Viviana Marcela Varón-Ramírez

Soil water erosion is the dominant soil degradation driver worldwide. Rainfall erosivity (also known as the R factor) quantifies the potential of rainfall to cause soil water erosion and is regarded as a dominant factor determining it. This contribution presents the first annual R factor database across the continental Mexico for three climate normals (1968–1997, 1978–2007, and 1988–2017). The workflow comprised three main steps. Firstly, a harmonaized daily rainfall time series dataset was obtained by compiling and harmonizing (through quality control, homogeneity analysis and gap-filling) 5410 raw rainfall time series distributed across Mexico. Secondly, three combinations of the α and β coefficients in a power model were tested to estimate daily R factors using three validation databases at global, national, and local scales. Thirdly, a validated and continuously distributed annual R factor for all three climate normal was obtained. Thus, the database presented herein encompasses 1369 (climate normal 1968–1997), 1678 (climate normal 1978–2007), and 1676 (climate normal 1988–2017) rainfall time series and the corresponding R factor. Our results indicate that the median values of the R factor for the three climate normals were 3245; 3070; and 3327 MJ mm ha ⁻¹ h ⁻¹ yr ⁻¹ , respectively. The statistical distribution of the R factor is right-skewed for the three climate normals, with high erosivity values reaching >12 000 MJ mm ha ⁻¹ h ⁻¹ yr ⁻¹ in all cases. The R factor across Mexico showed a strong ecoregional differentiation, with the highest values concentrated in the Tropical Rain Forest and Tropical Dry Forest ecoregions, whereas the North American Deserts and Mediterranean California exhibited the lowest values. The annual behavior of the R factor was similar for the three climate normals. However, September had the highest contribution to the annual R factor. By identifying areas with higher susceptibility to soil loss due to rainfall action and providing spatially distributed and well-documented estimates, we believe that the present R factor database has the potential to support the study of soil water erosion in the continental Mexico at country scale. Said database is available from a scholarly-accepted repository at https://doi.org/10.6073/pasta/dd2b30e28ee25ff2d60d8a9f436951d2 (Varón-Ramírez et al., 2026) for public consultation.

The infield transport of harvested sugarcane stalks (transshipment operation) during mechanized harvesting is widely recognized as the operation with the greatest potential to induce soil compaction. Nevertheless, there is still a lack of experimental data on the effect of compaction resulting from transshipment vehicles on soil physical functionality. We assessed the effects of realistic infield traffic from different transshipment configurations on soil structural and functional properties and their effects on crop yield. Three transshipment systems under controlled traffic farming system were evaluated: a tractor pulling one four-axle trailer unit with 21 Mg carrying capacity (1T/21), a tractor pulling two axle trailer units with 10 Mg carrying capacity (2T/10), and an autonomous truck with four axles and one trailer with 20 Mg carrying capacity (1TT/20). Several analyses were conducted, including degree of compaction (DC), macroporosity (MaP), air-filled porosity (ε_a10), soil air permeability (k_a10), and saturated hydraulic conductivity (Ks). Soil samplings were performed in surface and subsurface layers of an Oxisol in southeastern Brazil at the planting row and inter-row, and at the midpoint between these positions, over two consecutive sugarcane harvests. Although machine traffic occurred at low soil water content, all transshipment configurations promoted soil compaction during the first harvest, with the greatest changes in soil physical attributes in the 0–10 and 10–20 cm layers in the inter-row center and, in some cases, at the midpoint. However, all treatments preserved soil conditions in the planting row. The 1TT/20 transshipment induced the greatest compaction, with significant effects on DC, MaP, and ε_a10 in the inter-row and midpoint positions. Despite structural alterations, no significant differences were observed among treatments for k_a10 and Ks. However, after the first harvest, k_a10 frequently reached critical thresholds of low permeability in trafficked areas, indicating functional degradation of soil aeration. Sugarcane yield was not affected by the transshipment configurations. The absence of productivity differences reflects the effectiveness of controlled traffic in confining compaction to the inter-row center and midpoint while preserving the planting row. Although short-term yield was not affected, structural degradation in trafficked areas and the persistence of high subsoil compaction indicate the potential for cumulative long-term impacts. Continuous monitoring and integrated soil management strategies remain essential to mitigate progressive compaction under mechanized sugarcane harvesting.

Context: High-mountain páramos provide essential hydrological services across the Andes. However, green onion cultivation in these ecosystems has led to nitrogen (N) losses, compromising water quality. Aims: This study aimed to improve understanding of soil N dynamics in páramo agroecosystems by analyzing the vertical and temporal distribution of total and mineral N in páramo soils, and by developing pedotransfer functions (PTFs) to predict these pools. Method: From two cultivated plots and one native grassland plot, 288 soil samples were collected over 7 and 4 months, respectively, at three depths. Samples were analyzed for soil N concentrations, mineral N stocks, and 16 edaphic properties. Using random forest and multiple linear regression (MLR), PTFs were developed incorporating edaphic and remote-sensing covariates. Key results: Green onion cultivation altered mineral N distribution, with subsurface nitrate-N reaching 114 mg kg−1, compared to 1.76 mg kg−1 in native grassland. Monthly declines in mineral N stocks reached 75.8 kg N ha−1 in cultivated soils, nearly triple the losses in uncultivated soil. The MLR using edaphic properties performed best for total N and nitrate-N prediction (R2 = 0.80 and 0.64, respectively), while ammonium-N was poorly predicted. Conclusions: Shifts in N dynamics were consistent with soil acidity neutralization associated with repeated chicken manure applications, fallow history, and soil texture. These conditions favored subsoil mineral N accumulation, increasing leaching risk. The developed PTFs demonstrate potential to inform site-specific assessments of soil N pools in páramos. Implications: Future research should include additional N pools, microbial processes, and isotopic tracers to deepen understanding of N dynamics.

