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Article

Plant Growth Regulators Reduce Flower and Pod Shedding and Optimize Pod Distribution in Soybean in Northwest China

by
Hao Cheng
,
Qinglan Xu
,
Chenfang Ding
,
Ziyi Meng
,
Feifei Zhao
,
Yuchen Gan
,
Xinghu Song
* and
Qiang Zhao
*
College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(4), 924; https://doi.org/10.3390/agronomy15040924
Submission received: 27 February 2025 / Revised: 4 April 2025 / Accepted: 7 April 2025 / Published: 10 April 2025
(This article belongs to the Section Innovative Cropping Systems)
Browse Figures
Versions Notes

Abstract

:
The soybean yield per unit area in Xinjiang has reached a high level, with the crop maturing quickly because of the higher temperatures and levels of mechanization. However, environmental factors cause flowers and pods to shed easily, limiting yield potential. Efficient plant growth regulators (PGRs) used to increase crop yields have gained popularity, but their effectiveness in reducing flower and pod shedding, considering factors such as environment, crop variety, and time of spraying, remains unclear. This study investigated whether spraying several PGRs could reduce soybean flower and pod shedding. Field experiments were conducted from 2022 to 2024 in Ili, Xinjiang, China, using α-naphthaleneacetic acid (NAA), prohexadione-calcium (Pro-Ca), and iron chlorine e6 (ICE6) with foliar applications of 300, 450, and 45 g ha−1 at the four-node stage (V4) and full pod stage (R4). All PGR treatments reduced flower and pod shedding over the years and resulted in an increase in the average flower and pod numbers compared to normal-growth-treated (CK) soybeans. The effective slowing of flower and pod shedding during the critical pod formation stage (R4) ensured a stable yield potential. The flower-to-pod conversion rate was higher after spraying plants with PGRs than for the CK group, and pod retention was higher at the beginning of maturity (R7). Our results demonstrated that spraying PGRs (NAA, Pro-Ca, and ICE6) effectively reduced soybean flower and pod shedding, optimized pod distribution, and increased soybean yield potential. The study findings provide a useful reference for global soybean growers to optimize planting methods.

1. Introduction

Soybean (Glycine max (L.) Merr.) is an important crop for “grain, oil, and feed” production in China, offering substantial nutritional value and holding a vital position in both the industrial and agricultural sectors. In recent years, due to China’s inability to meet domestic soybean demand owing to low per-unit area yields [1], the government has introduced various policies [2] to safeguard soybean yield and reduce growing imports [3]. As an emerging soybean-producing area, the Xinjiang Uygur Autonomous Region in northwestern China has distinctive advantages in terms of climatic conditions and mechanization levels [4,5], which effectively contribute to soybean yield. In 2022, the region’s average soybean yield per unit area reached 3021.46 kg ha−1, surpassing China’s average unit area yield by 52.6% [6]. During the growth and development of soybeans, the shedding of many flowers and pods is influenced by the cultivar and environmental factors [7]. In Xinjiang, flower and pod shedding is particularly prevalent due to limited soybean variety adaptation to the environmental conditions, high temperatures, and seasonal water shortages in summer [4]. Changes in soybean yield are related to the number of pods and seeds per unit area [8], and the excessive shedding of reproductive organs leads to lower yields [9]. Consequently, reducing the shedding of flowers and pods to enhance yield per unit area is an urgent issue that needs to be addressed. The application of plant growth regulators (PGRs) is known to increase crop yields and improve quality [10]. α-Naphthaleneacetic acid (NAA), a synthetic PGR, exhibits physiological effects similar to indole acetic acid (IAA) but is more economical and stable, making it more suitable for application in crop cultivation [11]. NAA reduces fruit drop by increasing the production of abscission-inducing ethyene and enhancing competition among fruitlets [12]. Currently, research on NAA has primarily focused on seed germination, activity improvement, and plant resistance to adverse conditions [13,14,15]. Prohexadione-calcium (Pro-Ca) is a plant growth inhibitor that impedes gibberellin (GA) synthesis [16], reduces fruit drop by regulating GA biosynthesis and promoting fruit ripening [17], reduces vegetative growth, and confers stress resistance to crops [18,19,20]. Iron chlorine e6 (ICE6), a PGR developed in China, triggers the inhibition of chlorophyll-degrading enzymes, the improvement of photosystem II efficiency, and a reduction in indoleacetic acid oxidase activity, resulting in a synergistic reduction in fruit drop, and is predominantly used to slow chlorophyll degradation and promote rooting and germination processes [21].
Research has shown that PGRs can reduce the shedding of reproductive organs and increase their weight and number. For example, NAA showed superior outcomes in improving the fruit set, number, and quality of peppers [22]. The pre-flowering application of NAA to cucumbers induces fruit development and improves fruit set [23]. Pro-Ca promotes apple flower production but negatively affects fruit set [24]. Pro-Ca increases the seed weight of lentil plants [25]. Spraying ICE6 and Pro-Ca increases the effective spike number and yield of rice [26,27]. However, the effects of PGRs (NAA, Pro-Ca, and ICE6) on decelerating flower and pod shedding and optimizing pod distribution in soybean plants remain uncertain.
In the present study, we investigated whether (i) PGRs were effective in reducing soybean flower and pod shedding, (ii) PGRs could optimize pod distribution in soybean plants and reduce empty branches, and (iii) PGRs could accelerate reproductive processes. The results of this study provide a reference for the application of PGRs to reduce soybean pod shedding, facilitating the further optimization and screening of rational PGR combinations.

