Estimation of Critical Nitrogen Concentration Based on Leaf Dry Matter in Drip Irrigation Spring Maize Production in Northern China

Abstract

The application of nitrogen (N) fertilizer not only increases crop yield but also improves the N utilization efficiency. The critical N concentration (N_c) can be used to diagnose crops’ N nutritional status. The N_c dilution curve model of maize was calibrated with leaf dry matter (LDM) as the indicator, and the performance of the model for diagnosing maize N nutritional status was further evaluated. Three field experiments were carried out in two sites between 2018 and 2020 in Ningxia Hui Autonomous Region with a series of N levels (application of N from 0 to 450 kg N ha⁻¹). Two spring maize cultivars, i.e., Tianci19 (TC19) and Ningdan19 (ND19), were utilized in the field experiment. The results showed that a negative power function relationship existed between LDM and leaf N concentration (LNC) for spring maize under drip irrigation. The N_c dilution curve equation was divided into two parts: when the LDM < 1.11 t ha⁻¹, the constant leaf N_c value was 3.25%; and when LDM > 1.11 t ha⁻¹, the N_c curve was 3.33LDM^−0.24. The LDM-based N_c curve can well distinguish data on the N-limiting and non-N-limiting N status of maize, which was independent of maize varieties, growing seasons, and stages. Additionally, the N nutrition index (NNI) had a significant linear correlation with the relative leaf dry matter (RLDM). This study revealed that the LDM-based N_c dilution curve could accurately identify spring maize N status under drip irrigation. NNI can thus, be used as a robust and reliable tool to diagnose the N nutritional status of maize.

1. Introduction

Nitrogen (N) is a key element for maintaining crop growth [1]. Under N deficiency, an increase in N application can dramatically stimulate the growth of crops and maintain a good grain yield [2]. However, the applied N in the soil cannot be fully absorbed by the crop, and consequently, about 60% of the N fertilizer is lost through ammonia volatilization, nitrification, denitrification, leaching, and runoff [3]. The N utilization efficiency has become an important factor restricting the sustainable development of agricultural production since N utilization efficiency is generally low as a result of excessive N application for maintaining high grain yield [4,5]. The major challenge is how to achieve optimum water-fertilizer management and obtain a continuous increase in crop production and efficiency in intensive planting [6,7]. At different growth stages of crops, optimizing its N application rate is especially helpful to further improve crop yield and quality [8,9]. Therefore, rapidly and accurately characterizing plant N status becomes particularly important.

Diagnosis of N nutrition in crops mainly includes the measurement of chlorophyll using a chlorophyll meter [10], spectral diagnosis [11], remote sensors [12], and image processing [13]. One of the common disadvantages of these methods is that there are large variations in the results obtained, especially under luxury absorption [14,15]. Greenwood et al. (1991) [15] summarized the characteristics of crop growth and N absorption and proposed the concept of critical N concentration (N_c), which is the minimum N required for maximum crop growth. N_c has received significant attention from numerous researchers related to reliable N diagnosis with sufficient accuracy, and it can be used as an index to evaluate the crop N nutritional status of crops. The N_c dilution curve is a crop diagnostic approach based on the allometry between the dynamics of N uptake and dry matter accumulation in crops. Plant dry matter (PDM)-based N_c dilution curves have been successfully constructed and applied in rice [14,16,17], wheat [18,19], and other crops [20]. However, some previous reports show that N_c dilution curves may vary with environmental conditions, crops, and varieties [21,22]. Therefore, a robust N_c dilution curve is especially important for diagnosing N requirements during the growth of specific crops.

