Eric M. A. Smaling

Integrated Soil Fertility Management: Operational Definition and Consequences for Implementation and Dissemination

Traditional farming systems in Sub-Saharan Africa depend primarily on mining soil nutrients. The African green revolution aims to intensify agriculture through the dissemination of integrated soil fertility management (ISFM). This paper develops a robust and operational definition of ISFM based on detailed knowledge of African farming systems and their inherent variability and of the optimal use of nutrients. The authors define ISFM as a set of soil fertility management practices that necessarily include the use of fertilizer, organic inputs and improved germplasm, combined with the knowledge on how to adapt these practices to local conditions, aimed at maximizing agronomic use efficiency of the applied nutrients and improving crop productivity. All inputs need to be managed in accordance with sound agronomic principles. The integration of ISFM practices into farming systems is illustrated with the dual-purpose grain legume–maize rotations in the savannas and fertilizer micro-dosing in the Sahel. Finally, the dissemination of ISFM practices is discussed.

Application of Fuzzy Cognitive Mapping in Livelihood Vulnerability Analysis

Feedback mechanisms are important in the analysis of vulnerability and resilience of social-ecological systems, as well as in the analysis of livelihoods, but how to evaluate systems with direct feedbacks has been a great challenge. We applied fuzzy cognitive mapping, a tool that allows analysis of both direct and indirect feedbacks and can be used to explore the vulnerabilities of livelihoods to identified hazards. We studied characteristics and drivers of rural livelihoods in the Great Limpopo Transfrontier Conservation Area in southern Africa to assess the vulnerability of inhabitants to the different hazards they face. The process involved four steps: (1) surveys and interviews to identify the major livelihood types; (2) description of specific livelihood types in a system format using fuzzy cognitive maps (FCMs), a semi-quantitative tool that models systems based on people’s knowledge; (3) linking variables and drivers in FCMs by attaching weights; and (4) defining and applying scenarios to visualize the effects of drought and changing park boundaries on cash and household food security. FCMs successfully gave information concerning the nature (increase or decrease) and magnitude by which a livelihood system changed under different scenarios. However, they did not explain the recovery path in relation to time and pattern (e.g., how long it takes for cattle to return to desired numbers after a drought). Using FCMs revealed that issues of policy, such as changing situations at borders, can strongly aggravate effects of climate change such as drought. FCMs revealed hidden knowledge and gave insights that improved the understanding of the complexity of livelihood systems in a way that is better appreciated by stakeholders.

Adaptive livelihood strategies employed by farmers to close the food gap in semi-arid south eastern Zimbabwe

Rural households in semi-arid areas of southern Africa are confronted with numerous hazards that threaten the household food base. The new wildlife policy of establishing transfrontier conservation areas aims to increase conservation of wildlife resources while improving local livelihoods. This policy can be better appreciated by local people if it embraces knowledge of the adaptive strategies they employ to close the food gap. We assessed how different households responded to the major hazard, drought, in order to gain insight into how these households addressed critical questions of food availability. Informal interviews, key informant interviews and focus group discussions were conducted to determine how households can be disaggregated according to their livelihood patterns and a questionnaire was applied to learn how each group responded to drought. Data were analysed within the three livelihood types that were identified and described at local level as cattle-based, crop-based and non-farm based. We found that factors that aggravated the effects of drought are specific to the different household types and their responses were also specific to that particular household type. Disaggregation of the livelihood types revealed within and between type relations and interactions that are important to people in order to cope. For example, even though cropping is an important activity across the three livelihood types, specifically in cattle and crop-based types, the non-farm type becomes important in restocking inputs after a serious drought through cross-border trading. Livestock and cross-border trading are important coping strategies for all three livelihood types, with the cattle-based trading cattle, the crop-based trading goats and poultry and the non-farm based linking with markets for trading livestock, drugs and restocked inputs for the cattle-based and crop-based groups. These linkages among livelihood types are important factors in reducing vulnerability to change that only become visible as a result of this disaggregation. We conclude that additional policies of enhancing the resilience of local food systems by stimulating across-border livestock trading and formal market set-up and enhancing systems of adaptation that are already in existence (e.g., crop production in the Banyeni) can add value to the success of transfrontier conservation areas in southern Africa.

ANALYSIS OF FACTORS THAT DETERMINE TEA PRODUCTIVITY IN NORTHEASTERN INDIA: A COMBINED STATISTICAL AND MODELLING APPROACH

SUMMARY This study analyses the factors affecting tea productivity in Northeast India using a combined statistical and modelling approach. The effects of a number of genotypic, environmental and management factors on tea yield are quantified and modelled, using a three-year (2007–2009) field trial in Assam, Northeast India. Simulations of the potential tea yield are obtained using the Cranfield University Plantation Productivity Analysis (CUPPA) Tea model to find out how well the predicted and observed values for tea production match. This combined approach shows that plantation age has a significant negative (R 2 = 0.77) effect on tea yield. Monthly rainfall had a significant positive effect on monthly yields (R 2 = 0.43). Rainfall was more strongly associated with tea yield when rainfall in month x was related to the tea yield in month x +1( R 2 = 0.49). When repeating the analysis for a hypothetical situation that the fields are fully planted, the correlation between monthly rainfall in month x and tea yield for month x +1i ncreases (R2 = 0.58). Adjusted yields show a higher correlation than actual yields. The results obtained show a close correspondence between predicted and observed yields, indicating that the model could be used on contrasting soil types, genotypes and also on daily, weekly and monthly weather data. It can be further calibrated and validated for Northeast Indian conditions if more required input parameters are collected in a series of plantations. Tea research might benefit from developing new versions of the CUPPA Tea model for the major clonal tea cultivars, with a more flexible module for fertiliser application as is currently the case.