2. Materials and Methods

2.1. Experimental Site

This field study was conducted in Chabchal County, Ili, Xinjiang Province, China, from 2022 to 2024, with the following nutrient contents within the 0–30 cm soil depth: 0.065%, 0.079%, 0.077% total N; 88.5 mg kg−1, 107.3 mg kg−1, 105.8 mg kg−1 alkaline N; 15.4 mg kg−1, 17.7 mg kg−1, 16.6 mg kg−1 P2O5; and 118.3 mg kg−1, 127.6 mg kg−1, 139.2 mg kg−1 K2O for the 2022, 2023, and 2024 years, respectively. Ili is located in the Ili River Valley in the northern Tianshan Mountains and has temperate continental and alpine climates. Meteorological data for this study were provided by the county agrometeorological bureau where the experimental site was situated; site layout and observation elements were set up with reference to the standards of international organizations such as the World Meteorological Organization. The weather conditions reported by the county agrometeorological bureau in Ili during the soybean growing season are shown in Figure 1.

2.2. Experimental Design and Management

The experiment was conducted in a completely randomized block design with three replications consisting of four foliar spray treatments: 5%-NAA 600 g ha−1 (T1), 8%-Pro-Ca 450 g ha−1 (T2), 0.01%-ICE6 90 g ha−1 (T3), and a normal growth treatment (CK). The CK plots were sprayed with equal amounts of water (450 L of tap water per hectare). Treatments were sprayed at the four-leaf stage (V4) (1 July) and full pod set stage (R4) (27 July) using a manually pressurized backpack sprayer.
The 5% NAA was provided by Zhengzhou Zhengshi Chemical Co., Ltd., Zhengzhou City, China; 8% Pro-Ca was provided by Chengdu Guanzhi Agricultural Technology Co., Ltd., Chengdu City, China; and 0.01% ICE6 was provided by Nanjing Biotek Biological Engineering Co., Ltd., Nanjing City, China.
Soybean cultivar HeiHe-45, characterized as a subindeterminate plant, was sown on 5 June as a short-term cultivar. Each plot was 2.1 m wide and 10 m long, with a plant spacing of 5 cm and a row spacing of 35 cm, and was irrigated by drip.
During the soybean growing season, the plants were irrigated five times with different amounts of water: 3750 m3 ha−1 in 2022, 3600 m3 ha−1 in 2023, and 3900 m3 ha−1 in 2024. Fertilizer application was carried out according to local farmers’ practices as follows: at sowing, 70 kg ha−1 N, 150 kg ha−1 P2O5, and 30 kg ha−1 K2O were applied, and during the soybean growing season, 150 kg ha−1 urea was applied via water. The management of fertilization, weeds, diseases, and insects was conducted in accordance with local practices.

2.3. Data Collection

2.3.1. Growth Stage Surveys

Data were recorded for each soybean growth stage (VE, V4, R1, R2, R3, R4, R5, R6, R7, and R8, indicating emergence, fourth trifoliolate, beginning flowering, full bloom, beginning pod, full pod, beginning seed, full seed, beginning maturity, and full maturity, respectively) for all the treatments from 2022 to 2024.

