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To design policies for sustainable irrigation planning, public decision-makers need knowledge about the potential environmental impacts of irrigated farming systems. In a previous paper, we proposed to couple Life Cycle Assessment (LCA) and Agrarian System Diagnosis (ASD) to build the Life Cycle Inventory (LCI) of a farming region in a context of data scarcity and farming system diversity. This method was applied to the semi-arid irrigated plain of Kairouan (Tunisia). The aim of the present paper is to demonstrate how this new ASD-based LCA method is used to conduct the impact assessment of this farming region, under current conditions and for two prospective scenarios. Two functional units (FU) were used: the area and the gross product generated. Life Cycle Impact Assessment (LCIA) was conducted based on a multilevel process, by converting LCI data collected for each cropping/ livestock system into LCIA outputs, which were then consolidated at the farming system (FS) and at the regional scales. Two scenarios, based on observed trends, were assessed: the "agrarian" scenario (driven by a shift from family to corporate farming) and the "hydrological" scenario (driven by a water table drawdown). Environmental hotspots were identified at each scale: the most impactful FS was the "mixed family farming" (intermediate access to water), because of its large share of land at the regional scale (45%). In this FS, the most harmful cropping system was input-intensive olive groves intercropped with a melon-pepper/ cereal/ tomato rotation, in which most impacts (35%) were attributable to the input-intensive "melon" crop, and more particularly to "manufacture and transport of fertilizer", including farmyard manure. Globally, the most harmful processes were water and fertilizer use (fertilizer manufacturing and transportation). This downscaling is very useful to inform public policies on the most harmful productions. The impacts in human, ecosystem and resource endpoints increased by 11%, 2%, 9% and 23%, 27% and 40%, for the agrarian and hydrological scenarios respectively. These results show that observed trends in farming practices evolution (more irrigation) should be stopped. Our method has three main strengths: i) inter-farms synergies are accounted for in the LCIA, which is essential to promote circularity; ii) thanks to the innovative and multilevel approach, the origins of environmental hotspots can be investigated and later mitigated by future policies; iii) ASD helps to design sustainable scenarios by identifying current trends and regional thresholds (resources, pollution and workforce) that cannot be overcome. [Display omitted] • Multilevel Life Cycle Impact Assessment (LCIA) was conducted in a semi-arid irrigated plain (Tunisia). • Agrarian System Diagnosis and Life Cycle Assessment methods were coupled. • Environmental hotspots are modeled from the plot /herd, up to the regional scale. • Main impacts are caused by water depletion, fertilizer manufacturing and manure transportation. • Environmental impacts may increase by 40% in trend evolution scenarios (watertable depletion and less familial farming). [ABSTRACT FROM AUTHOR]

Abstract This introduction to the special issue deploys a framework, inspired by realist synthesis and introduced in Section 1 , that aims to untangle the contexts, mechanisms, and outcomes associated with investments that link poverty reduction and rural prosperity within a broad agri-food systems perspective. Section 2 considers changes in contexts: Where are agricultural research investments most likely to be an engine of poverty reduction? Over the past 25 years, there have been profound changes in the development context of most countries, necessitating an update on strategic insights for research investment priorities relevant for the economic, political, social, environmental, and structural realities of the early 21st Century. Section 2 briefly surveys changes in these structural aspects of poverty and development processes in low-income countries, with particular attention to new drivers (e.g., urbanization, climate change) that will be of increasing salience in the coming decades. In Section 3 , we turn to mechanisms: What are the plausible impact pathways and what evidence exists to test their plausibility? Poor farmers in the developing world are often the stated focus of public sector agricultural research. However, farmers are not the only potential beneficiaries of agricultural research; rural landless laborers, stakeholders along food value chains, and the urban poor can also be major beneficiaries of such research. Thus, there are multiple, interacting pathways through which agricultural research can contribute to reductions in poverty and associated livelihood vulnerabilities. This paper introduces an ex ante set of 18 plausible impact pathways from agricultural research to rural prosperity outcomes, employing bibliometric methods to assess the evidence underpinning causal links. In Section 4 , we revisit the concept of desired impacts: When we seek poverty reduction, what does that mean and what measures are needed to demonstrate impact? The papers in this special issue are intended to yield insights to inform improvements in agricultural research that seeks to reduce poverty. History indicates that equity of distribution of gains matters hugely, and thus the questions of "who wins?" and "who loses?" must be addressed. Moreover, our understanding(s) of "poverty" and the intended outcomes of development investments have become much richer over the past 25 years, incorporating more nuance regarding gender, community differences, and fundamental reconsideration of the meaning of poverty and prosperity that are not captured by simple head count income or even living standard measures. Highlights • AR4D is a systems problem, requiring consideration of shifting contexts, interconnected mechanisms, and complex impacts. • Fundamental changes in context of poverty reduction affect likelihood of success in a given setting. • Given these contextual features, it is necessary to broaden from fields and farms to encompass the food system. • Food system perspectives reveal many plausible pathways, from conventional AR4D investments in breeding to food policy research. • Poverty impacts emerge at scales far different from AR4D interventions, which creates M&E and impact assessment challenges. [ABSTRACT FROM AUTHOR]

Climate change and salinisation present substantial challenges to the sustainability of cropping systems in south-west coastal Bangladesh. This is the third paper in a series reporting a study to assess the impacts of climate change and salinity on the productivity and economic viability of ten current and potential rice-based cropping systems in two coastal villages in Khulna District. In this paper, possible adaptations are assessed, including novel dry-season crops, changed fertilizer use, and changed sowing dates, across five climate and three salinity scenarios. Farmers' estimated, APSIM-simulated, and extrapolated yield distributions were incorporated in budgets for the ten cropping systems, using current and projected salinity levels. Current and projected future prices and costs were used to estimate different measures of profitability. Estimated variability in yields and prices was used to generate probability distributions for these profitability measures, permitting comparison of cropping systems based on profitability and risk. Adaptation through changed fertilizer use (higher or lower, depending on the crop) was projected to give higher returns for some cropping systems. However, larger improvements were obtainable with changes in sowing dates to avoid the worst stresses imposed by climate change and salinity. The loss of production of all crops except watermelon and pumpkin due to salinity was more than offset with changed sowing dates for 2030 and 2060 conditions, irrespective of season. With such adaptations, and allowing for risk, the rice/shrimp system maintained the top ranking in terms of net income per hectare in 2030 and 2060 and the rice/sunflower system maintained the second ranking. The rice/pumpkin/rice system ranked third for 2030 and fourth in 2060 while the rice/maize system moved up to third in 2060. [ABSTRACT FROM AUTHOR]

Ancient irrigation systems (AISs) have been providing a multitude of ecosystem services to rural farming and urban communities in Asia and Africa, especially in arid and semi-arid climatic areas with low rainfall. Many AISs, however have now been degraded. A systematic analysis of AISs on their typologies, causes of degradation, and their ecosystem services is lacking. The objective of this review was to synthesize the knowledge on AISs on their typologies, status and causes of degradation, ecosystem services and functions, and identify gaps in research in Asia and Africa. A critical review of peer-reviewed journal papers, conference and workshop proceedings, book chapters, grey literature, and country reports was conducted. Qualitative and quantitative information from journal papers were used to conceptualize the typologies and analyze the status and causes of degradation, and ecosystems services and functions provided by the AISs. Based on the review, we classified AISs into three groups by source of irrigation water: Rainwater harvesting system (RHS) with small reservoirs, ground water based system, and floodwater based system. The RHSs, which used to receive reliable rainfall and managed by well cohesive social organizations for their maintenance and functioning in past, have now been silting due to extreme rainfall pattern and breakdown of the cohesive organizations in recent decades. In ground water based systems, indiscriminate development of deep tube wells causing siltation of channels has been a major challenge. In floodwater irrigation systems, irregular rainfall in the highlands and the breakage of irrigation structures by destructive floods were the main causes of degradation. Lack of maintenance and increased soil erosion, inadequate skilled manpower, and declining support from the government for repair and maintenance were the main causes of degradation of all AISs. The main ecosystem service provided by all AISs is water for agriculture. In tank- and pond-based systems, fish farming is also practiced. Tank irrigation systems provide various types of provisioning, regulatory, cultural and supporting services, especially in India and Sri Lanka. Ground water based systems provide water for domestic purposes and various cultural services. Floodwater based systems provide water for power generation and wildlife habitat maintenance and help in flood control. The knowledge generated through the review provide evidence-based information, and help aware governments, private sectors and development agencies for improved policy planning and decision making, and prioritizing the restoration, rehabilitation, and management of various AISs. Community Managed Ancient Irrigation Systems in Asia and Africa: Typologies, Degradation and Ecosystem services. [Display omitted] • Ancient irrigation systems (AISs) in Asia and Africa provide a multitude of benefits to mankind but are at a threat of degradation. • A systematic review conducted to identify typologies, status and causes of degradation of, and ecosystems services provided by, AISs. • Three typologies (rainwater harvesting, groundwater, and surface water based) and four ecosystems services identified. • Cascade tanks in Sri Lanka and India provide irrigation water for farming communities and ecosystem services for local communities and societies. • Rehabilitation of AISs needs urgent attention by governments, private sector, and development partners. [ABSTRACT FROM AUTHOR]

Climate-smart agriculture (CSA) aims to address climate change, climate variability, and food security while sustaining productivity. The literature on acceptance and adoption of CSA technologies recognizes the importance of the policy environment in shaping farmers' decisions, particularly the policy mix, including economic, regulatory, and information instruments to stimulate CSA acceptance. Behavioral models have been used to better explain CSA acceptance, however, only a few studies integrate farmers' behavioral drivers and their policy mix appraisal, which in combination, are important determinants for the successful acceptance of CSA. This paper proposes a model that integrates behavioral drivers and policy mix appraisal influencing the acceptance of CSA technologies. We aim to examine how farmers' behavioral drivers and their appraisal of the policy environment influence the acceptance of CSA technologies and practices. We studied the Costa Rican coffee sector and conducted 523 surveys with coffee farmers and two focus groups with experts, extension agents, and cooperatives. An ordered probit model was used to identify factors explaining CSA acceptance. The results indicate that besides the influence of behavioral drivers, policy consistency and comprehensiveness and the type of instrument targeting farmers' behaviors play an important role in explaining CSA acceptance. Our results suggest that a positive appraisal of policy consistency and comprehensiveness are important for increasing farmers' acceptance of CSA and sustainable practices. This deepens earlier thinking on "policy packages" effectiveness by showing that the farmers' appraisal of the overall policy mix influences their decisions to engage with CSA. Our study shows the importance of considering system context effects (policy environment) on farmers' decision-making. Since the integration of behavioral drivers and the appraisal of the policy mix characteristics is relatively underexplored for CSA, our empirical results may help to unravel farmers' decision-making processes. Thus, it can be used for rethinking and adjusting policy interventions toward more balanced and comprehensive policy mixes, as it enables feedback from policy implementation and can further induce policy learning. [Display omitted] • We determine the factors influencing farmers' acceptance of Climate-smart technologies • CSA acceptance results from behavioral predictors, socioeconomic drivers, and policy appraisal • Policy consistency and comprehensiveness are key drivers for increasing farmers' acceptance of CSA • Integrating behavioral drivers and policy appraisal enhances the understanding of system contextual effects on farmers' decisions [ABSTRACT FROM AUTHOR]

Agricultural food production is both affected by and contributing to climate change. At the global scale, agri-food systems are responsible for one-third of total greenhouse gas emissions. With progressing climate change, the risks of crop failure increase. Thus, an urgent need is to reduce emissions from food systems while increasing their resilience to climate change. Enormous untapped potentials to achieve these dual goals lie in transforming agri-food systems towards more diverse, plant-based, and regional food production systems. In this paper, we present an innovative approach for identifying climate-adapted alternative food crops that could (1) help to diversify existing cropping systems and thus increase their climate resilience and can be (2) nutritious elements of plant-based regional diets with reduced emissions. The approach builds on the model ecocrop to select food crops that could benefit from regionally projected changes in climate. The model-based analysis is complemented with a literature review to examine the ecocrop results for their plausibility and provide a broader assessment of potentials for cultivation, utilization, and nutritional values of model-selected crops. The approach is applied to Switzerland, where we identify eight alternative crops with the potential to increase climate resilience while contributing to healthy human diets of regional consumers with benefits for climate mitigation (almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage). The literature review indicated that the increasing demand for many of these crops suggests great potential for regional marketing of crop products. The results produced in this study provide an initial guide for researchers and innovative farmers interested in experimenting with alternative crops in Switzerland, thus promoting climate-smart food system transformation from the production side. Using our unbiased bottom-up screening approach, we identified climate-adapted alternative crops that can provide essential nutrients, cover nutritional gaps in Switzerland, diversify existing production systems, and improve sustainability. [Display omitted] • Innovative bottom-up screening approach identifies climate-adapted alternative crops. • Almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage are identified. • Identified crops provide essential nutrients and cover nutritional gaps in Switzerland. • Selected crops can diversify existing production systems to improve sustainability. [ABSTRACT FROM AUTHOR]

Conservation agriculture (CA) is promoted worldwide to enhance soil quality, improve farmers’ incomes and increase the resilience of rainfed agro-ecosystems under climate change. A major constraint to the adoption of CA is crop residue management in mixed crop–livestock systems. Farmers have competing uses of crop residues – for soil cover, as fodder or as additional income source – which may explain low CA adoption rates in some countries. This paper describes the social and income trade-offs of different crop residue uses in Mexico at regional, community and household level associated with the introduction of CA. We first spatially analyze the importance of crop residues for fodder supply and identify municipalities with fodder surplus at national and regional level. Second, we assess the likely social trade-offs and implications for farming communities of changing a typical farm households’ residue allocation. Third, we identify the effects of crop residue uses on gross margins of maize and barley and assess the economic optimal crop residue allocation at the farm level with short planning horizons. The paper focuses on maize and barley producers in the central Mexican highlands and combines primary quantitative and qualitative data with secondary data. Analysis shows that at a national level, Mexico has a fodder surplus while the central highlands have a deficit. Crop residues are a major fodder source in Mexico, contributing up to 40% of fodder availability. Crop residues are also an important income source which implies costs for introducing CA in the central highlands. Our analysis indicates that retaining crop residues in-situ influences gross margins and that retention of roughly 45% of residues maximizes gross margins in situations where opportunity costs for the use of crop residues exist. Partial residue retention, sequential introduction and combination of technologies may facilitate CA uptake but CA remains a challenge for resource-poor farmers given their limited liquidity, risky production environment and difficulty to forego current income for future benefits. More interdisciplinary research is needed around economic optimal residue retention levels under different farming and production conditions, to identify the ecological needed minimum cover of crop residues for CA and to develop alternative low-cost sustainable technologies with short-term benefits. [ABSTRACT FROM AUTHOR]

To contribute to building sustainable and effective climate change adaptation solutions avoiding usability gap, it is largely recommended to engage in the process of co-production, integrating expertise and knowledge from various academic and non-academic actors. We want to learn if and how co-production, believed to effectively link knowledge and decision-making, and thus suggested as the best practice in building decision-support frameworks, is really applied in the frameworks that are being implemented on the ground. A literature review allowed us to identify integrated decision-support frameworks for climate-change adaptation in agriculture developed and used over the period of the last 10 years and involving non-academic stakeholders. To analyse them, we chose as an assessment tool the four co-production principles proposed by Norström and colleagues: context-based, pluralistic, goal-oriented and interactive. The useful entry points for incorporating co-production in the design of decision-support that we found in the reviewed frameworks include among the others adequate participants selection strategy, building on existing interaction spaces, developing a theory of change with the participants, and involving participants in the design of different elements of the method. The architectures of the analyzed frameworks contained more elements that responded to pluralistic and interactive principles than to context-based and goal oriented principles, we have also identified gaps in the design, such as taking into account the personal characteristics of researchers that could strengthen a framework's implementation and its impact, or attempts at bridging different levels of decision making, to cover the triad of science, policy and practice. A detailed look at the decision-frameworks that are actually being applied allows for a critical reflection whether and how we as researchers use what we preach as an effective way of responding to sustainability challenges in agriculture. Co-production principles turn out to be a useful tool for analysis and we suggest they can be used as a check-list when designing decision-support frameworks for climate-change adaptation. This papers offers useful examples of how to shift the research-led processes of decision-support towards more co-production with non-academic actors, to increase chances of bridging the gaps between science, policy and practice. [Display omitted] • Knowledge co-production is largely suggested as the best practice to bridge the gaps between science, policy and practice. • Evidence is lacking for the actual use of co-production in decision-support for climate-change adaptation in agriculture. • We identify useful entry points for applying co-production principles from already used decision-support frameworks. • Researchers are rarely selected to build decision-support frameworks based on their ability to support co-production. • When designing decision-support frameworks co-production principles can be used as a guiding tool. [ABSTRACT FROM AUTHOR]

