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Studies of global environmental change make extensive use of scenarios to explore how the future can evolve under a consistent set of assumptions. The recently developed Shared Socioeconomic Pathways (SSPs) create a framework for the study of climate-related scenario outcomes. Their five narratives span a wide range of worlds that vary in their challenges for climate change mitigation and adaptation. Here we provide background on the quantification that has been selected to serve as the reference, or ‘marker’, implementation for SSP2. The SSP2 narrative describes a middle-of-the-road development in the mitigation and adaptation challenges space. We explain how the narrative has been translated into quantitative assumptions in the IIASA Integrated Assessment Modelling Framework . We show that our SSP2 marker implementation occupies a central position for key metrics along the mitigation and adaptation challenge dimensions. For many dimensions the SSP2 marker implementation also reflects an extension of the historical experience, particularly in terms of carbon and energy intensity improvements in its baseline. This leads to a steady emissions increase over the 21st century, with projected end-of-century warming nearing 4 °C relative to preindustrial levels. On the other hand, SSP2 also shows that global-mean temperature increase can be limited to below 2 °C, pending stringent climate policies throughout the world. The added value of the SSP2 marker implementation for the wider scientific community is that it can serve as a starting point to further explore integrated solutions for achieving multiple societal objectives in light of the climate adaptation and mitigation challenges that society could face over the 21st century. [ABSTRACT FROM AUTHOR]

This paper examines the development and use of scenarios as an approach to guide action in multi-level, multi-actor adaptation contexts such as food security under climate change. Three challenges are highlighted: (1) ensuring the appropriate scope for action; (2) moving beyond intervention-based decision guidance; and (3) developing long-term shared capacity for strategic planning. To overcome these challenges we have applied explorative scenarios and normative back-casting with stakeholders from different sectors at the regional level in East Africa. We then applied lessons about appropriate scope, enabling adaptation pathways, and developing strategic planning capacity to scenarios processes in multiple global regions. Scenarios were created to have a broad enough scope to be relevant to diverse actors, and then adapted by different actor groups to ensure their salience in specific decision contexts. The initial strategy for using the scenarios by bringing a range of actors together to explore new collaborative proposals had limitations as well as strengths versus the application of scenarios for specific actor groups and existing decision pathways. Scenarios development and use transitioned from an intervention-based process to an embedded process characterized by continuous engagement. Feasibility and long-term sustainability could be ensured by having decision makers own the process and focusing on developing strategic planning capacity within their home organizations. [ABSTRACT FROM AUTHOR]

Highlights: [•] We model farming systems evolution in the Kenyan highlands. [•] We link socio-economic scenarios with regional and household models. [•] Intensification, diversification and stagnation can occur simultaneously in a region. [•] Socio-political conditions modify the opportunity costs of resource use in farming systems. [•] Multi-scale models and scenarios useful for linking global change and local scale processes. [ABSTRACT FROM AUTHOR]

Abstract: In the three major maize producing countries in the East African region of Kenya, Tanzania and Ethiopia, up to 44% of the dietary calorie requirements is provided by maize. It is also recognized that livestock are an essential asset of poor farmers in the mixed crop-livestock systems in this region. One of the major constraints to their productivity is, however, feed availability. A significant proportion of this feed is sourced from maize stover. We engaged in a multi-disciplinary research of dual-purpose maize cultivars with the purpose of contributing to smallholder food security. The specific objective of our endeavor is to better match new maize cultivars to farmers’ needs by including fodder traits in maize improvement programs in Ethiopia, Tanzania and Kenya. We explored a novel approach for targeting maize breeding research. Agricultural development strategies must recognize heterogeneity in bio-physical, economic, socio-cultural, institutional and environmental factors when devising interventions and investments. The research effort into maize as a food and feed resource was, therefore, carried out in a cross-section of bio-physically and socio-economically contrasting areas across the three study countries. To this effect the International Maize and Wheat Improvement Centre''s traditional targeting framework, maize mega-environments (MMEs), was combined with recommendation domains for dual-purpose maize using a Geographical Information System (GIS). The GIS-based approach provided a spatial framework for the structured exploration of opportunities to transfer knowledge and technologies. Results show that maize is potentially an important feed resource in areas with high feed demand. Throughout the different MMEs, a range of different incentives for dual-purpose varieties can be found. The maps with recommendation domains for dual-purpose maize will facilitate better targeting of new maize cultivars. Cultivars with good quality stover can now be preferentially promoted in areas with high demand for stover as feed, while at the same time matched to the bio-physically most suitable mega-environment. This integrated approach is widely applicable and will help increase the impacts from agricultural research. [Copyright &y& Elsevier]

