Journal articleIFPRI3; CRP2; CRP7; A Ensuring Sustainable food production; DCA; ISIEPTD; PIMPRCGIAR Research Program on Policies, Institutions, and Markets (PIM); CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS

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a b s t r a c tAccurate 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 rainfed maize in four countries, assuming only 25% coverage of the national harvested crop area (to represent countries with poor data availability), national waterlimited 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.

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Bangladesh faces huge challenges in achieving food security due to its high population, diet changes, and limited room for expanding cropland and cropping intensity. The objective of this study is to assess the degree to which Bangladesh can be self-sufficient in terms of domestic maize, rice and wheat production by the years 2030 and 2050 by closing the existing gap (Yg) between yield potential (Yp) and actual farm yield (Ya), accounting for possible changes in cropland area. Yield potential and yield gaps were calculated for the three crops using well-validated crop models and site-specific weather, management and soil data, and upscaled to the whole country. We assessed potential grain production in the years 2030 and 2050 for six land use change scenarios (general decrease in arable land; declining ground water tables in the north; cropping of fallow areas in the south; effect of sea level rise; increased cropping intensity; and larger share of cash crops) and three levels of Yg closure (1: no yield increase; 2: Yg closure at a level equivalent to 50% (50% Yg closure); 3: Yg closure to a level of 85% of Yp (irrigated crops) and 80% of water-limited yield potential or Yw (rainfed crops) (full Yg closure)). In addition, changes in demand with low and high population growth rates, and substitution of rice by maize in future diets were also examined. Total aggregated demand of the three cereals (in milled rice equivalents) in 2030 and 2050, based on the UN median population variant, is projected to be 21 and 24% higher than in 2010. Current Yg represent 50% (irrigated rice), 48–63% (rainfed rice), 49% (irrigated wheat), 40% (rainfed wheat), 46% (irrigated maize), and 44% (rainfed maize) of their Yp or Yw. With 50% Yg closure and for various land use changes, self-sufficiency ratio will be > 1 for rice in 2030 and about one in 2050 but well below one for maize and wheat in both 2030 and 2050. With full Yg closure, self-sufficiency ratios will be well above one for rice and all three cereals jointly but below one for maize and wheat for all scenarios, except for the scenario with drastic decrease in boro rice area to allow for area expansion for cash crops. Full Yg closure of all cereals is needed to compensate for area decreases and demand increases, and then even some maize and large amounts of wheat imports will be required to satisfy demand in future. The results of this analysis have important implications for Bangladesh and other countries with high population growth rate, shrinking arable land due to rapid urbanization, and highly vulnerable to climate change.

Crop simulation models are used at the field scale to estimate crop yield potential, optimize current management, and benchmark input-use efficiency. At issue is the ability of crop models to predict local and regional actual yield and total production without need of site-year specific calibration of internal parameters associated with fundamental physiological processes. In this study, a well-validated maize simulation model was used to estimate yield potential for 45 locations across the U.S. Corn Belt, including both irrigated and rainfed environments, during four years (2011-2014) that encompassed diverse weather conditions. Simulations were based on measured weather data, dominant soil properties, and key management practices at each location (including sowing date, hybrid maturity, and plant density). The same set of internal model parameters were used across all site-years. Simulated yields were upscaled from locations to larger spatial domains (county, agricultural district, state, and region), following a bottom-up approach based on a climate zone scheme and distribution of maize harvested area. Simulated yields were compared against actual yields reported at each spatial level, both in absolute terms as well as deviations from long-term averages. Similar comparisons were performed for total maize production, estimated as the product of simulated yields and official statistics on maize harvested area in each year. At county-level, the relationship between simulated and actual yield was better described by a curvilinear model, with decreasing agreement at higher yields (>12 Mg ha-1). Comparison of actual and simulated yield anomalies, as estimated from the yearly yield deviations from the long-term actual and simulated average yield, indicated a linear relationship at county-level. In both cases (absolute yields and yield anomalies comparisons), the agreement increased with increasing spatial aggregation (from county to region). An approach based on long-term actual and simulated yields and year-specific simulated yield allowed estimation of actual yield with a high degree of accuracy at county level (RMSE ≤ 18%), even in years with highly favorable weather or severe drought. Estimates of total production, which are of greatest interest to buyers and sellers in the market, were also in close agreement with actual production (RMSE ≤ 22%). The approach proposed here to estimate yield and production can complement other approaches that rely on surveys, field crop cuttings, and empirical statistical methods and serve as basis for in-season yield and production forecasts.