Comparison of Agronomic and Physiological Characteristics for Rice Varieties Differing in Water Use Efficiency under Alternate Wetting and Drying Irrigation
by
, , , , and
Chen Wang
1, 
Xiaotong Fa
1,
Qinghao Meng
1,
Ying Zhang
1,
Weilu Wang
1,
Kuanyu Zhu
1,
Weiyang Zhang
1,
Junfei Gu
1,
Lijun Liu
1,
Jianhua Zhang
2,3 and
Hao Zhang
1,* 1
Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
2
Department of Biology, Hong Kong Baptist University, Hong Kong 999077, China
3
State Key Laboratory of Agrobiotechnology, Chinese University of Hong Kong, Hong Kong 999077, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(9), 1986; https://doi.org/10.3390/agronomy14091986 (registering DOI)
Submission received: 22 July 2024
/
Revised: 27 August 2024
/
Accepted: 29 August 2024
/
Published: 2 September 2024
(This article belongs to the Section Farming Sustainability)
Abstract
:Rice (Oryza sativa L.) stands as one of the most critical staple crops globally, with its yield and water use efficiency (WUE) being pivotal for food security. This study aimed to evaluate the agronomic and physiological traits and WUE of six rice varieties under two irrigation regimes: alternate wetting and drying (AWD) and conventional irrigation (CI). The results showed the significant improvements in grain yield and WUE with variety improvement under both irrigation treatments. Under AWD, high water use efficiency varieties (HWVs) demonstrated pronounced enhancements, including tillers and spikelet production, filled grain rate, 1000-grain weight, harvest index, leaf area index, non-structural carbohydrate remobilization, photosynthesis and catalase and peroxidase activities of leaf, root and shoot biomass, and root activity. AWD was observed to synchronize and amplify grain yield (2–14%) and WUE, including both leaf-level (13.94–20.72%) and yield-level (23.20–30.87%) water use efficiencies (WUEL and WUEY). The water use potential for HWVs was substantially enhanced under AWD. The integration of variety improvement with AWD irrigation strategies effectively achieves the dual objectives of high yield and WUE, offering a promising approach for sustainable rice production.
1. Introduction
Rice (Oryza sativa L.) is among the top 3 most essential staple crops globally, serving as a primary dietary staple for over half of the world’s population, highlighting its vital role in food security [1,2,3,4]. Enhancing rice yield per hectare has long been a primary goal of the global rice research community. Nonetheless, since the early 21st century, the global rice grain yield has exhibited an average yearly growth rate of under 1%, with some Asian countries experiencing stagnation in their annual productivity [5]. Over the last 80 years, rice breeding in China has evolved through multiple phases, transitioning from early tall varieties to dwarf, semi-dwarf, and eventually semi-dwarf hybrid varieties. This progression has led to significant gains in grain yield. Specifically, the average yield in China has surged from 2.0 tons per hectare to over 6.0 tons per hectare in the past 60 years [6]. To continue increasing average yields, it is crucial to intensify research focused on developing innovative rice varieties with greater grain yield potential. For innovative rice varieties, how to further realize their yield potential under appropriate cultivation and management techniques is a great challenge.
The scarcity of water resources has turned into a critical and widespread challenge in agriculture, substantially threatening global food security. Rice cultivation consumes the most water among crops, and water scarcity presents a pronounced issue, severely limiting production output [7,8,9]. Notably, rice irrigation constitutes approximately 80% of total water usage in Asia. Population, environmental degradation, shifting climate patterns, and industrial growth have led to a significant reduction in water resources available for agricultural irrigation. Consequently, this scarcity has severely limited the potential for increased rice production [10,11,12]. To address these challenges, research efforts have intensified, focusing on optimizing water productivity and increasing grain yields. These investigations have examined rice’s water needs and explored various irrigation strategies suitable for different cropping systems [13]. Advancements in agricultural water conservation have introduced several efficient irrigation methods. These include alternate wetting and drying irrigation (AWD) [14], which cycles between flooding and soil drying periods, as well as overhead irrigation [15], mulching cultivation, and growing drought-tolerant varieties [16]. These innovations show potential for reducing the impact of water scarcity on rice production and improving the sustainability of agricultural practices. AWD has been extensively adopted in rice cultivation, demonstrating substantial water-saving benefits. In AWD, the field does not require continuous water injection; instead, once the stagnant water evaporates, the soil dries out before being re-flooded for a day or several days [17]. The AWD method offers multiple advantages, such as decreased water consumption, lower methane release, reduced insect pest invasions and disease epidemics, enhanced quality of rice grain [18], and significant yield enhancements [19]. AWD has been implemented in Asia, such as China, Vietnam, and Bangladesh. Notably, this method can decrease water consumption by 30–35% [20]. However, the existing body of research on how different rice varieties respond to AWD remains limited, leaving significant gaps in our understanding of the variations in physiological and agronomic responses across different genotypes. This research sought to access the outcomes in yield and WUE of different rice varieties under AWD. We investigated that the yield and its components, percentage of productive tillers, number of tillers, and leaf area index (LAI) include total LAI, high-effective LAI and effective LAI, NSC content in sheaths and stems, NSC translocation amount, NSC remobilization, NSC contribution to grain, flag net leaf photosynthesis rate, flag leaf transpiration rate, activities of antioxidant enzyme (peroxidase, catalase, and superoxide dismutase), root–shoot ratio, root and shoot biomass, ROA, active surface area, total surface area, Z + ZR in the root bleeding sap, harvest index, and WUE. Next, we discussed the relationships between agronomic and physiological traits and yield and WUE. This investigation would provide actionable recommendations for attaining high yield and WUE by implementing an irrigation regime and selecting appropriate rice varieties.
