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Article

Impact of Key Agronomic Traits on Economic Yield Traits in Anhui Rice (Oryza sativa L. spp. japonica)

by
Yi Ren
1,†,
Ending Xu
2,†,
Peijiang Zhang
2,
Xinchun Zhan
2,
Qingyuan He
3,* and
Yu Zou
2,*
1
College of Agricultural, Anhui Science and Technology University, Chuzhou 233100, China
2
Anhui Province Key Laboratory of Rice Germplasm Innovation and Molecular Improvement, Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
3
College of Life and Health Sciences, Anhui Science and Technology University, Chuzhou 233100, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(6), 1101; https://doi.org/10.3390/agronomy14061101
Submission received: 15 April 2024 / Revised: 11 May 2024 / Accepted: 21 May 2024 / Published: 22 May 2024
(This article belongs to the Special Issue Sustainable Development of Rice Cultivation and Soil Nutrient Management)
Browse Figures
Versions Notes

Abstract

:
Nitrogen (N) application rates and planting density are key factors affecting grain yield and quality. In this experiment, four varieties, Huijing 753 (HJ 753), Huijing 855 (HJ 855), Huijing 866 (HJ 866), and Dangjing 8 (DJ 8), were used to investigate their effects on the yield and quality of japonica rice in Anhui, which has a lower soil N supply capacity. The current study set four levels of N application N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and three planting densities D1, D2, and D3 (high-density: 45 × 104; medium-density: 37.5 × 104; low-density: 32.2 × 104) hills ha−1 for a total of 12 treatments. The effects of N application and planting density on rice yield, composition, processing quality, appearance quality, and cooking quality were analyzed. The results showed that with the increase in N application rates and planting density, the effective panicle number per plant and grain number per panicle of each rice variety increased, while HJ 753, HJ 855, and HJ 866 reached the highest yield under the N3D1 treatment and then gradually declined, and DJ 8 had the highest yield in the N4D1 treatment. In addition, the processing quality of various varieties can be improved by increasing the N application rate and planting density, but the appearance quality will deteriorate, the amylose content will also decrease, and gel consistency will also be lower. Therefore, under medium-planting density conditions, N at a rate of 240 kg ha−1 was the best for HJ 753, HJ 855, and HJ 866, and using 300 kg ha−1 for DJ 8 resulted in high grain yields and superior rice grain quality. In the next step, physiological, biochemical, and genotypic analyses of these three japonica rice varieties were carried out to provide a scientific basis and technical support for accelerating the breeding of high-yield japonica rice varieties.

1. Introduction

Rice (Oryza sativa L.) is one of the world’s major cereal crops and is a staple food for more than half of the world’s population [1]. With rapid economic development and continuous population growth, demands and requirements for food are increasing, and there is more demand for japonica rice due to its better eating and cooking quality [2]. Anhui is one of the key areas for the expansion of the cultivation of japonica rice [3], and the vigorous development of japonica rice production in Anhui can increase the supply of japonica rice. However, the total soil N content of 1.25 g kg−1 in Anhui is significantly lower than that in areas suitable for japonica rice cultivation, which may limit the development of japonica rice in Anhui to a certain extent and reduce grain yields, thus affecting food security [4].
N is the most important element during the growth and development of rice and is crucial to the formation of yield and quality [5]. N deficiency in rice may lead to reduced resource utilization, productivity, and profitability [6]. Therefore, people tend to increase the amount of N applied to compensate for the N nutrient needs of rice grown in areas with low soil total N content; however, too much N application may cause problems such as reduced grain yield and quality [7,8]. Most studies have shown that under high-N supplies, rice’s nutritive growth period is prolonged relative to the reproductive growth period, which results in symptoms such as late maturity, plant collapse, a lower percentage of filling, and ineffective tillering [9,10,11]. In addition, the heavy application of N fertilizers can adversely affect biodiversity, human health, and climate and pose a major challenge to N cycles [12,13]. China is the world’s largest consumer of N fertilizer, and the N fertilizer utilization rate is only 30–40%, which is 15–20% lower than that of other major rice-producing countries [14]. Therefore, the rational application of N can improve the utilization rate of N fertilizer, reduce costs, and improve yield and quality. In addition to N application, planting density is also an important factor affecting rice growth and yield [15]. An appropriate planting density is conducive to the utilization of resources by rice. Several studies have shown that reasonable dense planting can optimize the quality of the rice plant population structure and coordinate the environment for individual development, thus achieving high yield and high quality [15,16].
The optimization of N application and planting density based on soil N fertility levels is essential to achieve the maximum yield and rice quality [17]. Previous studies have found that optimizing integrated cultivation management can improve the physiological activity of Wuyunjing 24, increase yields, and improve the processing quality of rice, but the appearance quality and cooking quality will deteriorate [18]. Additionally, some people have determined different cultivation methods based on the ecological conditions and quality characteristics of each rice-growing area. For example, in conventional fields, a N application rate of 300 kg ha−1 can easily achieve high yields, but combined with green production in rice fields that is cost-effective and efficient, the best effect is when the N application rate is 225 kg ha−1 and the planting density is of basic seedlings 180 × 104 ha−1 [19]. With a N application rate of 300 kg ha−1 and a planting density of 25 cm ×12 cm in saline-alkali land, grain yields will be increased [20]. Densification and N reduction increased the maximum number of tillers and aboveground biomass of rice, obtaining a higher number of spikes and seed grout, resulting in high yield and quality [21].
The Anhui region is the main grain-producing area in China, but the soil’s N supply capacity is low, and increasing N applications is becoming a new fertilizer method and density management measure for rice cultivation in the Anhui region in order to improve yields [22]. Although this method promotes the growth advantage of monocultures, it requires large amounts of fertilizer, which greatly increases the cost of cultivation and reduces the efficiency of fertilizer use. Moreover, the blind selection of planting density fails to realize grain yield potential and rice quality [16,23]. Therefore, cultivation practices that balance N application and planting density may be a key factor in improving rice yield and quality. In this study, four japonica rice varieties, HJ 753, HJ 855, HJ 866, and DJ 8, were used as materials for field experiments with different N application rates and planting densities in Anhui, China. The effects of different N application rates and planting densities on the yield and quality of HJ 753, HJ 855, HJ 866, and DJ 8 were investigated to provide a basis for improving the yield of japonica rice, taking into account high-quality rice production.

