農業情報研究
Online ISSN : 1881-5219
Print ISSN : 0916-9482
ISSN-L : 0916-9482
原著論文
Determinants of Paddy Yield of Individual Fields Measured by IT Combine: Empirical Analysis from the Perspective of Large-Scale Farm Management in Japan
Dongpo LiTeruaki NansekiYuji MatsueYosuke ChomeiShuichi Yokota
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ジャーナル フリー

2016 年 25 巻 1 号 p. 39-46

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Paddy production in Japan is currently undergoing a transition, moving away from the former acreage reduction policies of the 1970s to improve the sector’s efficiency and competitiveness. Meanwhile, agricultural production corporations and the adoption of information and communications technology (ICT) and good agricultural practices (GAP) have been steadily increasing over last decades. This study aims to identify the determinants of paddy yield measured by IT combine within large-scale farms. The sample includes 351 paddy fields from a farm corporation scaled over 113 hectares, located in the Kanto Region of Japan. The candidate determinants include the continuous variables of field area and condition evaluation scores, transplanting or sowing time, and amount of nitrogen, as well as stage-specific growth indicators for chlorophyll contain, number of panicles, plant height, and leaf plate value. Meanwhile, three discrete variables including variety, cultivation method, and soil type are also adopted. Empirical analysis is conducted using a multivariate linear regression, with logarithmic transformations of the continuous variables. Of the continuous variables, transplanting or sowing time is identified as possessing the largest absolute standardized regression coefficient, and thus be the most important determinant. The negative coefficient indicates that earlier transplanting or sowing benefits vegetative growth, thus panicle number and plant height in heading stage, which are identified as positively significant together with field area, and amount of nitrogen. Of the discrete determinants, Akidawara is measured as a productive variety; while the well-drained and submerged direct sowing methods are identified as negatively affecting the yield.

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© 2016 Japanese Society of Agricultural Informatics
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