Characteristics of Grain Yield, Dry Matter Production and Nitrogen Uptake and Transport of Rice Varieties with Different Grain Protein Content
Xinyang Key Laboratory of Rice Genetic Improvement, Ecology and Physiology/Xinyang High Quality Rice Research and Development Center, Xinyang Agriculture and Forestry University, Xinyang 464000, China
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Author to whom correspondence should be addressed.
Agronomy 2022, 12(11), 2866; https://doi.org/10.3390/agronomy12112866
Submission received: 25 September 2022
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Revised: 9 October 2022
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Accepted: 9 November 2022
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Published: 16 November 2022
Abstract
:Grain protein content (GPC) is an important index affecting rice quality and nutrition, and there is a large difference in the GPC among varieties. However, the differences in the grain yield, dry matter production, and nitrogen uptake and transport among varieties with a different GPC and their relationships with the GPC are still unclear. In this study, three japonica varieties with high GPC (H-GPC) and three japonica varieties with low GPC (L-GPC) were compared for their grain yield, dry matter production, and nitrogen uptake and transport, in field experiments under the same nitrogen application level in 2020 and 2021. The results showed that the grain yield of the L-GPC type was 26.87% higher in 2020, and 25.98% higher in 2021 than that of the H-GPC type at the same nitrogen rate, which might be related to the higher spikelet per panicle and larger sink capacity of the L-GPC type. Moreover, the varieties with L-GPC showed more dry matter production and total nitrogen content compared with the varieties with H-GPC at the heading stage and maturity, but the nitrogen uptake during the grain-filling period (NUP-GF) of the L-GPC varieties was lower than that of the H-GPC varieties. The leaf nitrogen translocation amount (L-NTA) of the L-GPC type was significantly higher than that of the H-GPC type. There was no significant difference in the leaf nitrogen translocation efficiency (L-NTE) between the different GPC types. The GPC was mainly determined by the amount of nitrogen available for developing the grain per unit sink capacity (NAV) and had a significant positive correlation with the NAV, indicating that sufficient NAV is necessary to obtain a high GPC. The direct restriction effect of the sink capacity on the NAV was the largest, and the leaf nitrogen content at the heading stage (LNC-H) had the largest direct promotion effect on the NAV, but the indirect restriction effect of the LNC-H on the NAV was also the strongest. The direct and indirect path coefficients of the NUP-GF to the NAV were both positive, indicating that increasing the NUP-GF can promote the improvement of the NAV.
1. Introduction
Rice is the most important staple food crop in the world, and it is an important source of energy and protein for half of the world’s population [1]. Rice grain protein content (GPC) can limit starch swelling and leaching during rice cooking, which increases hardness and reduces stickiness [2,3,4]. In East Asia, people prefer rice with a more elastic, sticky texture and lower hardness, so a high GPC is considered to negatively impact the rice-eating quality [5,6]. However, a high GPC is associated with reduced grain breakage during milling, and increasing the GPC is beneficial to increasing the head rice yield [7]. In addition, rice with a high GPC can be used to produce feed [8]. Therefore, different demands have different requirements on the GPC.
It has been known that there are significant genotypic differences in GPC [9]. However, the nitrogen required for rice grain protein synthesis mainly comes from the transportation of nitrogen accumulated by plants before anthesis and the nitrogen uptake by plants after anthesis [10]. Therefore, the nitrogen uptake and transport capacity of rice plants are closely related to their GPC [11]. A large number of studies have been conducted on the genotypic differences of the nitrogen uptake and utilization efficiency in rice, and the results showed that there were significant differences in the nitrogen uptake and transport among different rice genotypes [12,13]. It can be concluded that the differences in GPC among different genotypes may be related to the nitrogen uptake and transportation of different genotypes under the same nitrogen supply level [14]. For a long time, the research on rice protein content mainly focused on the effects of the nitrogen level, nitrogen application method, planting density, environmental factors and so on [15,16,17]. Although environmental factors have great influences on the GPC, genotype selection is still the key to maintaining the stability of the GPC [15]. However, little is known about the relationship between genotypic differences in the nitrogen uptake and transport and GPC.
To determine the differences in the grain yield, dry matter production, and nitrogen uptake and transportation of rice varieties with a different GPC, field experiments under the same nitrogen application level were operated over two years. The objectives of this study were to elucidate the relationships between the rice yield, dry matter production, nitrogen uptake and transport and the GPC, and to propose the key indexes affecting the GPC. This work will provide useful information for the GPC improvement of rice varieties in rice production.
2. Materials and Methods
2.1. Rice Varieties and Cultivation
Field experiments were conducted during the rice-growing season of 2020 and repeated in 2021 at Ganan Town, Shihe District, Xinyang City, Henan Province, China (114°2′ E, 32°18′ N). The field soil is sandy soil, containing 20.15 g/kg organic matter, 1.46 g/kg total N, 86.49 mg/kg alkali hydrolysable N, 10.61 mg/kg available P, and 97.25 mg/kg available K in 2020. It contained 21.21 g/kg organic matter, 1.39 g/kg total N, 87.21 mg/kg alkali hydrolysable N, 10.88 mg/kg available P, and 105.52 mg/kg available K as of 2021. Six japonica rice varieties with a different GPC (with Nanjing9108, Su1785, and Wuyunjing80 as the high GPC rice varieties, and Songzaoxiang NO.1, Huajing NO.5, and Suxiangjing NO.3 as the low GPC rice varieties) were used in this experiment. The heading and maturity dates of the tested varieties are shown in Table 1.