• Projected demand for livestock-derived foods to 2050 for different scenarios. • Scenarios explored changes in population, income, and income elasticity of demand. • 14% greater per person global demand for livestock-derived foods in 2050 vs. 2020. • 38% greater total global demand for livestock-derived foods in 2050 vs. 2020. In recent decades there has been a sustained and substantial shift in human diets across the globe towards including more livestock-derived foods. Continuing debates scrutinize how these dietary shifts affect human health, the natural environment, and livelihoods. However, amidst these debates there remain unanswered questions about how demand for livestock-derived foods may evolve over the upcoming decades for a range of scenarios for key drivers of change including human population, income, and consumer preferences. Future trends in human population and income in our scenarios were sourced from three of the shared socioeconomic pathways. We used scenario-based modeling to show that average protein demand for red meat (beef, sheep, goats, and pork), poultry, dairy milk, and eggs across the globe would increase by 14% per person and 38% in total between the year 2020 and the year 2050 if trends in income and population continue along a mid-range trajectory. The fastest per person rates of increase were 49% in South Asia and 55% in sub-Saharan Africa. We show that per person demand for red meat in high-income countries would decline by 2.8% if income elasticities of demand (a partial proxy for consumer preferences, based on the responsiveness of demand to income changes) in high-income countries decline by 100% by 2050 under a mid-range trajectory for per person income growth, compared to their current trajectory. Prices are an important driver of demand, and our results demonstrate that the result of a decline in red meat demand in high-income countries is strongly related to rising red meat prices, as projected by our scenario-based modeling. If the decline in the income elasticity of demand occurred in all countries rather than only in high-income countries, then per person red meat demand in high-income countries would actually increase in 2050 by 8.9% because the income elasticity-driven decline in global demand reduces prices, and the effect of lower prices outweighs the effect of a decline in the income elasticity of demand. Our results demonstrate the importance of interactions between income, prices, and the income elasticity of demand in projecting future demand for livestock-derived foods. We complement the existing literature on food systems and global change by providing quantitative evidence about the possible space for the future demand of livestock-derived foods, which has important implications for human health and the natural environment. [ABSTRACT FROM AUTHOR]

There is broad agreement that current food systems are not on a sustainable trajectory that will enable us to reach the Sustainable Development Goals by 2030, particularly in the face of anthropogenic climate change. Guided by a consideration of some food system reconfigurations in the past, we outline an agenda of work around four action areas: rerouting old systems into new trajectories; reducing risks; minimising the environmental footprint of food systems; and realigning the enablers of change needed to make new food systems function. Here we highlight food systems levers that, along with activities within these four action areas, may shift food systems towards more sustainable, inclusive, healthy and climate-resilient futures. These actions, summarised here, are presented in extended form in a report of an international initiative involving hundreds of stakeholders for reconfiguring food systems. • Food systems need to shift towards more sustainable, inclusive, healthy and climate-resilient futures. • Food systems reconfiguration needs to happen in the next ten years in order to achieve the Paris agreement and SDG agenda. • Food systems reconfiguration include rerouting trajectories, reducing risks and emissions, realigning enablers of change. • Actions for food systems reconfiguration need to be tailored to the diversity of farmer types. [ABSTRACT FROM AUTHOR]

• Research and science should not only inform food and environmental policy but should be adopted and mainstreamed into actions at all levels. • Food systems are faced with grander and interconnected challenges and constraints that bring about new research questions. • Research has a vital role in charting a positive and sustainable direction for global food security, nutrition, and health. • The status quo must be challenged to shape food systems transformation to deliver sustainable, healthier diets. • Global Food Security provides a platform where evidence is shared in an accessible manner for those who need to act on it. [ABSTRACT FROM AUTHOR]

