2025-09-30
【學術亮點】利用日曆天與熱函數以及光熱函數對玉米物候預測之穩定性
字體大小
小
中
大
Intelligent Detection: Development of expert diagnosis system for crop cultivation and management【Department of Agronomy / Bo-Jein Kuo / Professor】
智慧檢測：作物栽培管理專家診斷系統開發【農藝系郭寶錚教授】
上架日期2025/9/26
智慧檢測：作物栽培管理專家診斷系統開發【農藝系郭寶錚教授】
| 論文篇名 | 英文：Stability of Maize Phenology Predictions by Using Calendar Days, Thermal Functions, and Photothermal Functions 中文：利用日曆天與熱函數以及光熱函數對玉米物候預測之穩定性 |
| 期刊名稱 | Agriculture-BASEL |
| 發表年份, 卷數, 起迄頁數 | 2025, 15(19), 2020 |
| 作者 | Yen-Yu Liu, Yuan-Chih Su, Ping-Wei Sun , Hung-Yu Dai, and Bo-Jein Kuo (郭寶錚)* |
| DOI | 10.3390/agriculture15192020 |
| 中文摘要 | 準確地預測作物之物候 (phenology) 期為有效地進行作物之栽培管理之必要條件。物候期預測所提供之作物物候期的確切時間點不僅為安排栽培管理時程的依據，亦有助於瞭解關鍵物候期可能遭遇不良天氣之風險並進一步調整播種期。氣溫為決定玉米 (Zea mays L.) 發育的主要氣候因子，而光週期 (photoperiod) 則被視為次要因子。本研究利用玉米開花期與成熟期之田間觀測資料評估利用日曆天與熱函數以及光熱函數對物候期預測之穩定性。結果顯示，熱函數方法為最穩定的預測方法，其平均變異係數 (coefficient of variation) 為8.37%。進一步將不同熱函數方法分類為線性經驗、非線性經驗、程序基礎型的函數後，生育度日與葉熱單位以及農業系統模擬器這三種方法分別為三種類型之熱函數中最穩定的方法。其中葉熱單位又為所有熱函數中最穩定的方法。以上結果提供了研究玉米物候時選擇熱函數的參考依據，並有助於安排栽培管理的決策。 |
| 英文摘要 | Accurate prediction of crop phenological stages is essential for effective crop management. Such a prediction provides the timing of phenological stages, thus aiding in scheduling management practices, understanding the potential risks of adverse weather at critical phenological stages, and adjusting sowing dates. Temperature is the dominant climatic factor affecting maize (Zea mays L.) development, with photoperiod serving as a secondary influence. This study used maize field data with recorded flowering and ma turity dates to evaluate the stability of phenological stage predictions obtained using the calendar days method, thermal functions, and photothermal functions. These methods were used to calculate the number of days, accumulated temperature, and accumulated photothermal units from sowing to flowering and from flowering to maturity. Results showed that thermal functions produced the most stable predictions, with the lowest average coefficient of variation (CV) being 8.37%. The thermal functions were further categorized as empirical linear, empirical nonlinear, and process-based. Within each category, the functions with the lowest average CVs were growing degree days (GDD8,34; 9.12%), thermal leaf unit (GTI; 7.74%), and agricultural production system simulator (APSIM; 8.26%), respectively. Among them, GTI had the lowest CV, indicating its superior stability in predicting maize phenological stages. These results provide a basis for selecting thermal models in maize phenology research and can support improved decision-making in crop scheduling and management. |
| 發表成果與AI計畫研究主題相關性 | 本研究顯示以熱量函數(如 GDD、GTI、APSIM)能提供更穩定且準確的玉米物候期預測，其中以GTI 模型表現最佳。此成果可進一步應用在智慧農業中，作為精準物候期預期可協助農民進行田間管理與決策。 |
