From field to atlas: Upscaling of location-specific yield gap estimates

Authors

Lenny G.J. van Bussel, Wageningen University, The NetherlandsFollow
Patricio Grassini, University of Nebraska-LincolnFollow
Justin van Wart, University of Nebraska-LincolnFollow
Joost Wolf, Wageningen University, The Netherlands
Lieven Claessens, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Kenya
Haishun Yang, University of Nebraska-LincolnFollow
Hendrik Boogaard, Wageningen University, The Netherlands
Hugo de Groot, Wageningen University, The Netherlands
Kazuki Saito, Africa Rice Center, Benin
Kenneth Cassman, University of Nebraska-LincolnFollow
Martin K. van Ittersum, Wageningen University, The NetherlandsFollow

Document Type

Article

Date of this Version

2015

Citation

Field Crops Research 177 (2015) 98–108; doi: 10.1016/j.fcr.2015.03.005

Comments

Copyright (c) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

Abstract

Accurate estimation of yield gaps is only possible for locations where high quality local data are available,which are, however, lacking in many regions of the world. The challenge is how yield gap estimates based on location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence,insight about the minimum number of locations required to achieve robust estimates of yield gaps at larger spatial scales is essential because data collection at a large number of locations is expensive and time consuming. In this paper we describe an approach that consists of a climate zonation scheme supplemented by agronomical and locally relevant weather, soil and cropping system data. Two elements of this methodology are evaluated here: the effects on simulated national crop yield potentials attributable to missing and/or poor quality data and the error that might be introduced in scaled up yield gap estimates due to the selected climate zonation scheme. Variation in simulated yield potentials among weather stations located within the same climate zone, represented by the coefficient of variation, served as a measure of the performance of the climate zonation scheme for upscaling of yield potentials.We found that our approach was most appropriate for countries with homogeneous topography and large climate zones, and that local up-to-date knowledge of crop area distribution is required for selecting relevant locations for data collection. Estimated national water-limited yield potentials were found to be robust if data could be collected that are representative for approximately 50% of the national harvested area of a crop. In a sensitivity analysis for rain-fed maize in four countries, assuming only 25% coverage of the national harvested crop area (to represent countries with poor data availability), national water-limited yield potentials were found to be over- or underestimated by 3 to 27% compared to estimates with the recommended crop area coverage of ≥50%. It was shown that the variation of simulated yield potentials within the same climate zone is small. Water-limited potentials in semi-arid areas are an exception, because the climate zones in these semi-arid areas represent aridity limits of crop production for the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimates from field, i.e. weather station data supplemented by local soil and cropping system data, to regional and national levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countries with more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach.