1. Introduction
Rice (
Oryza sativa L.) is a staple food for more than half of the world’s population, and about 90% of rice is produced and consumed in Asia [
1]. Due to the shortage of arable land and water resources, it is not possible to increase the rice production area [
2]. Therefore, enhancing yield per unit area is always an important goal of crop cultivation and breeding, and it is also an important direction of rice research in China [
3,
4]. Furthermore, planting densities and nutrient management are important agronomic parameters that must be optimized to achieve high grain yields. Rice is a monocotyledonous plant [
5], and tillering is one of its characteristics. An excessive number of seedlings on a single hill and suboptimal plant row spacing will reduce the effectiveness of rice tillering [
6]. The main stems and tillers of rice compete for resources such as light, air, and nutrients [
7]. Therefore, an appropriate planting density can effectively increase yield, while improper planting density can decrease grain yields. Transplanting density and ecological site both have significant effects on rice yield, source-sink relationship, and dry matter accumulation [
8]. Therefore, to establish a high-efficiency rice population structure, the number of rice seedlings in each hill and plant spacing must be strictly managed to maximize the resource and light utilization efficiency of the rice population [
9].
Competition is prevalent in the agroecosystem [
10]. Within a rice population, competition is primarily intraspecific, and the competition between individual plants is essentially the competition at the organ level for limited resources [
11]. Competition causes changes in trait expression in plants [
12]. Seeding density is a major factor affecting plant plasticity as it determines the intensity of intraspecific competition for interactions between plants in different environments [
13]. In the process of rice development, the result of rice resource competition will ultimately be reflected in yield [
14]. Competitiveness is closely related to functional traits such as biomass allocation. Therefore, it is necessary to quantify the apparent and true plasticity of functional traits [
15]. Every plant expresses a certain allometric growth pattern under specific conditions; that is, plants exhibit allometric plasticity, which changes quantitative relationships between the growth and distribution of organs in an individual. The allometric growth of the plant itself is the “apparent plasticity,” while the change of the allometric growth curve is the true plasticity [
16]. Allometric characteristics of individual rice can be obtained by allometric analysis.
Planting structure not only affects the plasticity of rice phenotype but also affects nitrogen use efficiency. Nitrogen is a macronutrient for plant growth and development, and the increase in inorganic nitrogen usage is vital to improving crop growth and yield. Studies have shown that increasing planting density can compensate for the adverse effects of reduced nitrogen application and improve nitrogen use efficiency [
17]. In the early stage of plant growth, leaves are the nitrogen storage reservoir, and nitrogen is redistributed into the grain. The source-sink relationship of nitrogen exists among the roots and leaves in the early stage of plant growth and among the leaves and seed development in the mature stage [
18]. In cereal crops, 50–90% of the nitrogen in the grain is mainly transported through leaf nitrogen [
19]. Factors such as nitrogen distribution among stems and leaves, photosynthetic efficiency, rubisco activity, and leaf senescence determine nitrogen use efficiency [
20]. For instance, San-oh et al. found that the number of seedlings planted per hill affects rice’s photosynthetic rate and physiological process. Seedlings per hill affect the Ribulose bisphosphate carboxylase oxygenase/oxygen Enzyme (Rubisco) and nitrogen levels; there is a positive correlation between Rubisco levels and photosynthetic rate and between nitrogen levels, and Rubisco, the levels of Rubisco and N in the leaves of rice planted with one seeding per hill are higher; thus the leaf photosynthetic rate is higher during the maturation process [
21].
In this experiment, under the same basic seedling number, the allometric growth characteristics of rice under the synergistic change of rice seedlings per hill and plant row spacing were investigated, and the effect on yield and nitrogen use efficiency, which was significant for improving the efficiency of rice planting and simplifying the production process. Therefore, the objectives of the study were: (1) To explore the effect of synergistic changes in the number of seedlings per hill and row spacing on rice yield under the same basic seedlings; (2) To study the effect of synergistic changes in the number of seedlings per hill and row spacing on the biomass distribution and allometric growth characteristics of above-ground organs; (3) To compare the differences in nitrogen use efficiency of rice under different seedlings per hill and row spacing.
