從農業普查觀察我國精緻農業發展

An Observation of Taiwan's Refined Agriculture Development from Agriculture Census

沈芝貝(Chih-Pei Shen) ； 楊雅惠(Ya-Hui Yang) ； 蔡依倫(I-Lun Tsai)

《中國統計學報》 51卷2期 (2013/06) Pp. 147-185

摘要

為促進台灣農業永續發展，政府於2009年提出「精緻農業健康卓越方案」，期望朝向「健康、卓越、樂活」農業三大面向推動。本文運用2010年農林漁牧業普查資訊，在健康農業方面釐出參與有機農業之普查對象，藉由列聯表分析（Contingency Tables）及觀察追蹤資料，發現有機農牧業在地區分布及主要經營種類投入方面具差異性，且其經營指標優於全體農耕業者，另經營規模有擴大趨勢。在卓越農業部分，利用主成份分析（Principal Component Analysis）建構重要農漁產品之綜合性競爭力指標，析出具優勢競爭力之經營條件，供為農政單位與農家經營者未來制定農業經營決策參考。在樂活農業部分，結合地理資訊系統（GIS）及空間自相關分析（Spatial Autocorrelation）驗證休閒農業分布確具群聚現象，且由兩次普查結果發現群聚現象愈趨明顯。

關鍵字

有機農業 ； 卓越農業 ； 樂活農業 ； 列聯表分析 ； 主成份分析 ； 地理資訊系統 ； 空間自相關分析

並列摘要

In order to promote the sustainable development of Taiwan's agriculture, in 2009 the government proposed the ”Refined Agriculture with Health and Excellence Program”, expecting to promote three agricultural directions as ”Health, Excellence and LOHAS”. In this text, 2010 Census of Agriculture, Forestry, Fishery and Animal Husbandry information was used. In the part of health agriculture, to dig out the census objects who were involved in organic agriculture; by means of contingency tables analysis and the observed panel data, we found that organic agriculture had differences in geographical distribution and main industry, and they are better than the whole crops planting in operating indicators; moreover, their operating scale had expanding trend. In the part of excellence agriculture, we utilized principal component analysis to construct comprehensive competitiveness indicators of important agricultural and fishery products, and analyzed the operation conditions of competitive advantage as references for agricultural agencies and farm operators in future decision-making for agricultural business. In the part of LOHAS agriculture, we combined GIS and spatial autocorrelation to verify that the distribution of leisure agriculture was indeed a clustering phenomenon, and which was found increasingly obvious by two census results.

並列關鍵字

Organic agriculture ； LOHAS agriculture ； contingency tables analysis ； principal component analysis ； GIS ； spatial autocorrelation

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