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Genetic improvement of important agronomic traits in Chinese wheat breeding over the past 70 years

BMC Plant Biology volume 24, Article number: 1151 (2024) Cite this article

Abstract

Background

Understanding the genetic improvement patterns of agronomic traits in Chinese wheat (Triticum aestivum L.) breeding is essential for devising future breeding strategies. However, a systematic analysis of the genetic improvement of important traits in Chinese wheat is lacking. This study aimed to provide insights into the improvement progress of yield-related traits in the Chinese wheat breeding process and clarify the selection pressure on important agronomic traits in different agroecological zones. Phenotypic evaluations of 481 wheat accessions including 157 Chinese landraces (CLs) and 324 modern Chinese cultivars (MCCs), were carried out in multiple locations and years.

Results

The population structure analyses showed that all accessions could be basically divided into CLs and MCCs subpopulations. Pearson correlation analysis revealed that the negative correlation between grain number per spike and thousand-grain weight gradually decreased while thousand-grain weight, grain number per spike, and effective tiller number exhibited synergistic improvements during the modern breeding process. Phenotypic differences among MCCs released from the 1950s to the 2000s indicated that grain number per spike and grain weight-related traits increased linearly, whereas plant height and effective tiller number decreased significantly. Furthermore, since the 1950s, the heading date, flowering date, and maturity date have become earlier, while the spike length and spikelet number per spike have not changed significantly with the advancement of breeding years. The annual genetic gain analysis of agronomic traits showed that plant height had the greatest increase (‒0.96%), followed by thousand-grain weight (0.38%), while the lowest for grain number per spike (0.13%). Phenotypic difference analysis of CLs and MCCs with different geographical origins further revealed that heading date, flowering date, plant height, thousand-grain weight, grain width, and grain thickness experienced strong selection with the same trend in seven agroecological zones. Among zones, the northern winter wheat zone experienced the strongest selection pressure, and plant height and thousand-grain weight were strongly selected in all zones.

Conclusions

This study reveals that CLs and MCCs in China with obvious phenotypic differences, plant height and thousand-grain weight were strongly selected during wheat breeding, and further improvement of wheat in China will inevitably involve a continuous increase in grain number per spike.

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Introduction

Wheat (Triticum aestivum L.) is a major food crop worldwide, feeding approximately 35% of the global population [1]. China is the largest wheat producer and consumer in the world [2]. The total yield of wheat in China has increased greatly, from 13.81 million tons in 1949 to 136.9 million tons in 2021 (National Bureau of Statistics of China, https://data.stats.gov.cn/), which was mainly associated with a significant increase in the yield per unit area [3]. The wheat grain yield in China increased nearly nine-fold between 1949 (645 kg/ha) and 2021 (5741 kg/ha) [4, 5]. The cultivation and promotion of new varieties have been reported to contribute greatly to the improvement of wheat yield in China. Over the last few decades, a large number of elite modern wheat cultivars have been successfully cultivated based on Chinese landraces (CLs) and introduced modern cultivars (IMCs). Since 1949, more than 3,000 wheat cultivars have been released in China [6]. Over the past 70 years, wheat breeding in China has experienced three general stages: (1) disease resistance and stable yield, (2) dwarfism and high yield, and (3) high yield and quality advancement [7]. Therefore, increasing yield remains an important goal in current Chinese wheat breeding.

Thousand-grain weight, number of grains per spike, and effective tiller number are three important components that determine wheat yield and are commonly referred to as the “three yield component traits”. Furthermore, important agronomic traits such as flowering date, plant height, and spike length exhibit varying degrees of correlation with the three yield component traits, indirectly affecting wheat yield [8]. Human-mediated selection for morphological characteristics in the breeding process has caused phenotypic variation in important agronomic traits between landraces and modern cultivars [6, 9]. Analyzing the genetic improvement characteristics of important agronomic traits from landraces to modern cultivars can promote understanding past breeding objectives and yield-limiting factors, and provide a theoretical basis for the formulation of future breeding strategies.

Genetic progress in terms of wheat yield-related traits has been reported in many countries, e.g., the United States [10,11,12,13], Canada [14], France [15], Australia [16, 17], and Mexico [18,19,20]. In China, there have been many previous studies on the genetic gains for important agronomic traits in different wheat agroecological regions and provinces. For example, Zhou et al. (2007a) [21] revealed that genetic improvements in yield were mainly due to increased grain weight, decreased plant height, and an increased harvest index. Tian et al. (2011) [22] investigated the effects of genetic improvement on grain yield and agronomic traits across 35 winter wheat varieties in the Yangtze River Basin of China. These findings revealed a linear increase in yield, grains per spike, thousand-grain weight, and harvest index with the release of cultivars from the 1950s to the 2000s, while the number of spikes per unit area significantly decreased. Sun et al. (2014) [23] indicated that the harvest index of cultivars released in Shaanxi Province did not increase significantly after the 1980s. Song et al. (2013) [24] reported that the cultivars released in Shandong Province in recent years showed no significant changes in tiller number per unit area or grain number per spike, and only grain weight presented a significant increasing trend. Taken together, these studies depicted the genetic advancement in yield and related traits of registered cultivars in different periods and established future breeding goals according to the production practices in each province of China.