Maize (Zea mays L.) is one of the most important crops in the world, which is widely cultivated in tropical to temperate climatic zones [23]. Growth and yield of maize are significantly affected by the application of fertilizers, especially for N. Previous studies have reported calibrated N_c curves model for maize in different regions using plant dry matter (PDM) [21,24]. Leaves are the main tissues and organs of maize, while the N_c dilution curves model is also varied with tissues and organs [25]. Leaves are extremely sensitive to N nutritional status. For instance, when N was applied within a certain dosage, the leaf dry matter (LDM) and PDM were similar, and N of maize generally increased with increased N application. However, when N was applied above a certain dosage, the N concentration continued to increase, but LDM actually decreased [22]. With maize growth, the dry matter accumulation in leaves increases, but the leaves’ N concentration (LNC) decreases [22]. Therefore, LNC can be used as an important agronomic index for evaluating the status of crop growth. For instance, LDM based N_c curve has been successfully established and applied in several main crops, such as rice [26,27] and winter wheat [28]. Based on the LDM-derived N_c dilution curves, various diagnostic tools have been established for the assessment of the N status of crops for the purpose of improving N management practices [26,29]. However, the LDM-based N_c curve model in maize has not been calibrated under the condition of integrated drip irrigation and fertilizer. Therefore, this study aimed (1) to calibrate and validate the LDM-based N_c dilution curve in maize, (2) to compare the validated model with other crops’ N_c curve models and to evaluate its reliability in spring maize, and (3) to offer a novel method for precisely managing N fertilizer application in spring maize cultivated under drip irrigation conditions. Collectively, the N_c dilution curve model for spring maize established a rapid diagnostic method for N status, which further developed the N fertilizer topdressing model for the supply of N fertilizer at different growth stages under N deficiency conditions.

2. Materials and Methods

2.1. Field Experiment Design

Three experiments were carried out in two experimental sites, Pingjibu (38°25′ N, 106°1′ E) and Yongning (38°13′ N, 106°14′ E) in 2018, 2019, and 2020 in Ningxia Hui Autonomous Region of China. In this region, the annual cumulative total temperatures above 10 °C range from 2800 to 3600 °C day, and the annual total precipitation range from 200 to 300 mm per year, of which about 60% occurs between July and September [23]. The spring maize cultivars were, Tianci19 (TC19) and Ningdan19 (ND19). Six N fertilizer (urea) application rates included 0 (N0), 90 (N1), 180 (N2), 270 (N3), 360 (N4), and 450 kg ha⁻¹ (N5). A randomized complete block design with three replicates was used in the experimental plots. Each plot (15 m × 4.5 m) had eight rows of spring maize, with 40–70 cm row spacing and 20 cm distance between each hill within each row, following typical mechanically planted and harvested spring maize fields in the region. The plant density reached 90,000 plants ha⁻¹. The spring maize was sown by mechanical seeding at a soil depth of about 5 cm in late April and harvested from mid to late September in two experiment sites, where maize is planted one season per year. The basic soil properties are shown in

Table 1. Basic soil properties (0–60 cm soil depth).

Parameter	Units	Experiments
		Experiment 1		Experiment 2		Experiment 3
Year		2018		2019		2020
Site		Pingjibu	Yongning	Pingjibu	Yongning	Pingjibu	Yongning
Cultivar		TC19	ND19	TC19	ND19	TC19	ND19
pH value		7.82	8.53	7.98	8.44	7.76	8.57
Organic matter	g kg−1	14.83	10.56	11.45	8.07	12.82	14.83
Total N	g kg−1	0.92	0.96	0.8	0.98	0.75	0.92
Available N	mg kg−1	37.81	40.23	37.42	40.47	36.82	39.44
Available P	mg kg−1	20.63	18.96	19.04	18.33	19.37	20.63
Available K	mg kg−1	109.17	108.92	102.53	106.25	105.31	111.25
Sowing date		20/4	24/4	26/4	22/4	28/4	20/4
Harvesting date		22/9	20/9	16/9	18/9	18/9	22/9

Notes: Measurement of Soil properties were carried out according to Dalal and Nelsonand et al. [30,31].

, including sowing and harvesting date information.

The integrated technology of water and fertilizer was adopted in field experiments. The total irrigation amount applied during the maize growing season in each plot was 400 mm water via drip irrigation, mainly including 20 mm at the seedling, 100 mm at jointing-bell, 140 mm tasseling-silk, and 140 mm at the filling stage, respectively. Urea dissolved in drip irrigation was applied as N fertilizer, with 10% of the N applied at seedling, 45% applied at jointing-bell, 20% applied at tasseling-silk, and 25% applied at filling stage, and 138 kg potassium dihydrogen (P₂O₅ ha⁻¹) and 120 kg potassium sulfate (K₂O ha⁻¹) were applied as the phosphorus and potassium fertilizer for each plot. The other remaining management (i.e., irrigation times and fertilizer application times) practices were the same as those used by local farmers.