In both years, the seeds of the used varieties were sown on May 25 and transplanted into fields on June 15 with a hill spacing of 25 cm × 12 cm and four seedlings per hill. The field experiments were arranged in a randomized block design with three replications, and each plot covered 15 m2 in both years. Note that 270 kg N ha−1 was applied via urea (46% N) in 3 parts: 30% as basal fertilizer, 40% as tiller fertilizer, and 30% at the stage of panicle initiation. Urea was applied to all varieties at the same time. Calcium superphosphate (P2O5 content: 12%) was applied as a basal fertilizer at a rate of 135 kg P2O5 ha−1. Similarly, potassium chloride (K2O content: 60%) was split into two equal amounts (135 kg K2O ha−1) and applied around the emergence stage and booting stage. Field management followed normal agronomic procedures.
2.2. Sampling and Measurements
2.2.1. Dry Matter Production and Nitrogen Uptake
Plants were sampled at the heading stage and at maturity. Three representative plant samples were randomly collected from each plot at the heading and maturity stages. Root portions were rejected and the remainder separated into leaves, stems (internode plus sheath), and panicles. Each component of the rice plants was bagged and oven-dried separately at 105 °C for 30 min and then at 80 °C to a constant weight. The sum of the weights of these plant organs was taken as the total dry matter production.
The dried samples of leaves, stems, and panicles were powdered and analyzed via the Kjeldahl method using a Kjeltec-Foss 8200 Auto-analyzer (Kjeltec 8200, Foss, Copenhagen, Denmark). From the nitrogen concentrations of the plant parts, we determined the N uptake. The total nitrogen uptake by the rice was the sum of the N uptake by the stems, leaves, and panicles.
2.2.2. Grain Yield and Yield Components
The grain yield and its components were measured as follows, as per Wei et al. [18]: at maturity, the number of panicles per m2 was determined from three representative square meter regions that were randomly sampled from each plot. Five plants with the average panicle number were sampled randomly from each plot to determine the yield components, including the spikelet per panicle, filled grain rate, and 1000-grains weight. The grain yield was determined from a harvest area of 5 m2 in the middle of each plot at maturity, and the grain yield was weighed. The final grain yield was adjusted to 14% moisture content.
2.2.3. Grain Protein Content
Filled grain was polished for milled rice, and then ground to a powder, and the nitrogen content was measured by using a Kjeltec-Foss 8200 Auto-analyzer (Kjeltec 8200, Foss, Copenhagen, Denmark). The GPC was calculated by multiplying the nitrogen content by 5.95 [19].
2.3. Calculation Formulae
Sink capacity (g/m2) = spikelet per panicle × the number of panicles per m2 × 1000-grains weight/1000.
Dry matter production during grain-filling period (DMP-GF, g/m2) = total dry matter production at maturity − total dry matter production at heading stage.
Nitrogen harvest index (NHI, %) = panicle nitrogen content at maturity/the total above ground plant nitrogen content at maturity × 100.
Nitrogen translocation amount (NTA, g/m2) = stem (leaf) nitrogen content at heading stage − stem (leaf) nitrogen content at maturity [20].
Nitrogen translocation efficiency (NTE, %) = NTA/stem (leaf) nitrogen content at heading stage × 100 [20].
Nitrogen increase in panicle during grain-filling period (NIA-P, g/m2) = panicle nitrogen content at maturity − panicle nitrogen content at heading stage [20].
Nitrogen uptake during grain-filling period (NUP-GF, g/m2) = the total above ground plant nitrogen content at maturity − the total above ground plant nitrogen content at heading stage [20].
The amount of nitrogen available for developing grain per unit sink capacity (NAV, mg/g) = (1.6 × (the leaf nitrogen content (g/m2) at heading stage − 0.005 × the leaf dry matter weight (g/m2) at heading stage) + NUP-GF)/sink capacity × 103 [14].
2.4. Statistical Analysis
Analysis of variance was performed using SPSS version 22 (IBM, Armonk, NY, USA). The statistical model used included sources of variation due to year and GPC type, and the interaction of year × GPC type. Means were tested using the least significant difference (LSD) at p = 0.05. Path analysis was carried out according to the method provided by Song et al. [21]. Tables and figures were prepared using MS Excel 2013 (Microsoft Corporation, Redmond, WA, USA) for Windows.
3. Results
3.1. GPC and Growth Period
As shown in Figure 1, there were significant differences in the GPC among varieties in both years. The GPC levels of the tested varieties was in the range of 6.05–8.27% in 2020 and 6.25–8.53% in 2021. The GPC levels of Huajing NO.5, Songzaoxiang NO.1, and Suxiangjing NO.3 were all higher than 8%, and those of Nanjing 9108, Su1785, and Wuyunjing 80 ranged between 6.05 and 6.46% in both years. For the convenience of later analysis, we regard Huajing NO.5, Songzaoxiang NO.1, and Suxiangjing NO.3 as the high GPC (H-GPC) varieties, and Nanjing 9108, Su1785, and Wuyunjing 80 as the low GPC (L-GPC) varieties.
Compared with the H-GPC varieties, the whole growth periods of the L-GPC varieties were 8 days longer in 2020 and 7 days longer in 2021 (Table 1). However, the difference in the whole growth periods of the varieties with a different GPC was mainly caused by the difference in the growth periods from sowing to heading, but there was no difference in the days from heading to maturity between the two GPC types.
3.2. Grain Yield and Yield Components
The grain yield and its components were significantly different among the GPC types (Table 2). Compared with the H-GPC type, the grain yield of the L-GPC type was 26.87% higher in 2020 and 25.98% higher in 2021. The spikelet per panicle of the L-GPC type was significantly (p < 0.01) higher than that of the H-GPC type, but the filled grain rate showed an opposite rule. There was no significant difference in the 1000-grains weight or the number of panicles between the two GPC types. Therefore, the sink capacity of the L-GPC type was significantly (p < 0.01) higher than that of the H-GPC type, which may have been mainly due to their higher spikelet per panicle.
3.3. Dry Matter Production
The dry matter production at the heading stage and maturity were significantly different between the H-GPC type and L-GPC type (Table 3). The stem, leaf, panicle, and total dry matter production of the L-GPC type were 18.29%, 41.93%, 28.65%, and 27.63% in 2020 and 33.88%, 30.77%, 38.66%, and 36.14% in 2021 higher than those of the H-GPC type at maturity. The dry matter production regularity of the H-GPC type and L-GPC type at the heading stage was consistent with that at maturity, and the dry matter production of the L-GPC type was significantly (p < 0.01) higher than that of the H-GPC type. The DMP-GF of the L-GPC type was 747.64 g/m2 in 2020 and 751.08 g/m2 in 2021, 27.80% higher in 2020 and 32.67% higher in 2021 than those of the H-GPC type.
3.4. Nitrogen Accumulation, Distribution and Transport
As shown in Table 4, the TNC-H and TNC-M of the L-GPC type were significantly (p < 0.01) higher than those of the H-GPC type in both years. The SNC-H, LNC-H, and PNC-H of the L-GPC type were higher than those of the H-GPC type, and only the LNC-H had a significant difference (p < 0.01) between the H-GPC type and L-GPC type in both years. The SNC-M, LNC-M, and PNC-M of the L-GPC type were also higher than those of the H-GPC type, but there was no significant difference in the SNC-M, LNC-M or PNC-M of the H-GPC type and L-GPC type, except the PNC-M of 2021.
Nitrogen absorption and transportation during the grain-filling period is shown in Table 5. The S-NTA, L-NTA, and NIA-P were higher than those of the H-GPC type, but there was no significant difference in the S-NTA, S-NTE, L-NTE, and NIA-P between the H-GPC type and L-GPC type in both years. The NUP-GF and NAV of the H-GPC type were significantly (p < 0.01) higher than those of the L-GPC type in both years. The proportion of the L-NTA in the NIA-P of the L-GPC type was significantly (p < 0.05) higher than that of the H-GPC type, and the proportion of the NUP-GF in the NIA-P of the H-GPC type was significantly (p < 0.05) higher than that of the L-GPC type in both years (Figure 2).
3.5. Key Indexes Affecting GPC
The results of the simple correlation analysis (Table 6) showed that the spikelet per panicle, sink capacity, grain yield, SDM-H, LDM-H, TDM-H, LDM-M, PDM-M, TDM-M, and LNC-H were significantly (p < 0.05) or extremely significantly (p < 0.01) negatively correlated with the GPC in both years. The GPC was positively correlated with the filled grain rate, number of panicles, S-NTE, NUP-GF, and NAV, and the correlation between the GPC and the NAV was extremely significant (p < 0.01) in both years.
Stepwise regression analysis was used to determine the key indicators to the GPC (Table 7). The results showed that the NAV was the most closely related to the GPC in both years, and there was a significant linear relationship between the GPC and the NAV (R2 = 0.978 in 2020, R2 = 0.983 in 2021). According to the regression equation, when the NAV increased by 1 unit, the GPC increased by 0.495% in 2020 and 0.584% in 2021.
The NAV had a significant negative correlation with the sink capacity, and the correlation coefficients were the largest in both years, which may be related to the highest direct negative effect (−1.447 in 2020 and −1.610 in 2021) of the sink capacity on the NAV (Table 8). The LNC-H and LDM-H had great indirect negative effects on the NAV by affecting the sink capacity and NUP-GF, which led to a negative correlation between the LNC-H and LDM-H and NAV in both years. The direct and indirect path coefficients of the NUP-GF to the NAV were both positive, and there was a positive correlation between the NUP-GF and the NAV, indicating that increasing the NUP-GF can promote the improvement of the NAV.
4. Discussion
4.1. The Relationships between Grain Yield, Dry Matter Production, Nitrogen Uptake and Transport and GPC
Grain yield is the eternal focus of rice production. Previous studies have shown that rice varieties with a high grain protein content have a higher proteolytic enzyme activity, which accelerates the reduction of the leaf nitrogen content and the senescence process of rice leaves at the later growth stage, leading to the reduction of the leaf photosynthetic rate at the later growth stage, and then the decline of the dry matter production capacity and grain yield [22,23]. Imagawa et al. [24] found a significant increase in the protein content in the NADH-GOGAT2 mutant, but the grain yield of the NADH-GOGAT2 mutant was decreased by 40%. In this study, we also found that the grain yield of the L-GPC type was 26.87% higher in 2020 and 25.98% higher in 2021 than that of the H-GPC type (Table 2). The significantly (p < 0.01) negative correlation between the GPC and grain yield (Table 6) are in good agreement with previous studies [25,26]. However, sufficient sink capacity was the important base for achieving a high rice grain yield [27,28,29], and the sink capacity is mainly determined by the number of effective panicles, the spikelet per panicle, and single grain weight [27]. It should be noted that there was no significant difference in the number of effective panicles or single grain weight among the different GPC types, except for the spikelet per panicle (Table 2), which indicated that the main reason for the difference in sink capacity was the spikelet per panicle. Cultivars with large sink capacities have a higher demand for nitrogen during the grain-filling period, which is mainly met by remobilizing nitrogen from vegetative parts [28,29]. Nitrogen is an important component of chloroplasts. Transferring nitrogen from leaves will reduce chlorophyll content, which is not conducive to photosynthesis [30]. However, large panicle varieties need to have dark green leaves in the middle and late grain-filling stages to fill a larger sink size sufficiently [31]. The leaf nitrogen translocation amount (L-NTA) of the L-GPC type was higher than that of the H-GPC type, which resulted in a lower filled grain rate of the L-GPC type than the H-GPC type (Table 2). Therefore, more attention should be paid to the nitrogen source of leaves in the cultivation of the L-GPC cultivars with high yields.
