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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.

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.

Pinus greggii is a species of socioeconomic importance in terms of wood production and environmental services in Mexico, though it is restricted by particular environmental conditions to the Sierra Madre Occidental. Species distribution models are geospatial tools widely used in the identification and delineation of species' distribution areas and zones susceptible to climate change. The objectives of this study were to: (i) model and quantify the environmentally suitable area for Pinus greggii in Mexico, and possible future distributions under four different scenarios of climate change; (ii) identify the most relevant environmental variables that will possibly drive changes in future distribution; and (iii) to propose adequate zones for the species' conservation in Mexico. Some 438 records of Pinus greggii from several national and international databases were obtained, and duplicates were discarded to avoid overestimations in the models. Climatic, edaphic, and topographic variables were used and 100 distribution models for current and future scenarios were generated using the Maxent software. The best model had an area under the curve (AUC) of 0.88 and 0.93 for model training and validation, respectively, a partial ROC of 1.94, and a significant Z test (p<0.01). The current estimated suitable area of Pinus greggii in Mexico was 617,706.04 ha. The most relevant environmental variables for current distribution were annual mean temperature, mean temperature of coldest quarter, and slope. For the 2041-2060 models, annual mean temperature, precipitation of coldest quarter, and slope were the most important drivers. The use of climatic models allowed to predict a future decrease in suitable habitat for the species by 2041-2060, ranging from 48,403.85 (7.8%-HadGEM2-ES RCP 8.5 model) to 134,680.17 ha (21.8%-CNRM-CM5 RCP 4.5). Spatial modeling of current and future ecological niche of Pinus greggii also allowed to delineate two zones for in situ conservation and restoration purpose in northeastern (Nuevo Leon) and central (Hidalgo) Mexico.

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