Climate change is a major constraint for common bean (Phaseolus vulgaris L.) cultivation in tropical regions, where elevated temperatures drastically affect reproductive efficiency and yield. This study aimed to evaluate the response of two local varieties, Matambú and Tayní, under passive induced heat using Open Top Chambers (OTC) in the humid tropics of Costa Rica. A factorial randomized block design with two genotypes and two environments (control and OTC) was applied to assess morphological, physiological, and yield-related traits. OTC increased daily maximum, minimum, and mean air temperatures by +2.29, +0.93, and +2.80 °C, respectively, and raised cumulative growing degree days by 325 °C·day⁻¹ compared with the control. Heat stress reduced grain yield by more than 80% (from 0.15 to 0.03 t·ha⁻¹) and significantly lowered the harvest index, confirming strong reproductive vulnerability. However, Matambú maintained higher nodulation and above-ground biomass under heat, whereas Tayní showed marked declines in pod set and nodule number. Correlation analyses revealed that pod number and harvest index were the strongest predictors of yield across environments. These results provide the first field evidence of local varietal responses to induced passive heat in Costa Rican common bean varieties and highlight Matambú as a valuable genetic resource for breeding climate resilient cultivars.

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.

Estimating nitrogen (N) and the corresponding crude protein (CP) content in forage crops is essential for optimizing fertilization and livestock nutrition. However, standard methods such as the Dumas and Kjeldahl techniques are destructive, costly, and impractical for field use in certain regions of developing countries. This study evaluated four non-destructive approaches—morphometric measurements, Pantone^® color scales, smartphone-based RGB analysis (ColorDetector app), and SPAD chlorophyll readings—for predicting N and CP in Megathyrsus maximus (Mombasa grass). A total of 120 samples were collected under three nitrogen fertilization levels and assessed using linear mixed-effects models with cross-validation. Morphometric variables showed poor performance (R² < 0.01), indicating low correlation with nutrient content. Pantone-based RGB models provided slightly better predictions (R² ≈ 0.30) but were limited by subjectivity and discrete data. SPAD-based models demonstrated moderate predictive accuracy (R² ≈ 0.53; RMSE ≈ 0.46%). The highest accuracy was achieved with smartphone-derived RGB data, where full RGB models reached R² = 0.60 and RMSE = 0.45%. Based on these results, a practical green color scale was developed from RGB values to support real-time, in-field nitrogen and crude protein assessment. This study highlights smartphone imaging as a scalable, low-cost, and accurate tool for non-destructive estimation of nitrogen and crude protein in tropical forages, offering an accessible alternative to laboratory methods for producers and field technicians.