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Article

Response of Yield and Protein Content of Forage Mulberry to Irrigation in North China Plain

by
Yujie Ren
1,2,3,
Guang Guo
1,2,3,
Zhaohong Wang
1,2,3,
Lin Zhu
1,2,3 and
Bing Geng
1,2,3,*
1
Shandong Institute of Sericulture, Yantai 264001, China
2
Shandong Academy of Agricultural Sciences, Jinan 250100, China
3
Shandong Engineering Technology Research Center, Yantai 264001, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(5), 1016; https://doi.org/10.3390/agronomy15051016
Submission received: 31 March 2025 / Revised: 19 April 2025 / Accepted: 22 April 2025 / Published: 23 April 2025
(This article belongs to the Section Water Use and Irrigation)
Browse Figures
Versions Notes

Abstract

:
Mulberry (Morus spp.) is resilient to water deficit conditions, and the high protein content of its leaves means they can be used as forage. Therefore, it could be a valuable resource for alleviating the animal feed crisis, but it is crucial that its high productivity and stable traits are sustained to achieve this. We conducted a 2-year field experiment in the North China Plain (NCP), which investigated different irrigation levels (W1 = 15 mm, W2 = 30 mm) and genotypes (Feng Yuan No. 1, Feng Chi). This study demonstrates that using water-saving irrigation coupled with selected genotypes can increase the leaf yield and protein content. We measured various physiological and ecological indicators of mulberry, including the leaf area, fresh leaf weight, dry leaf weight, net photosynthetic rate, leaf water use efficiency (WUEL) under limited irrigation, protein content, and yield. The results from both years indicate that, under deficit irrigation conditions, Feng Yuan No. 1 exhibited drought resistance while maintaining relatively high and stable growth traits. When the irrigation amount was increased (W2 = 30 mm), the net photosynthetic rate and leaf water use efficiency of Feng Yuan No. 1 were significantly better than those of Feng Chi. Additionally, Feng Yuan No. 1 combined with the W2 irrigation treatment led to a higher protein content of leaves (19.98 g/100 g and 21.19 g/100 g) and greater yield of leaves and branches (9.79 t·ha−1 and 11.19 t·ha−1) in the two years. Furthermore, under deficit irrigation conditions, Feng Yuan No. 1 effectively compensated for yield losses caused by water scarcity.

