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Abstract: We compared the production–ecological sustainability of biofuel production from several major crops that are also commonly used for production of food or feed, based on current production practices in major production areas. The set of nine sustainability indicators focused on resource use efficiency, soil quality, net energy production and greenhouse gas emissions, disregarding socio-economic or biodiversity aspects and land use change. Based on these nine production–ecological indicators and attributing equal importance to each indicator, biofuel produced from oil palm (South East Asia), sugarcane (Brazil) and sweet sorghum (China) appeared most sustainable: these crops make the most efficient use of land, water, nitrogen and energy resources, while pesticide applications are relatively low in relation to the net energy produced. Provided there is no land use change, greenhouse gas emissions of these three biofuels are substantially reduced compared with fossil fuels. Oil palm was most sustainable with respect to the maintenance of soil quality. Maize (USA) and wheat (Northwest Europe) as feedstock for ethanol perform poorly for nearly all indicators. Sugar beet (Northwest Europe), cassava (Thailand), rapeseed (Northwest Europe) and soybean (USA) take an intermediate position. [Copyright &y& Elsevier]

• SOM dynamics vary across fields under equal management and soil depth. • To monitor carbon sequestration, the minimal sampling depth should be 60 cm. • For the conversion of SOM concentration to stock local bulk density is needed. • Monitoring for payment of carbon credits needs to take all farm fields into account. To meet the goals of the Climate Agreement, policy makers consider incentivizing soil carbon sequestration by carbon credits to offset greenhouse gas emissions from agriculture, industry and other sectors. Therefore, the interest in monitoring soil organic matter dynamics is growing rapidly, yet factors affecting the precision of the monitoring results are rarely quantified. We used the rhetorical scheme "The seven circumstances" to structure the methodological aspects of setting up a soil organic matter (SOM) monitoring program. The rhetorical scheme was applied during four years on two grass-based dairy farms in the Netherlands to assess in detail: conversion of SOM weight concentrations to SOM stocks (HOW), the effect of soil depth and the integration of scales field vs. farm (WHERE), and the effect of sampling date and sampling frequency on SOM estimates (WHEN). We found that all three circumstances affect conclusions on SOM stocks or SOM dynamics strongly. Considerable variation was found in the relationship between soil bulk density and SOM weight concentration (i.e. pedotransfer curves) among fields, depth and literature reference equations. Therefore, preferably a site specific pedotransfer curve should be used when comparing SOM stocks based on SOM weight concentrations across sites. Large differences in trends of SOM stock changes over time were found between fields and sampling depths. We conclude that a sampling depth in grassland soils up to 60 cm may be relevant to capture the dynamics in deeper layers. Furthermore, for quantitative underpinning of carbon payment schemes, the whole farm should be monitored rather than a few fields as trends between fields are highly variable. [ABSTRACT FROM AUTHOR]

The difference between the theoretical maximum (potential) production and the actual production realized by farmers is referred to as the yield gap. The objectives of this study are to develop a mechanistic model for dairy cows that allows yield gap analysis in dairy production systems and to evaluate model performance. We extended and adapted an existing model for beef cattle to dairy cattle, and the new model was named Livestock simulator for Generic analysis of Animal Production Systems—Dairy cattle (LiGAPS-Dairy). Milk production and growth of an individual cow over its entire lifespan were described as a function of the animal's genotype, the ambient climate, feed quality, and available feed quantity. The model was parameterized for Holstein-Friesian cows. After calibration, we evaluated model performance by comparing simulated results and measured results from experimental farms in the Netherlands, which were not used for model calibration. Cows were permanently housed in stables, where the diet consisted of predetermined amounts of concentrates and ad libitum high-quality roughage. The mean absolute error (MAE) for simulated milk production per lactation was 12% of the measured milk production, whereas the MAE for simulated daily milk yields was 19%. The MAE for simulated feed intake per lactation was 10% of the measured feed intake, whereas the MAE for simulated daily feed intake was 19%. The average yield gap for dairy cows was 11% of the potential milk production (Y P). Yield gap analysis indicated that for experimental farms in the Netherlands, the difference between Y P and feed quality limited milk production (Y L) of 1,009 kg fat- and protein-corrected milk was mainly explained by feed intake capacity (33%), protein deficiency (25%), cow weight at the start of experiments (23%), and heat stress (19%). The LiGAPS-Dairy model also indicated the periods during lactation in which these factors affected milk production. In our opinion, the overall model performance is acceptable for permanently housed cows under Dutch conditions. The model needs to be evaluated further for other production systems, countries and breeds. Thereafter, LiGAPS-Dairy can be used for yield gap analysis and exploration of options to increase resource use efficiency in dairy production. [ABSTRACT FROM AUTHOR]

