3.2 Aplicación de la Guía de Comparación
3.2.3 Búsqueda de soluciones alternativas
In this thesis, we developed two models to evaluate how agrobiodiversity and threshold- based pest management can be used to reduce pest pressure and risk in an apple orchard infested by codling moths. Model 1 represents a multi-variety apple orchard consisting of hard cider variety and commercial variety. Due to the impacts of volatiles and phenolics, the hard cider variety is naturally more resistant to pest infestation but has a lower market value compared to the commercial variety. It is a tree-level, spatially explicit, bioeconomic simulation model
represented using cellular automata. It determines the optimal mix of the two apple varieties over 25 apple-growing seasons, for different spatial configurations of the intercropping. We had a random spatial configuration in our baseline model, and we evaluated the ecological and economic trade-off which exists for our management practice. The ecological and economic objective was represented using the maximization of half-life and ENPVs, respectively. The bio- economically optimal proportion of cider was 20%. We analyzed the sensitivity of our policy variable, the proportion of cider variety in the orchard, to the market price difference of the two apple varieties (i.e., the opportunity cost of agrobiodiversity in the case of cider varieties). The optimal proportion of cider decreases at a decreasing rate as the price difference increases. To determine the sensitivity of the optimal proportion of cider to different spatial configurations, we compared the optimal half-life and ENPV of a random strategy against that of well-known spatial configurations suggested in the landscape ecology literature. The optimal policy variable value is sensitive to different spatial configurations and out of the nine spatial configurations, the diagonal configuration maximizes the ENPVs while the random configuration ranked seventh in terms of ENPV. Finally, we used the certainty equivalent measure to determine the optimal mix for a grower who has a moderate level of risk aversion to production risk posed by changing
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orchard temperature, and therefore infestations, over the years. We found that the optimal cider variety increases to 38% compared to the baseline case of 20% of a risk-neutral grower. We also documented the risk-reducing effect of apple agrobiodiversity by characterizing how the risk premium decreases with increasing proportions of cider.
Using Model 2, we determined the robust optimal pesticide application threshold, given an infested multi-variety orchard consisting of the optimal proportion of cider varieties, arranged in a random spatial configuration. The robustness of the solution ensures that it does not fail in the presence of data uncertainty. We built on our Model 1, by using historical degree-day data to add pesticide application features to our cellular automata and then used it as a simulator to generate data on infestation and damage level over time. We used Bayesian inference on our data set to determine the intrinsic pest growth rate and we modeled our problem as an MDP. We used RL technique on our MDP to find the optimal pesticide application threshold for both an average-case scenario and worst-case scenario. We found that the robust optimal threshold (worst-case optimal) is around 1,000 insects whereas the average-case optimal is around 250. Having obtained a different optimal policy for each case verifies the notion that robustness matters for the economic bottom line of a grower and a robust policy acts as high-confidence lower bound in terms of return. In order to identify the key parameters whose uncertainty impacts the model solution the most, we carried out a sensitivity analysis of the model solution to the uncertainty of the pest growth rate and the pesticide application cost. The model solution was sensitive to both, but the solution had a greater degree of sensitivity to pesticide application cost compared to the pest growth rate. This result indicated the importance of addressing the uncertainty of these parameters for such problem settings with a higher priority for addressing cost uncertainty.
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