Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Niño in 2015–2016, as well as the 6-month drought peak period. During the drought´s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from −31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions’ ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions.

This study focused on evaluating the growth and productivity of Coffea arabica var. Esperanza L4A5 in different agroforestry systems in the Caribbean region of Costa Rica, a non-traditional area for coffee cultivation due to its low altitude and challenging climatic conditions. Three tree coverages were investigated, in combination with two types of differentiated fertilization (physical and chemical), comparing the results with full sun coffee plots as a control: (1) Albizia saman, (2) Hymenaea courbaril + Erythrina poeppigiana, and (3) Anacardium excelsum + Erythrina poeppigiana. The results showed that tree associations significantly reduced the mortality of coffee plants and increased both the height and mature cherry production compared to full sun treatments. In particular, the tree coverages associated with chemical and physical fertilization achieved the highest growth and production rates, with A. excelsum + E. poeppigiana and H. courbaril + E. poeppigiana standing out with maximum mature cherry productions of 3.35 t/ha and 3.28 t/ha, respectively. Growth analysis revealed that rapid initial growth, especially under chemical fertilization, is crucial for maximizing productivity, although a rapid slowdown in growth was also observed after reaching the peak. These findings underscore the importance of combining tree coverages with appropriate fertilization strategies to optimize coffee production in agroforestry systems, particularly in low-altitude areas like the Costa Rican Caribbean. This study concludes that agroforestry systems not only improve the resilience of coffee crops to adverse environmental conditions but can also be a viable strategy for increasing productivity in non-conventional regions. This suggests the need for further research to assess the long-term impacts on soil health, biodiversity, and the economic viability of these systems.

This study focused on the phenology of Coffea arabica var. Esperanza L4A5, an F1 interspecific hybrid obtained by crossing commercial varieties with wild genotypes from Ethiopia and Sudan. Most phenological studies on C. arabica have been conducted in traditional high-altitude regions, leaving a gap in the understanding of its behavior in non-traditional areas such as the Caribbean region of Costa Rica. To establish a baseline on the phenological behavior of the Esperanza L4A5 hybrid in this region, we conducted a four-year study examining the effects of different agroforestry associations: (1) Albizia saman; (2) Hymenaea courbaril and Erythrina poeppigiana; (3) Anacardium excelsum and Erythrina poeppigiana; and coffee plots under full sun. Additionally, the phenology of the coffee plants was evaluated under differentiated fertilizations (physical, chemical, and without fertilization), considering meteorological factors such as temperature, humidity, and rainfall. The observed variables included the development of floral nodes, pre-anthesis, anthesis, and fruiting stages. To analyze the relationships between environmental factors, tree cover, fertilization, and the phenological stages, we employed multiple linear regression (MLR), which revealed that both tree cover and physical and chemical fertilizations had significant effects on the presence of developed floral nodes and, consequently, on fruit production. Furthermore, the random forest (RF) model was applied to capture complex interactions between variables and to rank the importance of meteorological factors, tree cover, and fertilization practices. These analyses demonstrated that the Esperanza L4A5 hybrid exhibited viable phenological development under the atypical conditions of the Caribbean region of Costa Rica, suggesting its potential to adapt and thrive in non-traditional coffee-growing areas.

Accurate yield prediction in perennial crops is challenging when hierarchical datasets contain repeated response values because lower-level evaluation can inflate apparent model performance. We re-audited a four-year Coffea arabica var. Esperanza L4A5 dataset from a lowland agroforestry trial in Costa Rica. The 2020–2022 development dataset comprised 45 response-unit-year records representing 15 recurring management × fertilization combinations, while the corresponding 15 combinations in 2023 were retained as a temporal holdout. Decision Tree, Random Forest (RF), Support Vector Regression (SVR), and ridge regression were evaluated using forward temporal validation and compared with training-mean and previous-year persistence benchmarks. On the 2023 holdout, SVR (R² = 0.53; RMSE = 0.308 kg plant⁻¹) and RF (R² = 0.51; RMSE = 0.312 kg plant⁻¹) improved on the training-mean baseline, but previous-year yield persistence performed substantially better (R2 = 0.96; RMSE = 0.091 kg plant−1). Leakage-aware aggregation therefore materially changed conclusions about model performance, and added algorithmic complexity did not improve prediction beyond recent yield history in this short time series. Longer multi-site time series and genuinely pre-harvest predictors are required before operational forecasting can be justified.