Suchergebnisse

Intro -- Acknowledgments -- Contents -- Chapter 1: An Introduction to the Climate-Smart Agriculture Papers -- 1.1 Tracking Progress -- 1.2 Overview of the Chapters -- 1.3 Implications for Development -- References -- Part I: Climate Risks and Impacts -- Chapter 2: Future Climate Projections in Africa: Where Are We Headed? -- 2.1 Introduction -- 2.2 Past and Present: Evidence Africa's Climate Has Already Changed -- 2.3 Future: Climate Model Projections for Africa -- 2.4 Implications for Development -- 2.4.1 Adapting African Agriculture to Climate Change -- 2.4.2 Collecting and Using Climate Data -- References -- Chapter 3: Climate Change and Infectious Livestock Diseases: The Case of Rift Valley Fever and Tick-Borne Diseases -- 3.1 Background -- 3.2 Case Studies -- 3.2.1 Rift Valley Fever -- 3.2.1.1 Drivers -- 3.2.1.2 Climate Change and RVF -- 3.2.2 Ticks and Tick-Borne Diseases (TBDs) -- 3.2.3 Inferences from the Case Studies -- 3.3 Mitigations and Adaptations -- 3.4 Conclusions and Implications for Development -- References -- Chapter 4: Large Scale Crop Suitability Assessment Under Future Climate Using the Ecocrop Model: The Case of Six Provinces in Angola's Planalto Region -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Sources of Climate Data -- 4.2.2 Analysis of Crop Suitability -- 4.3 Results and Discussion -- 4.3.1 Projected Climate Changes -- 4.3.2 Effects of Climate Change on Distribution of Crop Suitability -- 4.3.2.1 Cassava -- 4.3.2.2 Maize -- 4.4 Implications for Development -- References -- Chapter 5: Understanding the Role of Soils and Management on Crops in the Face of Climate Uncertainty in Zimbabwe: A Sensitivity Analysis -- 5.1 Introduction -- 5.2 Methods -- 5.2.1 Study Site -- 5.2.2 Climate Data -- 5.2.3 Crop Model Setup and Sensitivity Tests -- 5.3 Results.

Large-scale investment is needed to create climate-smart agriculture (CSA) systems. While many government and development agencies are integrating CSA into their policies, programmes, plans and projects, there is little guidance for operational planning and implementation on ways to be climate-smart. Here we present 'CSA-Plan'. CSA-Plan frames actions needed to design and execute CSA programmes into four components – (i) situation analysis, (ii) targeting and prioritising, (iii) programme design, and (iv) monitoring and evaluation. Each component yields concrete information to operationalise CSA development, separating it from traditional agriculture development. Already, CSA-Plan has shown the capacity to change the discussion around CSA implementation. With iterative co-development, the approaches will become ever more useful, relevant and legitimate to governments, civil society and the private sector alike.

environmental management; air quality control; air pollution; climate change; agriculture; atmospheric protection; soil science; conservation

Climate-smart agriculture (CSA) makes financial sense for businesses. Governments are increasingly holding the private sector responsible for their role in climate change impacts. Extreme weather events are incredibly costly for businesses. This is particularly true in agriculture, which relies heavily on favorable weather conditions. CSA practices and technologies are central to the transformative changes necessary to maintain the stability—and profitability—of the food system in the face of climate change. Where robust information on the benefits, costs, and risks of interventions is missing or incomplete, would-be investors, including donors, governments, businesses, and farmers, remain uninformed of the potentially massive dividends climate-smart investments could offer. This dearth of viable business models ultimately hinders the mainstreaming of productive, climate-resilient, low-emissions agriculture. Robust business-case analyses of CSA could accelerate the scaling of promising, profitable technologies by transparently and rigorously laying out the monetary and nonmonetary values of performance. We use existing data from Evidence for Resilient Agriculture (ERA, previously known as The Compendium) to develop a general framework for establishing the business case for specific farm-level agricultural technologies. The framework focuses on the costs, benefits, and risks of adoption of CSA by smallholder farmers. We illustrate the application of the framework with two case studies in Kenya and Malawi to highlight opportunities, challenges, and lessons learned from building business cases for CSA. These give potential investors the tools to screen and select appropriate technologies and help de-risk investments where data are few and far between.

