Reinhardt, J., S. Liersch, M. A. Abdeladhim, M. Diallo, C. Dickens, S. Fournet, F. F. Hattermann, C. Kabaseke, M. Muhumuza, M. L. Mul, T. Pilz, I. M. Otto, and A. Walz. 2018. Systematic evaluation of scenario assessments supporting sustainable integrated natural resources management: evidence from four case studies in Africa. Ecology and Society 23(1):5. https://doi.org/10.5751/ES-09728-230105

Water harvesting techniques (WHTs) are important climate change adaptation measures to better manage rainwater for domestic and agricultural purposes, but which WHT to plan where is subject to sustainability considerations. Moreover, suitability of different WHTs varies from one location to another, depending on physical and socio-economic conditions. This study aimed to identify suitable sites for WHTs taking into account stakeholders’ sustainability criteria. In a participatory assessment framework, Geographic Information Systems and the “Simple Multi-Attribute Rating Technique” were combined to generate suitability maps and to guide sustainable WHTs investments. Steps included the calculation of a sustainability index for a set of traditional and newly introduced WHTs from the perspective of two stakeholder groups, farmers and decision-makers, and its integration with layers of biophysical constraints. An application of the framework in the Oum Zessar watershed, southeast Tunisia, shows that traditional techniques are the most suitable and sustainable for farmers and fall within the highly suitable class in 76.4% of the total area, while decision-makers prefer innovative techniques that are highly suitable in 80.4% of the watershed. The framework offers a scalable transparent process for knowledge integration in support of WHT investment decisions that can be adapted to other dryland areas.

This paper analyzes the complex relationships of factors influencing residue biomass management in cereal–sheep production systems in semi-arid areas of Tunisia. The Bayesian belief network (BBN) methodology was applied to identify factors enabling the better management of crop residue (CR) at the farm level. Data were collected from 152 farms located in the governorate of Siliana in north-west Tunisia. After designing the complex interactions between different variables that have an influence on the allocation of CR, BBN was also applied as a predictive model by inserting evidence conditional probabilities on the quantity of CR left on the soil and simulating the incurrent changes in the probability state of the remaining network variables. The results show that around 70% of farmers in our sample retain an overall quantity of CR lower than 200 kg/ha. The share of livestock income, livestock herds, cost of livestock feed, and off-farm income are all factors that have a strong influence on residue biomass management.