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Agricultural systems in Sub Saharan Africa (SSA) are highly under pressure as they are required to meet the demand of food without further damage the natural resources. At the same time, smallholder farmers that have to deal with space and resource constraints are asked to increase their agricultural productivity in circumstances where often inputs are difficult or expensive to obtain.
Many programs were started in the last decade in SSA with the aim of promoting new agricultural approaches and technologies for smallholder farmers and Sustainable Intensification (SI) is believed to be among the possible solution for agricultural development in such countries.
This thesis focused on assessing the farm level performances of different farmer groups (mother, baby and control farms) that were included in a Participatory Action Research (PAR) from Africa RISING project in Malawi. The assessment of performances focused on 10 indicators and included the 5 domains identified by the Sustainable Intensification Assessment Framework (SIAF) : productivity, environmental, economic, human and social.
The main objective was to identify significant differences in performances by comparing the results of such indicators by Extension Planning Areas (EPAs) and by farmer group. The EPAs considered for this study were characterized by different bio-physical conditions that cause different productivity potentials in the experimented areas. These differences were at the base of the hypothesis that EPAs were expected to perform differently, especially when considering the productivity indicators. The results showed that the EPA of Linthipe which was identified as the one with the highest potential outperformed other EPAs for maize yields (5132 kg/ha), crop revenues (711 kMWK/year) and the labor (830,4 hours/h/year). The Pareto ranking also confirmed that this EPA counted 3 out of the 7 farmers that found the best compromise among the 10 indicators that were considered for this analysis.
On the other hand, the second hypothesis that supposed that mother farmers were expected to outperform the other farmer group due to the higher interaction with researchers and the higher implementation of SI technologies was not confirmed. Although this group of farmers resulted in having a higher implementation/awareness ratio, no indicators showed higher statistical difference when compared to other farmer groups.
This analysis also highlighted what previous studies already acknowledged: smallholder farmers are highly heterogeneous in their farm characteristics and endowments and if such variables are considered together with the local bio-physical conditions then it becomes clear that such technologies must be adapted to specific agroecological zones by supporting farmers in their decision process.
