Predicción del nivel de cosecha de camarón blanco: El caso de una peque\~na camaronera en la parroquia Tenguel del cantón Guayaquil, Ecuador; Prediction of white shrimp harvest: The case of a small shrimp farm in Tenguel, Guayaquil-Ecuador - Holger Cevallos-Valdiviezo; Ariana Rodríguez-Cristiansen; Patricia Valdiviezo-Valenzuela; Danny Arévalo-Avecillas; Carmen Padilla-Lozano (Revista de Métodos Cuantitativos para la Economía y la Empresa, 2020)
– 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 prediction 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 predictions 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 prediction error estimate by Cross Validation. Their good predictive performance was confirmed with good predictions on the next two production cycles. The use of statistical techniques can be of great help to improve predictions and therefore operational processes of small shrimp farms.
Keywords: C53; M11; Q22; 330; prediction; harvest; white shrimp Litopenaeus vannamei; statisticallearning; cross-validation; MARS