1. Introduction

The shortage of high-quality roughage restricts the development of the livestock industry in the North China Plain (NCP) [1]. Previous research has indicated that mulberry (Morus spp.) possesses significant forage [2,3] and medicinal value [4,5]. The supplementation of mulberry leaf powder in diets can improve livestock productivity and feed utilization rates [6]. Consequently, mulberry leaves have emerged as a potential resource to alleviate the animal feed supply crisis. Alpízar-Naranjo et al. (2017) showed that mulberry leaves used in feeding experiments instead of commercial concentrate reduced feed costs without affecting animal productivity [7]. However, the bioactive content of mulberry leaves is influenced by various factors, particularly genotype [8,9]. Also, the functional component content of the same variety of mulberry can change under different environmental factors, such as light, water, and fertilizer [10]. Water shortage is the key problem limiting China’s agricultural development [11]. Irrigation can mitigate water shortages and plays a crucial role in regulating plant growth [12]. In the context of limited water supply, irrigation volume is a critical factor in improving soil moisture content. Research indicated that deficit irrigation significantly enhances crop water use efficiency (WUEL) [13]. Reducing irrigation without compromising production is increasingly important for sustainable development.
As an alternative source of roughage, mulberry has the potential to enhance water resource utilization and reduce environmental pollution [14]. In most countries, particularly in Europe, corn is predominantly used as a feed source. Despite its extensive cultivation area, there are numerous disadvantages to using corn as animal feed. First, the short growing season is an important limiting factor [15]. Second, surveys indicated that the average yield of corn ranges from 8.0–10.6 t·ha−1; however, it often requires irrigation to achieve these yields [16]. Consequently, in regions such as the severely water-scarce NCP, corn is not appropriate as a feed source. Third, corn has a high demand for nitrogen, requiring about 300 kg of fertilizer per hectare of yield, 80% of which is added in the form of urea or manure [17,18,19]. This leads to substantial greenhouse gas emissions and poses significant adverse effects on air quality and human health [20].
Mulberry is internationally recognized as an important and sustainable crop [21,22]. Mulberry leaf protein, a type of plant leaf protein extracted from mulberry leaves, not only broadens the avenues for the development and utilization of mulberry resources but also plays a significant role in enriching China’s protein resource pool [23,24,25]. The widespread distribution of mulberry [26,27] allows it to thrive in various soil conditions and at altitudes up to an average sea level height of 4000 m [28]. This indicates its strong adaptability to diverse environmental conditions. It is estimated that the cultivation area of mulberry in China exceeds 6.3 × 105 ha, which is capable of producing between 15 and 37 t·ha−1 of leaves [29]. Furthermore, as a feed source, mulberry demonstrates high dry matter yield under a wide range of conditions [30], allowing for harvests every eight to ten weeks. Therefore, it has geographical and yield advantages as a feed source.
Photosynthesis provides an opportunity to promote crop growth [31]. Studies have shown that photosynthetic rates directly affect crop yield [32], but water availability is crucial. Regulating or limiting the variations in photosynthetic parameters is considered one of the means to enhance soil nutrient availability and improve leaf WUEL [33,34]. Under drought conditions, plants reduce transpiration by closing their stomata; however, this inevitably limits CO2 metabolism and consequently restricts photosynthesis [35,36]. Deficit irrigation and rainfall patterns subsequently influence the water supply and demand within cropping systems [37,38]. We focused on assessing the growth conditions of different mulberry varieties under deficit irrigation to provide a theoretical basis and technical support for the sustainable development of forage mulberry.
This study selected two of the most popular forage mulberry varieties from Shandong China: Feng Yuan No. 1 and Feng Chi [39]. These varieties are characterized by high yield and resilience to harsh climatic conditions. We hypothesized that both genotype selection and deficit irrigation could increase the yield and protein content of mulberry. Our primary objectives were to (a) understand the physiological and ecological indicators of different genotypes of mulberry and identify varieties that can maximize biomass yield under water deficit conditions; (b) investigate the photosynthetic characteristics and WUEL of selected genotypes of mulberry under deficit irrigation conditions to further understand the physiological stress resistance performance of various genotypes under water scarcity; and (c) determine which genotype can maximally enhance yield and crude protein content under deficit conditions, with the aim of supporting the development of the livestock industry.

2. Materials and Methods

2.1. Experimental Site

The experiments were conducted at the Shandong Institute of Sericulture from May to October in 2022 and 2023. The experimental site was located in Yantai City, Shandong Province, China (36°16′~36°10′ N, 120°50′~120°29′ E), at an elevation of 65 m. The area is in a warm temperate continental monsoon climate zone, with an average annual temperature of 13.4 °C and an average annual precipitation of 637.7 mm. The rainfall and temperature during the growth periods of mulberry in 2022 and 2023 are illustrated in Figure 1. Meteorological data were provided by the Meteorological Experimental Station of the Shandong Institute of Sericulture. The experimental soil was classified as loamy clay, with a field water-holding capacity of 32.4% (volumetric water content). The contents of alkali-hydrolyzed nitrogen, available phosphorus, and available potassium in the top 0–20 cm layer of soil were measured at 108.1 mg·kg−1, 16.1 mg·kg−1, and 92.4 mg·kg−1, respectively.

2.2. Experimental Materials

We selected local varieties known for their high yield, superior quality, robust stress resistance, and cold tolerance (Feng Yuan No. 1 and Feng Chi) for our experimental study.

2.3. Experimental Design

The experiment adopted a split-plot design with a randomized block arrangement. The main plots were two mulberry varieties (Table 1), Feng Yuan No. 1 (Y) and Feng Chi (C). The subplots had two different irrigation volumes, 15 mm (W1) and 30 mm (W2). Drip irrigation tapes were laid in the plot, and drip irrigation was delivered three times during the whole growth period, in May, July, and September for both years. This experiment had a total of four treatments, each repeated three times, making a total of 12 plots, arranged in a randomized block design. The plot was planted with row spacing of 50 cm and plant spacing of 30 cm. No tillage was allowed after planting. The mulberry was fertilized once 7–10 d after each harvest, for a total of three times each year during the growth period, with 225 kg·hm−1 urea applied each time.