Previous studies have reported that the dry matter production and nitrogen uptake of high yield varieties were also high [32,33]. Due to the high yield of the L-GPC type, the dry matter production and total nitrogen uptake of the L-GPC were significantly (p < 0.05) higher than those of the H-GPC type at the heading stage and maturity (Table 3 and Table 4). The higher nitrogen uptake ratio before heading was beneficial to the formation of a high yield population, and the smaller nitrogen uptake ratio during the grain-filling period was conducive to the transformation from nitrogen metabolism to carbon metabolism, which was conducive to the grain yield [34]. In this study, the nitrogen uptake during the grain-filling period (NUP-GF) of the L-GPC type was significantly (p < 0.05) lower than that of the H-GPC type (Table 5), which is consistent with previous studies [35]. During the grain-filling period, 70–90% of grain nitrogen is transported from the vegetative organs, and the rest (10–30%) is supplemented from the soil [10]. The result of this study also confirmed that the contributions of the S-NTA, L-NTA, and NUP-GF to the nitrogen increase in the panicle during the grain-filling period (NIA-P) were in the order L-NTA > S-NTA > NUP-GF (Figure 2). Previous studies have shown that adding panicle fertilizer can significantly increase the grain protein content, which may be related to the direct transport of nitrogen supplied in the later stage into the grain [36,37]. In this study, the proportion of the L-NTA in the NIA-P of the L-GPC type was significantly (p < 0.05) higher than that of the H-GPC type, whereas the proportion of the NUP-GF in the NIA-P showed a completely opposite pattern. These results indicated that the nitrogen uptake during the grain-filling period played an important role in increasing the grain protein content. In addition, there were significant genotypic differences in the leaf and stem nitrogen transport [38]. In this study, the S-NTA, especially the L-NTA, of the L-GPC type was significantly (p < 0.05) higher than that of the H-GPC type, whereas there was no significant difference in the S-NTE and L-NTE between the different GPC types, which may be related to the high SDM-M and LDM-M of the L-GPC type, resulting in more nitrogen residues [34]. However, the grain protein concentration is determined by the co-ordination of protein and nonprotein accumulation in rice [39]. Therefore, although the L-NTA and S-NTA of the L-GPC type were higher than those of the H-GPC type, its protein content was diluted due to the accumulation of more carbohydrates in the grain.
4.2. The Key Indexes Affecting GPC
It was proposed by Tsukaguchi et al. [14] that the difference in the GPC was mainly related to the amount of nitrogen available for developing the grain per unit sink capacity (NAV). The GPC was extremely significantly (p < 0.01) positively correlated with the NAV, and the correlation coefficient was the highest in this study (Table 6). The results of the stepwise regression analysis further showed that there was a significant linear relationship between the NAV and GPC (Table 7), indicating that the NAV was the key factor affecting the GPC. However, the NAV is calculated according to the LNC-H, LDM-H, NUP-GF, and the sink capacity, as it is a composite index. The path analysis results showed that the sink capacity had the most direct limiting effect on the NAV (Table 8). The principle of increasing the GPC by spikelet-thinning treatment [14,40,41] is to reduce the sink capacity and increase the NAV. The LNC-H had the most direct promotion effect on the NAV, but it also had the strongest indirect limiting effect on the NAV through the sink capacity and NUP-GF (Table 8), which may be related to sufficient nitrogen absorption in the early stage. This is conducive to the formation of a high-yield population with a high sink capacity [33,34,35]. The nitrogen absorbed by the plant roots will be directly transported to the grain at the late growth stage of rice [42], and it is effective for increasing the NUP-GF and improving the GPC by nitrogen topdressing at the late growth stage [14,16,43,44]. It was also found that the direct path coefficient and the indirect path coefficient of the NUP-GF to the NAV were both positive in this study, indicating that increasing the NUP-GF has a positive effect on improving the NAV (Table 8). In addition, the varieties of the L-GPC type had more days in the whole growth period, especially from sowing to heading, than the varieties of the H-GPC type, and the longer growth period is beneficial to increasing the grain yield and reducing the GPC [45]. Moreover, the grain protein content was inherited as a typical polygenic trait and thereby susceptible to environmental factors, especially for N, an essential component during grain protein synthesis [46]. Therefore, in order to control the grain protein content, we should not only select appropriate rice varieties, but also take into full consideration relevant cultivation measures, such as the operation of nitrogen fertilizer, etc. However, it is worth noting that the GPC data were obtained at maturity, which might indicate the result, rather than the cause, of the nitrogen source–sink capacity relationship. This suggests that we should pay more attention to the balance between the nitrogen source and sink capacity in improving the GPC, which needs further study.
5. Conclusions
Under the same nitrogen application level, the grain yield of the L-GPC type was significantly higher than that of the H-GPC type, because of its higher spikelet number per panicle and larger sink capacity. The dry matter production and total nitrogen content of the L-GPC type were both significantly higher than those of the H-GPC type at the heading stage and maturity, whereas the NUP-GF of the L-GPC varieties was significantly lower than that of the H-GPC varieties. The L-NTA of the L-GPC type was significantly higher than that of the H-GPC type. The NAV is the key index to determine the GPC, and has a significant positive correlation with the GPC, indicating that sufficient NAV is necessary to obtain a high GPC. The sink capacity and the LNC-H are two important indexes affecting the NAV. The sink capacity has the greatest direct limiting effect on the NAV, and the LNC-H has the greatest indirect limiting effect on the NAV. Increasing the NUP-GF is an effective way to improve the GPC. However, the materials used in this study were all conventional japonica rice varieties, whether indica rice and hybrid rice also have the same law needs to be further studied.
