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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 (>12Mgha−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.

Quantitative understanding of factors driving yield increases of major food crops is essential for effective prioritization of research and development. Yet previous estimates had limitations in distinguishing among contributing factors such as changing climate and new agronomic and genetic technologies. Here, we distinguished the separate contribution of these factors to yield advance using an extensive database collected from the largest irrigated maizeproduction domain in the world located in Nebraska (United States) during the 2005-to-2018 period. We found that 48% of the yield gain was associated with a decadal climate trend, 39% with agronomic improvements, and, by difference, only 13% with improvement in genetic yield potential. The fact that these findings were so different from most previous studies, which gave much-greater weight to genetic yield potential improvement, gives urgency to the need to reevaluate contributions to yield advances for all major food crops to help guide future investments in research and development to achieve sustainable global food security. If genetic progress in yield potential is also slowing in other environments and crops, future crop-yield gains will increasingly rely on improved agronomic practices. [ABSTRACT FROM AUTHOR]

Many assessments of crop yield gaps based on comparisons to actual yields suggest grain yields in highly intensified agricultural systems are at or near the maximum yield attainable. However, these estimates can be biased in situations where yields are below full yield potential. Rice yields in the US continue to increase annually, suggesting that rice yields are not near the potential. In the interest of directing future efforts towards areas where improvement is most easily achieved, we estimated yield potential and yield gaps in US rice production systems, which are amongst the highest yielding rice systems globally. Zones around fourteen reference weather stations were created, and represented 87% of total US rice harvested area. Rice yield potential was estimated over a period of 13–15 years within each zone using the ORYZA(v3) crop model. Yield potential ranged from 11.5 to 14.5 Mg ha −1 , while actual yields varied from 7.4 to 9.6 Mg ha −1 , or 58–76% of yield potential. Assuming farmers could exploit up to 85% of yield potential, yield gaps ranged from 1.1 to 3.5 Mg ha −1 . Yield gaps were smallest in northern California and the western rice area of Texas, and largest in the southern rice area of California, southern Louisiana, and northern Arkansas/southern Missouri. Areas with larger yield gaps exhibited greater annual yield increases over the study period (35.7 kg ha −1 year −1 per Mg yield gap). Adoption of optimum management and hybrid rice varieties over the study period may explain annual yield increases, and may provide a means to further increase production via expanded adoption of current technologies. [ABSTRACT FROM AUTHOR]

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases ( GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs ( CRU, NCEP/ DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data ( CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors ( RMSEs) that were 26-72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA- POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12-19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method. [ABSTRACT FROM AUTHOR]

Abstract: Yields of crops must increase substantially over the coming decades to keep pace with global food demand driven by population and income growth. Ultimately global food production capacity will be limited by the amount of land and water resources available and suitable for crop production, and by biophysical limits on crop growth. Quantifying food production capacity on every hectare of current farmland in a consistent and transparent manner is needed to inform decisions on policy, research, development and investment that aim to affect future crop yield and land use, and to inform on-ground action by local farmers through their knowledge networks. Crop production capacity can be evaluated by estimating potential yield and water-limited yield levels as benchmarks for crop production under, respectively, irrigated and rainfed conditions. The differences between these theoretical yield levels and actual farmers’ yields define the yield gaps, and precise spatially explicit knowledge about these yield gaps is essential to guide sustainable intensification of agriculture. This paper reviews methods to estimate yield gaps, with a focus on the local-to-global relevance of outcomes. Empirical methods estimate yield potential from 90 to 95th percentiles of farmers’ yields, maximum yields from experiment stations, growers’ yield contests or boundary functions; these are compared with crop simulation of potential or water-limited yields. Comparisons utilize detailed data sets from western Kenya, Nebraska (USA) and Victoria (Australia). We then review global studies, often performed by non-agricultural scientists, aimed at yield and sometimes yield gap assessment and compare several studies in terms of outcomes for regions in Nebraska, Kenya and The Netherlands. Based on our review we recommend key components for a yield gap assessment that can be applied at local to global scales. Given lack of data for some regions, the protocol recommends use of a tiered approach with preferred use of crop growth simulation models applied to relatively homogenous climate zones for which measured weather data are available. Within such zones simulations are performed for the dominant soils and cropping systems considering current spatial distribution of crops. Need for accurate agronomic and current yield data together with calibrated and validated crop models and upscaling methods is emphasized. The bottom-up application of this global protocol allows verification of estimated yield gaps with on-farm data and experiments. [Copyright &y& Elsevier]

