Precision Agriculture (PA) manages field heterogeneities and enables informed site-specific
management. While PA helps improve farming efficiency and profitability, challenges
prior to and following PA adoption can prevent many farmers from widely using it. This
paper aims to understand producers’ challenge perceptions using 1119 survey responses
from U.S. Midwest farmers. The majority (59%) of respondents have adopted at least
one PA technology, while the minority (14%) had not adopted any PA technologies. Cost
(equipment and service fee), brand compatibility, and data privacy concerns topped other
concerns from the average producer’s point of view. Among all producers, 60% regarded
PA equipment and service fee as too high, followed by 50% who viewed brand compatibility
and data privacy as their major concerns. Producers at more advanced adoption stage
indicated reduced concerns in most categories. Yet, there were similar concerns towards
data privacy issue regardless of the adoption status. Furthermore, brand compatibility issue
is more of a concern for adopters than for non-adopters. Estimation results from partial proportional
odds (PPO) models show that factors that frequently affect producers’ perceived
challenges include adoption status, cropland acres, age, education, information sources,
farming goals, soil characteristics, and region variables. Findings from this study can aid
PA stakeholders in identifying target groups, tailoring future development, research, and
outreach efforts, and ultimately promoting efficient PA usage on a broader scale.
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Understanding farmer views of precision agriculture profitability in the U.S. Midwest
Precision Agriculture (PA) technologies are well known to be useful in addressing field heterogeneities and enabling informed site-specific management decisions. While profitability is the foremost factor considered by farmers when making PA adoption decisions, information in this regard is lacking from the farmers' perspective. This paper analyzed 1119 farmer responses from a 2021 survey conducted in four states along the western margins of the U.S. Midwest. Our findings show that while most (around 60%) non-adopters indicate that they are unaware of PA profit change, adopters are likely to rate a major profit increase. About two thirds of adopters rated at least a 5% increase in profitability towards variable rate (VR) fertilizer application (72%), VR seed application (68%), and automatic section control (66%). We modeled farmers' profit change subsequent to PA adoptions. Our regression results demonstrate that the profits from PA usage increase over time and that use of conservation practices likely influences PA profitability in a positive way. As soil quality and weather factors also affect profit ratings, it would be beneficial to compare and demonstrate profitability potential of various PA technologies on a regional basis and tailor the promotion efforts to farmers most likely to benefit from them.
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- Award ID(s):
- 2119753
- PAR ID:
- 10479190
- Publisher / Repository:
- Ecological Economics
- Date Published:
- Journal Name:
- Ecological Economics
- Volume:
- 213
- Issue:
- C
- ISSN:
- 0921-8009
- Page Range / eLocation ID:
- 107950
- Subject(s) / Keyword(s):
- AdoptionConservation practicesFarm surveyProfit changePrecision agricultureSoil quality
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Precision agriculture (PA) has been defined as a “management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.” This definition suggests that because PA should simultaneously increase food production and reduce the environmental footprint, the barriers to adoption of PA should be explored. These barriers include (1) the financial constraints associated with adopting decision support system (DSS); (2) the hesitancy of farmers to change from their trusted advisor to a computer program that often behaves as a black box; (3) questions about data ownership and privacy; and (4) the lack of a trained workforce to provide the necessary training to implement DSSs on individual farms. This paper also discusses the lessons learned from successful and unsuccessful efforts to implement DSSs, the importance of communication with end users during DSS development, and potential career opportunities that DSSs are creating in PA.
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Abstract The majority of research to date has attributed the pollution problems associated with agriculture to the independent decisions of individual farmers; in this article, we illustrate how seed companies' subtle forms of coercion encourage farmers to become compliant polluters. We focus on corn farmers in the midwestern United States, whose management decisions have become increasingly controlled by seed companies. Beyond obvious forms of control, seed companies influence farmers in ways that are less apparent. Drawing from Foucault's concept of disciplinary power, we explore how seed companies have encouraged farmers to increase their application of nitrogen fertilizer through disciplinary mechanisms that naturalize the imposition of constraints. We argue that seed companies employ disciplinary techniques related to the biology of the seed, the product life cycle, and knowledge. While most farmers believe their fertilizer decisions are made independently, these disciplinary techniques compel farmers to increase nitrogen fertilizer application, resulting in increased water pollution and greenhouse gas emissions.
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Abstract BACKGROUND The Oklahoma Mesonet (the statewide environmental and weather monitoring network) has monitored changes in weather patterns since 1994 to provide accurate and timely mesoscale weather information to farmers and other groups. Studies are still scarce that would quantitatively assess farmers' perceptions about the value of the Oklahoma Mesonet contributions to agricultural operations, profitability of land management, and decision making. This paper aims to analyze those questions by means of an exploratory empirical study in Oklahoma for two groups of Mesonet users and non‐users.
RESULTS Familiarity with and application of Mesonet information determines farmers' profitability assessments and decision making. Farmers' perceptions are also influenced by the degree of previous exposure to weather‐related losses. The median estimate of the economic value of Mesonet information is $1000 per year. Mesonet users perceive higher profitability from the application of Mesonet data at 7.6/10, whereas Mesonet non‐users provided an average assessment of 2.6/10.
CONCLUSIONS Consistent use of Mesonet information results in a higher assessment of the importance of Mesonet. This research provides some initial insights into farmers' perceptions about the value of Oklahoma Mesonet information, which could guide stakeholders in developing measures to better serve farmers with environmental monitoring data for improved farm decisions. © 2018 Society of Chemical Industry
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Abstract Artificial intelligence (AI) represents technologies with human‐like cognitive abilities to learn, perform, and make decisions. AI in precision agriculture (PA) enables farmers and farm managers to deploy highly targeted and precise farming practices based on site‐specific agroclimatic field measurements. The foundational and applied development of AI has matured considerably over the last 30 years. The time is now right to engage seriously with the ethics and responsible practice of AI for the well‐being of farmers and farm managers. In this paper, we identify and discuss both challenges and opportunities for improving farmers’ trust in those providing AI solutions for PA. We highlight that farmers’ trust can be moderated by how the benefits and risks of AI are perceived, shared, and distributed. We propose four recommendations for improving farmers’ trust. First, AI developers should improve model transparency and explainability. Second, clear responsibility and accountability should be assigned to AI decisions. Third, concerns about the fairness of AI need to be overcome to improve human‐machine partnerships in agriculture. Finally, regulation and voluntary compliance of data ownership, privacy, and security are needed, if AI systems are to become accepted and used by farmers.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.ecolecon.2023.107950