2.3.2. Investigation of Flower and Pod

Before flowering, five consecutive uniformly developed soybeans were labeled per plot. The flower-to-pod conversion rate (FCR) was determined by counting the number of flowers and pods on these five labeled soybeans in each plot every 5 days, starting from the R3 stage. The FCR was calculated according to Ibrahim [28] as (effective pods)/(effective pods + flowers) per plant. The number of flowers and pods shed (NFPS) was ascertained by counting the number of flowers and pods shed within a certain range (three rows wide by two meters in length) every 5 days, starting from R2 until the end of the R6 stage using a sieve fabric. If the subsequent stage was reached within five days, the count was restarted.
The soybean pods were equally divided into three sections of soybean plants based on their position along the main stem: lower, middle, and upper (branching did not occur). In cases where the division was not even, additional pods were allocated to the lower part, and so on. The temporal and spatial distributions of flowers and pods were calculated by recording their numbers in each section of the main stem (upper, middle, and lower) at the midpoint of each reproductive stage, starting from the R1 stage until the end of the R6 stage. Photographic evidence was collected at the R6 stage for comparative purposes. The spatial distribution of pods was determined by counting the number of pods per plant stem node for the five labeled soybeans at the R7 stage.

2.4. Statistical Analysis

All data were recorded using Excel 2021 (Microsoft Corp., Redmond, WA, USA). Analysis of variance of pod-related data was performed using SPSS (version 26.0; SPSS Inc., Chicago, IL, USA). The Least Significant Difference (LSD) test at the p < 0.05 level was used to determine significant differences between treatments. SPSS 26.0 was also used for path analysis and to examine correlations between yield and pod-related parameters. The figures were plotted using Origin 2024 (OriginLab Co., Northampton, MA, USA) and PowerPoint 2021 (Microsoft Corp., Redmond, WA, USA).

3. Results

3.1. Growth Stages

Across all years, the application of PGRs at V4 resulted in flowering and maturation occurring 1 to 3 days earlier compared to the CK treatment (Figure 2). NAA (T1) demonstrated the most significant advancement of flowering, while ICE6 (T3) had the greatest impact on expediting harvest maturity. Pro-Ca (T2) also contributed to earlier flowering and harvesting, but the effect was not as obvious as the other PGRs.
In addition, in 2022 and 2023, NAA and Pro-Ca shortened the critical pod formation phase (R1-R4), while ICE6 extended its duration (Figure 2a,c). In 2024, the development stage was shorter than in 2022 and 2023 (Figure 2), the sole distinction being that NAA, Pro-Ca, and ICE6 concluded the R4 stage sooner in 2023 (Figure 2b).

3.2. NFPS and FCR

The total NFPS (TNFPS) varied significantly between years; the PGR treatments reduced shedding numbers by 18–40% compared to the CK treatment (Figure 3c). The NFPS gradually increased and reached a maximum from R2 to R4, followed by a decrease from R4 to R6. The application of PGRs significantly reduced shedding compared to CK from R2 to R5; whilst the difference was not significant at R6, a slight reduction was still observed (Figure 3a).
Furthermore, the transformation of flowers into pods occurred swiftly within 10–20 days after R3, with the PGRs exhibiting a more rapid conversion rate compared to the CK treatment (Figure 3d). Owing to the differences in the number of days in the soybean developmental stages, the conversion rates were varied in different years (Figure 3d). Within 20–30 days after R3, the FCR gradually increased and stabilized. At 30 days, PGRs promoted FCR to a certain extent, but the differences were not significant (Figure 3d).