2. Materials and Methods

2.1. Test Materials and Locations

HJ 753, HJ 855, HJ 866, and DJ 8 were used as test materials. Among them, HJ 753, HJ 855, and HJ 866 are japonica rice varieties provided by the Rice Research Institute of the Anhui Academy of Agricultural Sciences, and DJ 8 is a regional trial control variety for medium-maturing japonica rice in Anhui Province (Table 1). Field cultivation trials were conducted at the Quanjiao County Experimental Base in Chuzhou City, Anhui Province, in 2022 and 2023 (elevation 30 m, 118°21′67″ E, 32°06′79″ N), as shown in Figure 1. The physical and chemical properties of the soil surface are shown in Table 2. The changes in temperature, rainfall, and relative humidity during rice growth in 2022 and 2023 are shown in Figure 2. Compared with 2023, the overall temperature in 2022 was higher, there was less rainfall in May, and rainfall increased from June to August.

2.2. Experimental Design

This experiment adopted a split–split plot experimental design, with the N application rate as the main plot, planting density as the sub-plot, and variety as the sub-sub-plot [24]. Three levels of N fertilizer treatment were set up: low N (N1) 120 kg ha−1; medium-low N (N2) 180 kg ha−1; medium N (N3) 240 kg ha−1; and high N (N4) 300 kg ha−1. There are three gradients for planting density: high density (D1): 45 × 104 hills ha−1; medium density (D2): 37.5 × 104 hills ha−1; and low density (D3): 32.2 × 104 hills ha−1. There were a total of 12 treatments and 3 replications, and the plot area was 30.24 m2 [25]. Random block arrangements were used. The main areas were separated by field ridges. Different sub-plots (planting density) and sub-sub-plots (variety) in the same main area do not have field ridges. In order to prevent fertilizers from flowing between different main areas, the field ridges were covered with plastic film and embedded 20 cm below the soil layer, leaving 30 cm above the ground. A 30 cm wide drainage and irrigation ditch was set up, a drainage ditch with a width and depth of 25–30 cm was created at the center of the drainage and irrigation ditch, and single-row irrigation was implemented in the main section. The amount of N application is divided into four applications: base fertilizer (1 day before transplanting), tillering fertilizer (7 days after transplanting), flower-promoting fertilizer (when the leaf age remainder is 3), and flower-preserving fertilizer (when the leaf age remainder is 1). The N application ratio is 4:2:2:2. Superphosphate (containing 12% P2O5) at 120 kg ha−1 and potassium chloride (containing 60% K2O) at 120 kg ha−1 were applied to all plots in the experimental field. Water management was carried out according to local high-yield cultivation, and diseases, insect pests, and weeds were strictly controlled. Seeds were sown on May 10 and transplanted on June 10 (seedling age: 7.5 leaves) in 2022 and 2023, respectively, and they were harvested in late October of the same year, with previous crops in a vacant field.

2.3. Measurement Items and Methods

2.3.1. Grain Yield and Its Components

After the crop matured, 30 rice plants were taken from the middle range of each plot. The yield was recorded according to the actual harvest and converted into yield per hectare. From each plot, five representative plants were selected based on the average panicle number and then air-dried and sun-dried for indoor seed testing, respectively. Tests include the following: effective panicles (m2), spikelets per panicle (m2), seed setting rate (%), and thousand-grain weight (g).

2.3.2. Grain Quality

After harvesting, the rice was naturally dried to a moisture content of 12 threshed, and impurities were removed. The quality was tested, including processing, appearance, and cooking qualities. The measurement method refers to the national standard of the People’s Republic of China GB/T1354-2018 [25].

2.4. Statistical Analyses

An analysis of variance and multiple comparisons were performed using SPSS 27 software (IBM Corporation, Armonk, NY, USA), and the least significant difference (LSD) with p < 0.05 was used to determine significance. All graphs were plotted with Origin 2021 (Origin Lab, Northampton, MA, USA), and the standard error of the mean was calculated and displayed as error bars in the graphs. The ANOVA did not show any significant difference between the two years (Table 3 and Table 4). Therefore, we reanalyzed the data using a simplified model by removing the non-significant factor (in this case, the year factor). The average shown is the average result for different locations and years.

3. Results

3.1. Effect of N Application Rate and Planting Density on Yield and Its Components

3.1.1. Effect of N Application Rate on Yield and Its Components

The yield of japonica rice is significantly affected by the year (p < 0.001), and both the variety and N application rates have a significant impact on the yield (Table 3). The yields of HJ 753, HJ 855, and HJ 866 increased first and then decreased with the increase in N application rate, and the highest yields were obtained at N3, which were 11.4 t ha−1, 11.43 t ha−1, and 9.90 t ha−1. The yield of DJ 8 increased with an increase in N application, reaching the highest yield of 10.75 t ha−1 at N4 (Table 5).
The amount of N application has a significant impact on the effective number of panicles, number of grains per panicle, and seed setting rate, but it has no significant impact on the thousand-grain weight (Table 3). The effective number of panicles, number of grains per panicle, and seed setting rate of each japonica rice variety first increased and then decreased with the increase in N application. Compared with N1, the increase in the effective panicle number (5.5%, 16.6%, and 10.4%) and grain number per panicle (4.5%, 8.4%, and 5.9%) of HJ 753, HJ 855, and HJ 866 reached the highest in N3, contrasting the effective panicle number of DJ 8. The increase rate of the grain number (19.5%) and grain number per panicle (9.8%) reached the highest in N4. Although the increase in the effective panicle number and grain number per panicle of DJ 8 is higher than that of other varieties, the total yield of DJ 8 is lower than that of the other varieties; thus, the yield of other varieties is higher than that of DJ 8.

