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 project 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 semiautomatic 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.
