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.

One of the conifers that survived after the last glaciation is the Pseudotsuga menziesii (Mirb.) Franco. Due to the gradual increase in temperature, this species was forced to move from the south to the north and to higher elevation, causing a fragmented and intermittent distribution in Mexico. The main objective of this study was to model and identify suitable areas for the future conservation of the P. menziesii in Mexico. The specific objectives of this research were: (i) to model the habitat suitability of P. menziesii in Mexico, (ii) to identify the most relevant environmental variables based on its current and future habitat suitability (2030, 2050, 2070 and 2090) and (iii) to suggest areas for the conservation of the species in Mexico. Records were compiled from different national and international sources. Climate and topographic variables were used. With MaxEnt software version 3.4.3 (Phillips, New York, NY, USA) 100 distribution models were obtained, where the model showed an area under the curve of 0.905 for training and 0.906 for validation and partial ROC of 1.95 and Z reliable (p < 0.01), with TSS values > 0.80. The current area of the P. menziesii was 31,580.65 km². The most important variables in the current and future distribution were maximum temperature of the hottest month, precipitation of the coldest trimester and average temperature of the coldest trimester. The percentage of permanence (resilience) for the 2030, 2050, 2070 and 2090 climate horizons was 49.79%, 25.14%, 17.45% and 16.46%, respectively, for the SSP 245 scenario. On the other hand, for the SSP 585 scenario and the analyzed horizons, the percentage resilience in areas of suitable habitat zones was 41.45%, 27.42%, 9.82% and 2.89%.

Background. The use of synthetic fertilizers has contributed to environmental pollution and the impact of areas for agricultural use, generating the need to look for ecological alternatives. Currently it has been shown that the application of the commercial biofertilizer Azofert® (Rhizobium leguminosarum, CF1, 1x109 CFU mL-1) increases the growth, development and yield of some crops, including corn, which is of great importance in the world due to its high consumption. Objetive. To evaluate the agronomic response of two corn hybrids (ADV-9789 and Emblema Ultra), under the application of said organic biofertilizer Azofert®. Methodology. A completely randomized block experimental design (BCA) with a bi-factorial arrangement was used, applying doses of 4, 8 and 12 mL Kg-1 seed and a control treatment. Results. It was observed that, in the growth, production and yield variables, the three Azofert® treatments were superior to the control treatment, which assume that the inoculation in seed was efective as a treatment recommendation for this crop. The highest dose (12 mL Kg-1 seed) was the one that presented the significantly higher values in both varieties. Implications. Azofert® is a biofertilizer whose main use is in legumes however, this opens an area of opportunity to study it in other types of crops. Conclusions. The organic biofertilizer Azofert® has a high potential to increase the growth, development and yield of the two corn hybrids studied.