2.2. Sampling and Measurement

Three spring maize plants with uniform growth were randomly selected from each plot at the fourth leaf collar (V4), the sixth leaf collar (V6), the tenth leaf collar (V10), the twelfth leaf collar (V12), tasseling stage (VT), and silking stage (R1). The plants were separated into stems and leaves. Plant tissues were oven-dried at 105 °C for 30 min and then at 80 °C until reaching a constant weight. All sampled leaves were weighted to calculate the LDM and stored in hermetic bags for the downstream chemical analysis. The leaf N content was measured by using the micro-Kjeldahl method [31].

2.3. Critical Nitrogen Dilution Curve

The critical nitrogen dilution (N_c) was calculated following the method reported by Justes et al. [32]. A typical power-law function was adopted to regress the correlation between N_c and LDM. The two-year data (i.e., experiments 1 and 2) were used to calibrate the N_c model for the two spring maize cultivars. The calibrated curve model was further validated by an independent dataset from experiment 3. N_c was derived from the equation described by Plénet and Lemaire [33]:

N_c = a LDM^−b

(1)

where N_c means the N_c concentration (%), LDM represents the leaf dry matter (t ha⁻¹), a indicates the N_c concentration when LDM equals to 1 t ha⁻¹, while b is the statistical parameter controlling the slope of the curve, representing the ratio of the relative dry matter accumulation rate to the relative N content accumulation rate.

2.4. Nitrogen Nutrition Index

The N nutrition index (NNI) was derived from the equation reported by Plénet and Lemaire [33]:

NNI = LNC/N_c

(2)

NNI = 1, >1, and <1 mean plant N nutritional status is optimal, excessive, and deficient, respectively.

2.5. Relative LDM

Relative LDM (RLDM) was calculated by dividing the LDM at each growth stage by the maximum LDM in the whole growing season. The following equation was used:

RLDM_i = LDM_i/LDM_max (0 < RLDM_i < 1)

(3)

where RLDM_i is the relative leaf dry matter within growth stage i, LDM_i is the measured leaf dry matter within growth stage i (t ha⁻¹), and LDM_max is the maximum dry matter of maize leaves in the whole growing season (t ha⁻¹) [27,34].

2.6. Statistical Analysis

ANOVA was used to analyze the significant difference between LDM and LNC in different nitrogen fertilizer application rates. Means were determined using the least significant difference at the p < 0.05 level, and analytical data were classified into the N-limiting and non-N-limiting group. The non-linear relationship between NNI and RLDM was fitted using Origin 2018 (Origin Lab Corporation, Northampton, MA, USA).

3. Results

3.1. The Dynamic Change in the Status of Both Spring Maize Cultivar’s LDM and LNC

The leaf dry matter (LDM) of both spring maize cultivars increased as the growth stage increased in all the six N fertilizer application rates. The increase followed an S-shaped pattern (See Figure 1). The LDM of TC19 and ND19 ranged from 0.32 t ha⁻¹ (N0) at the fourth leaf collar stages (V4) to 4.06 (N5) t ha⁻¹ at the silking stages (R1) (See Figure 1a) and 0.23 t ha⁻¹ (N0) at the V4 stages to 3.73 (N5) t ha⁻¹ at R1 stages (See Figure 1b) in 2018, and from 0.39 t ha⁻¹ (N0) at the V4 stages to 4.30 (N5) t ha⁻¹ at R1 stages (See Figure 1c) and 0.34 t ha⁻¹ (N0) at the V4 stages to 3.97 (N5) t ha⁻¹ at R1 stages (See Figure 1d) in 2019, respectively. Significant differences in LDM were observed from N0 to N3 rates in both 2018 and 2019. However, no significant differences in LDM of both maize cultivars were observed between N4 and N5 rates.