2. Methods and Materials
2.1. Description of the Plant Materials and Experimental Site
Field trials were carried out at the research farm of Yangzhou University throughout the rice cultivation period between May and October 2023 in Jiangsu Province, China (32°30′ N, 119°25′ E). The soil of the test field is sandy loam and has the following characteristics: 22.5 g kg−1 organic matter, 91.23 mg kg−1 exchangeable K, 23.4 mg kg−1 Olsen P, and 101.9 mg kg−1 alkali-hydrolyzable N. Weather conditions (mean air temperature, solar radiation, and precipitation) during the rice growing season in 2023 were measured at a weather station close to the experimental site (Supplementary Table S1).
Five mid-season japonica rice varieties, Jinnanfeng, Xudao 2, Huaidao 5, Nanjing 9108, and Wuyunjing 24, and one indica-japonica hybrid rice variety, Yongyou 2640, were utilized in this study, which were applied in the 1960s, 1970s, 1990s, 2000s, and 2010s in the production throughout the last 60 years at the Yangtze River Basin, respectively. On 15 May, seedlings were planted after being grown in seedbeds. On 10 June, with two seedlings per hill, they were transplanted at a spacing of 10.7 cm × 30.0 cm. Each parcel of rice field measured 3 m × 5 m. An overall was applied at a 4:2:2:2 ratio amount of 240 kg of pure nitrogen per hectare at the main growth period. Each plot was applied with 300 kg per hectare of superphosphate (containing 13.5% P2O5) and 195 kg per hectare of potassium chloride (containing 52% K2O) before transplanting.
2.2. Treatment
The test utilized a two-split zone design, where irrigation treatment is the main area and rice varieties are the secondary area. Each parcel of rice field measured 5 m × 3 m. Both AWD and CI applied from 10 days after transplanting until plants matured. Under the CI regime, the water layer with a depth of 2.0–3.0 cm was maintained except throughout the mid-tillering stage drainage, and was re-established 1 week prior to the harvest. Under the AWD regime, irrigation was stopped right down the soil water potential at a depth of 15–20 cm, reaching –15 ± 5 kPa. Five tension meters (Institute of Soil Science, Chinese Academy of Sciences, Nanjing, Jiangsu, China) were installed in each parcel of rice field to record readings at 1200 h. When the soil water potential reached the threshold value, the re-hydration depth was 1.0~2.0 cm.
2.3. Sampling and Measurements
Table 1 provides an outline of measured methods. See Supplementary Information for specific determination indexes.
2.4. Harvest
The yield and its components were determined using a method by Zhang et al. [21].
2.5. Statistical Analysis
All data were processed using Microsoft Office Excel 2021 (Microsoft Corporation, Redmond, WA, USA). Analysis of variance (ANOVA) was conducted with IBM SPSS Statistics software (v27, IBM Corporation, Armonk, NY, USA). Graphs were generated using Origin 2021 (OriginLab Corporation, Northampton, MA, USA) and R software (v4.2.1, R Foundation for Statistical Computing, Vienna, Austria).
3. Results
3.1. Yield and Its Components
Both alternate wetting and drying (AWD) and conventional irrigation (CI) led to gradual increases in grain yield with the improvement of varieties. AWD markedly boosted the yield of each grain type (Table 2). The yields of various varieties increased by 14%, 9%, 6%, 2%, 5%, and 6%, respectively, under AWD. Under AWD, the grain yields of JNF and XD 2 were below 8 t ha−1, the yields of HD 5 and NJ 9108 was between 8 and 10 t ha−1, and the yield of WYJ 24 and YY 2640 was above 10 t ha−1. AWD reduced the number of panicles but increased the number of spikelets per panicle across all rice varieties. The 1000-grain weights under AWD were 24.23 g, 24.16 g, 29.38 g, 27.68 g, 28.27 g, and 23.95 g for the respective species.
3.2. Water Use Efficiency (WUE) and Harvest Index (HI)
The yield-level water use efficiency (WUEY), leaf-level water use efficiency (WUEL), and HI substantially enhanced with the improvement of varieties, aligning with the trends observed in grain yield. Notably, AWD resulted in substantially higher values for WUEY and WUEL, and the HI of Huaidao 5, Wuyunjing 24, and Yongyou 2640 increased (Table 3). Specifically, under AWD, WUEL increased by 25%, 35%, 32%, 14%, 32%, and 18% at the PI stage across the different varieties, respectively. The trends of the other main growth stages were consistent with those observed at the PI stage. Under AWD, the WUEY of JNF and XD 2 was below 1.6 kg m−3, the WUEY of HD 5 and NJ 9108 was between 1.6 and 1.8 kg m−3, and the WUEY of WYJ 24 and YY 2640 was above 1.8 kg m−3. Similar to WUEY, the WUEL of JNF and XD 2 was below 2.8 μmol mmol−1, HD 5 and NJ 9108 were between 2.8 and 2.9 μmol mmol−1, and WYJ 24 and YY 2640 were above 2.9 μmol mmol−1.