• Global area of different multiple cropping systems quantified for the first time. • Twelve percent of global cropland are used for growing two or three crops in a sequence. • Potential for increasing harvest frequency smaller than previously estimated. Multiple cropping, defined as harvesting more than once a year, is a widespread land management strategy in tropical and subtropical agriculture. It is a way of intensifying agricultural production and diversifying the crop mix for economic and environmental benefits. Here we present the first global gridded data set of multiple cropping systems and quantify the physical area of more than 200 systems, the global multiple cropping area and the potential for increasing cropping intensity. We use national and sub-national data on monthly crop-specific growing areas around the year 2000 (1998–2002) for 26 crop groups, global cropland extent and crop harvested areas to identify sequential cropping systems of two or three crops with non-overlapping growing seasons. We find multiple cropping systems on 135 million hectares (12% of global cropland) with 85 million hectares in irrigated agriculture. 34%, 13% and 10% of the rice, wheat and maize area, respectively are under multiple cropping, demonstrating the importance of such cropping systems for cereal production. Harvesting currently single cropped areas a second time could increase global harvested areas by 87–395 million hectares, which is about 45% lower than previous estimates. Some scenarios of intensification indicate that it could be enough land to avoid expanding physical cropland into other land uses but attainable intensification will depend on the local context and the crop yields attainable in the second cycle and its related environmental costs. [ABSTRACT FROM AUTHOR]

The various ways in which livestock production systems can be incorporated into economic, partial-equilibrium, multi-market models are presented, and the challenges outlined. A particular focus and illustrative case is livestock feed. Foremost among the challenges is the reconciliation of scientific understanding of livestock feed requirements and production characteristics with the available national data. Another challenge is in estimating herd structures. An economic, multi-market modeling approach is presented which has been widely used in policy analysis and advocacy, and an account is given of the necessary recent enhancements for addressing livestock. [ABSTRACT FROM AUTHOR]

• Food systems face big challenges that publicly-funded agricultural research for development must help address. • Alignment between development goals and research portfolios needs to be monitored. • Such monitoring is crucial in times of rapid change if the Sustainable Development Goals are to be met. • More details on research expenditure and impacts would facilitate such monitoring. Global food systems are currently facing unprecedented challenges with respect to production and nutritional targets, inclusivity and environmental footprint. Several recent reports highlight the need for major, rapid reconfiguration of our food systems as a result. International publicly funded agricultural research for development will play an increasingly vital role in support of such goals as reducing poverty, improving food and nutrition security, and improving natural resources and ecosystem services. Here we take stock of the work over the last decade of CGIAR, one of the major players in the agricultural research for development arena, from the perspective of published, peer-reviewed science. We do this with respect to several elements of its vision as set out in 2011, elements that are shared by many other organisations that are also working towards achieving the Sustainable Development Goals. Overall, we found a strong association between number of CGIAR publications and countries with large numbers of rural poor and high child stunting prevalence. At the same time several countries were identified that are anomalous, being either relatively over- or under-represented in the peer-reviewed literature in relation to numbers of rural poor and stunting prevalence. On average, 30% of the calories consumed in national food baskets come from food sources that are not currently the commodity focus of CGIAR research, such as fruit and vegetables. We identify possible ways in which the alignment between the strategic objectives of an agricultural research for development organisation such as CGIAR and its publicly funded science outputs might be further strengthened, for maximum impact in the nine years that are left for the world to achieve the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

The Australian red meat industry is an important contributor to the national economy and international markets. A focus on reducing greenhouse gas (GHG) emissions from this sector presents an important opportunity for the sustainability of the industry and to enable Australia to achieve its commitments set under the Paris climate agreement. Here we show that through changes in land management and application of technologies to reduce enteric methane emissions from grazing livestock it is possible for the Australian red meat industry to substantially reduce GHG emissions, and even become carbon neutral. We calculated baseline (2005) and current (2015) GHG emissions for the red meat sector, then examined the mitigation potential and costs of potential pathways to reduce and offset emissions. Emissions from the Australian red meat industry have decreased from 124.1 Mt CO 2 e in 2005 to 68.6 Mt CO 2 e in 2015, primarily through reductions in land clearing. Achieving carbon neutrality is possible with continued improvements in vegetation management combined with methods to reduce livestock emissions, sequester carbon, and maintain animal numbers. While possible, this ambitious target will require timely and substantial investment and policy support from private and government bodies. • Greenhouse gas emissions from the red meat sector have decreased since 2005. • The main sources of greenhouse gas emissions are land clearing and enteric methane. • Improved land and animal management are key to further reducing emissions. • Methods to reduce enteric methane must be applicable to extensive grazing systems. • Consistent and sustained policy support and incentives are required. [ABSTRACT FROM AUTHOR]

