The Effect of Two Irrigation Regimes on Yield and Water Use Efficiency of Rice Varieties in Eastern China

Abstract

The way in which alternate wetting and drying irrigation (AWD), as a water-saving practice promoted in rice (Oryza sativa L.) production systems, could enhance the productivity and water use efficiency (WUE) attracts broad attention. This study selected six mid-season indica rice varieties to investigate the impacts of AWD and conventional irrigation (CI) on grain yield, WUE, grain filling, and root traits. A two-year field experiment demonstrated that grain yields and WUE were significantly increased with varietal improvements. With the improvement of varieties, the maximum grain filling rate and mean grain filling rate for both apical superior and basal inferior spikelets were progressively enhanced during the grain filling stage. Compared to CI, AWD significantly enhanced grain yield and WUE. Flag leaf photosynthetic rate and root characteristics, including root weight, root length, root absorbing surface area, root oxidation activity, and zeatin (Z) + zeatin riboside (ZR) contents in panicles, roots, and root bleeding, were superior under AWD across early, mid, and late grain filling stages. Correlation and path analysis showed that improved grain filling in basal inferior spikelets was attributed to delayed root senescence during the grain filling stage under AWD. These results indicated that AWD would be a better irrigation regime to improve yield and WUE by optimizing grain filling and root growth for modern varieties.

1. Introduction

As the world population grows, the demand for food is expected to increase significantly. By the mid-21st century, crop production must rise by an estimated 1.1% to 1.3% annually to meet human needs [1]. Rice (Oryza sativa L.) is a critical staple crop that serves as the main food source for over 60% of China’s population [2]. Over the past 60 years, China’s rice production has seen remarkable growth, primarily due to increased productivity per unit area rather than the expansion of arable land. The introduction of the dwarfing gene between the 1950s and 1980s significantly improved the rice harvest index, contributing to higher yields [3]. Furthermore, the development of high-yielding varieties, especially hybrids, and advances in crop management techniques have played a key role in improving rice productivity since the 1980s [4,5]. Enhancing the productivity and efficiency of crop varieties, especially modern cultivars, is a critical research priority.

Grain filling in rice, particularly the filling of apical superior and basal inferior spikelets, is a key factor influencing yield. This process is closely related to the source–sink relationship, and large panicle rice varieties often exhibit asynchronous filling of apical superior and basal inferior spikelets, which can limit yield potential. While the performance of apical superior spikelets is well studied, the filling of basal inferior spikelets has received less attention, even though it is crucial for both yield and resource use efficiency [6,7,8]. Understanding the physiological mechanisms that regulate grain filling, particularly in basal inferior spikelets, is essential to fully realizing rice’s yield potential. Additionally, plant roots are essential for the absorption of water and nutrients and function as critical sites for the production of plant hormones, organic acids, and amino acids. The structure and physiological traits of roots are strongly connected to the growth of aboveground parts, as well as to yield and quality formation [9,10,11]. Since Weaver first studied the connection between roots and ecology in 1919, research on plant roots has advanced significantly [12]. Key areas of progress include root structure and function, research methods, growth and metabolism, stress responses, and interactions between roots and soil within ecological systems [13,14,15,16]. However, compared to aboveground parts, rice root characteristics and their role in yield formation have received less attention. During the improvement of rice varieties, little research has focused on how roots affect the grain yield and its grain filling characteristic.

Water management is a critical factor in rice cultivation, as rice is the crop that consumes the most irrigation water, accounting for about 30% of global irrigation usage [17]. To mitigate water scarcity and enhance water use efficiency (WUE) in rice farming, researchers have introduced several water-saving irrigation techniques. Such techniques encompass the implementation of alternate wetting and drying (AWD) [18], overhead irrigation [19], film-covering cultivation [20], and drought tolerance [21]. Among these, AWD is highly recommended and widely adopted, with over 12 million hectares of rice fields using this method annually, especially in China [22,23,24]. This irrigation regime involves alternating between flooding, drying, and rehydrating the soil based on specific moisture levels [25]. Previous studies showed that AWD could reduce 19–30% of irrigation water and enhance WUE by 17–40% [26,27]. It was reported that AWD could either maintain or increase rice yield [28]. However, other studies suggest that AWD may lead to a reduction in yield. Carrijo et al. [29] found that AWD was associated with yield reductions, which ranged between 3% and 23%, when comparison with continuous flooding. The mechanisms behind the changes in rice yield under AWD irrigation are still not fully understood. How AWD regulates root growth to promote aboveground growth and development, as well as its role in increasing yield and WUE, requires further investigation.

In this study, we hypothesized that the combination of rice variety improvement and alternate wetting drying irrigation could synergistically enhance yield and WUE through improving root growth and grain filling. To test this hypothesis, this study investigated the grain filling characteristics, as well as the physiological and agronomic traits of the shoots and roots. This research aimed to establish a theoretical foundation and provide practical guidance for variety screening and high-yield cultivation.

2. Materials and Methods

2.1. Cultivation

Field trials were conducted at the research farm of Yangzhou University, situated in Jiangsu Province, China (32°30′ N, 119°25′ E), over the rice cultivation period (May to October) during 2022 and 2023. The topsoil (0–20 cm) at the experimental site was classified as Typic Epiaquents according to the Soil Taxonomy. The soil had a sandy loam texture. Key physicochemical characteristics included a pH of 6.3 (measured in a 1:2.5 soil–water suspension), 22.5 g kg⁻¹ of organic matter, 101.9 mg kg⁻¹ of alkali hydrolyzable nitrogen (N), 23.4 mg kg⁻¹ of Olsen phosphorus (P), and 91.2 mg kg⁻¹ of exchangeable potassium (K). The cation exchange capacity (CEC) was 12.5 cmol kg⁻¹, the moisture soil content at field capacity was 0.2 g g⁻¹, and the soil bulk density was 1.3 g cm⁻³ (

Table 1. Soil classification and physical–chemical properties of the experimental field (0–20 cm layer).

Parameter	Value	Unit	Classification
Soil taxonomy	–	–	Typic Epiaquents
Texture class	–	–	Sandy loam
pH (H2O, 1:2.5)	6.3	–	–
Organic matter	22.5	g kg−1	–
Alkali-hydrolyzable nitrogen (N)	101.9	mg kg−1	–
Olsen phosphorus (P)	23.4	mg kg−1	–
Exchangeable potassium (K)	91.2	mg kg−1	–
Cation exchange capacity (CEC)	12.5	cmol kg−1	–
Field capacity	0.2	g g−1	–
Bulk density	1.3	g cm−3	–

). Weather parameters, such as precipitation, solar radiation, and mean air temperature, were monitored over a two-year period at a meteorological station near the experimental site (Figure A1).