3.3. Category for Defining Rice Varieties with Different Water Use Efficiencies
Under AWD, the grain yield of JNF and XD 2 was below 8 t ha−1, the yield of HD 5 and NJ 9108 was between 8 and 10 t ha−1, and the yield of WYJ 24 and YY 2640 was above 10 t ha−1. Under AWD, the WUEY of JNF and XD 2 was below 1.6 kg m−3, the WUEY of HD 5 and NJ 9108 was between 1.6 and 1.8 kg m−3, and the WUEY of WYJ 24 and YY 2640 was above 1.8 kg m−3. Similar to WUEY, the WUEL of JNF and XD 2 was below 2.8 μmol mmol−1, HD 5 and NJ 9108 were between 2.8 and 2.9 μmol mmol−1, and WYJ 24 and YY 2640 were above 2.9 μmol mmol−1. Based on this result, JNF and XD 2 can be defined as LWVs, HD 5 and NJ 9108 as MWVs, and WYJ 24 and YY 2640 as HWVs (Table 4).
3.4. Number of Tillers and Leaf Area Index (LAI)
The number of tillers saw a significant reduction under two treatments with the improvement of varieties. AWD notably reduced the tiller count across three types (LWVs, LWVs, and HWVs) throughout the main growth stages (Table 5). Additionally, LAI showed a significant enhancement with the improvement of varieties. AWD had a significant impact on LAI at the HD stage. Specifically, AWD markedly improved the total LAI, effective LAI, and high effective LAI (Supplementary Table S2).
3.5. NSC Translocation in Sheaths and Stems
The amount of NSC translocation substantially increased under both irrigation methods with the improvement of varieties (Table 6). AWD substantially increased the NSC translocation amount of rice. However, AWD substantially reduced NSC content in sheaths and stems at the HD stage and the MA stage of rice. AWD substantially increased the amount of NSC translocation, NSC remobilization, and NSC contribution to the grain. There was reduced NSC content in sheaths and stems.
3.6. Leaf Net Photosynthetic and Transpiration Rate
The flag leaf net photosynthesis rate throughout the PI, HD, and MF stages enhanced substantially with the improvement of varieties (Figure 1A,B). Additionally, with the improvement of rice varieties, the transpiration rate of leaves throughout the PI stage and the HD stage substantially increased. The transpiration rate of leaves throughout the MF stage showed a trend of first increasing and then decreasing with the improvement of rice varieties. Unlike the CI treatment, AWD substantially increased the flag leaf net photosynthesis rate of three types from the PI stage to the MF stage and reduced the transpiration rate throughout the three main growth stages. AWD had a significant impact on the net photosynthetic rate of leaves. The leaf net photosynthetic rate of AWD increased by 6.90% to 14.75% at the HD stage. The transpiration rate of AWD decreased by 0.58% to 12.86% at the HD stage.
3.7. Catalase (CAT), Peroxidase (POD), and Superoxide Dismutase (SOD) Activities in Leaves
CAT and POD activity increased substantially at the PI stage, HD stage, and MF stage with the improvement of rice varieties (Supplementary Figure S1A,B). Compared with low water use efficiency varieties (LWVs), the CAT of high water use efficiency varieties (HWVs) increased by 34.96% under AWD. Similarly, POD increased by 43.48%. SOD activity first expanded and then decreased at the HD stage (Supplementary Figure S1C). Compared with CI, AWD substantially increased CAT, POD, and SOD in the three main growth stages. The CAT of AWD increased by 23.90–29.03%, POD increased by 4.00–9.77%, and SOD increased by 10.35–22.08%.
3.8. Root, Shoot Biomass, and Root–Shoot Ratio
Shoot biomass, root biomass, and root–shoot ratio showed significant increases under both treatment from the PI to MA stages with the improvement of rice varieties. AWD enhanced shoot biomass in all three types (LWVs, MWVs, and HWVs) throughout this period (Figure 2A). The shoot biomass of AWD increased by 4.71–7.41%, the root biomass increased by 18.80–24.38%, and the root–shoot ratio increased by 13.46–16.28% at the HD stage. Under CI, the root biomass and root–shoot ratio were lower than those observed under AWD. The root biomass and root–shoot ratio enhanced and then decreased as the growth stage progressed (Figure 2B,C).
3.9. Root Absorbing Surface Area and Root Oxidation Activity (ROA)
In three types (LWVs, MWVs, and HWVs), the root absorbing surface area and ROA were substantially lower under CI compared with AWD. At each main growth stage, either ROA or the root absorbing surface area substantially increased with the improvement of rice varieties in the three types (Figure 3 and Supplementary Figure S2A,B).