Abstract Climate-smart agriculture (CSA) is widely promoted as an approach for reorienting agricultural development under the realities of climate change. Prioritising research-for-development activities is crucial, given the need to utilise scarce resources as effectively as possible. However, no framework exists for assessing and comparing different CSA research investments. Several aspects make it challenging to prioritise CSA research, including its multi-dimensional nature (productivity, adaptation and mitigation), the uncertainty surrounding many climate impacts, and the scale and temporal dependencies that may affect the benefits and costs of CSA adoption. Here we propose a framework for prioritising agricultural research investments across scales and review different approaches to setting priorities among agricultural research projects. Many priority-setting case studies address the short- to medium-term and at relatively local scales. We suggest that a mix of actions that span spatial and temporal time scales is needed to be adaptive to a changing climate, address immediate problems and create enabling conditions for enduring change. Highlights • Prioritising CSA research activities is challenging, primarily because it is multi-dimensional. • We propose and illustrate a simple six-element framework as a "map" to guide prioritisation. • Most priority setting activities need a mix of different quantitative and qualitative methods. • This mix needs to address actions that span spatial and temporal time scales to ensure CSA research effectiveness. [ABSTRACT FROM AUTHOR]

Abstract There are large yield gaps in the mixed smallholder farming systems of Africa, with limited opportunities to sustainably increase productivity and adapt to climate change. In this study, the ex-ante potential of residue retention and fertilization measures to meet this challenge is assessed using a positive mathematical programming (PMP) model. This micro-economic model captures decision making at the farm level for a sample population in Northern Burkina Faso for the 2010 to 2045 simulation period. In contrast to previous studies of mixed farms in this area, we model each individual farm in the sample population, instead of one or a small number of representative farms. We are therefore able identify groups of farms for which each measure is profitable, applied either individually or as a combined package. This approach also enables simulation of the economic impacts from indiscriminate applications of the measures or "smart" applications which are restricted to the farms that profit from the measures. Our findings are aligned with other studies showing that residue retention causes trade-offs between crop and livestock production, while fertilization can synergistically raise returns to both production activities. The annual profit losses from the "middle of the road" RCP6 trajectory of climate change assumed in this study were estimated to reach 15% by 2045. The smart package of measures increased aggregate profit the most, although not by nearly enough to claw back the losses from climate change. The fertilizer measures were the next most profitable, with indiscriminately applied residue retention being the only measure to reduce aggregate profit relative to this climate change baseline. Importantly, the measures that are the most profitable at the aggregate level are not necessarily those that would be the most widely adopted. For example, residue retention is profitable for a larger share of the sample population than fertilization. The advantage of the population scale analysis used in this study is that it prevents measures such as residue retention, which can benefit a significant share of farms, from being disregarded by practitioners because they appear to be unprofitable at the aggregate level or when viewed through the lens of an average representative farm. Finally, amidst the growing emphasis of studies on the benefits of packages compared to individual measures, the findings from this study are more equivocal about this choice, suggesting that extension programs should have the flexibility to apply measures individually or as a package. Highlights • Annual profit losses from climate change (RCP6) reach 15% by 2045. • Most profitable adaptation options at aggregate level unlikely to be most adopted. • Residue retention reduces livestock profits, but improves profits for 51% of farmers. • Packages of adaptation options have no clear advantage over individual options. [ABSTRACT FROM AUTHOR]

