Developing a farm-level productivity indicator for coffee and cocoa via NDVI time series and National Agricultural Survey data in Peru

Robinson López-Monzon; Jose Otoya-Barrenechea; Juan Romero-Vasquez; Diana Carolina Quintero-Giraldo · Research Square (2026) · DOI: 10.21203/rs.3.rs-9464917/v1

Resumen

Abstract Context: Farm-level productivity indicators are still limited in Peruvian agricultural statistics, particularly for smallholder perennial crops such as coffee and cocoa, where strong within-region heterogeneity and complex management conditions make productivity difficult to measure consistently. Moreover, increasing access to satellite imagery creates new opportunities to complement survey-based agricultural information with spatially explicit indicators. Aims: This study aimed to develop and validate a farm-level indicator of productive capacity for smallholder coffee and cocoa systems in Peru by integrating National Agricultural Survey (ENA) microdata with Sentinel-2 normalized difference vegetation index (NDVI) time series. Methods: ENA 2023–2024 microdata were linked to georeferenced farm polygons and cloud-filtered 10 m Sentinel-2 normalized difference vegetation index (NDVI) imagery aggregated into 12 bimonthly composites. NDVI snapshots and temporal features were derived and used first in ridge and random forest regression models for continuous yield prediction and then in gradient boosting ensemble models for ordinal classification into low-, medium-, and high-productivity classes. A simplified decision-tree indicator was subsequently derived for operational use. Key Results: Continuous-yield regression performed poorly (R² ≈ 0; RMSE ≈ 340 kg ha⁻¹), indicating that the NDVI alone was insufficient for reliable parcel-level yield estimation. In contrast, ordinal classification performed well, reaching 0.73 validation accuracy, 0.85 evaluation accuracy, and Cohen’s κ = 0.75. The simplified indicator achieved lower but still useful performance (56.7% accuracy; κ = 0.31). Conclusion: Survey microdata and NDVI time series can be combined to generate an operational, farm-level indicator of productive capacity for smallholder perennial systems, even when continuous yield prediction remains weak. Implications and Impacts: The proposed indicator provides a practical way to translate satellite time series and survey microdata into an operational tool for official statistics. In coffee and cocoa systems, it can support stratified sampling, improve the review of anomalous survey records, and guide more spatially explicit interventions, with potential relevance for other contexts after local calibration.