有機農法採用之空間計量分析：以農業普查稻作農家為例

Spatial Econometric Analyses of Organic Farming Adoption: The Case of Agricultural Census Rice Farm Households

在提倡環境友善與永續發展的聲浪中，有機農法在全球逐漸受到重視。臺灣的有機農地面積占比於21世紀快速成長，至2020年已成長至1.37%，期間多以有機稻米呈現最高占比。本研究以2015年農林漁牧普查整理而得之鄉鎮層級資料，針對臺灣地區稻農有機農法的採用率進行影響因子之分析。本研究與過往研究之主要差異在於同時考量各鄉鎮農家主要經營者及家戶成員之社會經濟特性，並同時以兩種空間計量迴歸方法－空間落遲模型 (spatial lag model)、空間誤差模型 (spatial error model) 解決有機農法採用率空間自相關性所造成之估計偏誤問題。根據全域型空間自相關統計分析結果，有機農法採用率之空間自相關指標Moran’s I值為0.366，顯示整體而言臺灣鄉鎮在有機農法採用率上有空間聚集情形。此外，由空間計量模型之估計結果，可知影響臺灣稻米有機農法 採用的因子主要為鄉鎮之經營管理者年齡組成、中高教育程度比例、全年工作日數比例，以及戶內成員之性別、教育程度及非農工作比例。本研究結果有助於日後有機農業發展相關政策之制定，進而提升政府有機農業推動之政策效益。

 

Under the notion of environmental-friendly and sustainable development, organic farming is gaining traction worldwide. In Taiwan, farmland allocated to organic farming has grown rapidly, up to 1.37% by 2020. Among different crop categories, rice has taken the largest share during the past two decades. Drawn from the 2015 Agriculture Census data at the township level, this study analyzed the determinants of the adoption rate of organic farming for rice farm households in Taiwan. This study departs from the previous research by considering the socioeconomic characteristics of both farm operators and household working population at average township levels. In addition, two spatial econometric models—spatial lag model and spatial error model—are used to deal with the estimation bias resulting from the spatial autocorrelation of organic adoption rates. The spatial autocorrelation index, Moran’s I, which is estimated from the global spatial autocorrelation analysis, equals to 0.366, indicating that there are spatial clusters in the township adoption rate of organic farming. Results from spatial econometric analyses suggest that, the proportion of major operators at different age groups, educational levels and workday groups are the major determinants of township-level adoption rates. In addition, working-age members’ gender ratio, proportion of different educational levels and portion of non-agricultural workers also explains the variations in organic adoption rate. This study provides essential references for policy design aiming at organic agriculture development in the future, which thus enhances the efficiency of organic agriculture development.