Leveraging digital agriculture for on-farm testing of technologies - Puntel, Laila A.; Thompson, Laura J.; Mieno, Taro (Frontiers in Agronomy, 2024)
The Precision Nitrogen Project (PNP) worked with more than 80 corn and winter wheat producers to inexpensively design and implement randomized, replicated field strip trials on whole commercial farm fields, and to provide site-specific testing of current nitrogen (N) technologies. This article proposes a conceptual framework and detailed procedure to select the N technology to be tested; design and implement field trials; generate, process, and manage field trial data; and automatically analyze, report, and share benefits from precision N technology. The selection of the N technology was farmer-driven to ensure a good fit and to increase the likelihood of future technology adoption. The technology selection method was called the “N tiered approach”, which consisted of selecting a technology that progressively increases the level of complexity without exceeding the farmer’s learning process or farm logistic constraints. The N tools were classified into (1) crop model-based, (2) remote sensing-based, (3) enhanced efficiency fertilizers, and (4) biologicals. Field strip trials comparing producers’ traditional management and the selected N technology were combined with site-specific N rate blocks placed in contrasting areas of the fields. Yield data from the N rate blocks was utilized to derive the site-specific optimal N rate. The benefits of current N technologies were quantified by comparing their yield, profit, and N use efficiency (NUE) to growers’ traditional management and to the estimated site-specific optimal N rate. Communication of the trial results back to the growers was crucial to ensure the promotion and adoption of these N technologies farm wide. The framework and overall benefits from N technologies was presented and discussed. The proposed framework allowed researchers, agronomists, and farmers to carry out on-farm precision N experimentation using novel technologies to quantify benefits of digital ag technology and promote adoption.