Agriculture is a resource-intensive enterprise. The manner in which food production systems utilize resources has a large influence on environmental quality. To evaluate prospects for conserving natural resources while meeting increased demand for cereals, we interpret recent trends and future trajectories in crop yields, land and nitrogen fertilizer use, carbon sequestration, and greenhouse gas emissions to identify key issues and challenges. Based on this assessment, we conclude that avoiding expansion of cultivation into natural ecosystems, increased nitrogen use efficiency, and improved soil quality are pivotal components of a sustainable agriculture that meets human needs and protects natural resources. To achieve this outcome will depend on raising the yield potential and closing existing yield gaps of the major cereal crops to avoid yield stagnation in some of the world's most productive systems. Recent trends suggest, however, that increasing crop yield potential is a formidable scientific challenge that has proven to be an elusive goal. [ABSTRACT FROM AUTHOR]

Rice yields in Uruguay have increased rapidly (159 kg−1 ha−1 y−1) between 1990 and 2013. There is evidence, however, of an incipient yield plateau in recent years. The aim of this study was to determine if the recent slowdown in yield gains is because average yield (Ya) has approached the yield potential (Yp) ceiling, which makes it increasingly difficult for farmers to sustain further yield gains. We followed the methodology developed by the Global Yield Gap Atlas to estimate Yp and associated yield gaps for irrigated rice supported by data from high-yield experiments to calibrate the rice simulation model Oryza (v3). Subsequently, the model was used to simulate Yp using long-term daily weather data from seven locations, representing 90 % of total rice area in Uruguay. The exploitable yield gap (Yeg) was calculated as the difference between 80 % of Yp and Ya. Estimated national average Yp was 13.9 Mg ha−1, with relatively small variation across sites, from 13.1 to 15.1 Mg ha−1. Average Ya was 8.3 Mg ha−1, ranging from 7.9 to 8.5 Mg ha−1 across sites, and representing 60 % of Yp. Our analysis suggests there is still room to further increase rice yields in Uruguay, because the Yeg is 2.8 Mg ha−1, which means the current yield plateau is not due to Ya approaching Yp, as has occurred in other high-yield irrigated rice systems in China and California, USA. The approach followed here can help determine whether yield plateaus are occurring due to a small Yeg or other factors. • There is evidence of an incipient yield plateau for irrigated rice in Uruguay. • We used yield-gap analysis to determine if the yield plateau was related to a biophysical limit on yield potential. • Yield potential was estimated using a well-calibrated crop model coupled with local weather and management data. • Our analysis showed that a relatively large exploitable yield gap still exists. • The yield-gap analysis approach can be used to understand causes for yield plateaus in other regions and for other crops. [ABSTRACT FROM AUTHOR]

Accurate estimation of a crop’s yield potential (Yp) is critical to addressing long-term food security via identification of the exploitable yield gap. Due to lack of field data, efforts to quantify crop yield potential typically rely on crop models. Using the ORYZA rice crop model, we sought to estimate Yp of irrigated rice for two widely used rice varieties (M-206 and CXL745) in three major US rice-producing regions that together represent some of the highest yielding rice regions of the world. Three major issues with the crop model had to be addressed to achieve acceptable simulation of Yp; first, the model simulated leaf area index (LAI) and biomass agreed poorly for all direct-seeded systems using default settings; second, cold-induced sterility and associated yield losses were poorly simulated for environments with a large diurnal temperature variation; lastly, simulated Yp was sensitive to the specified definition of physiological maturity. Except for the simulation of cold-induced sterility, all issues could be remedied within the existing model structure. In contrast, simulation of cold-induced sterility posed a continuing challenge to accurate simulation—one that will likely require changes to ORYZA's formulation. Estimates of Yp from the modified model were validated against large multi-year data sets of experimental yields covering the majority of US rice production areas. Validation showed the adjusted model simulated Yp well, with most top yields falling within 85% of Yp for both varieties (77% and 78% observed yields within 15% of Yp for CXL745 and M-206 respectively). Maximum estimated Yp was 14.3 (range of 8.2–14.5) and 14.5 (range of 8.7–15.3) t ha ‐1 for the Southern US and CA, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: The green revolution was initiated by introduction of modern high yielding rice and wheat varieties in the 1960s. In recent years, however, there are signs of stagnating yields in major rice producing areas of Asia, which suggests a lack of genetic gain in yield potential in rice improvement program. We examined the grain yield of IR8, which was the first modern rice variety of the green revolution, and found that it has decreased by 15% compared to levels it achieved in the 1960s. To determine if this yield difference was due to genetic changes in IR8 seed through repeated cycles of planting, or to the lack of adaptation to changing environmental conditions, we compared IR8 stored in a gene bank for 30 years with continuously grown IR8 in both field and pot experiments. In the field experiments, IR8 was also compared with recently developed varieties in yield performance. Plants from both seed sources had identical agronomic and morpho-physiological characteristics. Yield of IR8 from both seed sources were 15% less than the yields of recently developed varieties. These findings suggest that the low yield of IR8 was resulted from the lack of adaptation to changed environmental conditions, and maintenance breeding plays a critical role in improving adaptation of newly developed varieties to environmental changes that have a negative impact on older varieties. Our study provides strong justification for continuous maintenance breeding efforts to preserve rice yield potential through improved resistance to rapidly evolving biotic and abiotic stresses. [ABSTRACT FROM AUTHOR]