Most previous studies have focused on the improvement of Chinese wheat in individual agroecological regions or provinces [4, 25]. Moreover, the number of materials used in previous studies was relatively limited, ranging from 8 to 35 varieties, and the collections of phenotypic data from multiple years and locations were limited. Due to variances in wheat germplasm resources, genetic improvement strategies, climatic conditions, and cultivation systems among different countries, there are differences in the progress of wheat genetic improvement. To widely explore the advancements in wheat genetic improvement in China, a total of 481 wheat accessions from 10 Chinese wheat agroecological zones were used for subsequent analysis based on phenotypic data collected in multiple environments. In this study, we hypothesized that the genetic improvement direction of traits would not always be fixed with the advancement of breeding in China. Furthermore, we hypothesized that different wheat agroecological zones of China have various breeding selection patterns and subsequent zone-specific yield performances. To test our hypothesis, we proposed the following objectives: (1) determine phenotypic differences in important agronomic traits between CLs and modern Chinese cultivars (MCCs); (2) clarify the genetic improvement pattern of yield-related traits in the Chinese wheat breeding process; (3) detect the selection pressure and breeding goals for important agronomic traits in different wheat agroecological zones; and (4) understand the effects of past breeding improvements on important agronomic traits and provide new strategies for future breeding goals for Chinese wheat.

Materials and methods

Plant materials

A panel of 481 hexaploid wheat accessions with wide geographical distributions from 10 agroecological zones was used in the present study, including 157 CLs and 324 MCCs (Supplementary Fig. 1). The materials encompassed the complete set of the Chinese wheat mini core collections [26]. Prior to 2000, major varieties were replaced four to six times in China [25], and the replacement of wheat varieties in China occurred approximately every 10 years. Thus, following the approach of Hao et al. (2020) [6], the MCCs included accessions released in the 1950s (19 accessions), 1960s (23), 1970s (26), 1980s (10), 1990s (57), and 2000s (187). In addition, the distribution of all accessions in each wheat agroecological zone in China included the northern winter wheat zone (Zone I, 99 accessions), Yellow and Huai River valley winter wheat zone (Zone II, 159), low and middle Yangtze River valley winter wheat zone (Zone III, 65), southwestern winter wheat zone (Zone IV, 52), southern winter wheat zone (Zone V, 9), northeastern spring wheat zone (Zone VI, 26), northern spring wheat zone (Zone VII, 15), northwestern spring wheat zone (Zone VIII, 25), Qinghai-Tibet spring-winter wheat zone (Zone IX, 16), and Xinjiang winter-spring wheat zone (Zone X, 15). Zones I to V are winter wheat regions, with sowing periods varying from late September in Beijing to November in Guangdong. Zones VI to VIII are spring-sown wheat regions, with sowing periods in March and April. Zones IX and X belong to spring-sown spring wheat and autumn-sown winter wheat regions [25]. Detailed information on the 481 accessions is listed in Supplementary Table 1.

Experimental design

Field experiments were conducted at the Chinese Academy of Agricultural Sciences (CAAS) -Xinxiang Experimental Station, Henan (113.5°E, 35.2°N) from 2013 to 2019 (seven seasons) and at the CAAS-Shunyi Experimental Station, Beijing (116.3°E, 40.0°N) from 2016 to 2017 (two seasons), using 481 wheat accessions. Hereafter, the nine identified environments were designated as 2013XX, 2014XX, 2015XX, 2016XX, 2016SY, 2017XX, 2017SY, 2018XX, and 2019XX (Supplementary Table 2). The Xinxiang and Shunyi Experimental Stations are located in Zone II and Zone I, respectively. The Xinxiang Experimental Station was sown in mid to late October and the Shunyi Experimental Station was sown in early October every year. Field experiments were conducted in a randomized block design with three replications. Each accession was planted in a 2-m four-row plot, with 25 cm between rows and 20 seeds per row. Field management followed local practices. The total amount of nutrients, irrigation applied in each season, and meteorological data for the different environments are shown in Supplementary Table 3. Agronomic traits were investigated regarding the Descriptors and Data Standard for Wheat [27], including heading date (days), flowering date (days), maturity date (days), plant height (cm), effective tiller number, spike length (cm), spikelet number per spike, grain number per spike, thousand-grain weight (g), grain length (mm), grain width (mm), and grain thickness (mm). More precisely, the heading date was recorded when 50% of the plant spikes exserted halfway from the flag leaf sheath, the flowering date was recorded when 50% of the middle spikelets had flowered or exposed anthers, and the maturity date was recorded when the grains had firmed and hardened, and plants had become completely yellow. At physiological maturity, 10 plants from the middle rows in each plot were selected to determine the other traits. Plant height was measured as the length from the soil surface to the top of the spike (excluding awns); effective tiller number was determined by counting the main stem spikes and effective tiller spikes, and a spike with more than five grains was considered an effective spike; spike length was measured as the length from the base of the rachis to the top of the rachis (excluding awns); spikelet number per spike was the number of spikelets with grain; and grain number was the count of total grains per spike. Spikes in the middle two rows were hand-harvested after physiological maturity to determine thousand-grain weight, grain length, grain width, and grain thickness. Harvested wheat grains were air-dried to a 12% water content and then weighed. Grain-related traits were measured semiautomatically by an SC-G automatic grain analyzer (Hangzhou Wanshen Testing Technology Co., Ltd.).

