Relationship between Leaf Area Index and Yield Components in Farmers’ Paddy Fields

Naoyuki Hashimoto, Yuki Saito, Shuhei Yamamoto, Taro Ishibashi, Ruito Ito, Masayasu Maki, Koki Homma
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Abstract

Estimation of rice yield components is required to optimize cultivation management in fields. The leaf area index (LAI) can be a parameter for this estimation, but it has not been evaluated in farmers’ fields. In this study, we analyzed the relationship between the LAI and yield components using data collected over a five-year period in farmers’ fields for the cultivar Hitomebore. Leaf area dynamics (LAD) were parameterized by fitting a growth function to the time-series data of LAI measured using a canopy analyzer. The contribution of LAD to yield components was analyzed using multiple regression. The LAIs at five points during the growing season (effective integrated temperatures of 200, 400, 600, 800, and 1000 °Cd) were calculated using the growth function and the relationship between them and the yield components were analyzed using linear regression. The results of the multiple regression analysis showed that all function parameters significantly affected the yield components at the 5% probability level, with the greatest contribution from the LAI. The LAI at effective integrated temperatures of 400 to 600 °Cd significantly affected most of the yield components. However, the correlation coefficients between the LAI and yield components were not high (R = 0.18–0.61). The LAIs at almost all periods significantly affected the grain number per panicle and 1000-grain weight at the 5% probability level. These results suggest that the LAI could be used for monitoring trends in yield components, while further research on the development of accurate estimation methods is needed.
稻田叶面积指数与产量成分的关系
估算水稻产量构成要素是优化田间栽培管理的必要条件。叶面积指数(LAI)可以作为估算的一个参数,但尚未在农民田间进行评估。在本研究中,我们利用在农民田间收集的5年数据,分析了LAI与产量成分之间的关系。利用冠层分析仪对LAI时间序列数据拟合生长函数,对叶面积动态进行参数化。采用多元回归分析LAD对产量成分的贡献。利用生长函数计算生长季5个点(200、400、600、800和1000°Cd有效综合温度)的lai,并利用线性回归分析其与产量成分之间的关系。多元回归分析结果表明,各功能参数在5%概率水平上显著影响产量成分,其中LAI贡献最大。400 ~ 600°Cd有效综合温度下的LAI显著影响了大部分产量分量。然而,LAI与产量成分的相关系数不高(R = 0.18-0.61)。在几乎所有时期,lai对每穗粒数和千粒重的影响都在5%的概率水平上显著。这些结果表明,LAI可以用于监测产量成分的趋势,但需要进一步研究开发准确的估算方法。
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CiteScore
4.70
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