Daily solar radiation is an important variable in many models. In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatán Peninsula, México. Model performance was assessed with statistical indicators such as root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The performance assessment indicates that the SVM technique with requirements of daily maximum and minimum air temperature, extraterrestrial solar radiation and rainfall has better performance than the other techniques and may be a promising alternative to the usual approaches for predicting solar radiation

Estimation of reference evapotranspiration (${\mathrm{ET}}_{\mathrm{o}}$ ) using air temperature is particularly attractive for places where solar radiation, wind speed, and air humidity data are not readily available. In this study, seven temperature-based (TET) models and the standardized reference evapotranspiration equation for short canopies method were compared. Using only temperature data from the Yucatán Peninsula, México, the Food and Agriculture Organization of the United Nations (FAO)-Penman-Monteith (PMT) model was used to estimate ${\mathrm{ET}}_{\mathrm{o}}$ . Results from the temperature-based models are compared with FAO-56 daily ${\mathrm{ET}}_{\mathrm{o}}$ calculations using the Nash-Sutcliffe model efficiency coefficient (NSE), the coefficient of determination ($R^2$ ), mean absolute bias error (MAE), mean absolute percentage error (MAPE) and root mean square (RMSE). The results show that the noncalibrated PMT expression using temperatures alone produced the best results, with $\mathrm{RMSE}=0.7\mathrm{mm}{\mathrm{d}}^{-1}$ . The Hargreaves-Samani calibrated ($\mathrm{RMSE}=0.74\mathrm{mm}·{\mathrm{d}}^{-1}$ ) and Camargo calibrated ($\mathrm{RMSE}=0.78$ ) models exhibited the next best performance. RMSE values were as high as $1.56\mathrm{mm}·{\mathrm{d}}^{-1}$ for the other models.

The Yucatan Peninsula, located in southern Mexico, is a central honey-producing region with extraordinary biodiversity of melliferous plants. Approximately 900 plant species have been described as being a source of nectar and pollen for bees and other pollinators. They provide ecosystem services that help to keep plant biodiversity high and mitigate the effects of climate change. This study aimed to reveal the diversity of the pollen content in honey through a melissopalynological analysis of 22 honey samples collected in February–August 2021 from the north-central area of Campeche, Mexico. The extraction of pollen from the honey was carried out using standard methods for melissopalynological analysis. The honeys were classified by botanical origin to determine their floral sources and a diverse spectrum of 19 pollen types from 13 families was identified. Only eight were predominant: Milleria quinqueflora, Gymnopodium floribundum, Terminalia buceras, Amaranthus spinosus, Zea mays, Talisia floresii, Guazuma ulmifolia, and Croton icche. Our research shows the high quality of the honey analyzed and highlights the diversity and critical role of local melliferous flora and crops in beekeeping development in southern Mexico. The results in this study are useful for confirming the botanical origins of honey, generating information for designing nature conservation and agroecosystem management strategies, and increasing the knowledge of beekeepers in Campeche, Mexico.