Author Contributions
Funding acquisition, Q.L. and Y.W.; methodology, Q.L. and Y.W.; investigation, Q.L., M.L., X.J. and J.L.; writing—original draft, Q.L.; writing—review and editing, X.J., J.L. and F.W. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Key Research and Development and Promotion Project of Henan Province, China, grant number 222102110129; the Innovation Application Special Project of Xinyang City, China; grant number 20210006; Key Research Project of Higher Education Institutions in Henan Province, China, grant number 22A210009 and 22B210010; the Young Teachers Scientific Research Fund Project of Xinyang Agriculture and Forestry University, grant number 20200102 and QN2021021; the Science and Technology Innovation Team Construction Project of Xinyang Agriculture and Forestry University, grant number KJCXTD-202006.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
GPC of tested varieties in 2020 and 2021. Different lowercase letters after values indicate a significant difference at the 0.05 probability level.
Figure 1.
GPC of tested varieties in 2020 and 2021. Different lowercase letters after values indicate a significant difference at the 0.05 probability level.
Figure 2.
The proportions of S-NTA, L-NTA and NUP-GF in NIA-P. S-NTA, stem nitrogen translocation amount; L-NTA, leaf nitrogen translocation amount; NUP-GF, nitrogen uptake during grain-filling period; NIA-P, nitrogen increase in panicle during grain-filling period. Different lowercase letters after values indicate a significant difference at the 0.05 probability level.
Figure 2.
The proportions of S-NTA, L-NTA and NUP-GF in NIA-P. S-NTA, stem nitrogen translocation amount; L-NTA, leaf nitrogen translocation amount; NUP-GF, nitrogen uptake during grain-filling period; NIA-P, nitrogen increase in panicle during grain-filling period. Different lowercase letters after values indicate a significant difference at the 0.05 probability level.
Table 1.
Dates of heading and maturity.
| Variety | Heading | Maturity | S-H (d) | H-M (d) | Total Growth Duration (d) |
|---|---|---|---|---|---|
| 2020 | |||||
| Huajing NO.5 | 9-Aug | 3-Oct | 76 | 55 | 131 |
| Songzaoxiang NO.1 | 9-Aug | 1-Oct | 76 | 53 | 129 |
| Suxiangjing NO.3 | 11-Aug | 4-Oct | 78 | 54 | 132 |
| Nanjing9108 | 17-Aug | 10-Oct | 84 | 54 | 138 |
| Su1785 | 17-Aug | 12-Oct | 84 | 56 | 140 |
| Wuyunjing80 | 19-Aug | 11-Oct | 86 | 53 | 139 |
| 2021 | |||||
| Huajing NO.5 | 8-Aug | 1-Oct | 75 | 54 | 129 |
| Songzaoxiang NO.1 | 9-Aug | 1-Oct | 76 | 53 | 129 |
| Suxiangjing NO.3 | 8-Aug | 1-Oct | 75 | 54 | 129 |
| Nanjing9108 | 14-Aug | 7-Oct | 81 | 54 | 135 |
| Su1785 | 14-Aug | 9-Oct | 81 | 56 | 137 |
| Wuyunjing80 | 18-Aug | 9-Oct | 85 | 52 | 137 |
S: sowing, H: heading, M: maturity.
Table 2.
Grain yield and yield components in 2020 and 2021.
| Type | Variety | Spikelet Per Panicle | Filled Grain Rate (%) | 1000-Grains Weight (g) | No. of Panicles (×104 hm−2) | Sink Capacity (t hm−2) | Grain Yield (t hm−2) |
|---|---|---|---|---|---|---|---|
| 2020 | |||||||
| H-GPC | Huajing NO.5 | 92.25 | 96.78 | 28.57 | 322.77 | 8.51 | 7.83 |
| Songzaoxiang NO.1 | 87.44 | 94.82 | 25.93 | 371.79 | 8.43 | 7.57 | |
| Suxiangjing NO.3 | 83.78 | 96.22 | 20.93 | 456.36 | 8.00 | 7.58 | |
| Mean | 87.83 b | 95.94 a | 25.14 a | 383.64 a | 8.31 b | 7.66 b | |
| L-GPC | Nanjing9108 | 120.80 | 91.13 | 26.37 | 345.23 | 11.00 | 9.65 |
| Su1785 | 114.15 | 88.93 | 26.73 | 386.83 | 11.80 | 10.02 | |
| Wuyunjing80 | 127.92 | 90.03 | 24.32 | 353.08 | 10.98 | 9.49 | |
| Mean | 120.96 a | 90.03 b | 25.81 a | 361.72 b | 11.26 a | 9.72 a | |
| 2021 | |||||||
| H-GPC | Huajing NO.5 | 101.30 | 96.52 | 28.52 | 301.73 | 8.72 | 7.98 |
| Songzaoxiang NO.1 | 92.89 | 91.93 | 26.23 | 343.80 | 8.38 | 7.63 | |
| Suxiangjing NO.3 | 88.47 | 95.91 | 21.33 | 433.88 | 8.19 | 7.69 | |
| Mean | 94.22 b | 94.79 a | 25.36 a | 359.80 a | 8.43 b | 7.77 b | |
| L-GPC | Nanjing9108 | 118.03 | 90.88 | 26.70 | 352.35 | 11.10 | 9.84 |
| Su1785 | 117.43 | 86.20 | 27.07 | 373.05 | 11.85 | 9.97 | |
| Wuyunjing80 | 130.90 | 88.16 | 24.39 | 332.70 | 10.62 | 9.55 | |
| Mean | 122.12 a | 88.41 b | 26.05 a | 352.70 b | 11.19 a | 9.79 a | |
| Results of ANOVA | |||||||
| Year | * | ns | * | ** | ns | ns | |
| Type | ** | ** | ** | ** | ** | ** | |
| Year × Type | Ns | ns | ns | ns | ns | ns | |
Values within the same year followed by different letters are significantly different at the 0.05 probability level. * Significant at the 0.05 probability level, ** Significant at the 0.01 probability level, ns not significant at the 0.05 probability level.
Table 3.
Dry matter production at heading stage and maturity (g/m2).
| Type | Variety | SDM-H | LDM-H | PDM-H | TDM-H | SDM-M | LDM-M | PDM-M | TDM-M | DMP-GF |
|---|---|---|---|---|---|---|---|---|---|---|
| 2020 | ||||||||||
| H-GPC | Huajing NO.5 | 480.57 | 258.04 | 141.12 | 879.74 | 451.73 | 233.17 | 846.28 | 1531.18 | 651.44 |
| Songzaoxiang NO.1 | 459.78 | 282.74 | 145.88 | 888.40 | 439.24 | 215.64 | 807.32 | 1462.20 | 573.80 | |
| Suxiangjing NO.3 | 458.46 | 252.90 | 144.13 | 855.49 | 383.54 | 208.03 | 793.76 | 1385.32 | 529.83 | |
| Mean | 466.27 b | 264.56 b | 143.71 b | 874.54 b | 424.84 b | 218.94 b | 815.79 b | 1459.57 b | 585.02 b | |
| L-GPC | Nanjing9108 | 542.18 | 338.16 | 147.13 | 1027.47 | 457.57 | 285.39 | 1022.31 | 1765.28 | 737.81 |
| Su1785 | 638.53 | 367.49 | 195.60 | 1201.63 | 573.25 | 337.47 | 1124.77 | 2035.48 | 833.86 | |
| Wuyunjing80 | 588.08 | 345.83 | 182.62 | 1116.53 | 476.81 | 309.39 | 1001.58 | 1787.78 | 671.25 | |
| Mean | 589.60 a | 350.49 a | 175.12 a | 1115.21 a | 502.55 a | 310.75 a | 1049.55 a | 1862.85 a | 747.64 a | |
| 2021 | ||||||||||
| H-GPC | Huajing NO.5 | 468.68 | 259.65 | 136.11 | 864.44 | 411.94 | 242.85 | 860.00 | 1514.79 | 650.34 |
| Songzaoxiang NO.1 | 438.92 | 281.00 | 133.39 | 853.31 | 403.72 | 212.34 | 812.35 | 1428.41 | 575.10 | |
| Suxiangjing NO.3 | 444.68 | 245.85 | 135.36 | 825.88 | 350.63 | 194.03 | 754.19 | 1298.85 | 472.96 | |
| Mean | 450.76 b | 262.17 b | 134.96 b | 847.88 b | 388.76 b | 216.41 b | 808.85 b | 1414.01 b | 566.13 b | |
| L-GPC | Nanjing9108 | 586.55 | 324.63 | 173.83 | 1085.00 | 480.66 | 252.66 | 1054.70 | 1788.03 | 703.03 |
| Su1785 | 717.68 | 350.84 | 209.97 | 1278.50 | 596.70 | 300.57 | 1246.34 | 2143.62 | 865.12 | |
| Wuyunjing80 | 614.16 | 346.90 | 197.18 | 1158.24 | 484.08 | 295.73 | 1063.53 | 1843.34 | 685.10 | |
| Mean | 639.46 a | 340.79 a | 193.66 a | 1173.91 a | 520.48 a | 282.99 a | 1121.52 a | 1925.00 a | 751.08 a | |
| Results of ANOVA | ||||||||||
| Year | ns | ns | ns | ns | Ns | ns | Ns | ns | ns | |
| Type | ** | ** | ** | ** | ** | ** | ** | ** | ** | |
| Year × Type | ns | ns | ns | ns | Ns | ns | Ns | ns | ns | |
SDM-H, stem dry matter weight at heading stage; LDM-H, leaf dry matter weight at heading stage; PDM-H, panicle dry matter weight at heading stage; TDM-H, total dry matter weight at heading stage; SDM-M, stem dry matter weight at maturity; LDM-M, leaf dry matter weight at maturity; PDM-M, panicle dry matter weight at maturity; TDM-M, total dry matter weight at maturity; DMP-GF, dry matter production during grain-filling period. Values within the same year followed by different letters are significantly different at the 0.05 probability level. ** Significant at the 0.01 probability level, ns not significant at the 0.05 probability level.
Table 4.
Nitrogen accumulation, distribution at heading stage and maturity.