Statistical analysis

The best linear unbiased prediction (BLUP) values of each phenotypic trait were calculated using a mixed linear model with the “lme4” package in R [28]. The formula was as follows: Y = µ + Line + Loc + (Line × Loc) + Rep (Loc) + ε; where Y represents the phenotype; Line and Loc denote the respective random effects of the genotype and the environment; Line × Loc is the interaction effect between genotype and environment; Rep (Loc) is the random effect of replication nested in the environment; and ε represents the random residual error effect. The basic descriptive statistics of the CLs and MCCs were performed with SPSS v25.0 software (https://www.ibm.com/spss), and multiple comparisons between the different types of accessions were performed using the Bonferroni correction test (P < 0.05). Analysis of variance (ANOVA) tests for traits was calculated using the general linear model (GLM) procedure in SPSS v25.0. The formula \(\:{h}^{2}\)=\(\:{{\upsigma\:}}_{G}^{2}/({{\upsigma\:}}_{G}^{2}+\frac{{{\upsigma\:}}_{GE}^{2}}{n}+\frac{{{\upsigma\:}}_{e}^{2}}{nr})\) was used to calculate broad-sense heritability (H2), where\(\:{\:{\upsigma\:}}_{G}^{2}\:\), \(\:{\:{\upsigma\:}}_{GE}^{2}\:\), and\(\:{\:{\upsigma\:}}_{e}^{2}\:\)represent the variance components for genotype, interaction between genotype and environment, and error, respectively, r is the number of replicates, and n is the number of environments for phenotype identification. Pairwise phenotypic correlations between agronomic traits were computed with the “chart.Correlation” function implemented in the R package Performance Analytics (https://github.com/braverock/PerformanceAnalytics) based on the BLUP values. Additionally, histogram plots, box plots, and violin plots were constructed using the ‘‘ggplot2’’ package in the R environment [29] to visualize the phenotypic differences of traits between CLs and MCCs. Improvement intensity was defined according to the method of Liu et al. (2021) [30] with minor modifications, and then the intensity of breeding improvement in different wheat regions and for different traits was assessed by the absolute value of the (MCCs-CLs)/CLs. Due to the relatively small number of accessions for Zones V, IX, and X, improvement intensity was assessed only for the other seven wheat zones.

Based on the BLUP values of the 12 phenotypic traits, Origin software (https://www.originlab.com/) was used for cluster analysis, and the “scatterplot3d” R package [31] was used for principal component analysis (PCA) to evaluate population structure. The phenotypic diversity of each trait in different subpopulations was estimated using the Shannon-Weaver diversity index (H’) (Eq. (1)) [32], where Pi represents the ratio of the number of accessions in the corresponding subpopulation to the total number of accessions based on phenotypic data.

$$\:H{\prime\:}=-\sum\:{P}_{\text{i}}{lnP}_{\text{i}}$$
(1)

A total of 322 cultivars with specific breeding periods were used for genetic gain analysis. The genetic gain was calculated via the “Fitting” function of Origin software. The absolute (phenotypic change in the traits per year) or exponential (percentage change in the trait per year) genetic gains of grain yield-related traits were estimated using Eqs. (2) and (3), where yi represents the average value of a trait of accession i, ln(yi) represents the natural logarithm of yi, and \(\:{x}_{i}\:\)represents the year of accession release. In Eq. (2), the slope b is the absolute genetic gain of traits, while in Eq. (3), multiplying the slope b by 100% results in the exponential genetic gain of traits. Additionally, a and u are the intercept and residual error of each equation, respectively [33].

$$\:{y}_{i}=a+{bx}_{i}+u\:\:\left(2\right)$$
$$\:\text{ln}\left({y}_{i}\right)=a+{bx}_{i}+u\:\:\left(3\right)$$

Results

Population structure of Chinese wheats based on phenotypic data

Among the 481 wheat accessions, many important agronomic traits, such as plant height, spike length, and grain size, presented high phenotypic diversity (Fig. 1A; Table 1). Cluster analysis performed using the BLUP values of the 12 phenotypic traits indicated that all accessions could be basically divided into two subpopulations, i.e., the CLs and MCCs (Fig. 1B). However, 12 CLs clustered together with the MCCs, accounting for 7.6% of the CLs, and 36 MCCs clustered together with the CLs, accounting for 11.2% of the MCCs. Interestingly, the proportion of MCCs clustered with CLs was the highest (79.0%) during the 1950s, and it decreased significantly with the advancement of breeding years (Supplementary Table 4). Furthermore, there were significant phenotypic differences in heading date, flowering date, maturity date, plant height, effective tiller number, and spike length between the 12 CLs clustered together with the MCCs and other landrace subgroups. Except for spikelet number per spike, there were significant phenotypic differences in all traits between the 36 MCCs clustered together with the CLs and other modern cultivar subgroups (Supplementary Table 5). Similar to the results of the cluster analysis, PCA also showed a distinctly different population structure between the CLs and MCCs (Fig. 1C). Specifically, the cumulative contribution of the first three principal components reached 82.18%, indicating that these three principal components could reflect most of the information. The contribution rate of the first principal component was 53.67%. Heading date, flowering date, maturity date, and plant height had greater effects on the first principal component, followed by thousand-grain weight, grain width, and grain thickness. The contribution rate of the second principal component was 16.21%, and effective tiller number, spike length, and spikelet number per spike had greater effects. The contribution rate of the third principal component was 12.85%, and loads of grain number per spike and grain length were high (Fig. 1C; Supplementary Table 6).