Dietary changes are closely intertwined with land use changes, and understanding the relative importance of different food items and their driving forces is crucial. Here, we analyzed the changes in China's global cropland footprint for food and feed consumption from 1987 to 2013 and explored the driving forces behind these changes for each food item. China's per capita protein consumption increased by 57%. The global cropland footprint of China's food and feed consumption expanded by 40% during this period. Decomposition analysis demonstrated that population growth was the primary driver of the increased total cropland footprint until 1993, with the subsequent rise in per capita protein consumption becoming the driving force. Thereafter, the increased efficiency of cropland use offset 49% of the total cropland expansion primarily due to improved management technologies. Among the food items analyzed, pork, eggs, and vegetables were identified as the main contributors to the increased total cropland footprint, primarily driven by changes in dietary patterns and their increased inclusion in the human diet. In conclusion, changing human diet towards less monogastric livestock products and improving productivity of concentrate feed crops are essential for mitigating domestic land pressure and ecological degradation in exporting countries. [ABSTRACT FROM AUTHOR]

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]

The physiological age of potato seed tubers evolves with chronological age, influenced by growth history and storage conditions. Effective quantification of physiological ageing requires a dynamic and flexible modelling approach. In this study, we conducted three cycles of field production and storage of seed tubers, using contrasting and widely grown cultivars. Different storage temperatures were employed as the sole treatment to introduce variation in the physiological age of the same cultivar. Throughout the storage seasons, periodic assessments of sprouting behaviour were conducted, both at the onset and at the end of a sprouting test. The total sprout dry weight per tuber produced in the sprouting test emerged as the best measure of seed tuber vigour, exhibiting cultivar-specific responses to storage temperatures. To capture these diverse sprouting behaviours, we devised a piecewise sprouting behaviour function. This is the first mathematical model that effectively describes the evolving sprouting patterns and provides biologically relevant parameters for the analysis of cultivar-specific sprouting trends. The temporal parameters of the function displayed sensitivity to storage temperature and year-to-year variation in growth conditions, highlighting distinct physiological phases. Consequently, the function facilitated insightful analysis of these key factors, enabling interpretation of underlying processes involved in the physiological ageing of potato seed tubers. • Large dataset and thorough analyses of the sprouting behaviours of potato seed tubers. • Innovative devise of a mathematical function capable of describing various sprouting patterns. • Proposed framework of biological interpretation of the underlying physiological ageing process. [ABSTRACT FROM AUTHOR]

Wheat yields in Ethiopia need to increase considerably to reduce import dependency and keep up with the expected increase in population and dietary changes. Despite the yield progress observed in recent years, wheat yield gaps remain large. Here, we decompose wheat yield gaps in Ethiopia into efficiency, resource, and technology yield gaps and relate those yield gaps to broader farm(ing) systems aspects. To do so, stochastic frontier analysis was applied to a nationally representative panel dataset covering the Meher seasons of 2009 and 2013 and crop modelling was used to simulate the water-limited yield (Yw) in the same years. Farming systems analysis was conducted to describe crop area shares and the availability of land, labour, and capital in contrasting administrative zones. Wheat yield in farmers' fields averaged 1.9 t ha− 1 corresponding to ca. 20% of Yw. Most of the yield gap was attributed to the technology yield gap (> 50% of Yw) but narrowing efficiency (ca. 10% of Yw) and resource yield gaps (ca. 15% of Yw) with current technologies can nearly double actual yields and contribute to achieve wheat self-sufficiency in Ethiopia. There were small differences in the relative contribution of the intermediate yield gaps to the overall yield gap across agro-ecological zones, administrative zones, and farming systems. At farm level, oxen ownership was positively associated with the wheat cultivated area in zones with relatively large cultivated areas per household (West Arsi and North Showa) while no relationship was found between oxen ownership and the amount of inputs used per hectare of wheat in the zones studied. This is the first thorough yield gap decomposition for wheat in Ethiopia and our results suggest government policies aiming to increase wheat production should prioritise accessibility and affordability of inputs and dissemination of technologies that allow for precise use of these inputs. [ABSTRACT FROM AUTHOR]