3.1.2. Effect of Planting Density on Yield and Components of Different Japonica Rice Varieties

Planting density has a significant impact on japonica grain yield (Table 3). The yield of each japonica rice variety increases with the increase in planting density. When the planting density is D1, the yields of each variety were the highest at 11.09 t ha−1, 10.72 t ha−1, 9.79 t ha−1, and 10.42 t ha−1 (Table 6).
Planting density has a significant impact on the effective number of panicles, number of grains per panicle, and thousand-grain weight, but it has no significant impact on the seed setting rate (Table 3). The effective panicle number of each japonica rice variety increased with the increase in planting density, while the number of grains per panicle and thousand-grain weight decreased with the increase in planting density. Compared with D3, the increase in the effective panicle number (30.3%, 22.8%, 34.1%, and 19.2%) of HJ 753, HJ 855, HJ 866, and DJ 8 reached the highest in D1.

3.1.3. Effects of Interaction between N Application Rate and Planting Density on Yield and Yield Components

The interactions between the N application rate and planting density, variety and N application rate, and variety and planting density have a significant impact on yield (Table 3).
N-intensive interactions have different effects on the yield of different varieties (Table 7). Under the N3D1 treatment condition, the yields of HJ 753, HJ 855, and HJ 866 reached the highest, which were 12.32 t ha−1, 12.09 t ha−1, and 10.22 t ha−1, respectively. Under the N4D1 treatment condition, the yield of DJ 8 reached the highest, which was 10.77 t ha−1. Under the same N fertilizer level, the yield of each variety was the highest at the D1 treatment density, and the yield decreased as planting density decreased. The yields of HJ 753, HJ 855, and HJ 866 were not significantly different between treatments D2 and D3, while the yields of DJ 8 were significantly different between treatments D2 and D3. Under the same planting density level, the yields of HJ 753, HJ 855, and HJ 866 first increased and then declined with the increase, while the yield of DJ 8 continuously increased. These results suggest that high yields of HJ 753, HJ 855, and HJ 866 can be obtained under lower N fertilizer conditions, while the yield of DJ 8 depends on higher N fertilizer levels.
The effective number of panicles per unit area of each variety increased with the increase in N application rate and density (Table 7). The effective number of panicles per unit area of HJ 753, HJ 866, and DJ 8 all reached the highest numbers under the N4D1 treatment, which were 421.10 m−2, 394.82 m−2, and 397.80 m−2, respectively. The effective number of panicles per unit area of HJ 855 was the highest in the N3D1 treatment, which was 438.02 m−2.

3.2. Effects of N Application Rate and Planting Density on Grain Quality

3.2.1. Effects of N Application Rate and Planting Density on Processing Quality

The processing quality of rice is significantly affected by year and variety (Table 4). In addition, as the amount of N application increased, the brown rice rate, polished rice rate, and whole rice rate of each variety showed an upward trend. With the increase in planting density, the brown rice rate, polished rice rate, and whole-milled rice rate of each variety decreased slightly (Figure 3 and Figure S1).

3.2.2. Effects of N Application Rate and Planting Density on Appearance Quality

The chalky grain rate and chalkiness are affected by year, variety, N application rate, and planting density (p < 0.01), but the interaction between the N application rate and planting density was not significant (Table 4).
Under the same N application rate, the chalky grain rate and chalkiness of each variety increased with an increase in planting density. Under the same planting density, the chalky grain rate and chalkiness among various varieties increased with an increase in N application (Figure 4 and Figure S2).

3.2.3. Effects of N Application Rate and Planting Density on Cooking Quality

Amylose content and gum consistency were significantly affected by year, variety, N application rate, and planting density (p < 0.01), but they were not affected by the interaction between the N application rate and planting density (Table 4). At the same planting density, the amylose content and gum consistency of each variety decreased with an increase in N application. Under the same N application rate, the amylose content and gum consistency of each variety decreased as planting density increased (Figure 5 and Figure S3).

3.3. Correlation Analysis of N Application Rate and Planting Density on Grain Yield, Composition, and Quality

The yield and yield components (grain filling), processing quality, and appearance quality (grain length/width ratio) of HJ 753 have a significant positive correlation with the amount of N application, and the cooking quality has a significant negative correlation with the amount of N application (Figure 6). For HJ 855, yield, effective panicles, the number of grains per panicle, polished rice rate, fully polished rice rate, chalkiness, and chalky grain rate have a significant positive correlation with the amount of N application. The seed setting rate, thousand-grain weight, brown rice rate, and cooking quality have a significant negative correlation with the amount of N application. For HJ 866, the effective panicle number, grain number per panicle, processing quality, and appearance quality (grain length/width ratio) have a significant positive correlation with the amount of N application, and the yield, amylose content, and gel consistency have a negative correlation with the amount of N application. For DJ 8, the yield and effective panicle number have a significant positive correlation with the amount of N application, and gel consistency has a significant negative correlation with the amount of N application.
The yield and effective panicle number of HJ 753 showed a significant positive correlation with planting density, and the number of grains per panicle showed a significant negative correlation with planting density. For HJ 855, yield, effective panicle number, and seed setting rate have a significant positive correlation with planting density. For HJ 866, yield and effective panicle number have a significant positive correlation with planting density, and the grain number per panicle, seed setting rate, and thousand-grain weight have a significant negative correlation with planting density. For DJ 8, yield and effective panicle numbers have a significant positive correlation with planting density, while the grain number per panicle has a significant negative correlation with planting density. The appearance quality of each variety has a positive correlation with the planting density, while processing quality and cooking quality have negative correlations with planting density.