Fig. 1
figure 1

Phenotypic diversity and population structure of the wheat accessions used in this study. (A) The highly diverse agronomic phenotypes among 7 accessions, including 3 Chinese landraces and 4 modern Chinese cultivars. From top to bottom are plant height, spike length, grain size, grain length, and grain width. The bars represent 26.10, 2.37, 2.55, and 2.25 cm respectively. (B) Dendrogram tree of all accessions based on the BLUP values of the 12 phenotypic traits. Blue and pink represent the CLs and MCCs, respectively. (C) Principal component analysis (PCA) plot of all accessions based on the BLUP values of 12 phenotypic traits. Percentages are the genetic variation ratios explained by the corresponding principal component

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Correlation analysis among agronomic traits

The Pearson correlation analysis showed different degrees of correlation between the 12 agronomic traits in CLs and MCCs populations (Supplementary Tables 7–8). Comparing the CLs with MCCs, it was found that some traits had identical correlations in the two subgroups. For example, plant height was negatively correlated with grain number per spike (Fig. 2A) and positively correlated with heading date (Fig. 2B). However, in the process of wheat breeding and improvement, the correlation between many traits changed from CLs to MCCs. For instance, there was no clear relationship between thousand-grain weight and plant height in the CLs, but there was a highly significant negative correlation between them in the MCCs (Fig. 2C). In addition, there was a significant negative correlation between the three yield component traits in the CLs, whereas there was a very weak negative correlation between grain number per spike and thousand-grain weight in the MCCs (Fig. 2D-F).

Fig. 2
figure 2

Correlation analysis between agronomic traits based on BLUP values. (A-F) Smoothing splines for PH and GN, PH and HD, TGW and PH, TGW and ETN, TGW and GN, GN and ETN in the CLs and MCCs. HD: heading date (days); PH: plant height (cm); ETN: effective tiller number (number); GN: grain number per spike (number); TGW: thousand-grain weight (g). *, **, and *** indicate significance at P < 0.05, P < 0.01, and P < 0.001, respectively. The blue and pink bars on the x-axis represent the materials of all CLs and MCCs, respectively. The blue bar consists of 157 dots, and each dot represents one CL; the pink bar consists of 324 dots, and each dot represents one MCC. The y-axis represents the phenotypic data of the traits. The values of r at the bottom of the figures represent the correlation coefficient between the two traits as described above in the respective CLs and MCCs

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Improvement progress of important agronomic traits in the Chinese wheat breeding process

Phenotypic variation between CLs and MCCs

The results of the ANOVA for the 12 traits indicate that the interaction effect between genotype and environment (G×E) was smaller than the main effects of genotype, environment, and block (Supplementary Table 9). Overall, the basic descriptive statistics of the two subgroups displayed that the coefficients of variation of heading date, flowering date, maturity date, spikelet number per spike, grain number per spike, grain length, grain width, and grain thickness were lower than 10% in both CLs and MCCs, indicating that the overall dispersion of these traits was low and the phenotypic differences between accessions were small, while the other traits exhibited high phenotypic diversity with coefficients of variation exceeding 10% in the CLs and MCCs. In terms of broad-sense heritability, plant height had the greatest value, followed by heading date, flowering date, thousand-grain weight, and effective tiller number, which had the lowest values, indicating that some traits (e.g., effective tiller number and grain number per spike) are more susceptible to environmental change. In addition, the range of the Shannon-Weaver diversity index for the CLs based on phenotypic data was 1.90–2.10 with an average of 2.02, while it was 1.62–2.06 with an average of 1.92 for the MCCs (Table 1).

Furthermore, analysis of phenotypic differences from the normal distribution curves revealed significant differences in the 12 phenotypic traits between CLs and MCCs (Fig. 3). Specifically, compared to the CLs, the MCCs exhibited an advancement of 5.33 days in heading date, 5.22 days in flowering date, and 1.82 days in maturity date. Additionally, plant height, effective tiller number, spikelet number per spike, and spike length decreased significantly by approximately 44.88 cm, 2.09, 0.61, and 0.77 cm, while grain number per spike, thousand-grain weight, grain length, grain width, and grain thickness increased significantly by 1.02, 10.46 g, 0.39 mm, 0.34 mm, and 0.25 mm, respectively. Moreover, the changing trend of the 12 phenotypic traits between CLs and MCCs tended to be stable in the nine environments examined in the present study (Supplementary Fig. 2). Compared with CLs, the accessions released in the 1950s showed significant phenotypic differences in heading date, flowering date, plant height, grain number per spike, thousand-grain weight, grain width, grain length, and grain thickness (Supplementary Fig. 3), strongly indicating that these agronomic traits were subject to primary selection by breeders during the early period of Chinese wheat breeding. There were significant phenotypic differences in plant height, thousand-grain weight, grain width, and grain thickness between the CLs and the MCCs released in each period (Supplementary Fig. 3; Supplementary Table 10), which demonstrated that reducing plant height and increasing thousand-grain weight have become two important goals for improvement in Chinese wheat breeding.

