Science to support climate-smart agricultural development: concepts and results from the MICCA pilot projects in East Africa.


This document reports on the concepts driving the scientific activities of FAO's Mitigation of Climate Change in Agriculture Programme's pilot projects in East Africa. It provides results from the research, briefly describes the analytical approaches used and concludes with key messages relevant to discussions on climate-smart agriculture (CSA). CSA links three critical issues that must be addressed to ensure a sustainable future. One, it supports society's potential to sustainably increase agricultural productivity to support the rapidly growing population. Two, it builds the resilience of food systems and the adaptive capacity of farmers to climate change. Three, it aims to reduce the impact of food, fuel and fiber production on the climate system and combats climate change when appropriate. The following list presents the main findings of the research that are pertinent to CSA practices: * In cereal-based cropping systems of Kolero in the United Republic of Tanzania, leguminous trees and mineral nitrogen fertilizer can sustainably intensify production by increasing productivity under conservation agriculture (CA) without significantly increasing GHG emissions. * In integrated crop-livestock systems of Kaptumo, Kenya, partial GHG budgets suggest that smallholder dairy production can be relatively climate-friendly when combined with agroforestry and when pasture is managed wisely. * The probabilistic model applied at both sites indicated that yield improvements anticipated with CA adoption were unlikely to be achieved given the social and ecological contexts of the sites. Using such probabilistic approaches may be a rapid way to target CSA interventions. The scientific approach that was followed permits a few general messages and suggestions for future efforts on 'research for development' or 'research to inform policy' that are aimed at quantifying the parameters of potential CSA practices and their implications at nested scales. * The data precision and variability of a wide range of factors, including farming systems, inputs, farming configurations, the timing of farm activities, ecosystem characteristics, weather and socioeconomic conditions, characterizing the emissions associated with different practices that are assumed to be climate-smart will continue to present challenges. * In estimating emissions for integrated farming systems, it may be important to implement whole-farm measurements or estimates, as quantifying the impacts of only one part of the farming system may miss critical emission hotspots or mitigation leverage points, or overlook options that simultaneously address adaptation and food security. For example, to identify mitigation leverage points, a probabilistic analysis of alternative farm systems could help to focus measurements on parts of the system where there are the greatest uncertainties and that have a large impact on total emissions. * Research and development stand to benefit from greater integration. Development practitioners, working with local communities, can ensure that research is demand driven and grounded in reality. Research carried out with the active participation of farmers can validate the practices being promoted through development initiatives and estimate the potential impacts of different activities. However, integrating research and development presents some challenges. For example, the time period required to guarantee robust research results may exceed the time span allocated for development programmes. Because of these mismatched timeframes, development programmes and policies may be based on limited empirical evidence. * Because CSA is meant to have impacts far beyond the farm gate, and is influenced by policy and enabling environment, so the continued integration of research, development and policy is essential. National policies and action plans geared toward enhancing climate change adaptation and mitigation from agriculture need to be continually shaped by the evidence associated with practices that are most likely to succeed, and the feasibility of their implementation and scaling up must be based on field experience and lessons learned.

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