Econ. Socio., 1971, 6(2), 162–186.
  • Dervin, R., The everyday information needs of the average citizens. A taxonomy for analysis. In Information for the Community (ed. Kochen, M.), American Library Association, IL, Chicago, USA, 1976.
  • Rogers, E. M., Diffusion of Innovations, The Free Press, New York, USA, 1995, p. 12.
  • McNamara, K. T. and Weiss, C., Farm household income and on-and-off farm diversification. J. Agric. Appl. Econ., 2005, 37, 37–48.
  • Mukherjee, A., Wang, S. Y. S. and Promchote, P., Examination of the climate factors that reduced wheat yield in Northwest India during the 2000s. Water, 2019, 11(2), 343.
  • Bhattacharyya, S., Burman, R. R., Sharma, J. P., Padaria, R. N., Paul, S. and Singh, A. K., Model villages led rural development: a review of conceptual framework and development indicators. J. Commun. Mobil. Sustain. Dev., 2018, 13(3), 513–526.
  • Birthala, P. S., Negia, D. S., Jha, A. K. and Singh, D., Income sources of farm households in India: determinants, distributional consequences and policy implications. Agric. Econ. Res. Rev., 2014, 27(1), 37–48.
  • Junakar, P. N., Land tenure and Indian agricultural productivity. J. Dev. Stud., 1976, 13(1), 42–60.
  • Agriculture Census, 2023; www.agcensus.nic.in (accessed on 24 April 2023).
  • Just, R. E. and Antle, J. M., Interactions between agricultural and environmental policies: a conceptual framework. Am. Econ. Rev., 1990, 80, 197–202.
  • Swinton, S. M., Lupi, F., Robertson, G. P. and Landis, D. A., Ecosystem services from agriculture: looking beyond the usual suspects. Am. J. Agric. Econ., 2006, 88, 1160–1166.
  • Smith, K. R., Public payments for environmental services from agriculture: precedents and possibilities. Am. J. Agric. Econ., 2006, 88, 1167–1173.
  • Roy, P., Hansra, B. S., Burman, R. R., Bhattacharyya, S., Roy, T. N. and Ahmed, R., Can farm mechanization enhance small farmers’ income? Lessons from Lower Shivalik Hills of the Indian Himalayan Region. Curr. Sci., 2022, 123(5), 667–676.
  • Roy, P., Hansra, B. S., Burman, R. R., Roy, T. N., Bhattacharyya, S. and Ahmed, R., An introspection into impact of combine harvester: a tale of sustainable livelihood security. Indian J. Extens. Educ., 2022, 58(1), 66–72.
  • Singh, I., Squire, L. and Strauss, J. (eds), Agricultural Household Models: Extensions, Applications, and Policy, John Hopkins University Press, Baltimore, USA, 1986, p. 335.
  • McCann, E., Sullivan, S., Erickson, D. and Young, R. de., Environmental awareness, economic orientation, and farming practices: a comparison of organic and conventional farmers. Environ. Manage., 1997, 21, 747–758.
  • Knowler, D. and Bradshaw, B., Farmers’ adoption of conservation agriculture: a review and synthesis of recent research. Food Policy, 2007, 32, 25–48.
  • Bisaliah, S., Capital formation, agriculture growth, and poverty: conceptual and empirical constructs, Food and Agricultural Organization, Rome, Italy, 2017; www.fao.org (accessed on 20 October 2021).
  • incometaxindia.gov.in/Pages/default.aspx (accessed on 21 April 2023).
  • Akber, N. and Paltasingh, K. R., Public financing in Indian agriculture and its return – some panel evidence. Agric. Econ. Res. Rev., 2020, 33, 1–15.
  • Gulati, A. and Sharma, A., Subsidy syndrome in Indian agriculture. Econ. Polit. Wkly, 1995, 30(39), 93–102.
  • https://www.tribuneindia.com/news/comment/punjabs-agri-policy-must-be-all-encompassing-472846 (accessed on 24 April 2023).
  • Hardie, I. W., Parks, P. J. and Van Kooten, J. C., Land use decisions and policy at the intensive and extensive margins. In Intern Yearbook of Environment and Resource Economics, Edward Elgar, London, UK, 2004, pp. 101–139.
  • Goetz, R. U. and Zilberman, D., The economics of land use regulation in the presence of an externality: a dynamic approach. Optim. Control Appl. Methods, 2007, 28, 21–43.
  • Lichtenberg, E., Agriculture and the environment. In Handbook of Agricultural Economics (eds Gardner, B. and Rausser, G.), Elsevier Science, Amsterdam, The Netherlands, 2002, vol. 2, pp. 1249–1313.
  • Singh, K. M., Agricultural price policy in India. In Market-led Agricultural Extension: Concept and Practices, ICAR Research Complex for Eastern Region, Patna, 2017, pp. 147–156; www.researchgate.net (accessed on 22 May 2020).
  • Agarwal, T., Horons, M. and Hardy, A. G., Understanding farmers’ cropping decisions and implications for crop diversity conservation: Insights from Central India. Curr. Res. Environ. Sustain., 2021, 3, 100068.
  • Khan, N. and Khan, M. M., Marketing of agricultural crops in rural Indian economy: a case study. J. Econ. Sustain. Dev., 2012, 3(2), 1–9.
  • Manjunatha, S. and Kannan, E., Effect of rural infrastructure on agricultural development: a district level analysis of Karnataka, India. J. Infrastruct. Dev., 2017, 9(2), 1–14.
  • Yogi, V., Farm mechanization in India. Biotech Article, Category: Agriculture, 2017; www.biotecharticles.com (accessed on 12 December 2022).
  • Singh, G., Farm mechanization in Punjab: social, economic and environmental implications, 2021; www.un-csam.org (accessed on 30 October 2022).
  • Basuroy, T., Internet usage activities across rural India. 2022; www.statistika.com (accessed on 17 November 2022).
  • Saroj, N., ICT usage by farmers of Punjab. J. Emerg. Technol. Innov. Res., 2019, 6(6), 692–695.
  • Agarwal, R. G., How technology can benefit Indian farmers, 2020; www.businessworld.in (accessed on 2 January 2023).
  • Chavas, J. P. and Kim, K., Economies of diversification: a generalization and decomposition of economies of scope. Int. J. Prod. Econ., 2010, 126, 229–235.
  • Chavas, J. P., Chambers, R. G. and Pope, R. D., Production economics and farm management: a century of contributions. Am. J. Agric. Econ., 2010, 92, 356–375.
  • Dhondayal, S. P., Farm Management – An Economic Analysis, Aman Publishing House, Meerut, 2002.
  • Emran, S. A., Krupnik, T. J., Aravindakshan, S., Kumar, V. and Pittelkow, C. M., Factors contributing to farm-level productivity and household income generation in coastal Bangladesh’s rice-based farming systems. PLoS ONE, 2021, 16(9); https://www.ncbi.nlm.nih.gov/ (accessed on 19 March 2022).
  • Hassan, I., Chattha, M. B., Chattha, T. H. and Ali, M. A., Factors affecting wheat yield: a case study of mixed cropping zone of Punjab. J. Agric. Res., 2010, 48(3), 403–408.
  • Serra, T., Zilberman, D. and Gil, J., Differential uncertainties and risk attitudes between conventional and organic producers: the case of Spanish arable crop farmers. Agric. Econ., 2008, 39, 219–229.
  • Takahashi, K., Muraoka, R. and Otsuka, K., Technology adoption, impact, and extension in developing countries’ agriculture: a review of the recent literature. Agric. Econ., 2020, 51(1), 31–45.
  • Pillay, D. P. K. and Kumar, T. M., Food security in India: evolution, efforts, and problems. Strat. Anal., 2018, 42(6), 595–611.
  • Saxena, N. C., Hunger, under-nutrition and food security in India. In Poverty, Chronic Poverty, and Poverty Dynamics, Springer, Singapore, 2018, pp. 55–92.
  • Das, S., Agricultural products exports in India. Agricultural Marketing, 2014, p. 339.
  • www.devdiscourse.com (accessed on 10 March 2023).
  • www.indiabudget.gov.in (accessed on 29 April 2023).
  • https://core.ac.uk/reader/188049490 (accessed on 29 April 2023).
  • Sethy, V., Top three causes of low agricultural productivity in India; www.yourarticlelibrary.com (accessed on 7 January 2023).