3. Discussion
In this study, ‘Yexiangyou2’ (V4) was a hybrid rice with strong tillering ability, with the highest yield, grain biomass allocation (early season), grain-to-leaf ratio (early season), NGPE, and NHI. The largest variety with NDMPE was ‘Guiyu9’ (V1), ‘Zhenguiai’ (V2) also had high NGPE, and ‘Zhuangxiangyou5’ (V3) had the highest GN, TNA, and NHI. Numerous studies have been carried out to investigate the effects of seedlings per hill and row spacing on rice growth [
22,
23]. Transplanting 1–2 seedlings per hill has a wide range of sources of rice tillers. Transplanting 3–4 seedlings per hill can promote the occurrence of the central tillering position, which is the main source of tiller panicles and total panicles [
24]. However, the study by Wiangsamut et al. [
25] showed that transplanting 1 seedling per hill yielded higher yields than 4 seedlings per hill saving production costs. At the same time, there are many discoveries in the study of densely planted rice. At high planting density (25 cm × 11 cm), although the number of panicles per square meter of single seedling machine transplanting (SMT) was lower than that of conventional machine transplanting (CMT), the number of spikelets per panicle and the grain filling rate of each panicle were higher [
26]. Machine-planting single seedlings at high density can improve single tiller performance by reducing non-productive tillers, increasing bank size by increasing secondary shoots per panicle, and increasing dry matter yield in late heading, thereby increasing grain yield [
27]. In this experiment, yield was significantly affected by the number of effective panicle and percentage of filled grains. The number of effective panicles of T1 was higher than that of T4; in the two growing seasons, the yield of 1 seedling hill
−1 (T1) increased by 16.27% and 27.32%, respectively, compared with 9 seedlings hill
−1 (T4), with an average increase of 21.80%.
The regulation of biomass allocation among plant organs is a survival strategy for plants to cope with environmental changes. It has been shown that the allocation of organ biomass is specific among species and influenced by plant size and growing environment [
28]. Restrictive regulation of population density and individual growth is the key to determining biomass allocation in competitive growth environments [
29,
30,
31]. When the number of seedlings transplanted in each hill is increased in rice planting, the competition for resources among individuals intensifies [
32]. This intensified competition between plants on a hill will inevitably lead to changes in biomass allocation strategies, and the result of our study showed that the row spacing of T1 (12.93 cm) was narrower than that of T4 (38.80 cm). Under the conditions of this experiment, the row spacing of T1 (12.93 cm) is narrower than that of T4 (38.80 cm), but T1 is more conducive to increasing the biomass distribution of grains than T4. In the early and late seasons, the distribution of grain biomass in T1 treatment ranged from 56.7% to 65.82%, and the ratio of grain to leaf increased by 26.81% and 37.68% compared with T4.
The result of differences in biomass allocation between organs is a change in allometric relationships. This study showed that the allometric relationship appeared more frequently in T4, and the allometric relationship between leaf biomass and aboveground biomass was the most obvious. Among the six allometric relationships of leaves, stems, and grain organs, four were related to leaves. In addition, leaf and grain organs showed c-type allometric growth, indicating that the differences in allometric characteristics of T1 and T4 were related to the significant differences in individual biomass. The leaves of the plant will change accordingly as the density changes [
33]. With the increase in altitude, the leaf length, leaf width, girth, and other functional traits of the leaves bamboo decreased significantly, but the leaves of the middle-altitude bamboo species had the highest specific leaf area and the lowest leaf dry matter content, and the change of altitude was obvious. Inspired bamboo growth potential and morphological plasticity [
34]. When testing the response of rape (
Brassica napus L.) to temperature, it was also found that the leaf mass per area (LMA) at high temperature was significantly smaller than that at medium and low temperature, but the leaves at high temperature were significantly wider, and the leaves grew through the leaves. Modeling the functional structure of plants can predict plant growth under different environmental conditions [
35]. Allometric growth allows plants to adapt to environmental changes and maximize favorable phenotypes to increase their niche breadth [
36,
37,
38]. This also proves that the regulatory function of leaves plays an important role in plant growth when the environment changes.
