AI Based Prediction of Crop Loss Risk in Tropical Agricultural Systems of Panama with Implications for Food Security Planning

Paul R. Peterson ^1,2,* and Jay Molino ^2,*
1 Food and Nutrition Security, Faculty of Biosciences and Public Health, Universidad Especializada de las Américas (UDELAS), Los Santos, Panama
2 Environmental Analysis Laboratory, Center for Biotechnology, Green Energies and Climate Change, Faculty of Biosciences and Public Health, Universidad Especializada de las Américas (UDELAS), Panama City, Panama
This article belongs to the Special Issue Green and Bio-based Technologies for Climate-Resilient Tropical Agriculture
* Correspondence: paul.peterson.2@udelas.ac.pa (P.R.P.); jay.molino@udelas.ac.pa (J.M.)
Received: 4 August 2026 Revised: 11 October 2026 Accepted: 15 October 2026 Published: 29 October 2026

ABSTRACT

This study evaluated a Random Forest model for estimating crop-loss risk in small- and medium-scale maize and rice production systems in Los Santos, Guararé, and Tonosí, in the Azuero region of Panama. The dataset comprised approximately 720 repeated monthly records from 20 production units across three agricultural cycles, combining climatic, agronomic, phytosanitary, and production information. Crop loss was expressed relative to a common yield reference of 6.0 t/ha, with operational categories of low (<10%), medium (10–25%), and high (>25%) risk. The analysis used an 80/20 training-validation partition and five-fold cross-validation. Internal validation yielded R² = 0.78, while the fold-specific R² values shown in the cross-validation output ranged from approximately 0.75 to 0.80. Accumulated precipitation (28.6%) and average temperature (22.1%) had the greatest model importance, followed by irrigation frequency (15.4%) and pest incidence (13.7%). A matrix-based classification analysis of 81 observations yielded 64 correct risk assignments (79.0%), with an 85.7% high-risk recall. These findings support localized crop-loss risk assessment within the participating production units and identify potential applications in irrigation management, pest surveillance, and planting decisions relevant to food-security planning.
Keywords: Random Forest; crop-loss risk; tropical agriculture; climate variability; food security; Panama