3.3. Temporal and Spatial Distribution of Flowers and Pods

The number of flowers and pods in each part demonstrated an increasing trend from R1 to R3, stabilized during R4–R6, peaked at R3, and subsequently decreased gradually until R6. This pattern was largely consistent across the years (Figure 4).
Field observations on the same day at the R6 stage revealed more fully developed pods in the upper section of the plants treated with PGRs compared to the CK treatment (Figure 5a–d,i–l). The pod numbers (PNs) in the middle sections did not significantly differ, whereas the lower sections exhibited a significant retention of pods in the PGR-treated plants (Figure 5e–h).
Overall, the CK plants produced fewer flowers and pods than those treated with PGRs at all stages. At the R6 stage, the impact of various PGR treatments differed among varieties, but all treatments promoted an increase in flower and pod production, especially in the upper sections (Figure 4a–c) and lower sections (Figure 4g–i), as well as in the middle sections (Figure 4d–f), though the effect on MFP was erratic.
Following the application of PGRs, soybean plants exhibited a significant increase in the number of pods per node on the main stem nodes compared to the CK treatments. This increase was particularly pronounced in the upper and lower plant sections, whereas the middle portion showed a less marked change (Figure 6a). PGRs improved the distribution ratio of soybean pods to some extent, primarily by increasing the number of pods retained on the soybeans in the lower and upper sections, resulting in a more uniform pod distribution (Figure 6b).

3.4. Quantifying the Contribution of PGRs to Soybean Yield

The primary goal of agricultural production is to enhance yield. In the context of flower and pod shedding, the final yield serves as the key trait that measures whether PGRs reduce pod shedding. The correlation matrices shown in Figure 7a–c illustrate the correlations and distributions between the yield, pod number (PN), flower-to-pod conversion rate (FCR), and total number of flowers and pods shed (TNFPS) for different years. The results showed that the yield was significantly and positively correlated with all these variables, except for TNFPS. Among all traits, the yield–PN correlation demonstrated the highest correlation coefficients, ranging from 0.950 to 0.995, indicating PN’s crucial role in reducing flower and pod shedding in soybeans (Figure 7d). The correlation coefficients with yield followed the order of PN > FCR > TNFPS. Consequently, with the exception of TNFPS, the other two indicators can serve as primary factors in assessing NAA-, Pro-Ca-, and ICE6-induced yield increase in soybean in a flower and pod shedding context.
To further evaluate which indicators were primarily utilized by the NAA, Pro-Ca, and ICE6 treatments to alleviate the inhibitory effects of flower and pod shedding on soybean yield, a path analysis was conducted. This analysis aimed to assign direct and indirect effects between yield and the three biological traits related to PN, TNFPS, and FCR (Figure 7d). The results, as depicted in Figure 7d, revealed a distinct variation in the impact of the three biological traits on soybean yield under NAA, Pro-Ca, and ICE6 treatments. Following NAA treatment, PN exhibited the greatest and most positive direct effect on yield, demonstrating that PN was the primary factor contributing to greater yield after the exogenous application of NAA in a flower and pod shedding context. PN, FCR, and TNFPS significantly influenced yield, whereas TNFPS exhibited significantly negative correlations with the direct pathway coefficients of 0.758, 0.340, and 0.340, respectively (Figure 7d). Further analysis showed that FCR primarily contributed to the improvement in PN, demonstrating an indirect path coefficient of 0.252 between FCR and PN. Comparable to NAA, PN exhibited the greatest and most positive direct effect on yield following Pro-Ca and ICE6 treatments, indicating that Pro-Ca and ICE6 partially reversed the inhibitory effects of flower and pod shedding on PN, thereby increasing soybean yield.