In addition, there were also differences in nitrogen accumulation in stem, leaf, and panicle organs. Among the four varieties, the nitrogen accumulation in the grain was the highest. Compared with the T4 treatment (4 seedlings per hill, row spacing 38.80 × 38.80 cm), the T1 treatment (1 seedling per hill, row spacing 12.93 × 12.93 cm) had higher N accumulation, dry matter N use efficiency, and N harvest index, indicating that 1 seedling per hill and narrow rows were more conducive to improving N use efficiency. Because of the low efficiency of nitrogen use, only a very minor part of the nitrogen applied to the soil is absorbed by the rice seedlings. In contrast, the excess nitrogen applied can have a negative impact on the environment [
39]. To improve nitrogen use efficiency, researchers studied a reduced nitrogen densely planting (RNDP) cultivation mode, which can make rice obtain similar yields to conventional high-yield practice (CHYP). This is due to the increased storage capacity of grains per panicle per unit area, increased biomass accumulation after heading, and improved nitrogen use efficiency [
17]. It can be seen that dense planting plays a crucial role in improving the rice yield. When studying the interaction effect of nitrogen application rate and density on rice grain yield and nitrogen use efficiency, Hou et al. [
40] found that nitrogen recovery efficiency (NRE) increased by 12.7 to 40.0% with the increase of planting density.
4. Materials and Methods
4.1. Experiment Site, Time, and Materials
The experiment was carried out in an experimental field at the Rice Research Institute of Guangxi University (N22°50′28.41″ N, E108°17′9.00″ E) in the early (April to July) and late seasons (August to November) of 2020. A total of four rice cultivars were tested, including two conventional rice varieties (‘Guiyu9’and ‘Zhenguiai’) and two hybrid rice varieties (‘Zhuangxiang5’ and ‘Yexiangyou2’). In this article, V1, V2, V3, and V4 are used to represent ‘Guiyu9’, ‘Zhenguiai’, ‘Zhuangxiang5’, and ‘Yexiangyou2’. The “Zhenguiai” seeds were obtained from the research group, which maintains this variety, and the seeds of the other varieties were provided by the breeding unit of Guangxi University. Before the experiment, the basic properties of the soil were: pH 6.7, total nitrogen 1.72 g kg−1, total phosphorus 1.62 g kg−1, total potassium 5.90 g kg−1, hydrolyzed nitrogen 187.4 mg kg−1, organic carbon 18.3 g kg−1, and organic matter 31.48 g kg−1.
4.2. Experiment Design
The experiment was laid out in a two-factor split-plot experimental design with varieties × seedlings per hill. The number of seedlings per hill was divided into split plots, and the varieties were the main plots. The treatments were randomly arranged among the main plots of four different cultivars and four seedling patterns (T1 = 1 seedling (12.93 × 12.93 cm), T2 = 3 seedlings (22.33 × 22.33 cm), T3 = 6 seedlings (31.67 × 31.67 cm), and T4 = 9 seedlings (38.80 × 38.80 cm). The experiment consisted of sixteen treatment combinations. The basic number of transplanted seedlings of all varieties was 60 seedlings m
−2. In October 2019, the 0–10 cm layer of the experimental field’s soil was excavated, air-dried, and sieved. In April 2020, a plot was made where the surface soil had been excavated. Wooden boards were cut to the plots’ lengths (or widths) (given in
Table 10); they were 3 cm thick and 15 cm wide. The boards were glued together to form plot cells. The sieved soil was then backfilled into the plots to the height of 10 cm. The main interval is kept at a distance of 50 cm, which is convenient for field management and investigation, and the split intervals are side by side without interval. A 5 cm depth gap was left on one board of each plot to facilitate irrigation and drainage. Each plot was drained and irrigated separately.
Rice was managed by conventional seedlings and field management. The row spacing, plant spacing, and the number of seedlings per hill are shown in
Table 10. The recommended dose of N-P-K fertilizer 180 kg N, 180 kg K
2O, and 90 kg P
2O
5 per hectare was applied, respectively. The nitrogen, phosphorus, and potassium fertilizers applied in the experiment were urea, superphosphate, and potassium chloride, respectively. Nitrogen and potassium fertilizers were applied in three split doses, i.e., 50% basal dose, 30% tillering stage dose, and 20% panicle initiation stage dose. All phosphorus fertilizers were applied in the basal fertilizer dose. The basal fertilizer was applied two days before transplanting.
