Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
There is increasing interest in how big data will affect U.S. crop production, yet little is known about the field‐level effects of “small” (i.e., individual farm) data. We help to fill this void by studying the relationship between Midwest corn production and the information contained in yield and soil maps. Research on this relationship is lacking, perhaps because maps are information inputs that may not enter the production function in a way comparable to conventional inputs. Using detailed USDA survey data, we implement a stochastic frontier analysis to evaluate how mapping technologies influence field productivity. Controlling for farmers' endogenous choice of technologies, we find evidence of direct (frontier‐shifting) and indirect (efficiency‐enhancing) productivity effects. Depending on model, field output increases by 5.6% or 11.9% as a result of map adoption. Yield maps increase expected efficiency by 8.5%, and soil maps increase expected efficiency by 7.2%, on average. These effects differ by operator demographics, such as years of experience with the field, and structural characteristics, such as whether the field is insured and if it is owned by the operator. Given that yield and soil maps are not universally adopted, our results suggest there remain opportunities to increase productivity through field‐level information use.