The Data‐Intensive Farm Management Project: Changing Agronomic Research Through On‐Farm Precision Experimentation
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- David S. Bullock
- Dep. of Agricultural and Consumer Economics Univ. of Illinois at Urbana‐Champaign 307 Mumford Hall. 1301 W. Gregory Urbana IL 61801‐3028
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- Maria Boerngen
- College of Applied Science and Technology Illinois State Univ. Clarence R Ropp Agriculture Building RAB 143 Normal IL 61790‐5020
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- Haiying Tao
- Dep. of Crop and Soil Sciences Washington State Univ. Johnson Hall 245 Pullman WA 99164‐1009
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- Bruce Maxwell
- Land Resources and Environmental Sciences Montana State Univ. Bozeman 334 Leon Johnson Hall Bozeman MT 59717‐2000
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- Joe D. Luck
- Biological Systems Engineering Univ. of Nebraska 204 L.W. Chase Hall Lincoln NE 68588
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- Luciano Shiratsuchi
- School of Plant, Environmental and Soil Sciences Louisiana State Univ. Sturgis Hall 104 Baton Rouge LA 70803‐2804
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- Laila Puntel
- Dep. of Agronomy and Horticulture Univ. of Nebraska 102 D Keim Hall Lincoln NE 68583
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- Nicolas F. Martin
- Dep. of Crop Sciences Univ. of Illinois at Urbana‐Champaign 1102 S Goodwin Ave. Urbana IL 61801‐3028
説明
<jats:p>The Data‐Intensive Farm Management (DIFM) project works with participating farmers, using precision technology to inexpensively design and run randomized agronomic field trials on whole commercial farm fields, to provide data‐based, site‐specific farm input management guidance, thus providing economic and environmental benefits. This article lays out a conceptual framework used by the multidisciplinary DIFM research team to facilitate collaboration and then presents details of DIFM's procedures for what it calls on‐farm precision experimentation (OFPE), which includes field trial design and implementation, data generation, processing, and management, and analysis. It is argued that DIFM's data and the agricultural “Big Data” currently being collected with remote and proximal sensors are complementary; that is, more of either increases the value of the other. In 2019, DIFM and affiliates conducted over 120 trials, ranging from 10 to 100 ha in size, on maize, wheat, soybeans, cotton, and barley in eight US states, Argentina, Brazil, and South Africa. The DIFM is developing cyberinfrastructure to “scale up” its activities, to permit researchers and crop consultants worldwide to work with farmers to conduct trials, then process and manage the data. In Addition, DIFM is in the early stages of developing a software system for semi‐automatic data analytics, and a cloud‐based farm management aid, the purpose of which is to facilitate conversations between agronomists and farmers about implementing data‐driven input management decisions. The proposed framework allows researchers, agronomists, and farmers to carry out on‐farm precision experimentation using novel digital tools.</jats:p><jats:p><jats:bold>Core Ideas</jats:bold> <jats:list list-type="bullet"> <jats:list-item><jats:p>The Data‐Intensive Farm Management project's on‐farm trials can generate massive amounts varied managed input data.</jats:p></jats:list-item> <jats:list-item><jats:p>The Data‐Intensive Farm Management project's data fill a gap in agricultural “Big Data,” to enable data‐intensive crop management.</jats:p></jats:list-item> <jats:list-item><jats:p>The Data‐Intensive Farm Management project's protocols support trial design, data processing and analysis.</jats:p></jats:list-item> <jats:list-item><jats:p>The Data‐Intensive Farm Management project can be implemented by researchers, consultants, and farmers in diverse agronomic scenarios.</jats:p></jats:list-item> </jats:list></jats:p>
収録刊行物
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- Agronomy Journal
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Agronomy Journal 111 (6), 2736-2746, 2019-11
Wiley