India's agricultural systems are increasingly affected by climate change's adverse effects. The Government of India has an impressive set of programs to address this issue, but they have substantial shortcomings, especially in reaching women farmers. We aim to understand policy and implementation gaps in reaching women farmers with climate-smart agriculture (CSA) practices and study how women and men farmer's different roles in agriculture shape their needs and access to complementary services needed to adapt to climate change. An extensive review of India's agriculture and climate policies and program and a series of focus group discussions with farmers in Gujarat, India to discuss constraints and potential entry points for better reaching women farmers with CSA practices. Women's increased vulnerability to climate change and reduced access to CSA practices can be attributed to limited land ownership, poor access to credit, reduced access to information and formal extension, and multiple pressures on their time. Village cooperatives and self-help groups can be leveraged to support women's access to agricultural information and adoption of CSA practices. This paper highlights constraints to information and extension access by Indian women farmers that could impede the widespread adoption of CSA practices. It fills an important knowledge gap in designing gender-responsive policies and inclusive agricultural extension systems to promote adoption of CSA practices among smallholder farmers. [Display omitted] • Women farmers have limited land ownership, access to credit, access to information and formal extension, and time, constraining adoption of climate smart practices. • Building gender-responsive extension systems is critical for promoting adoption of climate smart agriculture practices. • Knowledge of gendered experience of climate change, availability of gender-disaggregated data and gendered analyses are important aspects. • Participatory approaches to policy making and involvement of local and community organizations needs to be strengthened in India. • Village cooperatives and self-help groups can be key intermediary organizations to support women's access to information and adoption of CSA practices. [ABSTRACT FROM AUTHOR]

Abstract: The idea of the decision support system (DSS) for farmers remains an enigma. Clever technology to bridge the gap between agricultural science and farming practice still seems appropriate. Many more of the conditions for success appear to exist today than ever before. Yet the DSS has yet to significantly colonise farm decision making practice. This paper comes late in a long program of research conducted to see if, and under what conditions, computer simulation of farming scenarios, on which a DSS generally depends, can be valued by farmers. The research approach used an unconventional prototypic information system (IS), comprising local measurements, models, and facilitated discussions that evolved in an action research program. The aim has been to elucidate the means by which successful simulation-based decision support intervention can take place and why it usually does not. This required a significant expansion of the researchers’ concept of the farm as a system to include the farmer’s internal system of practical knowing and learning. This paper reports on a cognitive framework model with transactions at interfaces with both the production system and the analytical IS. Its coarse structure is the classical perception–action cycle influenced by goals and outcome feedback. In the highly uncertain production environment of Australian dryland farming, personal judgement plays a significant mediating role between perception and action, and theory of a continuum between the judgement modes of intuition and analysis adds to framework structure. Further structure comes from the theoretical distinctions between holistic and arbitrary intuition, and between causal and probabilistic analysis. Analytic interventions influence: (a) awareness of current situation conditions and (b) expectations of future conditions and action outcomes, and these serve as primary cognitive resources for evaluation of possible actions in planning and decision making. A theory that matches our research experience in bridging the gap between analytic intervention and intuitive practice posits that virtual situations simulated with analytic models and outputs represented graphically can facilitate vicarious experiential learning. This dovetails with theory concerning the education of intuition. The paper concludes by applying criteria from the field of cognitive engineering to test whether the framework presents a concept of mind that is workable for informing practical model-based research and development aimed at supporting farmers’ judgments and decisions. [Copyright &y& Elsevier]

The livestock sector in Sub-Saharan Africa (SSA) is under increasing pressure to define its role in jointly addressing food security and climate change. Climate-smart agriculture (CSA) has been widely leveraged as an approach to achieving both food security and climate change outcomes through suites of interventions that maximize synergies and reduce tradeoffs among three pillars: productivity, climate change resilience, and climate change mitigation. However, operationalization of the CSA approach in the livestock sector is hindered by a lack of clarity around what the pillars mean for livestock systems, given their fundamental attributes compared to crops and the spatial and temporal dimensions of these attributes. A conceptual framework is also lacking for assessing and comparing the potential CSA synergies and tradeoffs that different livestock systems and interventions may generate. In this paper we aim to offer guidance on the operationalization of the CSA approach in the livestock sector. We draw on a literature review to explore the essential attributes of livestock systems in SSA as they relate to CSA objectives over different temporal and spatial scales. Based on this review, we propose a practical and flexible framework for assessing and comparing the synergies and tradeoffs that different livestock systems may generate among food security and climate change objectives over different spatial and temporal scales. The framework consists of four elements: CSA pillars, spatial-temporal scales, CSA objectives mapped to each spatial-temporal scale, and indicator guidance. Using farm survey data and national statistics, an illustrative application of the framework to two dairy farms in Rwanda is presented and discussed. The illustrative application demonstrates how the framework can be used to identify important, spatial-temporal CSA synergies and tradeoffs that otherwise may go unrecognized. Additional applications are needed to assess the utility, practicality, and potential of the framework to guide CSA operationalization in the livestock sector in SSA. Maximizing synergies and reducing tradeoffs among food security and climate change outcomes in the livestock sector is critical for a sustainable food future. With an emphasis on flexibility for tailoring to specific development contexts and compatibility with varying levels of data availability and methodological complexity, the framework is intended to support diverse stakeholders involved in policy and development seeking to identify those livestock systems that contribute most to food security and climate change objectives over time and space. [Display omitted] • Maximizing synergies among food security & climate change in the livestock sector is critical for a sustainable future. • However, the climate-smart agriculture (CSA) approach has not been fully operationalized in livestock sector development. • Spatial & temporal dimensions determine when, where, and for whom CSA synergies & tradeoffs in the livestock sector arise. • We propose a spatial-temporal framework to assess and compare the CSA synergies & tradeoffs of different livestock systems. • Applying the framework can help identify important CSA synergies & tradeoffs that otherwise may go unrecognized. [ABSTRACT FROM AUTHOR]

Food insecurity is a complex phenomenon with biophysical, climatic, economic, and infrastructure facets. Despite this understanding, there are few stakeholder-based modelling tools that can capture these dynamics and thereby evaluate the direct and indirect impacts that climatic change, economic change, and policy interventions can have on food security. To address this need, we have developed the Food Distributed Extendable COmplementarity (Food-DECO) model. The Food-DECO model represents individual aggregated stakeholders as decision-makers within the agricultural, transportation, and economic systems. In this paper, we demonstrate the model's capabilities by applying it to a food system based on characteristics of Ethiopia, a frequently food-insecure country. Food-DECO produces results that show the effects of seasonality and regional distribution networks on human nutrition while disaggregating those effects by age, gender, and per capita income. We explore the impacts of a regional crop failure and evaluate the possible effectiveness of several commonly proposed food security interventions. The economic integration of agriculture and transportation in Food-DECO enables us to see, counterintuitively, that improving the capacity of the existing food distribution network between regions can negatively impact the nutritional outcomes in the region experiencing crop failure; the increased ability to meet high demand elsewhere leads to an increase in regional exports – even during a food shortage. [ABSTRACT FROM AUTHOR]

This paper provides an ex-ante assessment of the impacts of climate and price variability on household income and food security in Ethiopia and Ghana. The study applies an agent-based modelling approach to highlight the role of coping and adaptation strategies under climate and price variability. Our simulation results show that climate and price variability adversely affects income and food security of households in both countries. Self-coping mechanisms are found to be important but insufficient to mitigate the adverse effects of variability, implying the need for policy interventions. Adaptation strategies composed of a portfolio of actions such as the provision of production credit and access to improved seeds are found to be effective in reducing the impacts of climate and price variability in Ethiopia. Similarly, policy interventions aimed at improving the provision of short-term production credit along with the existing irrigation facilities are important in Ghana. Finally, this study highlights the importance of capturing the distributional aspects of adaptation options by highlighting heterogeneous effects of variability and adaptation options. [ABSTRACT FROM AUTHOR]

