A Modified Critical Nitrogen Dilution Curve for Winter Wheat to Diagnose Nitrogen Status Under Different Nitrogen and Irrigation Rates

Zhao, Yu; Chen, Pengfei; Li, Zhenhai; Casa, Raffaele; Feng, Haikuan; Yang, Guijun; Yang, Wude; Wang, Jianwen; Xu, Xiru

doi:10.3389/fpls.2020.549636

Cited by 14 publications

(4 citation statements)

References 53 publications

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“…Most N c dilution curves fitted for organ biomass and LAI bases are different, reflecting the effect of G × E × M interaction. This implies that changes of wheat genotypes, planting environment, and management can affect the curve parameters, leading to potential errors in crop N nutrition status diagnosis and N demand estimations [ 28 , 39 , 40 ]. The uncertainty of the fitted parameter A 1 was significantly less than that of parameter A 2 , indicating that the curve parameter A 1 for organ basis was less affected by G × E × M. The organ-specific N c dilution curves showed that the N concentration in the leaf for all experiments was higher than in the stem (Fig.…”

Section: Discussionmentioning

confidence: 99%

Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat

Yao

Wang

et al. 2023

Plant Phenomics

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The organ-specific critical nitrogen (N c ) dilution curves are widely thought to represent a new approach for crop nitrogen (N) nutrition diagnosis, N management, and crop modeling. The N c dilution curve can be described by a power function (N c = A 1 ·W −A2 ), while parameters A 1 and A 2 control the starting point and slope. This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions. By using hierarchical Bayesian theory, parameters A 1 and A 2 of the organ-specific N c dilution curves for wheat were derived and evaluated under 14 different genotype × environment × management (G × E × M) N fertilizer experiments. Our results show that parameters A 1 and A 2 are highly correlated. Although the variation of parameter A 1 was less than that of A 2 , the values of both parameters can change significantly in response to G × E × M. Nitrogen nutrition index (NNI) calculated using organ-specific N c is in general consistent with NNI estimated with overall shoot N c , indicating that a simple organ-specific N c dilution curve may be used for wheat N diagnosis to assist N management. However, the significant differences in organ-specific N c dilution curves across G × E × M conditions imply potential errors in N c and crop N demand estimated using a general N c dilution curve in crop models, highlighting a clear need for improvement in N c calculations in such models. Our results provide new insights into how to improve modeling of crop nitrogen–biomass relations and N management practices under G × E × M.

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Section: Discussionmentioning

confidence: 99%

Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat

Yao

Wang

et al. 2023

Plant Phenomics

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“…Winter wheat is a typical crop with a long growth cycle, large differences in AGB during the different growth stages, and significant nitrogen “dilution” effects, which in turn produce large differences in the time series of AGB and PNC (Justes et al, 1994; Zhao et al, 2020). The leaf-related parameters are like the VI in the phenological stage, which is consistent with previous studies (Zhao et al, 2021; Feng et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Table 1 gives the number of samples collected in each experiment. The measurement methods and equations for calculating AGB, LGB, PNC, LNC, and LAI are the same as used by Zhao et al (2020).…”

Section: Methodsmentioning

confidence: 99%

Predicting plant-related and leaf-related parameters of winter wheat using vegetation index: Should phenological correction be applied across growth stages?

Zhao

Meng

Feng

et al. 2022

Preprint

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Most existing plant-related and leaf-related parameters models of winter wheat vary across growing seasons, but it is an open question whether a unified statistical model can be developed to predict plant-related and leaf-related parameters using VI across multiple growing seasons, or whether the phenological correction is necessary for each parameter across multiple growing seasons. To explore this question, a hierarchical linear model (HLM) automatically adapts the relationship between VIs and their corresponding parameter across growing seasons and assesses the contribution of phenological variables by applying a sensitivity analysis. The estimates of VI-plant-related parameters [aboveground dry biomass (AGB) and plant nitrogen concentration (PNC)] were scattered over a given growing season, unlike the relationship between VI-leaf-related parameters [leaf dry biomass (LGB), leaf nitrogen concentration (LNC), leaf area index (LAI) and soil and plant analysis development (SPAD)]. In contrast, the AGB, PNC, LGB, LNC, LAI, and SPAD HLM models are stable and can be popularized across growing seasons, with the determination coefficient R2 ranging from 0.84 to 0.86, 0.79 to 0.87, 0.70 to 0.71, 0.68 to 0.86, 0.75 to 0.81, and 0.68 to 0.70, respectively. The sensitivity index of the phenological information in the AGB and PNC models was 0.56-0.78 and 0.66-0.72, respectively, whereas that in the LGB, LNC, LAI, and SPAD models was 0.01-0.06, 0.01-0.10, 0.02-0.06, and 0.00-0.01, respectively. Although phenological effects have little effect on leaf-related indicators, HLM has a strong potential for application to other crops and regions.

