Propuesta de implementación de un modelo de estimación de cosecha para incrementar la asertividad en el cultivo de arándano en una empresa agrícola, Trujillo

This research proposes a harvest estimation model to increase yield prediction accuracy in the blueberry crop of an agricultural company in Trujillo. Using tools such as Pareto analysis, cause-and-effect diagrams, and phenological studies, five critical variables were identified: phenological cycle, fruit ripening, number of productive plants, fruit weight, and plant census. The implementation of the model, along with standardized instructions and staff training, improved the forecasting process. The economic impact was significant: losses due to estimation errors were reduced from USD 2.14 million to USD 1.54 million between 2021 and 2023, representing a savings of more than USD 600,000. Furthermore, the forecast accuracy increased from 85% to 91%, consolidating the technical and financial viability of the proposed model.