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.

Classification and regression trees (CARTs) for data analysis, an hourly weather dataset, and a 3 year field incidence and severity dataset of winter wheat rust were integrated to forecast pathogens’ presence/absence. The field dataset of incidence and severity was collected for three production cycles. Measured records of 88 Automatic Meteorological Stations and the indirect weather dataset generated in the Weather Research and Forecasting environment interpolated to each Automatic Meteorological Station location were analysed in the Python ecosystem. The focal point of the analysis was the severity of the disease. The analysis of direct weather data revealed the association of leaf rust severity with a night temperature of <14.25°C and global radiation of <521.67 W·m^–2, while the estimated dataset showed that its severity is better explained by the dew point temperature of <13.7°C and a mean temperature of <19.06°C. The direct dataset also indicated that stripe rust severity was associated with relative humidity of <88.73%, global radiation of <597.39 W·m^–2 and dew point temperature of <16.09°C, whereas the estimated data revealed that pathogen severity is better explained by a model composed of a dew point temperature of <14.6°C, night temperature of <20.4°C and a maximum temperature of <27.9°C. The severity and intensity analysis indicated the pathogen's preference for non‐dry ambient conditions and the preference of stripe rust pathogen for humid and warmer temperatures than leaf rust. The weather thresholds of both pathogens, and CART analysis, unveiled that winter wheat rust can be forecasted. This constitutes the foundation of a more efficient extension programme based on the internet of things.

Remotely sensed imageries were used to analyze the response of desert vegetation to physiographic factors and accumulated precipitation in drier and wetter years within a region of >16,500 km² sampled with 5,000 random pixels of 30 m. Vegetation development was indexed by the annual maximum values for greenness (SAVI) and canopy water content (NDII). Precipitation was interpolated from the 0.25° grid of the Tropical Rainfall Measurement Mission satellite-based estimates, showing a regional average of ∼55 mm in the wetter year. The vegetation indices were only weakly related to total precipitation, often in a negative sense. Terrain factors that most often affected the vegetation indices, in multiple regression models, were Topographic Wetness Index, elevation, and slope gradient; these often had different signs for SAVI and for NDII. Models for NDII on intrusive igneous rocks gave better results than on extrusive igneous rocks. The strongest patterns in vegetation development were the contrast among Pacific coast, Cordillera, and Gulf coast subregions and the generally stronger results for NDII than SAVI.

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.

Deforestation is an important environmental problem and a key promoter of regional climate change through modifying the surface albedo. The objective of this research was to characterize the impact of deforestation and land use changes on surface albedo (α) and climate patterns in a tropical highland region of Mexico, between the years 2014 and 2021. The main land cover types are coniferous forests (CF), oak and gallery woodlands (OGW), and annual agriculture (AA), which represent more than 88% of the regional territory. We used 2014 and 2021 Landsat 8 OLI images with topographic and atmospheric correction in order to develop an inventory of albedo values for each land cover type in both time scenarios. Albedo images were generated by using the equation proposed by Liang in 2001, which is based on the reflectance of the bands 2, 3, 4, 5, and 7. Differences in albedo values were calculated between the years 2014 and 2021, and those differences were correlated with variations in climate parameters, for which we used climate data derived from the WRF model. In addition, the different land use changes found were classified in terms of triggers for increasing or decreasing surface albedo. We used the Mann–Whitney U Test to compare the 2021 − 2014 climatic deviations in two samples: Sample A, which included sites without albedo change in 2021; and Sample B, including sites with albedo change in 2021. Results showed that between 2014 and 2021, at least 38 events of land use change or deforestation occurred, with albedo increments between 1 and 11%, which triggered an average increment of 2.16% (p < 0.01; Mann–Whitney U Test) of the regional surface albedo in comparison to the 2014 scenario. In this period, the albedo for CF, OGW, and AA also increased significantly (p < 0.001; Mann–Whitney U Test) by +79, +12, and +9%, respectively. In addition, the regional albedo increment was found to be significant and negatively correlated (p < 0.01 Spearman’s coefficient) with relative humidity (RH), maximum temperature (Tmax), and minimum temperature (Tmin), and correlated (p < 0.01) positively with diurnal temperature range (DTR). The Mann–Whitney U Test revealed that 2021 climatic variations in Sample B sites are statistically different (p < 0.05) to 2021 climatic variations in Sample A sites, which demonstrates that albedo changes are linked to a decrease in minimum temperature and relative humidity and an increase in DTR. Conversion of CF and OGW into AA, perennial agriculture (PA), or grassland (GR) always yielded an albedo increment, whilst the conversion of AA to irrigation agriculture (IA) or PA triggered a decrease in albedo, and finally, the pass from GR or AA to protected agriculture (PA) caused albedo to increase or decrease, depending on the greenhouse covers materials. Reducing deforestation of CF and OGW, conversion of AA or GR into PA, and selecting adequate greenhouse covers could help to mitigate regional climate change.

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.