The aim of this report is to provide a detailed review of documented social learning processes for climate changeand natural resource managementas described in peer-reviewed literature. Particular focus is on identifying (1) lessons and principles, (2) tools and approaches, (3) evaluation of social learning, as well as (4) concrete examples of impacts that social learning has contributed to.
This facilitation guide was developed to support the training of scientists who are members of the CCAFS Working Group on impact pathways and M&E for results-based management. The group attended a highly participatory introductory training from 1-5th April 2014 in Segovia, Spain. The objectives of the workshop were: 1. To introduce working group members to outcome thinking; 2. To present elements of the CCAFS theory of change (TOC), impact pathway (IP) and monitoring and evaluation (M&E) framework; 3.
This evaluation report discusses the findings, conclusions and recommendations on the project “Strengthening Community Resilience to Change: Combining Local Innovative Capacity with Scientific Research (CLIC-SR)” under the umbrella of the network Promoting Local Innovation in ecologically oriented agriculture and NRM (PROLINNOVA). This project was implemented in four Eastern African countries, namely Ethiopia, Kenya, Tanzania and Uganda.
The CLIC–SR project started on 1 September 2012, ended on 31 August 2016, and was implemented in four countries: Ethiopia, Kenya, Tanzania and Uganda. This report covers the work done in the final project period: January–August 2016. The report adds a chapter that reviews the achievements of the project over the full project cycle. The report from an independent external evaluation was a major source of information for this final chapter.
This paper outlines key areas of intervention that are identified as the core of FAO's strategy on strengthening Agricultural Innovation Systems (AIS) across multiple areas of work (e.g. research and extension, agroecology, biotechnology, green jobs, resourcing etc.) for achieving sustainable rural development.
Agricultural water management is a vital practice in ensuring reduction, and environmental protection. After decades of successfully expanding irrigation and improving productivity, farmers and managers face an emerging crisis in the form of poorly performing irrigation schemes, slow modernization, declining investment, constrained water availability, and environmental degradation. More and better investments in agricultural water are needed.
The sector review includes seven chapters and one annex. This first chapter is an overview of agriculture, irrigation and the purpose and content of this report. The second chapter provides a review of the Bank s own strategy and priorities for irrigation and drainage within its portfolio of investments, from the time of its 2004 Strategy until the present. It also includes a short summary of key lessons learned in this sector.
The Climate Change and Social Learning (CCSL) Initiative is a cross-organisation group working to build a body of evidence on how social learning methodologies and approaches contribute towards development targets. Together with a select number of participating initiatives from a variety of organisations, we are working towards establishing a common monitoring and evaluation (M&E) framework for new projects and programmes using a social learning-oriented approach.
Understanding spatial patterns of land use and land cover is essential for studies addressing biodiversity, climate change and environmental modeling as well as for the design and monitoring of land use policies. The aim of this study was to create a detailed map of land use land cover of the deforested areas of the Brazilian Legal Amazon up to 2008.
One of the most important challenges for the researchers in the 21st Century is related to global heating and climate change that can have as consequence the intensification of natural hazards. Another problem of changes in the Earth's climate is its impact in the agriculture production. In this scenario, application of statistical models as well as development of new methods become very important to aid in the analyses of climate from ground-based stations and outputs of forecasting models. Additionally, remote sensing images have been used to improve the monitoring of crop yields.