2.4. Measurements

2.4.1. Leaf Area

The mulberry leaf (cm2) surface area was measured by an LA-S (Guangzhou Shenhua Biotechnology Co., Ltd., Hangzhou, China) multi-function leaf area analyzer system. In the harvest periods of June, August, and October, 15 representative leaves of 10 plants were selected for measurement. Images from the leaf area analyzer were uploaded, and Photoshop CS5 was used to calculate the leaf area. Each measurement was taken three times, and the average was used as the final value.

2.4.2. Fresh Leaf Weight and Leaf Dry Weight

In May, August, and September of 2022 and 2023, 10 representative leaves from each plot were systematically collected and weighed using an electronic balance to obtain their fresh weight. The samples were then placed in marked envelopes and subjected to an initial drying process at 105 °C for 20 min, followed by a subsequent drying phase at 80 °C until reaching a constant weight. Finally, the dry weight was measured with an electronic balance.

2.4.3. Photosynthetic Rate and Leaf Water Use Efficiency (WUEL)

The net photosynthetic rate (Pn) and transpiration rate (Tr) were measured using an LI-6400 Portable Photosynthetic Assay System (LI-COR, Lincoln, NE, USA). On a typical sunny day, the Pn and Tr of 10 representative leaves were measured between 9:00 and 11:00 a.m. Healthy leaves fully expanded from the middle canopy of the plant were selected. The artificial light source was set to 1400 μmol·m−2·s−1, the CO2 concentration was the atmospheric CO2 concentration, the relative humidity was 60–70%, the air flow rate was 500 μmol·s−1, and the leaf chamber temperature was 30 °C. The leaf water use efficiency (WUEL) was calculated as the ratio of the net photosynthetic rate to the transpiration rate (Pn/Tr).

2.4.4. Protein Content Measurement

The content of crude protein was determined by a Kjeldahl nitrogen analyzer (K9840, Jinan Haineng Instrument Co., Ltd., Jinan, China). A total of 10 representative mulberry leaves were selected, and the branches and leaves were separated. After separation, 20 g of each sample was taken and dried in an oven at 65 °C for 48 h to a constant weight. Then, the samples were crushed through a 0.1 mm screen with a miniature plant crusher. The sifted 20 g samples were transferred into a digestion tube, to which 0.4 g of copper sulfate, 6 g of potassium sulfate, and 12 mL of sulfuric acid were added. Then, the samples were put into a digestion furnace for digestion. After reaching a furnace temperature of 420 °C, the samples were digested for 1 h. The samples were then removed and cooled before adding 20 mL of water, followed by distillation on an automatic Kjeldahl nitrogen analyzer for 7 min. Then, 1–2 drops of indicator mixed solution and 10 mL of boric acid solution (20 g·L−1) were added to the receiving bottle. Distillation was carried out to 200 mL, and then the solution was titrated with standard hydrochloric acid solution (0.100 mol·L−1). The end point was determined when the solution turned light gray-red.
The protein content was calculated as follows:
X = ( V 1 − V 2 ) × c × 0.0140 m × V 3 / 100 × F × 100
where X is the protein content in the sample; V1 (mL) is the volume of the titrated standard hydrochloric acid solution consumed by the sample; V2 (mL) is the volume of the titrated standard hydrochloric acid solution consumed by the reagent blank; V3 (mL) is the volume of digested fluid; C (mol·L−1) is the concentration of the titrated standard hydrochloric acid solution; M (g) is the sample size; F is the coefficient of the conversion of nitrogen into protein.

2.4.5. Yield Measurement

The yield was measured three times a year in June, August, and October, and the total annual leaf output per square meter was calculated cumulatively.

2.5. Statistical Analysis

Data Processing System V18.10 statistical analysis software and Microsoft Excel 2007 were used for data processing and statistical analysis. We used Origin 8.0 software for the mapping. In order to find differences in the measurement data, analysis of variance (ANOVA) was used to evaluate the effects of the treatments. The ANOVA was performed at a significance level of α = 0.05 to determine if differences existed among the treatment means. When significance was observed, the least significant difference (LSD) post hoc test was used to conduct multiple comparisons. Prior to the ANOVA, we tested for the normality of the variances.

