Responses of the Lodging Resistance of Indica Rice Cultivars to Temperature and Solar Radiation under Field Conditions

Abstract

Much attention has shifted to the effects of temperature and solar radiation on rice production and grain quality due to global climate change. Meanwhile, lodging is a major cause of rice yield and quality losses. However, responses of the lodging resistance of rice to temperature and solar radiation are still unclear. To decipher the mechanisms through which the lodging resistance might be affected by temperature and solar radiation, 32 rice cultivars with different lodging resistance were grown at two eco-sites on three sowing dates over a period of three years. Based on the field observation, 12 indica rice cultivars which did not lodge were selected for analysis. Significant differences were found in the lodging resistance of the indica rice cultivars at different temperature and solar radiation treatments. The results showed that temperature was the main factor that affected the lodging resistance of indica rice cultivars under the conditions of this study. With the increased average daily temperature, the lodging resistance decreased rapidly, primarily due to the significant reduction in physical strength of the culm, which was attributed to the longer and thinner basal second internode. Among the 12 indica rice cultivars, the lodging-moderate cultivar Chuanxiang 29B was most sensitive to temperature, and the lodging-resistant cultivar Jiangan was least responsive to temperature. These results suggested that rice breeders could set the shorter and thicker basal internode as the main selection criteria to cultivate lodging-resistant indica cultivars to ensure a high yield at a higher ambient temperature.

1. Introduction

Lodging severely reduces the grain yield and quality of rice [1]. Furthermore, it increases production costs by adversely affecting the harvest manipulations and heightening the grain drying demand [2,3]. According to a study by Nakajima et al. [4], rice lodging could also aggravate mycotoxin pollution that threatens animal and human health. Since the initiation of the ‘‘Green revolution’’ in the 1960s, semi-dwarf cultivars of rice and wheat have been developed, which have enhanced the lodging resistance significantly and increased global grain production [5,6,7]. However, with the large-scale cultivation of high-yielding cultivars, extensive use of fertilizers, and simplified planting techniques, such as direct-seeding, the potential risk of lodging has increased in recent years [8,9,10]. Thus, lodging-resistant cultivars have been developed as a genetic improvement strategy to increase the yield of rice, wheat, and other crops [11,12,13,14].

Lodging, which results from a loss of balance in plant bodies, refers to the lasting vertical stem displacement of plants [15]. In the case of rice, there are three types of lodging: culm bending, culm breaking and root lodging [3,16]. Culm breaking is generally seen at the lower internodes (including the third and fourth internodes from the plant top), which happens when the bending moment of the upper plant part is excessive [2,17]. The manner in which rice resists against culm breaking is usually assessed by the lodging index (LI) [18,19,20,21]. A decrease in the LI indicates a stronger lodging resistance capability. Several studies have investigated the correlation of LI with lodging-related traits [22,23,24]. However, as the LI is a ratio of the bending moment of the whole plant (BM) to the bending moment at breaking (M), it is inadequate for evaluating the lodging resistance capability under certain special conditions. For example, when the multiple differences in BM values between two cultivars are similar to the multiple differences in M values between the two cultivars, the calculated LI values of the two cultivars would exhibit no significant difference, which could be contradictory to the actual lodging resistance of the rice cultivars. As a result, an optimized parameter △BM (equal to the value of 2M minus the value of BM), which was defined as the external force that the basal second internode could withstand, was proposed to be used along with the LI for a further accurate evaluation of the lodging resistance [25]. An increase in the △BM indicates a stronger lodging resistance capability. Lodging generally happens at the stage of grain filling [26,27]. In the research of Ichii and Hada [28], the stem-breaking strength decreased to the minimum value and the LI increased to the maximum at 30 days after heading, which indicated that the grain-filling stage is the period when lodging often occurs. Lodging is associated with several biotic and abiotic factors: the height and weight of the plant, the length of the panicle, the plumpness of the leaf sheath, as well as the length, diameter and thickness of basal internodes influence the lodging resistance of rice [29,30,31,32,33,34]. Regarding the morphological factors, the external diameter and thickness of the cross-section from basal internodes strongly influence the breaking strength of the stem [21,35]. Additionally, the stem contents of soluble sugars, K, Si, cellulose, starch and lignin affect the basal stem-breaking strength pronouncedly [36,37,38,39,40]. However, some researchers have suggested that the basal stem-breaking strength is decided by structural carbohydrates (lignin and cellulose) rather than non-structural carbohydrates (soluble sugars and starch) [20,41]. Growth conditions, such as the application of different fertilizers, planting density, direct seeding methods, and sheath blight attacks, strongly affect the lodging resistance of rice plants [10,19,42,43,44,45]. Lodging is also correlated with varying environmental parameters, such as rain, wind, CO₂, deep water, and resource complementarities [46,47,48,49,50].

With the increase in depletion of the stratospheric ozone, atmospheric levels of greenhouse gases, land-use alterations and aerosol outputs, an increase in global temperatures (global warming) and a decrease in solar radiation in Asia have been recorded in recent decades [51,52,53,54]. In the last century, the average global surface temperature recorded an elevation by 0.5 °C, and its estimated range of elevation is 0.3–6.4 °C by the end of this century [55]. An average annual reduction of 0.51 ± 0.05 W m⁻² in solar radiation in Asia has been reported [56,57]. Many studies have shown a significant influence of the increase in global temperatures on the yield and quality of rice grains [58,59]. Additionally, the positive role and significance of solar radiation in rice grain output have also been shown [60,61]. However, few studies have considered the influences imposed on rice lodging resistance by the solar radiation and temperature variations. One study reported that an increase in the soil temperature increased the lodging risk of rice plants [18]. The low solar radiation reduced the physical strength of the stem and, thus, increased lodging susceptibility in rice [62].

More than half of the global population consumes rice as a staple food. Since lodging, high temperature, and low solar radiation have detrimental effects on rice production, understanding the effects of temperature and solar radiation on lodging is important for growing rice that is adapted to the changing global climate. In the present work, we chose two eco-sites to carry out 3-year field experimentations on three sowing dates per year, to grow rice at different temperatures and under different solar radiation treatments. A total of 32 rice cultivars with different lodging resistance capabilities were evaluated under different combinations of temperature and solar radiation. Among them, 12 indica rice cultivars, which did not lodge in all the sowing dates based on the field observation, were selected for analysis. The objectives of the present study were to: (1) investigate the responses of the lodging resistance of indica rice cultivars to different temperature and solar radiation treatments; (2) evaluate the most and least affected cultivars among the 12 indica rice cultivars under different temperature and solar radiation treatments; (3) explore the relationship of the morphological, mechanical, and biochemical characteristics associated with lodging resistance with temperature and solar radiation. To cope with global climate change, a greater understanding of the climatic impact on lodging in indica rice will provide guidelines for rice breeders to adopt appropriate strategies for developing lodging-resistant indica rice cultivars in the future.

2. Materials and Methods

2.1. Experimental Materials

Thirty-two rice cultivars in total, including 18 Chinese accessions, 11 cultivars from a mini-core subset of the United States Department of Agriculture (USDA) rice gene bank [63], as well as Kasalath from India, Lemont from the USA and IR58025B from the IRRI, were selected as the experimental materials. These 32 rice cultivars were categorized into three groups (Groups 1, 2 and 3) with different lodging-resistance capabilities based on the principal component analysis and hierarchical clustering analysis of the lodging index in our previous study [25]. Group 1 (the lodging-susceptive group) comprised 3 indica cultivars and 4 japonica cultivars. There were 18 indica cultivars and four japonica cultivars in Group 2 (the lodging-moderate group). The remaining three rice cultivars in Group 3 (the lodging-resistant group) all belong to indica. Table S1 lists the detailed grouping information.

2.2. Experimental Design

For phenotypic information acquisition of the tested cultivars at varying temperatures and solar radiations, we chose two eco-sites to carry out 3-year field experiments on three sowing dates (SDs) per year, which totaled three experiments. The location of the first experiment was Xindu in China’s Sichuan province (30°49′51.54″ N, 104°06′3.44″ E; 547.7 m altitude), and the experiment was conducted during the rice-growing season from April to September in 2015. The location of the other two experiments was Ezhou in China’s Hubei province (30°22′20.75″ N, 114°45′7.78″ E; 23.6 m altitude), and the experiments were conducted during the rice-growing seasons from May to October in 2017 and 2018, respectively. Every experiment was arranged in a split plot design with sowing dates (SDs) as main plots and cultivars as subplots. The 32 cultivars were all sown on April 11 (SD1), April 20 (SD2) and April 28 (SD3) in 2015 at the Xindu site. Following growth to about the fourth leaf stage, we transplanted seedlings from each SD to paddies in an experimental block roughly sizing 16 m × 6 m (96 m²). Three blocks were set up for the three SDs. Each block was randomly arranged with 32 plots (each 2 m × 0.6 m in size; 1.2 m²) for the 32 cultivars. Each cultivar was planted in triplicate rows (10 plants per row) at each experimental plot, where the hill spacing was 20 cm × 20 cm. Isolation of consecutive plots was accomplished at one-row spacing of 20 cm so that the growth impact on adjacent plants could be minimized and the plant cultivar could be clarified. In the case of the Ezhou site, the 32 cultivars were all sown on May 8 (SD4), May 23 (SD5) and June 7 (SD6) in 2017 for the second experiment, and on May 10 (SD7), May 25 (SD8) and June 9 (SD9) in 2018 for the third experiment. The seedling transplantation and plot designs for each SD were identical to those for Xindu.

In each experiment, 375 kg ha⁻¹ of basal fertilizer in total, which was the compound fertilizer containing each 15% w/w N, P and K, was applied to each block one day before the seedling transplantation. As an N source, urea was split-applied during the tillering stage at 150 kg ha⁻¹ and during the panicle initiation stage at 75 kg ha⁻¹. About a 5-cm water depth was maintained in the experimental field post-transplant and remained flooded until 7 days before maturity. Intensive control of diseases, insects and weeds was implemented, in order to avoid biomass or yield loss.

2.3. Meteorological Data Collection

The sources of the sunshine hours and temperature data, including the daily maximum, average and minimum temperatures, were the China Meteorological Data Service Centre [64] and the relevant local meteorological stations near the experimental fields (Figure 1). Due to the lack of equipment for measuring solar radiation, the solar radiation data could not be directly recorded at the meteorological stations. Therefore, an Angstrom empirical model was employed to simulate the 2015 solar radiation data for Xindu, and the 2017 and 2018 solar radiation data for Ezhou based on the sunshine hours [65,66]:

R_G/R_A = a + b × n/N(1)

where R_G and R_A, respectively, denote the global and extraterrestrial solar radiations (both MJ m⁻² day⁻¹), whereas n and N, respectively, represent the actual and potential daily sunshine hours. Following the method in a study by Chen et al. [67], “a” and “b”, the empirical factors, were found to be 0.15 and 0.55 at Xindu, and 0.13 and 0.52 at Ezhou, respectively. The Nash–Sutcliffe equation (NSE) values were 0.81 and 0.84 for Xindu and Ezhou, respectively, which indicated that the model ran well [68].

The effective accumulated temperature (EAT) in the determined growth duration was calculated as:

EAT (°C) =∑(T − T₀) × Growth duration (d)(2)

where T and T₀ (10 °C for rice) are the daily average temperature and the biological zero temperature, respectively [58,69].

The cumulative solar radiation (CSR) in the determined growth duration was calculated as:

CSR (MJ m⁻²) =∑R × Growth duration (d)(3)

where R (MJ m⁻² d⁻¹) is the daily solar radiation, which is calculated as the R_G from the formula (1).

2.4. Plant Sampling and Measurements

The growth period, which spanned from the sowing to the maturation stages, varied from 90 to 146 days depending on the different cultivars and environmental factors. According to the cultivar growth period in each plot, we recorded the full-heading date, which was defined as the date on which 80% emergence of all panicles occurred from the flag leaf sheath. At both experimental sites, we assessed the lodging-related traits of the plants in each plot 25 days following the full-heading date, i.e., the grain filling stage. The days of the determined growth duration from the sowing date to the measuring date were also recorded for each cultivar plot (Table 1). For the phenotypic determination, eight plants were picked from the middle of each plot to avoid the marginal effects. These harvested plants, which had all tillers and intact roots, were transferred to the laboratory in plastic buckets filled with water for subsequent analyses. For lodging-related traits determination, the representative samples of plant main tillers were collected. We examined only the traits associated with the second elongated internode (starting from the main tiller root) in view of the common occurrence of culm-breaking-lodging type at the lower internodes [2,70]. Measurement of the culm length (CL) was accomplished between the lower node of the basal second internode and the panicle tip. Measurement of the stem length (SL) was accomplished between the lower nodes of the basal second and third internodes. Panicle length (PL) measurement was accomplished from the bottom node to the tip of a panicle. Determination of the fresh weight (FW) was accomplished for the zone between the lower node of the basal second internode and the panicle tip. A YYD-1 plant lodging tester (TOP Instrument, Zhejiang China) was utilized for determining the breaking resistance (BR) at the middle point of the basal second internode with a leaf sheath as per a priorly reported procedure [29]. Each sampled basal second internode with a leaf sheath was placed on the groove of supporting pillars that were 5 cm apart. The plant lodging tester, which was arranged perpendicularly to the middle internode, was then progressively loaded. BR measurement was accomplished when the internode was pushed to break, and the tester readout was recorded as the BR value (kg). Measurements of the culm diameter (CD), culm wall thickness (CT), as well as the external and internal diameters of the minor and major axes in an oval cross-section at the basal second internode center were accomplished using a digital vernier caliper. The physical parameters were calculated following the studies of Ookawa and Ishihara [71] and Ookawa et al. [72], and our previous study [25] as follows:

Bending moment of the whole plant (BM, g cm) = CL (cm) × FW (g)(4)

where CL is the culm length from the lower node of the basal second internode to the panicle tip, FW is the fresh weight from the lower node of the basal second internode to the panicle tip.

Bending moment at breaking (M, g cm) = 1/4 × BR (kg) × Spacing between supporting pillars (cm) × 10³(5)

where BR is the breaking resistance of the basal second internode with leaf sheath; spacing between supporting pillars is 5 cm.

Lodging index (LI, %) = BM/M × 100%(6)

where BM and M are calculated from formulas (4) and (5), respectively.

The external force that the basal second internode could withstand (△BM, g cm) = 2M − BM(7)

where BM and M are calculated from formulas (4) and (5), respectively.

Section modulus (SM, mm³) = π/32 × (a₁³b₁ − a₂³b₂)/a₁(8)

Culm diameter (CD, mm) = (a₁ + b₁)/2(9)

Culm wall thickness (CT, mm) = (a₁ – a₂ + b₁ – b₂)/4(10)

where a₁ and b₁ respectively denote the outer diameters of the minor and major axes in an oval cross-section, whereas a₂ and b₂ respectively represent the inner diameters of the minor and major axes in an oval cross-section.

Bending stress (BS, g mm ⁻²) = M/SM × 10(11)

where M and SM are calculated from formulas (5) and (8), respectively.

Following the determination of all lodging-related parameters, the basal second internode with the leaf sheath was subjected to 105 °C oven-drying for 30 min, then dried at 70 °C until the weight was constant. The dried basal second internodes were then ground to a fine powder for determining structural carbohydrates. The cellulose content (CC) was measured using a commercial cellulose content assay kit (Boxbio Science, Beijing, China), which was modified following the method of Updegraff [73]. Since the acetyl bromide method for the determination of lignin appears to have earned the most widespread acceptance [74], we used a commercial lignin content assay kit (Boxbio Science, Beijing, China), which was modified following the method of Johnson et al. [75], to measure the lignin content (LC) of the samples. The CD, CT, LC and CC were only measured in 2018 at Ezhou.

2.5. Data Analysis

Acording to the field observation, 12 indica rice cultivars, including the nine cultivars (Chuan 106B, 345B, Huanghuazhan, Jinlongsimiao, Chuanxiang 29B, Chenghui 3203, Guichao 2, II-32B and Teqing) in the lodging-moderate group and the three cultivars (R379, 9311 and Jiangan) in the lodging-resistant group, which did not lodge on all the sowing dates, were selected for the following data analysis. The measured data were analyzed via the Statistical Product and Service Solutions software for Windows (SPSS, Ver. 20.0; IBM, NY, USA). For each rice cultivar, the mean of eight main tillers was determined as the lodging-related parameter for that cultivar plot. The average daily temperature (T_mean) and solar radiation (R_mean), effective accumulated temperature (EAT) and cumulative solar radiation (CSR) for each SD were calculated based on the mean values of all the 12 indica rice cultivars in the determined growth durations. A Tukey test was employed to accomplish the significance analysis of the T_mean, R_mean, EAT, CSR, LI and △BM for the different sowing dates in each year, where p = 0.05 was set as the statistical significance level. To determine the relationship of the temperature and solar radiation parameters with the lodging-related parameters, we minimized the inter-cultivar differences in traits using the standardized data according to the method described in a study by Fan and Liu [76]. The standardized data, which were referred to as “relative data”, were calculated according to the formula below:

The relative lodging index = the lodging index of a given cultivar on one sowing date/average lodging index of this cultivar on all the sowing dates.