Six mid-season indica rice varieties, including hybrid combinations, were utilized in this research, cultivated in the lower Yangtze River basin during the past 60 years. These varieties were chosen because they were very famous and widely planted at that time, and each had large planting acreage (>6.67 × 10⁴ ha) and could normally head at Yangzhou. The varieties were classified into three groups: dwarf variety (DV), semi-dwarf variety (SDV), and semi-dwarf hybrid (SDH), as presented in

Table 2. Mid-season indica rice varieties evaluated in this study.

Year of Release	Variety	Type	Growth Period (d)
1960s–1970s	Taizhongxian	Dwarf variety (DV)	130
1960s–1970s	Zhenzhu’ai	Dwarf variety (DV)	130
1980s–1990s	Yangdao 2	Semi-dwarf variety (SDV)	145
1980s–1990s	Yangdao 6	Semi-dwarf variety (SDV)	145
2000–	Yangliangyou 6	Semi-dwarf hybrid rice (SDH)	150
2000–	Liangyoupeijiu	Semi-dwarf hybrid rice (SDH)	150

. In 2022 and 2023, the seedlings were initially grown in a seedbed, sown on 15 May, followed by transplanting on 10 June of both years. After leveling the seedbed, seedling trays were neatly arranged in the field with appropriate spacing between varieties (about 20 cm). Bird netting was installed to protect the seedlings. For the first 7 days after sowing, no water was applied to the seedbed. After this period, the field was irrigated with a water layer until transplanting. One week before transplanting, urea was applied as a supplementary fertilizer. Transplanting was conducted with a hill spacing of 30.0 cm × 11.0 cm, with two seedlings placed per hill. Each experimental plot measured 5.0 m × 3.0 m. A total of 240 kg N ha⁻¹ was applied at a ratio of 4:2:2:2 during the pre-transplanting, mid-tillering, panicle initiation, and spikelet differentiation stages. One day prior to transplanting, phosphorous (P₂O₅ 13.5%) as calcium superphosphate (CaH₆O₉P₂) and potassium chloride (K₂O 52.0%) were applied at rates of 300 kg ha⁻¹ and 195 kg ha⁻¹, respectively. These application rates were determined based on local agronomic recommendations for high-yielding indica rice production in the Yangtze River basin, taking into account the baseline soil fertility status at the experimental site (Table 1). The phosphorus and potassium levels were adjusted to ensure adequate nutrient availability during early vegetative and reproductive development.

2.2. Treatment

The field trial utilized a split-plot design with three replications, where the main block was designated for the irrigation treatment and the subplots were assigned to different varieties. Each variety plot measured 5 m × 3 m. The total test field area: variety plot 15 m² × 6 varieties × 2 irrigation treatments × 3 replications. We applied two irrigation treatments: conventional irrigation (CI) and alternate wetting and drying irrigation (AWD). In the CI treatment, a water layer of 2.0–3.0 cm depth was maintained from 10 days after transplanting (20 June) to one week before the final harvest, except during the mid-tillering stage when drainage occurred. AWD was applied from 10 days after transplanting to one week before the final harvest. In this irrigation regime, plot field was not irrigated until the soil water potential had reached (−15 ± 5) kPa at 15–20 cm depth. Five tensionmeters were installed in each plot, and readings were recorded at 12:00 in every day. When soil water potential reached the threshold, a flood with 1.0–2.0 cm water depth was applied to the plots. Diseases, pests, and weeds were completely controlled to avoid yield loss.

2.3. Irrigation System

A centrifugal water pump was used to extract water from the pond near the experimental plots. The pump operated at a pressure of approximately 300 kPa and was capable of delivering a flow rate of 25 L s⁻¹. This flow rate ensured sufficient water delivery to all plots under both irrigation regimes. Water was transported through a network of pipes and distributed evenly to each plot. Water meters were installed in each plot to monitor and record the volume of irrigation water applied. The irrigation system’s design aimed to minimize water wastage while ensuring adequate water supply to rice during the growing season.

2.4. Sampling and Measurements

From each plot, 200 panicles that flowered on the same day were selected and marked. The date of flowering and the location of each spikelet on the marked panicles were documented. Every 4 days, a sample of ten to twelve marked panicles was collected from each plot, starting from anthesis and continuing until maturity. Apical superior spikelets, which flower within the first 2 days in the apical panicle, and basal inferior spikelets, which flower within the last 2 days, were separated from the basal panicle. Each panicle consisted of 120–150 spikelets (grains) at each sampling point. The difference in flowering dates between superior and inferior spikelets was 3 days within a panicle. The sampled spikelets were dried at 70 °C until achieving a stable weight, then dehulled and weighed. The grain filling process in rice typically follows an asymmetric sigmoidal pattern, with superior spikelets exhibiting rapid early growth and inferior spikelets showing a delayed but accelerated filling phase. Common models such as linear, polynomial, or Logistic equations are limited in describing such complexity due to their fixed curvature or lack of biological interpretability. In contrast, the Richards’ growth equation [30] provides greater flexibility and allows extraction of biologically meaningful parameters [31]. Therefore, we adopted the Richards’ growth equation in this study to describe grain filling processes, as previously outlined by Zhu et al. [31]:

$$W={\displaystyle \frac{A}{(1+B{e}^{-kt}{)}^{\frac{1}{N}}}}$$

(1)

The grain filling rate (R) was calculated using Equation (1).

$$R={\displaystyle \frac{AkB{e}^{-kt}}{N(1+B{e}^{-kt}{)}^{\frac{(N+1)}{N}}}}$$

(2)

where W represents the value of grain weight; A denotes the maximum grain weight; t represents the number of days elapsed since anthesis; and B, k, and N refer to the coefficients determined via regression analysis.

In each plot, samples were taken from five hills, and plants were separated into roots, leaves, stems and sheaths, and panicles to measure the dry weights of root and shoot components at different growth stages: mid-tillering (MT), panicle initiation (PI), heading (HD), and maturity (MA). Following a drying process at 70 °C for 72 h, the dry weights of individual components were recorded. To ensure consistent canopy conditions throughout the experiment, any gaps created by sampling for root and shoot biomass measurements were promptly filled with replacement hills taken from the border areas. These transplanted hills were excluded from subsequent sampling to avoid any potential bias in the data collection process.

The photosynthetic rate of the flag leaf was assessed during the early, middle, and late grain filling stages. The LI-6400 portable photosynthesis system (LiCor Corp., Lincoln, NE, USA) was employed to measure the rate. The data collection took place between 09:00 and 12:00 h, under photosynthetically active radiation levels ranging from 1300 to 1500 μmol m⁻² s⁻¹ above the canopy. For each variety, measurements were taken using 10 leaves (including 3 leaves in the first variety plot, 3 leaves in the second replication plot, and 4 leaves in the third replication plot).

In the root sample, two root-containing soil samples (20 cm × 20 cm × 20 cm) were collected from each plot. There were 6 samples per irrigation treatment in each sampling stage (n = 6), during the early, middle, and late grain-filling stages. The roots were carefully separated from the soil core through a hydropneumatic elutriation process (Gillison’s Variety Fabrications, Benzonia, MI, USA). Subsequently, the roots were arranged in a glass dish (30 cm × 30 cm) with a shallow water layer to avoid overlapping of roots. After the root image was scanned using an Epson Expression 1680 Scanner (Seiko Epson Corp., Tokyo, Japan), root length was determined with the WinRHIZO root analysis system (Regent Instruments Inc., Quebec, QC, Canada). The sample was baked in an oven at 105 °C for 30 min and subsequently dried at 75 °C until a stable weight was achieved before being weighed.

The total and active root absorbing surface areas were determined using the methylene blue method following Xiao et al. [32]. The roots were thoroughly rinsed with distilled water, dried with absorbent paper, and then immersed for 1.5 min in three sequential beakers containing methylene blue solution. After returning to the original beakers, the solution was allowed to drain naturally. An ultraviolet spectrophotometer (UV-2450, Shimadzu, Tokyo, Japan) was used to measure the solution volume (V1, V2, and V3) in the three beakers. The absorbance of the methylene blue solution, diluted tenfold, was recorded at 660 nm. Based on the standard curve of methylene blue concentration (C′1, C′2, and C′3), the total and active root absorbing surface area were determined. Root oxidation activity (ROA) was determined by alpha-naphthylamine (α-NA) oxidation, following Ramasamy et al. [33], and expressed as μg α-NA g⁻¹ DW h⁻¹. Approximately 1 g of fresh root tissue (cut into 1–2 cm segments) was preincubated for 10 min in 50 mL of 20 μmol L⁻¹ α-NA solution to minimize rapid adsorption during the assay. The roots were then transferred to a fresh 50 mL aliquot of the same solution and incubated for 4 h. After incubation, the solution was filtered, and a 2 mL aliquot of the α-NA sample was mixed with 10 mL of 0.1% sulfanilic acid (prepared in 3% acetic acid) and 2 mL of 50 μmol L⁻¹ NaNO₂, then diluted to 25 mL with distilled water. The absorbance of the resulting solution was measured at 530 nm using a UV spectrometer (7752 G, Shanghai Yidian, Shanghai, China).

At the measurement stage, five plants per plot were selected under both irrigation regimes and trimmed 10 cm above the soil surface at 18:00 h to collect root bleeding sap, resulting in a total of 30 sampled plants (2 irrigation treatments × 3 replications × 5 plants). The severed stems were covered with absorbent cotton and a polyethylene sheet. To protect against dew and sunlight, a paper bag was placed over the cotton-coated stems. At 06:00 h the following morning, absorbent cotton with sap was collected, and root bleeding rate was determined from the increase in cotton weight. The panicles and roots used for Z + ZR determination were collected simultaneously with the root-containing soil samples during root sampling, while root bleeding sap was obtained as described above. All samples were immediately frozen in liquid nitrogen and stored at −80 °C for subsequent hormone analysis. The extraction methods for Z + ZR were described by Pan et al. [34] and Müller et al. [35], using [²H₅]-Z, [²H₅]-ZR, [²H₆]-iP, and [²H₆]-iPR as internal standards. Detection of Z + ZR was conducted through liquid chromatography–tandem mass spectrometry (LC–MS/MS) with multiple reaction monitoring (MRM) mode, using a TSQ Vantage system (Thermo Fisher Scientific, Waltham, MA, USA). Quantification was achieved through a calibration curve with pre-determined amounts of standards, calculated based on the ratio of the cumulative MRM transition areas for cytokinins to their respective internal standards. The Xcalibur Data System (Thermo Fisher Scientific, Waltham, MA, USA) was employed for both data acquisition and analysis.

For the calculation of water use efficiency and crop growth rate, we employed the following formulas:

Water use efficiency (kg m⁻³) = grain yield/the amount of irrigation water.

(3)

Crop growth rate (g m⁻² d⁻¹) = (W₂ − W₁)/(t₂ − t₁)

(4)

where W₁ refers to the initial measurement of shoot biomass (in g m⁻²), whereas W₂ indicates the later shoot biomass measurement (also in g m⁻²). The time points t₁ and t₂ represent the respective days when these measurements were recorded.

2.5. Harvest

Panicle number, filled grain rate, and grain weight were measured from 50 randomly chosen non-border plants in each plot. Grain yield was determined from plants in a 5 m² area excluding border rows and normalized to a moisture level of 0.14 g H₂O g⁻¹ fresh weight. Grain fill percentage and spikelets per panicle were calculated following the method described by He et al. [36].

2.6. Statistical Analysis

All data were analyzed using Microsoft Office Excel 2021 (Microsoft Corp., Redmond, WA, USA), and analysis of variance (ANOVA) was conducted with IBM SPSS Statistics 27 software (IBM Corp., Armonk, NY, USA). Significant differences between means were assessed using Fisher’s protected least significant difference (LSD) test at the 5% level (p ≤ 0.05). Before analysis, normality (Shapiro–Wilk test) and homogeneity of variance (Levene’s test) were checked. A three-way ANOVA was applied to evaluate the effects of year (Y), treatment (T), variety (V), and their interactions (Y × T, Y × V, T × V, and Y × T × V) in

Table A1. Analysis of variance of grain yield and main measurements with variety as a factor.

