The ability of communities in the drylands of Africa to adapt to or manage shocks and stresses while maintaining trajectories towards sustainable development, or “resilience”, fea- tures prominently in the objectives of donors and development partners. This concept is, however, poorly understood, particularly when it comes to measuring the impact of invest- ments on resilience. Most de nitions of resilience in development scenarios hinge upon the response of social, ecological and economic systems to shocks and stressors. It is, however, extremely dicult to quantify this response, as it is impossible to observe the full range of possible disturbances, hence assessments of system resilience normally fall short of providing comprehensive evaluations. In this paper, we discuss the use of system models to overcome this constraint in the chroni- cally food insecure region of the Horn of Africa. As this method allows a simulation of system responses to the full range of plausible disturbances, it then becomes possible to produce long-term system performance projections that convey insights into system resilience. Com- plex systems are, however, dicult to model, and there are always substantial uncertainties about model parameters, model structure and consequently model outputs. We therefore propose a modeling approach that is adequate for dealing with multiple uncertainties and that produces assessments of system resilience that can be used in planning for development activities aimed at enhancing resilience. Our method draws on Applied Information Economics, a well-established approach to sup- porting business decisions under multiple uncertainties. Rather than aiming to specify system dynamics with high precision – the level of precision rarely justi ed by available information – this approach emphasizes the adequate framing of uncertainties with respect to data concern- ing input parameters, system processes and potentially even resilience de nitions. Estimates of con dence intervals for input variables are obtained from historic and current datasets, as well as solicited from calibrated experts. Probability distributions of all input variables are used for constructing high-level system models that are then subjected to Monte Carlo analysis. Outputs from this procedure are analyzed with data mining techniques to deter- mine which uncertainties constrain our ability to evaluate resilience and are thus priorities for further research
Tag: decision
Factors influencing crop rotation strategies on organic farms with different time periods since conversion to organic production
Productive crop rotations are central to the success of organic production systems. The selection and sequence of crops are determined by a combination of agronomic and economic factors as well as the principles and standards of organic farming. Semi-structured interviews were conducted with sixteen organic farmers in Central-east Sweden to explore the factors that influence the design of crop rotations and the trade-offs between these factors, taking into account the length of time since conversion to organic production. We discerned three crop rotation strategies: strict, flexible and liberal, based on how crop(s) are repeated over time. A major trade-off for arable farmers was between perennial leys to provide nitrogen and control weeds, and the use of more inputs such as purchased nutrients and mechanical weed control to allow continuous cereal production. Critical considerations for livestock farmers were the length of ley for feed production and weed control, cost of re-seeding leys and decisions about whether to grow crops to feed animals or cereals to sell. Farmers practicing organic for a long time (more than 10 years) often had flexible rotations to adapt to changing conditions, but they generally included leys and a selection of annual crops in line with the principles of crop rotation and organic farming. Recently converted organic farmers usually concentrated on controlling weeds and producing sufficient livestock feed by following strict crop rotations. We conclude that farm type and experience strongly influenced rotation strategies and that weed management and market prices were the most important influences. © 2016 Informa UK Limited, trading as Taylor & Francis Group.