How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis

Numerous studies have been published during the past two decades that use simulation models to assesscrop yield gaps (quantified as the difference between potential and actual farm yields), impact of climatechange on future crop yields, and land-use change. However, there is a wide range in quality and spatialand temporal scale and resolution of climate and soil data underpinning these studies, as well as widelydiffering assumptions about cropping-system context and crop model calibration. Here we present anexplicit rationale and methodology for selecting data sources for simulating crop yields and estimatingyield gaps at specific locations that can be applied across widely different levels of data availability andquality. The method consists of a tiered approach that identifies the most scientifically robust require-ments for data availability and quality, as well as other, less rigorous options when data are not availableor are of poor quality. Examples are given using this approach to estimate maize yield gaps in the stateof Nebraska (USA), and at a national scale for Argentina and Kenya. These examples were selected torepresent contrasting scenarios of data availability and quality for the variables used to estimate yieldgaps. The goal of the proposed methods is to provide transparent, reproducible, and scientifically robustguidelines for estimating yield gaps; guidelines which are also relevant for simulating the impact of cli-mate change and land-use change at local to global spatial scales. Likewise, the improved understandingof data requirements and alternatives for simulating crop yields and estimating yield gaps as describedhere can help identify the most critical “data gaps” and focus global efforts to fill them. A related paper(Van Bussel et al., 2015) examines issues of site selection to minimize data requirements and up-scalingfrom location-specific estimates to regional and national spatial scales