Water productivity (WP) is a robust benchmark for crop production in relation to available water supply across spatial scales. Quantifying water-limited potential (WPw) and actual on-farm (WPa) WP to estimate WP gaps is an essential first step to identify the most sensitive factors influencing production capacity with limited water supply. This study combines local weather, soil, and agronomic data, and crop modeling in a spatial framework to determine WPw and WPa at local and regional levels for rainfed cropping systems in 17 (maize) and 18 (wheat) major grain-producing countries representing a wide range of cropping systems, from intensive, high-yield maize in north America and wheat in west Europe to low-input, low-yield maize systems in sub-Saharan Africa and south Asia. WP was calculated as the quotient of either water-limited yield potential or actual yield, and simulated crop evapotranspiration. Estimated WPw upper limits compared well with maximum WP reported for field-grown crops. However, there was large WPw variation across regions with different climate and soil (CV = 29% for maize and 27% for wheat), which cautions against the use of generic WPw benchmarks and highlights the need for region-specific WPw. Differences in simulated evaporative demand, crop evapotranspiration after flowering, soil evaporation, and intensity of water stress around flowering collectively explained two thirds of the variation in WPw. Average WP gaps were 13 (maize) and 10 (wheat) kg ha −1 mm −1 , equivalent to about half of their respective WPw. We found that non-water related factors ( i.e. , management deficiencies, biotic and abiotic stresses, and their interactions) constrained yield more than water supply in ca. half of the regions. These findings highlight the opportunity to produce more food with same amount of water, provided limiting factors other than water supply can be identified and alleviated with improved management practices. Our study provides a consistent protocol for estimating WP at local to regional scale, which can be used to understand WP gaps and their mitigation. [ABSTRACT FROM AUTHOR]

Circular resource use in agriculture and food systems could play an important role when aiming for sufficient food output with limited environmental impact and resource depletion. Circularity, however, is not a goal in itself. With respect to nutrient use and emissions, agricultural system sustainability is currently commonly assessed by nutrient output/input ratio (O/I , nutrient use efficiency) or surplus per ha (I O). Our aim is to assess how these sustainability indicators are related to nutrient cycling. Starting from basic circularity concepts, a set of equations (frame) is presented that relates nitrogen (N) and phosphorus (P) cycling to food product output, or to food use by human consumers. Circularity indicators express how many times a nutrient input cohort completes a full cycle (CyCt), or passes through the system's top trophic compartment (UseCt). Examples of such compartments are the crop (arable systems), the herd (livestock farms), and the human population (regional food systems). UseCt governs export in useful product. The frame allows to predict equilibrium O / I from system properties, and to attribute parts of O / I to direct (linear) and cycled flow. CyCt R quantifies how many cycles could be completed by nutrients in absence of product export. CyCt R allows to assess the efficacy of returning waste from exported products. Above indicators are compared against Finn cycling index and Figge circularity index, more commonly used in ecological and industrial research respectively. All indicators are calculated for systems of increasing complexity: (i) a UK wheat field, (ii) a Dutch dairy farm, and (iii) the Flanders regional food system. Their responses to changes in system properties are analysed for examples ii and iii. Nutrient flows in UK arable field and Flanders are almost linear. In UK arable field, O / I equals 0.74 (N) and 0.66 (P), with small contributions from cycled flow (9% for N, 5% for P). In Flanders, cycled flow constitutes only 2% of total N and P flows that reach the human consumer in Flanders. The dairy farm shows largest contributions of cycled flow: 35% (N) and 60% (P) of O / I comes from cycled flow, but O / I itself is only 0.28 (N) and 0.72 (P). The presented frame allows to assess the impacts of system changes on productivity, nutrient cycling, resource use and nutrient emissions. This is useful for ex-ante assessment of measures that reduce nutrient losses from the system or increase the retrieval of external waste flows. [Display omitted] • Nutrient cycling receives much attention but its contribution to nutrient use efficiency & system output remains elusive. • Nutrient cycling indicators enable the calculation of equilibrium Output/Input ratio (O/I) from basic system properties. • O/I responds more than proportionally to the fraction of flow retained per cycle. • Analyses of three case studies show that nutrient cycling in farming and food systems is currently very limited. • Proposed circularity indicators help prioritize between measures minimizing losses & promoting return of external waste. [ABSTRACT FROM AUTHOR]

