a b s t r a c tAfrican 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' technologiestechnologies 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 typologies have proven useful to target technologies between farmers of different production objectives and resource endowment (notably in terms of land, labour and capacity for investment). In such regions we could categorise types of fields on the basis of their responsiveness to soil improving technologies along soil fertility gradients, relying on local indicators to differentiate those that may be managed through 'maintenance fertilization' from fields that are highly-responsive to fertilizers and fields that require rehabilitation before yields can improved. Where human population pressure on the land is less intense, farm and field types are harder to discern, without clear patterns. Nutrient cycling through livestock is in principle not efficient for increasing food production due to increased nutrient losses, but is attractive for farmers due to the multiple functions of livestock. We identified trade-offs between income generation, soil conservation and community agreements through optimising concurrent objectives at farm and village levels. These examples show that future analyses must focus at farm and farming system level and not at the level of individual fields to achieve appropriate targeting of technologies -both between locations and between farms at any given location. The approach for integrated assessment described here can be used ex ante to explore the potential of bes...

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Once again, agricultural mechanization is top on the policy, research, and development agendas in sub-Saharan Africa. However, whether labor is limiting in this region-and mechanization is in demand-remains debated. The hypothesis of this study is that labor is a major limiting factor to the productivity of most farming systems in Africa. We used farm-level data (including detailed labor data) from eight sites dominated by smallholder agriculture and spanning four countries in Eastern and Southern Africa, and analyzed this unique dataset using multivariate methods (generalized linear models, boundary line analysis, and binary classification and regression trees). Labor and/or other sources of farm power (draught power or tractor power) were found to limit land productivity in all study sites. We also found that the overall contribution of female labor to farming was much lower than commonly stated-between 7 and 35%-and that the labor intensity experienced by women in agriculture was dependent to a large degree on men's tasks. Our results reveal a much higher demand for mechanization than previously found by macroeconomic analyses, and point to a problem of access rather than demand. Our results also add to recent evidence debunking the persistent myth that women provide the bulk of the farming labor, and demonstrate that reducing labor intensity experienced by women in farming depends to a large degree upon understanding labor intensity experienced by men, rather than poorly founded generalizations about how women are overworked. This is the first time farm-level data containing detailed labor assessment and spanning four countries are used to assess mechanization demand in Africa. This paper also plays a pioneering role in debunking a number of myths related to labor in African smallholder agriculture, with implications for the mechanization agenda of the region.

SUMMARYInnovation Platforms (IPs) have become a popular vehicle in agricultural research for development (AR4D). The IP promise is that integrating scientific and local knowledge results in innovations that can have impact at scale. Many studies have uncovered how IPs work in various countries, value chains and themes. The conclusion is clear: IPs generate enthusiasm and can bring together stakeholders to effectively address specific problems and achieve 'local' impact. However, few studies focus on 'mature' IPs and whether or not these achieve impact at a 'higher' scale: address systems trade-offs to guide decision making, focus on integration of multiple commodities, reach a large number of beneficiaries and learn from their failures. This study evaluates the impact of mature IPs in AR4D by analysing the success factors of eight case studies across three continents. Although we found pockets of IP success and impact, these were rarely achieved at scale. We therefore critically question the use of IPs as a technology dissemination and scaling mechanism in AR4D programs that aim to benefit the livelihoods of many farmers in developing countries. Nevertheless, we do find that IPs can fulfil an important role in AR4D. If the IP processes are truly demand-driven, participatory and based on collective investment and action, they have the ability to bring together committed stakeholders, and result in innovations that are technically sound, locally adapted, economically feasible for farmers, and socially, culturally and politically acceptable. Several of our cases show that if these IPs are firmly embedded in other public and private extension mechanisms and networks, they can allow the technologies or other types of innovations to scale out beyond the original IP scope, geographical focus or target audience. We see a need for more rigorous, accurate and continuous measurement of IP performance which can contribute to adaptive management of IPs, better understanding of 'what works' in terms of process design and facilitation, as well as to cost-benefit analysis of IPs as compared to other approaches that aim to contribute to agricultural development.

Although the development of improved seeds has witnessed significant advances over the last decades, the adoption of improved seeds and varieties by smallholder farmers is variable. This suggests that research methods for studying farmers’ seed demand are not yielding information that reflects the real-life decisions and behaviours of farmers in the choice and acquisition of their seeds. We suggest that research methods for analysing farmers’ seed demand shape seed availability. This is supported by the theory of social life of methods. We argue that access to and attractiveness of seed are highly context-specific for a farmer, for example, influenced by his/her social position, the role of the crop or variety in the farming system, the linkage to the market, agro-ecological conditions, and that context is highly variable. We also argue that many of our research methods are weak on capturing real-life context and provide fragmented snapshot-nature understanding and biases of farmers preferences and needs for seeds. We call for more integrated understanding of seed systems as a whole and a more holistic methodological research approach that better captures the variable real-life context of farmers while providing the metrics that are needed by seed actors and policymakers to enable informed decisions.