Grain Yield and Main Measurements	Year (Y)	Treatment (T)	Variety (V)	Y × T	Y × V	T × V	Y × T × V
Grain yield	**	**	**	NS	NS	*	NS
Number of panicles	**	NS	**	NS	**	**	**
Spikelets per panicle	**	**	**	NS	**	NS	NS
Total spikelets	NS	**	**	NS	**	**	**
Filled grain rate	**	**	**	*	**	**	NS
1000-grain weight	**	**	**	NS	**	*	**
Water use efficiency	**	**	**	NS	NS	**	NS
Aboveground dry matter at MT	NS	NS	**	NS	NS	NS	NS
Aboveground dry matter at PI	NS	NS	**	NS	NS	NS	NS
Aboveground dry matter at HD	NS	*	**	NS	NS	NS	NS
Aboveground dry matter at MA	*	**	**	NS	NS	NS	NS
Crop growth rate at BMT	NS	NS	**	NS	NS	NS	NS
Crop growth rate at MT–HD	NS	NS	**	NS	NS	NS	NS
Crop growth rate at PI–HD	**	**	**	**	**	**	**
Crop growth rate at HD–MA	NS	**	**	NS	NS	NS	NS
Flag leaf photosynthetic rate at EF	NS	**	**	NS	**	**	NS
Flag leaf photosynthetic rate at MF	**	**	**	NS	*	*	NS
Flag leaf photosynthetic rate at LF	**	**	**	NS	**	NS	*
Root weight at EF	**	**	**	NS	NS	*	NS
Root weight at MF	**	**	**	NS	NS	*	NS
Root weight at LF	**	**	**	NS	NS	*	NS
Root length at EF	**	**	**	NS	NS	**	NS
Root length at MF	**	**	**	NS	NS	**	NS
Root length at LF	**	**	**	NS	NS	NS	NS
Root total absorbing surface area at EF	**	**	**	NS	NS	**	NS
Root total absorbing surface area at MF	**	**	**	NS	NS	NS	NS
Root total absorbing surface area at LF	**	**	**	NS	NS	NS	NS
Root active absorbing surface area at EF	**	**	**	NS	NS	NS	NS
Root active absorbing surface area at MF	*	**	**	NS	NS	NS	NS
Root active absorbing surface area at LF	NS	**	**	NS	NS	NS	NS
Root oxidation activity at EF	**	**	**	NS	NS	**	NS
Root oxidation activity at MF	**	**	**	NS	NS	**	NS
Root oxidation activity at LF	**	**	**	NS	NS	*	NS
Root bleeding rate at EF	**	**	**	NS	*	**	*
Root bleeding rate at MF	**	**	**	NS	**	**	**
Root bleeding rate at LF	**	**	**	NS	NS	NS	NS
Z + ZR in panicles at EF	**	**	**	NS	**	**	*
Z + ZR in panicles at MF	**	**	**	NS	**	**	NS
Z + ZR in panicles at LF	**	**	**	NS	**	**	**
Z + ZR in roots at EF	**	**	**	NS	*	**	NS
Z + ZR in roots at MF	**	**	**	NS	**	**	NS
Z + ZR in roots at LF	**	**	**	NS	NS	NS	NS
Z + ZR in root bleeding at EF	*	**	**	NS	NS	*	NS
Z + ZR in root bleeding at MF	*	**	**	NS	NS	**	NS
Z + ZR in root bleeding at LF	NS	**	**	NS	NS	NS	NS

*, ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01, respectively. NS indicates not significant at p ≤ 0.05.

. Another three-way ANOVA was conducted for year (Y), treatment (T), and type (Ty), with interactions (Y × T, Y × Ty, T × Ty, and Y × T × Ty) shown in

Table A2. Analysis of variance of grain yield and main measurements with type as a factor.

Grain Yield and Main Characteristics	Year (Y)	Treatment (T)	Type (Ty)	Y × T	Y × Ty	T × Ty	Y × T × Ty
Grain yield	*	**	**	NS	NS	NS	NS
Number of panicles	**	*	**	NS	NS	NS	NS
Spikelets per panicle	**	**	**	NS	**	NS	*
Total spikelets	NS	**	**	NS	**	**	**
Filled grain rate	**	**	**	NS	*	**	NS
1000-grain weight	**	**	**	NS	NS	NS	NS
Water use efficiency	*	**	**	NS	NS	*	NS
Aboveground dry matter accumulation at MT	**	**	**	NS	*	*	*
Aboveground dry matter accumulation at PI	NS	NS	**	NS	NS	NS	NS
Aboveground dry matter accumulation at HD	*	NS	**	NS	NS	NS	NS
Aboveground dry matter accumulation at MA	**	**	**	NS	*	NS	NS
Crop growth rate at BMT	NS	NS	**	NS	NS	NS	NS
Crop growth rate at MT–HD	NS	NS	*	NS	NS	NS	NS
Crop growth rate at PI–HD	NS	NS	*	NS	NS	NS	NS
Crop growth rate at HD–MA	NS	*	**	NS	NS	NS	NS
Flag leaf photosynthesis rate at EF	NS	**	**	NS	*	NS	NS
Flag leaf photosynthesis rate at MF	**	**	**	NS	**	NS	NS
Flag leaf photosynthesis rate at LF	**	**	**	NS	**	NS	NS
Root weight at EF	**	**	**	NS	NS	NS	NS
Root weight at MF	*	**	**	NS	NS	*	NS
Root weight at LF	**	**	**	NS	NS	*	NS
Root length at EF	**	**	**	NS	NS	**	NS
Root length at MF	**	**	**	NS	NS	**	NS
Root length at LF	**	**	**	NS	NS	NS	NS
Root total absorbing surface area at EF	**	**	**	NS	NS	*	NS
Root total absorbing surface area at MF	*	**	**	NS	NS	NS	NS
Root total absorbing surface area at LF	NS	**	**	NS	NS	NS	NS
Root active absorbing surface area at EF	*	**	**	NS	NS	NS	NS
Root active absorbing surface area at MF	*	**	**	NS	NS	NS	NS
Root active absorbing surface area at LF	NS	**	**	NS	NS	NS	NS
Root oxidation activity at EF	**	**	**	NS	NS	NS	NS
Root oxidation activity at MF	**	**	**	NS	NS	NS	NS
Root oxidation activity at LF	**	**	**	NS	NS	NS	NS
Root bleeding rate at EF	**	**	**	NS	NS	**	NS
Root bleeding rate at MF	**	**	**	NS	NS	**	NS
Root bleeding rate at LF	**	**	**	NS	NS	NS	NS
Z + ZR contents in panicles at EF	**	**	**	NS	NS	*	NS
Z + ZR contents in panicles at MF	**	**	**	NS	*	**	NS
Z + ZR contents in panicles at LF	**	**	**	NS	NS	NS	NS
Z + ZR contents in roots at EF	**	**	**	NS	NS	NS	NS
Z + ZR contents in roots at MF	**	**	**	NS	NS	*	NS
Z + ZR contents in roots at LF	NS	**	**	NS	NS	NS	NS
Z + ZR contents in root bleeding at EF	NS	**	**	NS	NS	*	NS
Z + ZR contents in root bleeding at MF	NS	**	**	NS	NS	**	NS
Z + ZR contents in root bleeding at LF	NS	**	**	NS	NS	NS	NS

*, ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01, respectively. NS indicates not significant at p ≤ 0.05.