Table 1 Basic descriptive statistics of the 12 phenotypic traits in Chinese landraces (CLs) and modern Chinese cultivars (MCCs). Note: HD: heading date; FD: flowering date; MD: maturity date; PH: plant height; ETN: effective tiller number; SL: spike length; SN: spikelet number per spike; GN: grain number per spike; TGW: thousand-grain weight; GL: grain length; GW: grain width; GT: grain thickness. *, P < 0.05; **, P < 0.01; ***, P < 0.001. S.E: standard error; CV: coefficient of variation; H': Shannon-Weaver diversity index; H2: broad-sense heritability
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Fig. 3
figure 3

Phenotypic differences in agronomic traits between CLs and MCCs based on BLUP values. HD: heading date (days); FD: flowering date (days); MD: maturity date (days); PH: plant height (cm); ETN: effective tiller number (number); SL: spike length (cm); SN: spikelet number per spike (number); GN: grain number per spike (number); TGW: thousand-grain weight (g); GL: grain length (mm); GW: grain width (mm); GT: grain thickness (mm). Blue and pink in both the histogram and the normal distribution curves represent the CLs and MCCs, respectively. The spacing and numerical values of the bidirectional red arrows at the top represent the specific phenotypic differences between CLs and MCCs

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Selective pressure on agronomic traits in Chinese wheat agroecological zones

The phenotypic differences of CLs and MCCs with different geographical origins were analyzed to gain insights into the selection pressure on important agronomic traits in different wheat agroecological zones imposed by wheat breeding. The analysis of phenotypic differences in CLs among the seven zones showed no significant difference in plant height. Furthermore, the thousand-grain weight in the spring wheat region was higher than that in the winter wheat region, especially in Zone VIII (Fig. 4A, Supplementary Fig. 4A). However, the phenotypic differences in MCCs among the seven zones indicated that the plant height in the spring wheat region was significantly higher than that in the winter wheat region, but the thousand-grain weight was lower than that in the winter wheat region (Supplementary Fig. 4B; Supplementary Table 11). Phenotypic difference analysis between the CLs and MCCs indicated that the seven wheat agroecological zones had the same trends in terms of heading date, flowering date, maturity date, effective tiller number, plant height, thousand-grain weight, grain width, and grain thickness, with considerable advancements in heading date, maturity date and flowering date, decreases in plant height and effective tiller number, and increases in thousand-grain weight, grain width and grain thickness.

Furthermore, as the two main wheat production regions in China, Zone I and Zone II showed greater breeding improvement intensities compared with the other wheat zones, followed by Zone IV and Zone III. Regarding the spring wheat region, the breeding improvement intensity was relatively high in Zone VI (Fig. 4B). The subsequent analysis of trait improvement intensity in the breeding process combining the ranges of variation of the 12 phenotypic traits in the seven wheat zones showed that thousand-grain weight underwent the strongest selection during wheat breeding improvement, followed by plant height and effective tiller number (Fig. 4C). Genetic gain analysis indicated that plant height (−1.00%), effective tiller number (−0.50%) and grain number per spike (0.40%) had the largest annual genetic gain in Zone I. Thousand-grain weight (0.45%) had the largest annual genetic gain in Zone II. Spike length (0.41%) and grain length (0.26%) had the largest annual genetic gain in Zone VI. Grain width (0.26%) had the largest annual genetic gain in Zone VIII (Supplementary Table 12).

Fig. 4
figure 4

Phenotypic differences and improvement intensity analysis of agronomic traits based on BLUP values between CLs and MCCs in different Chinese wheat agroecological zones. (A) Comparison of phenotypic differences between CLs and MCCs in seven wheat agroecological zones. The colors blue and pink represent the CLs and MCCs, respectively. Roman numerals represent the seven Chinese wheat agroecological zones: I, northern winter wheat zone; II, Yellow and Huai River valley winter wheat zone; III, low and middle Yangtze River valley winter wheat zone; IV, southwestern winter wheat zone; VI, northeastern spring wheat zone; VII, northern spring wheat zone; VIII, northwestern spring wheat zone. (B) Improvement intensity of different wheat zones according to the accumulation of this parameter for all 12 phenotypic traits. Different colors correspond to different traits. (C) Improvement intensity of important agronomic traits based on the accumulation of this parameter for the seven wheat agroecological zones. The different colors show different zones

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Genetic progress in important agronomic traits in Chinese wheat breeding

The phenotypic changes in the 12 agronomic traits of the accessions from the six periods showed that the growth period traits (heading date, flowering date, and maturity date) all decreased as the years of breeding progressed, but the growth period of the accessions released in the 1970s was delayed (Fig. 5). Compared with those from the 1950s, accessions released in the 2000s headed and flowered 3–4 d earlier and matured 1–2 d earlier, with an average annual genetic progress of −0.04%, −0.04%, and −0.02% over the past 70 decades, respectively. Plant height and effective tiller number decreased by 47 cm and 3 from the 1950s to the 2000s, and the average annual genetic progress was −0.96% and −0.36%, respectively. In contrast, several traits, such as grain number per spike, thousand-grain weight, grain length, grain width, and grain thickness, showed different increasing trends during the modern breeding process. However, in the 1980s, there was an inflection point in the grain number per spike, and the grain weight-related traits decreased among accessions released during the 1970s. Overall, the thousand-grain weight of accessions released in the 2000s increased by nearly 8 g compared with that of accessions released in the 1950s; simultaneously, grain number per spike increased by approximately 4, and grain length, grain width, and grain thickness increased by approximately 0.5 mm, 0.3 mm and 0.2 mm, respectively. The average annual genetic progress of the above traits was 0.13%, 0.38%, 0.14%, 0.17% and 0.12%, respectively. Nevertheless, spike length and spikelet number per spike did not change significantly with the advancement of breeding years (Fig. 5; Table S13). The variation trend of all traits was relatively stable across years and locations in different breeding decades (Supplementary Fig. 5).

Fig. 5
figure 5

Genetic improvement pattern and genetic gain analysis of agronomic traits based on BLUP values in modern Chinese cultivars released in different decades. The fitted value for each decade and each trait is the mean for MCCs released from the 1950s to the 2000s. The solid line represents the fitted line obtained through linear fitting, while the dashed line connects the means of traits across different decades. Multiple comparisons among decades for each trait were performed according to one-way analysis of variance (ANOVA), and different lowercase letters indicate significant differences (P < 0.05). The percentages in brackets at the top of each chart represent the annual rate of increase in genetic progress, or annual genetic gain, for each trait

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Discussion

Phenotypic differentiation and synergistic improvement among traits in Chinese wheat breeding

Phenotype refers to the sum of the characteristics of individuals with specific genotypes under certain environmental conditions. The genotype is the internal factor of phenotype expression, and different phenotypes are inevitably associated with different genotypes [34]. An increasing number of studies on wheat population structure using SSR markers, SNP arrays, or whole-genome resequencing have suggested that Chinese landraces and modern Chinese cultivars can be divided into two subgroups at the molecular level [6, 26, 35]. In this study, a similar genetic structure was obtained based on phenotypes of the 12 important agronomic traits in Chinese wheat accessions (Fig. 1B-C), which strongly indicated that there was obvious phenotypic differentiation in important agronomic traits between the CLs and MCCs. This study revealed that the Shannon-Weaver diversity index of CLs was significantly higher than that of MCCs for most traits (Table 1), which indicates that the MCCs experienced strong selection pressure and had decreased phenotypic diversity. Nevertheless, phenotypic differences were the lowest between landraces and modern cultivars released in the 1950s compared with those released during other breeding periods (Supplementary Fig. 3), suggesting that excellent landraces form the basis of early modern Chinese wheat breeding, which is consistent with the finding of a previous study [36]. The phenotypic differentiation reached a maximum between landraces and modern cultivars released in the 2000s for important agronomic traits evaluated (Supplementary Fig. 3), which supports the previously reported result that the genetic differentiation coefficient (Fst) of landraces and modern cultivars released in the 2000s reached a maximum at the genomic level [37]. As described above, such rich phenotypic variation found in this study will make these materials an ideal population to further perform high-density SNP array genotyping, and to identify marker-trait associations for breeding through genome wide association study in the future.

Due to differences in genetic backgrounds among the studied populations, various conclusions have been drawn about the correlations between wheat traits. For example, Würschum et al. (2018) [38] reported a highly significant positive correlation between plant height and thousand-grain weight. However, Zhou et al. (2007b) [39] showed a highly significant negative correlation between these two traits, which is consistent with the findings of the present study. Furthermore, yield components are often negatively correlated, indicating that increasing one yield trait is usually achieved at the expense of other traits [40]. Würschum et al. (2018) [38] revealed that as the number of grains per spike increased, the thousand-grain weight of grains in the distal spike positions decreased. Interestingly, this study revealed a significant negative correlation between thousand-grain weight and grain number per spike in CLs, while there was a very weak negative correlation between these two traits in MCCs (Fig. 2E). The negative correlation between grain number per spike and thousand-grain weight gradually decreased, implying that genetic improvement was developing in the direction of the synergistic increase in the three yield component traits during the Chinese wheat breeding process. Additionally, aggregative breeding enhanced the correlations between traits, especially between plant height and grain weight-related traits (Supplementary Table 8). A correlation between traits is the external manifestation of gene pleiotropy or tight gene linkage. Therefore, in future breeding programs, it will be necessary to further integrate molecular marker-assisted selection and whole-genome selection techniques to overcome the unfavorable linkage of the yield-related genes and achieve synergistic improvement in traits.

