Objective:To measure the seasonal effect on endocrinological profiles and sex behavioural profiles during rainy and dry summer seasons in male Andaman local goat in Andaman and Nicobar Islands,India.Methods:Ten adult ...Objective:To measure the seasonal effect on endocrinological profiles and sex behavioural profiles during rainy and dry summer seasons in male Andaman local goat in Andaman and Nicobar Islands,India.Methods:Ten adult male Andaman local goats were selected from the goat breeding farm,ICAR-Central Island Agricultural Research Institute,Port Blair,Andaman and Nicobar Islands,India.Endocrinological profiles such as follicle stimulating hormone(FSH),luteinizing hormone(LH),testosterone,thyroid stimulating hormone(TSH),triiodothyronine(T3),thyroxine(T4),cortisol and prolactin were analysed with enzyme-linked immunosorbent assay kits.Sex behavioural profiles such as libido score,mating ability score and sex behavioural score were measured during rainy and dry summer seasons in male Andaman local goat.Results:Endocrinological profiles as well as sex behavioural profiles differed significantly between rainy season and dry summer season(P<0.05).The levels of FSH,LH,testosterone,TSH,T3,and T4 were significantly higher in rainy season than in dry summer season whereas cortisol and prolactin were significantly higher in dry summer season than in rainy season(P<0.05).The ratio of T3:T4 was significantly higher in rainy season than in dry summer season.Similarly,libido score,mating ability score and sex behavioural score were significantly higher in rainy season than in dry summer season(P<0.05).Conclusions:Rainy season has significantly higher beneficial effects than summer season on reproduction and artificial breeding programmes in semi-intensive management of goat under the tropical humid island ecosystem in Andaman and Nicobar Islands.展开更多
[ Objective] The research aimed to study differences of temperature and humidity and vertical distribution of human comfort between city and countryside of Heilongjiang in summer. [Method] By using temperature, humidi...[ Objective] The research aimed to study differences of temperature and humidity and vertical distribution of human comfort between city and countryside of Heilongjiang in summer. [Method] By using temperature, humidity and wind velocity data at 10 and 70 m of two iron towers in urban and rural districts of Heilongjiang from June 1 to August 31,2010, the characteristics at temperature and humidity fields and change rule of human comfort index in urban and rural areas in summer were analyzed. [Result] Compared with rural areas, heat island effect of urban districts was obvious, and it was the strongest during 21：00 -23：00 and the weakest during 05：00 -06：00. Daily change rules of wind velocity at 10 m of two anemometer towers were basically consistent. Wind velocity was big at daytime and small at night. Daily change of wind velocity at 70 m of urban districts was consistent with that at 10 m, while wind velocity change at 70 m of rural areas was different from that at 10 m, which had obvious high-altitude wind characteristics. Daily change rules of humidity in two towers were basically consistent, and occurrence time of extremum at 70 m delayed for 1 h. At vertical height, humidity at 10 m was higher than that at 70 m at daytime, and was lower than that at 70 m at night. [ Conclusion] The city had obvious wet island effect during 00：00 -06：00 and dry island effect at nightfall. Comfort index of urban districts was higher than that in rural areas, and difference was the maximum during 19：00 -21：00. Comfort index at 10 m was higher than that at 70 m at daytime in the city and countryside, and was lower than that at 70 m at night. Occurrence frequencies of Grade Zero of comfort at 10 and 70 m of urban districts were both over 60%, while occurrence frequency of ≥ Grade Three of comfort was very small. At vertical height, human comfort had small difference at night. At daytime, as height rise, human comfort index significantly declined, and human feeling was more comfortable than that at low layer.展开更多
The objective of this work is to analyze the temporal and spatial variability of the monthly mean aerosol index (AI) obtained from the Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) in c...The objective of this work is to analyze the temporal and spatial variability of the monthly mean aerosol index (AI) obtained from the Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) in comparison with the available ground observations in Nigeria during 1984-2013. It also aims at developing a regression model to allow the estimation of the values of AI in Nigeria based on the data from ground observations. TOMS and OMI data are considered and treated separately to provide continuity and consistency in the long-term data observations, together with the meteorological variable such as wind speed, visibility, air temperature and relative humidity that can be used to characterize the dust activity in Nigeria. The results revealed a strong seasonal pattern of the monthly distribution and variability of absorbing aerosols along a north to south gradient. The monthly mean AI showed higher values during the dry months (Harmattan) and lower values during the wet months (Summer) in all zones. From December to February, higher AI values are observed in the southern region, decreasing progressively towards the north, while during March-October, the opposite pattern is observed. The AI showed clear maximum values of 2.06, 1.93, and 1.87 (TOMS) and 2.32, 2.27 and 2.24 (OMI) in the month of January and minimum values in September over the north-central, southern and coastal zones, while showing maximum values of 1.76 (TOMS) and 2.10 (OMI) during March in the Sahel. New empirical algorithms for predicting missing AI data were proposed using TOMS data and multiple linear regression, and the model co-efficient was determined. The generated coefficients were applied to another dataset for cross-validation. The accuracy of the model was determined using the coefficient of determination R<sup>2</sup> and the root mean square error (RMSE) calculated at the 95% confidence level. The AI values for the missing years were retrieved, plotted and compared with the measured monthly AI cycle. It is concluded that the meteorological variables can significantly explain the AI variability and can be used efficiently to predict the missing AI data.展开更多
基金This research was funded by a Grant from All India Coordinated Research Project on Goat Improvement(ICAR-AICRP on Goat Improvement),Indian Council of Agricultural Research,New Delhi,India with Grant number AICRP-Goat Improvement/ICAR-CIARI.
