Effects of different irrigation treatments on dry matter accumulation, allocation and yield of grapes in solar greenhouse

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The study explored the effects of three irrigation levels (I1: 65–85% θ f , I2: 60–80% θ f , I3: 55–75% θ f ) and a fully irrigated control (CK: 70–90% θ f ) on grape dry matter, yield, and resource use efficiency in solar greenhouse from 2023 to 2024. Results showed that irrigation treatments significantly affected dry matter accumulation in organs and aboveground parts, especially during fruit swelling and maturity stages. The logistic model simulated dry matter accumulation, with the maximum theoretical accumulation (A) being most sensitive to water changes. I3 treatment reduced A by 12.4-43.04% in stem, 3.80-15.09% in leaf, 3.87–26.45% in fruit, and 8.23–35.27% in aboveground parts. Lower irrigation amount shortened the rapid growth stage duration ( T 2 ) and decreased the maximum aboveground dry matter rate time ( X max ) and the dry matter accumulation maximum ( V max ) and average ( V avg ) rates. At maturity, lower irrigation amount promoted dry matter allocation to leaves and fruits but reduced yield. The Mantel test revealed that seven dry matter accumulation characteristic parameters were significantly and positively correlated with yield and radiation use efficiency (RUE) ( p < 0.05, r ≥ 0.2). The random forest model identified y 3 and y 1 (the dry matter accumulation during the gradually and slow growth stages) as critical parameters influencing yield and RUE. I1 treatment was optimal that increased water use efficiency (WUE) and fruit allocation index by 7.36 and 8.37%, 2.78 and 2.78% in 2023 and 2024, with no significant impact on yield or RUE ( p > 0.05). Dry matter accumulation and allocation Grape yield Water and radiation use efficiency Photo-thermal products Logistic equation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Grapes, as globally significant cash crops, play a pivotal role in Chinese agriculture (Albasha and Bartlett, 2024 ; Niu et al., 2025 ; Sun et al., 2024 ). By the end of 2022, China ranked fourth worldwide in total grape viticultural area and was the largest producer of table grapes (FAO, 2022 ). Northeastern China, with its high latitude, large diurnal temperature differences, and abundant light resources, is an ideal region for grape cultivation. However, extreme weather conditions are also more frequent (Fu et al., 2021 ). Approximately two-thirds of China’s solar greenhouses are located here, where table grapes are mainly grown under greenhouses conditions to mitigate the impacts of extreme meteorological conditions (Wang et al., 2021a ; Zheng et al., 2024b ). Under greenhouse cultivation, grape production is largely dependent on artificial irrigation. Irrational irrigation amount can result in an imbalance between the nutritional and reproductive growth in greenhouse grapes (Albrizio et al., 2023 ; Yan et al., 2022c ). Irrational irrigation amount can also cause several problems, including redundant branch growth (Chaves et al., 2007 ; Peng et al., 2024b ), low grape yield, and poor use of greenhouse water, light, and heat resources (Jiang et al., 2022 ; Peng et al., 2024a ; Zhou et al., 2024 ). These problems have become bottlenecks constraining the development of the local greenhouse grape industry and sustainable use of resources. Temperature, radiation, and water are central factors influencing dry matter accumulation and yield formation in grapes, playing crucial roles in grape growth and development. Previous studies have shown that both temperature and water significantly affect crop growth, development, and physiological processes. Unsuitable temperature conditions or excessive water stress can severely inhibit the growth and yield formation processes in grapes (Davide et al., 2023 ; Valentín et al., 2023 ). Radiation is also a critical factor influencing crop productivity, with approximately 90% of the dry matter originating from radiation interception (Anda et al., 2021 ; Buesa et al., 2020b ; Droulia and Charalampopoulos, 2021 ). Greenhouse environments are characterized by their complexity, with environmental factors such as light intensity, air and soil temperature, and humidity being influenced by human-induced irrigation and fertilization practices (Rahimikhoob et al., 2020 ). Greenhouse production enhances crop yield and quality. In contrast, field environments are primarily influenced by a single factor, predominantly rain-fed irrigation, with supplementary artificial irrigation. The interplay of multiple environmental factors results in distinct response mechanisms of dry matter accumulation and yield to water between vineyard and greenhouse-grown grapes, even under identical irrigation conditions. Yan et al. ( 2023 ) used photo-thermal products (PTP) to establish a prediction model for pumpkin growth and development based on the quality indicators of pumpkin rootstock seedlings (hypocotyl length, stem diameter, shoot dry weight, root dry weight, root shoot ratio, and seedling quality index), providing theoretical guidance for the regulation of light and temperature environments in the greenhouse cultivation of pumpkin rootstock seedlings. Zheng et al. ( 2024a ) used growing degree days (GDD) and high temperature degree days (HDD) to investigate the optimal sowing time for different soybean varieties in the Huanghuaihai farming region of China. GDD and HDD ignore the effect of radiation, while the PTP comprehensively considers the combined effects of temperature and radiation. PTP has been widely applied to simulate the growth and development of various crops, such as bell peppers (Diao et al., 2008 ) and lettuce (Hang et al., 2019 ). The logistic (Verhulst, 1838 ), Richards (Richards, 1959 ), and Gompertz (Gompertz, 1825 ) equations are common PTP models that have been successfully applied to crops, such as tomato (Teng et al., 2012 ) and potato (Wen et al., 2024 ). Numerous studies have shown that grapes are subjected to the combined effects of temperature and radiation during their growth and development stages (Hunter et al., 2021 ; Lu et al., 2024 ; Prats-Llinàs et al., 2020 ). The use of the PTP to construct crop models provides a powerful tool for exploring the characteristics of grape dry matter accumulation and allocation. However, model parameters and applicability can vary significantly depending on factors such as crop variety, experimental location, and irrigation management (Li et al., 2023b ; Yan et al., 2022a ). Despite considerable research on the mechanisms of crop growth responses to varying moisture and light-temperature conditions, few studies have specifically focused on the growth processes (Oliver-Manera et al., 2024 ; Zhou et al., 2023 ). While some studies have described the effects of moisture on grape growth across different fertility stages, the description of the differences in growth response to moisture at each stage remains insufficiently systematic (Basile et al., 2012 ; Junquera et al., 2012 ). Additionally, the analysis and modelling of the accumulation and allocation processes of dry matter in grapes under temperate monsoon climates with different irrigation treatments remain limited. The application of PTP to simulate dry matter accumulation and allocation processes in grapes, especially in greenhouse with more complex light and temperature environments, is not yet common. Optimal irrigation strategies are critical for greenhouse viticulture. Excessive irrigation can lead to vigorous branch growth, which may inhibit grape yield and reduce berry exposure and quality. Conversely, inadequate irrigation amount can decrease the stomatal conductance, weaken photosynthesis, and lead to flower and fruit abscission, thereby decreasing yield and dry matter accumulation (Romero et al., 2013 ; Sun et al., 2024 ). The application of deficit irrigation strategies to regulate nutritional growth may compromise fruit dry matter accumulation and yield. In numerous crops, nutritional organs exhibit greater sensitivity to water stress than reproductive organs, thereby providing the potential to control canopy development without significantly diminishing yield (Calvo et al., 2022 ). Previous studies have shown that appropriate control of irrigation can help achieve a balance between nutrient and reproductive growth in crops, which is beneficial for yield formation and quality enhancement (Ben-Gal et al., 2024 ; Oliver-Manera et al., 2023 ; Peng et al., 2024b ). Wang et al. ( 2021b ) reported that water stress in maize in Xinjiang promoted early maturity and facilitated the transfer of photosynthetic