Despite recent improvements in living standards, a substantial proportion of farm households in sub-Saharan Africa (SSA) is food insecure, and increasing crop productivity could help address this problem. We estimated the effect of increasing maize yields with mineral fertilizer on household food security and on regional and national maize supply in two East African countries - Uganda and Tanzania. We estimated maize yield response to nitrogen (N) fertilization with a machine learning model trained on 15,952 observations of maize responses to fertilizer across SSA. Together with spatial price data, we used this model to quantify the profit-maximizing N fertilizer input for a nationally-representative sample of 4188 agricultural households in the two countries. We computed a food availability indicator for all households. The mean profit-maximizing N input was 82 kg/ha in Tanzania, but it was much lower in Uganda (24 kg/ha) mostly because of less favorable prices. The profit-maximizing N input was above the reported N input for 95% of the households in Tanzania and for 43% of the households in Uganda. It was predicted to increase the food availability ratio of food insecure maize growers by 95% in Tanzania, and by 25% in Uganda. The administrative regions where maize supply could increase most were not the same as the regions where the increase in household-level food security was largest. With increased fertilization, food insecure maize growing households (35% in Tanzania and 42% in Uganda) could only contribute about 20% of the overall increase in maize supply, whereas the 20 to 30% food secure households that have a larger area planted with maize could contribute >60%. Our study makes two key contributions: i) a substantial increase in national maize supply is more likely to come from already food secure households with relatively large farms, while food insecure households with small farms may nevertheless increase their household-level food security through maize intensification, and ii) high potential areas to increase maize domestic production do not necessarily match with areas where there is immediate scope to improve household-level food security. [Display omitted] • Food security analysis with current and profit-maximizing N input in East Africa • Food secure households are main contributors of national increase in grain supply • Food insecure households also benefit for their food security • Variations in maize and fertilizer price impact strongly opportunities to reduce food insecurity [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]

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]

Phosphorus (P) is a finite and dwindling resource. Debate focuses on current production and use of phosphate rock rather than on the amounts of P required in the future to feed the world. We applied a two-pool soil P model to reproduce historical continental crop P uptake as a function of P inputs from fertilizer and manure and to estimate P requirements for crop production in 2050. The key feature is the consideration of the role of residual soil P in crop production. Model simulations closely fit historical P uptake for all continents. Cumulative inputs of P fertilizer and manure for the period 1965-2007 in Europe (1,115 kg·ha-1 of cropland) grossly exceeded the cumulative P uptake by crops (360 kg·ha-1). Since the 1980s in much of Europe, P application rates have been reduced, and uptake continues to increase due to the supply of plant-available P from residual soil P pool. We estimate that between 2008 and 2050 a global cumulative P application of 700-790 kg·ha-1 of cropland (in total 1,070-1,200 teragrams P) is required to achieve crop production according to the various Millennium Ecosystem Assessment scenarios [Alcamo J, Van Vuuren D, Cramer W (2006) Ecosystems and Human Well-Being: Scenarios, Vol 2, pp 279-354]. We estimate that average global P fertilizer use must change from the current 17.8 to 16.8-20.8 teragrams per year in 2050, which is up to 50% less than other estimates in the literature that ignore the role of residual soil P. [ABSTRACT FROM AUTHOR]

Abstract: Cropping system models are powerful tools for regional impact assessment, but their input data requirements for large heterogeneous areas are difficult to fulfil. Hence, the objectives of this paper are to present low-data approaches for specifying detailed management data required by cropping system models, and for calibrating default crop parameters applied to 12 regions in the European Union (EU). Various downscaling and upscaling procedures for different data types are applied to address both objectives. The Agricultural Production and Externalities Simulator (APES) model is used for illustrative purposes. Combining easy-to-collect regional crop management information and expert knowledge enables to develop generic, expert-based rules for specifying crop management. Effects of these expert-based management rules on simulated yields and nitrogen leaching are illustrated using APES. Simulated yields of grain maize, soft wheat and durum wheat using default crop parameters for phenology are compared with crop yields observed in 12 EU regions. The accuracy of the simulated yields was variable, but generally poor. A regional calibration factor Kpheno is developed based on the temperature sum of the average sowing and harvest dates of the three crops in each region. Applying this calibration factor improved the simulated yields in all cases. Results suggest that it is possible to develop expert-based management rules and to capture yield variation across the EU by using the presented low-data approaches. [Copyright &y& Elsevier]