The other relative values of lodging-related parameters were calculated using the formula for the relative lodging index described above. The stepwise regression analysis and path analysis were performed to determine the relationships of temperature and solar radiation parameters to the lodging resistance. The correlation analysis was conducted using Pearson’s r coefficient, and the correlations were regarded as statistically significant when p < 0.05. The correlation coefficient was decomposed into direct effects and indirect effects by path analysis, with the result that the relative importance of the temperature and solar radiation for the lodging resistance were revealed. The values of direct and indirect effects were calculated according to the study of Luo et al. [77]. The GraphPad Prism software for Windows (Ver. 5.0; San Diego, CA, USA) was utilized for depicting the scatter diagrams and histograms.

3. Results

3.1. Effects of Sowing Date on Determined Growth Durations and Temperature and Solar Radiation Conditions

The determined growth durations of all the 12 indica rice cultivars were shortened as the sowing dates were delayed in each year (Table 1). Compared with the first sowing date, the number of days of the determined growth durations in the last sowing date were reduced by 5−14 d, 2−11 d, and 1−12 d in 2015, 2017, and 2018, respectively. The average determined growth durations of the three sowing dates at Ezhou were reduced by 6−30 d compared with those at Xindu.

The average daily temperature (T_mean) of the experiment during the determined growth durations increased significantly with the delay of the sowing dates in each year (Figure 2A). T_mean on SD1 was 0.4 and 0.8 °C lower than that on SD2 and SD3, respectively. T_mean in SD4 was 0.2 and 0.3 °C lower than that on SD5 and SD6, respectively. Additionally, T_mean on SD7 was 0.5 and 0.8 °C lower than that on SD8 and SD9, respectively. Furthermore, the T_mean showed significant differences between locations and years (Table S2). The T_mean from SD1 to SD3 in 2015 at Xindu was significantly lower than that from SD4 to SD6 in 2017 and from SD7 to SD9 in 2018 at Ezhou by 4.3 and 5.1 °C, respectively. However, the effective accumulated temperature (EAT) showed no significant variations among the three sowing dates in each year. Similar to the T_mean, the EAT at Xindu was significantly lower than that at Ezhou (Figure 2B, Table S2).

The average solar radiation (R_mean) of the experiment during the determined growth durations showed an inconsistent tendency with the delayed sowing dates in each year (Figure 2C). From SD1 to SD3, the R_mean was relatively constant. However, the R_mean on SD4 was 0.7 and 1.1 MJ m⁻² day⁻¹ higher than that on SD5 and SD6, respectively. Compared to the declining trend from SD4 to SD6, the R_mean displayed the opposite trend from SD7 to SD9, where the R_mean on SD7 was lower than that on SD8 and SD9 by 0.3 and 0.4 MJ m⁻² day⁻¹. In addition, the R_mean was also significantly affected by locations and years (Table S2). The R_mean from SD1 to SD3 in 2015 at Xindu was significantly lower than that from SD4 to SD6 in 2017 and from SD7 to SD9 in 2018 at Ezhou by 1.8 and 3.9 MJ m⁻² day⁻¹, respectively. As a result of delayed sowing dates, the cumulative solar radiation (CSR) decreased significantly from SD1 to SD3 and from SD4 to SD6 (Figure 2D). In addition, the CSR displayed an insignificant declining trend from SD7 to SD9. On the other hand, the CSR in 2015 at Xindu and in 2017 at Ezhou were significantly lower than those in 2018 at Ezhou (Figure 2D, Table S2).

Thus, a total of nine temperature and solar radiation treatments with significant differences were established for the twelve indica rice cultivars by setting nine sowing dates across two eco-sites and three years.

3.2. Effects of Sowing Date on LI and △BM

The LI and △BM, which were used to evaluate the lodging resistance of rice, were significantly affected by the sowing dates (Figure 3). With the delayed sowing dates, the LI increased significantly in each year (Figure 3A). The LI on SD3 was 22.65% higher than SD1. The LI on SD6 was 19.82% higher than SD4. Additionally, the LI on SD9 was 17.58% higher than SD7. Although the tendency of LI was not always significant in some cultivars, the overall increasing tendencies of LI of the 12 indica cultivars were identical (Table 2). In addition, the LI was also significantly affected by locations and years (Table S2). The average LI of the three SDs (SD1, SD2, and SD3) in 2015 at Xindu was significantly lower than that of the three SDs (SD4, SD5, and SD6) in 2017 and the three SDs (SD7, SD8, and SD9) in 2018 at Ezhou by 41.03% and 36.97%, respectively. Contrary to the LI, the △BM decreased significantly with the delay in the sowing dates in each year (Figure 3B). Compared to the first sowing date, the △BM of the last sowing date was lower by 35.52%, 57.62% and 47.93% in 2015, 2017 and 2018, respectively. The decreasing trends of △BM of all the 12 indica cultivars were significant (Table 3). Furthermore, the average △BM of the three SDs in 2015 at Xindu was significantly higher than that of the three SDs in 2017 and 2018 at Ezhou by 255.88% and 179.51%, respectively. However, there was no significant difference in the △BM between 2017 and 2018 at Ezhou (Table S2). These results indicated that the lodging resistance of 12 indica rice cultivars weakened with the delay in the sowing dates in each year and it was greater at Xindu than that at Ezhou.

3.3. Relationship of Temperature and Solar Radiation Parameters with Relative LI and △BM

The relative LI increased significantly with the T_mean in each year (Figure 4A). Contrastingly, the relative △BM decreased significantly with the T_mean in each year (Figure 4E). No significant correlations between the EAT and the relative LI or △BM were observed in each year (Figure 4B,F). Significant linear correlations between the R_mean and the relative LI and △BM were found in each year except in 2015 (Figure 4C,G). However, the linear correlations were reversed in 2017 and 2018. The relative LI showed a significantly negative correlation with the CSR in 2015 and 2017 (Figure 4D). The relative △BM displayed the opposite result, as was expected (Figure 4H). However, both the relative LI and △BM showed no significant correlation with the CSR in 2018.