4. Discussion

4.1. Effect of N Application Rate and Planting Density on Yield and Its Components of N

N is one of the elements necessary for plant growth and development. Previous research results show that with the increase in N application, grain yields first increase and then decrease [26]. Reasonably increasing the amount of N application can promote rice tillering and increase rice spikelet differentiation and the panicle rate [27,28,29]. However, when too much N is applied, it will increase the number of ineffective tillers, reduce the panicle rate, result in the late maturity of rice, and affect grain yields [30]. The increase in storage capacity is the main factor in increasing yields, and the increase in panicle number and grain number per panicle is the main method for increasing storage capacity [31,32]. Similarly, this study observed that the effective panicle number, grain number per panicle, and seed setting rate of HJ 753, HJ 855, and HJ 866 first increased and then decreased with the increase in N application. However, the effective panicle number and grain number per panicle of DJ 8 continued to increase, and this is possibly because the maximum N application rate did not reach the optimal N fertility level of DJ 8. It can be observed that various rice varieties have different levels of assimilation and varied N fertilizer absorption. Some varieties, such as HJ 753, HJ 855, and HJ 866, are more likely to realize their potential in areas with medium to lower N fertilizer levels.
The amount of N application mainly increases the number of panicles by promoting tillering, while planting density mainly increases the number of panicles by increasing the number of basic seedlings. The number of spikelets per panicle is restricted by the number of panicles [33]. Previous studies have shown that within a certain range of planting density, grain yield increases with increasing density [34,35]. The current study found that increasing planting densities can increase the effective panicle number of rice but lead to a decrease in the number of grains per panicle. Higher planting density can ensure the initial population number of rice, such as the number of tillers. However, too many tillers will lead to the production of ineffective tillers, resulting in a waste of nutrients, reducing the rice panicle rate, and thus leading to a reduction in the number of grains per panicle [36,37]. In summary, these results indicate that reasonable planting densities are conducive to the mutual coordination of yield components and, thus, can increase yield.

4.2. Effects of N Application Rate and Planting Density on Grain Quality

Rice grain quality is a comprehensive index composed of four aspects: processing quality, appearance quality, nutritional quality, and cooking and eating quality. Previous studies have shown that as N application increases, the processing quality of varieties gradually improves. This may be attributed to the increase in protein content caused by increased N, which provides elasticity to the endosperm and increases the hardness of the rice grains, thereby reducing grain breakage during milling [38,39]. In this study, it was found that the amount of N applied had a significant effect on the processed varieties, and increasing the amount of N applied could improve the processing quality of rice, while increasing planting densities could increase processing quality, but it had no significant effect. The difference is that studies have also shown that with the increase in N fertilizer application, the processing quality of rice presents a parabolic change, and the processing quality increases first and then decreases with the increase in N application [40]. It is also possible that the cost-effective trial setting used less N fertilizer treatment and did not reach the critical point of decline in the processing quality of each variety. However, previous studies showed that with the increase in N application and planting density, the total grain number increased, which affected the grain filling speed and degree, loosened the starch grain arrangement, and resulted in an increase in rice chalkiness [8,41]. In addition, an increased planting density may reduce the photosynthetic rate, which may result in insufficient nutrient supplies for the developing endosperm, reduce the endosperm’s ability to synthesize starch, and disrupt amyloplast development, thereby increasing the chalky grain rate and chalkiness [42,43]. Similarly, the current study found that increasing N applications significantly improved the appearance quality of each variety, but it had no significant effect on planting density. Therefore, it is necessary to choose the appropriate N application amount and planting density, taking into account the processing quality and appearance quality.
In addition, the current study also found that as the amount and density of N application increased, the amylose content and gum consistency of each variety decreased. Rice endosperm contains obvious A-type and B-type starch granules. Generally speaking, A-type starch granules have slightly higher apparent and total amylose content than B-type starch granules, and increasing N fertilizer applications will reduce the proportion of A-type starch granules, thereby reducing the amylose content. The change in gum consistency is primarily due to the fact that the high protein content at high N levels restrained the swelling and maintained the swollen starch granules [15,17,44]. However, some studies have shown that the amount of N application has no significant effect on amylose content [45,46]. In short, the appropriate N application rate and planting density can take into account the relationship between rice processing quality, appearance quality, and cooking quality [15].

5. Conclusions

In the Anhui region where the soil N supply capacity is low, different varieties show differences in yield and quality under different N applications and planting densities, and choosing reasonable N applications and planting densities for growing japonica rice in the Anhui region is a must. In future studies, the physiological and biochemical characterization of high yields of HJ 753, HJ 855, and HJ 866 japonica varieties obtained at medium fertility levels will be carried out, in addition to genotypic analyses, which can provide scientific bases and technical support for selecting good parents, developing breeding strategies, and accelerating the selection and breeding of high-yield japonica varieties.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy14061101/s1, Figure S1: Effect of nitrogen application rate and planting density on processing quality; (a) brown rice percentage; (b) milled rice percentage; (c) head rice percentage. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and in each group, different letters indicate significant differences at p < 0.05. Figure S2: Effect of nitrogen application rate and planting density on appearance quality. (a) Grain length/width ratio; (b) chalky grain percentage; (c) chalkiness. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and in each group, different letters indicate significant differences at p < 0.05. Figure S3: Effect of nitrogen application rate and planting density on cooking quality. Influence of quality. (a) Amylose content; (b) gum consistency. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and different letters in each group indicate significant differences at p < 0.05.

