In this paper we analysed the economic payoffs from marine reserves using a stochastic optimal control model, with a jump-diffusion process.The results show that even if the reserve and harvested populations face the same negative shocks, harvesting is optimal, the population is persistent and there is no uncertainty over current stock size, a reserve can increase resource rents. Using fishery data we demonstrate that the payoffs from a reserve, and also optimum reserve size, increase the larger is the magnitude of the negative shock, the greater its frequency and the larger its relative impact on the harvested population.

Using data from what was once one of the world's largest capture fisheries, the northern cod fishery, the economic value of a marine reserve is calculated using a stochastic optimal control model with a jump-diffusion process. The counterfactual analysis shows that with a stochastic environment an optimal-sized marine reserve in this fishery would have prevented the fishery's collapse and generated a triple payoff: raised the resource rent even if harvesting had been 'optimal'; decreased the recovery time for the biomass to return to its former state and smoothed fishers' harvests and resource rents; and lowered the chance of a catastrophic collapse following a negative shock.

Analysing Vietnam's rice export policy and recent export ban in the context of rising food prices, this study combines insights from a regionally‐disaggregated or ‘bottom‐up’ CGE model and a micro‐simulation using household data. Three main conclusions are drawn. First, although there is little impact on GDP, there are substantial distributional impacts across regions and households from different export policies and market conditions. Second, both rural and urban households, including poor households, benefit from free trade, even though domestic rice prices are higher. Finally, under free trade, relatively large gains accrue to rural households, where poverty is most pervasive in Vietnam.

Previous foot‐and‐mouth disease (FMD) outbreaks and simulation‐based analyses suggest substantial payoffs from detecting an incursion early. However, no economic measures for early detection have been analysed in an optimising framework. We investigate the use of bulk milk testing (BMT) for active surveillance against an FMD incursion in Australia. We find that BMT can be justified, but only when the FMD entry probability is sufficiently high or the cost of BMT is low. However, BMT is well suited for post‐outbreak surveillance, to shorten the length of time and size of an epidemic and to facilitate an earlier return to market.

Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modeling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration, and the total area under control (AUC). The study involved two modeling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree, and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration, and the total AUC. The number of infected premises (IPs), number of pending culls, AUC, estimated dissemination ratio, and cattle density around the index herd at days 7, 14, and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the AUC had the highest predictive value (R2 = 0.51–0.9) followed by the number of IPs (R2 = 0.3–0.75) and outbreak duration (R2 = 0.28–0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85–0.98 and negative predictive values of 0.52–0.91, with 79–97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations. Our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions.