Journal
EUROPEAN JOURNAL OF AGRONOMY
Volume 90, Issue -, Pages 139-151Publisher
ELSEVIER
DOI: 10.1016/j.eja.2017.08.001
Keywords
Grain quality; Nitrogen dilution curve; Prediction models; Protein; Amylose; Rice ecotypes
Categories
Funding
- National Key Research and Development Program of China [2016YFD0300604]
- National Natural Science Foundation of China [31571566]
- Special Program for Agriculture Science and Technology from Ministry of Agriculture in China [201303109]
- Jiangsu Agricultural Science and Technology Independent Innovation Fund Project [CX(16)1039]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
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Effective nitrogen (N) management strategies ensure optimal N status in rice (Oryza sativa L.) plants for improving crop growth and grain quality with optimal N use efficiency. In-season plant N status affects rice grain quality. The critical N (Nc) dilution curves have been applied for predicting in-season nitrogen requirement (NR) and grain yield in rice, however, its application for estimating rice grain quality at harvest is yet to be tested. This research was endeavored to establish the quantitative relationships of protein content (PC) and amylose content (AC) with nitrogen nutrition index (NNI), accumulated nitrogen deficit (AND), and NR at various growth stages during vegetative growth period of rice and to validate these relationships in Japonica and Indica rice ecotypes. Five multi-locational field trials with five rice cultivars and varied N application rates were carried out in eastern China. The quantitative relationships of PC and AC with NNI, AND, and NR at various growth stages in both rice ecotypes were highly significant (for Japonica and Indica, R-2 > 0.88 and 0.85 for PC, R-2 > 0.85 and 0.81 for AC, respectively). The strongest relations were observed for both rice ecotypes at later vegetative growth stages and periods. The validation of the developed quantitative relationships with the independent dataset revealed a solid model performance, especially during later vegetative growth period (R2 > 0.90, root mean square error < 18%) and confirmed their robustness as reliable predictors for assessing in-season grain quality in rice. The projected results can be used for estimating in-season grain quality and precision N management for rice.
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