Emerging or alternative crops are those that have been recently introduced in response to new demands and commercial opportunities. The fig has been introduced as an alternative fruit crop in Mexico due to its high nutritional, nutraceutical, and antioxidant capacity. A total of 644 points of presence were downloaded and filtered according to climatic ranges and agricultural areas, leaving 68 records. The MaxEnt algorithm was used to develop the habitat suitability models for current and future climate. The bioclimatic variables of the global circulation models, Hadley Centre Global Environment Model version 2-EarthSystem (HADGEM2-ES) and Max Planck Institute for Meteorology-Earth System Model Low Resolution (MPI-ESM-LR), were used under scenarios 2.6 and 8.5. The changes in the fig area for Mexico were analyzed based on the generated models. Under the current climate, 359,575.76 km² were estimated for 2050, and a loss of area for the excellent category was estimated for both models and scenarios; however, for the MPI-ESM-LR model projected to 2070, an increase of 5.51% and 0.39% was estimated for scenarios 2.6 and 8.5, respectively. The effect of climate change on agronomic species such as figs will be expressed in variations in climatic ranges and areas suitable for their development. The results of this study reveal the negative and positive effects of climate change on fig habitat suitability in Mexico. The dynamics of changes in surface area will be reflected mainly in northern and central Mexico.

Nitrogen is one of the most important macronutrients for crops, and in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation ≥ 0.7 were selected for subsequent use in Random Forest, Gradient Boosting, and Artificial Neural Networks to determine their relationship with nitrogen levels measured in the laboratory. Random Forest showed no relationship, yielding an R2 of zero, whereas Artificial Neural Networks yielded the best results with an R2 of 0.93. Thus, it is reliable to estimate nitrogen levels using this algorithm by feeding it with data from TCARI, MCARI, TCARI/OSAVI, and MCARI/OSAVI, assisted by technological tools.