Soil water erosion (SWE) is the dominant soil degradation driver on a global scale. For quantifying SWE, erosivity is an index that reflects the potential (i.e., the energy) of rainfall to cause SWE. To support large-scale SWE studies and the assessment of the SWE process at the national scale in Mexico, the objectives of this research are a) to develop the first Mexican rainfall time series database for three climate normals CNs (1968–1997, 1978–2007, and 1988–2017) leveraging legacy climate data, and b) to estimate rainfall erosivity across continental Mexico by using daily rainfall time series. The workflow has three methodological moments: 1) development of the daily rainfall time series database, 2) identification of the best empirical relationship to estimate daily rainfall erosivity, and 3) estimation of the rainfall erosivity across Mexican territory. We compiled and harmonized 5410 rainfall time series (RTS) well distributed across the Mexican territory. We perform quality control and assurance, homogeneity analysis (using the normal homogeneity test), and the data gap-filling process (using the proportion method). Then, we tested three combinations of the α and β coefficients, proposed by three authors, in a power model to estimate rainfall erosivity; in this step, we used three validation databases (global, national, and local scales). Finally, we estimated the annual rainfall erosivity for all three CNs with multiple combinations of α and β coefficients. As principal results, the new database includes 1370, 1678, and 1676 RTS for each CN and its corresponding rainfall erosivity. The best parameter combination is the one proposed by Richardson et al. (1983) for all three validation databases. For the global and national databases, we observe a positive bias (Mean error of 956 and 324 MJ mm ha^-1 h^-1 yr^-1, respectively); in contrast, for the local database, results show a negative and higher bias (Mean error of -3699 MJ mm ha^-1 h^-1 yr^-1). About the erosivity estimation across the Mexican territory, the median values for rainfall erosivity for the three CNs were 3245, 3070, and 3327 MJ mm ha^-1 h^-1 yr^-1, respectively. The statistical distribution of the erosivity values was right-skewed for the three CNs, with high erosivity values reaching >12000 MJ mm ha^-1 h^-1 yr^-1 in all three CNs. The behavior throughout the year of the rainfall erosivity was similar for the three CNs. However, September had the highest contribution to the rainfall erosivity. The new database provides daily climatological data and analysis across Mexican territory through a multi-year period (1968 to 2017). Rainfall erosivity results support the study of SWE at the national scale by identifying areas with higher susceptibility to soil loss due to rainfall action and providing a more spatially dense and well-documented rainfall erosivity database. Following the FAIR principles (Findability, Availability, Interoperability, and Reproducibility) for scientific data, this database is available from a scholarly accepted repository https://doi.org/10.6073/pasta/e0dc8bd3501f8c19bb750e853c3289cb (Varón-Ramírez et al., 2025) for public consultation.

The consistency between quality-based erosion assessments and erosion modeling varies across Mexico. Erosion studies, from regional to global scales, typically use either expert knowledge or data-driven approaches. Expert knowledge involves photo-interpretation of satellite imagery combined with fieldwork to classify erosion degrees. Data-driven approaches use models such as the Revised Universal Soil Loss Equation (RUSLE) to estimate soil loss rates. In Mexico, both strategies have been applied nationally, but discrepancies persist. This study examined the relationship between expert-assessed soil erosion by water (SEW) degrees and the erosivity, erodibility, coverage, and topography factors (or their modifications) of the RUSLE model, as well as erosion rates (ER). A dataset of 2,185 SEW degree observations (categorized as No evidence, Slight, Moderate, and Severe) was analyzed using a random forest classification algorithm to explore non-linear relationships between SEW degrees and RUSLE factors. Nationally, the average ER was estimated at 207 t ha⁻¹ year⁻¹. Areas with the highest ER (>500 t ha⁻¹ year⁻¹) are concentrated in the rainiest regions, particularly around the Isthmus of Tehuantepec, while the lowest ER (