. The split-plot design was accounted for by specifying appropriate error terms for main plots (irrigation treatment) and subplots (variety), following the standard mixed-model approach for split-plot experiments. Plotting was carried out using Origin 2021 software (OriginLab Corp., Northampton, MA, USA). Mantel test and partial least squares path modeling (PLS-PM) were conducted using the “ggcor” package and “plspm” package in RStudio software (version 4.2.1), respectively.

3. Results

3.1. Three-Way Analysis of Variance (ANOVA)

Three-way ANOVA revealed that grain yield, yield components, WUE, photosynthetic rate, and most root-related traits were significantly affected by year, irrigation treatment, and genotype (Table A1 and Table A2). Interaction effects between year and genotype or treatment were generally not significant, indicating consistent patterns across growth seasons. Notably, significant treatment × variety interactions were observed for grain yield, crop growth rate, root activity, and Z + ZR contents (Table A1), while type-based analysis showed fewer interactions and more stable main effects (Table A2).

3.2. Grain Yield, Its Yield Components, and Water Use Efficiency (WUE)

The grain yields and WUE were progressively enhanced through variety improvements from the 1960s to the 2000s. Yields achieved under AWD were significantly higher than those obtained under CI over the two-year period. In two years, compared to CI, AWD significantly increased grain yield by an average of 14.13%, 19.58%, and 23.80% in DV, SDV, and SDH, respectively (Figure 1A). In terms of yield components, the increase in grain yield was mainly attributed to an increase in the total number of spikelets, calculated as panicles multiplied by spikelets per panicle. In two years, compared to CI, AWD significantly increased the number of total spikelets by an average of 3.08%, 11.11%, and 6.11% in DV, SDV, and SDH, respectively (

Table 3. The grain yield components of various rice varieties evaluated under distinct irrigation regimes.

Year/ Treatment †	Type ‡	Variety	Number of Panicles (×104 ha−1)	Spikelets per Panicle	Total Spikelets (×106 ha−1)	Filled Grain Rate (%)	1000-Grain Weight (g)
2022/AWD	DV	Taizhongxian	249.22 d §	134.44 j	335.06 j	66.53 d	25.93 f
		Zhenzhu’ai	255.45 b	147.08 h	375.73 h	64.35 de	26.53 e
		Mean	252.34	140.76	355.40	65.44	26.23
	SDV	Yangdao 2	239.88 f	174.06 e	460.71 b	77.12 a	29.45 a
		Yangdao 6	245.22 e	166.18 g	414.15 g	73.40 bc	27.78 c
		Mean	242.55	170.12	437.43	75.26	28.62
	SDH	Yangliangyou 6	228.07 i	193.53 c	422.03 e	74.46 b	28.00 c
		Liangyoupeijiu	230.53 h	203.22 a	468.47 a	75.67 ab	29.17 ab
		Mean	229.30	198.38	445.25	75.07	28.59
2022/CI	DV	Taizhongxian	255.45 b	129.06 k	329.69 k	65.98 de	25.85 f
		Zhenzhu’ai	261.68 a	134.93 j	353.08 i	58.53 f	26.52 e
		Mean	258.57	132.00	341.39	62.26	26.19
	SDV	Yangdao 2	246.11 e	169.77 f	417.82 f	75.73 ab	26.98 d
		Yangdao 6	252.34 c	140.68 i	354.98 i	63.82 e	28.97 b
		Mean	249.23	155.23	386.40	69.78	27.98
	SDH	Yangliangyou 6	227.41 i	197.55 b	449.26 c	59.74 f	29.07 ab
		Liangyoupeijiu	233.64 g	184.26 d	430.50 d	71.92 c	26.77 de
		Mean	230.53	190.91	439.88	65.83	27.92
2023/AWD	DV	Taizhongxian	255.66 e	151.27 f	386.30 g	66.46 h	24.97 f
		Zhenzhu’ai	269.78 b	147.91 g	339.42 j	68.78 f	26.98 d
		Mean	262.72	149.59	362.86	67.62	25.98
	SDV	Yangdao 2	264.27 d	159.34 e	407.37 e	85.69 a	26.85 d
		Yangdao 6	243.76 g	143.31 h	386.62 g	84.94 a	29.32 a
		Mean	254.02	151.33	397.00	85.32	28.09
	SDH	Yangliangyou 6	255.37 e	190.41 b	492.63 a	84.91 a	28.05 bc
		Liangyoupeijiu	229.48 i	196.84 a	479.82 b	83.37 b	27.92 bc
		Mean	242.43	193.63	486.23	84.14	27.99
2023/CI	DV	Taizhongxian	267.42 c	134.36 j	362.75 h	63.39 i	23.60 g
		Zhenzhu’ai	278.16 a	125.21 k	348.28 i	67.52 g	26.77 d
		Mean	272.79	129.79	355.52	65.46	25.19
	SDV	Yangdao 2	269.98 b	148.69 g	401.43 f	71.02 e	28.35 b
		Yangdao 6	236.12 h	138.46 i	326.93 k	77.72 c	27.84 c
		Mean	253.05	143.58	364.18	74.37	28.10
	SDH	Yangliangyou 6	235.83 h	186.87 c	440.70 c	76.56 d	26.07 e
		Liangyoupeijiu	248.49 f	175.24 d	435.45 d	69.22 f	28.93 a
		Mean	242.16	181.06	438.08	72.89	27.50

^† Alternate wetting and drying irrigation (AWD) and conventional irrigation (CI) denote two distinct water management regimes. ^‡ DV, SDV, and SDH refer to the dwarf variety, semi-dwarf variety, and semi-dwarf hybrid, respectively. ^§ Statistical significance at the p ≤ 0.05 level (n = 6) for comparisons conducted within the same year is denoted by different letters.

