Determination of critical nitrogen concentration and dilution curve based on leaf area index for summer maize

Zhao, Ben; Ata-Ul-Karim, Syed Tahir; Duan, Aiwang; Liu, Zhandong; Wang, Xiaolong; Xiao, Junfu; Liu, Zugui; Qin, Anzhen; Ning, Dongfeng; Zhang, Weiqiang; Lian, Yanhao

doi:10.1016/j.fcr.2018.09.005

Cited by 44 publications

(20 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prost and Jeuffroy indicated that the 1LFT showed poor defined relationship with N indicators, this might be due to the time difference in maturity of the first expanded leaves, due to these differences and wide variation in 1LFT [41]. Wang et al reported that the lower leaf (3,4LFT) responded more to nitrogen supply than upper leaf (1,2LFT), and also suggested that lower leaf (4LFT) could be the ideal sample leaf for diagnosis of plant nitrogen nutrition [18]. Yuan et al also proved that lower leaves afforded more reliable estimations of crop N status [17].…”

Section: Discussionmentioning

confidence: 99%

“…Nitrogen (N) is one of the most important yield-limiting factors [1]. Appropriate N management is essential to achieve relatively high yields with low N input, particularly to ensure maximum rice yield and quality [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Non-destructive diagnostic strategies use various devices to monitor crop growth and N status [7,8]. A chlorophyll meter (Soil Plant Analysis Development, SPAD-502, Minolta Camera Co., Osaka, Japan) has been widely used for simple, rapid, and non-destructive assessment of leaf chlorophyll concentrations [9], however, the readings are significantly influenced by growth stage, plant leaf position, leaf measurement location, leaf thickness, leaf weight, the cultivar, solar radiation, and environmental stress [1,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we defined the relationships between NDSI, LNC, and NNI during different growth periods: (1) to accurately access N nutrition status using SPAD-based index, and (2) to draw a dynamic-critical curve showing when yields were lower than required by the NDSI target to guide N fertilizer topdressing, thus aiding rice production in eastern China.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…However, the N c equation coefficients in China were lower than those obtained in France (Yue et al 2012;Zhao et al 2017). Therefore, it is essential to calculate and calibrate critical N dilution curve based on regional climatic condition and crop species (Zhao et al 2018). Developing the N c equation requires frequent and accurate measurements during the plant growth period.…”

Section: Introductionmentioning

confidence: 99%

Development of a Critical Nitrogen Dilution Curve for Basil (Ocimum basilicum L.) Under Greenhouse Conditions

Rahimikhoob

Sohrabi

Delshad

2020

J Soil Sci Plant Nutr

View full text Add to dashboard Cite

Nitrogen (N) is the most limiting nutrient for crops growth and development. The optimal application of N significantly enhances crop metabolic process. On the other hand, overuse of nitrogenous fertilizer leads to excess N accumulation in crop tissues that increases the risk of health-related problems. Therefore, it is mandatory to develop a quantitative approach for monitoring the crop N status during growth period. The main objective of this study was to establish the critical nitrogen dilution curve as an effective method to diagnose nitrogen status of sweet basil (Ocimum basilicum L.) grown under controlled greenhouse conditions. Two experiments were carried out with seven N application rates in the research greenhouse of University of Tehran, Iran. Values of crop shoot biomass (t ha −1) and N concentration (%) were measured in each sampling date to develop the critical nitrogen concentration (N c) equation. The relationship between N c and shoot dry matter (DM) was described by a power function (N c = 4.80DM-0.38 , R 2 = 0.98). The validation results showed that the developed equation effectively distinguishes N-limiting from non-N-limiting treatments. The N nutrition index (NNI) ranged from 0.52 to 1.18 and 0.53 to 1.26 for the first and second experiment, respectively. It was concluded that the developed N dilution curve could provide useful insight into determination of basil N nutrition status and proper fertilizer management.

show abstract

Optimising non-destructive sampling methods to study nitrogen use efficiency throughout the growth-cycle of giant C4 crops

Poultney

Christina²,

Versini

2020

Plant Soil

View full text Add to dashboard Cite

Determination of critical nitrogen concentration and dilution curve based on leaf area index for summer maize

Cited by 44 publications

References 30 publications

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

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

Development of a Critical Nitrogen Dilution Curve for Basil (Ocimum basilicum L.) Under Greenhouse Conditions

Optimising non-destructive sampling methods to study nitrogen use efficiency throughout the growth-cycle of giant C4 crops

Contact Info

Product

Resources

About