product from stems to grains. In contrast, Yan et al. ( 2022a ) showed that water stress in winter wheat in northwest China increased the grain-filling capacity and promoted dry matter accumulation in stems and grain. Dry matter allocation patterns are influenced by multiple factors and exhibit diverse response mechanisms to water stress. Therefore, it is essential to further delineate the water stress threshold for regulating the growth of nutritional organs, in accordance with the characteristics of grapes and prevailing environmental conditions, to optimize the accumulation and allocation of dry matter in grapes. Despite the abundance light resources in Northeast China, greenhouse irrigation mainly relies on manual intervention. Improper management practices have led to common problems of low water use efficiency (WUE) and yield (Yu et al., 2017 ; Zhang et al., 2023 ). Improving WUE and yield is critical for achieving sustainable agricultural development in the region. Numerous studies have shown that appropriate water stress can reduce water consumption while increasing WUE without significantly affecting yield (Liao et al., 2022 ; Wang et al., 2021b ). Some researchers have shown that mild water stress can result in insignificant yield reduction, which can improve WUE and enhance grape quality (Jiang et al., 2021 ; Soltekin and Altındişli, 2021 ). Conversely, some studies have shown that medium water stress can still satisfy growth and development requirements of grapes, ensuring yield and quality while improving WUE (El-Sayed et al., 2024 ; Salazar et al., 2024 ). Li et al. ( 2023a ) reported superior results with mild water stress in tomatoes in the Northwest, and Al-Qthanin et al. ( 2024 ) observed similar benefits in orange trees in Egypt. Therefore, the differences in WUE and yield responses to water stress are influenced by crop variety, irrigation level, crop environment, and climatic characteristic (Ali et al., 2024 ; Wang et al., 2023 ). Crop productivity is intricately linked to canopy sunlight interception and the ability to utilize light and heat energy. Inadequate water supply can constrain crop canopy growth, reduce photosynthetic capacity, and thereby suppress crop yield and dry matter accumulation (Liu et al., 2021 ; Wang et al., 2023 ; Zhao et al., 2024 ). Conversely, excessive irrigation may lead to mutual shading within the canopy, which not only impairs radiation absorption and photosynthetic efficiency of leaves (Gao et al., 2019 ; Wu et al., 2023 ), but also increases competition among organs for environmental resources. Inadequate or excessive irrigation amount can reduce radiation use efficiency (RUE). Resource competition increases the proportion of dry matter allocated to nutrient organs (Wang et al., 2021b ; Zhang et al., 2020 ) and decreases fruit dry matter allocation, ultimately lowering the harvest index (HI) (Li et al., 2015 ). Water stress can increase HI to varying degrees. However, the response of HI to water stress varies among crops (Diez et al., 2023 ). The idea that the combined effects of light, heat, and water significantly influence grape growth was supported by Buesa et al. ( 2020a ) and Bambach et al. ( 2022 ). Numerous studies on grape yield, dry matter accumulation, and resource use efficiency have been conducted by previous researchers (Chen et al., 2021b ; Cheng et al., 2021 ; Han et al., 2023 ; Li et al., 2020b ; Salazar et al., 2024 ). However, studies focusing on optimizing irrigation strategies based on the potential interlinkages among multiple indicators, such as grape yield, WUE, RUE, and the characteristic parameters of aboveground dry matter accumulation, are still relatively limited. Therefore, the objectives of this study were (1) to explore the effects of different irrigation amount on grape yield and aboveground dry matter accumulation and allocation, and to construct an aboveground dry matter accumulation and allocation model based on PTP. This model was used to examine the dynamic trends of dry matter accumulation and to analyze the characteristic parameters of aboveground dry matter accumulation in grapes; (2) to analyze the effects of irrigation amount on grape WUE, RUE, and HI; and (3) to explore the correlation response mechanisms between the characteristic parameters of aboveground dry matter accumulation and grape yield, as well as the indicators of RUE and WUE. By integrating yield, dry matter accumulation and allocation characteristics, and greenhouse resource use efficiency indicators, this study aimed to determine the optimal irrigation amount for solar greenhouse grapes in the cold region of Northeast China, thereby providing a theoretical basis for optimizing irrigation strategies in this region. 