). When compared with CI, AWD led to a reduction in panicle number but resulted in increases in spikelets per panicle, filled grain rate, and 1000-grain weight across all rice varieties examined. It was observed that the increase in spikelets per panicle exceeded the reduction in panicle numbers, resulting in an overall increase in grain yield (Table 3).

The improvement of rice varieties also led to a significant increase in WUE. Across the two years, the average WUE of the three types progressively increased with variety improvement. Under AWD, the average WUEs of DV, SDV, and SDH were 1.23, 1.71, and 1.96 kg m⁻³, respectively. Under CI, the corresponding values were 0.77, 1.02, and 1.14 kg m⁻³, respectively. Over two years, WUE observed under AWD was significantly greater compared to that under CI. Compared to CI, AWD significantly increased WUE by an average of 59.35%, 66.95%, and 72.86% in DV, SDV, and SDH, respectively (Figure 2B). Additionally, the total irrigation water applied under CI was 740 mm, while under AWD was 530 mm, and AWD reduced water consumption by 28.38% compared to CI.

3.3. Grain Filling

Under two irrigation regimes, a rapid initial increase, followed by a plateau, was observed in the grain filling of apical superior spikelets across all rice varieties. The grain weights of apical superior and basal inferior spikelets were progressively enhanced with the improvement of varieties. Compared with CI, AWD enhanced the apical superior grain weight during the mid-grain filling stage and markedly improved the basal inferior grain weight. Similar trends in the grain filling processes of apical superior and basal inferior spikelets were observed over the two years (Figure 2).

With the improvement of varieties, the maximum grain filling rate (G_max) and mean grain filling rate (G_mean) for both apical superior and basal inferior spikelets were progressively enhanced throughout the grain filling stage. Under the same treatment, basal inferior spikelets reached their G_max later than apical superior spikelets in all rice varieties. Compared to CI, AWD prolonged the time to reach the maximum grain filling rate (T_max) and lengthened the effective grain filling period for both apical superior and basal inferior spikelets, leading to a notable increase in the G_max of basal inferior spikelets (

Table 4. Grain filling characteristics of various rice varieties evaluated under distinct irrigation regimes.

Treatment †	Type ‡	Variety	Maximum Grain Filling Rate (mg grain−1 d−1)		Mean Grain Filling Rate (mg grain−1 d−1)		Time to Reach the Maximum Grain Filling Rate (d)
			Superior	Inferior	Superior	Inferior	Superior	Inferior
AWD	DV	Taizhongxian	1.32 b §	0.97 de	1.22 e	0.39 efg	16.49 de	17.77 f
		Zhenzhu’ai	1.30 b	0.95 ef	1.20 e	0.36 g	16.26 e	17.18 g
		Mean	1.31	0.96	1.21	0.38	16.38	17.48
	SDV	Yangdao 2	1.70 a	1.06 c	1.52 cd	0.41 cde	16.52 de	18.69 e
		Yangdao 6	1.68 a	1.08 bc	1.55 abc	0.44 bc	16.69 cde	19.11 d
		Mean	1.69	1.07	1.54	0.43	16.61	18.90
	SDH	Yangliangyou 6	1.71 a	1.15 ab	1.57 a	0.46 b	16.54 de	19.95 c
		Liangyoupeijiu	1.73 a	1.18 a	1.56 ab	0.49 a	16.79 bcd	20.02 c
		Mean	1.72	1.165	1.57	0.48	16.67	19.99
CI	DV	Taizhongxian	1.31 b	0.88 fg	1.20 e	0.33 h	16.59 de	19.41 d
		Zhenzhu’ai	1.29 b	0.86 g	1.19 e	0.31 h	16.75 cd	19.11 d
		Mean	1.30	0.87	1.20	0.32	16.67	19.26
	SDV	Yangdao 2	1.68 a	0.95 ef	1.51 d	0.37 fg	17.12 bc	21.18 b
		Yangdao 6	1.66 a	0.97 de	1.53 bcd	0.4 def	17.21 b	20.97 b
		Mean	1.67	0.96	1.52	0.39	17.17	21.08
	SDH	Yangliangyou 6	1.72 a	1.07 bc	1.54 abcd	0.43 bcd	17.83 a	22.04 a
		Liangyoupeijiu	1.70 a	1.05 cd	1.55 abc	0.42 cde	18.15 a	21.88 a
		Mean	1.71	1.06	1.55	0.43	17.99	21.96

^† Alternate wetting and drying irrigation (AWD) and conventional irrigation (CI) denote two distinct water management regimes. ^‡ DV, SDV, and SDH refer to the dwarf variety, semi-dwarf variety, and semi-dwarf hybrid, respectively. ^§ Statistical significance at the p ≤ 0.05 level (n = 6) for comparisons conducted within the same column is denoted by different letters.

).

3.4. Aboveground Dry Matter Accumulation and Crop Growth Rate (CGR)

With improvement in varieties, both aboveground dry matter accumulation and the CGR showed a significant increase under the two irrigation regimes during the main growth stages. From MT to MA, AWD led to higher aboveground dry matter accumulation across all three types (DV, SDV, and SDH) compared to CI. In two years, the aboveground dry matter accumulations under AWD increased on average by 2.87%, 2.71%, and 8.58% in DV, SDV, and SDH, respectively, compared to CI (Figure 3A–D). The CGR under CI was lower compared to AWD. It initially increased, followed by a decline during the progression of the growth stages. In two years, compared to CI, the CGRs under AWD increased on average by 3.77%, 7.14%, and 8.64% in DV, SDV, and SDH, respectively (Figure 3E–H).

3.5. Leaf Photosynthesis

Under two irrigation regimes, the photosynthetic rate of rice flag leaves increased progressively with improvement in rice varieties. Compared to CI, AWD led to a significant increase in the flag leaf photosynthetic rate throughout the entire grain filling stage (Figure 4). Over two years, the photosynthetic rate of flag leaves under AWD increased on average by 11.58%, 7.03%, and 3.31% in DV, SDV, and SDH, respectively, compared to CI (Figure 4).

