This study represents the first nationwide survey regarding the distribution of Cd content in cacao-growing soils in Colombia. The soil Cd distribution was analyzed using a cold/hotspots model. Moreover, both descriptive and predictive analytical tools were used to assess the key factors regulating the Cd concentration, considering Cd content and eight soil variables in the cacao systems. A critical discussion was performed in four main cacao-growing districts. Our results suggest that the performance of a model using all the variables will always be superior to the one using Zn alone. The analyzed variables featured an appropriate predictive performance, nonetheless, that performance has to be improved to develop a prediction method that might be used nationwide. Results from the fitted graphical models showed that the largest associations (as measured by the partial correlation coefficients) were those between Cd and Zn. Ca had the second-largest partial correlation with Cd and its predictive performance ranked second. Interestingly, it was found that there was a high variability in the factors correlated with Cd in cacao growing soils at a national level. Therefore, this study constitutes a baseline for the forthcoming studies in the country and should be reinforced with an analysis of cadmium content in cacao beans.

Se utilizó el modelo regionalizado de lluvia Regionalisierte Niederschlage (Regnie) para interpolar la lluvia y temperatura media del aire de las regiones Andina, Caribe y Pacífica de Colombia. Este modelo integró los datos de la pendiente y exposición del terreno obtenidos del modelo digital de elevación, así como los promedios climatológicos de 1440 series de precipitación y 258 series de temperatura registradas en igual número de estaciones meteorológicas de superficie para cada variable. Se establecieron los modelos de regresión lineal múltiple de la precipitación media anual y temperatura media anual del aire, además se utilizaron herramientas de geoprocesamiento para la generación de las superficies interpoladas. Los estadísticos de prueba de las superficies Regnie fueron similares a los obtenidos con interpolaciones con los métodos Spline e IDW para precipitación (coeficiente de determinación 0.81 y error medio de 55.6 mm) y mejores para la temperatura media (coeficiente de determinación 0.99 y error medio de 0.02°C).

Evapotranspiration (ET) is an essential agroclimatic variable, fundamental for estimating crop water requirements and managing irrigation. However, quantifying ET has been challenging due to limited monitoring stations, leading to the development of alternative methods, including the use of datasets and gridded products derived from satellite remote sensing or reanalysis of global models. Despite its complexity, the FAO-Penman Monteith reference evapotranspiration (ET₀) model is widely used, but challenges arise in upscaling it over extensive areas, especially where data are insufficient or heterogeneous as in the Colombian Thornthwaite regions. Datasets from platforms such as TERRACLIMATE, NASA POWER, and DMETREF-EUMETSAT provide valuable information for estimating ET₀ at different spatial and temporal resolutions; however, there are limitations in terms of their coverage and accuracy. In this study, the ET₀ derived from these three satellite-based datasets were compared with the monthly ET₀ estimated from national legacy data from the available IDEAM weather stations (1981–2020) in Colombia, using robust linear estimators for the entire country. Results show that TERRACLIMATE (R ² = 0.75, RMSE = 311 mm) performs better than NASA POWER (R ² = 0.5, RMSE = 531 mm) and DMETREF-EUMETSAT (R ² = 0.20, RMSE = 1666 mm) using Ordinary Least Squares regression, particularly in certain Thornthwaite regions. However, challenges remain, especially in arid areas, where the analyzed datasets show lower accuracy. The results of this study suggest that using certain ET data and gridded products, particularly TERRACLIMATE, is an effective approach for characterizing and monitoring this important agroclimatic variable.

Gross primary productivity (GPP) is an essential parameter to assess the efficiency of terrestrial ecosystems on carbon transfer. Although GPP is regularly measured with eddy covariance (EC) systems, these are restricted to the tower footprint area, and remote sensing (RS) products have estimated GPP using multispectral vegetation indexes (VIs) from farms to whole ecosystems. Indeed, nowadays, unmanned aerial vehicle (UAV)-based RS technology is becoming more accessible. Accordingly, we propose the estimation of GPP using VIs at high spatial resolutions using UAVs and multi-spectral cameras. A small typical farm in Colombia was cultivated with carrot as our base crop. An EC system was installed to estimate GPP and was used as a reference. A total of 24 VIs from UAV-based RS products were selected and compared with the GPP of the EC system. A cross-validation process was performed, and seven VIs obtained a high R² score (0.76–0.78). The accumulated GPP, estimated with the best index (NIRv) was 520.3 g C m⁻², while the GPP-EC estimate was 580.4 g C m⁻² (10.3% error). This work showed that it is possible to estimate the GPP of carrot crops using UAV-based RS, VIs, and linear regression models, which can be used in further research on GPP using UAVs.