The leaves’ nitrogen content (LNC) of both spring maize cultivars also increased as the growth stage increased in all the six N fertilizer application rates. In general, for all the crop growth stages, a decrease in LNC was accompanied by an increase in LDM (See Figure 2). The LNC of TC19 and ND19 ranged from 1.38% (N0) at V4 stages to 3.74% and 1.85% (N0) at V4 stages to 3.53% (N5) at R1 stages in 2018 (See Figure 2a,b), and from 1.42% (N0) at V4 stages to 3.61% (N5) at R1 stages and 1.43% (N0) at V4 stages to 3.48% (N5) at R1 stages in 2019 (See Figure 2c,d), respectively. The LNC of the same cultivar had a similar variation tendency.

3.2. Calibration of N_c Curves

The N_c values were calculated from V6 to R1 for the two spring cultivars (TC19 and ND19) using experimental data 1 and 2 between 2018 and 2019. The N_c dilution curves for the two spring cultivars (TC19 and ND19) were calibrated using twenty data points (LDM range from 1.11 to 4.25 t ha⁻¹) during the vegetative growth stages of spring maize (See Figure 3a). The N_c values decreased with an increase in LDM. The coefficients of equations determination of TC19 and ND19 cultivars were 0.90 and 0.95 (p < 0.01), respectively (Figure 3a).

TC19: N_c = 3.41LDM^−0.22

(4)

ND19: N_c = 3.28 LDM^−0.27

(5)

The coefficients of the two N_c curves for TC19 and ND19 (See Figure 3a) were analyzed based on the method described by Mead and Curnow [35]. We did not observe a significant difference (t_slope = 0.548 < t (0.05, 20) = 2.086, and t_intercept = 0.435 < t (0.05, 20) = 2.086) at the 95% confidence level. Therefore, the N_c curves of the two spring cultivars were combined and fitted to obtain the unified N_c curve of maize under drip-irrigated (See Figure 3b). However, we could not obtain a robust N_c curve in the early growth stages (V4) of spring maize since LNC does not change significantly with the increase in the low LDM. There is no obvious competition between plants’ utilization of water, fertilizer, light, and other resources. When LDM increases, the LNC does not be significantly reduced. The low LDM data were acquired in the early growth stage of maize when the plants were very small. In this study, the N_c curves were not applied to the early growth of maize. Instead, a minimum LNC value (3.31%) was defined for the non-N-limiting group, while a maximum LNC value (3.19%) for the N-limiting groups. The LNC value of 3.25% was defined as the constant leaf N_c value for the early growth stage of maize (See Figure 3b) when LDM < 1.11 t ha⁻¹ (See Figure 3b). Therefore, the N_c curve can be described as follows:

$$\{\begin{array}{l}{\mathrm{N}}_{\mathrm{c}}=3{.33\mathrm{LDM}}^{-0.24} \mathrm{LDM}\ge 1.11\\ \\ {\mathrm{N}}_{\mathrm{c}}=3.25\% \mathrm{LDM}<1.11\end{array}$$

(6)

3.3. Validation of the N_c Curve

The LDM-based N_c model was validated using data from a one-year independent experiment 3 in two spring maize cultivars (TC19 and ND19) in 2020 (n = 72) (See Figure 4). The results showed that the non-N-limiting and N-limiting group data were located above and below the N_c curve, respectively. The N_c curve effectively distinguished the N-limiting from the non-N-limiting groups in the independent experimental data and was not affected by the growing seasons, growth stages, and cultivars. Therefore, the calibrated LDM-based N_c curve in this study can be applied for evaluating and diagnosing the N nutritional status of spring maize.

3.4. Changes of NNI Values under Various N Levels

There was substantial variation in NNI between different N levels, cultivars growing seasons, and stages (See Figure 5). NNI increased with an increase in N application. NNI values of TC19 and ND19 ranged from 0.61 (N0) to 1.45 (N5) and from 0.60 (N0) to 1.32 (N5), respectively (Figure 5a,b) in 2018, and from 0.55 (N0) to 1.29 (N5) and from 0.62 (N0) to 1.37 (N5) (See Figure 5c,d) in 2019, respectively. Furthermore, the NNI values were N0, N1, and N2 rates less than 1.0 in 2018 and 2019, which indicated that LNC was low and that the N fertilizer application rates were insufficient. However, the NNI values in N4 and N5 rates in 2018 and 2019 were greater than 1.0, which indicated that LNC was high and that the N fertilizer rates were excessively applied. NNI values in the N3 rate were around 1.0, which indicated the N application dose of 270 kg ha⁻¹ not only ensured the accumulation of LDM but also prevented N extravagant absorption. Therefore, this is a cost-effective method to get NNI value in N diagnosis and management compared with the traditional destructive and laboratory analysis methods.

