David S. Bullock

The Data‐Intensive Farm Management Project: Changing Agronomic Research Through On‐Farm Precision Experimentation

Agronomy Journal · 2019-11-01 · 96 citations

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…

Spatial variability of crop responses to agronomic inputs in on-farm precision experimentation

Precision Agriculture · 2020 · 72 citations

Abstract Within-field variability of crop yield levels has been extensively investigated, but the spatial variability of crop yield responses to agronomic treatments is less understood. On-farm precision experimentation (OFPE) can be a valuable tool for the estimation of in-field variation of optimal input rates and thus improve agronomic decisions. Therefore, the objectives of this study were to investigate the spatial variability of optimal input rates in OFPE and the potential economic benefi…

Modeling the economic and environmental effects of corn nitrogen management strategies in Illinois

Field Crops Research · 2020 · 29 citations

Eco-efficient use of nitrogen (N) fertilizer in Corn (Zea Mays L.) requires timely information about the supply of N from the soil and the response to N by the crop. These are complex processes, and multiple N management strategies (NMS) have been proposed over time. In this work, we used APSIM (Agricultural Production Systems sIMulator) to simulate the response of Corn's Yield to N for ca. 4200 fields in the state of Illinois in different weather scenarios. Ten different N management strategies…

Can machine learning models provide accurate fertilizer recommendations?

Precision Agriculture · 2024-03-25 · 28 citations

Abstract Accurate modeling of site-specific crop yield response is key to providing farmers with accurate site-specific economically optimal input rates (EOIRs) recommendations. Many studies have demonstrated that machine learning models can accurately predict yield. These models have also been used to analyze the effect of fertilizer application rates on yield and derive EOIRs. But models with accurate yield prediction can still provide highly inaccurate input application recommendations. This…

The value of conducting on-farm field trials using precision agriculture technology: a theory and simulations

Precision Agriculture · 2020 · 23 citations