|Title:||Using Air Temperature to Predict Forage Production of Nilegrass||Other Titles:||利用氣溫預測尼羅草之牧草產量||Authors:||Chwen-Ming Yang
|Keywords:||air temperature;forage yield prediction;day-night temperature difference;Nilegrass;model validation;氣溫;牧草產量預測;日夜溫差;尼羅草;模式驗證||Issue Date:||1-Dec-2006||Publisher:||中華民國雜草學會||Journal Volume:||27||Journal Issue:||2||Start page/Pages:||79-90||Source:||中華民國雜草學會會刊||Abstract:||
The aboveground fresh weights (forage fresh yield) were measured and the maximum, mean, and minimum hourly air temperatures were recorded for nilegrass (Acroceras macrum cv. Taishi No. 1) vegetation grown in the experimental pasture of Taiwan Livestock Research Institute (Hsinhua, Taiwan) during the nine growing seasons in 2002-2004. Data from 6 seasons were used to establish the forage yield-air temperature relationships and the rest data from the other 3 seasons were used for models' validation. As the results shown, the relationships between forage fresh yield and accumulated hourly air temperatures were found fitted to a quadratic function with R^2 value greater than 0.66 (P-value less than 0.0001), irrespective to use maximum, mean, or minimum hourly air temperature as temperature variable. When using the regression equation with mean hourly air temperature as input for validation, result demonstrated its applicability in accounting for more than 93% variability of yield prediction. Results further indicated that the accumulated mean air temperatures of daytime and nighttime hours were also corresponded to forage fresh yield. The relationship can be improved by substituting with the values of temperature difference between daytime and nighttime hours, yet the applicability was in a lesser extent. As a result, the established regression equations using air temperature as a predictor have proved their feasibility and applicability as tools for predicting forage yield. However, more data covering a wider range of environmental variation is needed for models' improvement and validation if an accurate yield prediction if of concern.
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