Wheat stem rust, a fungal disease that can be highly devastating under the right environmental conditions, was reduced to non-economically damaging levels during the Green Revolution. However, it has reemerged as a global threat to wheat production due to the appearance of new virulent strains in Uganda in 1999 that have spread steadily to other geographic areas. Wheat experts warn that the disease could pose a catastrophic threat to the global wheat supply if not monitored. Considering the importance of wheat as a principal source of calories, nutrients, and farm income throughout the world, assessments of the potential impacts of the disease are urgently required in order to formulate an appropriate response. Published assessments so far vary widely in method and results, and generally focus on wheat production losses alone, without considering how markets may offset or aggravate impacts (spillover effects). Here we take an integrated assessment approach and examine a set of "what-if" scenarios to account for direct and indirect economic and food security impacts of wheat stem rust in various world regions over the years 2026-2050. The severity and frequency of epidemics is introduced into the modeling framework based on a survey of international wheat experts. The results suggest that global market incentives may offset the worst impacts of wheat stem rust in most affected areas via international trade. However, the market mechanism simultaneously precipitates considerable food insecurity in areas far from any epidemic, as farms in these areas reallocate resources from the domestic cereal market to the wheat export market, in response to price signals.

ABSTRACT Understanding the subnational dynamics of food demand, while accounting for the evolution of the agrifood system at the global level, remains a challenge for designing food policy to ensure food security. To link global agrifood systems with subnational food policy planning, we downscale per capita food demand for 62 food commodities at the subnational level across all world regions over the next 5 years, based on downscaled projections from a global economic model. The novelty of our approach lies in being the first to downscale global food demand projections to the subnational level, thereby enabling the assessment of within‐country heterogeneity in future food demand while explicitly linking it to global drivers and the broader agrifood system. The results show considerable within‐country heterogeneity: subnational changes in food demand often move in the opposite direction of national projections. Using Kenya as a case study, we reveal nuanced subnational demand patterns and discuss them in the context of Kenya's Food Security Policy Framework. The downscaled estimates confirm that national averages can be misleading—while national per capita maize demand is projected to decline, several counties may experience increases, and national gains in dietary diversity are largely driven by already medium–high diversity areas. Different global scenarios result in heterogeneous subnational dietary patterns, showing how global drivers affect local demand. For sparsely populated areas, model predictions are less reliable and should be interpreted with caution. Overall, our results highlight the importance of accounting for subnational heterogeneity when designing forward‐looking food policies to ensure food security at the subnational level.

In Africa, water conflicts are increasing, posing significant threats to livelihoods. Unraveling the spatial pattern and drivers of water conflicts is essential for anticipating risks and targeting water policies. However, there is a lack of evidence to support this need. Using a multi-scale spatial approach, we examine the spatially heterogeneous influence of drought on intrastate water conflicts and how this may shape future water conflict patterns. We construct a unique dataset of water conflicts at the pixel level from 2010 to 2024 for 21 African countries. Results show that drought increases water conflict fatalities, with a 0.7% rise per 10 km closer to a country's border. Future droughts are anticipated to result in different trends in water conflict fatalities across areas. This pattern is not explained by the overall security situation or factors like irrigation, but may stem from weaker water governance in border areas, implying that stronger water governance may partially mitigate the impact of drought-driven water scarcity on intensifying water conflicts. Our study highlights the importance of identifying areas that face a dual risk from drought and inadequate governance to inform decision-making that strengthens water governance and de-escalates water conflicts.

Purpose - The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country. Design/methodology/approach - A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift. Findings - Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind. Originality/value - This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.