3.6. Root Weight and Root Length

The root weight and root length increased progressively with the improvement of rice varieties under both AWD and CI (Figure 5). In two years, the root weights under AWD increased on average by 9.15%, 15.27%, and 4.94% in DV, SDV, and SDH, respectively, compared to CI (Figure 5A–C). Compared to CI, the root lengths under AWD increased on average by 7.93%, 20.88%, and 9.43% in DV, SDV, and SDH, respectively (Figure 5D–F). The increase in root length under AWD may be attributed to improved soil aeration and intermittent mild water stress, which promote root elongation and penetration into deeper soil layers. These conditions reduce anaerobic inhibition and enhance physiological activity in root meristems, resulting in more extensive root systems compared to CI [37,38].

3.7. Root Absorbing Surface Area, Root Oxidation Activity (ROA), and Root Bleeding Rate

With improvement in varieties, the root absorbing surface area, ROA, and root bleeding rate gradually increased under the two irrigation regimes (Figure 6 and Figure 7). In two years, compared to CI, the above-mentioned four parameters of DV under AWD increased by an average of 9.59%, 14.31%, 14.81%, and 18.60%, respectively. For SDV under AWD, these parameters showed average increases of 18.63%, 21.32%, 8.84%, and 5.04%, respectively, compared to CI. For SDH under AWD, these parameters showed average increases of 15.05%, 8.93%, 6.38%, and 26.92%, respectively, compared to CI (Figure 6 and Figure 7). The different responses among genotypes may be related to variations in root vigor and the ability to maintain root function under fluctuating soil moisture. AWD improves soil aeration and reduces anaerobic stress, which could enhance root activity, especially during key growth stages. Varieties with stronger aboveground growth may further promote root development through improved source–sink interaction.

3.8. Zeatin (Z) + Zeatin Riboside (ZR) in Panicles, Roots, and Root Bleeding

The contents of zeatin (Z) and zeatin riboside (ZR) in panicles, roots, and root exudates varied significantly among different irrigation regimes during the grain filling stage (Figure 8). Similar trends in the contents of zeatin (Z) and zeatin riboside (ZR) were observed in panicles, roots, and root bleeding. As rice varieties improved and grain filling progressed, Z + ZR contents gradually decreased. Over two years, compared to CI, the Z + ZR contents in panicles, roots, and root bleeding of DV under AWD increased by an average of 15.84%, 14.42%, and 14.75%, respectively. For SDV under AWD, these parameters showed average increases of 24.40%, 23.38%, and 17.62%, respectively, compared to CI. Compared to CI, AWD significantly increased the above-mentioned three parameters of SDH by an average of 32.66%, 16.65%, and 29.80%, respectively (Figure 8).

3.9. Relationships Between Grain Yield, WUE, Grain Filling Characteristics, and Main Agronomic and Physiological Traits

Aboveground dry matter accumulation and crop growth rate at the main growth stage, flag leaf photosynthetic rate, root weight, root length, root total absorbing surface area, root active absorbing surface area, ROA, root bleeding rate, and Z + ZR contents in panicles, roots, and root bleeding during grain filling stage were significantly or very significantly and positively correlated with grain yield, WUE, and Gmean of inferior spikelets (Figure 9). Partial least squares path modeling (PLS-PM) was applied to explore the relationships between various factors. The multivariate statistical approach is particularly suitable for complex biological systems where multiple interrelated variables influence the outcome, and it does not require strict assumptions of data normality. The model’s goodness-of-fit was 0.647, indicating that approximately 64.7% of the variation in yield and WUE could be explained by the variables included. This reflects a moderately strong model fit. Simulation results indicated that AWD and type (variety improvement) enhanced yield and WUE primarily by promoting grain filling, shoot biomass accumulation, photosynthetic performance, and root activity (Figure 10).

4. Discussion

High-yielding varieties are key to maintaining both productivity and stability in rice production. In this study, six mid-season indica rice varieties, including hybrid combinations, were used in this research, cultivated in the lower Yangtze River basin over the past 60 years. These varieties were chosen because they were very famous at the time of their release, and each had large planting acreage and could normally head at Yangzhou. Compared to early varieties, modern rice varieties generally demonstrate higher yields due to improvements in genetic potential and agronomic practices [39]. This study observed that rice yield was progressively enhanced through variety improvements, primarily due to the increase in the total number of spikelets (Figure 1A, Table 3). The increase in total spikelets was primarily attributed to a significant rise in the spikelets per panicle. This finding indicates that rice yield improvements are achievable in the rice-growing regions of the middle and lower reaches of the Yangtze River, stabilizing the number of panicles while substantially increasing the spikelets per panicle to expand yield sink capacity (i.e., increasing total spikelets), which is a crucial strategy for achieving high and even higher yields (Table 3). In general, the number of grains per panicle is strongly inversely associated with the number of panicles per unit area [40]. The way in which to increase the number of spikelets per panicle while simultaneously improving the filled grain rate is a critical issue for achieving higher yields. Increasing rice yield often requires the consumption of more agricultural water resources [41]. Consequently, enhancing the WUE of rice while maintaining high yields is crucial. It was reported that modern rice varieties generally exhibit higher WUE [42]. In this study, WUE improved significantly with the improvement in varieties (Figure 1B). The results indicated that genetic improvement and breeding strategies could simultaneously enhance both efficiency and yield of rice.

Balancing water conservation with yield maintenance remains a key challenge in scaling up the application of alternate wetting and drying (AWD). Various studies have proposed different thresholds for soil water potential in AWD systems to prevent yield loss. For instance, a field water depth threshold of −15 cm (15 cm below the ground surface) has been suggested as a safe threshold for preventing yield loss [43]. Other studies suggest that safe soil water potential-based thresholds for AWD range from −15 to −20 kPa [26,29]. The results of our study indicate that the optimal soil water potential for AWD lies between −15 and −20 kPa. Significant increases in both yield and WUE were observed under AWD for all rice varieties, with modern varieties showing a more pronounced positive response (Figure 1). These findings align with Monaco et al. [44], who reported that modern rice varieties achieve higher yields and WUE under AWD. The results indicate that effective water-saving irrigation strategies like AWD can enhance the storage potential of modern varieties, leading to improved yield and water use efficiency.