3. Results

3.1. Leaf Area, Fresh Leaf Weight, and Leaf Dry Weight

The results presented in Figure 2 indicate that there were significant differences in the leaf area, fresh weight, and dry weight of the mulberry leaves among the different treatments, with the YW2 treatment exhibiting the highest values and the CW1 treatment showing the lowest. The leaf area of the YW2 treatment was 8.0% and 6.1% higher than those of the YW1 and CW2 treatments, respectively, in 2022. The leaf area of the CW2 treatment was 11.5% higher than that of the CW1 treatment. In 2023, the YW2 treatment again showed superior performance compared to YW1 and CW2 by 6.9% and 11.2%, respectively; meanwhile, CW2 outperformed CW1 by 5.6% (Figure 2). These findings indicate that the YW2 treatment significantly enhanced the mulberry leaf area.
Comparing the fresh weights of leaves, in 2022, the fresh weight in the YW2 treatment was greater than those in YW1 and CW2 by 23.8% and 9.3%, respectively; furthermore, CW2 exceeded CW1 by a substantial margin of 41.7%. In 2023, the fresh weight in YW2 continued to outperform both the YW1 and CW2 treatments by margins of 24.0% and 14.2%, respectively. Meanwhile, CW2 maintained an advantage over CW1 at a rate of 26.4% (Figure 2). This indicates that the YW2 treatment significantly increased the fresh leaf weight.
The comparison of the dry leaf weight across the different treatments showed that, over the 2-year period, the dry weights of mulberry leaves with each treatment were in the following order: YW2 > CW2 > YW1 > CW1. In 2022, the dry leaf weight in the YW2 treatment was 27.0% and 9.8% higher than in the YW1 and CW2 treatments, respectively. In addition, the CW2 treatment exhibited a 31.0% increase compared to the CW1 treatment. In 2023, the dry leaf weight in the YW2 treatment was 31.8% and 18.4% greater than that in the YW1 and CW2 treatments, respectively. Furthermore, CW2 (Figure 2) showed a 30.3% increase relative to CW1. These findings indicate that the YW2 treatment significantly enhanced the mulberry leaf dry weight.

3.2. Photosynthetic Rate

The net photosynthetic rates of mulberry in the growing seasons of 2022 and 2023 are illustrated in Figure 3. The variation trends of the net photosynthetic rates under different treatments exhibit a unimodal curve, with the maximum transpiration rate occurring in July. In May of 2022 and 2023, the Y treatment net photosynthetic rates were significantly higher than those of C by 6.2% and 5.3%, respectively. Furthermore, the W2 treatment outperformed the W1 treatment by 8.5% and 9.2%, respectively. In July of both years, the net photosynthetic rates in the W2 treatment exceeded those of W1 by 6.1% and 5.5%, respectively. When considering the genotype–irrigation interactions, YW2 demonstrated a significant increase in the net photosynthetic rate compared to CW1 by values of 10.9% and 12.6%, respectively. In September of both years, no significant differences were found between the genotypes; however, the W2 treatment still showed higher net photosynthetic rates than W1 by margins of 5.8% and 6.0%, respectively. Furthermore, when examining the genotype–irrigation combinations again, YW2 exhibited significantly greater net photosynthetic rates compared to CW1 by 6.0% and 5.3% (Figure 3). These findings indicate that both genotype and irrigation had a significant impact on the net photosynthetic rates of mulberry. This suggests that irrigation markedly enhanced the photosynthetic rates during later stages of growth.