CONTEXT: In Africa, farmer -herder conflicts can be partially attributed to linguistic differences that impede communication and conflict resolution. This tension can be further amplified by climatic shocks that increase incentives to fight for resources. Yet existing innovations and interventions in the agro-pastoralism system that aim to de-escalate farmer -herder conflicts often do not account for the interactive effect of linguistic diversity and climate shocks in affecting farmer -herder conflicts. BJECTIVE: Our objective is to assess the interactive effect of climate shocks and linguistic diversity on intensifying farmer -herder conflicts in multiple African countries. We also examine the implications on enabling linguistically inclusive innovations for agro-pastoralism system transformation in the face of climate shocks. METHODS: We employ a Spatial Difference -in -Discontinuities design to causally assess the impact of linguistic diversity and its interaction with climate extremes in affecting farmer -herder conflict. We use month -district panel data between 2010 and 2023 for six African countries. The conflict data is obtained from the Armed Conflict Location & Event Data Project. Climatic shocks are calculated from the Standardized Precipitation Index. Estimation results are then combined with drought projections to examine the spatially heterogeneous evolution of climate -related farmer -herder conflicts in the future. RESULTS AND CONCLUSIONS Farmer -herder conflicts are systematically more fatal in mixed -language districts compared to single -language districts. The fatality rate increases significantly when drought occurs in mixed -language districts. The differential starting value of conflict fatalities and the future drought may result in strikingly different trends in conflict fatality across districts in the future. SIGNIFICANCE: Our findings suggest that building linguistically inclusive innovations in agro-pastoralism systems may contribute to de-escalating climate -related farmer -herder conflicts and mitigate the repercussions on rural livelihoods. Linguistic diversity ought to be assessed and used by donors and practitioners, to develop innovative solutions that foster communication and conflict resolution.

Trade-off analysis (TOA) is central to policy and decision-making aimed at promoting sustainable agricultural landscapes. Yet, a generic methodological framework to assess trade-offs in agriculture is absent, largely due to the wide range of research disciplines and objectives for which TOA is used. In this study, we systematically reviewed 119 studies that have implemented TOAs in landscapes and regions dominated by agricultural systems around the world. Our results highlight that TOAs tend to be unbalanced, with a strong emphasis on productivity rather than environmental and socio-cultural services. TOAs have mostly been performed at farm or regional scales, rarely considering multiple spatial scales simultaneously. Mostly, TOAs fail to include stakeholders at study development stage, disregard recommendation uncertainty due to outcome variability and overlook risks associated with the TOA outcomes. Increased attention to these aspects is critical for TOAs to guide agricultural landscapes towards sustainability.

Although the policy impacts on farms accumulate year by year, most farm decision models focus on short-term decisions, evaluating policies based on snapshots. Structural changes are gradually built; therefore, farm decision models should consider the sequences within the period under study. Multiyear data from the arable sector in Thessaly, Greece, have fed a newly developed farm-level recursive linear programming model mainly to simulate farm structural change dynamics. The proposed model incorporates new evidence on the strategic decision of arable crop farms regarding their remaining in the production system and farm expansion. Results reveal an evident gradual farmland concentration in relatively large farms, accompanied by a gradual expansion of the most profitable cropping activities, verifying the real-world survival strategy of farms.

We analyze the farm-level economic and environmental impacts of the post-2020 reform of the Common Agricultural Policy (CAP) of the European Union, examining six scenarios constructed around the budget allocated to eco-schemes and the stringency of enhanced conditionality. Results suggest that the CAP post-2020 can improve environmental performance but at a cost for farms. Enhanced conditionality appears to play a greater role than eco-schemes in delivering environmental improvements. The new CAP provides the Member States ample options to choose among different measures. The optimal policy mix will depend on the balancing of income support versus environmental performance that reflects policy priorities.

Banana Xanthomonas wilt (BXW) is one of the most important diseases threatening banana production in Africa south of the Sahara (SSA). In this study, we examine the potential impacts of BXW on banana production, demand, and food security in SSA, if the disease spread across all banana-producing countries in the region. The analysis is based on a multidisciplinary approach that combines a mathematical model of field-level BXW spread over time with a dynamic global partial equilibrium economic model. Since BXW control relies exclusively on management, we analyze three scenarios of BXW spread that are constructed around assumptions about the level of policy response to the disease, and about how this response may affect the adoption of appropriate management practices by farmers to control BXW. Modeling results suggest that if the disease is left uncontrolled, banana production in SSA within 10 years can decrease by as much as 55%, compared to a BXW-free baseline scenario, resulting in economic losses of around 25 billion USD. At the same time, the population at risk of hunger in countries that highly depend on bananas as a staple food is projected to increase by more than 4.6%. Even a limited policy response to BXW can reduce infections and mitigate some of the production, economic, and food security consequences. BXW impacts are almost completely negated when farmers have good knowledge of the disease and fully adopt the appropriate management practices. This result highlights the need for policy frameworks which rely on sustained and coordinated efforts by public and private stakeholders, within and across SSA countries and at different geographical scales. It also aims to raise awareness and promote the adoption of such practices, while also considering local peculiarities and socioeconomic conditions.