Abstract: The ex-ante assessment of the likely impacts of policy changes and technological innovations on agriculture can provide insight into policy effects on land use and other resources and inform discussion on the desirability of such changes. Integrated assessment and modeling (IAM) is an approach that can be used for ex-ante assessment. It may combine several quantitative models representing different processes and scales into a framework for integrated assessment to allow for multi scale analysis of environmental, economic and social issues. IAM is a challenging task as models from different disciplines have a different representation of data, space and time. The aim of this paper is to describe our strategy to conceptually, semantically and technically integrate a chain of models from different domains to assess land use changes. The models that were linked are based on different modeling techniques (e.g. optimization, simulation, estimation) and operate on different time and spatial scales. The conceptual integration to ensure consistent linkage of simulated processes and scales required modelers representing the different models to clarify the data exchanged and interlinking of modeling methodologies across scales. For semantic integration, ontologies provided a way to rigorously define conceptualizations that can be easily shared between various disciplines. Finally, for technical integration, OpenMI was used and supplemented with the information from ontologies. In our case, explicitly tackling the challenge of semantic, conceptual and technical integration of models forced researchers to clarify the assumptions of their model interfaces, which helped to document the model linkage and to efficiently run models together. The linked models can now easily be used for integrated assessments of policy changes, technological innovations and societal and biophysical changes. [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]

In northern West Bengal and southern Bangladesh, Rabi crops, sown in winter and harvested in the spring, are an important source of income and nutrition for the target communities. In the study areas, NGOs and extension services have been engaging with farming communities on selecting suitable crops for the upcoming season. This engagement took place in the absence of quantitative tools to discuss trade offs and what-if scenarios to support an informed discussion. The objective of this study was to design a crop-choice model to support extension agronomists in engaging with the farming communities more effectively using a quantitative analysis tool. In this process, we explored how agricultural system models can be inclusive and allow participation and eventual application by NGO and government extension agents, using a process of gender sensitive contribution. The crop-choice model includes several constraints related to available resources, including land, labour, capital and technologies to calculate optimal crop areas within the Rabi season. The crop choices are determined considering gender specific community perceptions of risks, labour use, market, price-volatility, self-consumption, water use and cash flow. The model was used to explore the consequences of different crop choices on income, gender specific labour, use of inputs and markets, and to reveal the trade-offs of pursuing different crop choice pathways in the context of agricultural intensification. The initial model was demonstrated to the farmers and the extension agents, and based on their reflections, it was fine-tuned further to make the engagement process more effective. For better communication with the participating farmers, the model related questions and findings were translated in the local language (Bengali). We found that the modelling process can be made more inclusive from the outset by including farmers, NGO and extension agents as co-contributors of the model at each of the modelling steps and incorporating their reflections. Such an inclusive and reflective approach provides easy-to-use interfaces and enables translation of model results in ways that more effectively benefit farming communities. The process of engagement with farmers during tool development has been very valuable to farmers and researchers alike and the use of the tool has made the farmers informed about alternative scenarios and led to actual benefits for crop choice decision making. Inclusive farming systems models need to consider the gender dimension and its critical role in farm decision making and how this can be included in models to reflect the diversity of decision process. To maximise relevance of the model for next users and farmers, it was important that these key stakeholders were part of the model development from the beginning, this study has focused on the same throughout. This study demonstrated that appropriate design and development principles enabled bioeconomic farming systems models to be used by NGO and government extension agents to engage with farming communities as discussion support tools in farming decisions. Such an approach would make the engagement process more convincing and effective. Besides, it would also provide useful insights to the NGO and extension agents in revising their intervention strategies. [Display omitted] • We developed a crop-choice model to support extensionists in engaging with the farming communities more effectively • Model used to explore consequences of different crop choices on income, gender specific labour, use of inputs and markets • Gender sensitive co-contribution/participatory model is inclusive allowing participation and application by practitioners • Inclusive model development facilitated better communication between extension partners and farming communities [ABSTRACT FROM AUTHOR]