Climate-smart agriculture (CSA) is increasingly seen as a promising approach to feed the growing world population under climate change. The review explored how institutional perspectives are reflected in the CSA literature. In total, 137 publications were analyzed using institutional analysis framework, of which 55.5% make specific reference to institutional dimensions. While the CSA concept encompasses three pillars (productivity, adaptation, and mitigation), the literature has hardly addressed them in an integrated way. The development status of study sites also seems to influence which pillars are promoted. Mitigation was predominantly addressed in high-income countries, while productivity and adaptation were priorities for middle and low-income countries. Interest in institutional aspects has been gradual in the CSA literature. It has largely focused on knowledge infrastructure, market structure, and hard institutional aspects. There has been less attention to understand whether investments in physical infrastructure and actors' interaction, or how historical, political, and social context may influence the uptake of CSA options. Rethinking the approach to promoting CSA technologies by integrating technology packages and institutional enabling factors can provide potential opportunities for effective scaling of CSA options.

The fire severity of the 2013–2014 fire season within Sudanian ecosystems in Burkina Faso was evaluated from Landsat 8 images using derivatives of the Normalized Burn Ratio algorithm (NBR). The relationship between the image-derived severity and the field observed severity i.e. Composite Burn Index (CBI) was best described by a nonlinear model of the form y = a + b*EXP(CBI *c) (R2 = 0.66). Classification of the image-derived burned area into burn severity classes achieved a classification Kappa accuracy statistic of 0.56. Highly severely burned areas were mapped with the highest accuracy (user's accuracy 77%, producer's accuracy 86%). The severity of the burn varied across phyto-geographical zones, protected status, land cover regimes, and forest management practices. The south Sudanian zone burned with a higher severity (low = 7%, moderate = 16% and high = 13%) than the north Sudanian zone (low = 5%, moderate = 10% and high = 5%). The mean of the highly severely burned areas differed significantly among the forest management practices (P = 0.005). A pair-wise comparison of the severity mean area indicated that the highly burned areas within forests managed for wildlife purposes differed significantly with that of both forests under the joint management (P = 0.006) and those under no management (P = 0.024). Among the management practices, forests jointly managed by the local communities and the government had the highest unburned area and the least highly severely burned areas reflecting the impacts of bottom-up forestry management where the local communities are actively involved in the management.

Governments around the world have agreed to end hunger and food insecurity and to improve global nutrition, largely through changes to agriculture and food systems. However, they are faced with a lot of uncertainty when making policy decisions, since any agricultural changes will influence social and biophysical systems, which could yield either positive or negative nutrition outcomes. We outline a holistic probability modeling approach with Bayesian Network (BN) models for nutritional impacts resulting from agricultural development policy. The approach includes the elicitation of expert knowledge for impact model development, including sensitivity analysis and value of information calculations. It aims at a generalizable methodology that can be applied in a wide range of contexts. To showcase this approach, we develop an impact model of Vision 2040, Uganda's development strategy, which, among other objectives, seeks to transform the country's agricultural landscape from traditional systems to large-scale commercial agriculture. Model results suggest that Vision 2040 is likely to have negative outcomes for the rural livelihoods it intends to support; it may have no appreciable influence on household hunger but, by influencing preferences for and access to quality nutritional foods, may increase the prevalence of micronutrient deficiency. The results highlight the trade-offs that must be negotiated when making decisions regarding agriculture for nutrition, and the capacity of BNs to make these trade-offs explicit. The work illustrates the value of BNs for supporting evidence-based agricultural development decisions.