文摘Objective:To measure the seasonal effect on endocrinological profiles and sex behavioural profiles during rainy and dry summer seasons in male Andaman local goat in Andaman and Nicobar Islands,India.Methods:Ten adult male Andaman local goats were selected from the goat breeding farm,ICAR-Central Island Agricultural Research Institute,Port Blair,Andaman and Nicobar Islands,India.Endocrinological profiles such as follicle stimulating hormone(FSH),luteinizing hormone(LH),testosterone,thyroid stimulating hormone(TSH),triiodothyronine(T3),thyroxine(T4),cortisol and prolactin were analysed with enzyme-linked immunosorbent assay kits.Sex behavioural profiles such as libido score,mating ability score and sex behavioural score were measured during rainy and dry summer seasons in male Andaman local goat.Results:Endocrinological profiles as well as sex behavioural profiles differed significantly between rainy season and dry summer season(P<0.05).The levels of FSH,LH,testosterone,TSH,T3,and T4 were significantly higher in rainy season than in dry summer season whereas cortisol and prolactin were significantly higher in dry summer season than in rainy season(P<0.05).The ratio of T3:T4 was significantly higher in rainy season than in dry summer season.Similarly,libido score,mating ability score and sex behavioural score were significantly higher in rainy season than in dry summer season(P<0.05).Conclusions:Rainy season has significantly higher beneficial effects than summer season on reproduction and artificial breeding programmes in semi-intensive management of goat under the tropical humid island ecosystem in Andaman and Nicobar Islands.
文摘[ Objective] The research aimed to study differences of temperature and humidity and vertical distribution of human comfort between city and countryside of Heilongjiang in summer. [Method] By using temperature, humidity and wind velocity data at 10 and 70 m of two iron towers in urban and rural districts of Heilongjiang from June 1 to August 31,2010, the characteristics at temperature and humidity fields and change rule of human comfort index in urban and rural areas in summer were analyzed. [Result] Compared with rural areas, heat island effect of urban districts was obvious, and it was the strongest during 21：00 -23：00 and the weakest during 05：00 -06：00. Daily change rules of wind velocity at 10 m of two anemometer towers were basically consistent. Wind velocity was big at daytime and small at night. Daily change of wind velocity at 70 m of urban districts was consistent with that at 10 m, while wind velocity change at 70 m of rural areas was different from that at 10 m, which had obvious high-altitude wind characteristics. Daily change rules of humidity in two towers were basically consistent, and occurrence time of extremum at 70 m delayed for 1 h. At vertical height, humidity at 10 m was higher than that at 70 m at daytime, and was lower than that at 70 m at night. [ Conclusion] The city had obvious wet island effect during 00：00 -06：00 and dry island effect at nightfall. Comfort index of urban districts was higher than that in rural areas, and difference was the maximum during 19：00 -21：00. Comfort index at 10 m was higher than that at 70 m at daytime in the city and countryside, and was lower than that at 70 m at night. Occurrence frequencies of Grade Zero of comfort at 10 and 70 m of urban districts were both over 60%, while occurrence frequency of ≥ Grade Three of comfort was very small. At vertical height, human comfort had small difference at night. At daytime, as height rise, human comfort index significantly declined, and human feeling was more comfortable than that at low layer.
文摘The objective of this work is to analyze the temporal and spatial variability of the monthly mean aerosol index (AI) obtained from the Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) in comparison with the available ground observations in Nigeria during 1984-2013. It also aims at developing a regression model to allow the estimation of the values of AI in Nigeria based on the data from ground observations. TOMS and OMI data are considered and treated separately to provide continuity and consistency in the long-term data observations, together with the meteorological variable such as wind speed, visibility, air temperature and relative humidity that can be used to characterize the dust activity in Nigeria. The results revealed a strong seasonal pattern of the monthly distribution and variability of absorbing aerosols along a north to south gradient. The monthly mean AI showed higher values during the dry months (Harmattan) and lower values during the wet months (Summer) in all zones. From December to February, higher AI values are observed in the southern region, decreasing progressively towards the north, while during March-October, the opposite pattern is observed. The AI showed clear maximum values of 2.06, 1.93, and 1.87 (TOMS) and 2.32, 2.27 and 2.24 (OMI) in the month of January and minimum values in September over the north-central, southern and coastal zones, while showing maximum values of 1.76 (TOMS) and 2.10 (OMI) during March in the Sahel. New empirical algorithms for predicting missing AI data were proposed using TOMS data and multiple linear regression, and the model co-efficient was determined. The generated coefficients were applied to another dataset for cross-validation. The accuracy of the model was determined using the coefficient of determination R<sup>2</sup> and the root mean square error (RMSE) calculated at the 95% confidence level. The AI values for the missing years were retrieved, plotted and compared with the measured monthly AI cycle. It is concluded that the meteorological variables can significantly explain the AI variability and can be used efficiently to predict the missing AI data.