3.3. Leaf Water Use Efficiency (WUEL)

The WUEL of the leaves was significantly influenced by genotype and irrigation volume (Figure 4), with YW2 exhibiting the highest values and CW1 the lowest. In May 2022 and 2023, the WUEL in the Y treatment were significantly higher than in the C treatment by 13.4% and 12.0%, respectively. The W2 treatment outperformed the W1 treatment by 7.0% and 9.6%, respectively. The interaction between the genotype and irrigation amount showed that the YW2 WUEL rates were significantly greater than those of CW1, with differences of 20.4% and 23.3%, respectively. In July of both years, the WUEL in the Y treatment was also significantly higher than in the Feng Chi by 18.8% in 2022 and by 21.7% in 2023. In addition, the W2 treatment surpassed the W1 treatment by margins of 16.9% and 14.6%. Again, when considering genotype alongside irrigation conditions, YW2 demonstrated a significant increase over CW1 (37.2% and 41.3%, respectively). In September of both years, no significant differences were observed between the genotypes; however, it is noteworthy that the W2 treatments exceeded the W1 treatments by 6.4% and 7.6%. Furthermore, when examining the interplay between genotype and irrigation, it became evident that the WUEL of YW2 remained superior to that of CW1 (a 10.4% difference was noted in one case, with a subsequent observation showing a difference as high as 12.2%). These findings indicate that both Y genotypes plus the application of W2 (Figure 4) could substantially enhance the WUEL. Moreover, during later growth stages within this study context, the irrigation amount appeared to exert a more pronounced effect on mulberry WUEL compared to genetic factors alone.

3.4. Protein Content

The effects of genotype and irrigation on the crude protein content of mulberry leaves and branches are illustrated in Figure 5. Irrigation did not show a significant difference in the crude protein content of branches; however, genotype had a significant impact on this parameter. Over the 2 years, the crude protein contents of Y branches were significantly higher than those of C by 16.6% and 20.1%, respectively. When considering the interaction between genotype and irrigation, the crude protein content of YW2 branches was significantly greater than that of CW1 branches (20.2 g/100 g vs. 15.4 g/100 g). Regarding the leaf crude protein content, both genotype and irrigation significantly influenced the crude protein levels in mulberry leaves. Throughout the 2-year period, Y leaves exhibited a significantly higher crude protein content compared to C leaves (17.3% and 14.1%, respectively). The W2 treatment resulted in higher crude protein contents than W1 by 20.4% (2022) and 15.2% (2023). Furthermore, when examining the combined effect of genotype and irrigation, the YW2 leaves showed a markedly higher crude protein level compared to the CW1 leaves (40.4% vs. 32.9%). In conclusion, from the perspective of interactions between the genotype and irrigation treatments, both the Y genotype and W2 (Figure 5) irrigation effectively enhanced the crude protein content in mulberry branches and leaves.

3.5. Yield

In this 2-year study, both genotype and irrigation significantly influenced the number of leaves, branch weight, branch number, and yield of mulberry, as shown in Table 2. With fixed irrigation conditions, the leaf count of Y was significantly higher than that of C by 11.7% and 11.8%, respectively; the branch weight of Y exceeded that of C by 10.8% and 12.0%; the number of branches of Y surpassed that of C by 21.9% and 24.6%. Consequently, the yield of Y was also significantly greater than that of C by 11.3% and 11.5%. For each genotype, W2 exhibited a significant increase in the leaf count over W1 by 14.7% and 14.8%; the branch weight with W2 was higher than with W1 by 15.0% and 14.3%; the number of branches with W2 outperformed W1 by rates of 11.86% and 16.1%. Therefore, the yield with W2 was significantly elevated compared to that with W1, with increases of 9.1% and 11.2%, indicating that treatment with W2 enhanced yields across different varieties of mulberry.
The interaction between genotype and the irrigation method resulted in the following ranking order for yield: YW2 > YW1 > CW2 > CW1. The yields under treatment W1 were recorded at approximately 8.45 t·ha−1 and 9.73 t·ha−1; those under treatment YW1 were about 8.98 t·ha−1 and10.47 t·ha−1; CW1 yielded around 7.91 t·ha−1 and 8.97 t·ha−1 (Table 2). This suggests that treatment C exacerbated yield losses, while treatment Y effectively mitigated production declines caused by insufficient irrigation.

