Authors
Ken E Giller, Pablo A Tittonell, Mariana C Rufino, Mark T van Wijk, Shamie Zingore, Paul Mapfumo, Samuel Adjei-Nsiah, Mario T Herrero, Regis Chikowo, Marc Corbeels, E Rowe, Frederick P Baijukya, A Mwijage, Jimmy W Smith, E Yeboah, J Burg, M Sanogo, Michael T Misiko, NA de Ridder, S Karanja, C Kaizzi, J Kungu, M Mwale, D Nwaga, C Pacini, Bernard Vanlauwe
Publication date
2011
Publisher
Elsevier
Description
African farming systems are highly heterogeneous: between agroecological and socioeconomic environments, in the wide variability in farmers’ resource endowments and in farm management. This means that single solutions (or ‘silver bullets’) for improving farm productivity do not exist. Yet to date few approaches to understand constraints and explore options for change have tackled the bewildering complexity of African farming systems. In this paper we describe the Nutrient Use in Animal and Cropping systems–Efficiencies and Scales (NUANCES) framework. NUANCES offers a structured approach to unravel and understand the complexity of African farming to identify what we term ‘best-fit’technologies–technologies targeted to specific types of farmers and to specific niches within their farms. The NUANCES framework is not ‘just another computer model’! We combine the tools of systems analysis and experimentation, detailed field observations and surveys, incorporate expert knowledge (local knowledge and results of research), generate databases, and apply simulation models to analyse performance of farms, and the impacts of introducing new technologies. We have analysed and described complexity of farming systems, their external drivers and some of the mechanisms that result in (in) efficient use of scarce resources. Studying sites across sub-Saharan Africa has provided insights in the trajectories of change in farming systems in response to population growth, economic conditions and climate variability (cycles of drier and wetter years) and climate change. In regions where human population is dense and land scarce, farm …
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