Ongoing and projected changes to rural communities in sub-Saharan Africa (SSA) are unprecedented in scale and pace. This paper investigates to what extent significant changes in livelihoods, poverty and food security performance are already taking place. The study focuses on households in Lushoto district (n = 147), a remote but urban linked area of Tanzania. Within the short time period between 2012 and 2015, 77% of households made changes in farm resources or farm characteristics. Households in the study site can be broadly classified as ‘Rising high value crop’, ‘Rising livestock’, ‘Subsisting mixed’ and ‘Subsisting crops’. Some of the most substantial changes we observed in the three year period of study were most likely not related to any of the agricultural orientated interventions that are being promoted in the region, but are likely endogenous changes. The land expansion seen in the ‘Rising’ households (n = 58) provides a counterpoint to the trend established in the literature of decreasing farm sizes across lower income countries more broadly, and specifically in Africa. The strategy of land expansion is risky, potentially representing a future of winners and losers, ultimately with some land-holders falling further into poverty rather than leveraging their agricultural enterprises to improve their well-being. Our results show that in sites like Lushoto with a good rural to urban connection (increasingly common in SSA), households can be agile and diverse and agency interventions are aiming for a moving target. In order to achieve income and food security outcomes, targeted and rapid monitoring tools will be needed. [ABSTRACT FROM AUTHOR]

Livestock provides an important source of income and nourishment for around one billion rural households worldwide. Demand for livestock food products is increasing, especially in developing countries, and there are opportunities to increase production to meet local demand and increase farm incomes. Estimating the scale of livestock yield gaps and better understanding factors limiting current production will help to define the technological and investment needs in each livestock sector. The aim of this paper is to quantify livestock yield gaps and evaluate opportunities to increase dairy production in Sub-Saharan Africa and South Asia, using case studies from Ethiopia and India. We combined three different methods in our approach. Benchmarking and a frontier analysis were used to estimate attainable milk yields based on survey data. Household modelling was then used to simulate the effects of various interventions on dairy production and income. We tested interventions based on improved livestock nutrition and genetics in the extensive lowland grazing zone and highland mixed crop-livestock zones of Ethiopia, and the intensive irrigated and rainfed zones of India. Our analyses indicate that there are considerable yield gaps for dairy production in both countries, and opportunities to increase production using the interventions tested. In some cases, combined interventions could increase production past currently attainable livestock yields. [ABSTRACT FROM AUTHOR]

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a “pre-competitive” space for model development to a “competitive space” for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools. [ABSTRACT FROM AUTHOR]

Current approaches to estimate freshwater use in livestock production systems generally fail to consider the competition for water resources with alternative uses, such as production of food crops food or other ecosystem services. This article presents a new method to account for the competition for freshwater use between food crops and animal feed, while assessing freshwater use in livestock production systems. The developed water use ratio (WUR) is defined as the maximum amount of human digestible protein (HDP) derived from food crops from the consumptive water use (CWU) appropriated to produce 1 kg of animal-source food (ASF) over the amount of HDP in that 1 kg of ASF. The CWU for livestock production is first categorized according to the land over which it is consumed, based on the suitability of that land to produce food crops. Then, the method assesses food-feed competition by determining the amount of HDP that could have been produced from food crops, using the same CWU currently used to produce ASF. The method enables identification of livestock production systems that contribute to global food supply without competing significantly over water resources with food production, based on their CWU. Three beef production systems in Uruguay are used to illustrate the method. During the backgrounding and the finishing stages, which are analyzed in this study, cattle can be kept on natural pasture (NP), seeded pasture (SP) or in feedlots (FL). The following three systems were analysed: i) NP-NP, ii) SP-SP and iii) SP-FL. Results show that the NP-NP system uses the largest amount of water per kg of beef output. However, results also show that the SP-SP and SP-FL systems can potentially produce more HDP by growing food crops than by producing beef. Based on the traditional measure for water productivity, i.e. the quantity of CWU per kilo of beef produced, we would conclude that the NP-NP system is least efficient, whereas based on the WUR the NP-NP system is the only system producing HDP more efficiently than food crops. Sustainable intensification not only implies improving agriculture and livestock productivity per unit of resource used, but also improving the number of human beings nourished. Results from this study illustrate the importance of considering competition and trade-offs with other uses when evaluating water use efficiency of livestock systems to promote sustainable intensification. [ABSTRACT FROM AUTHOR]

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models. [ABSTRACT FROM AUTHOR]

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data. [ABSTRACT FROM AUTHOR]

