Climate Smart Agriculture in Tanzania main messages

What is and what is not climate-smart agriculture (CSA)? That existential question sparks debate, complicates implementation and fractures the development community. Many institutions are developing 4-10 page ‘technical briefs’ describing the ‘climate-smartness’ of interventions (ie, the impact of interventions on indicators of productivity, resilience and mitigation) to answer this question. Oftentimes, technical briefs, however, are data-lite increasing potential for biased assessments. CSA X-Rays were designed to provide a counter point to this: to be pithy and detailed analysis of what science and scientists tell us about the ‘climate-smartness’ of CSA interventions or CSA in a specific location. In short, this pilot project intended to innovate on the ‘CSA technical brief’. This CSA-Xray examine the climate-smartness of interventions in Tanzania

Collecting development data with mobile phones: Key considerations from a review of the evidence

Growth in mobile phone access and ownership presents an opportunity to collect more data, more frequently, from more people, and for less money. There are multiple ways to collect data with mobile phones (SMS, voice calls, etc.), each with particular strengths and weaknesses. n The best mode of data collection depends on the characteristics of the target population (e.g. literacy, network access, acceptability of using mobile phones, etc.) and of the data to be collected (e.g. quantitative vs. qualitative, number of questions, sensitivity of information, etc)

What is the scientific basis for climate-smart agriculture?

Climate-smart agriculture (CSA) is a systematic approach to agricultural development. It intends to address climate change and food security challenges simultaneously across levels, from field management to national policy, with goals to 1) improve food security and agricultural productivity, 2) increase the resilience of farming systems to climate change, and 3) mitigate greenhouse gas (GHG) emissions or sequester carbon. After the introduction of the CSA concept in 2010, development organizations, national governments, and donors have quickly adopted a “climate-smart” agenda

‘CSA-Plan’: strategies to put Climate-Smart Agriculture (CSA) into practice

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

‘CSA Plan’: A guide to scaling climate-smart agriculture - Concepts and lessons from designing CSA programs and policies in sub-Saharan Africa

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 programs into four components—1) situation analysis, 2) targeting and prioritizing, 3) program design, and 4) monitoring and evaluation. Each component yields concrete information to operationalize 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 only more useful, relevant and legitimate to governments, civil society and the private sector alike

Democratizing data for agricultural transformation in Africa

Presentation at the GLF Bonn, June 4 202

Bundles of Joy? Using the ERA database to explore the outcomes of CSA practice interactions

Farmers rarely apply CSA technologies in isolation and there is a strong demand for evidence about which bundles of practices work together to enhance outcome performance. The ERA database brings together thousands of African CSA studies giving us unprecedented power to explore trade-offs when bundling a diverse suite of practices together across a diverse range of outcome indicators. We have developed a range of analytical algorithms and plotting functions to assess performance of technology bundles to be integrated as apps on the ERA website

Evidence- and risk-based planning for food security under climate change

Planning robust climate-smart development programs can be done today with existing information. We propose a risk-household-option modeling approach to address household food security under climate change in Africa. Through a case study in Niger, we demonstrate that prioritizing CSA is possible by taking into account livelihood status, risks, and potential effects of CSA practices

Predicting the Performance of CSA Technologies under current and future conditions

Predicting the Performance of CSA Technologies under current and future conditions. Presentation at the Global Science Conference for Climate-Smart Agriculture 8-11 October 2019. Bali, Indonesia

Evidence- and risk-based planning for food security under climate change: Results of a modeling approach for climate-smart agriculture programming

Planning robust climate-smart development programs can be done today with existing information. We propose a risk-household-option modeling approach to address household food security under climate change in Africa. hrough a case study in Niger, we demonstrate that prioritizing CSA is possible by taking into account livelihood status, risks, and potential effects of CSA practices