Genetic improvement patterns of important agronomic traits in Chinese wheat breeding

Over the past 70 years of Chinese wheat breeding, there have been significant differences between CLs and MCCs in many phenotypic traits, such as plant height, thousand-grain weight, and other yield-related traits [41], which is consistent with the findings of this study (Supplementary Fig. 6). In particular, earliness and increase in thousand grain weight are common breeding objectives [42,43,44]. Nevertheless, this study revealed that with the advancement of the breeding process, the maturity date has not always decreased, and the thousand-grain weight has not always increased. The maturity of modern cultivars released in the 1970s was delayed (Fig. 5). From the late 1960s to the early 1970s, China extensively introduced foreign varieties for use in wheat breeding, so many accessions released in the 1970s carried the genetic lineage of foreign varieties, which is consistent with the results of Hao et al. (2020) [6]. However, most of the introduced varieties, such as “Lovrin 10”, “St2422/464”, “Neuzucht” and “Mexipak 66” presented excellent comprehensive traits but a late maturity period [4]. Therefore, the maturity of modern cultivars released in the 1970s was delayed. Interestingly, the grain weight-related traits of modern cultivars released in the 1970s also decreased. We speculate that due to a major outbreak of stripe rust in the 1970s, early breeding efforts focused on disease resistance, resulting in the extensive use of disease-resistant cross combinations [4]. Therefore, in this process, the genes or traits related to thousand-grain weight have not received sufficient attention and selection. Grain size is an important target for increasing thousand-grain weight. This study showed that the genetic improvement patterns of grain length, grain width, and grain thickness were exactly consistent with thousand-grain weight (Fig. 5). The application of semidwarf genes in the “Green Revolution” of the 1960s greatly reduced plant height and increased lodging resistance in wheat [45, 46]. Previous reports have revealed that the spike number per unit area was approximately stable, or that a reduction in spike number was beneficial for improving yield [23, 47, 48]. The results of the present study indicated that plant height and effective tiller number have significantly decreased since the 1950s, which is in accordance with a previous report [48]. The plant height of the modern cultivars released in the 2000s was 70–80 cm, which meets the optimum plant height proposed by breeders, and whether grain yield can be improved by further reducing plant height in the future is debatable [4, 49]. Spike architecture has undergone strong human selection in wheat [50,51,52]. Usually, a greater spikelet number implies a higher grain number per spike, thereby increasing the yield potential of wheat [53, 54]. The current study demonstrated that the improvement direction of spike-related traits changed in the 1980s, resulting in a decrease in spike length and grain number per spike (Fig. 5). Taken together, the above results support our hypothesis that the genetic improvement characteristics of traits have not been fixed with the advancement of breeding.

Furthermore, the Chinese wheat accessions released from the 1950s to the 2000s had the greatest genetic gain in reducing plant height, followed by thousand-grain weight, indicating that these traits experienced strong selection pressure for genetic improvement. Compared with grain length and thickness, genetic improvement in grain width played an important role in increasing thousand-grain weight. Conversely, the annual genetic gain in spikelet number per spike and grain number per spike were relatively low (Fig. 5), consistent with previous results in Shaanxi Province [23], illustrating that the selection of grain number per spike should be further increased in the future to achieve wheat breeding improvements [55]. However, this study focused on analyzing important agronomic traits of wheat, and the yield data of the plots were not evaluated. As a result, the exploration of crucial traits for improving future yield potential was somewhat limited in this study. In the future, we will further investigate the relationships between yield and important agronomic traits, explore the key traits for increasing yield, and lay the foundation for developing future breeding strategies.

Implications of selection signals based on phenotypic analysis in different Chinese wheat agroecological zones

Chinese wheat can be divided into autumn-sown wheat and spring-sown wheat based on sowing date. Autumn-sown wheat producing areas include Zone I, Zone II, Zone III, Zone IV, and Zone VI, which account for approximately 90% of the total wheat area of China [56]. An important finding of this study is that the CLs from the spring wheat region had higher thousand-grain weight, especially in Zone VIII; the landraces from the winter wheat region carried more grain number per spike, especially in Zone IV. These results lay a foundation for the exploration of excellent germplasm resources, especially among the abundant Chinese landraces, which have high thousand-grain weight and higher grain number per spike (Supplementary Fig. 4A). Heading date, flowering date, maturity date, effective tiller number, plant height, grain weight and related traits had the same trends throughout the genetic improvement process in seven wheat agroecological zones (Fig. 4A), indicating that these traits had common selection targets in different Chinese wheat regions. Moreover, there were large differences in the selection intensity of traits according to differences in climatic conditions, soil types, and adaptability to different wheat regions. For instance, adaptability-related traits such as heading date were strongly selected in the winter wheat region, and the heading and flowering dates of the winter wheat region were significantly earlier than those of the spring wheat region. However, spike-related traits in different wheat regions showed different selection directions (Supplementary Table 11). For example, in contrast to Zone I, the modern cultivars of Zone VI have increased spike length compared to landraces. According to Guo et al. (2020) [37], the number of selection signals was much higher in Zones I-VI than in Zones VII-X. Similarly, the current study confirmed that the improvement intensity of the winter wheat zone was stronger than that of the spring wheat zone, and the genetic improvement intensity of Zone I was the largest. This was likely because the winter wheat zones are the main wheat production areas in China [56]. Researchers have shown that the improvement in wheat yield relies primarily on increasing both the thousand-grain weight and the number of grains per spike [57]. Notably, our findings demonstrated that increasing grain number per spike and thousand-grain weight played critical roles in yield improvement in Zone I and Zone II, respectively. Previous reports indicated that Rht8 was distributed mainly in Zones II and III, Rht1 was more frequently distributed in Zone I, and Rht2 was more frequently distributed in Zone II [58]. According to the genetic gain results in this study, dwarf genes were widely applied in Zone I, Zone II, and Zone III (Supplementary Table 12), which is consistent with the above findings. Otherwise, there were no significant changes in spike length during the breeding process in the winter wheat region, while this trait was subject to strong selection pressure in Zone VI (Supplementary Table 12). Most importantly, the breeding of winter wheat was focused primarily on Zone I and Zone II. Future breeding in Zone I needs to focus on increasing thousand-grain weight, and that in Zone II needs to focus on increasing grain number per spike to boost wheat yield. The breeding focus of the spring wheat zone was primarily concentrated in Zone VI. In the future, the emphasis in this region will be on increasing the number of grains per spike and the thousand-grain weight, while further reducing plant height. Based on our results, we suggest that all seven wheat agroecological zones focused on improving wheat yield through breeding, even though there were differences in breeding targets and the intensity of trait improvement among the agroecological zones, which directly supports our hypothesis.