The agricultural sector is being called upon to reduce its greenhouse gas emissions (GHG). A scenario approach was developed to explore the plausible futures of the French bovine sector and their impact on climate change. These scenarios encompass a Business As Usual scenario (S1-BAU) and alternative contrasting scenarios: (S2) cattle production increase to meet a high global demand under a liberal policy, (S3) refocus on internal demand within France, with an upmarket move to ‘green’ products, (S4) committed public policy to reduce GHG emissions. This paper analyses how key drivers of these scenarios (e.g. subsidies on investment, reduction of market risks, carbon tax, limitation of concentrate feed in animal diets) affect the evolution of production, economics, and environmental impact on climate change of typical French suckler cow and dairy farms, by means of simulations performed with a bio-economic model. To adapt their farming systems to the scenarios, farms can opt for variably intensive/integrated practices per animal and per unit land area. Some technological progress in animal production, crop production, and farm equipment is also modeled. Results show that in S1-BAU, milk production, net income and impact on climate change of dairy farms rise. Beef production and impact on climate change decrease slightly in suckler cow farms. Impact on climate change per unit of product decreases owing to higher productivity per animal and to a more integrated management of crop production. Alternative scenarios underline that reorienting public support toward farm investment would further intensify dairy farms and increase their income, but would reduce production and income of suckler cow farms and favor crop production (S2). Climate change impact per unit of product is more strongly reduced in S3 (organic farming with low feed concentrate) than in S2, but with a reduced production, particularly for milk. A carbon tax decreases emissions, but to the detriment of cattle production, especially suckler cow farms. [ABSTRACT FROM AUTHOR]

Rangeland productivity is strongly conditioned by the amount and temporal distribution of precipitation. Thus, the worsening of droughts with climate change could be a serious threat to their existence. This paper presents a modelling study aimed at evaluating the sensitivity of a valuable type of commercial rangelands, namely Spanish dehesas, to increases in the frequency and intensity of droughts driven by climate change. The assessment consisted in a multi-way ANOVA carried out on the basis of 5400 simulations of a multidisciplinary integrated model. It included two blocking factors linked to climate change scenarios, namely Representative Concentration Pathway and downscaling method, and two treatment factors, namely return period and severity of droughts. The levels of all factors were included as part of the simulation scenarios. The response variables constituted a summary of model's behaviour throughout one simulation. They were average profits per farmer and average stocking rate, both calculated over the entire simulation period, and remaining soil depth at the end of the simulation. The effects of the treatment factors on the response variables were small for all blocks, thereby suggesting that the sensitivity, and thus the vulnerability, of Spanish dehesas to the worsening of droughts would be low under climate change. Farmers were defined as conservative in all model simulations, that is, they minimized changes in the size of their herds and bought supplementary feed to meet shortfalls in livestock feed unless it was excessively expensive. Thus, we conclude that this group strategy could explain the adaptive capacity of Spanish dehesas to droughts. This paper shows that multidisciplinary integrated models are valuable learning tools to acquire insights into the relationships between climate, ecologic and socio-economic factors. Although there is a recurrent call for holistic studies, they are still rare in the rangeland literature. Hopefully, this paper will motivate some researchers to consider this approach. Unlabelled Image • Climate change heralds the worsening of droughts threatening rangeland sustainability. • Modelling assessment of the sensitivity of a dehesa rangelands to increases in the frequency and intensity of droughts. • The sensitivity, and thus the vulnerability, of dehesas to the worsening of droughts would be low. • A widespread conservative strategy would provide Mediterranean commercial rangelands with adaptive capacity to droughts. [ABSTRACT FROM AUTHOR]

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers. [ABSTRACT FROM AUTHOR]

Agricultural productivity and growth in Mali are under threat from erratic rainfall, resulting in more frequent dry years. The national economy is vulnerable to climate change due to 50% of the gross domestic product coming from the agricultural sector and 75% of the population living in rural areas. The Climate-Smart Agriculture (CSA) concept arises from a need to provide innovative solutions towards the complex and integrated goals of increasing yields, improving resilience, and promoting a low emissions agricultural sector. A major challenge for policymakers to operationalize CSA is the identification, valuation (cost-benefit), and subsequent prioritization of climate-smart options and portfolios (groups of CSA options) for investment. This paper presents the process, results, and lessons learned from a yearlong pilot of the Climate-Smart Agriculture Prioritization Framework (CSA-PF) in Mali. Key national and international stakeholders participated in the co-development and prioritization of two CSA portfolios and related action plans for the Malian Sudanese zone. Initial steps towards outcomes of the process include inclusion of prioritized CSA practices in ongoing development projects and prompting discussion of modifications of future calls for agricultural development proposals by regional donors. [ABSTRACT FROM AUTHOR]

Place-based social, cultural, institutional and political dynamics not only influence the innovation capacity of agricultural systems, but also the willingness of relevant actors to be involved in participatory research processes, and the dynamics of their participation. This paper critically discusses the modification and application of one particular participatory approach to agricultural systems analysis (Rapid Appraisal of Agricultural Innovation Systems [RAAIS]) to agricultural adaptation in Southeast Kazakhstan. We consider the overall effectiveness of the method as a research tool, practical issues in the implementation of workshops, definition of and selection of participant groups, as well as the questions of participation and empowerment within the workshops themselves. We find that although RAAIS is adaptable to alternative theoretical frameworks, its implementation in different socio-cultural and political contexts may require more consideration than is apparent in previous discussions. In particular, the appropriate training of workshop organisers is of crucial importance to the success of this methodology. These findings will be useful to those adapting participatory research methods to different research topics and contexts more broadly. [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]

This paper describes an investigation of various adaptations of rice based cropping systems to climate variability in India's Telangana State. All adaptations were generated through participatory engagement and were field-tested with local smallholder households before being evaluated through cropping system simulation analysis. This approach contrasts with most research about adaptation of cropping systems to climate variability and climate change that is mostly based on simplifying assumptions about current farmer management practices and where the feasibility of implementing proposed adaptations is rarely tested. In this study, the investigation commenced with discussions about climate related issues in rice based farming systems between researchers, farmers and NGOs in three villages in three Mandals of the state of Telangana. Participatory intervention was used to identify new practices that could provide more adaptive and robust responses to climate variability. Suggested adaptations were implemented in on-farm experimentation. Fields demonstrating these adaptations were monitored and results were discussed with participating farmers at regular ‘Climate Club’ village meetings. Crop and soil data from these fields were used to locally parameterise the cropping systems simulator APSIM. Local adaptations that were trialled in the villages were simulated using local soil and long term historical weather data. In each of the case studies, a number of adaptations that were developed and implemented in the villages were shown through simulation to be successful in terms of agricultural production, stability of yields and resource use efficiency. Of the adaptations investigated, sowing rules to reduce the chance of crop failure due to early dry spells were most readily adopted and are also relatively easy to extend to other villages. Strategic irrigation of rainfed crops such as maize and cotton resulted in significant gains to profitability and stability of these crops but cannot be considered in isolation where access to water is limited. Reduced irrigation of rice resulted in over 60 mm/ha/yr. savings in water and some improvements in gross margins but this adaptation was not popular with farmers due to its burden on labour and added risks associated with unreliable supply of electricity for pumping at critical times. The reduced rice area for strategic irrigation of rainfed crops adaptation resulted in improved gross margins per hectare per year and higher net water productivity. This adaptation is most promising but will require institutional change around water use policy and more equitable allocation of limited water resources within villages. These results led us to the proposition that participatory action research with smallholder farmers, coupled with field-testing and simulation analysis can produce practical and productive adaptations to climate variability. [ABSTRACT FROM AUTHOR]

The Mediterranean Basin has been identified as a climate change "hotspot", a region where increased warming and drying are expected to occur at rates faster than the global average. Increasing scientific attention has therefore been drawn to the repercussions of climate change for the Mediterranean's diverse and valued agricultural sector. The extent to which these repercussions vary throughout the region, owing both to a non-uniform distribution of climatic hazards and to uneven adaptive capacities of farmers, remains however unquantified. We hereby provide a comprehensive spatial assessment of the potential to implement different farm-based land and water management adaptations across the Mediterranean's croplands, and evaluate the extent to which different regions see a match or a mismatch between areas of adaptation need and areas of adaptation capacity. This approach made use of spatial multi-criteria analysis to evaluate the suitability of different farm adaptations, and relied on the Ecocrop climate suitability model and climate data on changing duration or frequency of extremes to identify areas of adaptation need under a high-end climate change scenario. The adaptations explored in this work span a range of sustainable approaches addressing different components of the farming system (soil-based, water-based, and crop-based adaptations). For each sub-system, we compared the potential to implement an adaptation representing an incremental change vs. one involving greater transformation. Results indicate a worsening of climatic conditions for all croplands in the region, and particularly within the Mediterranean extents of Egypt, Turkey, Greece, Spain, Morocco, Bosnia and Herzegovina, and Israel. In more than half of these countries, >60% of highly affected Mediterranean areas, on average, see no tangible potential to implement adaptations. Importantly, potentials for adaptation, and particularly of transformational adaptations having the greatest hypothesized capacity to buffer against the effects of climate change, are found to be lower within areas where the most adverse climate change hazards are forecast to occur. For regions found to be approaching so-called limits to adaptation, this research contributes to growing calls for consideration of more transformational options, alongside the opportunities and sacrifices these options entail. For regions where considerable (yet unrealized) adaptation capacity is found to match areas of adaptation need, this paper instead presents a basis for further investigation on how to bridge adaptation capacity and preparedness. [Display omitted] • Mediterranean croplands will witness increased drought and heat stress under RCP 8.5. • Croplands in the south-west and east will face the greatest increases in climatic extremes. • Most regions have a > 50% spatial mismatch between adaptation potential and needs. • Adaptation potential is lower where most needed, especially for transformational adaptations. • Greater reflection on transformation needed as limits to adaptation are approached. [ABSTRACT FROM AUTHOR]