3.10. Z + ZR in Root Bleeding
The contents of Z + ZR in root bleeding varied between both treatments at each main growth stage. The Z + ZR in every type increased gradually with the improvement of rice varieties (Supplementary Figure S2C,D). From the PI stage to the MF stage, the Z + ZR under both treatments increased and then decreased. The Z + ZR under AWD was substantially higher than those under CI.
3.11. Relationship between Aboveground Agronomic Traits, Photosynthetic System, Antioxidant System, and Root System with WUE and Yield
In this PCA analysis, PC 1 explains 70.1% of the variance, while PC2 accounts for 12.8%, collectively capturing 82.9% of the data variability. The sample points, differentiated by treatments (AWD and CI), exhibit significant separation in the principal component space, particularly along the PC 1 axis, indicating a marked difference between the two treatment groups. The red arrows represent variable loadings, illustrating each original variable’s contribution to the principal components. The length and direction of the arrows indicate the importance and relationship of the variables to each component. The angle between the arrows reflects the correlation between variables, where smaller angles suggest higher correlation, and larger angles indicate lower correlation. Overall, PC 1 primarily drives the distinction between AWD and CI groups, reflecting the significant impact of variables along the PC 1 axis on treatment group differences. Therefore, the spikelets per panicle, total spikelets, filled grain rate, 1000-grain weight, harvest index, percentage of productive tillers, total leaf area index, effective leaf area index, high effective leaf area index, WUE, NSC, CAT, POD, SOD, flag leaf net photosynthesis and transpiration rate, root and shoot biomass, root–shoot ratio, ROA, surface area (total and active), and Z + ZR were very considerable and had a positive association with grain yield and WUE (Supplementary Figure S3).
Correlation coefficients and path coefficients exhibited that AWD directly improved water use efficiency, which explained 95.2% of their variations, then indirectly enhanced yield (Figure 4). AWD affected aboveground agronomic traits, the photosynthetic system, the antioxidant system, and the root system, where it explained 53.8%, 50.3%, 28.4%, and 45.8% of their variations, respectively. WUE was improved with the improvement of rice varieties, which explained 75.2% of their variations, then indirectly enhanced yield. Variety improvement affected aboveground agronomic traits, the photosynthetic system, the antioxidant system, and the root system, where it explained 41.5%, 14.3%, 22.5%, and 53.9% of their variations, respectively (Figure 4).
4. Discussion
4.1. Variations in Yield and WUE among Varieties
Enhancing yield while conserving water is crucial for food security in China. Advanced cultivation techniques and improved rice varieties have significantly boosted yields [22]. Research indicates that modern high-yield varieties, with enhanced response diversity and adaptability, outperform older ones, especially under challenging conditions [23,24]. Our findings confirm this, showing that yield increases are primarily due to a rise in total spikelets, as modern breeding strategies focus on developing larger sink sizes by increasing panicle numbers (Table 2). This innovation ensures higher productivity in both optimal and suboptimal environments, underscoring the need for continued advancements in rice breeding.
The increasing consumption of freshwater resources necessitates improving WUE alongside rice variety enhancement. Biological water saving, through strategic irrigation based on rice growth stages, aims to maximize yields with reduced water use and enhance natural WUE [25]. Modern varieties generally show higher WUE than earlier ones. Our study confirmed that varietal improvements significantly boosted WUE at both grain yield and leaf levels (Table 3), demonstrating that breeding and varietal enhancement can achieve both high yield and efficiency simultaneously.
4.2. Response of Yield and WUE to Different Irrigation Methods
The root system’s physiological and morphological traits, along with aboveground growth and water and nutrient uptake, directly affect grain yield. AWD impacts yield variably; some studies show that it can enhance grain yield by 6.00% to 12.63% [26]. One study found that re-watering at a soil water potential of −15 kPa under AWD significantly increased yield. Another study reported no significant yield difference between AWD and CI when soil water was 15 cm below the surface, while a different study found that AWD reduced grain yield [27]. A meta-analysis by Carrijo et al. [28] found that AWD reduced grain yield by 3% to 23% compared with flood irrigation. Other research suggests that AWD can decrease rice yield and increase weed presence, requiring a reliable water source and involving complex processes, which limits its use. Therefore, further study is needed to understand AWD’s effects on rice yield. In our study, AWD significantly enhanced grain yield and WUE across all rice varieties compared with CI, primarily due to increases in the percentage of filled grains, 1000-grain weight, and spikelets per panicle under AWD (Table 2).
4.3. The Response of Physiological and Agronomic Traits to AWD across Different Varieties
Boosting biomass accumulation, harvest efficiency, or both can enhance cereal grain yields, though their relative importance remains debated [29]. Our study showed that rice shoot dry biomass increased with varietal improvement, with AWD further boosting shoot biomass in all rice types (Figure 2). Modern varieties exhibited superior LAI at the HD stage, along with higher ROA, root biomass, and leaf net photosynthetic rates throughout the growth period (Supplementary Table S2, Figure 2 and Figure 3, and Supplementary Figure S2A,B). These findings suggest that the source–sink relationship, regulated by hormones, enhances population quality as rice varieties improve, leading to higher grain yields. However, reduced filling rates in modern varieties may result from decreased ROA and leaf net photosynthetic rates during the MF stage. Increasing these during the grout process could improve filling rates, as proposed by Zhang et al. [30].