Climate variability is a major source of risk to smallholder farmers and pastoralists, particularly in dryland regions. A growing body of evidence links climate-related risk to the extent and the persistence of rural poverty in these environments. Stochastic shocks erode smallholder farmers' long-term livelihood potential through loss of productive assets. The resulting uncertainty impedes progress out of poverty by acting as a disincentive to investment in agriculture – by farmers, rural financial services, value chain institutions and governments. We assess evidence published in the last ten years that a set of production technologies and institutional options for managing risk can stabilize production and incomes, protect assets in the face of shocks, enhance uptake of improved technologies and practices, improve farmer welfare, and contribute to poverty reduction in risk-prone smallholder agricultural systems. Production technologies and practices such as stress-adapted crop germplasm, conservation agriculture, and diversified production systems stabilize agricultural production and incomes and, hence, reduce the adverse impacts of climate-related risk under some circumstances. Institutional interventions such as index-based insurance, adaptive safety nets and climate services play a complementary role in enabling farmers to manage risk, overcome risk-related barriers to adoption of improved technologies and practices, and protect their assets against the impacts of extreme climatic events. While some research documents improvements in household welfare indicators, there is limited evidence that the risk-reduction benefits of the interventions reviewed have benefited significant numbers of chronically poor farmers. We discuss the roles that climate-risk management interventions can play in efforts to reduce rural poverty, and the need for further research on identifying and targeting environments and farming populations where improved climate risk management could accelerate efforts to reduce rural poverty.

Governments around the world have agreed to end hunger and food insecurity and to improve global nutrition, largely through changes to agriculture and food systems. However, they are faced with a lot of uncertainty when making policy decisions, since any agricultural changes will influence social and biophysical systems, which could yield either positive or negative nutrition outcomes. We outline a holistic probability modeling approach with Bayesian Network (BN) models for nutritional impacts resulting from agricultural development policy. The approach includes the elicitation of expert knowledge for impact model development, including sensitivity analysis and value of information calculations. It aims at a generalizable methodology that can be applied in a wide range of contexts. To showcase this approach, we develop an impact model of Vision 2040, Uganda's development strategy, which, among other objectives, seeks to transform the country's agricultural landscape from traditional systems to large-scale commercial agriculture. Model results suggest that Vision 2040 is likely to have negative outcomes for the rural livelihoods it intends to support; it may have no appreciable influence on household hunger but, by influencing preferences for and access to quality nutritional foods, may increase the prevalence of micronutrient deficiency. The results highlight the trade-offs that must be negotiated when making decisions regarding agriculture for nutrition, and the capacity of BNs to make these trade-offs explicit. The work illustrates the value of BNs for supporting evidence-based agricultural development decisions.

This paper offers an overview of how climate change is already affecting farmers across eastern and southern Africa, and how it will continue to affect them in the future. The rising temperatures and increased rainfall variability associated with climate change are undermining the livelihoods and food security of Africa's farmers, most of whom work at a subsistence level and also face problems of poverty, inadequate infrastructure and poor governance. To address these problems, governments and development organizations have promoted climate-smart agriculture (CSA). These projects, however, have been constrained by inadequate data and predictions regarding future climate change. In particular, farmers in Africa need better projections of the climate hazards for specific regions. Historical weather data at the local level contains many gaps, and the continuing collection of such data could be much improved. Strengthening the database of observed weather is critical to understanding the changes that have occurred already, to project future changes, and to plan appropriately to address them. Once collected and analyzed, climate data must be communicated in ways that help decision-makers understand climate impacts. Good tools are available—such as ClimateWizard.org and Servir ClimateServ—but practitioners at the local level must have the access and training to use them. Even in places where projections are uncertain, steps can be taken now to implement CSA practices and make farmers more resilient in the face of climate change.

Climate variability is a major source of risk to smallholder farmers and pastoralists, particularly in dryland regions. A growing body of evidence links climate-related risk to the extent and the persistence of rural poverty in these environments. Stochastic shocks erode smallholder farmers' long-term livelihood potential through loss of productive assets. The resulting uncertainty impedes progress out of poverty by acting as a disincentive to investment in agriculture – by farmers, rural financial services, value chain institutions and governments. We assess evidence published in the last ten years that a set of production technologies and institutional options for managing risk can stabilize production and incomes, protect assets in the face of shocks, enhance uptake of improved technologies and practices, improve farmer welfare, and contribute to poverty reduction in risk-prone smallholder agricultural systems. Production technologies and practices such as stress-adapted crop germplasm, conservation agriculture, and diversified production systems stabilize agricultural production and incomes and, hence, reduce the adverse impacts of climate-related risk under some circumstances. Institutional interventions such as index-based insurance and social protection through adaptive safety nets play a complementary role in enabling farmers to manage risk, overcome risk-related barriers to adoption of improved technologies and practices, and protect their assets against the impacts of extreme climatic events. While some research documents improvements in household welfare indicators, there is limited evidence that the risk-reduction benefits of the interventions reviewed have enabled significant numbers of very poor farmers to escape poverty. We discuss the roles that climate-risk management interventions can play in efforts to reduce rural poverty, and the need for further research on identifying and targeting environments and farming populations where improved climate risk management could accelerate efforts to reduce rural poverty.