Author Contributions

Y.R., Q.H. and Y.Z.: conceptualization; E.X. and X.Z.: methodology; Y.R. and P.Z.: investigation; Y.R., E.X., X.Z. and P.Z.: formal analysis; Q.H. and Y.Z.: project administration; Y.R.: writing—original draft; E.X., Q.H. and Y.Z.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Key Research and Development Program of China (No. 2021YFD1200500), Anhui Provincial Key Research and Development Project (No. 202204c06020018), and the Open Fund of Anhui Province International Joint Research Center of Forage Bio-breeding (No. AHIJRCFB202302).

Data Availability Statement

All data generated during this study are included in this published article and its Supplementary Materials, and the raw data used or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area: (a) the location of Chuzhou; (b) the location of the test site in Quanjiao County.
Figure 1. Study area: (a) the location of Chuzhou; (b) the location of the test site in Quanjiao County.
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Figure 2. (a) Temperature, relative humidity, and rainfall during rice growth in 2022; (b) temperature, relative humidity, and rainfall during rice growth in 2023.
Figure 2. (a) Temperature, relative humidity, and rainfall during rice growth in 2022; (b) temperature, relative humidity, and rainfall during rice growth in 2023.
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Figure 3. Effect of N application rates on processing quality. (a) Brown rice percentage; (b) milled rice percentage; (c) head rice percentage. Effect of planting density on processing quality: (d) brown rice percentage; (e) milled rice percentage; and (f) head rice percentage. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and in each group, different letters indicate significant differences at p < 0.05.
Figure 3. Effect of N application rates on processing quality. (a) Brown rice percentage; (b) milled rice percentage; (c) head rice percentage. Effect of planting density on processing quality: (d) brown rice percentage; (e) milled rice percentage; and (f) head rice percentage. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and in each group, different letters indicate significant differences at p < 0.05.
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Figure 4. Effect of N application rate on appearance quality. (a) Grain length/width ratio; (b) chalky grain percentage; (c) chalkiness. Effect of planting density on appearance quality: (d) grain length/width ratio; (e) chalky grain percentage; and (f) chalkiness. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and in each group, different letters indicate significant differences at p < 0.05.
Figure 4. Effect of N application rate on appearance quality. (a) Grain length/width ratio; (b) chalky grain percentage; (c) chalkiness. Effect of planting density on appearance quality: (d) grain length/width ratio; (e) chalky grain percentage; and (f) chalkiness. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and in each group, different letters indicate significant differences at p < 0.05.
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Figure 5. Effect of N application rate on cooking quality. (a) Amylose content; (b) gum consistency. Effect of planting density on cooking quality: (c) amylose content; (d) gum consistency. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and different letters in each group indicate significant differences at p < 0.05.
Figure 5. Effect of N application rate on cooking quality. (a) Amylose content; (b) gum consistency. Effect of planting density on cooking quality: (c) amylose content; (d) gum consistency. N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Data are means of two years, and different letters in each group indicate significant differences at p < 0.05.
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Figure 6. Correlation analysis of N application rate and planting density on grain yield, yield factors, and quality: (a) HJ 753, (b) HJ 855, (c) HJ 866, and (d) DJ 8. * p < 0.05; ** p < 0.01. Note: p, panicle per m2; S, spikelet per m2; F, grain filling; W, thousand-grain weight; Y, grain yield; BRP, brown rice percentage; MRP, milled rice percentage; HRP, head rice percentage; LWR, length/width ratio; CKP, chalky kernel percentage; CK, chalkiness; AAC, amylose content; GC, gum consistency.
