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Food production is at the heart of global sustainability challenges, with unsustainable practices being a major driver of biodiversity loss, emissions and land degradation. The concept of foodscapes, defined as the characteristics of food production along biophysical and socio-economic gradients, could be a way addressing those challenges. By identifying homologues foodscapes classes possible interventions and leverage points for more sustainable agriculture could be identified. Here we provide a globally consistent approximation of the world's foodscape classes. We integrate global data on biophysical and socio-economic factors to identify a minimum set of emergent clusters and evaluate their characteristics, vulnerabilities and risks with regards to global change factors. Overall, we find food production globally to be highly concentrated in a few areas. Worryingly, we find particularly intensively cultivated or irrigated foodscape classes to be under considerable climatic and degradation risks. Our work can serve as baseline for global-scale zoning and gap analyses, while also revealing homologous areas for possible agricultural interventions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Jung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Increasing demand for food is driving a worldwide trend of agricultural input intensification. However, there is no comprehensive knowledge about the interrelations between potential yield gains and environmental trade-offs that would enable the identification of regions where input-driven intensification could achieve higher yields, yet with minimal environmental impacts. We explore ways of enhancing global yields, while avoiding significant nitrogen (N) emissions (N e ) by exploring a range of N and irrigation management scenarios. The simulated responses of yields and N e to increased N inputs (N in ) and irrigation show high spatial variations due to differences in current agricultural inputs and agro-climatic conditions. Nitrogen use efficiency (NUE) of yield gains is negatively correlated with incremental N e due to N in additions. Avoiding further intensification in regions where high fractions of climatic yield potentials, ≥ 80%, are already achieved is key to maintain good NUE. Depending on the intensification scenarios, relative increases in N e could be reduced by 0.3-29.6% of the baseline N e with this intensification strategy as compared to indiscriminate further intensification, at the cost of a loss of yield increases by 0.2-16.7% of the baseline yields. In addition, irrigation water requirements and N in would dramatically decrease by considering this intensification strategy.

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.

We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.

Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, especially below national scales, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys of 3,008 households and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries (Indonesia, Madagascar, Papua New Guinea, Philippines, and Tanzania). Our study reveals three key findings: First, overall potential losses to fisheries are higher than potential losses to agriculture. Second, while most locations (> 2/3) will experience potential losses to both fisheries and agriculture simultaneously, climate change mitigation could reduce the proportion of places facing that double burden. Third, potential impacts are more likely in communities with lower socioeconomic status.

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes. [ABSTRACT FROM AUTHOR]

Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980–2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility. [ABSTRACT FROM AUTHOR]

Summary: Phosphorus (P) is a major agricultural nutrient and, in its mineable form, a potentially scarce resource. Countries with limited physical access to P should hence develop an effective national P governance. This requires analyses of trends and variations in P flows and stocks over time. Here, we present a long‐term P flow analysis for the Indian agri‐food sector from 1988 to 2011. Major P flows are imports of mineral P, fertilizer application, and uptake of animal fodder. The mineral P import dependency ratio is constant at around 93%. On average, 20% of P inputs to soils are lost through erosion. Key drivers of changes in P flows include population growth, dietary change, and agricultural intensification. To reduce its P fertilizer import dependence, India could, for example, substitute up to 19% of the presently applied mineral P if manure used as a household fuel were recycled, and up to 21% if P was fully recovered from wastewater and household waste. Comparing selected indicators for P use in agriculture with China and the European Union (EU) reveals that there are structural similarities, such as increasing fertilizer application rates and P accumulation in soils, with the first but large differences compared to the latter. The analyses highlight that in contrast to static indicators, the time‐continuous tracking of P flows provides substantial advantages, such as the identification of long‐term trends, drivers, and intervention options for sustainable P management, given that it allows for the interpretation of present indicators in the context of past trends and legacies. [ABSTRACT FROM AUTHOR]

Highlights: [•] Inputs and crop yields under different management levels are quantified. [•] Climate and soil related yield constraints are identified for different regions. [•] Highest maize yield potential was identified for tropical regions. [•] Modest improvement in nutrient supply could double or triple maize yields. [Copyright &y& Elsevier]

Food security and water scarcity have become two major concerns for future human's sustainable development, particularly in the context of climate change. Here we present a comprehensive assessment of climate change impacts on the production and water use of major cereal crops on a global scale with a spatial resolution of 30 arc-minutes for the 2030s (short term) and the 2090s (long term), respectively. Our findings show that impact uncertainties are higher on larger spatial scales (e.g., global and continental) but lower on smaller spatial scales (e.g., national and grid cell). Such patterns allow decision makers and investors to take adaptive measures without being puzzled by a highly uncertain future at the global level. Short-term gains in crop production from climate change are projected for many regions, particularly in African countries, but the gains will mostly vanish and turn to losses in the long run. Irrigation dependence in crop production is projected to increase in general. However, several water poor regions will rely less heavily on irrigation, conducive to alleviating regional water scarcity. The heterogeneity of spatial patterns and the non-linearity of temporal changes of the impacts call for site-specific adaptive measures with perspectives of reducing short- and long-term risks of future food and water security. [ABSTRACT FROM AUTHOR]

Abstract: Spatially explicit large-scale crop growth models are often applied at the global scale with no or little adjustments to regional conditions, which may produce unreliable model results. To tackle this issue, we have regionalized a large-scale crop model for simulating maize cultivation in sub-Saharan Africa (SSA). Planting dates were estimated using reported planting seasons, plant growth parameters were adopted from literature to reflect a low-yielding cultivar, and agricultural practice was mimicked by simulating continuous cultivation of maize with removal of plant residues. The analysis of different estimates of planting date showed that a monthly time step was too coarse in (semi-)arid regions and a weekly step should be used. Limiting planting date estimates by reported seasons is especially important in regions with bimodal rain seasons. The parameterization of a low-yielding cultivar by decreasing the maximum and minimum harvest index (HI) in the model resulted in HI estimates within the range of values reported in the literature. The most important step in the model adjustment was found to be the removal of plant residue. This leads together with little fertilizer inputs to soil nutrient and organic carbon depletion, which has been taking place in most parts of SSA during the past decades. If residue removal is not taken into account, the simulation results in organic carbon sequestration and only minor nutrient depletion. With the adjustments of cultivar, planting dates, and agricultural practice in the model setup, crop growth is in most areas of SSA mainly constrained by nutrient stress as compared to water and temperature. The estimated national and regional average yields compared well with reported yields for the major maize producing countries, suggesting that the regionalized model is suitable for supporting policies on water and soil management in SSA. [Copyright &y& Elsevier]