Vegetation growth conditions strongly indicate coffee flowering anomalies

Abstract Quantifying the timing of vegetation growth, particularly coffee plant flowering, is vital for estimating yield in advance. While satellite-based vegetation indices are effective in mapping crop growth and have a strong correlation with coffee yield, the potential contribution of plant conditions alongside climate factors in predicting coffee flowering anomalies remains underexplored. Here, our investigation aimed to determine whether satellite-based vegetation indices, in addition to climate variables, could enhance the model's predictive power for flowering anomalies of coffee trees. Utilizing a dataset on flowering dates over ten years of 558 coffee farms spread over four provinces (Dak Lak, Gia Lai, Dak Nong, and Lam Dong) in the Central Highlands of Vietnam, we analysed climate variables (rainfall and temperature) and the Normalized Difference Vegetation Index (NDVI) at various intervals prior to flowering dates. Using a Generalized Additive Model (GAM) and model selection based on Akaike’s Information Criteria (AIC), we identified the most influential predictors. Then, we performed Structural Equation Modelling (SEM) to further investigate the complex causal relationships among flowering anomalies, climate, vegetation, and management factors. Our results show that the NDVI prior to flowering dates held the most explanatory power, outperforming climate variables. Lower NDVI during the dormancy period indicated the ripe-to-flower condition of the coffee tree, informing earlier onset of the flowering stage, while higher NDVI during bud initiation and development stage suggested a delayed flowering. The best model incorporating both climate and NDVI predictors achieved good explanatory performance with an adjusted R2 of 0.87. The analysis highlighted the advantages of vegetation indices over climate predictors in capturing plant conditions through its growing cycle, with the accumulated effects of environmental factors and agricultural management activities, especially during critical phenological stages. Our findings suggest further studies utilising vegetation indices from remote sensing data sources at multiple scales to thoroughly understand plant conditions at different crop growth phases, especially at early stages, for site-specific, timely and strategic management interventions.