When the nine temperature and solar radiation treatments were combined, significant correlations were also found between the temperature and solar radiation parameters and the relative LI and △BM (Figure 5). There were significant positive linear correlations between the relative LI and T_mean and EAT (Figure 5A,B). However, a significant quadratic relationship between the relative LI and R_mean was observed. The relative LI increased significantly with the R_mean from 2015 at Xindu to 2017 at Ezhou, and then decreased slightly from 2017 to 2018 at Ezhou (Figure 5C). In addition, the relative LI and △BM showed no significant correlation with the CSR (Figure 5D,H). When referred to the correlations between the relative △BM and T_mean, EAT and R_mean, they displayed the reversed results of the relative LI (Figure 5E–G).

In order to determine the main temperature and solar radiation parameters that affected the lodging resistance of 12 indica rice cultivars, a stepwise regression analysis was conducted (Figure 6). The results of the stepwise regression analysis indicated that only the T_mean and R_mean had significant effects on the lodging resistance within the temperature and solar radiation parameters. The relative LI increased with the T_mean but decreased with the R_mean (Figure 6A). The relative △BM decreased with the T_mean but increased with the R_mean (Figure 6B).

Following this, the direct and indirect effects of the T_mean and R_mean on the relative LI and △BM were investigated using a path analysis to reveal the relative importance of the T_mean and R_mean on the lodging resistance of 12 indica rice cultivars. The results of the path analysis are shown in Table 4. The direct effect of the T_mean on the relative LI was 1.556, and the indirect effect of T_mean on the relative LI through the R_mean was −0.673. The direct effect of the R_mean on the relative LI was –0.759, and the indirect effect of the R_mean on the relative LI through the T_mean was 1.380. These results indicated that the T_mean and R_mean independently had a positive and negative effect on the relative LI, respectively. Additionally, the absolute value of the direct effect of the T_mean (1.556) was higher than that of the R_mean (0.759), and the correlation coefficients of the relative LI with the T_mean and R_mean were all positive values (r = 0.883 ^** and 0. 621 ^**, respectively), which implied that the T_mean had a greater effect on the lodging resistance than the R_mean. The same results could also be obtained from the direct and indirect effects of the T_mean and R_mean on the relative △BM. Thus, the correlations between the R_mean and lodging-related traits were not analyzed in the following part.

3.4. Correlation analysis of the T_mean and Relative Lodging-Related Traits

In order to clarify the lodging-related traits affected by the T_mean, the correlations between them were analyzed (Table 5). The relative values of the breaking resistance (BR) and bending moment at breaking (M) were mostly affected by the T_mean with the highest coefficient r on the three sowing dates of each year and on all the sowing dates across two locations and three years. Furthermore, the relative value of the stem length (SL) had highly significant positive correlations with the T_mean on the three sowing dates of each year and on all the sowing dates across the two locations and three years. However, the rest lodging-related traits showed inconsistent correlations with the T_mean. These results implied that the BR, M and SL were the major lodging-related traits affected by the T_mean.

The M value, which is used to determine the physical strength of the culm, can be further divided into two parts: the section modulus (SM), which is directly influenced by the culm diameter (CD) and culm wall thickness (CT); the bending stress (BS), which is an indicator of culm stiffness. In order to determine the reason behind the significant variation in the M value affected by the T_mean, the SM, BS, and other lodging-related traits were measured in 2018 at Ezhou. Additionally, correlation analysis was also conducted between the measured lodging-related traits and the T_mean (Table 6). The results of the correlation analysis showed that the T_mean had significant negative correlations with the relative value of CD, CT, SM and cellulose content (CC), but displayed significant positive correlations with the relative value of lignin content (LC), total content of lignin and cellulose (TC), and bending stress (BS). In addition, the absolute value of r between the T_mean and the relative value of SM was 0.837, which was higher than that between the T_mean and the relative value of BS (0.437).

4. Discussions

As both of the experimental sites have a subtropical monsoon type of climate, the temperatures gradually increase from spring to summer and decrease in autumn (Figure 1). However, Xindu is in the basin area, while Ezhou is in the plain area. Xindu is situated at a higher altitude than Ezhou. The differences in topography and altitude generate different temperature and solar radiation conditions between the two locations (Figure 2). Rice is often sown in the late spring at Xindu and Ezhou, and delayed sowing will make rice suffer from high temperatures sooner. Since high temperatures accelerated the growth of rice and advanced the rice maturity [78,79], the determined growth durations reduced with the delay in the sowing dates (Table 1). Hence, the average daily temperatures (T_mean) during the determined growth durations increased with the delayed sowing dates (Figure 2). As a high temperature tended to be accompanied by higher solar radiation [69], the average daily solar radiation (R_mean) also increased with the T_mean from SD7 to SD9. However, the R_mean varied little from SD1 to SD3 and even decreased from SD4 to SD6. This was due to the increase in cloudy and rainy days, which resulted in low sunshine hours and solar radiation. Delayed sowing can enhance the lodging resistance in winter wheat [80]. However, the lodging resistance of indica rice cultivars weakened with the delayed sowing dates in our research (Figure 3). Therefore, optimizing sowing dates could be a useful strategy for improving lodging resistance and growing crops to adapt to climate change.