Figure 6. Correlation analysis of N application rate and planting density on grain yield, yield factors, and quality: (a) HJ 753, (b) HJ 855, (c) HJ 866, and (d) DJ 8. * p < 0.05; ** p < 0.01. Note: p, panicle per m2; S, spikelet per m2; F, grain filling; W, thousand-grain weight; Y, grain yield; BRP, brown rice percentage; MRP, milled rice percentage; HRP, head rice percentage; LWR, length/width ratio; CKP, chalky kernel percentage; CK, chalkiness; AAC, amylose content; GC, gum consistency.
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Table 1. Detailed information of the experimental varieties in the present study (1).
Table 1. Detailed information of the experimental varieties in the present study (1).
VarietiesGrowth PeriodReleased YearAgroclimatic ZoneParental InformationDistinctive Remark
HJ 753152.9 (2)2020Anhui ProvinceZhendao 15 × Ningjing 5Lodging resistance, high yield, high quality
HJ 855133.5 (3)2021Anhui ProvinceZhendao 14 × Ningjing 5Lodging resistance, high quality
HJ 866152.1 (2)2021Anhui ProvinceZhendao 15 × Ningjing 5High yield, high quality
DJ 8153.0 (2)2013Anhui Province95–22 × Bing 98–110Stable yield
Note: (1) Detailed information is available from the China Rice Data Center (http://www.ricedata.cn. accessed on 8 May 2024); (2) late-maturing and mid-season rice; (3) early-maturing and late-season rice.
Table 2. Physical and chemical properties of soil surface (0–20 cm) at Quanjiao County cotton seed farm test base.
Table 2. Physical and chemical properties of soil surface (0–20 cm) at Quanjiao County cotton seed farm test base.
Soil TypepHOrganic Matter (g kg−1)Total N (g kg−1)Available P (mg kg−1)Available K (mg kg−1)Available Fe (mg kg−1)Available Zn (mg kg−1)
Paddy soil6.720.741.2513.5185.3237.70.93
Table 3. Analysis of variance p values for grain yield and its components as affected by the trial cultivar (C), N rate (N), and planting density (D) and their interactions for field experiments conducted in 2022 and 2023.
Table 3. Analysis of variance p values for grain yield and its components as affected by the trial cultivar (C), N rate (N), and planting density (D) and their interactions for field experiments conducted in 2022 and 2023.
TreatmentPanicle per m2Spikelet per m2Grain Filling (%)1000-Grain Weight (g)Yield
(t ha−1)
p Value
Year (Y)<0.0010.0010.003<0.0010.002
N rate (N)<0.001<0.001<0.0010.821<0.001
Density (D)<0.001<0.0010.4080.007<0.001
Cultivar(C)<0.001<0.001<0.001<0.001<0.001
Y × N0.2110.0670.4160.9980.132
Y × D0.3650.5370.9000.8550.232
Y × C0.6870.2940.0620.1560.795
N × D<0.0010.9450.3190.142<0.001
N × C<0.001<0.001<0.0010.002<0.001
D × C<0.0010.0160.1560.930<0.001
Y × N × C0.0490.0020.6890.991<0.001
Y × D × C0.2180.9740.7440.9760.048
Y × N × D0.3490.9430.9790.9710.488
N × D × C<0.0010.9350.8830.411<0.001
Y × N × D × C0.4871.0001.0001.0000.889
Table 4. Analysis of variance p values for rice grain quality traits as affected by the trial cultivar (C), N rate (N), and planting density (D) and their interactions for field experiments conducted in 2022 and 2023.
Table 4. Analysis of variance p values for rice grain quality traits as affected by the trial cultivar (C), N rate (N), and planting density (D) and their interactions for field experiments conducted in 2022 and 2023.
Source of VariationBRPMRPHRPLWRCKPCKACGC
p Value
Year (Y)0.002<0.0010.001<0.001<0.001<0.001<0.001<0.001
N rate (N)<0.001<0.001<0.0010.335<0.001<0.001<0.001<0.001
Density (D)0.0170.1080.0450.798<0.001<0.001<0.001<0.001
Cultivar(C)<0.001<0.001<0.0010.008<0.001<0.001<0.001<0.001
Y × N1.0001.0001.0000.9790.9510.8690.9960.995
Y × D0.9960.9970.9950.9900.9320.9400.9940.880
Y × C0.1170.5830.1870.3190.9230.0520.7630.106
N × D0.9981.0000.9990.8770.9960.9910.2860.731
N × C<0.0010.8100.2560.5380.7880.7240.0000.020
D × C0.9941.0000.9990.0550.0180.0010.0230.845
Y × N × C1.0001.0001.0001.0001.0001.0001.0001.000
Y × D × C1.0001.0001.0001.0001.0000.9981.0000.999
Y × N × D1.0001.0001.0001.0001.0001.0001.0001.000
N × D × C1.0001.0001.0000.0191.0000.9970.4281.000