Rapid economic development in East Africa is matched by extremely dynamic smallholder livelihoods. To quantify the changes in poverty of smallholder farmers, to evaluate the potential of farm and off-farm activities to alleviate poverty, and to evaluate the potential barriers to poverty alleviation. The analyses were based on a panel survey of 600 households undertaken in 2012 and re-visited approximately four years later in four sites in East Africa. The sites represented contrasting smallholder farming systems, linked to urban centres undergoing rapid economic and social change (Nairobi, Kampala, Kisumu, and Dar-es-Salaam). The surveys assessed farm management, farm productivity, livelihoods, and various measures of household welfare. Almost two thirds of households rose above or fell below meaningful poverty thresholds – more than previously measured in this context – but overall poverty rates remained constant. Enhanced farm value production and off-farm income proved to be important mechanisms to rise out of poverty for households that were already resource-endowed. However, households in the poorest stratum in both panels appeared to be stuck in a poverty trap. They owned significantly fewer productive assets in the first panel compared to other groups (land and livestock), and these baseline assets were found to be positively correlated with farm income in the second panel survey. Equally these households were also found to be among the least educated, while education was found to be an important enabling factor for the generation of high value off-farm income. Rural development that aims to stimulate increases in farm produce value as a means to alleviate poverty are only viable for already resource-endowed households, as they have the capacity to enhance farm value production. Conversely, the alleviation of extreme poverty should focus on different means, perhaps cash transfers, or the development of more sophisticated social safety nets. Furthermore, while off-farm income presents another important mechanism for poverty alleviation in rural areas, these opportunities are restricted to those households that have had access to education. As more households turn to off-farm activities to supplement or replace their livelihoods, farming approaches will also change affecting the management of natural resources. These dynamics ought to be better understood to better manage land-use transitions. [Display omitted] • A panel survey of 600 households was undertaken around four years apart in four sites in East Africa • Enhanced farm value production as a pathway out of poverty was only open to resource-endowed households • The participation in high-value off-farm income as a pathway out of poverty was only open to educated households • Households participating in low-value off-farm activities were trapped in a vicious cycle of diminishing resources • Intensification efforts may support medium-poverty households, alleviation of extreme poverty should be by different means [ABSTRACT FROM AUTHOR]

Crop-livestock production systems play an important role in the livelihoods of many rural communities in sub-Saharan Africa (SSA) but are vulnerable to the adverse impacts of climate change. Understanding which farming options will give the highest return on investment in light of climate change is critical information for decision-making. While there is continued investment in testing adaptation options using on-farm experiments, simulation models remain important tools for ‘ex-ante’ assessments of the impacts of proposed climate-smart agricultural technologies (CSA). This study used the Ruminant model and the Trade-offs Analysis model for Multi-Dimensional Impact Assessment (TOA-MD) to assess how improved livestock management options affect the three pillars of CSA: increased productivity, improved food security, and reduced greenhouse gas (GHG) emissions. Our sample was stratified into: 1) households with local cow breeds ( n = 28); 2) households with improved dairy cow breeds ( n = 70); and 3) households without dairy cows ( n = 66). Results showed that the predicted adoption rates for improved livestock feeding among households with improved dairy cows (stratum 2) were likely to be higher compared to households with only local cows (stratum 1). Both households with local cows and those with improved cows had increased income and food security. However, overall poverty reduction was only modest for households with local cows. Expected methane emissions intensity declined with adoption of improved livestock feeding strategies both in stratum 1 and stratum 2, and greater impacts were observed when households in stratum 2 received an additional improved cow breed. Providing a cow to households that were not keeping cows showed substantial economic gains. Additional research is, however, needed to understand why those farms currently do not have cows, which may determine if the predicted adoption rates are feasible. [ABSTRACT FROM AUTHOR]