4. Discussion

The combination of genotype Feng Yuan No. 1 and W2 irrigation significantly enhanced mulberry growth traits. Leaf area, fresh leaf weight, and dry leaf weight are important parameters for assessing crop growth, as they are highly correlated with crop biomass and yield [40]. In this study, the most significant adaptive response of mulberry to deficit irrigation was a reduction in the physiological indicators, including a decrease in the leaf area, fresh leaf weight, and dry leaf weight. However, Feng Yuan No. 1 was able to compensate for these decreases caused by deficit irrigation. Over 2 years, the W2 treatment exhibited significantly higher values for the leaf area, fresh leaf weight, and dry leaf weight compared to the W1 treatment. The YW1 treatment showed significantly greater values for these parameters compared to CW1. We concluded that Feng Chi is a drought-sensitive variety of mulberry; conversely, Feng Yuan No. 1 demonstrated drought resistance by maintaining relatively high and stable growth traits under conditions of deficit irrigation. Guha et al. (2010) similarly pointed out that water scarcity severely hindered mulberry growth, while emphasizing that the severity of water stress varies according to crop genotype [41]. Therefore, we propose that the YW2 treatment can significantly enhance mulberry growth traits.
The net photosynthetic rate serves as a direct indicator of a plant’s photosynthetic capacity and effectively reflects its growth status [42]. Under conditions of water limitation, the leaves of Feng Yuan No. 1 exhibited strong physiological plasticity in gas exchange, resulting in a high net photosynthetic rate and significant organic matter accumulation. In May and July of both years, the net photosynthetic rate of Feng Yuan No. 1 was significantly higher than that of Feng Chi. However, no significant difference was observed between the genotypes in September for both years, with both genotypes showing a marked decline in their net photosynthetic rates. Analysis combined with rainfall data indicated that there was a sharp reduction in precipitation during September and October across both years. Under drought conditions, the net photosynthetic rate is closely positively correlated with rainfall [43]. The primary reason for the downregulation of the mulberry photosynthetic rate under drought stress may be attributed to stomatal closure, which serves as an adaptive strategy to mitigate drought effects [44]. This could have led to a significant decrease in the net photosynthetic rates for both genotypes during this period. During this time frame, the net photosynthetic rate under the W2 treatment was higher by 5.8% (2022) and 6.0% (2023) compared to the W1 treatment, respectively. This may be because irrigation at later growth stages delayed leaf senescence.
The WUEL varied between the genotypes. In May 2022 and 2023, the WUEL in treatment Y was significantly higher than in control C by 13.4% and 12.0%, respectively; in July of both years, the WUEL in treatment Y exceeded that of control C by 18.8% and 21.7%, respectively. Overall, Feng Yuan No. 1 mulberry exhibited a higher WUEL, which may have helped to maintain adequate moisture levels and mitigated leaf drop and wilting caused by prolonged drought conditions. This finding is consistent with the growth traits and photosynthetic results observed in mulberry. The results are consistent with those of Yi [45]. According to the underlying mechanisms, an increase in the WUEL can be achieved either through enhanced net CO2 assimilation rates or reduced stomatal conductance. However, within a broad diversity of plant species, there exists a correlation between the net CO2 assimilation rates and stomatal conductance [46]. Particularly in C3 plants, a reduction in stomatal conductance tends to improve the WUEL but often at the expense of lowering the net CO2 assimilation rates [47]. Consequently, significant improvements in the WUEL were not observed among drought-sensitive mulberry germplasm due to their inability to maintain high net photosynthetic rates under water stress conditions; this result aligns with the findings of Konings et al. (2021) [44].
The crude protein content refers to the total nitrogenous substances in feed, which includes pure proteins and various essential amino acids. It is a crucial factor in determining the nutritional value of feed for silkworms [48]. Proteins play an important role in animal growth, development, reproduction, and organ repair [49]. Research has shown that incorporating mulberry leaves as a partial substitute can enhance the nutritional value of conventional feed protein [50,51]. The crude protein content in mulberry varies depending on the genotype and environmental conditions. Crude protein is primarily found in the leaves and branches of mulberry plants, with significant differences observed among varieties. Over 2 years, the crude protein content of Y branches was recorded at 6.0% and 6.4%, while the leaf protein levels were measured at 18.2% and 19.8%. In contrast, C branches exhibited crude protein content of 5.5% and 5.8%, with leaf protein levels of 15.5% and 17.4%. Under consistent genotypic conditions, there was no significant change in the branch crude protein content between the irrigation treatments; however, the leaf crude protein content with the W2 treatment was higher than that with W1 treatment (20.4% vs. 15.2%). The variation in the leaf crude protein content corresponds consistently with leaf biomass changes. We hold that increased leaf biomass could lead to the upregulation of key proteins involved in photosynthetic pathways. Concurrently, proteins associated with respiratory metabolism and energy metabolism also exhibit similar trends. These changes ultimately improve the plant’s energy production to support the growth and development of new organs and tissues [52].
In terms of yield, both the genotype and irrigation treatment exhibited significant differences. The limitation in the leaf yield due to deficit irrigation was attributed to a marked decline in the physiological and ecological indicators of mulberry leaves, including the net photosynthetic rate and WUEL. Yield loss varies with the severity of water stress depending on the crop genotype [53,54]. Under conditions of water deficiency, drought-resistant genotypes show less fluctuation in yield components compared to susceptible genotypes. The results from two years indicate that the yield under the YW1 treatment was significantly higher than that under the CW1 treatment by 14.5% and 16.7%, respectively; therefore, under deficit irrigation conditions, Feng Yuan No. 1 compensated for the yield losses caused by water scarcity. It is widely accepted that as a conservative water use strategy to adapt to arid environments, plants tend to reduce leaf numbers in order to minimize moisture loss [41]. However, superior genotypes possess stable resistance traits that enable them to mobilize other resources effectively, thereby stabilizing their leaf morphological and physiological characteristics [52]. Achieving stable yields requires the optimization of key physiological processes during plant responses to soil dehydration. Genotype Y demonstrated the largest leaf area and net photosynthesis with the irrigation treatments, and it also excelled in growth-related yields.

