氣象因子對盤固草產量之影響分析

Analysis of Climatic Factors on Forage Yield of Pangolagrass

張芳銘(Fang-Ming Chang) ； 楊純明(Chwen-Ming Yang) ； 洪國源(Kuo-Yuan Hong) ； 施意敏(Yih-Min Shy) ； 許福星(Fu-Hsing Hsu) ； 卜瑞雄(Ruey-Hsiung Buu) ； 金文蔚(Wen-Wei King)

《中華農業研究》 51卷1期 (2002/03) Pp. 15-24

https://doi.org/10.29951/JARC.200203.0002

摘要

Pangolagrass (Digitarium decumbens Stent.) is one of the major forage crops cultivated in Taiwan. Both forage yield and quality of this crop are greatly affected by the fluctuations of climatic environment. The objectives of this study were to investigate the influence of climatic factors on forage yield of pangolagrass and to determine the relationships between the climatic variables and forage yield. Field experiments were carried out in the experimental fields at Hsinhua headquartes and Hsinchu Branch Station of Taiwan Livestock Research Institute during the growing seasons of 1999-2000. Data of forage yields from each plots and climatic factors (i.e., daily mean air temperature, daily precipitation, and daily irradiance) of each growing seasons were collected for analyses. The results indicated that forage yield of pangolagrass was not determined by single climatic factor but by the complex interactions of climatic factors in both sites. By multiple regression analysis, it was shown that forage yield could be expressed in a multiple linear regression equation: Y=aX1+bX2+cX3+d, where Y is forage yield (fresh-based and dry-based) of small plot (kg m^(-2)), X1 is the accumulated daily mean air temperature, X2 is the accumulated daily precipitation, and X3 is the accumulated daily irradiance. The constants a, b, c, and d are site and season specific respectively. Except the dry-based yield from Hsinchu (R^2=0.106), the coefficients of determination (R^2) were all greater than 0.860. Due to the limited numbers of cutting (5 cuts from Hsinhua and 6 cuts from Hsinchu) for statistical analysis, the P value was all larger than 0.05. As the fresh-forage yields from two sites passed the t-test, data were pooled for further analysis. It showed that the determination coefficient reached 0.854 with the P value of 0.003.

關鍵字

氣象因子 ； 盤固草 ； 牧草產量 ； 複回歸分析

並列摘要

Pangolagrass (Digitarium decumbens Stent.) is one of the major forage crops cultivated in Taiwan. Both forage yield and quality of this crop are greatly affected by the fluctuations of climatic environment. The objectives of this study were to investigate the influence of climatic factors on forage yield of pangolagrass and to determine the relationships between the climatic variables and forage yield. Field experiments were carried out in the experimental fields at Hsinhua headquartes and Hsinchu Branch Station of Taiwan Livestock Research Institute during the growing seasons of 1999-2000. Data of forage yields from each plots and climatic factors (i.e., daily mean air temperature, daily precipitation, and daily irradiance) of each growing seasons were collected for analyses. The results indicated that forage yield of pangolagrass was not determined by single climatic factor but by the complex interactions of climatic factors in both sites. By multiple regression analysis, it was shown that forage yield could be expressed in a multiple linear regression equation: Y=aX1+bX2+cX3+d, where Y is forage yield (fresh-based and dry-based) of small plot (kg m^(-2)), X1 is the accumulated daily mean air temperature, X2 is the accumulated daily precipitation, and X3 is the accumulated daily irradiance. The constants a, b, c, and d are site and season specific respectively. Except the dry-based yield from Hsinchu (R^2=0.106), the coefficients of determination (R^2) were all greater than 0.860. Due to the limited numbers of cutting (5 cuts from Hsinhua and 6 cuts from Hsinchu) for statistical analysis, the P value was all larger than 0.05. As the fresh-forage yields from two sites passed the t-test, data were pooled for further analysis. It showed that the determination coefficient reached 0.854 with the P value of 0.003.

並列關鍵字

Climatic factors ； Pangolagrass ； Forage yield ； Multiple regression analysis