[Display omitted] • Participatory method assesses future sustainability and resilience of farming systems. • Application to an extensive sheep farming system reveals a perceived low robustness. • Cascading thresholds increase vulnerability of the sheep farming system. • Proposed alternative systems have better social, economic and environmental performance. • However, proposed alternative systems are not well aligned with expected future developments. Finding pathways to more sustainability and resilience of farming systems requires the avoidance of exceeding critical thresholds and the timely identification of viable alternative system configurations. To serve this purpose, the objective of this paper is to present a participatory, integrated and indicator-based methodology that leads researchers and farming system actors in six steps to a multi-dimensional understanding of sustainability and resilience of farming systems in the future. The methodology includes an assessment of current performance (Step 1), identification of critical thresholds whose exceedance can lead to large and permanent system change (Step 2), impact assessment when critical thresholds are exceeded (Step 3), identification of desired alternative systems and their expected improved performance of sustainability and resilience (Step 4), identification of strategies to realize those alternative systems (Step 5), and an assessment on the compatibility of alternative systems with the developments of exogenous factors as projected in different future scenarios (Step 6). The method is applied in 11 EU farming systems, and the application to extensive sheep production in Huesca, Spain, is presented here, as its problematic situation provides insights for other farming systems. Participants in the participatory workshop indicated that their farming system is very close to a decline or even a collapse. Approaching and exceeding critical thresholds in the social, economic and environmental domain are currently causing a vicious circle that includes low economic returns, low attractiveness of the farming system and abandonment of pasture lands. More sustainable and resilient alternative systems to counteract the current negative system dynamics were proposed by participants: a semi-intensive system primarily aimed at improving production and a high-tech extensive system primarily aimed at providing public goods. Both alternatives place a strong emphasis on the role of technology, but differ in their approach towards grazing, which is reflected in the different strategies that are foreseen to realize those alternatives. Although the high-tech extensive system seems most compatible with a future in which sustainable food production is very important, the semi-intensive system seems a less risky bet as it has on average the best compatibility with multiple future scenarios. Overall, the methodology can be regarded as relatively quick, interactive and interdisciplinary, providing ample information on critical thresholds, current system dynamics and future possibilities. As such, the method enables stakeholders to think and talk about the future of their system, paving the way for improved sustainability and resilience. [ABSTRACT FROM AUTHOR]

Yield gap (Yg) analysis is useful to map the scope for sustainable intensification of agriculture, but explaining and quantifying the underlying causes of yield gaps remains a considerable challenge. The objective of this study was to decompose maize yield gaps under different nitrogen (N) application rates and soil quality conditions across irrigated and rainfed cropping systems in China. A comprehensive database consisting of 5228 on-farm trials located in three major maize production regions of China was used for this purpose. The on-farm trials contained detailed information for four different treatments: fertilizer omission (control), optimal N rate (optimal N), 50% of optimal N rate (low N) and 150% of optimal N rate (high N). These were combined with biophysical and yield potential data from the Global Yield Gap Atlas (http://yieldgap.org). An analytical framework integrating stochastic frontier analysis and principles of production ecology was applied to decompose the overall maize yield gap into components of efficiency (and respective management and soil quality effects, Yg-M and Yg-S), resource (Yg-R), and technology Yg (Yg-T). The potential yield (Yp) of irrigated maize averaged 14.5 Mg/ha in Northeast China (NE) and 11.9 Mg/ha for North China Plain (NCP), and the water-limited potential yield (Yw) of rainfed maize averaged 12.0 Mg/ha in NE and 10.5 Mg/ha in Southwest China (SW), respectively. Maize yield gaps were highly variable across N treatments and cropping systems and ranged between 27–56% of Yp or Yw. Larger absolute yield gaps were observed in irrigated cropping systems in NE (4.8–8.1 Mg/ha) than in NCP (3.8–6.1 Mg/ha) and in rainfed cropping systems in NE (3.6–6.7 Mg/ha) and SW (2.8–5.9 Mg/ha). The components of the yield gap differed in size across cropping systems and N treatments. Yg-T was fairly small and consistent across N treatments ranging between 7.0% and 12.0% of Yp for irrigated maize and only ca. 2.0% of Yw for rainfed maize in NE. Yg-R was strongly associated with the N treatment explaining between 16.0–26.0% of Yp (or Yw) for control and low N treatments and being close to negligible for the optimal and high N treatments. Yg-M due to inefficient crop management accounted for 6.0–14.0% of Yp (or Yw) across cropping systems and N treatments, which is equivalent to 0.7–1.8 Mg/ha. The Yg-S explained the largest proportion of the total yield gap, especially in trials with low and medium soil quality levels, accounting for 11.0–24.0% of Yp or Yw (1.3–3.1 Mg/ha). The Yg-S was linked to partly manageable soil properties, such as low soil organic matter contents and low available P and/or K. This study is one of the first to incorporate the effects of soil quality in yield gap analysis and provides a basis to target management practices that can improve soil quality and N use efficiency while narrowing maize yield gaps in China. [ABSTRACT FROM AUTHOR]

