Texture is a fundamental soil property for multiple applications in environmental and earth sciences. Knowing its spatial distribution allows a better understanding of the response of soil conditions to changes in the environment, such as land use. This paper describes the technical development of Colombia's first texture maps, obtained via a spatial ensemble of national and global digital soil mapping products. This work compiles a new database with 4203 soil profiles, which were harmonized at five standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm) and standardized with additive log ratio (ALR) transformation. A compilation of 83 covariates was developed and harmonized at 1 km² of spatial resolution. Ensemble machine learning (EML) algorithms (MACHISPLIN and landmap) were trained to predict the distribution of soil particle size fractions (PSFs) (clay, sand, and silt), and a comparison with SoilGrids (SG) products was performed. Finally, a spatial ensemble function was created to identify the smallest prediction errors between EML and SG. Our results are the first effort to build a national texture map (clay, sand, and silt fractions) based on digital soil mapping in Colombia. The results of EML algorithms showed that their accuracies were very similar at each standard depth, and were more accurate than SG. The largest improvement with the spatial ensemble was found at the first layer (0–5 cm). EML predictions were frequently selected for each PSF and depth in the total area; however, SG predictions were better when increasing soil depth in some specific regions. The final error distribution in the study area showed that sand presented higher absolute error values than clay and silt fractions, specifically in eastern Colombia. The spatial distribution of soil texture in Colombia is a potential tool to provide information for water-related applications, ecosystem services, and agricultural and crop modeling. However, future efforts need to improve aspects such as treating abrupt changes in the texture between depths and unbalanced data. Our results and the compiled database (https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db, Varón-Ramírez and Araujo-Carrillo, 2022 , https://doi.org/10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e, Varón-Ramírez et al., 2022 ) provide new insights to solve some of the aforementioned issues.

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.

Spatial soil databases can help model complex phenomena in which soils are a decisive factor – for example, evaluating agricultural potential or estimating carbon storage capacity. The Latin America and Caribbean Soil Information System, SISLAC, is a regional initiative promoted by the Food and Agriculture Organization's (FAO) Latin America and the Caribbean Soil Partnership to contribute to sustainable management of soil. SISLAC includes data from 49 084 soil profiles distributed unevenly across the continent, making it the region's largest soil database. In addition, there are other soil databases in the region with about 40 000 soil profiles that can be integrated into SISLAC and improve it. However, some problems hinder its usages, such as the quality of the data and their high dimensionality. The objective of this research is evaluate the quality of the SISLAC data and the other available soil databases to generate a new improved version that meets the minimum quality requirements to be used for different purposes or practical applications. The results show that 15 % of the existing soil profiles had an inaccurate description of the diagnostic horizons and 17 % of the additional profiles already existed in SISLAC; therefore, a total of 32 % of profiles were excluded for these two reasons. Further correction of an additional 4.5 % of existing inconsistencies improved overall data quality. The improved database consists of 66 746 profiles and is available for public use at https://doi.org/10.5281/zenodo.7876731 (Díaz-Guadarrama and Guevara, 2023). This revised version of SISLAC data offers the opportunity to generate information that helps decision-making on issues in which soils are a decisive factor. It can also be used to plan future soil surveys in areas with low density or where updated information is required.

Research on soil erosion by water (SEW) includes studies on different spatial and temporal scales. Nevertheless, in Mexico (as in many countries of the Latin American and the Caribbean Region), a lack of studies on SEW is constantly reported in high‐impact journals. We argue that this lack is mainly due to the language in which the majority of the scientific research about SEW in Mexico is published, which is Spanish. Therefore, the objective is to identify the main trends in SEW research in Mexico, through a review of peer‐reviewed studies published in Spanish. The review is centred on two sources of studies published in Spanish that are relevant to Mexican soil sciences communities: Scielo (Scientific Electronic Library Online) and Redalyc (Diamond Open Access scientific journal network non‐commercial global infrastructure owned by academia). As a result, we identified 37 relevant peer‐reviewed articles about SEW, and the principal article aim of these works is modelling and measuring SEW (45.9 and 29.7% of the total articles compiled, respectively). We observed that research on SEW in Mexico follows the same trends as global reports. For example, the most frequently implemented models are those in the Universal Soil Loss Equation and its variants. We observed no data‐sharing culture in most of the revised articles. Our results suggest that large‐scale studies on SEW are needed in Mexico to better understand its role in large‐scale earth system models. Therefore, we conclude that despite the scarce international visibility of Spanish‐written SEW research, multiple valuable studies have indeed been carried out and they provide relevant information for the development of baseline references towards enabling nationwide soil erosion monitoring programmes in Mexico.

The physical characteristics of the soil are defined through the interaction between its properties, and they, in turn, can indicate its physical quality. The study area is located in the Centro Agropecuário Marengo (Marengo Agricultural Center), with an extension of 94.5 ha, subdivided into 17 plots. The objective of this research was to study characteristics such as water storage capacity, structure, consistency, and soil compaction in the two surface horizons (H1 and H2), in order to define management zones through the soil index. Properties such as soil penetration resistance were an indicator of soil degradation, with values higher than 2 MPa in 13 plots. The total porosity exhibited medium or low values in the H1 and H2 horizons, with a predominance of micropores, which means that the presence of water could be lower than 14%. Four management zones were identified for each horizon, where the first zone represents the area where the soil is capable of retaining between 17% and 21% of the available water, with a bulk density of around 1 g cm^-3 and organic carbon content close to 6%. By contrast, the fourth zone represents the sites where the soils are the finest, with contents above 45% clay, available water less than 11%, and a mean compaction of 4.39 MPa.

Soils have the ability to maintain plant growth and biological activity due to their physical and chemical properties. The aim of this study was to observe the spatial distribution of some chemical properties of the soil, such as pH, organic matter (OM), electrical conductivity (EC), effective cation exchange capacity (ECEC), sulfur (S) and aluminum (Al) content and to establish zones with homogeneous chemical characteristics using the MULTISPATI-PCA technique and the fuzzy c-means algorithm. The study area was located in the Tundama and Sugamuxi Valleys (Boyacá, Colombia) with an area of ​​8,017 ha. Chemical properties such as pH, OM, EC, S, Al, and ECEC were indicators of the chemical degradation of these soils. Four homogeneous zones were identified. The first zone represents areas with acidity and excessive sulfur, with a pH of 4.54, 15.88% OM, 3.19 dS m^-1 EC, 2.47 meq 100 g^-1 Al and 365.59 meq 100 g^-1 S. In contrast, the second zone represents areas with a high self-neutralizing capacity, with a pH of 5.98, 4.22% OM, 0.75 dS m^-1 EC, 0.20 meq 100 g^-1 Al and 44.64 meq 100 g^-1 S. Zone three showed a high similarity with the first two, except for its EC and S contents. Finally, zone four showed similarity with the first, except in OM, EC and S contents. These data show that S and EC influenced the homogeneous zones because the soils in this area are called acid sulfate soils.