Abstract: Unlike the Central and Eastern U.S. Corn-Belt where maize is grown almost entirely under rainfed conditions, maize in the Western Corn-Belt is produced under both irrigated (3.2millionha) and rainfed (4.1millionha) conditions. Simulation modeling, regression, and boundary-function analysis were used to assess constraints to maize productivity in the Western Corn-Belt. Aboveground biomass, grain yield, and water balance were simulated for fully irrigated and rainfed crops, using 20-year weather records from 18 locations in combination with actual soil, planting date, plant population, and hybrid-maturity data. Mean values of meteorological variables were estimated for three growth periods (pre- and post-silking, and the entire growing season) and used to identify major geospatial gradients. Linear and stepwise multiple regressions were performed to evaluate variation of potential productivity in relation to meteorological factors. Boundary functions for water productivity and water-use efficiency were derived and compared against observed data reported in the literature. Geospatial gradients of seasonal radiation, temperature, rainfall, and evaporative demand along the Western Corn-Belt were identified. Yield potential with irrigation did not exhibit any geospatial pattern, depending instead on the specific radiation/temperature regime at each location and its interaction with crop phenology. A linear and parabolic response to post-silking cumulative solar radiation and mean temperature, respectively, explained variations on yield potential. Water-limited productivity followed the longitudinal gradient in seasonal rainfall and evaporative demand. Rainfed crops grown in the Western Corn-Belt are frequently subjected to episodes of transient and unavoidable water stress, especially around and after silking. Soil water at sowing ameliorates, but does not eliminate water stress episodes. Boundary functions for water productivity had slopes of 46 and 28kgha−1 mm−1, for aboveground biomass and grain yield, respectively. At high seasonal water supply, productivity was weakly correlated with water supply because many crops did not fully utilize seasonally available water due to percolation below the root zone or water left in the ground at physiological maturity. Fitted boundary functions for water-use efficiency had slopes (≈seasonal transpiration-efficiency) of 54 and 37kgha−1 mm−1 for aboveground biomass and grain yield, respectively, and an x-intercept around 25–75mm (≈seasonal soil evaporation). Data collected from experiments conducted in low-rainfall environments indicated that the boundary functions for water-use efficiency, derived from this study, are broadly applicable. [Copyright &y& Elsevier]

Simulating crop yield and yield variability requires long-term, high-quality daily weather data, including solar radiation, maximum ( T max ) and minimum temperature ( T min ), and precipitation. In many regions, however, daily weather data of sufficient quality and duration are not available. To overcome this limitation, we evaluated a new method to create long-term weather series based on a few years of observed daily temperature data (hereafter called propagated data). The propagated data are comprised of uncorrected gridded solar radiation from the Prediction of Worldwide Energy Resource dataset from the National Aeronautics and Space Administration (NASA–POWER), rainfall from the Tropical Rainfall Measuring Mission (TRMM) dataset, and location-specific calibration of NASA–POWER T max and T min using a limited amount of observed daily temperature data. The distributions of simulated yields of maize, rice, or wheat with propagated data were compared with simulated yields using observed weather data at 18 sites in North and South America, Europe, Africa, and Asia. Other sources of weather data typically used in crop modeling for locations without long-term observed weather data were also included in the comparison: (i) uncorrected NASA–POWER weather data and (ii) generated weather data using the MarkSim weather generator. Results indicated good agreement between yields simulated with propagated weather data and yields simulated using observed weather data. For example, the distribution of simulated yields using propagated data was within 10% of the simulated yields using observed data at 78% of locations and degree of yield stability (quantified by coefficient of variation) was very similar at 89% of locations. In contrast, simulated yields based entirely on uncorrected NASA–POWER data or generated weather data using MarkSim were within 10% of yields simulated using observed data in only 44 and 33% of cases, respectively, and the bias was not consistent across locations and crops. We conclude that, for most locations, 3 years of observed daily T max and T min data would allow creation of a robust weather data set for simulation of long-term mean yield and yield stability of major cereal crops. [ABSTRACT FROM AUTHOR]