Climate change is expected to adversely affect agricultural production, particularly in Iran where the agricultural sector forms the backbone of the economy. This is true for the agriculture sector of the southwest region of Iran which is largely rain-fed and dominated by smallholder farmers with minimal livelihood alternatives. Although several studies have been conducted on climate change, little has been done on the adaptive capacity of farmers within farming systems, especially in Iran. The main objective of this study was to evaluate the adaptive capacity of four common farming systems (i.e., family farming system, cooperative farming system, commercialized farming system, and agro-enterprise farming system) in Fars province, southwestern Iran. A seven-step approach was used to normalize, weigh, and aggregate 102 individual indicators within a composite adaptive capacity index that is dimensionless, ranging from 0 to 1, and 1 is the optimal value. The required data were collected using a cross-sectional survey from 1472 farmers within the four mentioned farming systems. The results showed that the highest value was recorded by agro-enterprise for knowledge, perception and awareness, transport and physical accessibility, and institution and economic resources. Agro-enterprise scored higher in terms of awareness and action, which shows that these farming systems have a higher capacity for adaptation to the threat posed by climate diversity. Farmers in this farming system benefit from high formal education, high perceptions about the threat and risk of climate change, the occurrence of climatic events, causes for the change in climatic parameters, and awareness of climate change impacts. This paper recommends resilience-building interventions in the study area, which target individuals with low adaptive capacities, especially farmers. The results can also assist agricultural policymakers to recognize which components and determinants of adaptive capacity should be prioritized to mitigate the threat of climate change to farming systems. [Display omitted] • This study aimed to investigate the adaptive capacity of common agricultural systems • Climate change has a negative impact on Iran's agricultural production systems • The results showed that farmers at agro-enterprises have a well awareness of climate change adaptation • It is essential to obtain information about potential, effective, and cost-effective adaptation options [ABSTRACT FROM AUTHOR]

Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensification of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a priority. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are applied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. Developing the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems related to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks. [ABSTRACT FROM AUTHOR]

Brazil is one of the first major developing countries to commit to a national greenhouse gas (GHG) emissions target that requires a reduction of between 36.1% and 38.9% relative to baseline emissions by 2020. The country intends to submit agricultural emissions reductions as part of this target, with livestock production identified as offering significant abatement potential. Focusing on the Cerrado core (central Brazilian savannah), this paper investigates the cost-effectiveness of this potential, which involves some consideration of both the private and social costs and benefits (e.g. including avoided deforestation) arising from specific mitigation measures that may form part of Brazil's definition of Nationally Appropriate Mitigation Measures (NAMAs). The analysis used an optimisation model to define abatement costs. A baseline projection suggests that beef production in the region will emit 2.6 Gt CO 2 e (CO 2 equivalent) from 2010 to 2030, corresponding to 9% of national emissions (including energy, transport, waste, livestock and agriculture). By implementing negative-cost measures identified in a marginal abatement cost curve (MACC) by 2030, the 2.6 Gt CO 2 e could be reduced by around 24%. Pasture restoration, involving avoided deforestation, offers the largest contribution to these results. As the Brazilian Cerrado is seen as a model for transforming other global savannahs, the results offer a significant contribution by identifying alternatives for increasing productivity while minimizing national and global external costs. [ABSTRACT FROM AUTHOR]

The paper has two main objectives: (i) to assess the eco-efficiency of spring barley cultivation for malting in Denmark in a future changed climate (700 ppm [CO 2 ] and +5 °C) through Life Cycle Assessment (LCA) and (ii) to compare alternative future cultivation scenarios, both excluding and including earlier sowing and cultivar selection as measures of adaptation to a changed climate. A baseline scenario describing the current spring barley cultivation in Denmark was defined, and the expected main deviations were identified (differences in pesticide treatment index, modifications in nitrate leaching and change in crop yield). The main input data originate from experiments, where spring barley cultivars were cultivated in a climate phytotron under controlled and manipulated treatments. Effects of changed climate on both crop productivity and crop quality were represented, as well as impacts of predicted extreme events, simulated through a long heat-wave. LCA results showed that the changed climatic conditions will likely increase the negative impacts on the environment from Danish spring barley cultivation, since all environmental impact categories experienced increased impact for all investigated scenarios, except under the very optimistic assumption that the pace of yield improvement by breeding in the future will be the same as it was in the last decades. The main driver of the increased environmental impact was identified as the reduction in crop yield. Therefore, potential adaptation strategies should mainly focus on maintaining or improving crop productivity. The LCA also showed that selection of proper cultivars for future climate conditions including the challenge from extreme events is one of the most effective ways to reduce future environmental impacts of spring barley. Finally, if yield measurements are based on relative protein content, the negative effects of the future climate seem to be reduced. [ABSTRACT FROM AUTHOR]

Agricultural production in “open-sky systems” such as extensive cattle ranching on natural grasslands is subject to inter-annual climatic variability and other market fluctuations. To tackle the dependency of livestock breeding on these factors, we conducted participatory modeling with cattle producers in Uruguay. The methodology consists of simulating possible scenarios to collectively evaluate the different herd management practice alternatives. In this paper, we present an Agent-Based Model built with stakeholders and designed to represent a breeding system on a typical extensive grazing area in the basaltic soils region (BR) of Uruguay. This model has three main modules: environment, biophysical and decisional sub-models. This modularity allows the conducting of virtual experiments to reveal how some herd management decisions (such as seasonal stocking rate adjustments) combined with a climatic series can result in resilience against drought periods and market movements. Long-term simulations were implemented to analyze the sensitivity of the model to key management parameters with varying climate conditions. The inter-annual climatic variability can seriously affect cattle production, even with conservative stocking rates. Rigid strategies are bound to fail and cause systems to break. Adaptive management emerged as a critical option for the sustainability of livestock breeding. The inter-annual climatic variability can seriously affect cattle production, even with conservative stocking rates. This result highlights the importance of adaptive management, one that can react to a changing environment, for the sustainability of livestock breeding. [ABSTRACT FROM AUTHOR]

Highlights: [•] Impact of climate change and water policy on rice industry is empirically evaluated. [•] The paper finds stronger evidence of structural adjustments in rice farming system. [•] The regional CGE model demonstrate cost to the regional economy. [•] Relocation of some rice production to the northern Australia could offset potential impact. [•] Strong government support would be crucial to implement any possible relocation. [ABSTRACT FROM AUTHOR]

