Knowing the total Nitrogen content (Nt) of forage maize (Zea mays) is important so that decisions can be made quickly and efficiently to adjust the timing and amount of both irrigation and fertilizer. In 2017 and 2018 during three growing cycles in two study plots, leaf samples were collected and the Dumas method was used to estimate Nt. During the same growing seasons and on the same sampling plots, a Parrot Sequoia camera mounted on an unmanned aerial vehicle (UAV) was used to collect high resolution images of forage maize study plots. Thirteen multispectral indices were generated and, from these, a Random Forest (RF) algorithm was used to estimate Nt. RF is a machine-learning technique and is designed to work with extremely large datasets. Overall analysis showed five of the 13 indices as the most important. One of these five, the Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index, was found to be the most important for estimation of Nt in forage maize (R² = 0.76). RF handled the complex dataset in a time-efficient manner and Nt did not differ significantly when compared between traditional methods of evaluating Nt at the canopy level and using UAVs and RF to estimate Nt in forage maize. This result is an opportunity to explore many new research options in precision farming and digital agriculture.

Reference evapotranspiration (ET₀) is the first step in calculating crop irrigation demand, and numerous methods have been proposed to estimate this parameter. FAO-56 Penman–Monteith (PM) is the only standard method for defining and calculating ET₀. However, it requires radiation, air temperature, atmospheric humidity, and wind speed data, limiting its application in regions where these data are unavailable; therefore, new alternatives are required. This study compared the accuracy of ET₀ calculated with the Blaney–Criddle (BC) and Hargreaves–Samani (HS) methods versus PM using information from an automated weather station (AWS) and the NASA-POWER platform (NP) for different periods. The information collected corresponds to Module XII of the Lagunera Region Irrigation District 017, a semi-arid region in the North of Mexico. The HS method underestimated the reference evapotranspiration (ET₀) by 5.5% compared to the PM method considering the total ET₀ of the study period (26 February to 9 August 2021) and yielded the best fit in the different evaluation periods (daily, 5-day mean, and 5-day cumulative); the latter showed the best values of inferential parameters. The information about maximum and minimum temperatures from the NP platform was suitable for estimating ET₀ using the HS equation. This data source is a suitable alternative, particularly in semi-arid regions with limited climatological data from weather stations.

Climate defines the hydrological cycle of each region and climate change will undoubtedly affect the recharge processes of the world’s water tables and the water resources currently available at the basin and microbasin scale. The objective of the present paper is to evaluate future changes in evapotranspiration and flows from the Sardinas River basin in North Durango, Mexico. The Rural Genius Model (GR2M) is an aggregated monthly hydrological model, which is used to reconstruct flows from precipitation and evapotranspiration by applying two functions: a production function and a transfer function. A transfer function has been used under four shared socioeconomic pathway scenarios (126, 245, 370, and 585). Pettitt and Mann–Kendall statistical tests were used to determine trends, which were identified by the breakpoint in the evapotranspiration and flow time series. Results showed that under climate change scenarios, evapotranspiration shows an increase over time. Under the climate scenario, SSP 126, and the application of the statistical test in the flow series show an increasing trend with a break in May for 2090, with a mean of 1658 mm before and 2238 mm after the break, with an excess of 34.98%. The flow under the SSP 245 climate scenario predicts a mean flow of 1703.11 mm and a break in May of the 2090 horizon, with a mean before and after the break of 1624 mm and 2168 mm, respectively, with an excess of 33.49%. Under the SSP 370 scenario, the mean is expected to be 1710.81 mm, with a break in May 2090, before and after means of 1633 mm and 2166 mm, respectively, with an excess of 32.63%. Under climate change scenario SSP 585, the mean expected will be 1701.43 mm and the break in the flow series will occur in May of the 2090 horizon, with a mean of 1628 mm before the break and 2132 mm after, with a flow excess of 30.95%. The results of this study can be a basis for decision-makers for better management and protection of water resources in northern Durango, Mexico.

Droughts are a climatic phenomenon with local, regional, and large-scale repercussions. Historical knowledge of droughts generated by modeled data allows the development of more accurate climate reconstructions to propose better approaches for the management of hydric resources. The objective of this research was to evaluate the association of precipitation and temperature with data from the NLDAS-002 to develop a reconstruction of droughts in the center of Chihuahua, Mexico using the SPEI from tree rings. We also identified the influence of ocean–atmospheric phenomena on the reconstructed drought index. The best association among chronologies was obtained with the earlywood band and accumulated seasonal precipitation from November of the previous year to June of the current year (r = 0.82, p < 0.05) and for temperature from January to July (r = −0.81, p < 0.05). The reconstructed drought index extended from 1775 to 2017 (243 years), where seven extreme drought events were identified. We found significant correlations between the reconstructed Standardized Precipitation Evapotranspiration Index and the Pacific Decadal Oscillation (r = 0.46, p < 0.05), Atlantic Multidecadal Oscillation (r = −0.34, p < 0.05), Multivariate El Niño Southern Oscillation Index (r = 0.29, p < 0.05), and Southern Oscillation Index (r = −0.22, p < 0.05). The historical reconstruction of hydroclimatology in the center of Chihuahua is important for planning a long-term assessment and for the management of water resources shared by Mexico and the United States.

A superficie de ecosistemas vírgenes en México es casi inexistente, no obstante, árboles longevos en bosques de coníferas son factibles de ubicar en sitios remotos y escarpados de difícil acceso, situación que ha permitido conocer sus características fenotípicas. Una de las coníferas más antiguas en bosques mixtos de las Sierras Madre Occidental, Oriental y Eje Neovolcánico es Pseudotusga menziesii, que en algunos sitios, particularmente de la primera cadena montañosa, logran edades mayores a 600 años al igual que Pinus hartwegii y son de gran valía para estudios paleoclimáticos; otros como Pinus cembroides, Pinus pinceana, Pinus culminicola y Pinus lumholtzii, que habitan sitios más secos, alcanzan edades de 300 años o más. Taxodium mucronatum, propio de comunidades riparias, es la especie más longeva en México e individuos milenarios se han ubicado en bosques de galería de los estados de San Luis Potosí, Durango y Querétaro. Ejemplares de este tipo también se les encuentra en ecosistemas áridos y tropicales que aun no han sido estudiados. Algunos árboles añejos han estado ligados con hechos históricos relacionados con la Guerra de Independencia y la Revolución Mexicana, situación que les confiere gran importancia para su conservación. La ubicación de organismos de mucha edad es primordial y constituye un elemento adicional para fundamentar acciones de protección, restauración de ecosistemas degradados, formulación de proyectos ecoturísticos y conservación de la biodiversidad.