Identifying untapped opportunities for crop production improvement in current cropland is crucial to guide food availability interventions. Here we integrated an agronomically robust bottom-up approach with machine learning to generate global maps of yield potential of high resolution (ca. 1 km 2 at the Equator) and accuracy for maize, wheat and rice. These maps serve as a robust reference to benchmark farmers' yields in the context of current cropping systems and water regimes and can help to identify areas with large room to increase crop yields., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies -WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias-corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995-2014), 2030 (2020-2039), and 2050 (2040-2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water-limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights about agro-meteorological conditions and water-limited crop output to inform adaptation priorities and benchmark (gridded) crop simulations. Finally, the weather and simulated growth data can also be used for training machine learning techniques for extrapolation purposes., (© 2024 The Author(s).)

Africa produces around 60% of the rice the continent consumes, relying heavily on rice imports to fulfill the rest of the domestic demand. Over the past 10 years, the rice-agricultural area increased nearly 40%, while average yield remained stagnant. Here we used a process-based crop simulation modelling approach combined with local weather, soil, and management datasets to evaluate the potential to increase rice production on existing cropland area in Africa and assess cropland expansion and rice imports by year 2050 for different scenarios of yield intensification. We find that Africa can avoid further increases in rice imports, and even reduce them, through a combination of cropland expansion following the historical trend together with closure of the current exploitable yield gap by half or more. Without substantial increase in rice yields, meeting future rice demand will require larger rice imports and/or land conversion than now., (© 2024. The Author(s).)

Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Y ig ) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Y ig , covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Y ig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Y ig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

It is not known whether dietary guidelines proposing a limited intake of animal protein are compatible with the adoption of circular food systems. Using a resource-allocation model, we compared the effects of circularity on the supply of animal-source nutrients in Europe with the nutritional requirements of the EAT-Lancet reference diet. We found the two to be compatible in terms of total animal-source proteins but not specific animal-source foods; in particular, the EAT-Lancet guidelines recommend larger quantities of poultry meat over beef and pork, while a circular food system produces mainly milk, dairy-beef and pork. Compared with the EAT-Lancet reference diet, greenhouse gas emissions were reduced by up to 31% and arable land use reduced by up to 42%. Careful consideration of the feasible substitutability between animal-source foods is needed to define potential roles of animal products in circular human diets., (© 2022. The Author(s).)

Food security interventions and policies need reliable estimates of crop production and the scope to enhance production on existing cropland. Here we assess the performance of two widely used 'top-down' gridded frameworks (Global Agro-ecological Zones and Agricultural Model Intercomparison and Improvement Project) versus an alternative 'bottom-up' approach (Global Yield Gap Atlas). The Global Yield Gap Atlas estimates extra production potential locally for a number of sites representing major breadbaskets and then upscales the results to larger spatial scales. We find that estimates from top-down frameworks are alarmingly unlikely, with estimated potential production being lower than current farm production at some locations. The consequences of using these coarse estimates to predict food security are illustrated by an example for sub-Saharan Africa, where using different approaches would lead to different prognoses about future cereal self-sufficiency. Our study shows that foresight about food security and associated agriculture research priority setting based on yield potential and yield gaps derived from top-down approaches are subject to a high degree of uncertainty and would benefit from incorporating estimates from bottom-up approaches., (© 2021. The Author(s).)