Colombia has a total of 27.2 million heads of cattle, ranking fourth among the Latin American countries. Identifying sustainable strategies to mitigate greenhouse gas emissions (GHGE) will help the Colombian government meet their goal of a 51% reduction in national emissions by 2030. Estimation of yield gaps for identifying the potential to improve cattle farms productivity and efficiency in Colombia help on reducing the GHGE intensities from the cattle sector. This paper aims to calculate the gap between attainable and actual milk and meat yields for specialized dairy, dual-purpose, cow-calf, and fattening production systems in 3 agro-ecological zones (AEZ) in Colombia; to identify the main aspects that restrict the meat and milk yields in these production systems; and analyze how closing yield gaps affect the carbon footprint (CF) of meat and milk production. The most suitable AEZs for cattle activities were identified by considering environmental, climatic, edaphic, and land characteristics. From a dataset of 1505 surveyed farms, a yield gap benchmarking analysis for estimating the potential to increase meat and milk yields in each of the identified AEZ was applied. The most productive farms were included in the "best farms" while the rest of the farms belonged to the "farms operating below potential". A "cradle to farm-gate" Life Cycle Assessment was used to calculate the CF. Three scenarios were proposed for closing the yield gaps by 50, 75, and 100%, between the two groups of farms. Three AEZs likely to support cattle activities in Colombia were identified. Average milk production from the farms operating below potential was 45–50% of potential production, and meat was 34–51%, indicating that a potential to achieve increases in milk and meat productivity exists. CFs of 1 kg milk or meat were lower in the groups of best-performing farms than in the groups of farms operating below potential. Yield gaps for milk and meat production can be closed by improving cattle management practices and better technologies. As a general trend, closing the yield gaps decreases the CFs. Our findings contribute to understand the farms' current productive performance and provides key insights into the possible technological and managerial changes for improving the productivity of cattle systems in Colombia. In addition, the study showed how milk and meat CFs can be lowered with the adoption of proper cattle management practices, and better technologies. [Display omitted] • Cattle are responsible for 21% of Colombian GHG emissions, identifying sustainable strategies for its mitigation is crucial. • Yield gap analysis is useful for exploring opportunities to increase yields by identifying factors constraining production. • This study calculated the yield gaps for Colombian cattle systems, their carbon footprints, and proposed mitigation options. • Substantial cattle yield gaps were found, hence there is a considerable scope to improve milk and meat yields in Colombia. • Closing the yield gaps by increasing cattle productivity is an important mitigation strategy of climate change in Colombia. [ABSTRACT FROM AUTHOR]

Abstract: The Ricardian approach has been applied to many different geographic contexts and most applications concern large countries. Only few studies deal with Europe. This paper applies the Ricardian approach to measure the impact of climate on the agricultural system of a small Italian Alpine region where permanent cultivations are the main crop. Structural and economic data about farmers growing apples and grapes are extracted from the Italian Farm Accountancy Data Network (FADN). It tests three possible functional forms and it highlights the importance of controlling not only for farms’ characteristics but also for farmers’ strategic decisions such as specialization and quality certification. In contrast with overall beneficial effects of climate change found for broad areas in Europe (Germany and England), climate change seems to imply a reduction in annual net revenues in our study area. From a methodological point of view, results show that if there is enough climatic variation across the sample and suitable control variables are available, the Ricardian approach can be applied also on a small territorial scale, even though the results depend heavily on the adopted functional form. [Copyright &y& Elsevier]

Abstract: Climate change will alter the environmental conditions for crop growth and require adjustments in management practices at the field scale. In this paper, we analyzed the impacts of two different climate change scenarios on optimal field management practices in winterwheat and grain maize production with case studies from Switzerland. Management options included nitrogen fertilization (amount, timing and allocation) as well as irrigation. Optimal solutions that maximize the farmer’s utility were sought with the help of a bioeconomic modeling system that integrated the process-based crop growth model CropSyst into an economic decision model. The latter accounted not only for the crop specific average profit margins, but also for production risks, reflecting the utility (expressed as the certainty equivalent) of a risk-averse farmer’s management decisions at field scale. In view of the non-linearity and complexity of the problem, we used a genetic algorithm as optimization technique. For grain maize, our results showed that climate change will foster the use of irrigation, not only at sites prone to water limitation already under current climatic conditions, but more in general for climate change scenarios projecting a substantial decrease in summer precipitation. For winterwheat, irrigation was never identified as an optimal management option. For both crops and sites, climate change reduced the optimum nitrogen fertilization amount and decreased for winterwheat the number of fertilization applications. In all cases, the farmer’s certainty equivalent decreased between 7% and 25% under climate change, implying negative impacts on winterwheat and grain maize production even under the assumption of an adjustment of the optimum management practices. [Copyright &y& Elsevier]

Abstract: Modern agriculture must meet new challenges such as production of healthy food, adaptation to climate change, protection of natural resources, and conservation of landscape. These challenges require changes in current agricultural systems and therefore, environmentally-friendly agricultural systems must be designed and their sustainability assessed. Over the past several years, various methods have been developed for making such assessments (e.g. the Balancing and Ranking Method, MEACROS, MODAM, the modelling framework of Pacini et al., ROTAT+FarmImages, MASC, and ROTOR) but few studies put forward simple solutions for selecting one method over another. In this paper, we propose a simple guide to distinguish methods one from another. Categories of the guide include the type of systems to assess, the spatial and temporal scales at which systems are assessed, the dimensions of sustainability for which systems are assessed, the type of visualisation for comparing options, the target users, and the ability to generate alternative systems. The guide was developed and tested with a group of farm advisors involved in a three-year project called RotAB, which aimed to assess the sustainability of organic arable farming systems: the advisors looked for a method for sustainability assessment of cropping systems. We presented seven recent assessment methods as well as the guide to advisors. The guide’s key points allowed them to clearly express their requirements: the method they looked for had to evaluate cropping systems and helped advisors to propose new ones; it had to evaluate multiple sustainability criteria that are easily understandable by farmers; indicators had to be scientifically based, without the need for many input data; the method had to be easy to use and produced graphical output that can be discussed with farmers. Finally, the guide helped advisors to choose one of the seven methods (in that case they chose MASC). This guide can help decision-makers distinguish assessment tools from one another using simple categories and choose the one best adapted to their expectations. [Copyright &y& Elsevier]

Abstract: Agro-meteorological metrics are indicators of weather determined environmental conditions on which agricultural management decisions are made. Metrics derived from an estimated future climate provide an opportunity to characterise the impacts of climate change on a wide range of agricultural systems, land use practices and ecosystem services. Such indications are vital for determining how changes in the biophysical environment can lead to land management and policy adaptations to achieve multiple objectives of financial viability, food security, biodiversity conservation and environmental sustainability. They provide valuable links between probable management adaptation responses and capacity for mitigating greenhouse gas emissions. However, there are large uncertainties associated with projected future climates, including the climate models’ spatial scale of representation and those at which agro-meteorological metrics are applied. This paper describes the estimation of agro-meteorological metrics derived from observed weather and downscaled Regional Climate Model projection data for 12 sites in Scotland. Results show that projected changes to seasonal rainfall distribution, growing season length, soil moisture deficits and accessibility will be substantially different from the present climate. Fundamentally, the metrics indicate a substantial shift in land management requirements and potential need for substantial changes in agricultural systems and land use that will have implications across a wide range of research disciplines. [Copyright &y& Elsevier]

Abstract: Sub-Saharan Africa (SSA) is predicted to experience considerable negative impacts of climate change. The IPCC Fourth Assessment emphasizes that adaptation strategies are essential. Addressing adaptation in the context of small-scale, semi-subsistence agriculture raises special challenges. High data demands including site-specific bio-physical and economic data are an important constraint. This paper applies a new approach to impact assessment, the Tradeoff Analysis model for Multi-Dimensional Impact Assessment (TOA-MD), which simulates technology adoption and associated economic, environmental and social outcomes in a heterogeneous farm population for a regional impact assessment. The methodology uses the kinds of survey, experimental and modeled data that are typically available in countries where semi-subsistence systems are important, combined with future socio-economic scenarios based on new scenario pathway concepts being developed by the climate change and impact assessment modeling communities. Characteristics of current and future agricultural systems, including land use, output, output price, cost of production, and farm and household size are analyzed and compared for both current and projected future climate (2030), with and without adaptation, and for different socio-economic scenarios. The methodology is applied to two study areas in Kenya. These case studies show the potential of this approach to provide a flexible, generic framework that can use available and modeled data to evaluate climate impact and adaptation strategies under a range of socio-economic scenarios. [Copyright &y& Elsevier]

Abstract: The FARMSCAPE Information System emerged in a long-running research program aimed at making simulation models useful to Australian farmers in managing climatic variability. This paper is about how well it has worked. This is reported in relation to two standards: (1) the value to thinking and action expressed by farmers and their consultants, (2) correspondence with theory about learning and judgement in uncertain external environments. The former utilises recorded narrative interviews with participants over many years. The latter uses a cognitive framework drawn from theory of judgment and decision making featuring the relationship between intuition and analysis (). The cognitive theory framework makes sense of several evaluation surprises. The first was high enthusiasm by largely-intuitive farmers for an analytic approach to soil water in conjunction with a newly-appreciated “bucket” metaphor for water balance. The second surprise was the virtual absence of soil water measurement 10years later. This had been replaced by various intuitive estimates, calibrated to maintain a heuristic relationship with regard to the “bucket” as a resource. Farmers and their advisers were facilitated in using simulation for thought experiments and planning under climatic uncertainty. Benchmarking enabled problem solving in documented conditions. Scenario analysis using historical climate records supported thought experiments by providing probability distributions that were valued for shaping expectations as a “history of the future”. In retrospective evaluation interviews, researchers were surprised to find that yield forecasting and tactical decision making, anticipated to be analyses that were both site- and season-specific forecasts, had served farmers as “management gaming” simulations to aid formulating action rules for such conditions, thus reducing the need for an on-going decision-aiding service. Equipped with their soil monitoring techniques and with their heuristic rules, farmers still reserved a place for simulation “when you’ve got a planting situation out of the ordinary.” [Copyright &y& Elsevier]