Understanding rice root water uptake is crucial, as strong root systems correlate with higher yields and stress tolerance [31,32]. In our study, HWVs had greater root biomass and an increasing root–shoot ratio at key growth stages compared with LWVs and MWVs (Figure 2), indicating that enhanced root growth boosts grain yield and water uptake. Robust root physiological activity is vital for WUE, with water utilization closely linked to root activity, hormone levels, and other physiological indicators [33,34]. Higher Z + ZR contents in roots and root bleeding under AWD suggest that mild water stress increases Z + ZR, improving environmental adaptation. Thus, integrating AWD in varietal improvement enhances root morphology and physiology, boosting shoot growth, yield, and WUE.
We have conducted a comprehensive analysis of the primary agronomic and physiological traits using path analysis based on principal component analysis (PCA) results (Supplementary Figure S3). The harvest index and root–shoot ratio demonstrated the most direct impact on yield and WUE, followed by the NSC contribution activities to grain and spikelets per panicle. In agricultural production, effective strategies should focus on enhancing the root–shoot ratio, NSC contribution to grain, harvest index, and spikelets per panicle to enhance rice yield and WUE.
5. Conclusions
This research underscores the variability in WUE and yield noted among different rice varieties and the significant benefits of high water use efficiency varieties (HWVs) under alternate wetting and drying (AWD) irrigation, leading to marked improvements in both yield and water utilization. The increased sink size of spikelets, photosynthetic production ability, antioxidant system, and root activities were attributed to the enhanced WUE and yield for HWVs. The application of AWD was found to enhance both WUE and yield, with HWVs benefiting most from AWD. This combination of superior genetics and innovative agronomic practices offers a sustainable solution for boosting rice production while conserving water, highlighting a resilient path for global food security and environmental stewardship.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14091986/s1, Table S1: Mean air temperature, precipitation, and solar radiation during the rice growing season; Table S2: Leaf area index of varieties differing in water use efficiency at heading stage; Figure S1: The activities of catalase (CAT), peroxidase (POD), and super oxide dismutase (SOD) in leaves of rice varieties differing in water use efficiency; Figure S2: Root total absorbing surface area (A), root active absorbing surface area (B), Z +ZR contents in roots (C) and root bleeding sap (D) of rice varieties differing in water use efficiency; Figure S3: Principal component analysis (PCA) of key agronomic and physiological traits of rice under various irrigation methods. References [35,36,37,38,39] are cited in the supplementary materials.
Author Contributions
Conceptualization, X.F. and Q.M.; data curation, Y.Z.; formal analysis, W.W. and K.Z.; funding acquisition, J.Z. and H.Z.; methodology, W.Z. and J.G.; software, L.L. and J.Z.; validation, J.Z.; writing—original draft preparation, C.W.; writing—review and editing, H.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Key Research and Development Program of China (2022YFD2300304), the National Natural Science Foundation of China (32272197, 32071944), the Hong Kong Research Grants Council (GRF 14177617, 12103219, 12103220, AoE/M-403/16), the State Key Laboratory of Agrobiotechnology (Strategic Collaborative Projects) in the Chinese University of Hong Kong, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Data Availability Statement
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Flag leaf photosynthesis rate (A) and transpiration rate (B) of varieties differing in WUE. CI denotes conventional irrigation and AWD denotes alternate wetting. LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties. PI: panicle initiation stage; HD: heading stage; and MF: middle grain filling stage. Vertical bars represent the ± standard error of the mean where they exceed the symbol size. Different letters above the columns denote statistical significance at p ≤ 0.05 (n = 4) within the same year.
Figure 1.
Flag leaf photosynthesis rate (A) and transpiration rate (B) of varieties differing in WUE. CI denotes conventional irrigation and AWD denotes alternate wetting. LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties. PI: panicle initiation stage; HD: heading stage; and MF: middle grain filling stage. Vertical bars represent the ± standard error of the mean where they exceed the symbol size. Different letters above the columns denote statistical significance at p ≤ 0.05 (n = 4) within the same year.
Figure 2.
Shoot biomass (A), root biomass (B), and root–shoot ratio (C) of rice varieties differing in water use efficiency. CI denotes conventional irrigation and AWD denotes alternate wetting. LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties. MT: mid-tillering stage, PI: panicle initiation stage, HD: heading stage; MA: maturity stage. Vertical bars represent the ± standard error of the mean where they exceed the symbol size. Different letters above the columns denote statistical significance at p ≤ 0.05 (n = 4) within the same year.
Figure 2.
Shoot biomass (A), root biomass (B), and root–shoot ratio (C) of rice varieties differing in water use efficiency. CI denotes conventional irrigation and AWD denotes alternate wetting. LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties. MT: mid-tillering stage, PI: panicle initiation stage, HD: heading stage; MA: maturity stage. Vertical bars represent the ± standard error of the mean where they exceed the symbol size. Different letters above the columns denote statistical significance at p ≤ 0.05 (n = 4) within the same year.
Figure 3.