In this study, we found that whether the range of the T_mean was less than 1 °C across the three sowing dates in each year or more than 4 °C across the two locations (Figure 2), the increased T_mean significantly decreased the lodging resistance of indica rice cultivars (Figure 4A,E and Figure 5A,E), which indicated that the lodging resistance was more sensitive to the T_mean than the other temperature and solar radiation parameters. This could also be demonstrated by the results of stepwise regression analysis and path analysis. However, the response of the lodging resistance to other crops could be different. Previous research found that high temperatures showed an inconsistent effect on the stem-lodging resistance in canola plants, but reduced the root-lodging resistance significantly [81]. The results of stepwise regression analysis and path analysis also showed that the R_mean had a positive effect on the lodging resistance (Figure 5, Table 4). That is to say, an increase in the R_mean would improve the lodging resistance. However, this effect was not detectable under the greater negative influence of increased T_mean on the lodging resistance across the three sowing dates in 2018 and across the two locations (Figure 4C,G and Figure 5C,G). However, the lodging resistance displayed a slightly increasing trend from 2017 to 2018 at Ezhou (Figure 5C,G). We inferred that the positive effect of the increased R_mean on the lodging resistance outweighed the negative effect of the increased T_mean because of the larger differences (△R_mean = 2.1 MJ m⁻² d⁻¹) in the R_mean than the differences (△T_mean = 0.8 °C) in the T_mean between 2017 and 2018 at Ezhou (Figure 2). Several researchers found that low solar radiation could promote the vertical elongation of plants, such as an increase in the internode length and plant height, and restrain the lateral growth, such as a decrease in the culm diameter and wall thickness, and reduce cell wall carbohydrates; thus, low solar radiation could cause crop lodging [82,83,84,85,86,87]. Our results were similar to the previous findings, i.e., the R_mean was positively correlated with the lodging resistance. However, under the conditions of our present study, since the T_mean had a greater effect than the R_mean (Table 4), we could not establish the relationship between solar radiation and lodging-related traits.

According to the correlation analysis between the T_mean and lodging-related traits (Table 5), we found that with the increased T_mean, it was mainly the reduction in breaking resistance (BR) and the bending moment at breaking (M) of the basal second internode that weakened the lodging resistance of indica rice cultivars. The decreased stem-bending strength due to short periods of high temperature stress could also be found in canola [88]. In addition, the stem length (SL) of the basal second internode increased significantly with the increased T_mean, which could be a reason for the decline in the BR [89].

The culm stiffness of rice plants, which is expressed by the bending stress (BS), is a product of the structural carbohydrates (primarily lignin and cellulose) and non-structural carbohydrates contents at the lower internode. Carbohydrates in rice culms are accumulated before heading and transported to the ears after heading and it is mainly the non-structural carbohydrates which are transported to grains for grain filling [90,91]. Thus, the culm stiffness increases with more structural carbohydrates in the basal stems [33,41]. Li et al. [92] observed that the cellulose content (CC) of rice brittle culm1 (bc1) was lower than that of the wild type, but the lignin content (LC) was higher. However, the total content of lignin and cellulose (TC) of bc1 was lower than that of the wild type, which led to the weaker culm strength of bc1. In our study, the increase in LC was more than the decrease in CC as the T_mean increased. As a result, the TC increased with the T_mean, which caused the increased BS (Table 6). The physical strength of the culm, which was represented by the M value, was significantly and positively correlated with the BS and section modulus (SM) [20,32]. Despite the BS increasing with the T_mean, the decrease in the SM was even more important with the higher correlation coefficient, which was responsible for the significant reduction in the M value (Table 6). Additionally, the decrease in the SM was attributed to the reduced culm diameter (CD) and culm wall thickness (CT) of the basal second internode with the increased T_mean. As a consequence, CD, CT, and SL were the major factors influencing the physical strength of the culm under the effect of temperature.

The soil properties of the experimental fields at Xindu and Ezhou were different, which might affect the lodging resistance of tested rice cultivars. However, rice had been cultivated in the experimental fields for many years, which indicated that the soil properties were suitable for rice growth and development. In addition, the fertilizer applications between the experimental fields at Xindu and Ezhou were identical, resulting in a similar uptake of nitrogen, potassium and silicon from the soil, which caused the soil properties to have little influence on the lodging resistance [93]. In the research of Niu, Feng, Ding and Li [47], strong wind and heavy rain were the two most important causes of lodging. Even though there was no strong wind and heavy rain during the determined growth durations in our study, we selected 12 indica rice cultivars which did not lodge in the fields for analysis in order to exclude the influence of wind and rain on the plant lodging. Therefore, ordinary wind and rain had little effect on the lodging resistance of indica rice cultivars.

Although the lodging resistance of 12 indica rice cultivars significantly weakened with the increased temperature, each of the cultivars responded differently to the temperature (Table 3). The lodging resistance of lodging-moderate cultivar Chuanxiang 29B was most affected by the temperature with the highest coefficient of variation, and that of lodging-resistant cultivar Jiangan responded least to the temperature with the lowest coefficient of variation. This implied that the lodging-resistant indica cultivar had the potential to adapt to a higher temperature.

5. Conclusions

The average daily temperature gradually increased by about 1 °C across the three sowing dates in each year and significantly increased by about 5 °C across the two locations. The average solar radiation varied inconsistently across the three years but prominently increased by about 4 MJ m⁻² day⁻¹ across the two locations. Under this condition, the temperature had greater negative effects on the lodging resistance of indica rice cultivars than the positive effects of solar radiation. That is to say, temperature was the main factor that affected the lodging resistance of indica rice cultivars. With the delay in the sowing dates, the lodging resistance of indica rice cultivars showed a significant decrease (LI increased by 22.65%, 19.82% and 17.58% in 2015, 2017 and 2018, respectively; △BM decreased by 35.52%, 57.62% and 47.93% in 2015, 2017 and 2018, respectively) along with an increase in the average daily temperature. Among the morphological traits, the culm diameter and culm wall thickness decreased, and the stem length increased significantly with the increased average daily temperature, which led to the slender basal second internode. As the biochemical trait of the lignin content increased more than the cellulose content decreased, the total content of the lignin and cellulose increased with the increased average daily temperature, which resulted in the increased culm stiffness. In summary, owing to the slender basal second internode, the mechanical trait of the bending moment at breaking decreased significantly with the increased average daily temperature, increasing the potential risk of lodging in indica rice cultivars. The slender basal internodes would become a critical reason for the indica rice lodging with rising temperatures due to global warming.

Author Contributions

Conceptualization, R.Z. and X.W.; Methodology, X.L., Z.W., L.F. and X.W.; Software, X.L.; Formal Analysis, X.L. and Z.W.; Investigation, X.L., Z.W., L.F., Z.Y. and T.L.; Resources, R.Z.; Data Curation, X.L. and Z.W.; Writing—Original Draft Preparation, X.L.; Writing—Review & Editing, Z.D., W.L. and X.W.; Visualization, X.L., Z.D. and X.W.; Supervision, R.Z. and Z.H.; Project Administration, X.W. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their relevance to an ongoing Ph.D. thesis.

Acknowledgments

We thank the staff of the State Key Laboratory of Hybrid Rice, Wuhan University and the Crop Research Institute, Sichuan Academy of Agricultural Science for their experimental support.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. The daily average, minimum, and maximum temperatures and solar radiation during the whole rice growth season in 2015 at Xindu (A), and in 2017 (B) and 2018 (C) at Ezhou.

Figure 2. Differences in the average daily temperature (Tmean) (A) and solar radiation (Rmean) (B), effective accumulated temperature (EAT) (C) and cumulative solar radiation (CSR) (D) of the twelve indica rice cultivars on the three sowing dates of each year during the determined growth durations. SD1, 11 April 2015; SD2, 20 April 2015; SD3, 28 April 2015; SD4, 8 May 2017; SD5, 23 May 2017; SD6, 7 June 2017; SD7, 10 May 2018; SD8, 25 May 2018; SD9, 9 June 2018. All abbreviations imply the same below as well. Vertical bars indicate standard errors (±), n = 12 for each SD. Different lowercase letters on the bars of the three SDs in each year indicate significant differences determined by the Tukey test at 5% probability level.

Figure 3. Comparison of the LI (A) and △BM (B) for the three sowing dates in each year. LI, lodging index; △BM, the external force that the basal second internode could withstand. All abbreviations imply the same below as well. Vertical bars indicate standard errors (±), n = 12 for each SD. Different lowercase letters on the bars indicate significant differences determined by the Tukey test at 5% probability level.

Figure 4. Correlations between the relative LI of the twelve indica rice cultivars and average daily temperature (Tmean) (A) and solar radiation (Rmean) (B), effective accumulated temperature (EAT) (C) and cumulative solar radiation (CSR) (D) on the three sowing dates of each year. Correlations between the relative △BM of the twelve indica rice cultivars and Tmean (E), Rmean (F), EAT (G), and CSR (H) on the three sowing dates of each year. The green, blue, and red points represent the cultivars measured in 2015 at Xindu, and in 2017 and 2018 at Ezhou, respectively. n = 36 in each year. * and **, significant differences at p < 0.05 and p < 0.01, respectively.

Figure 5. Correlations between the relative LI of the twelve indica rice cultivars and Tmean (A), Rmean (B), EAT (C), and CSR (D) on all the sowing dates across two locations and three years. Correlations between the relative △BM of the twelve indica rice cultivars and Tmean (E), Rmean (F), EAT (G), and CSR (H) on all the sowing dates across two locations and three years. The green, blue, and red points represent the cultivars measured in 2015 at Xindu, and in 2017 and 2018 at Ezhou, respectively. n = 36 in each year. **, significant differences at p < 0.01.

Figure 6. Stepwise regression analysis of the temperature and solar radiation parameters with the relative LI (A) and △BM (B) of twelve indica rice cultivars. Tmean, average daily temperature. Rmean, average daily solar radiation. n = 108. **, significant differences at p < 0.01.

Table 1

The determined growth durations (days) of twelve indica rice cultivars on all the sowing dates across two locations and three years.

Cultivars	Growth Duration (d)
	Xindu/2015			Ezhou/2017			Ezhou/2018
	SD1 a	SD2	SD3	SD4	SD5	SD6	SD7	SD8	SD9
Chuan 106B	112	108	106	104	101	102	102	100	101
345B	114	113	109	106	105	104	106	105	102
Huanghuazhan	126	123	119	109	107	105	110	105	105
Jinlongsimiao	126	121	117	115	112	107	111	108	102
Chuanxiang 29B	124	126	119	112	109	107	112	107	106
Chenghui 3203	136	129	122	116	114	105	113	107	108
Guichao 2	126	121	117	111	108	106	111	106	103
II-32B	128	127	121	116	121	111	117	116	113
Teqing	128	125	119	116	114	106	114	107	107
R379	144	139	134	125	122	116	125	121	115
9311	129	124	121	112	111	106	112	110	108
Jiangan	147	142	136	117	115	107	118	110	106

^a SD1, 11 April 2015; SD2, 20 April 2015; SD3, 28 April 2015; SD4, 8 May 2017; SD5, 23 May 2017; SD6, 7 June 2017; SD7, 10 May 2018; SD8, 25 May 2018; SD9, 9 June 2018. All abbreviations imply the same below as well.

Table 2

The lodging index of twelve indica rice cultivars on all the sowing dates across two locations and three years.

Cultivars	Lodging Index (%)
	Xindu/2015			Ezhou/2017			Ezhou/2018
	SD1	SD2	SD3	SD4	SD5	SD6	SD7	SD8	SD9
Chuan 106B	47.11 b a	69.62 a	78.72 a	107.73 c	143.22 b	164.70 a	117.37 b	139.24 a	147.66 a
345B	63.46 b	88.30 a	89.25 a	123.65 b	138.31 ab	147.49 a	117.79 b	122.94 b	142.64 a
Huanghuazhan	70.98 b	69.87 b	84.58 a	136.87 b	165.57 a	181.87 a	116.95 b	137.56 a	150.58 a
Jinlongsimiao	78.09 b	86.15 ab	93.18 a	145.71 b	161.86 ab	178.79 a	125.62 b	152.64 a	154.23 a
Chuanxiang 29B	80.84 b	99.29 a	94.52 a	163.60 a	169.84 a	178.62 a	155.36 b	165.75 ab	179.44 a
Chenghui 3203	83.25 a	92.10 a	91.25 a	151.27 b	159.50 ab	176.02 a	145.68 a	153.73 a	152.95 a
Guichao 2	83.72 a	86.04 a	93.91 a	131.29 b	161.73 a	163.69 a	127.07 b	139.46 a	148.41 a
II-32B	89.32 b	109.54 a	114.86 a	162.52 a	166.74 a	184.31 a	155.90 b	178.68 a	186.06 a
Teqing	99.70 b	110.16 b	127.81 a	155.11 a	172.41 a	172.32 a	147.48 c	162.21 b	177.00 a
R379	86.56 a	100.41 a	98.20 a	127.60 b	142.23 a	152.40 a	118.40 b	131.65 a	127.62 ab
9311	91.19 b	106.94 a	117.20 a	160.74 a	173.83 a	170.69 a	149.48 b	162.77 a	167.92 a
Jiangan	94.39 b	103.09 a	104.54 a	110.10 b	118.95 b	137.43 a	103.25 b	115.53 a	123.70 a

^a Within a row for each year, means followed by the same letters are not significantly different determined by the Tukey test at 5% probability level.

Table 3

The △BM of twelve indica rice cultivars on all the sowing dates across two locations and three years.

Cultivars	△BM (g cm)									CV a (%)
	Xindu/2015			Ezhou/2017			Ezhou/2018
	SD1	SD2	SD3	SD4	SD5	SD6	SD7	SD8	SD9
Chuan 106B	2644.12 a b	2039.94 b	1346.41 c	1150.50 a	671.60 b	349.58 c	1177.42 a	758.29 b	517.23 b	63.19
345B	2501.73 a	1790.85 b	1642.24 b	922.84 a	617.56 b	477.06 b	1078.02 a	926.45 a	642.13 b	56.76
Huanghuazhan	3149.41 a	2629.95 ab	2036.62 b	736.20 a	341.49 b	146.86 c	1082.53 a	660.49 b	473.25 b	86.74
Jinlongsimiao	3123.09 a	2740.98 a	2137.34 b	811.33 a	550.77 ab	292.45 b	1176.19 a	733.90 b	635.89 b	76.76
Chuanxiang 29B	2600.97 a	2163.80 b	1957.99 b	540.39 a	416.68 ab	264.07 b	684.26 a	481.05 ab	250.41 b	88.95
Chenghui 3203	3174.04 a	2443.57 b	2444.77 b	850.97 a	591.92 ab	300.71 b	1003.42 a	696.71 b	695.78 b	76.57
Guichao 2	2213.49 a	2108.17 a	1613.65 b	897.83 a	420.38 b	353.33 b	1046.41 a	725.54 b	551.51 b	64.28
II-32B	1793.73 a	1436.09 ab	1176.57 b	480.55 a	354.43 ab	159.34 b	559.40 a	302.48 ab	133.49 b	85.05
Teqing	1891.21 a	1511.49 b	1039.69 c	586.78 a	329.64 b	312.78 b	703.07 a	451.53 b	252.74 c	73.69
R379	5442.30 a	3728.34 b	3329.22 b	1836.23 a	1144.77 b	847.96 c	2181.02 a	1483.45 b	1454.91 b	62.88
9311	3678.55 a	2560.75 b	1839.95 c	727.80 a	407.82 b	461.61 ab	1043.13 a	663.80 b	488.15 b	86.82
Jiangan	5089.65 a	4192.39 ab	3563.15 b	2838.87 a	2033.76 b	1280.59 c	3062.08 a	2090.83 b	1609.43 c	43.87

^a CV, coefficient of variation of the △BM across the nine sowing dates. ^b Within a row for each year, means followed by the same letters are not significantly different determined by the Tukey test at 5% probability level.

Table 4

The direct and indirect effects of the T_mean and R_mean on the relative LI and △BM of twelve indica rice cultivars.

Dependent Variable	Independent Variable	Correlation Coefficient	Direct Effect	Indirect Effect
				Tmean a	Rmean a
Relative LI	Tmean	0.883 ** b	1.556	-	−0.673
	Rmean	0.621 **	−0.759	1.380	-
Relative △BM	Tmean	−0.912 **	−1.473	-	0.561
	Rmean	−0.674 **	0.633	−1.307	-

^a T_mean and R_mean indicate the average daily temperature and average daily solar radiation, respectively. ^b ** indicate significant differences at p < 0.01.

Table 5

Analysis of the correlations between the relative value of lodging-related traits of twelve indica rice cultivars and the T_mean during the determined growth durations on the three sowing dates of different years and on all the sowing dates across two locations and three years.

Year/Location	CL a	SL	PL	BR	FW	BM	M
2015/Xindu	0.424 ** b	0.708 **	−0.645 **	−0.912 **	−0.365 *	−0.207	−0.912 **
2017/Ezhou	0.024	0.456 **	−0.547 **	−0.603 **	−0.202	−0.148	−0.603 **
2018/Ezhou	−0.303	0.782 **	−0.795 **	−0.882 **	−0.638 **	−0.627 **	−0.882 **
All	0.794 **	0.789 **	−0.014	−0.864 **	−0.273 **	0.195 *	−0.864 **

^a CL, culm length; SL, stem length; PL, panicle length; BR, breaking resistance; FW, fresh weight; BM, bending moment of the whole plant; M, bending moment at breaking. ^b * and ** indicate significant differences at p < 0.05 and p < 0.01, respectively.

Table 6

Analysis of the correlations between the relative value of lodging-related traits of twelve indica rice cultivars and the T_mean during the determined growth durations on the three sowing dates in 2018 at Ezhou.

Year/Location	CD a	CT	SM	LC	CC	TC	BS
2018/Ezhou	−0.851 ** b	−0.754 **	−0.837 **	0.685 **	−0.551 **	0.332 *	0.437 **

^a CD, culm diameter; CT, culm wall thickness; SM, section modulus; LC, lignin content; CC, cellulose content; TC, total content of lignin and cellulose; BS, bending stress. ^b * and ** indicate significant differences at p < 0.05 and p < 0.01, respectively.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12112603/s1, Table S1: The genetic background and the origin of the experimental rice cultivars classified into three different lodging resistance groups; Table S2: Analysis of variance of temperature and solar radiation parameters, LI, and △BM.

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