Rationale for field-specific on-farm precision experimentation

Abstract

Uncertainties in farming necessitate detailed knowledge of the production efficiencies to maintain sustainability. To accomplish ecologically based agriculture, with the goal of intensification by maximizing production and profit as well as minimizing environmental impact, we hypothesized that a site-specific knowledge base can be efficiently achieved through modern precision agriculture (PA) technologies at the field scale. The two goals of this study were to quantify the spatiotemporal variation of crop responses and the variables driving crop production, crop quality, and field-scale farmer net-return. We conducted on-farm experimentation (OFE) on several fields for three years where we varied nitrogen fertilizer rate as a management input, to induce changes in crop response. Using a Monte Carlo approach, we assessed the probability that crop responses varied across fields and between years. To determine the drivers of crop production, quality, and net-return, we performed sensitivity analyses to assess the impact of variation in the environment with the most influence on crop responses and farmer profits. Our analysis provided evidence that the degree of the response of winter wheat yield and protein content to variable nitrogen fertilizer rates are not homogenous across time and space. Elevation as a covariate to nitrogen fertilizer rate was the primary influence on predicted yields and protein across most fields, yet not among all fields and across years in fields. The drivers of net-return varied among fields and across years primarily between yield and protein. However, in some cases the most influential factor was the base price received, controlled by the grain elevators that growers sell to, indicating that in some fields and years, farmer’s net-returns are dictated by variables outside of a farmer’s control or ability to manage. These results provide basic evidence justifying the use of OFE for farm management and suggest that management needs to be specific to each field and point in time, with recommendations being made specifically for a field based on information gathered from that field. On-farm experimentation will enable farmers to identify these drivers and understand how their inputs influence yield and protein within fields. Using information provided by OFE with decision support systems can enable farmers to make informed management decisions that maximize their profits and increase the efficiency of chemical inputs, such as nitrogen fertilizer.