Root oxidation activity of varieties differing in water use efficiency. CI denotes conventional irrigation and AWD denotes alternate wetting. LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties. PI: panicle initiation stage; HD: heading stage; MF: middle grain filling stage. Vertical bars represent the ± standard error of the mean where they exceed the symbol size. Different letters above the columns denote statistical significance at p ≤ 0.05 (n = 4) within the same year.
Figure 3.
Root oxidation activity of varieties differing in water use efficiency. CI denotes conventional irrigation and AWD denotes alternate wetting. LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties. PI: panicle initiation stage; HD: heading stage; MF: middle grain filling stage. Vertical bars represent the ± standard error of the mean where they exceed the symbol size. Different letters above the columns denote statistical significance at p ≤ 0.05 (n = 4) within the same year.
Figure 4.
The correlation coefficients and path coefficients of aboveground agronomic traits, photosynthetic system, antioxidant system, root system, WUE, and yield for different types of varieties under AWD. Solid lines with arrows represent positive relationships, while dashed lines with arrows indicate negative relationships. The proportion of variance explained is expressed and shown as R2. * and ** indicate notable differences at p ≤ 0.05 and p ≤ 0.01, respectively.
Figure 4.
The correlation coefficients and path coefficients of aboveground agronomic traits, photosynthetic system, antioxidant system, root system, WUE, and yield for different types of varieties under AWD. Solid lines with arrows represent positive relationships, while dashed lines with arrows indicate negative relationships. The proportion of variance explained is expressed and shown as R2. * and ** indicate notable differences at p ≤ 0.05 and p ≤ 0.01, respectively.
Table 1.
The outline of measured methods.
| Indexes Measured | Assay Methods (1) |
|---|---|
| Tiller dynamics | [1] |
| Shoot biomass and root biomass | [2] |
| Leaf area index (LAI) | [3] |
| Flag leaf net photosynthetic and transpiration rates | [4] |
| Super oxide dismutase activity (SOD) | [5] |
| Peroxidase activity (POD) | [6] |
| Catalase activity (CAT) | [7] |
| Root oxidation activity (ROA) | [8] |
| Zeatin (Z) and zeatin riboside (ZR) in the root bleeding sap | [9] |
(1) The numbers 1–9 represent the measurement method of the corresponding indicator in Supplementary Information.
Table 2.
Yield and its components of varieties.
| Treatment (1) | Variety | Number of Panicles (×104 ha−1) | Spikelets per Panicle | Total Spikelets (×106 ha−1) | Filled Grain Rate (%) | 1000-Grain Weight (g) | Grain Yield (t ha−1) |
|---|---|---|---|---|---|---|---|
| CI | Jinnanfeng | 279.44 a (2) | 121.75 i | 340.22 i | 70.33 f | 24.10 f | 5.77 h |
| Xudao 2 | 269.22 b | 130.84 h | 352.25 h | 84.06 b | 24.15 f | 7.15 f | |
| Huaidao 5 | 257.79 c | 175.42 cde | 452.22 c | 82.14 c | 24.26 f | 9.01 d | |
| Nanjing 9108 | 249.31 d | 171.11 e | 426.59 e | 81.23 c | 24.78 e | 8.59 e | |
| Wuyunjing 24 | 246.71 def | 178.73 c | 440.94 d | 81.87 c | 25.60 cd | 9.24 cd | |
| Yongyou 2640 | 247.74 de | 172.35 de | 426.98 e | 84.67 ab | 25.74 bc | 9.31 cd | |
| AWD | Jinnanfeng | 268.46 b | 136.59 g | 366.69 g | 72.14 e | 24.23 f | 6.41 g |
| Xudao 2 | 266.19 b | 147.22 f | 391.88 f | 79.47 d | 24.19 f | 7.53 f | |
| Huaidao 5 | 241.68 f | 178.61 c | 431.66 e | 84.85 ab | 25.38 d | 9.30 cd | |
| Nanjing 9108 | 244.31 def | 176.35 cd | 430.84 e | 85.49 a | 25.68 bcd | 9.46 c | |
| Wuyunjing 24 | 242.97 ef | 189.65 b | 460.79 b | 85.83 a | 26.27 a | 10.39 b | |
| Yongyou 2640 | 234.69 g | 217.44 a | 510.31 a | 85.95 a | 25.95 b | 11.38 a | |
| Analysis of variance (3) | |||||||
| Treatment (T) | ** | ** | ** | NS | ** | * | |
| Variety (V) | ** | ** | ** | * | ** | ** | |
| T × V | ** | * | ** | ** | ** | ** | |
(1) CI: conventional irrigation; AWD: alternate wetting and drying irrigation. (2) Different lowercase letters indicate significant difference at the 0.05 probability level within the same column (n = 4). (3) *, ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01, respectively. NS indicates not significant difference at p ≤ 0.05.
Table 3.