The grain filling process in rice is influenced by water management practices and fertilization strategies [45]. It was observed that the G_max, G_mean, and grain weight of basal inferior spikelets were significantly enhanced, while the T_max of basal inferior spikelets was significantly reduced under AWD. Compared with apical superior spikelets, the grain filling characteristics of basal inferior spikelets showed a more positive response to AWD (Table 4). AWD did not result in a significant reduction in the grain filling rate and even promoted rice grain filling, which led to an increase in grain weight and yield. Additionally, AWD could increase the Z + ZR contents in panicles (Figure 8A–C), encouraging cell division in the endosperm, improving the movement of stored nutrients from the nutritional tissues to the grains, and significantly increasing grain filling rate, thus leading to a higher yield. These results indicate that AWD significantly enhances grain filling in basal inferior spikelets, and this effect was stronger in modern varieties, which showed greater improvement in grain weight and filling rate under AWD. This suggests that modern varieties are better adapted to benefit from water-saving irrigation during the grain filling stage. Studies demonstrated that once rice reaches the heading and grain filling stage, drought stress speeds up plant aging, lowers biomass production, shortens the grain filling period, and reduces grain weight. Although drought stress can raise the translocation amount of non-structural carbohydrates (NSC) from stems and sheaths to grains, promoting grain filling, the increased NSC translocation is insufficient to compensate for the reduced biomass production and the shortened grain filling duration, resulting in lower grain weight and reduced yield [46,47]. Yang et al. [48] found that AWD did not significantly inhibit rice plant photosynthesis and helped restore the plant’s water status during the night. The treatment enhanced the translocation of NSC from stems and sheaths to grains, thereby compensating for the losses caused by reduced photosynthesis and a shorter grain filling period. The apical superior spikelets exhibited a higher priority for carbohydrate allocation compared to the basal inferior ones. Research indicated that AWD could improve the mobilization of NSC for grain filling, especially in the basal inferior spikelets that flower later [49,50]. These results indicate that AWD significantly enhances grain filling in basal inferior spikelets, and this effect was stronger in modern varieties, which showed greater improvement in grain weight and filling rate under AWD. This suggests that modern varieties are better adapted to benefit from water-saving irrigation during the grain filling stage.

In this study, it was observed that aboveground dry matter accumulation, crop growth rate (CGR), and flag leaf photosynthetic rate were significantly enhanced through the improvement of varieties, and these parameters were significantly enhanced across all rice varieties under AWD (Figure 3 and Figure 4). The correlation analysis and partial least squares path modeling (PLS-PM) revealed significant and highly significant correlations between aboveground dry matter accumulation, CGR, flag leaf photosynthetic rate, and grain yield, as well as WUE (Figure 9 and Figure 10). These results indicated that enhanced shoot biomass and photosynthesis is important in realizing high yield and high resource use efficiency under AWD. Modern varieties benefited more from AWD through greater improvements in crop growth rate and leaf photosynthesis after anthesis, which contributed directly to higher yield and resource use efficiency. Previous studies have shown that rice yield is predominantly derived from post-anthesis photosynthates (accounting for 70–90%), with pre-anthesis reserves in stems and sheaths contributing around 10–30% [51]. Therefore, maintaining high photosynthetic capacity after heading is critical for yield formation. Liu et al. [52] also reported that sustained biomass production during grain filling helps reduce yield losses. These findings support our results, highlighting the importance of post-anthesis source strength in improving yield and WUE under AWD.

It has been demonstrated in previous studies that root trait is closely connected to the shoots growth. Photosynthesis in the aboveground tissues produces sufficient assimilates for the roots, supporting and enhancing root function. At the same time, increased root activity provides essential nutrients for the growth of aboveground parts. Good root growth promotes the accumulation of aboveground biomass and contributes to high yield. The establishment of a strong root system helps resist soil drought stress, and the impact of water on root growth and development directly affects nutrient uptake [53,54]. Moderate soil drought conditions can promote rapid root growth and deep rooting, which in turn increases root weight and root volume, as well as enhancing root activity. However, excessive reduction of soil moisture suppresses root activity. A reasonable irrigation regime helps maintain rice root vitality and delays root aging [55]. Our findings revealed that root weight, root length, absorbing surface area of root, ROA, root bleeding rate, and root Z + ZR contents and root bleeding increased with the improvement in rice varieties, and AWD significantly improved the root characteristics in all rice varieties (Figure 5, Figure 6 and Figure 7 and Figure 8D–I). Therefore, AWD could coordinate aboveground and root growth, improve root systems, promote grain filling, delay plant senescence, and enhance yield formation (Figure 10).

A previous study showed that the root activity of modern varieties decreased quickly during the late grain filling stage [56]. In this study, we observed that during the late grain filling stage, the leaf photosynthetic rate, ROA, and root absorbing surface area of semi-dwarf hybrid rice varieties declined at a faster rate than those of other rice types. This suggests that semi-dwarf hybrid rice varieties experience faster senescence of both leaves and roots during the grain filling stage, which is hypothesized to be a major factor contributing to their lower filled grain rate (Table 3). The filled grain rate is influenced not only by the genotype and intrinsic factors of the grains but also by the overall plant growth status, water and nutrient supply, and environmental conditions. Therefore, understanding the mechanisms underlying the low filled grain rate requires further extensive research. Further studies could focus on (i) analyzing how the factors, such as root-sourced signals and their transportations, and sink-source relationships, regulate grain filling in rice varieties; (ii) investigating the factors inside spikelets of rice varieties, including key enzymes involved in the sucrose–starch pathway, hormones, and genetic expression; and (iii) exploring the contribution of AWD regulation to grain filling under increasingly variable and extreme climatic conditions in the future. In conclusion, AWD is a practical and easy-to-apply irrigation method that can be widely used in irrigated rice systems. It relies on soil water potential thresholds and is not limited by soil type. AWD reduced irrigation times and labor input, which may help lower production costs and improve farm profitability. Further economic analysis is needed to quantify these benefits under different production conditions.

5. Conclusions

Significant variations in grain yield and water use efficiency (WUE) were observed among rice varieties, with modern varieties demonstrating a clear advantage. Compared to conventional irrigation (CI), alternate wetting and drying irrigation (AWD) enhanced both yield and WUE by 14.1–23.8% and 59.4–72.9%, respectively. Under both AWD and CI, modern varieties achieved higher yield and WUE than early varieties, and modern varieties benefited more from AWD. The combination of modern rice varieties and AWD synergistically achieved high yield and WUE through improving grain filling, enhancing the physiological metabolic capacity of roots and coordinating the growth of shoots and roots.