Although the U.S. accounts for 38% of global soybean production, and produces 85 Mt annually, quantification of U.S. soybean yield gaps and assessment of underpinning causes have not been performed. Likewise, no attempt has been made to estimate the attainable water productivity (kg seed per mm of water supply). Here we make an initial attempt at such analyses by evaluating yield gaps and water productivity of soybean in the state of Nebraska (western U.S. Corn Belt), which ranks fifth among U.S. soybean producing states with a 6.5 Mt annual production. Rainfed and irrigated production are both important in Nebraska, accounting for 46% and 54% of total state soybean output. We evaluated a database containing on-farm field yields, applied inputs, and management practices collected from 516 soybean fields in the eastern and central regions of Nebraska during three successive crop seasons (2010–2012). Yield gaps were estimated as the difference between yield potential and on-farm field yields. Yield potential was estimated by two approaches: (i) a soybean simulation model coupled with field-specific weather and management data, and (ii) derivation of a boundary function for the relationship between producer-reported soybean yields and seasonal water supply using quantile regression. Actual yields were evaluated in relation to applied inputs and management factors to identify the most likely causes of yield gaps. Across regions, average yield gap of irrigated soybean ranged from 10 to 30% of the simulated yield potential. Water supply set an upper limit to productivity, not only in rainfed fields, but also in irrigated fields in a drought year in which irrigation did not fully satisfy crop water requirements. The boundary function for the relationship between yield and seasonal water supply had a slope (≈attainable water productivity) of 9.9 kg ha −1 mm −1 and x -intercept (≈soil evaporation) of 73 mm. A seasonal water supply of ca. 650 mm appeared sufficient to maximize seed yield. Average rainfed and irrigated yields were 31 and 20% below their respective yield potential estimated from the boundary function. Sowing date also set an upper limit to producer yield, and this management practice alone explained the largest portion of the observed yield variation among fields. Soybean yield linearly decreased with sowing date delay, with the magnitude of the yield penalty varying among regions. Irrigated yields were not higher in no-till fields, and, in fact, a yield penalty was observed in no-till fields compared to reduced- or disked fields, especially in region-years with cooler early-season temperatures. Higher irrigated and rainfed yields were observed in fields that received starter fertilizer or P fertilizer application. Finally, application of in-season fungicide enhanced irrigated crop yields though this effect was inconsistent over years. Analysis of this database indicated that (i) soybean producers in this region obtained yields close (70–90%) to the estimated yield potential ceiling, and (ii) future agronomic on-farm yield improvement might be achieved by fine-tuning of current management practices, including earlier sowing date coupled with judiciously chosen tillage to achieve warmer soils in the springs plus suitably applied nutrient fertilizer application and in-season fungicide. [ABSTRACT FROM AUTHOR]

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 rainfed 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. [ABSTRACT FROM AUTHOR]

Numerous studies have been published during the past two decades that use simulation models to assess crop yield gaps (quantified as the difference between potential and actual farm yields), impact of climate change on future crop yields, and land-use change. However, there is a wide range in quality and spatial and temporal scale and resolution of climate and soil data underpinning these studies, as well as widely differing assumptions about cropping-system context and crop model calibration. Here we present an explicit rationale and methodology for selecting data sources for simulating crop yields and estimating yield gaps at specific locations that can be applied across widely different levels of data availability and quality. The method consists of a tiered approach that identifies the most scientifically robust requirements for data availability and quality, as well as other, less rigorous options when data are not available or are of poor quality. Examples are given using this approach to estimate maize yield gaps in the state of Nebraska (USA), and at a national scale for Argentina and Kenya. These examples were selected to represent contrasting scenarios of data availability and quality for the variables used to estimate yield gaps. The goal of the proposed methods is to provide transparent, reproducible, and scientifically robust guidelines for estimating yield gaps; guidelines which are also relevant for simulating the impact of climate change and land-use change at local to global spatial scales. Likewise, the improved understanding of data requirements and alternatives for simulating crop yields and estimating yield gaps as described here 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-scaling from location-specific estimates to regional and national spatial scales. [ABSTRACT FROM AUTHOR]

