Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 13; p. 2975
• Main Authors: Thao, Nguyen Thi Thanh, Khoi, Dao Nguyen, Denis, Antoine, Viet, Luong Van, Wellens, Joost, Tychon, Bernard
• Format: Journal Article Web Resource
• Language: English
• Published: Basel MDPI AG 01.07.2022; MDPI
• Subjects: Agricultural policy; Agricultural production; Agriculture & agronomie; Agriculture & agronomy; Agroforestry; CGMSstatTool; Climate change; Coffee; Coffee industry; coffee yield forecast; Crop growth; Crops; early prediction; Economic forecasting; El Nino; Engineering, computing & technology; FAPAR; Forecasting; Highlands; Ingénierie, informatique & technologie; LAI; Life sciences; Local government; Monitoring systems; NDVI; Ocean currents; Phenology; Physical, chemical, mathematical & earth Sciences; Physique, chimie, mathématiques & sciences de la terre; Precipitation; Regional analysis; Regional development; Regions; Regression analysis; Regression models; Remote sensing; remote sensing vegetation biophysical variables; Satellites; Sciences du vivant; Software; Statistical analysis; Statistical prediction; Statistics; Vegetation; Vietnam; Weather; Vietnam
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14132975

Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R2 = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies.