We study optimal adaptive grazing management under uncertain rainfall in a discrete-time model. As in each year actual rainfall can be observed during the short rainy season, and grazing management can be adapted accordingly for the growing season, the closed-loop solution of the stochastic...

Rain-index insurance is strongly advocated in many parts of the developing world to help farmers to cope with climatic risk that prevail in (semi-)arid rangelands due to low and highly uncertain rainfall. We present a modeling analysis of how the availability of rain-index insurance affects the...

We study the role of bush encroachment control for a farmer's income and income risk in a stochastic ecological-economic model of grazing management in semi-arid rangelands. In particular, we study debushing as an instrument of risk management that complements the choice of an adaptive grazing...

Commercial cattle farming in Namibia, a prime example of livestock farming in semi-arid rangelands, is subject to a variety of risks, predominant among which is precipitation risk. At the same time it suffers from rangeland degradation that is at least partly due to inadequate management. We...

Studying the sustainable use of ecosystem services under uncertainty requires the consideration of the stochastic dynamics of the system under study, risk and time preferences, risk management strategies and normative views pertaining to sustainability. To gather this information for an...

Bush encroachment is one of the most extensive changes in land cover in Namibia and an urgent problem for cattle farming, rapidly reducing the productivity of the rangeland. Despite the severity of these consequences, a complete and accurate assessment of bush encroached areas is still missing...

We propose a new three-step model-selection framework for size distributions in empirical data. It generalizes a recent frequentist plausibility-of-fit analysis (Step 1) and combines it with a relative ranking based on the Bayesian Akaike Information Criterion (Step 2). We enhance these...