Y × N × D × C1.0001.0001.0001.0001.0001.0001.0001.000
Note: BRP, brown rice percentage; MRP, milled rice percentage; HRP, head rice percentage; LWR, length/width ratio; CKP, chalky kernel percentage; CK, chalkiness; AC, amylose content; GC, gum consistency.
Table 5. Effect of N application rate on yield and composition.
Table 5. Effect of N application rate on yield and composition.
CultivarTreatmentPanicle per
m2		Spikelet per
m2		Grain Filling
(%)		1000-Grain Weight
(g)		Yield
(t ha−1)
HJ 753N1337.05 ± 3.86 c163.63 ± 1.89 b83.16 ± 1.91 ab26.18 ± 0.569.45 ± 0.06 c
N2342.62 ± 2.82 c168.33 ± 2.19 ab84.58 ± 2.17 ab26.53 ± 0.5310.11 ± 0.09 b
N3355.70 ± 4.33 b171.26 ± 3.78 a86.68 ± 1.02 a26.60 ± 0.6711.14 ± 0.10 a
N4367.58 ± 4.68 a165.01 ± 2.46 b82.16 ± 0.51 b26.39 ± 0.2910.16 ± 0.19 b
HJ 855N1337.02 ± 12.31 d140.12 ± 1.36 c86.87 ± 0.76 a25.95 ± 0.098.71 ± 0.40 c
N2348.92 ± 3.90 c145.25 ± 2.56 b87.44 ± 0.24 a26.37 ± 0.339.65 ± 0.44 b
N3392.90 ± 0.33 a151.88 ± 0.19 a87.66 ± 0.15 a26.12 ± 0.1011.43 ± 0.07 a
N4378.79 ± 3.57 b145.87 ± 0.53 b85.39 ± 0.28 b25.93 ± 0.1610.33 ± 0.05 b
HJ 866N1320.49 ± 1.12 c141.89 ± 0.28 c82.58 ± 2.50 a26.15 ± 0.329.06 ± 0.18 c
N2332.91 ± 2.07 b145.78 ± 1.57 b83.14 ± 0.88 a25.96 ± 0.229.54 ± 0.07 b
N3353.75 ± 1.90 a150.24 ± 1.04 a83.24 ± 1.31 a25.66 ± 0.489.90 ± 0.03 a
N4356.13 ± 2.06 a149.07 ± 2.10 a79.96 ± 1.56 a26.15 ± 0.278.86 ± 0.24 c
DJ 8N1287.57 ± 7.24 d131.46 ± 0.90 d87.43 ± 1.20 b27.44 ± 0.01 a8.19 ± 0.20 d
N2305.08 ± 5.74 c134.65 ± 0.66 c87.74 ± 0.70 ab27.19 ± 0.03 b8.75 ± 0.19 c
N3319.54 ± 2.47 b138.29 ± 1.59 b87.34 ± 0.85 b27.44 ± 0.02 a9.45 ± 0.11 b
N4343.74 ± 2.94 a144.28 ± 1.36 a89.76 ± 0.84 a27.45 ± 0.21 a10.75 ± 0.08 a
Note: N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 values with different letters after the columns are significant at p < 0.05. Data are averaged over three occasions in two years.
Table 6. Effect of planting density on yield and composition.
Table 6. Effect of planting density on yield and composition.
CultivarTreatmentPanicle per
m2		Spikelet per
m2		Grain Filling
(%)		1000-Grain Weight
(g)		Yield
(t ha−1)
HJ 753D1397.17 ± 5.65 a161.70 ± 2.49 b84.79 ± 0.5526.35 ± 0.2511.09 ± 0.13 a
D2350.20 ± 3.99 b167.78 ± 0.70 a84.37 ± 0.8626.57 ± 0.2110.29 ± 0.11 b
D3304.85 ± 2.07 c171.68 ± 1.04 a83.27 ± 0.6126.36 ± 0.149.22 ± 0.07 c
HJ 855D1400.15 ± 4.60 a141.89 ± 2.21 b86.84 ± 0.4325.93 ± 0.2610.72 ± 0.15 a
D2367.28 ± 5.36 b146.47 ± 1.81 ab87.11 ± 0.4226.24 ± 0.1410.34 ± 0.08 b
D3325.80 ± 1.50 c148.97 ± 1.77 a86.57 ± 0.5926.12 ± 0.159.03 ± 0.06 a
HJ 866D1373.53 ± 1.45 a144.28 ± 1.21 b81.33 ± 1.9925.87 ± 0.339.79 ± 0.15 a
D2335.59 ± 2.83 b147.22 ± 1.89 ab82.50 ± 0.4325.95 ± 0.309.35 ± 0.29 ab
D3313.34 ± 2.45 c148.73 ± 0.86 a82.87 ± 1.2525.97 ± 0.308.88 ± 0.15 b
DJ 8D1359.65 ± 2.34 a132.24 ± 0.89 c87.99 ± 0.8327.29 ± 0.1010.42 ± 0.17 a
D2314.13 ± 2.05 b137.07 ± 1.78 b88.39 ± 0.8727.55 ± 0.169.25 ± 0.05 b
D3268.17 ± 2.74 c142.20 ± 0.64 a87.82 ± 0.8727.29 ± 0.158.27 ± 0.07 c
Note: The values of D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1 with different letters after the column had significant differences (p < 0.05). The average data are from three times over two years.
Table 7. Effects of the N application rate and planting density on yields and compositions.
Table 7. Effects of the N application rate and planting density on yields and compositions.
CultivarTreatmentPanicle per
m2		Spikelet per
m2		Grain Filling
(%)		1000-Grain Weight
(g)		Yield
(t ha−1)
HJ 753N1D1378.19 ± 5.76 c157.93 ± 4.21 d83.93 ± 1.51 abc26.01 ± 0.2910.59 ± 0.42 cd
N1D2333.29 ± 7.04 e163.30 ± 4.44 bcd83.58 ± 2.21 abc26.20 ± 0.658.98 ± 0.23 gh
N1D3299.69 ± 1.22 f169.65 ± 5.04 ab81.96 ± 3.85 bc26.34 ± 0.818.78 ± 0.18 h
N2D1383.85 ± 8.15 c162.54 ± 2.32 bcd85.40 ± 1.92 abc26.15 ± 0.3710.81 ± 0.12 c
N2D2343.54 ± 8.27 de169.13 ± 4.48 abc85.46 ± 2.31 abc26.85 ± 0.6210.03 ± 0.48 def
N2D3300.45 ± 9.86 f173.31 ± 5.34 a82.89 ± 4.74 abc26.59 ± 0.859.50 ± 0.22 efgh
N3D1405.52 ± 9.25 b166.15 ± 2.03 abcd87.28 ± 1.14 a26.79 ± 0.6912.17 ± 0.22 a
N3D2353.75 ± 2.41 d172.55 ± 2.35 a86.81 ± 2.38 ab26.79 ± 0.7511.57 ± 0.45 ab
N3D3307.84 ± 2.01 f175.06 ± 7.75 a85.96 ± 0.18 abc26.23 ± 0.589.68 ± 0.20 efg