Soil Water Erosion (SWE) is the dominant soil degradation driver on a global scale. For quantifying SWE, erosivity is an index that reflects the potential (i.e., the energy) of rainfall to cause SWE. To enhance the assessment of the SWE process at the national scale—, the objectives of this research are a) to develop the first Mexican rainfall time series database for three climate normals CNs (1968–1997, 1978–2007, and 1988–2017) leveraging legacy climate data, and b) to estimate rainfall erosivity across continental Mexico by using daily rainfall time series. The workflow has three methodological moments: 1) development of the rainfall time series database, 2) estimation of rainfall erosivity, and 3) rainfall erosivity verification. First, we compiled and harmonized over 5000 useful rainfall time series (RTS) well distributed across the Mexican territory. We performed a quality assurance, homogeneity analysis (using the normal homogeneity test), and data gap-filling (using the proportion method). Then, we use a potential power law equation to estimate rainfall erosivity at daily resolution. Finally, we compared and verified our results with three external datasets (global, national, and local scales). The principal research product is a new database with 1370, 1678, and 1676 RTS for each CN and its corresponding rainfall erosivity. The mean values for rainfall erosivity for the three CNs were 3600, 3296, and 3461 MJ mm ha^-1 h-¹ yr^-1, respectively. The statistical distribution of the erosivity values was right-skewed for the three CNs, with high erosivity values reaching >8000 MJ mm ha^-1 h-¹ yr^-1 in all the three CNs. About the verification of erosivity values, we found that Tropical rain-forests, temperate Sierras, and the Great Plains are the ecoregions with more significant differences concerning the global database, a generalized underestimation of erosivity values concerning the national dataset, and an adjustment coefficient of 1.85 for a local condition in Michoacan state. This new database provides tools for daily climatological analysis across Mexican territory and through a multiyear period (1968 to 2017). Erosivity results trigger the study of SWE at the national scale by identifying areas with higher susceptibility to soil loss due to rainfall action and providing a more spatially dense erosivity database that follows the pattern of erosivity databases from higher time resolution. Following the FAIR principles (Findability, Availability, Interoperability, and Reproducibility) for scientific data, this database is available from a scholarly accepted repository (https://doi.org/10.6073/pasta/7479676e406aeb40127da7b096b28eb2) for public consultation.

This study evaluates and compares different methods for estimating evapotranspiration (ET) in potato crops (Solanum tuberosum L.) in the Bogotá Savannah, focusing on the FAO-56 method and direct measurements using eddy covariance (EC) systems. The research addresses the variability in crop coefficients (Kc) due to local climatic and edaphic conditions, which complicates precise ET estimation. Soil moisture was monitored throughout the crop cycle using tensiometers and volumetric moisture measurements, with an optimal water potential limit of 40 cbar. A fixed sprinkler irrigation system was employed, and soil moisture conditions were simulated using the CROPWAT 8.0 software. The study found that Method 1, which adjusts Kc values based on local conditions, provided the most accurate ET estimates, with a root mean square error (RMSE) of 0.45 mm and a Willmott’s index of agreement (d) of 0.86. The Kc values for Method 1 were 0.73, 0.87, and 0.80 for the initial, mid, and final stages, respectively. In contrast, Method 5, which uses standard FAO-56 values, showed the poorest performance. The CROPWAT simulations indicated that Method 1 also closely matched the experimental irrigation requirements, Methods 2 and 3 significantly overestimated irrigation needs, while Method 5 underestimated them in the initial stages but overestimated during maturation. These results highlight the importance of adjusting Kc values to local conditions to improve irrigation efficiency and crop health. The findings provide a robust methodology for irrigation planning and optimization, contributing to agricultural sustainability and water resource conservation.

En este libro se explora la agroindustria panelera a través de una cuidadosa recopilación de resultados de investigación en diversas áreas del conocimiento: 1) Cultivo y manejo agronómico, 2) Transformación, industrialización y diversificación de uso de la panela, y 3) Análisis socioeconómico y ambiental de la agroindustria panelera. La obra se presenta como una valiosa fuente de referencia, con capítulos estructurados en formato de artículos científicos, por lo cual ofrece información rigurosa y de alta calidad. Este enfoque garantiza que el libro se convierta en una herramienta invaluable para todos los actores implicados en la agroindustria panelera, abarcando desde los productores hasta los investigadores y los responsables de tomar decisiones. De esta manera, la obra se posiciona como una guía esencial para comprender los desafíos y oportunidades que enfrenta este sector en su evolución continua, ofreciendo una visión completa y actualizada que contribuye al desarrollo y la innovación dentro de la agroindustria.

En los últimos años, es evidente que los patrones climáticos predominantes se han alterado y han generado mayor incertidumbre en actividades económicas, como lo es la agricultura misma. En tal sentido, eventos climáticos extremos derivados de fluctuaciones climáticas son más comunes, frecuentes y, sobre todo, más severos que en ocasiones anteriores. Una de las soluciones es generar un fortalecimiento de la población objetivo, a través de metodologías y herramientas que coadyuven en una mejor gestión del riesgo agroclimático. Este manual obedece a un proceso de investigación que busca facilitar la conversación técnica entre todos los actores de la cadena de valor de la papa, en especial entre investigadores, asistentes técnicos y productores, por lo que podrá encontrar aspectos del contexto relacionados con el cambio climático, así como información regional sobre las características del suelo y el clima, la gestión del agua, el balance de carbono y la nutrición del cultivo, como uno de los puntos clave para mitigar el estrés por sequía.