4.3. Soil Properties
Soil samples were randomly collected from each treatment to determine basic soil properties. The soil pH was measured with a desktop pH meter. The contents of soil organic matter, available nitrogen, total phosphorus, and total potassium were measured by potassium dichromate volumetric method-external heating method, alkaline hydrolysis diffusion method, NaOH melting-molybdenum antimony anti-colorimetry method, and NaOH melting-flame photometry, respectively [
41]. Soil organic matter (SOM) content was estimated by multiplying soil organic carbon by 1.72 [
42,
43].
4.4. Yield and Yield Components
At maturity, plants from three hills were sampled. The number of effective panicles was investigated in four yield components: number of effective panicles, spikelets per panicle, percentage of filled grains, and 1000-grain weight. The rice in each plot was harvested, and then the rice yield per unit area was calculated according to the weight of the harvested rice.
4.5. Biomass and Total Nitrogen
The three-hill plant samples were split into three parts: leaves, stems, and panicle and placed in an oven at 105 °C for 30 min, then dried to a constant weight in an 85 °C oven and weighed. The dried stem, leaf, and ear samples were crushed, passed through a 100-mesh sieve, and boiled with H2SO4-H2O2, and the total nitrogen content was determined by a continuous flow chemical analyzer.
4.6. Data Processing
Rice panicle biomass was defined as grain biomass, denoted as GB. Leaf biomass per plant was expressed as LB. The total biomass of leaf sheaths and stems was taken as stem biomass, denoted by SB. The sum of the biomass of stems, leaves, and ears of a single plant was taken as the aboveground biomass, which was expressed as AGB. The reproductive leaf ratio was defined as the ratio of the total biomass of the reproductive organs of a single plant to the biomass of the leaf organs. Biomass allocation was calculated using the formula of ODM/TDM, where ODM was the total dry matter of panicle, leaf, and stem with leaf sheath (referred to as the stem). TDM was the total dry matter of aboveground organs.
The allometric equation can be used to describe the mechanism of allometric growth [
44,
45]:
where
Y is biological characteristic (organ size); M is individual size;
Ki is a species-specific constant, and b is the allometric index. When b = 1, there is isokinetic growth between individual biological characteristics and individual size; when b ≠ 1, there is allometric growth [
12].
Standardized Major Axis Tests were conducted in the ‘SMATR’s package in R (4.1.2) [
46]. There are four main allometric relationships between different species and organs [
47,
48]. Type A: significantly different slopes; Type B: significantly different intercepts, but with a common fit axis (the same slope); Type C: the same slopes, the same intercepts, but drift along the common fit axis; Type D: drift B and C occurs at the same time, i.e., the intercepts are different, and the fitted axes are not the same (the slopes are different).
The N accumulation, N use efficiency (NUE), and N harvest index (NHI) were computed as follows [
17]:
Nitrogen Use Efficiency Calculation | Formula | Abbreviations and Units |
N accumulation by grain | N content of grain × grain weight | GN, kg hm−2 |
N accumulation by stem | N content of stem × stem weight | SN, kg hm−2 |
N accumulation by leaf | N content of leaf × leaf weight | LN, kg hm−2 |
Total N accumulation | GN + LN + SN | TNA, kg hm−2 |
N dry matter production efficiency | total above-ground biomass/TNA | NDMPE, kg kg−1 |
N grain production efficiency | rice grain yield/TNA | NGPE, kg kg−1 |
N harvest index | GN/(GN + LN + SN) | NHI |
4.7. Statistical Analysis
Data among varieties and treatments were analyzed by ANOVA, and the mean comparisons between treatments were made using Duncan’s multiple range test. Statistical significance was taken at p < 0.05 and p < 0.01. All data analyses were performed using SPSS 26.0 software (SPSS Inc., Chicago, IL, USA).