| Type | Variety | SNC-H (g/m2) | LNC-H (g/m2) | PNC-H (g/m2) | TNC-H (g/m2) | SNC-M (g/m2) | LNC-M (g/m2) | PNC-M (g/m2) | TNC-M (g/m2) | NHI (%) |
|---|---|---|---|---|---|---|---|---|---|---|
| 2020 | ||||||||||
| H-GPC | Huajing NO.5 | 6.37 | 8.00 | 2.12 | 16.49 | 3.60 | 3.40 | 11.75 | 18.75 | 62.69 |
| Songzaoxiang NO.1 | 6.49 | 8.85 | 2.32 | 17.66 | 4.39 | 3.39 | 11.42 | 19.20 | 59.47 | |
| Suxiangjing NO.3 | 6.36 | 8.24 | 2.14 | 16.74 | 4.09 | 3.14 | 11.38 | 18.61 | 61.15 | |
| Mean | 6.40 a | 8.37 b | 2.19 a | 16.96 b | 4.03 a | 3.31 a | 11.52 a | 18.85 b | 61.10 a | |
| L-GPC | Nanjing9108 | 6.62 | 10.04 | 1.95 | 18.61 | 3.73 | 3.58 | 11.75 | 19.06 | 61.64 |
| Su1785 | 7.07 | 10.74 | 2.59 | 20.37 | 4.84 | 4.18 | 12.14 | 21.16 | 57.35 | |
| Wuyunjing80 | 6.67 | 9.36 | 2.47 | 18.45 | 4.49 | 3.69 | 11.14 | 19.32 | 57.66 | |
| Mean | 6.79 a | 10.04 a | 2.32 a | 19.14 a | 4.36 a | 3.82 a | 11.67 a | 19.84 a | 58.88 a | |
| 2021 | ||||||||||
| H-GPC | Huajing NO.5 | 6.14 | 8.18 | 1.93 | 16.26 | 3.08 | 3.38 | 11.92 | 18.38 | 64.85 |
| Songzaoxiang NO.1 | 5.90 | 8.70 | 1.98 | 16.58 | 3.51 | 3.25 | 11.58 | 18.34 | 63.13 | |
| Suxiangjing NO.3 | 6.17 | 7.86 | 1.96 | 15.98 | 3.78 | 2.88 | 10.64 | 17.30 | 61.53 | |
| Mean | 6.07 b | 8.24 b | 1.96 b | 16.27 b | 3.46 a | 3.17 a | 11.38 b | 18.00 b | 63.17 a | |
| L-GPC | Nanjing9108 | 6.80 | 9.81 | 2.34 | 18.95 | 3.77 | 3.12 | 12.68 | 19.56 | 64.82 |
| Su1785 | 7.31 | 10.48 | 2.79 | 20.58 | 4.25 | 3.66 | 14.11 | 22.02 | 64.07 | |
| Wuyunjing80 | 6.49 | 9.65 | 2.65 | 18.78 | 4.01 | 3.54 | 12.20 | 19.74 | 61.78 | |
| Mean | 6.87 a | 9.98 a | 2.59 a | 19.44 a | 4.01 a | 3.44 a | 12.99 a | 20.44 a | 63.56 a | |
| Results of ANOVA | ||||||||||
| Year | ns | ns | ns | ns | ns | ns | ns | ns | * | |
| Type | ** | ** | * | ** | ns | * | * | ** | ns | |
| Year × Type | ns | ns | ns | ns | ns | ns | ns | ns | ns | |
SNC-H, stem nitrogen content at heading stage; LNC-H, leaf nitrogen content at heading stage; PNC-H, panicle nitrogen content at heading stage; TNC-H, total nitrogen content at heading stage; SNC-M, stem nitrogen content at maturity; LNC-M, leaf nitrogen content at maturity; PNC-M, panicle nitrogen content at maturity; TNC-M, total nitrogen content at maturity; NHI, nitrogen harvest index. Values within the same year followed by different letters are significantly different at the 0.05 probability level. * Significant at the 0.05 probability level, ** Significant at the 0.01 probability level, ns not significant at the 0.05 probability level.
Table 5.
Nitrogen transport during grain-filling period.
| Type | Variety | S-NTA (g/m2) | S-NTE (%) | L-NTA (g/m2) | L-NTE (%) | NUP-GF (g/m2) | NIA-P (g/m2) | NAV (mg/g) |
|---|---|---|---|---|---|---|---|---|
| 2020 | ||||||||
| H-GPC | Huajing NO.5 | 2.77 | 43.50 | 4.61 | 57.56 | 2.26 | 9.63 | 15.20 |
| Songzaoxiang NO.1 | 2.09 | 32.24 | 5.46 | 61.74 | 1.53 | 9.09 | 16.58 | |
| Suxiangjing NO.3 | 2.27 | 35.69 | 5.10 | 61.85 | 1.87 | 9.24 | 15.98 | |
| Mean | 2.38 a | 37.14 a | 5.06 b | 60.39 a | 1.89 a | 9.32 a | 15.92 a | |
| L-GPC | Nanjing9108 | 2.89 | 43.64 | 6.46 | 64.38 | 0.45 | 9.80 | 11.95 |
| Su1785 | 2.23 | 31.57 | 6.52 | 60.93 | 0.79 | 9.55 | 12.40 | |
| Wuyunjing80 | 2.18 | 32.67 | 5.67 | 60.61 | 0.87 | 8.73 | 11.84 | |
| Mean | 2.44 a | 35.96 a | 6.22 a | 61.97 a | 0.70 b | 9.36 a | 12.06 b | |
| 2021 | ||||||||
| H-GPC | Huajing NO.5 | 3.06 | 49.85 | 4.80 | 58.69 | 2.12 | 9.99 | 14.69 |
| Songzaoxiang NO.1 | 2.39 | 40.48 | 5.45 | 62.65 | 1.76 | 9.59 | 16.04 | |
| Suxiangjing NO.3 | 2.39 | 38.73 | 4.98 | 64.37 | 1.31 | 8.68 | 15.25 | |
| Mean | 2.61 a | 42.99 a | 5.08 b | 61.57 a | 1.73 a | 9.42 a | 15.33 a | |
| L-GPC | Nanjing9108 | 3.04 | 44.65 | 6.69 | 68.23 | 0.61 | 10.34 | 11.90 |
| Su1785 | 3.06 | 41.91 | 6.82 | 65.05 | 1.44 | 11.32 | 12.52 | |
| Wuyunjing80 | 2.48 | 38.19 | 6.11 | 63.35 | 0.96 | 9.56 | 11.91 | |
| Mean | 2.86 a | 44.31 a | 6.54 a | 65.55 a | 0.82 b | 10.42 a | 12.11 b | |
| Results of ANOVA | ||||||||
| Year | ns | ns | ns | ns | ns | ns | ns | |
| Type | ns | ns | ** | ns | ** | ns | ** | |
| Year × Type | ns | ns | ns | ns | ns | ns | ns | |
S-NTA, stem nitrogen translocation amount; S-NTE, stem nitrogen translocation efficiency; L-NTA, leaf nitrogen translocation amount; L-NTE, leaf nitrogen translocation efficiency; NUP-GF, nitrogen uptake during grain-filling period; NIA-P, nitrogen increase in panicle during grain-filling period; NAV, the amount of nitrogen available for developing grain per unit sink capacity. Values within the same year followed by different letters are significantly different at the 0.05 probability level. ** Significant at the 0.01 probability level, ns not significant at the 0.05 probability level.