Conclusions

China will face major challenges in wheat production in the future. The global population is expected to increase to 9.9 billion by 2050, and wheat yield must increase by more than 2% annually to meet the growing demand [5]. Thus, improving wheat yield per unit area is still an important goal in future wheat breeding. This study found that great progress has been made in wheat genetic improvement over the past 70 years in China, and the important agronomic traits exhibited significant phenotypic changes between landraces and modern cultivars. Wheat breeding contributed to a decrease in plant height of 54.1 cm (40.9%), a decrease in effective tiller number of 2.1 (17.7%), an increase in thousand-grain weight of 11.8 g (34.5%), and an increase in grain number per spike of 1.94 (4.3%). The genetic improvement of wheat promoted significant enhancements in important agronomic traits and resulted in synergistic increases in the three yield component traits. Selection pressure analysis of seven Chinese wheat agroecological zones further demonstrated that Zones I and II, followed by Zones IV and III, experienced strong improvement, and reductions in plant height and increases in thousand-grain weight were two important major improvement targets. Future breeding programs should focus on the following aspects: effective strategies should be sought to synergistically increase thousand-grain weight and the grain number per spike. Secondly, attention should be paid to regional breeding in different agroecological zones to ensure the stability of yield under diverse environmental conditions. Additionally, combining modern biotechnology with traditional breeding methods can accelerate the breeding process and enhance wheat yield. This study contributes to our understanding of the selection characteristics of important agronomic traits during Chinese wheat breeding processes and offers valuable insights for future breeding strategies in China.

Data availability

All data generated and analyzed in this study are available from the corresponding author on reasonable requests.

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Funding

This work was financially supported by grants from the National Key Research and Development Program of China (2022YFD1201503), and the National Natural Science Foundation of China (32272081).

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  1. Lei Zhuang, Haixia Liu and Jian Hou contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Crop Gene Resources and Breeding, National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Lei Zhuang, Haixia Liu, Jian Hou, Chao Jian, Yunchuan Liu, Wei Xi, Jing Zhao, Pingan Hao, Shujuan Liu, Lina Cao, Yuxue Pan, Yinhui Zhang, Li Zhao, Chengzhi Jiao, Hongxia Liu, Xueyong Zhang, Tian Li & Chenyang Hao

  2. State Key Laboratory of Wheat and Maize Crop Science, and Center for Crop Genome Engineering, College of Agronomy, Henan Agricultural University, Zhengzhou, Henan, 450046, China

    Huifang Li

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Contributions

LZ1: Conceptualization, Investigation, Data curation, Formal analysis, Visualization, Writing - original draft. HXL1: Formal analysis, Visualization. JH: Investigation. CJ: Investigation. YCL: Investigation. HFL: Investigation. WX: Investigation. JZ: Investigation. PAH: Investigation. SJL: Investigation. LNC: Investigation. YXP: Investigation. YHZ: Investigation. LZ2: Investigation. CZJ: Investigation. HXL2: Investigation. XYZ: Conceptualization, Resources, Formal analysis, Funding acquisition, Writing - review & editing. TL: Investigation, Data curation, Formal analysis. CYH: Conceptualization, Resources, Formal analysis, Supervision, Project administration, Funding acquisition, Writing - review & editing. All authors reviewed the manuscript.

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Correspondence to Hongxia Liu, Xueyong Zhang, Tian Li or Chenyang Hao.

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Zhuang, L., Liu, H., Hou, J. et al. Genetic improvement of important agronomic traits in Chinese wheat breeding over the past 70 years. BMC Plant Biol 24, 1151 (2024). https://doi.org/10.1186/s12870-024-05841-8

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