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عنوان فارسی:
تنوع زیستی کشاورزی برای مدیریت آفات: یک رویکرد شبیه سازی یکپارچه اقتصادی زیستی و یادگیری ماشینی
عنوان انگلیسی:
Agrobiodiversity for Pest Management: An Integrated Bioeconomic Simulation and Machine Learning Approach
Abstract
A pressing challenge of modern agriculture is to develop means of decreasing the negative impacts of pesticides while maintaining low pest pressure and high crop yield. Certain crop varieties, especially wild relatives of domesticated crops, provide pest regulation ecosystem services through chemical defense mechanisms. Benefits from these ecosystem service can be realized by intercropping cash crops with repellent wild varieties to reduce pest pressure. An opportunity cost exists, however, which consists of lower yield and market value. Such is the case of heirloom apple varieties that are more resistant to the codling moth but have a lower market value compared to commercial apples such as Red Delicious and Gala. In this thesis, I first develop a model to identify the bioeconomically optimal intercropping level of commercial and wild varieties with the purpose of pest management in the specific case of the codling moth. Second, I develop a model that uses a machine learning technique to determine pesticide application policies for the multi-variety orchard, where the solution is robust to model and data uncertainty.
Model 1 is a tree-level, spatially-explicit, bioeconomic simulation model. In the baseline case, we find that the bioeconomically optimal variety mix consists of 20% cider variety and 80% commercial variety. We analyze the sensitivity of the optimal mix to the market price difference of the two apple varieties and find that the optimal proportion of cider decreases linearly and that 100% commercial variety is optimal if the price difference is greater than $0.3/lb. We consider eight different spatial configurations for the intercropping, in addition to the baseline random spatial intercropping and find that the diagonal configuration yields the highest net present value and requires the lowest amount of cider intercropping (4%). Random spatial intercropping, in contrast, ranks seventh and has the second-highest optimal proportion of cider (30%). We use the certainty equivalent measure to determine how the optimal mix changes for a grower who has a moderate level of risk aversion, where production risk is driven by the effect of temperature on codling moth infestation over the years. The optimal cider variety percentage for a moderately risk-averse grower increases to 38% compared to the baseline case of 20% of a risk-neutral grower. We also document the risk-reducing effect of apple agrobiodiversity by characterizing how the risk premium decreases with increasing proportions of cider.
In Model 2, we determine 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. We use historical degree-day (DD) data and associated established DD threshold-based spray recommendations to add pesticide application features to our Model 1 and then use it as a simulator to generate data on infestation and damage level over time. We then use Reinforcement Learning (RL) to find the robust optimal pesticide application threshold around 1,000 insects over the entire orchard. The model solution shows a greater degree of sensitivity to pesticide application costs compared to the pest growth rate, indicating the importance of addressing the data uncertainty of these parameters.
عنوان فارسی:
عوامل مؤثر بر پذیرش و شدت پذیرش فناوری‌های کشاورزی دقیق در داکوتای جنوبی
عنوان انگلیسی:
Factors Influencing Adoption and Adoption Intensity of Precision Agriculture Technologies in South Dakota
Abstract
Precision agriculture can play an important role in preserving the environment and improving the economic conditions of agricultural producers. This thesis analyzes the determinants of adoption and adoption intensity of precision agriculture technologies in South Dakota. This analysis uses survey data collected from 199 farms distributed over 28 different counties in South Dakota, accounting for approximately 500,000 acres of tillable agricultural land, to (1) discover the factors impacting precision technology adoption; (2) compare and contrast several characteristics among adopters and non-adopters; and (3) develop probit, count, and negative binomial models to determine the significance of explanatory variables impacting precision technology adoption and adoption intensity.
T-test results of the mean age of participants, Conservation Stewardship Program (CSP) enrollment, service center access, reliance on farm dealers for information, and computer usage for accounting purposes were statistically different between adopters and non-adopters of precision agriculture technologies. Probit model results indicate that age, spousal non-farm income, and service/repair access negatively influenced the decision to adopt, while the number of cropland acres, reliance on information from farm dealers, and use of computers for accounting activities positively impacted the decision to adopt. Results from the count model suggest that age, livestock owner status, spousal non-farm income, and service/repair access negatively influence the intensity of precision agriculture technologies adoption, while CSP enrollment, crop-land acreage, reliance on information from farm dealers, and using computers for accounting activities positively influenced the intensity of precision agriculture technologies adoption. Results of the negative binomial model indicate that only lack of access to service/repair facilities negatively affected the adoption intensity, and the adoption of different bundles of the six most popular precision technologies (auto-steer, variable rate systems, automatic section control/shut-offs, prescription field maps, yield monitors, and GPS guidance systems), while CSP enrollment, reliance on farm dealers as an information source, and using computers for accounting activities positively influenced precision technologies adoption intensity.
The results of this study may help policy makers understand how agricultural producers perceive precision agriculture technologies in general, and the degree to which these technologies may be used to enhance productivity, profitability, and environmental quality. The result also provides useful insights on key determinants of the adoption of precision agriculture technologies. The results may further help farm dealers and repair service providers as they consider marketing precision agriculture technologies to agricultural producers. Precision agriculture technologies manufacturers and sellers can use these results to identify the demand of their product and services in the future.