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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]

Rather than on crop modelling only, climate change impact assessments in agriculture need to be based on farming systems analysis and integrated assessment. With a case study for arable farming in Flevoland, the Netherlands (Reidsma et al., 2015), we illustrate that 1) crop models cannot account for all relevant climate change impacts and adaptation options, and 2) changes in technology, policy and prices have had and are likely to have larger impacts on farms than climate change. While crop modelling indicates positive impacts of climate change on yields of major crops in 2050, a semi-quantitative and participatory method assessing impacts of extreme events, shows that there are nevertheless several climate risks (Schaap et al., 2013). A range of adaptation measures are however available to reduce possible negative effects at crop level. In addition, at farm level, farmers can change cropping patterns, and adjust inputs and outputs (Kanellopoulos et al., 2014). Also farm structural change will influence impacts and adaptation (Mandryk et al., 2012). While the 5th IPCC report is more negative regarding impacts of climate change on agriculture compared to the previous report, also for temperate regions, our results show that when putting climate change in context of other drivers, and when explicitly accounting for adaptation at farm level, impacts may be less negative and opportunities are revealed. These results refer to a temperate region, but an integrated assessment may also change perspectives on climate change for other parts of the world. [ABSTRACT FROM AUTHOR]

In the Central Rift Valley of Ethiopia (CRV), actual productivity of most cereals is less than 3 t ha−1 associated with low input use and poor crop management. After calibrating and validating the Agricultural Production System Simulator (APSIM) using experimental data, we explored and prioritized promising intensification options for rainfed maize and wheat that enable to narrow prevailing yield gaps in the CRV, and quantified trade-offs with the water balance and gross margins. We set up a factorial simulation experiment combining Genetic x Environment x Management factors that influence crop yield and water use at field scale to simulate yield and water balance components under current and future climate scenarios (pessimistic scenario for mid-century). Varietal selection and nitrogen (N) fertilization were the most important factors contributing to yield gap closure. Although yields were maximized with N application rates up to 250 kg−1 in most soils and varieties, maximum gross margin and maximum water use efficiency (WUE) were attained at lower N rates, associated with a small yield reduction compared to the maximum. There was a trade-off between intensification and increased absolute water use through transpiration, while the water use per kg product was decreased. However, location-specific N application rates that allow producing at least 80% of the water-limited potential yield (Yw) of maize and wheat resulted in high water use efficiencies as well as favorable cost-benefit ratios. Climate change was projected to lower yield as it advanced maturity, and to result in decreased drainage and increased soil evaporation across all variety, location and management combinations for both crops. Climate change reduced crop yield by 15–25% for wheat and 2–30% for maize. We conclude that the locally-calibrated APSIM model could be used to derive key lessons from the genetic, environment and management interactions, and generate information on sustainable intensification pathways that combine narrowing yield gaps with maximizing WUE and gross margins. • Variety selection and N fertilization contribute most to closing yield gaps (Yg) • Yg can be narrowed at N rates that maximize water use efficiency and gross margin • The potential for yield gap closure is crop, variety, soil and climate specific • Crop intensification increases total water use but decreases water per kg product • Climate change reduces land productivity by 15–25% for wheat and 2–30% for maize [ABSTRACT FROM AUTHOR]

Abstract: A key issue in the debate on the contribution of organic agriculture to the future of world agriculture is whether organic agriculture can produce sufficient food to feed the world. Comparisons of organic and conventional yields play a central role in this debate. We therefore compiled and analyzed a meta-dataset of 362 published organic–conventional comparative crop yields. Our results show that organic yields of individual crops are on average 80% of conventional yields, but variation is substantial (standard deviation 21%). In our dataset, the organic yield gap significantly differed between crop groups and regions. The analysis gave some support to our hypothesis that the organic–conventional yield gap increases as conventional yields increase, but this relationship was only rather weak. The rationale behind this hypothesis is that when conventional yields are high and relatively close to the potential or water-limited level, nutrient stress must, as per definition of the potential or water-limited yield levels, be low and pests and diseases well controlled, which are conditions more difficult to attain in organic agriculture. We discuss our findings in the context of the literature on this subject and address the issue of upscaling our results to higher system levels. Our analysis was at field and crop level. We hypothesize that due to challenges in the maintenance of nutrient availability in organic systems at crop rotation, farm and regional level, the average yield gap between conventional and organic systems may be larger than 20% at higher system levels. This relates in particular to the role of legumes in the rotation and the farming system, and to the availability of (organic) manure at the farm and regional levels. Future research should therefore focus on assessing the relative performance of both types of agriculture at higher system levels, i.e. the farm, regional and global system levels, and should in that context pay particular attention to nutrient availability in both organic and conventional agriculture. [Copyright &y& Elsevier]

Sub-Saharan Africa's (SSA) demand for cereals is projected to more than double by 2050. Climate change is generally assumed to add to the future challenges of the needed productivity increase. This study aimed to assess (i) the potential climate change impact on four key rainfed cereals (maize, millet, sorghum and wheat) in ten SSA countries namely Burkina Faso, Ghana, Mali, Niger, Nigeria, Ethiopia, Kenya, Tanzania, Uganda, and Zambia using local data and national expertise, and (ii) the potential of cultivar adaptation to climate change for the four crops. We assessed effects on rainfed potential cereal yields per crop and aggregated these to regional level in West (WA), East and Southern Africa (ESA). We made use of a rigorous agronomic dataset for 120 locations in the ten countries and performed simulations of rainfed potential yield (Yw) using bias-corrected climate data from five GCMs, three time periods (1995–2014 as baseline, 2040–2059, and 2080–2099) and two scenarios (SSP3–7.0 as business as usual and SSP5–8.5 as pessimistic). We tested whether better adapted cultivars (taken from the pool of cultivars currently employed in the ten countries) could compensate for climate change. Results showed that climate change decreased aggregated Yw of cereals by around 6% in ESA by 2050, whereas projected impacts in WA were not significant. In 2090, however, the projected impact of climate change in both WA (−24%) and ESA (−9%). was significant. Cultivar adaptation partially compensated the negative impact of climate change. With the adaptation approach, 87% and 82% of potential production in ESA was estimated to occur with higher average Yw and lower variability in, respectively, 2050 and 2090, compared to the baseline period. In WA 67% and 43% of the potential production was estimated to experience such positive effects in 2050 and 2090, respectively. These results highlight remaining adaptation challenges for 13% (2050) and 18% (2090) in ESA and 33% (2050) and 57% (2090) in WA for potential production. In the context of the large yield gaps in SSA, this is likely to further increase challenges to meet cereal self-sufficiency for SSA, especially in WA. • By 2050, climate change impact on rainfed potential yields was insignificant in West and modest in East and Southern Africa. • By 2090, average impacts were much higher in West than in East and Southern Africa. • Early and late maturing cultivars were simulated to be effective for West Africa. • To offset climate change impacts, later maturing cultivars were simulated to be effective for East Africa. • Cultivar adaptation reduced negative impacts substantially. [ABSTRACT FROM AUTHOR]

Reliable estimates of crop nitrogen (N) uptake and offtake are critical in estimating N balances, N use efficiencies and potential losses to the environment. Calculation of crop N uptake and offtake requires estimates of yield of crop product (e.g. grain or beans) and crop residues (e.g. straw or stover) and the N concentration of both components. Yields of crop products are often reasonably well known, but those of crop residues are not. While the harvest index (HI) can be used to interpolate the quantity of crop residue from available data on crop product yields, harvest indices are known to vary across locations, as do N concentrations of residues and crop products. The increasing availability of crop data and advanced statistical and machine learning methods present us with an opportunity to move towards more locally relevant estimates of crop harvest index and N concentrations using more readily available data. The aim of this study was to investigate whether improved estimates of maize crop HI and N concentrations of crop products and crop residues can be based on crop data available at the global scale, such as crop yield, fertilizer application rates and estimates of yield potential. Experiments from 1487 different locations conducted across 31 countries were used to test various prediction models. Predictions from mixed-effects models and random forest machine learning models provided reasonable levels of prediction accuracy (R2 of between 0.33 and 0.68), with the random forest method having greater accuracy. Although the mixed-effects prediction models had lower prediction accuracy than random forest, they did provide better interpretability. Selection of which method to use will depend on the objective of the user. Here, the random forest and mixed-effects methods were applied to N in maize, but could equally be applied to other crops and other nutrients, if data becomes available. This will enable obtaining more locally relevant estimates of crop nutrient offtake to improve estimates of nutrient balances and nutrient use efficiency at national, regional or global levels, as part of strategies towards more sustainable nutrient management. [Display omitted] • Predictions of crop nitrogen (N) removal at national to sub-national scales will improve global nutrient budgeting. • Maize harvest index (HI), crop product N concentration (CPN) and crop residue N concentrations (CRN) were analyzed. • Predictor variables included crop product yield, fertilizer nitrogen application rates and yield potential. • Random forest (RF) models had greater prediction accuracy of HI, CPN and CRN compared with mixed-effects (ME) models. • Both methods could predict HI, CPN and CRN, but ME models were easier to interpret and extrapolate than RF models. [ABSTRACT FROM AUTHOR]

