依據農業貿易統計，2016年台灣文心蘭的出口額約為1200萬美元（COA，2016），說明它是台灣最重要、最有價值的觀賞作物和切花之一。鑑於氣候變遷日趨嚴重，其相對應的極端氣候可能會影響全球農業市場，並減少未來文心蘭的產量，因此，維持文心蘭的產量和質量非常重要。
為了定義天氣狀況與文心蘭產量與質量之間的相關性，本研究使用了台中九座文心蘭網室中自2015年到2018年的天氣條件（例如溫度、相對濕度、氣壓、降雨、風速、颱風和空氣污染）。但由於溫室內沒有過去氣象資料，故本研究採用了溫室附近18個中央氣象站的天氣狀況並以Kriging內插法估算網室天氣條件。同時依據文心蘭生長週期，計算由種植日起算24週的每週累積溫度，並使用決策樹選擇最重要的過往溫度條件。結果顯示，選擇切花日前24週和6週的累積高溫（ACHTW24和ACHTW6）作為產量的預測變量，切花日前23週和8週的累積低溫（ACLTW23和ACLTW8）作為質量的預測變量。再利用四個空間面板模型（最小平方法、空間誤差、空間滯後、空間自迴歸模型，後三者採用固定/隨機效應），以定義天氣條件對文心蘭產量和質量的相關性。基於赤池信息量準則（AIC），隨機效應的空間自迴歸模型（SAR-RE）在文心蘭的產量預測方面優於其他模型。而質量預測模型中，固定效應的空間滯後模型（SLM-FE）表現最好。結果顯示，在切花日前的23週內，夜晚溫度較低的區域其文心蘭產量較高。另一方面，文心蘭質量與切花日前六週的累積日高溫呈正相關，並與切花日前24週的累積日間溫度呈負相關。質量模型顯示極端天氣變量（例如颱風）和降雨分別對文心蘭的產量和質量產生負面影響。相對濕度和氣壓對文心蘭的產量和質量有正面影響。本研究的結果可用於評估天氣變化對文心產業的潛在影響，並有助於未來優化文心蘭網室的環境。

According to the agricultural trade statistics, Taiwan’s Oncidium export volume was valued at around US$12 million in 2016 (COA, 2016), making it one of the most important and valuable ornamental crops and cut flowers in Taiwan. Therefore, maintaining the quality and productivity of this flower becomes important. However, this work is difficult because of climate change, which results in extreme weather that significantly reduces Oncidium harvests. This study intends to define the relationship between several weather conditions (such as temperature, relative humidity, pressure, precipitation, and typhoon) to Oncidium production and quality.
Our study area includes nine Oncidium greenhouses in Taichung from 2015 to 2018. Since there are no weather stations inside greenhouses, Kriging interpolation has been used to estimate the weather conditions from 18 weather stations near the greenhouses. Based on the previous study execute by Chen and Hsu in 2003, the accumulation of historical temperature is important for Oncidium growth. Therefore, the accumulation of weekly temperature was calculated from planting day and up until the cutting day (24th weeks). This process generates 72 accumulation temperature variables. In order to choose the important historical temperature variables, a decision tree has developed. The selected variables were the 24th and 6th weeks for the accumulation of high temperatures and the 8th and 23rd weeks for the accumulation of low temperatures. Ordinary regression model (LM) and three spatial panel models (Spatial Error, Spatial Lag, and Spatial Autoregressive with fixed/random effect extensions) were utilized to capture the impact of weather conditions on Oncidium production and quality. Based on the Akaike Information Criterion (AIC): The Spatial Autoregressive with random effects (SAR-RE) model performed better than the other models for Oncidium productivity, and the Spatial Lag model with fixed effects (SLM-FE) perform the best for Oncidium quality. The results of the models have shown that areas with high accumulate night temperatures during the 23 weeks and lower accumulate day temperature during the six weeks before cutting day tend to have a larger amount of Oncidium production. On the other hand, the accumulated night temperature during eight weeks before cutting day has shown positive correlation while the accumulated day temperature during 24 weeks before cutting the flower has shown a negative correlation in terms of Oncidium quality. The quality models have shown that extreme weather variables (such as typhoons) and precipitation have a negative effect on Oncidium quality and statistically significant, but relative humidity and air pressure have a positive impact on Oncidium production and quality. The results of this study can be used to evaluate the potential future impacts of climate change on the Oncidium industries and help to optimize the environmental settings in Oncidium greenhouses.

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