Fertilizer nitrogen, soil chemical properties, and their determinacy on rice yield: Evidence from 92 paddy fields of a large-scale farm in the Kanto Region of Japan

3rd International Conference on Agricultural and Biological Sciences

农业科学;生物科学

Li, D.^1 ; Nanseki, T.^1 ; Chomei, Y.^1 ; Yokota, S.^2

Faculty of Agriculture, Kyushu University, Hakozaki 6-10-1, Fukuoka

812-8581, Japan^1

Yokota Nojo Co. Ltd., Nuruto 2047, Ryugasaki, Ibaraki

301-0803, Japan^2

关键词: Base elements;    Cation exchange capacities;    Multivariate regression;    Principal Components;    Silicic acids;    Soil chemical property;    Soil fertility;    Soil quality index;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/77/1/012011/pdf
DOI  :  10.1088/1755-1315/77/1/012011

来源: IOP

【 摘 要 】

Rice, a staple crop in Japan, is at risk of decreasing production and its yield highly depends on soil fertility. This study aimed to investigate determinants of rice yield, from the perspectives of fertilizer nitrogen and soil chemical properties. The data were sampled in 2014 and 2015 from 92 peat soil paddy fields on a large-scale farm located in the Kanto Region of Japan. The rice variety used was the most widely planted Koshihikari in Japan. Regression analysis indicated that fertilizer nitrogen significantly affected the yield, with a significant sustained effect to the subsequent year. Twelve soil chemical properties, including pH, cation exchange capacity, content of pyridine base elements, phosphoric acid, and silicic acid, were estimated. In addition to silicic acid, magnesia, in forms of its exchangeable content, saturation, and ratios to potassium and lime, positively affected the yield, while phosphoric acid negatively affected the yield. We assessed the soil chemical properties by soil quality index and principal component analysis. Positive effects were identified for both approaches, with the former performing better in explaining the rice yield. For soil quality index, the individual standardized soil properties and margins for improvement were indicated for each paddy field. Finally, multivariate regression on the principal components identified the most significant properties.