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In Mexico, beekeeping is one of the main activities of the agricultural sector due to its production of high quality honey which is exported to several European countries. Beekeeping is practiced by more than 40,000 producers; with around two million hives in apiaries distributed in the five honey producing regions of Mexico (North, Central and Highlands, Pacific, Gulf and Yucatan Peninsula). The Yucatan Peninsula comprising of Campeche, Quintana Roo and Yucatan, is considered as one the most important honey producing region since it contributes to about 95 % of Mexican honey production in the international markets. The region represents to 30 to 35 % of the total number of Mexican bee colonies, and beekeeping is considered as a secondary agricultural activity. With the purpose of characterizing the beekeeping activity in Campeche (in municipalities of Campeche, Champoton and Hopelchen), Yucatan Peninsula a comprehensive survey was conducted on 120 producers from July to December, 2016. The average age of beekeepers was found to be 57 years, with an average of 2.27 apiaries per producer and with 20.6 hives per apiary. The survey revealed that beekeepers devote two days a week to beekeeping and make on an average 3.67 harvests per year. Based on the survey results, it is recommended to promote generational change in the local beekeeping activity, with stronger emphasis on the young beekeepers of the region.

This study aimed to estimate live body weight from body measurements for Holstein × Zebu dairy cows (n = 156) reared under conditions of humid tropics in Mexico using multivariate adaptive regression splines algorithm (MARS) with several train-test proportions. The body measurements included withers height, rump height, hip width, heart girth, body length and diagonal body length. The data were divided into 65:35, 70:30 and 80:20 split data for training and testing sets, respectively. The MARS algorithm was used to construct a prediction model, which predicted the body weight from the body measurements of the test dataset. The results emphasized that the MARS algorithm had an explanation rate for 80:20 train and test set of 0.836 and 0.711, respectively, with minimum Akaike information criterion values. This indicates that it is a reliable way of predicting body weight from body measurements. The results suggest that body weight prediction can be performed with the MARS algorithm in a reliable way, therefore, this algorithm may be a useful tool for animal breeders and researchers in the development of feeding and selection-aimed approaches. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference of the abdomen, and semicircumference of the girth. A biometric parameter acquisition system was developed using a Kinect as a sensor. The results were contrasted with measurements obtained manually with a flexometer. The comparison gives an average root mean square error (RMSE) of 9.91 and a mean R 2 of 0.81. Subsequently, the parameters were used as input in a back-propagation artificial neural network. Performance tests were performed with different combinations to make the best choice of architecture. In this way, an intelligent body weight estimation system was obtained from biometric parameters, with a 5.8% RMSE in the weight estimations for the best architecture. This approach represents an innovative, feasible, and economical alternative to contribute to decision-making in livestock production systems. [ABSTRACT FROM AUTHOR]

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The incorporation of trees in livestock systems can influence the fluxes of CO2 from soil to the atmosphere. The aim of this study was to evaluate the diurnal and seasonal variations of soil CO2 fluxes in grazing livestock systems with and without trees in the State of Tabasco, southern Mexico. We selected two livestock systems, one with scattered trees in paddocks (STP) and the other pasture monoculture (PM) without trees. We measured the soil CO2 fluxes, soil temperature, and moisture in the winter-rainy, dry and summer-rainy seasons. Measurements were made five consecutive days a month from 06:00 to 14:00, with an infrared gas analyzer and a closed dynamic chamber (LI-COR). Pasture monoculture showed a higher soil CO2 flux, compared to the STP (6.72 vs. 4.30 μmol m−2 s−1). Soil moisture to 5 cm depth was higher in the PM, while soil temperature did not differ between these systems. Soil CO2 fluxes were higher in the summer rainy season compared to the winter-rainy and dry seasons in both livestock systems. Soil temperature and moisture also differed between seasons for both systems. Soil CO2 fluxes were higher in the afternoon, while soil moisture was lower in both livestock systems. Soil temperature did not differ between livestock systems (ANOVA, p > 0.05). Soil CO2 fluxes in STP showed significant positive correlations with soil temperature and soil moisture. We conclude that STP system is an alternative, with a high potential to mitigate CO2 emissions from the soil, and to promote the sustainable development of livestock farming in the tropics. [ABSTRACT FROM AUTHOR]

Direct collection of biometric measurements (BM) from sheep is an expensive and stressful procedure for animals; instead, indirect and novel methods have recently been used. The objective of this study was to use digital image analysis (DIA) to predict biometric measurements of Pelibuey sheep as a non-invasive approach under on-farm conditions. Withers height (WH), body length (BL), body diagonal length (BDL), and rib depth (RD) were predicted in Pelibuey ewes using DIA. Images were taken from the left flank of 65 nonpregnant and nonlactating Pelibuey ewes using a digital camera and analyzed by DIA. The BM determined from both in vivo and by DIA presented positive and moderate (P < 0.05) correlation coefficients (r) of 0.43, 0.66, 0.73, and 0.75 for BL, BDL, WH, and RD, respectively. Regression equations from BM by DIA had determination coefficients (r²) of 0.19, 0.44, 0.54, and 0.56 for BL, BDL, WH, and RD, respectively. The equations developed were from low to moderate precision (r² = 0.18 to 55), moderate to high accuracy with a bias correction factor (Cb > 0.69), and low to moderate reproducibility index (> 0.30). Overall, the use of DIA was able to predict the BM in Pelibuey ewes with low to moderate precision and accuracy. Factors affecting the accuracy and precision of this relationship should be further investigated. [ABSTRACT FROM AUTHOR]