Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network

Evidence of immunometabolic dysregulation and airway dysbiosis in athletes susceptible to respiratory illness

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Funding

Perspectives on biometeorological research on the African continent

Since the first issue of the International Journal of Biometeorology in 1957, a total of 135 papers have reported on research in or of African countries. The majority of these have been on topics of animal biometeorology (36%), and the greatest proportion (24%) are situated in Nigeria. There has been a considerable increase in papers on African biometeorology since 2011, with those from this past decade accounting for 58% of all African papers in the journal. This occurs concurrent to an increase in the total number of papers published in the journal, driven by a move to the Editorial Manager system. While 66% of the papers on African biometeorology in the journal are authored by at least one person with an affiliation in the African continent, only 15 African countries are represented in the total authorship. As much of the African continent is projected to experience climatic changes exceeding the global mean, as much of the region is involved in animal and plant farming, and as seasonally-fluctuating and climatically affected diseases are common place, this low representation of work in Africa is surprising. This points to the need for greater awareness among African researchers of the discipline of biometeorology, greater involvement of African biometeorologists in International Society of Biometeorology and Commission meetings, and the inclusion of a greater number of African academics in the review process. This would be beneficial to the Society in increasing diversity and encouraging a more cosmopolitan engagement, and to the recognition of scientific development in African countries.

Mild heat stress changes the microbiota diversity in the respiratory tract and the cecum of layer-type pullets

The present study aimed to research the effects of cyclic heat environment on the microbial diversity and structure of respiratory tract and cecum of chicken. A total of 360 layer-type pullets at 11 wk of age were subjected to different temperature treatments for 10 wk: constant 22°C; cyclic temperature 22°C to 24°C, 22°C to 26°C, 22°C to 28°C, 22°C to 30°C; the ambient temperature increased from 10:00, reached the set point within 1 h, and maintained until 18:00, thereafter the temperature was restored to 22°C; and the relative humidity was maintained at 60%. The result showed that feed intake of the chickens on ambient temperature 30°C group was significantly lower than that of the chickens on ambient temperature 24°C. The white blood cell, red blood cell, lymphocyte, hemoglobin, and pecked-cell volume content were highest at 24°C on 14, 16, and 18 wk. The ratio of CD3⁺CD4⁺/CD3⁺CD8⁺ T cells was lowest at 30°C. Meanwhile, the abundance of cecum bacteria in chickens at 30°C was lower than that at 24°C. Cyclic heat environment temperature treatment did not significantly affect the concentration of secretory immunoglobulin A in chicken bronchoalveolar lavage fluid (BALF) levels during 10 wk of trial. The diversity index analysis showed that the effect of 24°C on the cecum flora of chickens was optimal. Abundance of Firmicutes bacteria in the lung flora and cecum flora was lower at 30°C than at 24°C group. Similarly, the microorganism, Brevibacillus in the BALF was also significantly lower at 24°C. In conclusion, cyclic 24°C treatment was beneficial for the feed intake, blood routine indexes, microflora structure of the cecum, and respiratory tract in laying pullets.

Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits

This study was carried out to predict average number of kits per birth and mortality number of non-descript rabbits in Plateau State, Nigeria using artificial neural network (ANN). Data were obtained from a total of 100 rabbit farmers. The predicted mean value for number of kits per birth using ANN (6.60) was similar to the observed value (6.52). As regards mortality, the predicted mean value using ANN (17.75) was also similar to the observed value (17.80). Primary occupation, experience in rabbit keeping, flock size and credit type were the parameters of utmost importance in predicting number of kits per birth. The fairly high coefficient of determination (R2) (55.7%) and low root mean square error (RMSE) value of 1.22 conferred reliability on the ANN model. The R2 value obtained in the prediction of mortality using ANN implies that 61.1% of the variation in the number of mortality can be largely explained by the explanatory variables such as flock size, age of farmers, experience in rabbit keeping and average number of kits per birth. The low RMSE value of 3.82 also gave credence to the regression model. The present information may be exploited in taking appropriate management decisions to boost production.