Interviews with farmers from the US corn belt highlight opportunity for improved decision support systems and continued structural barriers to farmland diversification

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Matthew Nowatzke, Lijing Gao, Michael C. Dorneich, Emily A. Heaton, Andy VanLoocke
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Abstract

Diversifying high-input, monocropped landscapes like the US Corn Belt would provide both economic and ecosystem service benefits to the agricultural landscape. Decision support systems (DSS) and digital agriculture could help farmers decide if diversification is suitable for their operation. However, adoption of DSS by farmers remains low, likely due to lack of farmer engagement before and during the DSS development process. This study aimed to better understand the tasks, tools, and people involved in implementing farmland diversification with the goal to inform design of agricultural DSS. Semi-structured interviews were conducted with 11 farmers who had diversified their corn/soybean cropland with government-supported conservation programs (e.g., CRP, wetlands) and alternative crops (e.g., small grains, pasture) in the past four years. Interview data was transcribed and then analyzed using affinity diagramming. Results show farmers needed DSS to layer multiple sources of data and observations over several years to identify field productivity trends and drivers; spatial orientation of practices to fit management and field constraints; matching operation goals to alternative practices; financial planning and market exploration; and information on promising emerging practices like subsidized pollinator habitat. However, the interviews also highlighted structural barriers to diversification that DSS cannot or can only partially address. These included social pressures; market access; crop insurance policy; and quality of relationships with governmental agencies. Results indicate better DSS design can empower individual farmers to diversify cropland, but structural interventions will be needed to successfully diversify the agricultural landscape and support economic and ecosystem health.

Abstract Image

与美国玉米带农民的访谈强调了改进决策支持系统的机会以及农田多样化继续面临的结构性障碍
像美国玉米带这样的高投入、单一作物景观的多样化将为农业景观带来经济和生态系统服务效益。决策支持系统(DSS)和数字农业可以帮助农民决定多样化是否适合他们的经营。然而,农民对决策支持系统的采用率仍然很低,这可能是由于在决策支持系统开发之前和开发过程中缺乏农民的参与。本研究旨在更好地了解实施农田多样化所涉及的任务、工具和人员,从而为农业 DSS 的设计提供参考。本研究对 11 位农民进行了半结构式访谈,他们在过去四年中通过政府支持的保护计划(如 CRP、湿地)和替代作物(如小杂粮、牧草)实现了玉米/大豆耕地的多样化。对访谈数据进行了转录,然后使用亲和图对其进行了分析。结果表明,农民需要使用 DSS 系统对多年来的多种数据来源和观察结果进行分层,以确定田间生产力趋势和驱动因素;确定实践的空间定位,以适应管理和田间限制;将经营目标与替代实践相匹配;进行财务规划和市场探索;以及了解有前景的新兴实践,如补贴授粉者栖息地。不过,访谈也强调了多样化的结构性障碍,这是设计支持系统无法解决或只能部分解决的。这些障碍包括社会压力、市场准入、作物保险政策以及与政府机构关系的质量。结果表明,更好的设计可增强个体农民实现耕地多样化的能力,但要成功实现农业景观多样化并支持经济和生态系统健康,还需要结构性干预措施。
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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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