Abstract: Policy makers in the agricultural sector are confronted with challenges which might drive land use change and ultimately agricultural profitability to a substantial degree. The challenges include questions around climate variability, demographic changes, use of land for bio-fuel production and ensuring an increase in food production. As profitability triggers many agri-business decisions, knowledge about the existing socio-economic landscape and the economic profile of a region as well as potential impacts on profits provides useful contextual information when agricultural policies are designed. Given the upcoming challenges and their associated uncertainties, it is important to ensure that a map of agricultural profit can be reproduced in a scenario and simulation setting which will allow exploring uncertainties around the impacts on agricultural profits as well. There is however currently no flexible system in operation which allows for a consistent update of a map of agricultural profits in Australia or elsewhere. This paper describes a process that has been developed to produce a map of agricultural profit for Australia for the year 2005/2006. The process involves a complex data architecture that accounts for heterogeneous information that is collected by a variety of institutions across different scales. All information can be comfortably queried and query results can be forwarded for immediate processing and subsequent visualisation in a geographic information system (GIS). To facilitate the production of profit maps in the future, the system provides flexibility regarding an update of new economic information but it can also be linked to maps that show an updated distribution of land use. A map of agricultural profit on a large scale and regular updates thereof will help understand profit trends in time and across space. It will help identifying regions that have a lower economic profile and will inform decisions regarding the design of regulatory policies. As these maps are developed using national scale data, we do not recommend using the results at the farm level but we suggest using separate catchment scale profit assessments to calibrate the national scale profit map. The proposed system is well suited to be used in various land use management and economic scenarios and will represent a step forward regarding a scenario impact assessment on agricultural profits. It will also help understand the economic benefit of land use on a large scale. [Copyright &y& Elsevier]

Abstract: The links between water application, energy consumption and emissions are complex in irrigated agriculture. There is a need to ensure that water and energy use is closely considered in future industry planning and development to provide practical options for adaptation and to build resilience at the farm level. There is currently limited data available regarding the uncertainty and sensitivity associated with water application and energy consumption in irrigated crop production in Australia. This paper examines water application and energy consumption relationships for different irrigation systems, and the ways in which the uncertainty of different parameters impacts on these relationships and associated emissions for actual farms. This analysis was undertaken by examining the current water and energy patterns of crop production at actual farms in two irrigated areas of Australia (one using surface water and the other groundwater), and then modelling the risk/uncertainty and sensitivity associated with the link between water and energy consumption at the farm scale. Results showed that conversions from gravity to pressurised irrigation methods reduced water application, but there was a simultaneous increase in energy consumption in surface irrigation areas. In groundwater irrigated areas, the opposite is true; the use of pressurised irrigation methods can reduce water application and energy consumption by enhancing water use efficiency. Risk and uncertainty analysis quantified the range of water and energy use that might be expected for a given irrigation method for each farm. Sensitivity analysis revealed the contribution of climatic (evapotranspiration and rainfall) and technical factors (irrigation system efficiency, pump efficiency, suction and discharge head) impacting the uncertainty and the model output and water-energy system performance in general. Flood irrigation systems were generally associated with greater uncertainty than pressurised systems. To enhance resilience at the farm level, the optimum situation envisaged an irrigation system that minimises water and energy consumption and greenhouse gas emissions. Where surface water is used, well designed and managed flood irrigation systems will minimise the operating energy and carbon equivalent emissions. Where groundwater is the dominant use, the optimum system is a well designed and managed pressurised system operating at the lowest discharge pressure possible that will still allow for efficient irrigation. The findings might be useful for farm level risk mitigation strategies in surface and groundwater systems, and for aiding adaptation to climate change. [Copyright &y& Elsevier]

Abstract: Because its relevance for the sustenance and livelihood of human systems, the assessment of the impacts that future climatic conditions may have on agricultural productivity becomes a key piece of information for agricultural scientists and policy makers. Several authors have performed assessments of the impacts of climate change on agricultural productivity evaluating alternatives for adaptation that are closely related to current management practices and do not cover a wider range of options. The objective of this paper is to document the main impacts of climate change scenarios on maize productivity in central Chile, an irrigated Mediterranean region, and evaluate the possibility of double cropping as a response to changes in the length of the growing season. The results of this research have lead to the conclusion that maize can be affected by climate change, with yield reductions between 10% and 30%, depending on climate change scenario and the type of hybrid used. In addition, climate change will also affect other relevant variables such as the rate of development, allowing the crop to complete its growing cycle in shorter periods of time. In future climate scenarios it is also possible to see a reduction of total irrigation needs, because actual evapotranspiration is diminished. In extreme climate change scenarios, double cropping is a more effective alternative of adaptation compared to management practices such as the use of early sowing dates and reductions in nitrogen fertilization. Double cropping also results in an increase on water demands, nitrogen use, and will likely have an impact on weed, pest and diseases. If climate change also results into impacts on river flows, the incorporation of double cropping could be constrained by imbalances between water demand and supply. [Copyright &y& Elsevier]

Abstract: InfoCrop, a generic crop model, simulates the effects of weather, soils, agronomic management (planting, nitrogen, residues and irrigation) and major pests on crop growth, yield, soil carbon, nitrogen and water, and greenhouse gas emissions. This paper presents results of its evaluation in terms of its validation for rice and wheat crops in contrasting agro-environments of tropics, sensitivity to the key inputs, and also illustrates two typical applications of the model. Eleven diverse field experiments, having treatments of location, seasons, varieties, nitrogen management, organic matter, irrigation, and multiple pest incidences were used for validation. Grain yields in these experiments varied from 2.8 to 7.2tonha−1 in rice and from 3.6 to 5.5tonha−1 in wheat. The results indicated that the model was generally able to explain the differences in biomass, grain yield, emissions of carbon dioxide, methane and nitrous oxides, and long-term trends in soil organic carbon, in diverse agro-environments. The losses in dry matter and grain yield due to different pests and their populations were also explained satisfactorily. There were some discrepancies in the simulated emission of these gases during first few days after sowing/transplanting possibly because of the absence of tillage effects in the model. The sensitivity of the model to change in ambient temperature, crop duration and pest incidence was similar to the available field knowledge. The application of the model to quantify multiple pests damage through iso-loss curves is demonstrated. Another application illustrated is the use of InfoCrop for analyzing the trade-offs between increasing crop production, agronomic management strategies, and their global warming potential. [Copyright &y& Elsevier]

Agroforestry systems are widely promoted for their economic and environmental benefits. Food, energy, water and land resources in agroforestry systems are inextricably intertwined and expected to be severely impacted by climate change. Socioeconomic development and increasing populations have posed unique challenges for meeting the demand for food, energy, water and land, and the challenge will become more pressing under projected resource shortages and eco-environmental deterioration. Thus, a method of optimizing and sustainably managing the water-land-food-energy nexus in agroforestry systems under climate change must be developed. This paper develops an optimization model framework for the sustainable management of limited water-land-food-energy resources in agroforestry systems under climate change. The aims are to (1) quantify the interactions and feedbacks within water, land, food and energy subsystems; (2) provide trade-offs among water and energy utilization efficiency, economic benefits and environmental protection in agroforestry systems; and (3) generate optimal policy options among water and land resources for different crops and woodlands in different regions under different climate change patterns. The model framework is based on multiobjective fractional programming, and compromise programming is used to solve it. Climate change patterns are obtained from atmospheric circulation models and representative concentration pathways. The above aims are investigated through an actual nexus management problem in northeast China. Spatiotemporal meteorological and report-based databases, life cycle assessments, Pearson correlation analyses, data envelopment analyses and analytic hierarchy processes are integrated to realize practical application. The results show that climate variation will change the water and land allocation patterns and these changes will be more pronounced for major grain-producing areas. The optimized water allocation decreased (especially for rice, e.g., the optimal average value of the irrigation quota of rice was 4226 m3/ha, while the corresponding actual irrigation requirement of rice was [4200–7200] m3/ha) to improve the water use efficiency, and surface water allocation accounted for two-thirds. Maize had the largest planting area, although planting soybean generated the most greenhouse gases (greenhouse gas emissions from field activities for rice, maize, and soybean were 43.46%, 84.06% and 91.16%, respectively); However, these gases can be absorbed by forests. The model improved the harmonious degree of the resource-economy-environment system from 0.24 to 0.56 after optimization. Integrated models contribute to the sustainable management of water, food, energy and land resources and can consider the complex dynamics under climate change. It can be used as a general model and extended to other agroforestry systems that show inefficient agricultural production. [Display omitted] • Food, energy, water and land resources in agroforestry systems are inseparable and are impacted by climate change. • A cooperative optimization model is proposed to manage water-land-food-energy nexus (WLFEN) and cope with climate change. • Resources reallocation in WLFEN improve synergy of resource use efficiency, economic benefit and environmental impacts. • Inner linkage and synergy exist among resources in WLFEN in agroforestry systems and they are sensitive to climate change. • We put forward a quantitative way to assist managing agricultural resources sustainability in a change environment. [ABSTRACT FROM AUTHOR]