4. Discussion

This study demonstrated that PGRs were significantly effective in reducing flower and pod shedding, especially at the R4 stage (Figure 3a), and optimized the distribution of soybean pods across the upper, middle, and lower sections (Figure 6b). Additionally, PGRs promoted early flowering, which advanced the end date of the critical pod formation phase (R1–R4) [29], effectively circumvented hot weather, and facilitated earlier harvesting (Figure 1 and Figure 2). The application of PGRs reduced NFPS and increased PN at all stages of soybean growth to varying degrees (Figure 3, Figure 4 and Figure 6). This effect may be attributed to the ability of PGR treatments to enhance flowering synchronization in soybeans [30]. Empirical studies have shown that the timing of spraying and environmental conditions (sudden highs and sharp drops in temperature and precipitation) affect the efficacy of PGRs [31,32]. The comparable effectiveness of the PGRs (NAA, Pro-Ca, and ICE6) across different years indicates their consistent performance.
Notably, there was a sharp decrease in the number of pods in the lower part of the plant at the R6 stage in 2023 compared to the earlier R5 stage (Figure 4h). This phenomenon may be attributed to the higher average temperature (25 °C) during this stage and the increased precipitation (10.8 mm) [33] at 71–74 d after sowing (Figure 1). The upper-middle part also experienced a decrease in PN across treatments (Figure 4h), potentially due to the higher average temperatures (25 °C, previously 17 °C) at this stage. During this phase, the PGR treatment significantly increased the number of pods in the soybeans. However, the ICE6 treatment led to a significant decrease in the number of pods per plant during the R3–R6 stage (Figure 4f). This reduction could be linked to excessive leaf area formation during pod formation in ICE6, resulting in fewer pods [8]. It has been demonstrated that the number of soybean flowers and pods has increased; however, there is a paucity of evidence concerning relevant biological mechanisms, such as the content of endogenous hormones, etc. [15]. Our subsequent study will be conducted in this manner, encompassing the requisite research on the alterations in soybean grain quality and photosynthesis subsequent to the application of PGRs. This will facilitate a comprehensive exploration of the full impact of PGRs on soybean physiology. It is evident that research into whether the spraying of PGRs can alleviate the impact of adverse environmental factors on soybeans has yet to be undertaken [30], with only a preliminary result indicating that soybean damage can be mitigated during periods of elevated summer temperatures. A paucity of both pertinent physiological mechanisms and targeted research exists. Subsequent research will be conducted in response to the aforementioned issues.
Furthermore, the soybean pod count exhibited less variation in 2022 (Figure 4a,d,g). This can be attributed to the pods’ inability to properly flower and set before the R3 stage because of the higher rainfall, cooler temperatures, and the lower basal fertility of the soil at the planting location [34] during the critical period of pod development. The growth and development of soybeans is convergent between years. This may be indicative of the role of PGRs, which would also prove the stability of PGRs in different environments. However, this hypothesis requires verification through further experimentation. Subsequent experiments will focus on this.
Moreover, an empirical study has shown that the application of PGRs can increase the vigor of soybean plants [15], resulting in a swift increase in the accumulation of pod material in soybean plants [35]. Thus, the pods treated with PGRs in this study were fuller, suggesting a potential relationship between these observations and an increase in PN. However, the mechanism behind this is not clear because the relevant phytohormone profiles (e.g., determination of cytokinin, vinblastine, abscisic acid, gibberellin, etc.) and other tests have not yet been carried out, and the next step will be to conduct an in-depth research exploration of the pathways for the synthesis of this relationship.

5. Conclusions

In this field study, the regulatory effects of the three PGRs (NAA, Pro-Ca, and ICE6) on flower-to-pod conversion rate, pod number, and total number of flowers and pods shed in soybean plants varied in emphasis, significantly reduced soybean pod shedding, increased pod number, and potentially increased unit area yield.
NAA treatment was better and showed a more balanced performance; Pro-Ca and ICE-6 were somewhat weaker in flower-to-pod conversion rate. Therefore, spraying 300 g ha−1 5% NAA proved optimal for increasing pod numbers in Xinjiang soybean cultivation.
Our findings provide soybean growers with a method to increase yield at very low incremental costs. In addition, this study expands the use of plant growth regulators in soybean production, which is important for China and can be a useful reference for improving the soybean yield potential in other countries.

Author Contributions

H.C.: Writing—Original Draft; Writing—Review and Editing; Visualization; Methodology; Formal Analysis; Data Curation; Investigation; Conceptualization. Q.X.: Writing—Review and Editing; Visualization; Formal Analysis; Conceptualization. C.D.; Visualization; Investigation; Formal Analysis; Conceptualization. Z.M.: Visualization; Formal Analysis; Conceptualization. F.Z.: Investigation; Formal Analysis; Conceptualization. Y.G.: Investigation; Formal Analysis; Conceptualization. X.S.: Writing—Review and Editing; Visualization; Validation; Methodology; Investigation; Formal Analysis; Conceptualization. Q.Z.: Writing—Review and Editing; Visualization; Validation; Methodology; Investigation; Formal Analysis; Conceptualization; Final Approval of the Version to be Published. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Xinjiang Uygur Autonomous Region Major Science and Technology Projects: Screening of Resistant Varieties of Oilseed Crops and R&D and Integrated Demonstration of Green Yield and Efficiency Technology (2022A02008), and the Xinjiang Uygur Autonomous Region Tianchi Talent Program Project (6660184).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset is available from the authors upon request.

