DIGITAL FARMING PRACTICES AND CHANGES IN AGRICULTURAL PRODUCTIVITY OVER TIME

Authors

  • Dr. Honey Sinha Assistant professor, commerce, :  RM college Saharsa, Bhupendra Narayan Mandal University Madhepura, 852201

DOI:

https://doi.org/10.53555/gafs.v12i1.2577

Keywords:

Digital farming, Precision agriculture, Agricultural productivity, Corn yield, Technology adoption

Abstract

Digital farming is increasingly transforming agricultural production through precision technologies that support data-driven management and resource allocation. This study examined temporal changes in digital farming practices and their associations with agricultural productivity in U.S. corn production between 1996 and 2010. Precision-agriculture indicators were integrated with annual corn grain yield observations across matched years. Digital farming was characterized using precision agriculture, yield monitoring, yield mapping, GPS-based soil mapping, variable-rate technologies, and automated guidance systems. Temporal trends, Pearson and Spearman correlations, and parsimonious regression models were applied, alongside a composite Digital Farming Adoption Index. Precision-agriculture use increased from 17.29% of planted corn acres in 1997 to 72.47% in 2010, while yield-monitor use increased from 17.29% to 61.41%. Corn productivity increased by 20.1%, from 127.1 to 152.6 bu/acre. Precision-agriculture adoption was strongly associated with yield (r = 0.953, p < 0.001), while the Digital Farming Adoption Index also showed a significant positive association with productivity (r = 0.842, p = 0.035). Short-term changes in digital adoption were not significantly associated with corresponding yield changes. The findings demonstrate parallel long-term increases in agricultural digitalization and corn productivity while emphasizing the need for farm-level longitudinal evidence to establish causal effects.

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References

Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The digitisation of agriculture: a survey of research activities on smart farming. Array, 3, 100009.

Balafoutis, A., Beck, B., Fountas, S., Vangeyte, J., Van der Wal, T., Soto, I., ... & Eory, V. (2017). Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability, 9(8), 1339.

Barnes, A. P., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B., ... & Gómez-Barbero, M. (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land use policy, 80, 163-174.

Birner, R., Daum, T., & Pray, C. (2021). Who drives the digital revolution in agriculture? A review of supply‐side trends, players and challenges. Applied economic perspectives and policy, 43(4), 1260-1285.

Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and electronics in agriculture, 151, 61-69.

Cisternas, I., Velásquez, I., Caro, A., & Rodríguez, A. (2020). Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture, 176, 105626.

DeLay, N. D., Thompson, N. M., & Mintert, J. R. (2022). Precision agriculture technology adoption and technical efficiency. Journal of Agricultural Economics, 73(1), 195-219.

Fielke, S., Taylor, B., & Jakku, E. (2020). Digitalisation of agricultural knowledge and advice networks: A state-of-the-art review. Agricultural systems, 180, 102763.

Finger, R., Swinton, S. M., El Benni, N., & Walter, A. (2019). Precision farming at the nexus of agricultural production and the environment. Annual Review of Resource Economics, 11(1), 313-335.

Huang, Y., Tao, Y. U., & HUANG, X. Z. (2018). Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17(9), 1915-1931.

Ingram, J., & Maye, D. (2020). What are the implications of digitalisation for agricultural knowledge?. Frontiers in Sustainable Food Systems, 4, 66.

Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and electronics in agriculture, 143, 23-37.

Kendall, H., Naughton, P., Clark, B., Taylor, J., Li, Z., Zhao, C., ... & Frewer, L. J. (2017). Precision agriculture in China: Exploring awareness, understanding, attitudes and perceptions of agricultural experts and end-users in China. Advances in Animal Biosciences, 8(2), 703-707.

Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways?. Global Food Security, 24, 100347.

Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS: wageningen journal of life sciences, 90(1), 1-16.

Lowenberg‐DeBoer, J., & Erickson, B. (2019). Setting the record straight on precision agriculture adoption. Agronomy journal, 111(4), 1552-1569.

Monteiro, A., Santos, S., & Gonçalves, P. (2021). Precision agriculture for crop and livestock farming—Brief review. Animals, 11(8), 2345.

Patrício, D. I., & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and electronics in agriculture, 153, 69-81.

Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(2), 207.

Trendov, N. M., Varas, S., & Zeng, M. (2019). Digital technologies in agriculture and rural areas. FAO.

U.S. Department of Agriculture, Economic Research Service. (2023, February 24). ARMS farm financial and crop production practices—Tailored reports: Crop production practices. https://data.ers.usda.gov/reports.aspx?ID=4022

U.S. Department of Agriculture, National Agricultural Statistics Service. (2022). Quick Stats. https://quickstats.nass.usda.gov/results/345B58DA-D07D-3398-A6AE-84628CC9B45A

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Smart farming is key to developing sustainable agriculture. Proceedings of the National Academy of Sciences, 114(24), 6148-6150.

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69-80.

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Published

2026-03-28

How to Cite

Dr. Honey Sinha. (2026). DIGITAL FARMING PRACTICES AND CHANGES IN AGRICULTURAL PRODUCTIVITY OVER TIME. International Journal For Research In Agricultural And Food Science, 12(1), 68–83. https://doi.org/10.53555/gafs.v12i1.2577