A circular, bio-based economy could provide the pathway to a sustainable future. Here we present five ecological principles to guide biomass use towards a circular bioeconomy: safeguarding and regenerating the health of our (agro)ecosystems; avoiding non-essential products and the waste of essential ones; prioritizing biomass streams for basic human needs; utilizing and recycling by-products of (agro)ecosystems; and using renewable energy while minimizing overall energy use. Implementing these principles calls for a transformation of our current economic system, including fundamental changes to policies, technologies, organizations, social behaviour and markets., (© 2021. Springer Nature Limited.)

Cropping is responsible for substantial emissions of greenhouse gasses (GHGs) worldwide through the use of fertilizers and through expansion of agricultural land and associated carbon losses. Especially in sub-Saharan Africa (SSA), GHG emissions from these processes might increase steeply in coming decades, due to tripling demand for food until 2050 to match the steep population growth. This study assesses the impact of achieving cereal self-sufficiency by the year 2050 for 10 SSA countries on GHG emissions related to different scenarios of increasing cereal production, ranging from intensifying production to agricultural area expansion. We also assessed different nutrient management variants in the intensification. Our analysis revealed that irrespective of intensification or extensification, GHG emissions of the 10 countries jointly are at least 50% higher in 2050 than in 2015. Intensification will come, depending on the nutrient use efficiency achieved, with large increases in nutrient inputs and associated GHG emissions. However, matching food demand through conversion of forest and grasslands to cereal area likely results in much higher GHG emissions. Moreover, many countries lack enough suitable land for cereal expansion to match food demand. In addition, we analysed the uncertainty in our GHG estimates and found that it is caused primarily by uncertainty in the IPCC Tier 1 coefficient for direct N 2 O emissions, and by the agronomic nitrogen use efficiency (N-AE). In conclusion, intensification scenarios are clearly superior to expansion scenarios in terms of climate change mitigation, but only if current N-AE is increased to levels commonly achieved in, for example, the United States, and which have been demonstrated to be feasible in some locations in SSA. As such, intensifying cereal production with good agronomy and nutrient management is essential to moderate inevitable increases in GHG emissions. Sustainably increasing crop production in SSA is therefore a daunting challenge in the coming decades., (© 2019 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Ensuring an adequate food supply in systems that protect environmental quality and conserve natural resources requires productive and resource-efficient cropping systems on existing farmland. Meeting this challenge will be difficult without a robust spatial framework that facilitates rapid evaluation and scaling-out of currently available and emerging technologies. Here we develop a global spatial framework to delineate 'technology extrapolation domains' based on key climate and soil factors that govern crop yields and yield stability in rainfed crop production. The proposed framework adequately represents the spatial pattern of crop yields and stability when evaluated over the data-rich US Corn Belt. It also facilitates evaluation of cropping system performance across continents, which can improve efficiency of agricultural research that seeks to intensify production on existing farmland. Populating this biophysical spatial framework with appropriate socio-economic attributes provides the potential to amplify the return on investments in agricultural research and development by improving the effectiveness of research prioritization and impact assessment., (© 2018 The Author(s).)

Although global food demand is expected to increase 60% by 2050 compared with 2005/2007, the rise will be much greater in sub-Saharan Africa (SSA). Indeed, SSA is the region at greatest food security risk because by 2050 its population will increase 2.5-fold and demand for cereals approximately triple, whereas current levels of cereal consumption already depend on substantial imports. At issue is whether SSA can meet this vast increase in cereal demand without greater reliance on cereal imports or major expansion of agricultural area and associated biodiversity loss and greenhouse gas emissions. Recent studies indicate that the global increase in food demand by 2050 can be met through closing the gap between current farm yield and yield potential on existing cropland. Here, however, we estimate it will not be feasible to meet future SSA cereal demand on existing production area by yield gap closure alone. Our agronomically robust yield gap analysis for 10 countries in SSA using location-specific data and a spatial upscaling approach reveals that, in addition to yield gap closure, other more complex and uncertain components of intensification are also needed, i.e., increasing cropping intensity (the number of crops grown per 12 mo on the same field) and sustainable expansion of irrigated production area. If intensification is not successful and massive cropland land expansion is to be avoided, SSA will depend much more on imports of cereals than it does today., Competing Interests: The authors declare no conflict of interest.