Acknowledgments

We gratefully acknowledge Yating Xue, Shanyun Gao and Liuzhi Yang for their contribution to the field experiments. We thank the anonymous reviewers for their valuable suggestions for improving the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Temperature and precipitation in soybean growing seasons from 2022 to 2024.
Figure 1. Temperature and precipitation in soybean growing seasons from 2022 to 2024.
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Figure 2. Growth stages for each treatment from 2022 to 2024. (a) 2022 year, (b) 2023 year, (c) 2024 year. VE, V4, R1, R2, R3, R4, R5, R6, R7, and R8 indicate emergence, fourth trifoliolate, beginning flowering, full bloom, beginning pod, full pod, beginning seed, full seed, beginning maturity, and full maturity, respectively.
Figure 2. Growth stages for each treatment from 2022 to 2024. (a) 2022 year, (b) 2023 year, (c) 2024 year. VE, V4, R1, R2, R3, R4, R5, R6, R7, and R8 indicate emergence, fourth trifoliolate, beginning flowering, full bloom, beginning pod, full pod, beginning seed, full seed, beginning maturity, and full maturity, respectively.
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Figure 3. The number of flowers and pods shed (a,c) and flower-to-pod conversion rates (d) from 2022 to 2024. NFPS indicates the number of flowers and pods shed (a); flower and pod shedding collection pattern diagrams are shown in (b), red and yellow arrows indicate two adjacent planting rows, and white dotted lines indicate measured lengths; TNFPS indicates the total number of flowers and pods shed (c); FCR indicates the flower-to-pod conversion rate (d). The error bars represent the standard errors of the mean (n = 3); *, **, and *** represent significant differences at the 0.05, 0.01, and 0.001 levels, respectively; the different lowercase letters in the vertical direction indicate significant differences between treatments at the α = 0.05 level.
Figure 3. The number of flowers and pods shed (a,c) and flower-to-pod conversion rates (d) from 2022 to 2024. NFPS indicates the number of flowers and pods shed (a); flower and pod shedding collection pattern diagrams are shown in (b), red and yellow arrows indicate two adjacent planting rows, and white dotted lines indicate measured lengths; TNFPS indicates the total number of flowers and pods shed (c); FCR indicates the flower-to-pod conversion rate (d). The error bars represent the standard errors of the mean (n = 3); *, **, and *** represent significant differences at the 0.05, 0.01, and 0.001 levels, respectively; the different lowercase letters in the vertical direction indicate significant differences between treatments at the α = 0.05 level.
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Figure 4. Temporal and spatial distribution of flower and pod numbers from 2022 to 2024. UFP, MFP, and LFP indicate numbers of flowers and pods in upper, middle, and lower sections of soybeans, respectively; (a–c) 2022-2024 UFP; (d–f) 2022-2024 MFP; (g–i) 2022-2024 LFP; different lowercase letters in vertical direction indicate significant differences between treatments at α = 0.05 level.
Figure 4. Temporal and spatial distribution of flower and pod numbers from 2022 to 2024. UFP, MFP, and LFP indicate numbers of flowers and pods in upper, middle, and lower sections of soybeans, respectively; (a–c) 2022-2024 UFP; (d–f) 2022-2024 MFP; (g–i) 2022-2024 LFP; different lowercase letters in vertical direction indicate significant differences between treatments at α = 0.05 level.
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Figure 5. Comparison of soybean plants during full seed stage. Upper (a–d), middle, and lower (e–h) sections; close-up of upper part of single soybean plant (i–l). The dotted line indicates the location of the soybean midpoint.3.4. Spatial Distribution of Pods at R7 Stage.
Figure 5. Comparison of soybean plants during full seed stage. Upper (a–d), middle, and lower (e–h) sections; close-up of upper part of single soybean plant (i–l). The dotted line indicates the location of the soybean midpoint.3.4. Spatial Distribution of Pods at R7 Stage.
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Figure 6. Spatial distribution of flower and pod counts at R7 from 2022 to 2024. Distribution of pods per node (a); ratio of counts and distribution of pods in upper, middle, and lower sections (b). *, **, and *** represent significant differences at 0.05, 0.01, and 0.001 levels, respectively; different lowercase letters in vertical direction indicate significant differences between treatments at α = 0.05 level.
Figure 6. Spatial distribution of flower and pod counts at R7 from 2022 to 2024. Distribution of pods per node (a); ratio of counts and distribution of pods in upper, middle, and lower sections (b). *, **, and *** represent significant differences at 0.05, 0.01, and 0.001 levels, respectively; different lowercase letters in vertical direction indicate significant differences between treatments at α = 0.05 level.
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Figure 7. The correlation and path analysis of soybean yield, pod number (PN), flower-to-pod conversion rate (FCR), and total number of flowers and pods shed (TNFPS). A table of R-squared is plotted at the top right-hand side of the correlation matrices, while a scatter plot is plotted on their opposite sides. The bars on the diagonal are probability distribution graphs, and they all conform to a normal distribution (a–c). A schematic illustration of the effects of α-naphthaleneacetic acid (NAA), prohexadione-calcium (Pro-Ca), and iron chlorine e6 (ICE6) on the yield and causal direction of PN, flower-to-pod conversion rate (FCR), and total number of flowers and pods shed (TNFPS) (d). The direction of the arrows indicates a causal relationship between the indexes. Values on solid lines with two-way arrows indicate correlation coefficients. Values on solid lines with one-way arrows indicate direct path coefficients. Values on dashed lines with one-way arrows indicate indirect path coefficients. *, **, and *** represent significant differences at the 0.05, 0.01, and 0.001 levels, respectively. The orange segment of the figure denotes Pro-Ca, the red segment NAA, and the green segment ICE-6.
Figure 7. The correlation and path analysis of soybean yield, pod number (PN), flower-to-pod conversion rate (FCR), and total number of flowers and pods shed (TNFPS). A table of R-squared is plotted at the top right-hand side of the correlation matrices, while a scatter plot is plotted on their opposite sides. The bars on the diagonal are probability distribution graphs, and they all conform to a normal distribution (a–c). A schematic illustration of the effects of α-naphthaleneacetic acid (NAA), prohexadione-calcium (Pro-Ca), and iron chlorine e6 (ICE6) on the yield and causal direction of PN, flower-to-pod conversion rate (FCR), and total number of flowers and pods shed (TNFPS) (d). The direction of the arrows indicates a causal relationship between the indexes. Values on solid lines with two-way arrows indicate correlation coefficients. Values on solid lines with one-way arrows indicate direct path coefficients. Values on dashed lines with one-way arrows indicate indirect path coefficients. *, **, and *** represent significant differences at the 0.05, 0.01, and 0.001 levels, respectively. The orange segment of the figure denotes Pro-Ca, the red segment NAA, and the green segment ICE-6.
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MDPI and ACS Style