With the purpose of fostering better environmental performance of European farms, the CAP post-2020 reform will introduce new subsidy schemes to allocate direct payments that are conditional to environmental measures.In particular, the Commission's proposal for the CAP post-2020 introduces two layers of measures for achieving environmental objectives.First, "Enhanced conditionality" which will replace cross-compliance as the new, and more ambitious, minimum set of requirements for receiving Pillar I payments.Second, Member States are also asked to design a set of even more environmentally ambitious measures, referred to as "Eco-schemes", which will be voluntary for farmers and will be funded through Pillar I.This study assesses economic and environmental effects of the CAP post-2020 reform at farm level in Spain.By using the Individual Farm Model for Common Agricultural Policy analysis (IFM-CAP), a set of scenarios of different levels of environmental ambition are simulated.The results show the trade-off between farmers' income and environmental performance, suggesting that the provisions of the new CAP may be beneficial for the environment albeit at some cost to farmers.

Abstract Achieving multiple sustainable development goals simultaneously demands managing agricultural resources for different objectives and actively considering how these objectives compete (trade-offs) or complement (synergies). Trade-off analyses (TOA) are therefore central for policy and decision-making to achieve sustainable agricultural landscapes. Yet, evidence on TOA assessments in agriculture remains scattered due to the wide scope of research disciplines and objectives for which TOA is applied. We conducted a systematic review on 119 peer-reviewed articles to identify how TOAs are implemented within the agricultural context and what associated knowledge gaps exist. Our results highlight limited use of objectives that capture environmental and socioeconomic services from agriculture. Likewise, TOAs that consider effects or impacts across multiple spatial scales are an exception. Overall, our assessment identified that current TOA frameworks rarely include stakeholders in the co-development of the study, disregard TOA recommendations’ uncertainty due to outcome variability and overlook risks associated with the TOA outcomes. Increased attention to these aspects is critical for conducting TOAs that guide agricultural landscapes towards sustainability.

There is an emerging strand in the agricultural economics literature which examines the calibration of risk programming models using the principles of Positive Mathematical Programming (PMP). In a recent contribution to this journal, Liu et al. (2020) compare three different PMP approaches and attempt to find the ‘most practical’ method for calibrating risk programming models to be used in policy analysis. In this article, we argue that the comparison design by Liu et al. (2020) is problematic, as it is based on inappropriate metrics and it ignores recent advancements in PMP. This word of caution intends to provide constructive criticism and aims at contributing to the use of risk programming models in policy analysis.

Context: Past reviews of policy impact assessment studies using bio-economic farm models (BEFM) called for the development of a generic and modular implementation that can be maintained by a network of modellers. A main reason for these calls is the project-oriented way in which model developers receive funding. It favours the development of new models with case-study specific features over the maintenance and extension of well-tested, more generic ones which allow comparing results in a consistent way across many case-studies. The demand for more generic tools also reflects the dynamic landscape of policy measures within larger policy frameworks like the Common Agricultural Policy (CAP). These policy frameworks move increasingly away from a 'one-size-fitsall' approach of policy design towards more flexible systems, giving greater freedom to shape, implement, and target policy measures to specific regions, farm management systems and farm types. This creates new challenges for model-based impact assessment as applied models have to reflect the variety of policy measures and characteristics of targeted farmers and rural communities. Objective: The aim of this paper is to first address key questions regarding the functionality and implementation of such a modular BEFM that can be maintained and expanded by a user group, and second to develop concrete proposals of necessary model features, model design and shared development. Methods: This paper builds on literature research, including a detailed review of four models that are used extensively for impact assessment within the EU and were developed by multiple teams over a longer period of time. From there, necessary and desirable features of a generic and modular BEFM are identified and requirements for model design regarding modularity, software engineering, and shared development are discussed. Results and conclusions: This feeds into the development of concrete proposals of how modularity and flexibility can be addressed in the development, application and maintenance of a BEFM. At the end, a list of design decisions and implementation steps is proposed to build a modular BEFM that can be maintained by a network of researchers. Significance: The concept for a network-based generic and modular bio-economic farm model responds to the demand for analytical tools in agricultural policy impact analysis. The paper develops a research agenda to overcome observed limitations in the current landscape of such models.