3.5. Relationships between NNI and RLDM

The correlation between NNI and RLDM was studied with data from experiments 1 and 2, obtained in 2018 and 2019. NNI and RLDM showed a significant linear correlation at different growth stages, and RLDM increased with an increase in NNI (See Figure 6). The coefficients of determination from V4 to R1 were 0.84, 0.89, 0.83, 0.87, 0.65, and 0.67, respectively.

4. Discussion

4.1. Critical Nitrogen Dilution Curves Compared with Other Crops

Precise estimation of crops’ N nutritional status is essential for improving their N utilization efficiency. The N_c has been broadly utilized for diagnosing crops’ N nutritional status [36]. In the present study, the N_c value in the vegetative growth stage of spring maize gradually decreased with increased LDM (See Figure 3a), and this is consistent with that reported in rice (either the whole plant or the specific organ) [7,8,10,14,16,17,26], wheat [5,11,13,19,22,28,32,34] and cotton [37]. The N_c curve consisted of two parts: when LDM < 1.11 t ha⁻¹, the N_c value was defined as 3.25%, and when LDM ≥ 1.11 t ha⁻¹, the N_c curve was defined as 3.33LDM^−0.24 (See Figure 3b). Due to the few individual plants and minimal dry matter accumulation in leaves before the early growth stages (V4) of spring maize, there was no obvious competition for water, fertilizer, light, and other resources. The increase in LDM did not significantly reduce LNC. Therefore, the leaf N_c value was relatively stable in the early growth stage of spring maize. After the jointing stage (V6), leaf area index and leaf number increased, stalk growth occurred due to the phenomenon of shading, and this led to N dilution [23,38,39].

From a mathematical view, the parameter a equals the value of LNC when LDM is 1.0 t ha⁻¹, which represents the inherent N demand characteristics of the spring maize at the early growth stage. Parameter b indicates the LNC value with the change of LDM, whose value is determined by the proportional ratio between leaf N absorption and dry matter. In the present study, the value of parameter b (0.24) was less than that of the N_c dilution curve based on PDM for maize [5,22,38,39] (See Figure 3b). Low parameter b means that the leaf N dilution process is slow. The differences in PDM and LDM curves were mainly reflected in the calibrated model. Based on the N_c curve determined by LDM, the leaves were regarded as the central locations of crop growth. The N absorbed by the crop met the leaf growth, photosynthesis, and respiration needs and led to a slow decline in LNC [40,41]. For PDM based N_c curve, the stem N concentration was lower than that of leaf during the vegetative growth stages, while the dilution rate was lower than that of the stem [21,22], which was related to the leaf being the main organ of photosynthesis, and requires a certain N concentration to ensure efficient photosynthesis and yield formation [42,43]. Additionally, the N_c dilution curve b value of spring maize leaves under drip irrigation was higher than the reported N_c dilution curve b value of winter wheat and rice leaves (0.15, 0.22) [28,32,34] (See Figure 7). The b value of the N_c curve varies with crop types.

4.2. Application and Feasibility Analysis of LNC Based N_c Curve

The main purpose of calibrating the N_c dilution curve was to assess the N nutritional status of maize through agronomic research methods in this study. This scientific N application technology can reduce the cost of production and anthropogenic N pollution [44,45]. The N_c curve provides an alternative to assess the N nutritional status in the growth stage of maize under drip irrigation. Previous studies indicate that only the LDM and LNC of the maize can reflect its N nutritional status using the N_c curve. For example, when LNC values were on the N_c curve, which indicated that the N application rate was optimum, LNC values ranged between the lower limit curve (N_min) [27,34] and N_c, indicating a deficiency in the N application rate. LNC values were between N_c and the upper limit curve (N_max) [27,34], which indicated that excessive N was applied. The calibrated N_c curve can be used to distinguish the N deficiency from the surplus states of crops, and the Crop-Syst model [6,36,37,38,46] is used to calculate the N demand with the assistance of the N_c curve [47].

