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 (MICCA) 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. MICCA operates at the nexus between climate regulation and livelihood advancement. In October 2011, MICCA partners established two pilot projects in East Africa. As there are many unknowns about what farming approaches are best for reaching CSA's multiple objectives, the underlying premise of the MICCA pilot projects is that strong linkages between science and development are essential to the expansion of CSA in developing countries. Acting on this premise, the partners implementing the MICCA pilot projects have worked together to co-locate multidisciplinary and multiscale research to generate sound decision-relevant information for farmers, development organizations, communities and policy makers. With the goal of improving site-specific and socially appropriate interventions, the implementing partners have applied rigorous experimental approaches and advanced analytical tools to test methods and generate data that reduce the uncertainty about the social and environmental impacts of field and farm management practices, such as conservation agriculture (CA) and zero-grazing dairy production. The layers of quantitative and qualitative information serve to evaluate and generate CSA innovations and identify the constraints to their application. This information was created with the specific purpose of supporting the upscaling of prioritized and targeted innovations. In reviewing the broad research findings, it must be taken into account that because of the resources allocated for the pilot projects this was a study of limited duration (two-years) and scope. The following list presents the main site-specific findings that have been garnered from the research efforts described in this publication and that are pertinent to CSA practices: In cereal-based cropping systems of Kolero in the United Republic of Tanzania, leguminous trees and mineral nitrogen (N) fertilizer can sustainably intensify production by increasing productivity under 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.

Key words