AgStat: Automated variable-rate prescription generation for precision agriculture
Tipo:
Articulos de Divulgación
Autor:
Cadena-Díaz, A., Gutiérrez-Flores, H.*, Campos-Magaña, S.G., Valdés-Aguilar, L.A., Escobar-Oyervides, J.C.
Fecha:
2026-07-01
Descripción:
The adoption rate of information-intensive technologies (IITs) for precision agriculture (soil mapping, statistical analysis, prescription generation) is low because IITs require the user to have specialized knowledge and skills. Unlike embodied knowledge technologies (EKTs), whose adoption rates are much higher (autosteering, section control, among others), the operator does not need to be an expert to use them. Many potential IIT users have low information technology literacy, and to encourage them to use and benefit from software programs, easy-to-use applications are needed. This work introduces “AgStat,” an application that incorporates a fully automated prescription-generation algorithm capable of computing the appropriate input rate for individual field zones based on the needs of the crop and the spatial distribution of the variable of interest (i.e., nitrogen). AgStat was designed to provide farmers, researchers, and advisors with a user-friendly system to perform automated data processing. The application was developed in Python language and incorporates soil mapping, data visualization functionalities, and an automated variable-rate prescription generation (VRPG) algorithm for nitrogen application. VRPG is achieved through mathematical models and results in a ready-to-use ESRI shapefile that can be exported to an external USB drive. A case study is presented in which the prescriptions generated for urea application—both in granulated form and through fertigation—represent savings of 34.6% and 64.3%, respectively, compared with the farmer’s original strategy. AgStat provides insights for decision-making in an IIT without requiring the user to be a specialized geographic information systems operator or an expert agronomic advisor.
Keywords: Agronomic prescription, Precision agriculture, Spatial variability mapping, user-friendly application.
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