N4D1421.10 ± 9.20 a160.19 ± 4.18 cd82.57 ± 1.15 abc26.45 ± 0.3710.96 ± 0.35 bc
N4D2370.22 ± 5.68 c166.15 ± 2.59 abcd81.63 ± 2.36 c26.45 ± 0.5710.22 ± 0.41 cde
N4D3311.42 ± 1.07 f168.69 ± 4.33 abc82.30 ± 2.54 abc26.28 ± 0.249.32 ± 0.64 fgh
HJ 855N1D1357.02 ± 19.64 cd135.94 ± 2.59 e88.28 ± 0.66 ab25.87 ± 0.129.10 ± 0.54 de
N1D2344.46 ± 12.84 de141.47 ± 2.32 de87.08 ± 0.76 bcd25.87 ± 0.058.96 ± 0.45 de
N1D3309.59 ± 9.22 f142.95 ± 2.26 cd85.24 ± 0.93 e26.13 ± 0.148.07 ± 0.53 f
N2D1375.02 ± 7.65 bc141.68 ± 3.17 de87.41 ± 1.27 abc26.07 ± 0.8510.12 ± 0.61 bc
N2D2351.96 ± 1.77 d146.53 ± 4.88 bcd87.09 ± 0.86 bcd26.53 ± 0.259.66 ± 0.40 cd
N2D3319.77 ± 8.61 f147.52 ± 2.57 bcd87.84 ± 0.97 abc26.53 ± 0.199.17 ± 0.32 de
N3D1438.02 ± 8.68 a147.23 ± 4.33 bcd86.41 ± 0.28 cde25.97 ± 0.0612.09 ± 0.11 a
N3D2395.73 ± 10.86 b152.03 ± 4.13 ab88.89 ± 0.22 a26.53 ± 0.0811.94 ± 0.16 a
N3D3344.96 ± 6.48 de156.37 ± 3.26 a87.67 ± 0.37 abc25.90 ± 0.3910.26 ± 0.14 bc
N4D1430.52 ± 14.74 a142.70 ± 2.67 cde85.28 ± 0.52 e25.83 ± 0.1111.56 ± 0.07 a
N4D2376.97 ± 7.71 bc145.86 ± 1.93 bcd85.37 ± 0.91 e26.03 ± 0.3410.82 ± 0.18 b
N4D3328.88 ± 5.47 ef149.06 ± 3.94 bc85.53 ± 0.86 de25.93 ± 0.178.62 ± 0.24 ef
HJ 866N1D1350.02 ± 2.41 bc139.80 ± 1.61 f82.85 ± 0.42 ab25.86 ± 0.53 b9.31 ± 0.31 bcde
N1D2321.45 ± 3.71 de142.44 ± 1.80 ef81.82 ± 3.64 ab25.95 ± 0.25 ab8.97 ± 0.47 de
N1D3289.99 ± 1.94 f143.45 ± 0.52 def83.08 ± 4.21 ab26.64 ± 0.39 a8.92 ± 0.25 e
N2D1358.19 ± 7.08 b144.05 ± 2.37 def82.14 ± 1.43 ab25.97 ± 0.21 ab9.79 ± 0.29 abc
N2D2328.96 ± 9.36 d146.47 ± 0.45 bcde84.54 ± 1.50 a26.27 ± 0.25 ab9.64 ± 0.37 abc
N2D3311.59 ± 5.78 e146.81 ± 4.12 bcde82.74 ± 2.27 ab25.63 ± 0.27 b9.18 ± 0.31 cde
N3D1391.10 ± 4.69 a147.15 ± 1.71 bcd81.56 ± 2.58 ab25.60 ± 0.46 b10.22 ± 0.29 a
N3D2347.50 ± 2.06 c151.05 ± 1.74 ab83.69 ± 1.17 a25.76 ± 0.61 b9.90 ± 0.22 ab
N3D3322.65 ± 4.77 d152.53 ± 1.48 a84.47 ± 1.49 a25.62 ± 0.43 b9.58 ± 0.42 abcd
N4D1394.82 ± 4.18 a146.13 ± 0.34 cde78.74 ± 3.62 b26.06 ± 0.28 ab9.85 ± 0.28 ab
N4D2344.46 ± 3.71 c148.93 ± 4.47 abc79.93 ± 0.82 ab26.22 ± 0.25 ab8.89 ± 0.22 e
N4D3329.12 ± 2.32 d152.14 ± 1.53 a81.19 ± 0.27 ab26.18 ± 0.28 ab7.86 ± 0.28 f
DJ 8N1D1329.52 ± 10.39 d127.16 ± 2.20 h87.70 ± 1.30 bcd27.31 ± 0.37 abc9.13 ± 0.21 d
N1D2283.70 ± 7.62 ef131.43 ± 1.04 gh87.33 ± 1.23 cd27.78 ± 0.08 a8.36 ± 0.24 ef
N1D3249.49 ± 4.56 h135.78 ± 0.69 def87.28 ± 1.16 cd27.23 ± 0.42 abc7.99 ± 0.18 f
N2D1353.57 ± 1.30 b129.84 ± 1.39 h87.08 ± 1.02 d27.20 ± 0.23 bc9.68 ± 0.23 c
N2D2297.49 ± 7.05 e134.77 ± 1.64 efg88.57 ± 0.88 abcd27.34 ± 0.18 abc9.08 ± 0.27 d
N2D3264.17 ± 10.74 g139.34 ± 1.43 bcd87.57 ± 0.29 bcd27.02 ± 0.27 c8.45 ± 0.06 e
N3D1357.72 ± 8.75 b134.43 ± 1.87 fg87.83 ± 0.49 bcd27.35 ± 0.38 abc10.31 ± 0.06 b
N3D2331.33 ± 5.56 cd138.73 ± 2.31 cde87.37 ± 1.08 cd27.63 ± 0.34 ab9.99 ± 0.17 bc
N3D3269.57 ± 2.85 fg141.71 ± 4.08 bc86.81 ± 1.84 d27.35 ± 0.04 abc8.95 ± 0.18 d
N4D1397.80 ± 8.35 a137.51 ± 2.05 cdef89.36 ± 0.68 abc27.32 ± 0.31 abc10.77 ± 0.12 a
N4D2344.00 ± 5.73 bc143.35 ± 2.35 b90.29 ± 1.02 a27.47 ± 0.22 abc9.70 ± 0.05 c
N4D3289.43 ± 2.37 e151.98 ± 1.73 a89.64 ± 1.08 ab27.57 ± 0.26 abc8.97 ± 0.27 d
Note: Values with different letters after the columns are significantly different at p < 0.05 for N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and for D1, D2, and D3 (45 × 104, 37.5 × 104, and 32.2 × 104) hills ha−1. Averaged over two years of three data.
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Ren, Y.; Xu, E.; Zhang, P.; Zhan, X.; He, Q.; Zou, Y. Impact of Key Agronomic Traits on Economic Yield Traits in Anhui Rice (Oryza sativa L. spp. japonica). Agronomy 2024, 14, 1101. https://doi.org/10.3390/agronomy14061101

AMA Style

Ren Y, Xu E, Zhang P, Zhan X, He Q, Zou Y. Impact of Key Agronomic Traits on Economic Yield Traits in Anhui Rice (Oryza sativa L. spp. japonica). Agronomy. 2024; 14(6):1101. https://doi.org/10.3390/agronomy14061101

Chicago/Turabian Style

Ren, Yi, Ending Xu, Peijiang Zhang, Xinchun Zhan, Qingyuan He, and Yu Zou. 2024. "Impact of Key Agronomic Traits on Economic Yield Traits in Anhui Rice (Oryza sativa L. spp. japonica)" Agronomy 14, no. 6: 1101. https://doi.org/10.3390/agronomy14061101

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