In crop science, widely used theoretical concepts of production ecology comprise a hierarchy in growth defining, limiting, and reducing factors, which determine corresponding potential, limited, and actual production levels. These concepts give insight in theoretically achievable production, yield gaps, and yield gap mitigation. Concepts of production ecology have been demonstrated to be applicable to livestock science, but so far they have not been used quantitatively for livestock production. This paper aims to define theoretical concepts of production ecology for livestock systems in more detail, to express livestock production in suitable units, and to provide a framework to analyse production levels for livestock systems and combined crop–livestock systems. Growth defining (genotype and climate), growth limiting (feed quality and quantity), and growth reducing factors (diseases and stress) in livestock production are described analogous to the growth factors in crop production. Management practices, such as housing, feeding, culling, and slaughter are specified. From the perspective of a livestock system, production is expressed per animal, per unit of animal body mass, and per unit of feed intake, whereas from the perspective of a combined crop–livestock system, production is recommended to be expressed in kg livestock product ha − 1 year − 1 . The quantitative framework is illustrated for Charolais cattle ( Bos taurus subsp.) in two beef production systems in France, differing in feeding strategies. System A produces heavier calves than system B, whereas cattle in system B are fed a higher fraction of concentrates in the diet compared with system A. Potential beef production was similar for systems A and B, and estimated to be 152 kg beef animal − 1 year − 1 and 251 g beef kg − 1 live weight year − 1 , while there was a minor difference when expressed per unit of feed intake (54.5 vs 54.8 g beef kg − 1 DM). Actual production was lower for system A than for system B (24.9 vs 31.2 g beef kg − 1 DM). Potential production for combined crop–livestock systems was again similar for systems A and B (631 vs 634 kg beef ha − 1 year − 1 ), while actual production was much lower for system A than for system B (133 vs 180 kg beef ha − 1 year − 1 ). The yield gap for crop–livestock systems was 79% of potential production for system A and 72% for system B. We conclude that the framework is effective to reveal the scope to increase production and resource use efficiency in livestock production. [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]

