Maize yield is highly sensitive to climate change and extreme weather events. In some locations, it is projected to decrease due to an increase in the average growing season temperature. The present study analyzes changes in temperature and precipitation extremes in the Comarca Lagunera located in Northern Mexico, using the ETCCDI indices. We examined a 40-year period (1980–2020) using daily and monthly climate data provided by the National Meteorological Service. The climate databases were subjected to quality control, homogenization, and data filling using Climatol, and the ETCCDI indices were obtained using RClimDex software. Results indicate that the climate variable that most influences climate change in Comarca Lagunera is temperature, with increases in both maximum and minimum values. This situation is accentuating the drought in the Comarca Lagunera, which is supported by the increase in temperature-based indices. Furthermore, precipitation is the primary variable influencing the yield of rainfed maize, while maximum temperature affects the yield of irrigated maize. These results indicate that irrigation is functioning as a climate change adaptation strategy, reducing the impact of extreme weather on maize productivity, which could have a negative impact on water productivity in the study region in the short term.

This study examined the use of an artificial soil substrate in a mine waste reclamation area and its effect on plant metabolic functions. Research was conducted by determining the relationship between the plants’ biochemical features and the properties of plant growth medium derived from post-flotation coal waste, sewage sludge, crushed stone and fly ash on the surface of the mine waste disposal area. Trees and shrubs were established on the material and allowed to grow for eight years. The study determined that the applied plants and the naturally occurring Taraxacum officinale were suitable for physio-biochemical assessment, identification of derelict areas and reclamation purposes. An evaluation of a soil substrate applied to post-mining areas indicated that it was beneficial for plant growth since it activated the metabolic functions of herbaceous plants, shrubs, and trees. The study showed that soil substrate can be targeted to improve plant stress tolerance to potentially toxic elements (PTEs). These data suggest the potential for growth and slower susceptible response to Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn. It is possible that the constructed soil-substitute substrate (biosolid material) would be an effective reclamation treatment in areas where natural soil materials are polluted by PTEs. This observation may reflect a more efficient use of soil substrate released from the cycling of organic biogene pools, in accordance with the circular economy approach. In further studies related to land reclamation using sewage sludge amendments, it would be necessary to extend the research to other stress factors, such as salinity or water deficiency.

Los sistemas de captación del agua de lluvia constituyen una alternativa para reducir la explotación de los acuíferos. El objetivo de este trabajo fue diseñar tres sistemas, para destinar el agua a uso doméstico de un hogar de cuatro integrantes, a consumo humano en una institución educativa de mil personas y al riego de Stevia rebaudiana en un invernadero de media hectárea; y comparar, el costo de la captación del agua de lluvia con el de extracción del subsuelo. Los tres sistemas fueron propuestos para el municipio de Calpulalpan, Tlaxcala. El desarrollo del trabajo consistió en generar una metodología de diseño considerando la estimación de la precipitación a partir de una probabilidad de 75% de ocurrencia y el dimensionamiento del sistema de almacenamiento empleando el método de balance de masas. Adicionalmente se comparó el costo por metro cúbico del agua de lluvia captada contra el del agua subterránea. Los resultados sugieren que el aprovechamiento de agua lluvia puede llegar a ser económicamente más viable que la extracción del agua subterránea cuando la demanda de agua es baja, además de que representa una contribución al desarrollo sostenible del municipio

Field capacity (FC) and permanent wilting point (PWP) thresholds are critical parameters in climate-smart agriculture because they directly relate to soil water availability, which is essential for optimizing water use, improving crop yields, and ensuring resilience against climate variability. Using the continuous mosaic of SoilGrids data, pedotransfer functions based on bulk density, clay content, and sand content were applied to estimate the threshold values of FC and PWP across Mexico utilizing random forest (RF) algorithms. The selection of these parameters was based on their positive contribution to the model’s prediction: bulk density (0.51), clay content (0.21), and sand content (0.16). Soil organic carbon (SOC) contributed negatively; this negative importance score warrants careful interpretation. The 30–60 cm depth was chosen based on the assumption that it is reasonably uniform across other depths and lies below the highly variable surface horizon, which is strongly influenced by management practices and organic matter dynamics. Here we address key technical and scientific critiques regarding the use of SoilGrids for generating FC and PWP data. Additionally, the relevant role of FC and PWP thresholds in the context of climate-smart agriculture is highlighted, from the calculation of available soil water to their role in achieving sustainable development goals.

Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data.

Currently there are several precipitation reconstructions for northern Mexico; however, there is a lack of temperature reconstructions to understand past climate change, the impact on ecosystems and societies, etc. The central region of Chihuahua is located in a transition zone between the Sierra Madre Occidental and the Great Northern Plain, characterized by extreme temperatures and marked seasonal variability. The objectives of this study were (1) to generate a climatic association between variables from reanalysis models and the earlywood series for the center of Chihuahua, (2) to generate a reconstruction of mean temperature, (3) to determine extreme events, and (4) to identify the influence of ocean–atmosphere phenomena. Chronologies were downloaded from the International Tree-Ring Data Bank and climate information from the NLDAS-2 and ClimateNA reanalysis models. The response function was performed using climate models and regional dendrochronological series. A reconstruction of mean temperature was generated, and extreme periods were identified. The representativeness of the reconstruction was evaluated through spatial correlation, and low-frequency events were determined through multitaper spectral analysis and wavelet analysis. The influence of ocean–atmosphere phenomena on temperature reconstruction was analyzed using Pearson correlation, and the influence of ENSO was examined through wavelet coherence analysis. Highly significant correlations were found for maximum, minimum, and mean temperature, as well as for precipitation and relative humidity, before and after the growth year. However, the seasonal period with the highest correlation was found from May to June for mean temperature, which was used to generate the reconstruction from 1775 to 2022. The most extreme periods were 1775, 1801, 1805, 1860, 1892–1894, 1951, 1953–1954, and 2011–2012. Spectral analysis showed significant frequencies of 56.53 and 2.09 years, and wavelet analysis from 0 to 2 years from 1970 to 1980, from 8 to 11 years from 1890 to 1910, and from 30 to 70 years from 1860 to 2022. A significant association was found with the Multivariate ENSO Index phenomenon (r = 0.40; p = 0.009) and Pacific Decadal Oscillation (r = −0.38; p = 0.000). Regarding the ENSO phenomenon, an antiphase association of r = −0.34; p = 0.000 was found, with significant periods of 1 to 4 years from 1770 to 1800, 1845 to 1850, and 1860 to 1900, with periods of 6 to 10 years from 1875 to 1920, and from 6 to 8 years from 1990 to 2000. This study allowed a reconstruction of mean temperature through reanalysis data, as well as a historical characterization of temperature for central Chihuahua beyond the observed records.