NNI can serve as an ideal indicator to diagnose the N nutritional status of crops [10,24,29,48,49,50]. In this study, the NNI values of leaves in the two spring maize cultivars ranged between 0.55 and 1.45 under different N rates (See Figure 5). The NNI value per N rate was lowest in V12, which increased with an increase in the N fertilizer application rate from V12 to the R1 stage. This was associated with the vigorous spring maize growth and development in V12 to R1 stage, the high absolute and relative quantities of nutrients required by maize, and the high absorption rate during this stage [5,22,23]. Secondly, the NNI was recovered in R1(See Figure 5), which was linked to the N status of spring maize converting from vegetative growth to reproductive growth stage, and the demand for N fertilizer was not as urgent as that in V12 (See Figure 5). This indicates that the N status of spring maize can be affected by the amount of N fertilizer application at different fertilization periods, which NNI can confirm, and this was in line with the results of a few previous studies [10,24,29]. This conclusion could be adopted to obtain a quantitative NNI and to diagnose N deficiency in spring maize with sufficient efficiency and effectiveness. NNI values were changed dynamically during the growth stages in two spring maize cultivars (See Figure 6). Therefore, it could not be used an average value of certain stages to take the place of the NNI value of the whole period of spring maize. The present study found that there was a positive correlation between NNI and RLDM for two maize cultivars at various growth stages (from V4 to R1) in the 2018 and 2019 growing seasons (See Figure 6), especially for V6 stages (R² = 0.89^**) and V12 stages (R² = 0.87^**). Although there was a positive relationship between NNI and RLDM value, the efficient N fertilizer management strategy may need to assess not only maize yield but also soil residual N, soil type, and annual climatic conditions [5,22,23,38,39,46,48].

The N_c dilution curves of crops are calibrated under different N rates in most studies, and other conditions are found to be relatively suitable. However, crops are subjected to stress caused by different factors in real agricultural production. For example, severe drought stress severely affected the final yield of spring wheat during the growth stages in Northern China [51]. Previous studies show that N_c dilution curves and NNI values of crops under drought stress are lower than normal [24,37,48,50,52]. This can be associated with the lack of water, which limits plant growth and causes a reduction in dry matter, thus causing a decrease in N accumulation. Additionally, because water content has a close relationship with the N availability, water deficiency reduces the bio-available N in soils, thus causing a decrease in the soil N supply capacity [6,38,53,54,55]. Therefore, the crop N deficiency was seen when NNI was lower than 1.0, and topdressing with N was needed. Besides water, the interaction between other elements such as phosphorus and potassium with N also affected the N status of crops [37,56]. Therefore, clarifying the factors affecting the N_c dilution curve of crops under different conditions is of great significance for its application and the use of NNI in accurately diagnosing crops’ N nutritional status.

5. Conclusions

The N_c dilution model was calibrated for two spring maize cultivars with different N application rates in Northern China, and the N_c dilution curve equation used is N_c = 3.25%, when LDM < 1.11 t ha⁻¹, and N_c = 3.33 LDM^−0.24 when LDM ≥ 1.11 t ha⁻¹. The calibrated N_c curve effectively distinguishes the N-limiting from the non-N-limiting groups based on LDM, and the curve is not independent of the cultivars, growing seasons, and stages. The N_c dilution curve specific to spring maize could therefore be used to diagnose the N nutrition status. Further, the resulting NNI is utilized to assess the N nutritional state of maize plants. NNI of N3 fertilizer application (270 kg ha⁻¹) is considered to be closest to 1.0, the LDM_max can be obtained with the N3 application rate, and NNI is significantly positively correlated with RLDM. Therefore, the N3 application may be used as the reference for the N application for spring maize under drip irrigation in Northwest China. The results are expected to provide a theoretical basis for accurate management of N fertilizer application in spring maize production and the validation of future crop models on larger simulation scales.