As a result of population growth, urbanization and climate change, agricultural systems around the world face enormous pressure on the use of resources. There is a pressing need for wide-scale innovation leading to development that improves the livelihoods and food security of the world's population while at the same time addressing climate change adaptation and mitigation. A variety of promising climate-smart interventions have been identified. However, what remains is the prioritization of interventions for investment and broad dissemination. The suitability and adoption of interventions depends on a variety of bio-physical and socio-economic factors. Also their impacts, when adopted and out-scaled, are likely to be highly heterogeneous. This heterogeneity expresses itself not only spatially and temporally but also in terms of the stakeholders affected, some might win and some might lose. A mechanism that can facilitate a systematic, holistic assessment of the likely spread and consequential impact of potential interventions is one way of improving the selection and targeting of such options. In this paper we provide climate smart agriculture (CSA) planners and implementers at all levels with a generic framework for evaluating and prioritising potential interventions. This entails an iterative process of mapping out recommendation domains, assessing adoption potential and estimating impacts. Through examples, related to livestock production in sub-Saharan Africa, we demonstrate each of the steps and how they are interlinked. The framework is applicable in many different forms, scales and settings. It has a wide applicability beyond the examples presented and we hope to stimulate readers to integrate the concepts in the planning process for climate-smart agriculture, which invariably involves multi-stakeholder, multi-scale and multi-objective decision-making. [ABSTRACT FROM AUTHOR]

We use data from 500 households in Samburu County (Kenya) to explore how natural environment and market accessibility affect coping and adaptation strategies of pastoralists. In particular, we ask whether households accumulate livestock wealth and invest in structural and cognitive social capital to protect themselves against climate risks. We find weak evidence that households accumulate livestock wealth in response to living in a drier environment, and no evidence that households invest in either structural or cognitive social capital as insurance against climate risks. However, coping strategies vary across social groups. For example, while rainfall does not robustly affect cognitive social capital (trust)—we find that the “poor” and “financially-integrated” households (i.e., those who have relatively good access to credit and capacity to save money) show greater mutual trust in drier environments. The results from this study can be used for priority setting by policy makers and development agencies for programs aimed at safeguarding household livelihoods in arid and semi-arid lands (ASALs). [ABSTRACT FROM AUTHOR]

Abstract: The East African region exhibits considerable climatic and topographic variability. Much spatial and temporal variation in the response of different crops to climate change can thus be anticipated. In previous work we showed that a large part of this variation can be explained in terms of temperature and, to a lesser extent, water effects. Here, we summarise simulated yield response in two crops that are widely grown in the region, maize and beans, and investigate how the impacts of climate change might be addressed at two levels: the agricultural system and the household. Regionally, there are substantial between-country and within-system differences in maize and bean production responses projected to 2050. The arid-semiarid mixed crop-livestock systems are projected to see reductions in maize and bean production throughout most of the region to 2050. Yields of these crops in the tropical highland mixed systems are projected to increase, sometimes substantially. The humid–subhumid mixed systems show more varied yield responses through time and across space. Some within-country shifts in cropping away from the arid–semiarid systems to cooler, higher-elevation locations may be possible, but increased regional trade should be able to overcome the country-level production deficits in maize and beans caused by climate change to 2050, all other things being equal. For some places in the tropical highlands, maize and bean yield increases could have beneficial effects on household food security and income levels. In the other mixed systems, moderate yield losses can be expected to be offset by crop breeding and agronomic approaches in the coming decades, while more severe yield losses may necessitate changes in crop types, movement to more livestock-orientated production, or abandonment of cropping altogether. These production responses are indicative only, and their effects will be under-estimated because the methods used here have not accounted for increasing weather variability in the future or changes in the distribution and impacts of biotic and other abiotic stresses. These system-level shifts will take place in a context characterised by high population growth rates; the demand for food is projected to nearly triple by the middle of this century. Systems will have to intensify substantially in response, particularly in the better-endowed mixed systems in the region. For the more marginal areas, the variability in yield response, and the variability in households’ ability to adapt, suggest that, even given the limitations of this analysis, adaptation options need to be assessed at the level of the household and the local community, if research for development is to meet its poverty alleviation and food security targets in the face of global change. [Copyright &y& Elsevier]