Recent advances in approaches to quantitative strategic foresight have enabled new insights into understanding potential futures of the agriculture sector. Quantitative foresight approaches facilitate understanding of different plausible scenarios, especially as related to both endogenous and exogenous factors (e.g., global markets and climate change). These approaches tend to be macroeconomic in nature and resolve trends relative to coarse-grained drivers. In order translate these outputs into strategies that realistically benefit producers across scale, finer resolution and context specific understanding is needed. This paper offers perspective on how foresight analysis can be combined with more pointed assessment of the specific policies, institutions and market requirements needed create more inclusive agricultural investment strategies.

International crop‐related research as conducted by the CGIAR uses crop modeling for a variety of purposes. By linking crop models with economic models and approaches, crop model outputs can be effectively used as inputs into socioeconomic modeling efforts for priority setting and policy advice using ex‐ante impact assessment of technologies and scenario analysis. This requires interdisciplinary collaboration and very often collaboration across a variety of research organizations. This study highlights the key topics, purposes, and approaches of socioeconomic analysis within the CGIAR related to cropping systems. Although each CGIAR center has a different mission, all CGIAR centers share a common strategy of striving toward a world free of hunger, poverty, and environmental degradation. This means research is mostly focused toward resource‐constrained smallholder farmers. The review covers global modeling efforts using the IMPACT model to farm household bio‐economic models for assessing the potential impact of new technologies on farming systems and livelihoods. Although the CGIAR addresses all aspects of food systems, the focus of this review is on crop commodities and the economic analysis linked to crop‐growth model results. This study, while not a comprehensive review, provides insights into the richness of the socioeconomic modeling endeavors within the CGIAR. The study highlights the need for interdisciplinary approaches to address the challenges this type of modeling faces.

In many parts of Asia food security and poverty remain issues even though arable land is available to produce nutritious food. Sustainable intensification of agricultural systems has been proposed to produce more outputs on same land area whilst minimizing environmental degradation. Tradeoffs, however, are inevitable and evidence thereof is scant. In this study, we analyze effects of sustainable intensification of rice-based systems with potato and focus on the Eastern Indo-Gangetic Plains of India and Bangladesh. A literature review, expert consultation, and an economic surplus exercise are used to address our objective. We find that sustainable intensification has a huge potential and positive welfare effects. Socio-economic tradeoffs, especially for labour, need to be considered and balanced out. Increasing input efficiency for instance by improving farming practices may reduce negative environmental effects.

Much of the literature on future food supply in Asia focuses almost exclusively on the cereal crops overlooking the growing importance of other food commodities and their potential to help sustain Asian food systems and food security in the decades ahead. This study utilizes a multi-period, agricultural partial equilibrium economic model, linked with a set of crop, climate and water models to estimate potato supply in India for the period 2010 to 2030 according to three scenarios: high, moderate, and slow growth. According to the high growth scenario, potato supply could increase over 37 million metric tonnes while the more pessimistic scenario estimates increases in production of nearly 24 million metric tonnes. The findings point to opportunities for agribusiness initiatives in input markets and technical services for potato cultivation. They also call attention to the benefits to be derived from policy initiatives in support of future activities on and off the farm intended to optimize the potato sector’s contribution to food production, income, employment, and food security in India in the years ahead.

The widely recognized role of roots, tubers and bananas (RT&Bs) in achieving food security and providing income opportunities in the world’s poorest regions will be challenged by socioeconomic and climate related drivers. These will affect demand and production patterns and increase pressure on farming systems. Foresight results presented in this paper show that the importance of RT&B crops for food security will likely increase by 2050 despite these challenges. Furthermore, investments targeted at yield growth appear to be more effective than marketing improvements in alleviating production constraints and in strengthening the role of RT&B crops in future food systems.