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Agronomy 2019, 9(2), 106; https://doi.org/10.3390/agronomy9020106

Development of Chlorophyll-Meter-Index-Based Dynamic Models for Evaluation of High-Yield Japonica Rice Production in Yangtze River Reaches

1
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, 1 Weigang Road, Nanjing 210095, China
2
Key Laboratory for Information Agriculture, Jiangsu, Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, 1 Weigang Road, Nanjing 210095, China
3
Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Science, Nanjing 210008, China
4
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
*
Author to whom correspondence should be addressed.
Received: 28 January 2019 / Revised: 15 February 2019 / Accepted: 20 February 2019 / Published: 22 February 2019
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling)
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Abstract

Accurate estimation of the nitrogen (N) spatial distribution of rice (Oryza sativa L.) is imperative when it is sought to maintain regional and global carbon balances. We systematically evaluated the normalized differences of the soil and plant analysis development (SPAD) index (the normalized difference SPAD indexes, NDSIs) between the upper (the first and second leaves from the top), and lower (the third and fourth leaves from the top) leaves of Japonica rice. Four multi-location, multi-N rate (0–390 kg ha−1) field experiments were conducted using seven Japonica rice cultivars (9915, 27123, Wuxiangjing14, Wunyunjing19, Wunyunjing24, Liangyou9, and Yongyou8). Growth analyses were performed at different growth stages ranging from tillering (TI) to the ripening period (RP). We measured leaf N concentration (LNC), the N nutrition index (NNI), the NDSI, and rice grain yield at maturity. The relationships among the NDSI, LNC, and NNI at different growth stages showed that the NDSI values of the third and fourth fully expanded leaves more reliably reflected the N nutritional status than those of the first and second fully expanded leaves (LNC: NDSIL3,4, R2 > 0.81; NDSIothers, 0.77 > R2 > 0.06; NNI: NDSIL3,4, R2 > 0.83; NDSIothers, 0.76 > R2 > 0.07; all p < 0.01). Two new diagnostic models based on the NDSIL3,4 (from the tillering to the ripening period) can be used for effective diagnosis of the LNC and NNI, which exhibited reasonable distributions of residuals (LNC: relative root mean square error (RRMSE) = 0.0683; NNI: RRMSE = 0.0688; p < 0.01). The relationship between grain yield, predicted yield, and NDSIL3,4 were established during critical growth stages (from the stem elongation to the heading stages; R2 = 0.53, p < 0.01, RRMSE = 0.106). An NDSIL3,4 high-yield change curve was drawn to describe critical NDSIL3,4 values for a high-yield target (10.28 t ha−1). Furthermore, dynamic-critical curve models based on the NDSIL3,4 allowed a precise description of rice N status, facilitating the timing of fertilization decisions to optimize yields in the intensive rice cropping systems of eastern China. View Full-Text
Keywords: SPAD; leaf nitrogen concentration; nitrogen nutrition index; grain yield; dynamic model SPAD; leaf nitrogen concentration; nitrogen nutrition index; grain yield; dynamic model
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Zhang, K.; Liu, X.; Tahir Ata-Ul-Karim, S.; Lu, J.; Krienke, B.; Li, S.; Cao, Q.; Zhu, Y.; Cao, W.; Tian, Y. Development of Chlorophyll-Meter-Index-Based Dynamic Models for Evaluation of High-Yield Japonica Rice Production in Yangtze River Reaches. Agronomy 2019, 9, 106.

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