Abstract: African smallholder farming systems are complex, dynamic systems with many interacting biophysical subcomponents. In these systems the major inputs and outputs are managed by human agency – the farmers. To analyse potential developmental pathways of smallholder farming systems in sub-Saharan Africa (SSA), we recognised the need for a tool that can capture the effects and consequences of decision-making on the use of resources. Here we describe and apply such a new modelling tool, developed within the NUANCES framework (Nutrient Use in ANimal and Cropping systems: Efficiencies and Scales), called NUANCES-FARMSIM (FARM SIMulator), an integrated crop – livestock model developed to analyse African smallholder farm systems. NUANCES-FARMSIM was used to analyse a representative case study farm in the highlands of Western Kenya, a site for which each of the components of FARMSIM has been thoroughly tested. We present the results of a sensitivity analysis which showed the model to be sufficiently robust to identify key management options that explain most of the variability in farm productivity, and the long-term consequences of these options for the case study farm. The analyses showed clearly that the most important decisions are those related to the interactions between the different components of the farm and therefore justify the need of integrating crop and livestock components within one modelling tool. The allocation of limited resources across the farm, and the way organic matter is recycled or redistributed within the farm determines the long-term production capacity of the system. The results of the sensitivity analyses further showed that for the case study farm in Western Kenya a strong focus on improving the reliability of the subsystem level or process descriptions will only result in minor improvement in simulating productivity at farm level. [Copyright &y& Elsevier]

This paper describes the functioning and validation of the Decision Support Systems described in the first part of the paper. The DSS ran three case studies with different farm sizes that represent the range of farmers found in the Valley. The DSS results were validated against survey data for the same cases. Traditional technologies for maize and milk production were reproduced by the DSS including land use and cattle feeding systems. The generic nature of the DSS was demonstrated as well as its capacity to deal with the system''s socio-economic and biological aspects. The results suggest that the DSS was successful in reproducing the functioning of the farming system''s main components. More importantly it simulated the complex interactions observed between the farmers and their crops and cattle. Finally, it is acknowledged that despite the size and complexity of the DSS, it only was able to emulate the functioning of the main components of the farming system. [Copyright &y& Elsevier]

The objective of this work was to develop a Decision-Support System (DSS) in order to support the decision making process by campesino farmers of Central Mexico. Two biological models, one socio-economic model and a survey database form the DSS. The CERES-Maize model simulated the yield response of three local land-races of maize to different management systems. The second biological model, a cow model (dynamic hybrid model), was used to simulate alternative feeding systems. A multi-period mathematical programming model integrated the outputs of the previous models with the survey database. This model was used to find the optimal combination of resources and technologies that maximised farmers’ income. This model consists of 15,698 structural columns and 612 rows. The DSS successfully reproduced the functioning of the farming system''s main components. More importantly, it simulated the complex interactions observed between the farmers and their crops and cattle, including traditional maize management practices. [Copyright &y& Elsevier]

• Food system transformation is urgent, requiring rigorous, science-based monitoring to guide public and private decisions and support those who hold decision-makers to account. • Monitoring the whole of food systems and the interactions between their components is essential to support the immediate course corrections required to meet global sustainable development goals. • A food systems framework is proposed to define the architecture for a comprehensive monitoring agenda covering five thematic areas and their component indicator domains. • An inclusive process is called for that would select and track indicators for analysis of food systems performance and accountability. Food systems that support healthy diets in sustainable, resilient, just, and equitable ways can engender progress in eradicating poverty and malnutrition; protecting human rights; and restoring natural resources. Food system activities have contributed to great gains for humanity but have also led to significant challenges, including hunger, poor diet quality, inequity, and threats to nature. While it is recognized that food systems are central to multiple global commitments and goals, including the Sustainable Development Goals, current trajectories are not aligned to meet these objectives. As mounting crises further stress food systems, the consequences of inaction are clear. The goal of food system transformation is to generate a future where all people have access to healthy diets, which are produced in sustainable and resilient ways that restore nature and deliver just, equitable livelihoods. A rigorous, science-based monitoring framework can support evidence-based policymaking and the work of those who hold key actors accountable in this transformation process. Monitoring can illustrate current performance, facilitate comparisons across geographies and over time, and track progress. We propose a framework centered around five thematic areas related to (1) diets, nutrition, and health; (2) environment and climate; and (3) livelihoods, poverty, and equity; (4) governance; and (5) resilience and sustainability. We hope to call attention to the need to monitor food systems globally to inform decisions and support accountability for better governance of food systems as part of the transformation process. Transformation is possible in the next decade, but rigorous evidence is needed in the countdown to the 2030 SDG global goals. [ABSTRACT FROM AUTHOR]