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“…Many critical N concentration dilution curve models have been established for pepper [ 14 ], wheat [ 15 ], maize [ 16 ], cotton [ 17 ], winter oilseed rape [ 18 ], and tomatoes [ 19 ]. Nitrogen-deficit accumulation modeling based on critical nitrogen concentration curves is an effective method for the nitrogen nutritional diagnosis of crops [ 16 , 20 ]. Currently, most of the research on critical N concentration modeling focuses on the status of nitrogen gain or loss and utilization in facility crops under different water and fertilizer conditions [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Tomato Growth Status under Aerated Drip Irrigation Based on Critical Nitrogen Concentration and Nitrogen Nutrient Diagnosis

Lei,

Fan,

Xiao

et al. 2024

Plants

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In order to provide a theoretical basis for the rational application of nitrogen fertilizer for tomatoes under aerated drip irrigation, a model of the critical nitrogen dilution curve was established in this study, and the feasibility of the nitrogen nutrition index (NNI) for the real-time diagnosis and evaluation of the nitrogen nutrient status was explored. The tomato variety “FENOUYA” was used as the test crop, and aerated drip irrigation was adopted by setting three levels of aeration rates, namely, A1 (dissolved oxygen concentration of irrigation water is 5 mg L−1), A2 (dissolved oxygen concentration of irrigation water is 15 mg L−1), and A3 (dissolved oxygen concentration of irrigation water is 40 mg L−1), and three levels of nitrogen rates, namely, N1 (120 kg ha−1), N2 (180 kg ha−1) and N3 (240 kg ha−1). The model of the critical nitrogen concentration dilution of tomatoes under different aerated treatments was established. The results showed that (1) the dry matter accumulation of tomatoes increased with the increase in the nitrogen application rate in a certain range and it showed a trend of first increase and then decrease with the increase in aeration rate. (2) As the reproductive period progressed, the nitrogen concentration in tomato plants showed a decreasing trend. (3) There was a power exponential relationship between the critical nitrogen concentration of tomato plant growth and above-ground biomass under different levels of aeration and nitrogen application rate, but the power exponential curves were characterized by A1 (Nc = 15.674DM−0.658), A2 (Nc = 101.116DM−0.455), A3 (Nc = 119.527DM−0.535), N1 (Nc = 33.819DM−0.153), N2 (Nc = 127.759DM−0.555) and N3 (Nc = 209.696DM−0.683). The standardized root mean square error (n-RMSE) values were 0.08%, 3.68%, 3.79% 0.50%, 1.08%, and 0.55%, which were less than 10%, and the model has good stability. (4) The effect of an increased nitrogen application rate on the critical nitrogen concentration dilution curve was more significant than that of the increase in aeration rate. (5) A nitrogen nutrition index model was built based on the critical nitrogen concentration model to evaluate the nitrogen nutritional status of tomatoes, whereby 180 kg ha−1 was the optimal nitrogen application rate, and 15 mg L−1 dissolved oxygen of irrigation water was the optimal aeration rate for tomatoes.

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A Modified Critical Nitrogen Dilution Curve for Winter Wheat to Diagnose Nitrogen Status Under Different Nitrogen and Irrigation Rates

Cited by 14 publications

References 53 publications

Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat

Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat

Predicting plant-related and leaf-related parameters of winter wheat using vegetation index: Should phenological correction be applied across growth stages?

Comprehensive Evaluation of Tomato Growth Status under Aerated Drip Irrigation Based on Critical Nitrogen Concentration and Nitrogen Nutrient Diagnosis

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