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

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.

Spatiotemporal Heterogeneity of Chlorophyll Content and Fluorescence Response Within Rice (Oryza sativa L.) Canopies Under Different Nitrogen Treatments

“…While insufficient or excessive nitrogen stress would accelerate or delay the function transformation in rice leaves, respectively, thus affecting the vertical pattern. Cultivar differences would impact the absolute value of leaf Chl content without affecting the change tendency at different growth stages ( Peng et al, 2008 ; Wang et al, 2015 ; Zhang et al, 2019 ). Coupled with the source-sink regulation, it can be inferred that the spatiotemporal varations of the Chl content within canopy might be similar in ordinary rice varieties.…”

Development and Characterization of Near-Isogenic Lines Revealing Candidate Genes for a Major 7AL QTL Responsible for Heat Tolerance in Wheat

“…The smaller the reduction in SPAD chlorophyll content (ΔSCC), the more tolerant the plant should be to heat stress. SPAD meters have been used to estimate leaf chlorophyll content in research and agricultural practices because it is a quick, simple and non-destructive method ( Novichonok et al, 2016 ; Padilla et al, 2018 ; Zhao et al, 2018 ; de Souza et al, 2019 ; Galanti et al, 2019 ; Zhang et al, 2019 ). Here, agronomic traits such as grain number and grain yield were also measured to further confirm the true NIL pairs, which are valuable material for further studies of post-anthesis heat tolerance in wheat.…”

A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential

“…Many researchers have studied the inversion of rice nitrogen content using hyperspectral techniques, but the nitrogen content alone cannot directly guide the quantitative and accurate fertilization of rice in the production process ( Din et al, 2019 ; Ge et al, 2019 ; Li et al, 2019 ; Zhang et al, 2019 ; Liu et al, 2020 ; Wang et al, 2020 ). Ulrich (1952) was the first to introduce the concept of critical nitrogen concentration, or the minimum nitrogen concentration required for a crop to reach maximum dry matter, and developed a dilution model of critical nitrogen concentration as a power function using plant dry matter and plant nitrogen concentration ( Linquist et al, 2013 ; Zhao et al, 2016 ).…”