WUE and harvest index of varieties.
| Treatment (1) | Variety | Yield-Level Water Use Efficiency (kg m−3) | Leaf-Level Water Use Efficiency (μmol mmol−1) | Harvest Index | ||
|---|---|---|---|---|---|---|
| PI | HD | MF | ||||
| CI | Jinnanfeng | 0.78 i (2) | 2.50 cd | 2.29 d | 2.33 d | 0.34 g |
| Xudao 2 | 0.96 h | 2.15 e | 2.28 d | 2.35 d | 0.39 f | |
| Huaidao 5 | 1.27 f | 2.08 e | 2.28 d | 2.45 bcd | 0.47 e | |
| Nanjing 9108 | 1.60 d | 2.63 bcd | 2.18 d | 2.36 d | 0.49 de | |
| Wuyunjing 24 | 1.46 e | 2.52 cd | 2.68 bc | 2.60 abcd | 0.51 cd | |
| Yongyou 2640 | 1.85 c | 2.49 d | 2.67 c | 2.44 cd | 0.53 bc | |
| AWD | Jinnanfeng | 1.04 g | 2.72 abcd | 2.73 bc | 2.54 abcd | 0.31 h |
| Xudao 2 | 1.47 e | 2.46 d | 2.74 bc | 2.76 abc | 0.39 f | |
| Huaidao 5 | 1.87 c | 2.84 abc | 2.88 ab | 2.75 abcd | 0.49 d | |
| Nanjing 9108 | 1.86 c | 2.80 abcd | 2.87 bc | 2.85 ab | 0.49 de | |
| Wuyunjing 24 | 2.14 b | 3.04 a | 2.97 a | 2.95 a | 0.54 b | |
| Yongyou 2640 | 2.25 a | 2.95 ab | 2.99 bc | 2.93 abc | 0.56 a | |
| Analysis of variance (3) | ||||||
| Treatment (T) | ** | ** | * | NS | NS | |
| Variety (V) | ** | NS | NS | NS | ** | |
| T × V | ** | NS | NS | NS | NS | |
(1) CI: conventional irrigation; AWD: alternate wetting and drying irrigation. (2) Different lowercase letters indicate a significant difference at the 0.05 probability level within the same column (n = 4). (3) *, ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01, respectively. NS indicates not significant difference at p ≤ 0.05.
Table 4.
Category for defining varieties with different water use efficiencies.
| Type (1) | Variety | Yield (t ha−1) | Yield-Level Water Use Efficiency (kg m−3) | Leaf-Level Water Use Efficiency (μmol mmol−1) |
|---|---|---|---|---|
| LWVs | Jinnanfeng | <8.0 | <1.6 | <2.8 |
| Xudao 2 | <8.0 | <1.6 | <2.8 | |
| MWVs | Huaidao 5 | 8.0~10.0 | 1.6~2.0 | 2.8~2.9 |
| Nanjing 9108 | 8.0~10.0 | 1.6~2.0 | 2.8~2.9 | |
| HWVs | Wuyunjing 24 | >10.0 | >2.0 | >2.9 |
| Yongyou 2640 | >10.0 | >2.0 | >2.9 |
(1) LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties.
Table 5.
Tiller dynamics of different varieties with water use efficiency.
| Treatment (1) | Type (2) | Variety | Number of Tillers (× 104 ha−1) | Percentage of Productive Tillers (%) | |||
|---|---|---|---|---|---|---|---|
| MT | PI | HD | MA | ||||
| CI | LWVs | Jinnanfeng | 183.3 d (3) | 414.7 a | 342.5 b | 279.4 b | 67.4 j |
| Xudao 2 | 229.3 a | 412.1 a | 354.7 a | 259.227 d | 62.9 k | ||
| Mean | 206.3 | 413.4 | 348.6 | 269.3 | 64.1 | ||
| MWVs | Huaidao 5 | 163.7 i | 323.6 d | 278.1 f | 247.8 e | 76.6 h | |
| Nanjing 9108 | 178.1 e | 347.8 c | 298.9 d | 289.3 a | 83.2 f | ||
| Mean | 170.9 | 335.7 | 288.5 | 368.6 | 79.9 | ||
| HWVs | Wuyunjing 24 | 190.1 c | 327.3 d | 263.7 g | 266.7 c | 81.5 g | |
| Yongyou 2640 | 120.1 k | 258.9 f | 226.4 j | 217.7 g | 84.1 e | ||
| Mean | 155.1 | 293.1 | 245.1 | 242.2 | 82.8 | ||
| AWD | LWVs | Jinnanfeng | 177.1 f | 327.5 d | 321.9 c | 231.5 f | 70.7 i |
| Xudao 2 | 218.0 b | 404.9 b | 342.6 b | 275.2 b | 68.0 j | ||
| Mean | 197.5 | 366.2 | 332.3 | 253.3 | 69.3 | ||
| MWVs | Huaidao 5 | 151.4 j | 257.9 f | 252.7 i | 219.7 g | 85.2 d | |
| Nanjing 9108 | 165.3 h | 265.2 f | 290.5 e | 234.3 f | 88.4 b | ||
| Mean | 158.3 | 261.5 | 271.6 | 227.0 | 86.8 | ||
| HWVs | Wuyunjing 24 | 173.6 g | 286.5 e | 256.7 h | 249.0 e | 86.9 c | |
| Yongyou 2640 | 112.0 | 206.9 g | 205.7 k | 184.7 h | 89.2 a | ||
| Mean | 142.8 | 246.7 | 231.3 | 216.8 | 88.1 | ||
| Analysis of variance (4) | |||||||
| Treatment (T) | ** | ** | ** | ** | ** | ||
| Variety (V) | ** | ** | ** | ** | ** | ||
| T × V | NS | ** | * | NS | ** | ||
(1) CI: conventional irrigation; AWD: alternate wetting and drying irrigation. (2) LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties. (3) Different lowercase letters indicate a significant difference at the 0.05 probability level within the same column (n = 4). (4) *, ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01, respectively. NS indicates not significant difference at p ≤ 0.05.
