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Pbkp_rediction of white shrimp harvest: the case of a small shrimp farm in Tenguel, Guayaquil-Ecuador

Abstract:

Shrimp sector in Ecuador is nowadays one of the fastest-growing non-oil sectors towards the international market. In despite of this growth, to our knowledge most of the little producers of shrimps in Ecuador take important operational decisions based upon empirical knowledge, without considering historical data nor any scientific tool. In this work we implement and compare state-of-the-art statistical learning techniques for the pbkp_rediction of shrimp harvest (in pounds) for a little shrimp farm located in Tenguel, Guayaquil-Ecuador. For this study we used historical information collected by the farm biologist. The data was organized and put into a digital format by the authors. Data from n=35 past harvests, corresponding to 7 cycles of production, were used to train the models. We then made pbkp_redictions of shrimp harvest for the next two production cycles. We compare Multiple Linear Regression by means of ordinary least squares, CART Regression Tree, Random Forests, Multivariate Adaptive Regression Splines (MARS) and Support Vector Machines (SVM). In our analysis, MARS with no interaction terms allowed, Linear Regression with best subset variable selection and SVM with linear Kernel gave the lowest pbkp_rediction error estimate by Cross Validation. Their good pbkp_redictive performance was confirmed with good pbkp_redictions on the next two production cycles. The use of statistical techniques can be of great help to improve pbkp_redictions and therefore operational processes of small shrimp farms.