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  • Does Socio-Economic Dynamics Influence Crop Yield Variability?

Abstract Views: 77  |  PDF Views: 52

Authors

Avik Mukherjee
Maricopa County Cooperative Extension, University of Arizona, Phoenix, AZ 85040, United States
Pinaki Roy
Krishi Vigyan Kendra, Sitamarhi 843 320, India
Debankur Sanyal
Department of Environmental Science, University of Arizona, Tucson, AZ 85721, United States
T. N. Roy
Uttar Banga Krishi Viswavidyalaya, Coochbehar, West Bengal 736 165, India
Shih-Yu (Simon) Wang
Plants, Soils and Climate Department, Utah State University, Logan, UT 84321, United States

Abstract


The present study examines the current research on how socio-economic factors affect the decision-making process and adoption of agricultural technologies by farmers in crop production scheduling. It reviews existing literature to identify gaps in knowledge and determine the most relevant factors influencing crop production in Northwest India from 2016 to 2021. The study finds that socio-economic factors, such as education, age, awareness and financial limitations, significantly impact farmers’ decision-making when it comes to crop planning. Additionally, societal issues like politics and religion also influence crop output. The study suggests that government policies and subsidies can help improve farmers’ livelihoods, and effective communication from agricultural scientists can encourage the adoption of affordable and environmentally friendly production technologies. However, the study emphasizes the need for more primary data to address socio-economic constraints in intervetion efforts.

Keywords


Agricultural Technologies, Crop Production, Farmers, Policies and Subsidies, Socio-Economic Factors.

References





DOI: https://doi.org/10.18520/cs%2Fv125%2Fi8%2F846-852