Application of the Nitrogen Nutrition Index to Estimate the Yield of Indica Hybrid Rice Grown from Machine-Transplanted Bowl Seedlings

Abstract

The purpose was to comprehensively compare the prediction accuracy of different nitrogen nutrition indexes (NNILAI and NNIDM) derived from critical nitrogen concentration (Nc) models established by the leaf area index (LAI) and dry matter (DM) in estimating the grain yield of indica hybrid rice grown from machine-transplanted bowl seedlings. Therefore, field experiments were conducted with two high-yielding indica hybrid rice varieties and five nitrogen application rates in 2018 and 2019. The results show that NNIDM peaked in the stem elongation stage, while NNILAI had its maximal value in the mid-tillering stage during the growth stages. The NNILAI had the highest correlation with the relative effective panicle number in the tillering stage when compared with the NNIDM, and the threshold points of the NNI were 0.971 (active tillering stage) and 1.106 (mid-tillering stage). Moreover, the NNILAI had the highest correlation with the relative seed setting rate in the stem elongation–panicle initiation stage compared with the NNIDM, and its threshold points were 1.116 (stem elongation stage) and 1.053 (panicle initiation stage). In contrast, the NNIDM had the highest correlation with the relative seed setting rate in the heading stage compared with the NNILAI, and its threshold point was 1.050 (heading stage). Therefore, the NNILAI in the tillering–panicle initiation stage and NNIDM in the heading stage should be merged to effectively improve the nitrogen nutrition status and its evaluation in addition to the prediction accuracy of the yield of indica hybrid rice grown from machine-transplanted bowl seedlings. This study provides a theoretical basis for improved understanding of the nitrogen status and yield prediction of indica hybrid rice grown from machine-transplanted bowl seedlings.

Analysis of the Effects of Population Structure and Environmental Factors on Rice Nitrogen Nutrition Index and Yield Based on Machine Learning

“…In this study, the prediction of the NNI by the linear model was significantly better than that of the yield. This may be because the NNI is closely related to the population structure, such as LAI [50,51] and plant height, and the model for predicting the NNI based on LAI has been confirmed by a large number of studies [52,53].…”