Cheng, H.; Xu, Q.; Ding, C.; Meng, Z.; Zhao, F.; Gan, Y.; Song, X.; Zhao, Q. Plant Growth Regulators Reduce Flower and Pod Shedding and Optimize Pod Distribution in Soybean in Northwest China. Agronomy 2025, 15, 924. https://doi.org/10.3390/agronomy15040924

AMA Style

Cheng H, Xu Q, Ding C, Meng Z, Zhao F, Gan Y, Song X, Zhao Q. Plant Growth Regulators Reduce Flower and Pod Shedding and Optimize Pod Distribution in Soybean in Northwest China. Agronomy. 2025; 15(4):924. https://doi.org/10.3390/agronomy15040924

Chicago/Turabian Style

Cheng, Hao, Qinglan Xu, Chenfang Ding, Ziyi Meng, Feifei Zhao, Yuchen Gan, Xinghu Song, and Qiang Zhao. 2025. "Plant Growth Regulators Reduce Flower and Pod Shedding and Optimize Pod Distribution in Soybean in Northwest China" Agronomy 15, no. 4: 924. https://doi.org/10.3390/agronomy15040924

APA Style

Cheng, H., Xu, Q., Ding, C., Meng, Z., Zhao, F., Gan, Y., Song, X., & Zhao, Q. (2025). Plant Growth Regulators Reduce Flower and Pod Shedding and Optimize Pod Distribution in Soybean in Northwest China. Agronomy, 15(4), 924. https://doi.org/10.3390/agronomy15040924

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