5. Conclusions

Cultivation management of mulberry has a crucial influence on its yield and content of functional substances. Therefore, a better understanding of the optimum conditions for growth is crucial to ensure that mulberry leaves can be a viable alternative source of animal feed. The objective of this study was to understand the potential for sustainable yield among different genotypes of mulberry under deficit irrigation conditions, thereby contributing to productivity enhancement. According to our research, Feng Yuan No. 1 exhibited drought resistance and maintained relatively high and stable growth traits under deficit irrigation conditions. The YW2 treatment showed the highest net photosynthetic rate, WUEL, and leaf crude protein content. Under deficit irrigation conditions, Feng Yuan No. 1 was able to compensate for yield losses caused by water deficiency. The findings from this growth analysis can be used to enhance crop cultivation practices and offer breeders a better strategic understanding of mulberry growth.

Author Contributions

Y.R.: Experiment design, formal analysis, investigation, and writing—original draft. G.G.: Conceptualization, data curation, and writing—original draft. Z.W.: Writing—original draft, validation, and software. L.Z.: Validation and methodology. B.G.: Funding acquisition and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Shandong Academy of Agricultural Sciences Innovation Project (CXGC2025C18), the Shangdong Provincial Natural Science Foundation (ZR2023MC098), and the Yantai Comprehensive Test Station of National Silkworm Industry Technology System (CARS-18-SYZ08).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the policy of the institute.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The monthly total rainfall and mean monthly air temperature in the 2022 and 2023 mulberry growing seasons at the Shandong Institute of Sericulture.
Figure 1. The monthly total rainfall and mean monthly air temperature in the 2022 and 2023 mulberry growing seasons at the Shandong Institute of Sericulture.
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Figure 2. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. The vertical bars represent standard errors. The bars labeled at the top of the columns with different letters are significantly different (p < 0.05) among the treatments using the LSD post hoc test.
Figure 2. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. The vertical bars represent standard errors. The bars labeled at the top of the columns with different letters are significantly different (p < 0.05) among the treatments using the LSD post hoc test.
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Figure 3. Mulberry photosynthetic rates in the 2022 and 2023 growing seasons. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. The vertical bars are standard errors. The bars labeled at the top of the columns with different letters are significantly different (p < 0.05) among the treatments using the LSD post hoc test.
Figure 3. Mulberry photosynthetic rates in the 2022 and 2023 growing seasons. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. The vertical bars are standard errors. The bars labeled at the top of the columns with different letters are significantly different (p < 0.05) among the treatments using the LSD post hoc test.
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Figure 4. Mulberry leaf WUEL in the 2022 and 2023 growing seasons. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. The vertical bars are standard errors. The bars labeled at the top of the columns with different letters are significantly different (p < 0.05) among the treatments using the LSD post hoc test.