Abstract: Yield gap analysis, which evaluates magnitude and variability of difference between crop yield potential (Yp) or water limited yield potential (Yw) and actual farm yields, provides a measure of untapped food production capacity. Reliable location-specific estimates of yield gaps, either derived from research plots or simulation models, are available only for a limited number of locations and crops due to cost and time required for field studies or for obtaining data on long-term weather, crop rotations and management practices, and soil properties. Given these constraints, we compare global agro-climatic zonation schemes for suitability to up-scale location-specific estimates of Yp and Yw, which are the basis for estimating yield gaps at regional, national, and global scales. Six global climate zonation schemes were evaluated for climatic homogeneity within delineated climate zones (CZs) and coverage of crop area. An efficient CZ scheme should strike an effective balance between zone size and number of zones required to cover a large portion of harvested area of major food crops. Climate heterogeneity was very large in CZ schemes with less than 100 zones. Of the other four schemes, the Global Yield Gap Atlas Extrapolation Domain (GYGA-ED) approach, based on a matrix of three categorical variables (growing degree days, aridity index, temperature seasonality) to delineate CZs for harvested area of all major food crops, achieved reasonable balance between number of CZs to cover 80% of global crop area and climate homogeneity within zones. While CZ schemes derived from two climate-related categorical variables require a similar number of zones to cover 80% of crop area, within-zone heterogeneity is substantially greater than for the GYGA-ED for most weather variables that are sensitive drivers of crop production. Some CZ schemes are crop-specific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with crop rotations rather than single crop species. [Copyright &y& Elsevier]

Abstract: World population will increase 35% by 2050, which may require doubling crop yields on existing farm land to minimize expansion of agriculture into remaining rainforests, wetlands, and grasslands. Whether this is possible depends on closing the gap between yield potential (Yp, yield without pest, disease, nutrient or water stresses, or Yw under water-limited rainfed conditions) and current average farm yields in both developed and developing countries. Quantifying the yield gap is therefore essential to inform policies and prioritize research to achieve food security without environmental degradation. Previous attempts to estimate Yp and Yw at a global level have been too coarse, general, and opaque. Our purpose was to develop a protocol to overcome these limitations based on examples for irrigated rice in China, irrigated and rainfed maize in the USA, and rainfed wheat in Germany. Sensitivity analysis of simulated Yp or Yw found that robust estimates required specific information on crop management, +15 years of observed daily climate data from weather stations in major crop production zones, and coverage of 40–50% of total national production area. National Yp estimates were weighted by potential production within 100-km of reference weather stations. This protocol is appropriate for countries in which crops are mostly grown in landscapes with relatively homogenous topography, such as prairies, plains, large valleys, deltas and lowlands, which account for a majority of global food crop production. Results are consistent with the hypothesis that average farm yields plateau when they reach 75–85% of estimated national Yp, which appears to occur for rice in China and wheat in Germany. Prediction of when average crop yields will plateau in other countries is now possible based on the estimated Yp or Yw ceiling using this protocol. [Copyright &y& Elsevier]

Abstract: Quantifying the exploitable gap between average farmer yields and yield potential (Y P) is essential to prioritize research and formulate policies for food security at national and international levels. While irrigated maize accounts for 58% of total annual maize production in the Western U.S. Corn Belt, current yield gap in these systems has not been quantified. Our objectives were to quantify Y P, yield gaps, and the impact of agronomic practices on both parameters in irrigated maize systems of central Nebraska. The analysis was based on a 3-y database with field-specific values for yield, applied irrigation, and N fertilizer rate (n =777). Y P was estimated using a maize simulation model in combination with actual and interpolated weather records and detailed data on crop management collected from a subset of fields (n =123). Yield gaps were estimated as the difference between actual yields and simulated Y P for each field-year observation. Long-term simulation analysis was performed to evaluate the sensitivity of Y P to changes in selected management practices. Results showed that current irrigated maize systems are operating near the Y P ceiling. Average actual yield ranged from 12.5 to 13.6Mgha−1 across years. Mean N fertilizer efficiency (kg grain per kg applied N) was 23% greater than average efficiency in the USA. Rotation, tillage system, sowing date, and plant population density were the most sensitive factors affecting actual yields. Average yield gap was 11% of simulated Y P (14.9Mgha−1). Time trends in average farm yields from 1970 to 2008 show that yields have not increased during the past 8 years. Average yield during this period represented ∼80% of Y P ceiling estimated for this region based on current crop management practices. Simulation analysis showed that Y P can be increased by higher plant population densities and by hybrids with longer maturity. Adoption of these practices, however, may be constrained by other factors such as difficulty in planting and harvest operations due to wet weather and snow, additional seed and grain drying costs, and greater risk of frost and lodging. Two key points can be made: (i) irrigated maize producers in this region are operating close to the Y P ceiling and achieve high levels of N use efficiency and (ii) small increases in yield (<13%) can be achieved through fine tuning current management practices that require increased production costs and higher risk. [Copyright &y& Elsevier]