In the context of global climate change, droughts pose a serious threat to agricultural development and food security. Assessing the vulnerability and risk of regions to drought is important for its prevention. In this paper, to understand the vulnerability of maize to drought in different regions of China and quantify its risk, 241 prefecture-level administrative regions (including prefecture-level cities, autonomous prefectures, prefectures, and leagues) in the five main maize-growing regions of China are used as study area. By using a method of global sensitivity analysis, the extended Fourier amplitude sensitivity test (EFAST), we chose two parameters that are most sensitive to maize yield to calibrate the AquaCrop model. We then used it to simulate the water stress of maize in the study area under different irrigation scenarios as well as the corresponding production. We defined the drought hazard index (DHI) as the daily average of the crop water stress indicator during the growing season, and used it to describe the intensity of droughts. Vulnerability curves (the function of the DHI and rate of yield loss) of the entire growth season and various stages of growth were also formulated. These were used to determine the loss of maize yield under four levels of risk (return periods of 5, 10, 20, and 50 years). The results showed the following: 1) the vulnerability curve of maize for the entire growing season was consistent with logistic function, and the coefficient of determination of the equation of regression was R 2 = 0.93. The rate of yield loss began increasing rapidly once the DHI had reached 0.2 and approached its maximum value when the DHI was 0.6. 2) The coefficients of determination of the results of regression in 14 scenarios, in which drought had occurred in different stages of growth, were between 0.28 and 0.92. Drought from the tasseling stage to the milk stage had the most significant negative effect on the maize yield, followed by the seventh leaf stage to the tasseling stage and the sowing stage to the seventh leaf stage. Drought from the milk stage to physiological maturity had the least negative effect on the maize yield. 3) Under all four risk levels, the DHI and the yield loss rate of maize in China decreased from the northwest to the southeast. The Northwest Irrigated Maize Region had the highest drought risk among the five maize-growing regions, followed by the North Spring Maize Region, the Huang-Huai-Hai Summer Maize Region, the South Hilly Maize Region, and the Southwest Mountain Maize Region. 4) The DHI calculated by the average method was more representative than that calculated using the accumulative method. Unlabelled Image • A new drought hazard index was proposed for building drought loss curves. • Drought vulnerability curves were built for five main maize-growing regions in China. • Yield loss curves of maize were built for droughts occurred in various growth stages. • Risk of maize yield loss for return periods of 5, 10, 20, and 50 years were assessed. • The drought risk of maize in China decreases from northwest to southeast. [ABSTRACT FROM AUTHOR]

Mixed crop-livestock farming systems provide food for over half of the global population. However, some important food exporting countries, like Australia, are predicted to be vulnerable to climate change and may require transformative adaptations if they are to continue their role in food exportation. This paper assesses the potential impacts of projected climate change by 2030 (0.4–1.6° increase in mean temperature) on Australian mixed crop-livestock systems and examines the consequences of shifts in land allocations to cropping and grazing, in these systems, as an adaptation option. Farm bio-economic simulation models were developed for these mixed enterprise systems in several regions of Australia. These models were based on biophysically coupled crop, pasture, and livestock simulation models that in turn drew on site-based downscaled climate projection datasets. The farm models calculated farm profitability and risk measures. A range of land use changes was investigated. At drier locations facing adverse climate change, results showed a transition towards a greater emphasis on livestock production could be beneficial when assessed against multiple criteria of farm profit, downside financial risk, and environmental damage. We highlight some industry and government actions and policies that could facilitate these preferred adaptation strategies at such locations. • Temperature increase of 0.4–1.6° affect mixed crop-livestock systems in Australia. • Transition towards a greater emphasis on livestock production is often desirable. • Financially optimal land allocations vary between historical and future climate. • Increase in the land devoted to pasture is a strategy to manage future climate risk. [ABSTRACT FROM AUTHOR]

The impact of climate change on agricultural systems has become a reality that currently threatens food security in numerous regions worldwide. However, concrete consequences of climate change on agricultural yields still remain unknown in many countries around the world, including Romania. This study conducts a first-ever analysis of the impact of climate change on maize productivity in Romania, the European Union's leader in maize harvested production, which holds almost 30% of the European maize production. The paper explores complex statistical relationships between the climatic water balance (CWB)/its constituting parameters precipitation (P) and reference evapotranspiration (ETo) and maize yields (Zea mays L.) recorded nationally from 1990 to 2013, a key-period in Romania in terms of climate and socio-political changes. The analysis of various agro-climatic data, based on well-established methods (linear regression and bootstrapping), showed that the countrywide dependence of maize yields variability to changes in the analyzed climatic variables was considerable and reached peak values of roughly 60% for the CWB-yield relationship and around 50% for the P-yield and ETo-yield relationships. At the same time, a significant sensitivity of maize yield dynamics was found in response to a 1-unit climate change, which, on average, was quantified at 5 kg/ha/yr for a 1-mm variation of the CWB, and at 19 kg/ha/yr and 11 kg/ha/yr for a 1-mm change in P and ETo, respectively. In addition to this agricultural impact, our findings regarding the economic impact associated to concrete climatic trends (CWB decreases and ETo increases over the 24 years, consistent with an overall increase of the humidity deficit, and P increases, consistent with humidity surplus) indicate total costs of approximately 53 mil € (or almost 1% of the national agricultural gross domestic product) for the CWB-yield relationship, and of ~200 mil € (~3%) for ETo-yield. It was found that the separate influence of P in maize dynamics was positive, with net financial gains of up to ~130 mil € (~2%). However, our findings show that only the economic costs associated with the impact of ETo on maize productivity are highly statistically reliable. Our results issue a warning about the urgent measures to fight climate change effects on agriculture in Romania, which are necessary especially in the counties located in the Extra-Carpathian regions, highly vulnerable to climate dynamics. • Investigation of climate change impact on agricultural yields in Romania after 1990. • Analysis between climatic water balance (CWB)/precipitation (P)/reference evapotranspiration (ETo) and maize yields. • Up to ~60% dependence of maize yields variability to climatic changes, for the CWB – maize yields relationship. • Average sensitivity of 5 kg/ha/yr, 19 kg/ha/yr and 11 kg/ha/yr, for a 1-mm variation of the CWB, P and ETo. • Total economic costs of up to 53 mil €/200 mil €, associated with the negative impact of CWB/ETo on maize yields. [ABSTRACT FROM AUTHOR]

• Livestock systems in the United Kingdom will be affected by climate change. • A range of on-farm adaptation options are available at low-cost. • Adapting to observed and short-term impacts may overcome barriers. • Long-term changes should be planned for. Agriculture is a vital economic sector, providing food, fibre, and energy to a growing human population. Livestock are an important part of this sector, however the evidence and understanding of how a changing climate may affect livestock production systems, or how they may adapt to the changes, is a neglected area compared to the research into crop production. In this paper, we focus on livestock in the United Kingdom (UK), as an example of a temperate region likely to experience at least moderate changes in climate that will require changes to the way agricultural systems operate. We summarise the projected climate changes in this region, identify the main impacts likely to affect livestock agriculture, and discuss potential adaptation options at the farm level. We also categorise the adaptation options by the types of costs they incur, emphasising that many of these options involve management changes rather than investment and therefore no financial cost. Finally, we discuss the need for longer term planning to prepare for changes that have not yet been observed. [ABSTRACT FROM AUTHOR]