Figure 4. Mulberry leaf WUEL in the 2022 and 2023 growing seasons. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. The vertical bars are standard errors. The bars labeled at the top of the columns with different letters are significantly different (p < 0.05) among the treatments using the LSD post hoc test.
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Figure 5. Mulberry leaf and branch protein contents in the 2022 and 2023 growing seasons. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. The vertical bars are standard errors. The bars labeled at the top of the columns with different letters are significantly different (p < 0.05) among the treatments using the LSD post hoc test.
Figure 5. Mulberry leaf and branch protein contents in the 2022 and 2023 growing seasons. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. The vertical bars are standard errors. The bars labeled at the top of the columns with different letters are significantly different (p < 0.05) among the treatments using the LSD post hoc test.
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Table 1. Analyzed variables.
Table 1. Analyzed variables.
TreatmentMain Plot GenotypesSubplot Irrigation Water
YW1Feng Yuan No. 1 (Y)15 mm (W1)
YW2Feng Yuan No. 1 (Y)30 mm (W2)
CW1Feng Chi (C)15 mm (W1)
CW2Feng Chi (C)30 mm (W2)
YW1, YW2, CW1, and CW2 represent split-plot experiments with two genotypes and two irrigation volumes.
Table 2. Mulberry yield and yield components in the 2022 and 2023 growing seasons. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. Values followed by different letters are significantly different (p < 0.05) among treatments using LSD post hoc test.
Table 2. Mulberry yield and yield components in the 2022 and 2023 growing seasons. Y and C represent the Feng Yuan No. 1 and Feng Chi genotypes, respectively; W1 and W2 represent irrigation amounts of 15 mm and 30 mm, respectively; YW1, YW2, CW1, and CW2 represent Feng Yuan No. 1 with irrigation of 15 mm, Feng Yuan No. 1 with irrigation of 30 mm, Feng Chi with irrigation of 15 mm, and Feng Chi with irrigation of 30 mm, respectively. Values followed by different letters are significantly different (p < 0.05) among treatments using LSD post hoc test.
TimeLeaf NumberBranch WeightBranch NumberYield
(Piece/Plant)(g/Plant)(Branch/Plant)(t/ha)
2022
Interaction
Y109.29 a587.10 a7.67 a9.38 a
C97.82 b532.30 b6.29 b8.39 b
W196.65 b521.20 b6.61 b8.45 b
W2110.45 a599.09 a7.35 a9.22 a
Coupling
YW1100.38 b536.06 b7.37 c8.98 b
YW2118.19 a639.10 a7.79 a9.79 a
CW192.93 c507.03 c5.86 d7.91 c
CW2102.71 b558.02 b6.73 b8.86 b
2023
Interaction
Y112.72 a618.20 a8.11 a10.83 a
C100.78 b552.60 b6.51 b9.71 b
W199.410 b546.05 b6.77 b9.73 b
W2114.10 a624.03 a7.86 a10.86 a
Coupling
YW1100.38 b577.07 b7.54 b10.47 b
YW2121.56 a659.60 a8.68 a11.19 a
CW194.94 c515.80 c5.99 c8.97 c
CW2106.62 b588.01 b7.03 b10.46 b
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Ren, Y.; Guo, G.; Wang, Z.; Zhu, L.; Geng, B. Response of Yield and Protein Content of Forage Mulberry to Irrigation in North China Plain. Agronomy 2025, 15, 1016. https://doi.org/10.3390/agronomy15051016

AMA Style

Ren Y, Guo G, Wang Z, Zhu L, Geng B. Response of Yield and Protein Content of Forage Mulberry to Irrigation in North China Plain. Agronomy. 2025; 15(5):1016. https://doi.org/10.3390/agronomy15051016

Chicago/Turabian Style

Ren, Yujie, Guang Guo, Zhaohong Wang, Lin Zhu, and Bing Geng. 2025. "Response of Yield and Protein Content of Forage Mulberry to Irrigation in North China Plain" Agronomy 15, no. 5: 1016. https://doi.org/10.3390/agronomy15051016

APA Style

Ren, Y., Guo, G., Wang, Z., Zhu, L., & Geng, B. (2025). Response of Yield and Protein Content of Forage Mulberry to Irrigation in North China Plain. Agronomy, 15(5), 1016. https://doi.org/10.3390/agronomy15051016

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