Table 6.
NSC translocation in sheaths and stems of varieties differing in water use efficiency.
| Treatment (1) | Type (2) | Variety | NSC Content in Stems and Sheaths (g m−2) | NSC Translocation Amount (g m−2) | NSC Remobilization (%) | NSC Contribution to Grain (%) | |
|---|---|---|---|---|---|---|---|
| HD | MA | ||||||
| CI | LWVs | Jinnanfeng | 248.8 h (3) | 175.3 f | 73.5 g | 29.5 h | 11.0 cd |
| Xudao 2 | 256.7 g | 182.7 e | 73.9 g | 28.8 h | 10.4 f | ||
| Mean | 252.7 | 179.0 | 73.7 | 29.2 | 10.7 | ||
| MWVs | Huaidao 5 | 312.7 e | 197.9 d | 114.9 e | 36.7 e | 11.3 de | |
| Nanjing 9108 | 325.2 d | 211.2 c | 114.0 e | 35.0 fg | 11.7 f | ||
| Mean | 319.0 | 204.6 | 114.4 | 35.9 | 11.5 | ||
| HWVs | Wuyunjing 24 | 366.1 a | 218.1 b | 147.9 c | 40.4 d | 11.9 b | |
| Yongyou 2640 | 352.2 b | 222.2 a | 129.9 d | 36.9 e | 11.5 g | ||
| Mean | 359.1 | 220.2 | 138.9 | 38.7 | 11.7 | ||
| AWD | LWVs | Jinnanfeng | 215.4 j | 140.5 l | 74.9 g | 34.8 g | 11.6 bc |
| Xudao 2 | 225.8 i | 144.3 k | 81.5 f | 36.1 ef | 11.5 f | ||
| Mean | 220.6 | 142.4 | 78.2 | 35.4 | 11.5 | ||
| MWVs | Huaidao 5 | 273.0 f | 155.9 j | 137.2 e | 42.9 c | 12.8 e | |
| Nanjing 9108 | 325.6 d | 161.8 i | 143.8 a | 50.3 a | 12.6 a | ||
| Mean | 299.3 | 158.8 | 140.5 | 46.6 | 12.7 | ||
| HWVs | Wuyunjing 24 | 322.3 d | 167.3 h | 155.0 b | 48.1 b | 13.3 b | |
| Yongyou 2640 | 334.7 c | 169.9 g | 164.8 a | 49.2 ab | 13.1 b | ||
| Mean | 328.5 | 168.6 | 159.9 | 48.7 | 13.5 | ||
| Analysis of variance (4) | |||||||
| Treatment (T) | ** | ** | ** | ** | ** | ||
| Variety (V) | ** | ** | ** | ** | ** | ||
| T × V | * | NS | ** | ** | ** | ||
(1) CI: conventional irrigation; AWD: alternate wetting and drying irrigation. (2) LWVs: low water use efficiency varieties; MWVs: medium water use efficiency varieties; HWVs: high water use efficiency varieties. (3) Different lowercase letters indicate significant difference at the 0.05 probability level within the same column (n = 4). (4) *, ** indicate significant differences at p ≤ 0.05 and p ≤ 0.01, respectively. NS indicate not significant difference at p ≤ 0.05.
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Wang, C.; Fa, X.; Meng, Q.; Zhang, Y.; Wang, W.; Zhu, K.; Zhang, W.; Gu, J.; Liu, L.; Zhang, J.; et al. Comparison of Agronomic and Physiological Characteristics for Rice Varieties Differing in Water Use Efficiency under Alternate Wetting and Drying Irrigation. Agronomy 2024, 14, 1986. https://doi.org/10.3390/agronomy14091986
AMA Style
Wang C, Fa X, Meng Q, Zhang Y, Wang W, Zhu K, Zhang W, Gu J, Liu L, Zhang J, et al. Comparison of Agronomic and Physiological Characteristics for Rice Varieties Differing in Water Use Efficiency under Alternate Wetting and Drying Irrigation. Agronomy. 2024; 14(9):1986. https://doi.org/10.3390/agronomy14091986
Chicago/Turabian StyleWang, Chen, Xiaotong Fa, Qinghao Meng, Ying Zhang, Weilu Wang, Kuanyu Zhu, Weiyang Zhang, Junfei Gu, Lijun Liu, Jianhua Zhang, and et al. 2024. "Comparison of Agronomic and Physiological Characteristics for Rice Varieties Differing in Water Use Efficiency under Alternate Wetting and Drying Irrigation" Agronomy 14, no. 9: 1986. https://doi.org/10.3390/agronomy14091986
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