Table 6.
Correlations of grain yield, dry matter production, nitrogen uptake and translocation with GPC.
Table 6.
Correlations of grain yield, dry matter production, nitrogen uptake and translocation with GPC.
| Index | 2020 | 2021 | Index | 2020 | 2021 |
|---|---|---|---|---|---|
| Spikelet per panicle | −0.975 ** | −0.946 ** | LNC−H | −0.826 * | −0.876 * |
| Filled grain rate | 0.691 | 0.781 | PNC−H | −0.305 | −0.894 * |
| 1000-grains weight | −0.197 | −0.356 | TNC−H | −0.793 | −0.897 * |
| No. of panicles | 0.274 | 0.047 | SNC−M | −0.342 | −0.688 |
| Sink capacity | −0.973 ** | −0.961 ** | LNC−M | −0.751 | −0.506 |
| Grain yield | −0.977 ** | −0.981 ** | PNC−M | −0.203 | −0.726 |
| SDM-H | −0.901 * | −0.898 * | TNC−M | −0.542 | −0.774 |
| LDM-H | −0.936 ** | −0.914 * | NHI | 0.529 | 0.155 |
| PDM-H | −0.727 | −.924 ** | S−NTA | −0.119 | −0.424 |
| TDM-H | −0.908 * | −0.920 ** | S−NTE | 0.084 | 0.113 |
| SDM-M | −0.656 | −0.803 | L−NTA | −0.791 | −0.873 * |
| LDM-M | −0.935 ** | −0.857 * | L−NTE | −0.345 | −0.637 |
| PDM-M | −0.929 ** | −0.891 * | NIA−P | −0.026 | −0.574 |
| TDM-M | −0.886 * | −0.873 * | NUP−GF | 0.892 * | 0.728 |
| DMP-GF | −0.789 | −0.749 | NAV | 0.989 ** | 0.991 ** |
| SNC-H | −0.757 | −0.823 * |
*, significant at the 0.05 probability level; **, significant at the 0.01 probability level.
Table 7.
Stepwise regression of GPC to grain yield, dry matter production and nitrogen accumulation traits.
Table 7.
Stepwise regression of GPC to grain yield, dry matter production and nitrogen accumulation traits.
| Year | Selected Dependent | Regression Equation | R2 |
|---|---|---|---|
| 2020 | NAV | Y = −0.229 + 0.495·NAV | 0.978 ** |
| 2021 | NAV | Y = −0.688 + 0.584·NAV | 0.983 ** |
** indicates that R2 of the regression equation significant at the 0.01 probability level.
Table 8.
Path analysis of NAV and its components.
| Year | Trait | Correlation Coefficient | Direct Path Coefficient | Indirect Path Coefficient | ||||
|---|---|---|---|---|---|---|---|---|
| Sink Capacity | LDM-H | LNC-H | NUP-GF | Total | ||||
| 2020 | Sink capacity | −0.961 ** | −1.447 | 0.000 | 0.195 | 0.618 | −0.327 | 0.486 |
| LDM-H | −0.890 * | 0.202 | −1.396 | 0.000 | 0.646 | −0.342 | −1.091 | |
| LNC-H | −0.767 | 0.686 | −1.305 | 0.190 | 0.000 | −0.755 | −1.870 | |
| NUP-GF | 0.837 * | 0.374 | 1.264 | −0.184 | −0.618 | 0.000 | 0.462 | |
| 2021 | Sink capacity | −0.935 ** | −1.610 | 0.000 | −0.140 | 0.962 | −0.147 | 0.675 |
| LDM-H | −0.860 * | −0.146 | −1.538 | 0.000 | 0.972 | −0.148 | −0.714 | |
| LNC-H | −0.818 * | 1.004 | −1.542 | −0.141 | 0.000 | −0.137 | −1.821 | |
| NUP-GF | 0.723 | 0.263 | 0.900 | 0.082 | −0.522 | 0.000 | 0.460 | |
*, significant at the 0.05 probability level; **, significant at the 0.01 probability level.
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Liu, Q.; Li, M.; Ji, X.; Liu, J.; Wang, F.; Wei, Y. Characteristics of Grain Yield, Dry Matter Production and Nitrogen Uptake and Transport of Rice Varieties with Different Grain Protein Content. Agronomy 2022, 12, 2866. https://doi.org/10.3390/agronomy12112866
AMA Style
Liu Q, Li M, Ji X, Liu J, Wang F, Wei Y. Characteristics of Grain Yield, Dry Matter Production and Nitrogen Uptake and Transport of Rice Varieties with Different Grain Protein Content. Agronomy. 2022; 12(11):2866. https://doi.org/10.3390/agronomy12112866
Chicago/Turabian StyleLiu, Qiuyuan, Meng Li, Xin Ji, Juan Liu, Fujuan Wang, and Yunfei Wei. 2022. "Characteristics of Grain Yield, Dry Matter Production and Nitrogen Uptake and Transport of Rice Varieties with Different Grain Protein Content" Agronomy 12, no. 11: 2866. https://doi.org/10.3390/agronomy12112866
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