Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges. (11th July 2022)

• Record Type: Journal Article
• Authors: Khanna, Madhu; Atallah, Shady S.; Kar, Saurajyoti; Sharma, Bijay; Wu, Linghui; Yu, Chengzheng; Chowdhary, Girish; Soman, Chinmay; Guan, Kaiyu
• Other Names: Lele Uma guestEditor.; Vink Nick guestEditor.
• Is Part Of: Agricultural economics. Volume 53:Number 6(2022)
• Page Start: 924
• Page End: 937
• Publication Date: 2022-07-11
• Subjects: adoption -- environment -- precision farming -- site‐specific -- spatial variability; Agriculture -- Economic aspects -- Periodicals; 338.105
• DOI: 10.1111/agec.12733
• Languages: English
• ISSNs: 0169-5150

Abstract

Abstract: Agriculture faces key challenges of increasing productivity while reducing adverse impacts on the environment. Conventional practices that rely on tillage, inefficient and over‐application of chemicals, and monoculture row cropping are leading to growing resistance of weeds and pests to chemicals, nutrient and sediment run‐off, and declining soil carbon stocks in the United States. Digital technologies and artificial intelligence (AI) technologies are enabling the collection of vast amounts of geo‐referenced information about growing conditions within the field, automated implementation of spatially varying input applications, and reduced reliance on chemical inputs. We discuss the pathways by which digital agricultural technologies have the potential to address the challenge of herbicide‐resistant weeds, over‐application of nitrogen and irrigation water, and cover crop planting for restoring soil health and contribute to the environmental sustainability of agriculture. Then, we discuss the economic factors, behavioral preferences of farmers, peer pressure, and social networks that can be expected to play a role in adoption decisions. We conclude with a discussion of approaches for ex ante assessments of the determinants of farmer willingness to adopt digital technologies and their diffusion in a region.