Governments, donors, and non-governmental organizations are recognizing the need to integrate climate change and agriculture development goals in planning. Incorporating the climate-smart agriculture (CSA) concept can strengthen integration by explicitly emphasizing tradeoffs between investment options. Given the complex relationships between the food security, adaption, and mitigation goals of CSA, decision-support frameworks are needed that integrate stakeholder priorities, draw on the best scientific evidence available, and present complex results simply. Here we present a four phase stakeholder-driven framework for prioritizing CSA investment, designed to be globally applicable, for various users, for use from regional to sub-national levels, and adjustable given data and resource constraints. In the first phase, the scope and next-users of CSA portfolios are clarified, relevant practices are identified, and roughly ten indicators are selected/adapted from a suggested set of 29, based on scientific literature, to evaluate practices against CSA outcomes. A participatory workshop is used in phase 2 to short-list practices based on the results of the indicator evaluation and additional stakeholder criteria. A cost-benefit analysis is then conducted (phase 3) on these priority practices. In phase 4, stakeholders are reconvened to develop CSA investment portfolios that minimize trade-offs, maximize benefits and synergies, and address end user priorities. Barriers to adoption of practices and pathways to overcome these are used to adjust priorities or implementation plans. We present lessons learned from Guatemala and Mali, which demonstrate the scalability of the process, modifications based on institutional contexts, and strategies for refining the framework for use in Africa and Asia in 2015 with users including national agriculture ministries, agriculture development alliances, and bilateral and multilateral donors.

Climate-smart agriculture (CSA) is being widely promoted as a solution for food insecurity and climate change adaptation in food systems of sub-Saharan Africa, while simultaneously reducing the rate of greenhouse gas emissions. Governments throughout Africa are writing policies and programs to promote CSA practices despite uncertainty about the ability for practices to meet the triple CSA objectives of CSA. We conducted a systematic review of 175 peer-reviewed and grey literature studies, to gauge the impact of over seventy potential CSA practices on CSA outcomes in Tanzania and Uganda. Using a total of 6,342 observations, we found that practice impacts were highly context (i.e. farming system and location) specific. Nevertheless, practice effect across CSA outcomes generally agreed in direction. While our results suggest that CSA is indeed possible, lack of mitigation data precludes a more conclusive statement. Furthermore, the inclusion of potential adoption rates changes the potential of CSA practices to achieve benefits at scale. Given the uncertainty and variable impacts of practices across regions and outcomes, it is critical for decision makers to prioritize practices based on their desired outcomes and local context.

The GHG (greenhouse gas) mitigation potential from the agricultural sector is set to increase in coming decades. Much of the agricultural mitigation potential lies in developing countries where systems are dominated by smallholder farmers. There is therefore an opportunity for smallholders not only to gain environmental benefits from carbon friendly practices, but also to receive much needed financial input, either directly from carbon financing, or from development agencies looking to support carbon friendly activities. However, the problem remains of how to quantify carbon gains from mitigation activities carried out by smallholder farmers. Landscape-scale quantification enables farmers to pool resources and expertise, which can put participation in carbon markets and access to other funding sources, within their reach. Therefore, funding agencies, governments and NGOs are increasingly recognizing the benefits of taking a landscape approach to GHG quantification. This paper gives an overview of approaches that have been taken to date for landscape-scale GHG quantification, covering both measurement and modelling and the reliance of one upon the other. The discussion covers ground-based measurement approaches for carbon stock changes in biomass and soils, methods for measuring GHG flux and the application of remote sensing techniques. Computational approaches for estimating carbon stock changes and GHG emissions are discussed, in addition to the use of more complex dynamic ecosystem models. This is followed by an analysis of some of the resources that are available for those wishing to do GHG quantification at the landscape scale in areas dominated by smallholders. This analysis is intended to provide an aid to funding agencies, government agencies, NGOs, academics and others. Information for this section comes from questionnaires distributed to selected resource developers.