Current potential yield simulations of potato crops can be lower than actual yields observed in fields and the potential yield simulations miss important cultivar differences, for instance in terms of cultivar earliness. This causes issues not only for potential yield simulations but also when estimating the limiting effects of water (and nitrogen) and yield gaps in farmers' fields. To address this issue, we recalibrated potential yield simulations with WOFOST for five commercial Dutch potato cultivars. An experiment conducted during the 2020 season was used for model calibration while the 2019 version of the same experiment was used for evaluation. The focus of the calibration was on phenology, photosynthesis, and biomass allocation. To aid the latter, the WOFOST biomass allocation table functions were replaced with sigmoid functions and biomass reallocation from haulm to tubers was introduced. In total 19 parameters were adjusted and/or introduced. Twelve of these parameters were not cultivar-specific, e.g. the specific leaf area over the plant developmental stage (SLATB), and the maximum leaf CO 2 assimilation rate as function of developmental time (AMAXTB). Seven parameters differed between the cultivars, e.g. the thermal time between emergence and tuber initiation (TSUM1), and most of these were correlated to cultivar earliness. For the calibration year, final yields were underestimated by on average 5.4%, while for the evaluation year the final yield was overestimated by 4.4%. In comparison, the final dry matter yields ranged from 16.4 to 18.5 t ha−1 in 2019 and 15.5–17.5 t ha−1 in 2020. The old WOFOST parameters for potato resulted in an underestimation of 23.7% and 28.7% for, respectively, 2019 and 2020. Improving the parameter sets greatly increased accuracy of the simulations and provided insight in which parameters are cultivar specific and related to cultivar-earliness. • A new experimental dataset was used to calibrate WOFOST. • Potential tuber dry matter yield of potato crop ranged from 16.4 to 18.5 t ha−1. • Cultivar specific parameter calibration greatly increased model accuracy. • Cultivar specific parameter values can often be linked to cultivar earliness. [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: Rice production must increase to meet future food requirements amid strong competition for limited resources. Yield gap analysis is a useful method to examine how large the ranges are between potential, desirable rice yields and those actually realized in farmers’ fields. We analyzed farmers’ yields in wet and dry seasons in four intensively cropped rice areas in Southeast Asia and explored opportunities for reducing the yield gap to meet future food requirements. We found yield gaps of 2.0–5.0tha−1 between average and climatic yield potential and 1.2–2.6tha−1 between average and best farmers’ yields. In relative terms, average yields varied between 43% and 75% of the climatic yield potential and 61% and 83% of the best farmers’ yields. Farmers with best yields were generally more educated, and used fertilizers and labor more efficiently than average farmers. The yield gaps between average and best farmers’ yields are higher in rice-importing countries (Indonesia and Philippines) compared with rice-exporting countries (Thailand and Vietnam). Assuming no change in diet, closing the existing yield gap between average and best-yielding farmers can sufficiently cover the yield increase needed for 2050 in the three countries, except for the Philippines, where yield increase must be even higher. Trend analysis of yield increases of a population of farmers in Central Luzon (Philippines), which included a learning curve analysis, well described the process of technology adoption from 1966 to 2008, leading to higher yields. Using this analysis, for the Philippines, we predicted yields to increase (from 2007/2008 to 2050) by only 18% with current cultivars, production technologies, and prevailing conditions. Therefore, structural changes are needed to boost farmers’ yields to close the yield gap faster. Investments in technology transfer and institutional arrangements are suggested. [Copyright &y& Elsevier]

Yield gap analysis plays an important role in determining potential food availability. The Global Yield Gap Atlas maps yield gaps of crops from point to regional scale across the globe. The calculated yield gaps are based on comparisons between modelled potential yields with actual farmers' yields derived from statistical sources. The calculations are subject to uncertainty due to various sources, including measurement errors, modelling limitations, and scaling issues. An important goal of the Atlas is to convey an uncertainty evaluation of the yield gap analysis. The aim of this paper is to provide a practical methodology that can make the assessment of the uncertainty by experts explicit and accessible for users of the Atlas. We developed an uncertainty protocol and guidelines listing several sources of uncertainty to be considered by country agronomists who were involved in the calculation of the yield gaps. These experts are asked to score the level of uncertainty of each source, as well as the relative impact of each source. Both scores are combined into uncertainty scores for each source. Aggregated uncertainty scores for yield gaps, potential and actual yields are mapped as colours in the Atlas to indicate ranking. Moreover, experts are encouraged to provide a justification for their scores, which are also made available to users of the Atlas. The uncertainty protocol was applied to 189 country-crop combinations by fourteen experts. They ranked lack of data for model calibration, model sensitivity to specific conditions, weather data, and the data quality on cropping system as the most important uncertainty sources for potential yields. The quality of yield data was ranked as the highest source of uncertainty for actual yields. The justifications provided by experts suggest which uncertainty sources may be reducible with relatively little effort, while other uncertainty sources may be more difficult or impractical to address. The decision making on options to improve food production is better informed when uncertainties are accounted for. The proposed uncertainty protocol allows users to distinguish between different sources of uncertainty as well as their level and relative effect on the end result. The ranking of uncertainty sources suggests a prioritization of future effort to reduce the uncertainty around yield gaps. The justifications given by the experts can provide suggestions for options to reduce uncertainty. [Display omitted] • The Global Yield Gap Atlas maps yield gaps of crops from point to regional scale across the globe. • Many different uncertainty sources affect the calculation of yield gaps. • We present a protocol for experts to score uncertainties with their justifications made available to users • The expert scores of the uncertainty sources provide a ranking for users to consider per crop-country combination. • The expert justifications reveal which uncertainties can be reduced and which are not reducible [ABSTRACT FROM AUTHOR]