2 Materials and methods 2.1 Experimental site description The experiments were conducted between 2023 and 2024 in the solar greenhouse No. 44 at Beishan Scientific Research and Experimental Base (41.28°N, 123.57°E), located Shenyang city, Liaoning Province, China. The region experiences a temperate continental monsoon climate, characterized by hot and rainy summers, as well as cold and dry winters. The greenhouse type belonged to the Liaoshen III style solar energy-saving greenhouse, with steel frame construction. It had an east-west length of 60 m, the north-south width of 8 m, and a height of 4 m. The greenhouse roof was covered with a non-drip polyolefin plastic film, and a rainproof cotton blanket was employed for insulation (Fig. 1). The grape trellis were Y-shaped. The average soil bulk density at a depth of 30 cm was 1.44 g cm − 3 , the soil field capacity was 22.3 g g -1 , and the permanent wilting point was 9.0 g g -1 . In this study, a 7-year-old Muscat Hamburg grape was used as the research object. The size of the treatment test plot was 10.4 m long and 5.4 m wide, with a planting density of 45714 plants ha − 1 (row line spacing was 1.5 m, and planting spacing was 0.43 m). According to the "Irrigation experiment standard", the growing season was delineated into four phenological stages: the new shoot growth stage (Stage Ⅰ), the flowering and fruit setting stage (Stage Ⅱ), the fruit swelling stage (Stage III) and the maturity stage (Stage IV) (Table 1). Table 1 Timing of grape phenological growth stages Year New shoot growth stage Flowering and fruit setting stage Fruit swelling stage Maturity stage Stage Ⅰ Stage Ⅱ Stage Ⅲ Stage Ⅳ 2023 DOY 81-117 118-128 129-197 198-243 2024 DOY 92-116 117-128 129-196 197-244 2.2 Experimental design Four irrigation treatments were conducted based on the soil field capacity ( θ f ) during the growing seasons of 2023 and 2024, commencing after budburst and continuing until harvest (Table 2). Each treatment was replicated three times, resulting in a total of 12 experimental plots, each with an area of 4.68 m 2 (1.8 m × 2.6m). The water control test was conducted at all stages using an automatic drip irrigation system. The irrigation amount was measured using individual water meters installed at the inlet of the water pipes in each experimental plot. The cumulative irrigation amount in each treatment is shown in Fig. 2. Previous studies have indicated that the root distribution of grape in greenhouses is concentrated within the 0–60 cm soil layer, with a soil wetting depth of approximately 60 cm. However, the majority of the root system is predominantly located within the 0–30 cm soil layer (Li et al., 2020a). The TDT moisture probe (CS650, Campbell Scientific Inc.) was positioned at the 30cm soil depth to dynamically monitor soil water content, which was utilized to represent the average soil water content (SWC, %) (Fig. 2). Irrigation was automatically controlled via solenoid valves (Rain Bird Company, Glendora City, USA). Specifically, real-time data monitored by the TDT soil moisture sensors for each treatment were compared against the predefined lower threshold for irrigation. Irrigation was initiated when the monitored value fell below this threshold and ceased upon reaching the upper limit. The irrigation method employed was drip irrigation, utilizing pressure-compensated drip head with a flow rate of 2.0 L h − 1 . To prevent lateral soil moisture movement, PVC sheets (2 mm thick) were buried 80 cm deep and laid in each plot. Table 2 Experimental treatments applied Treatment Irrigation levels Water capacity (%) CK Full irrigation 70–90% I1 Mild water stress 65–85% I2 Medium water stress 60–80% I3 Severe water stress 55–75% 2.3 Measurements 2.3.1 Greenhouse micro-environment Meteorological data were measured using an automatic environmental monitoring system located within the greenhouse. Air temperature (Ta, ℃) was measured with a Pt100RTD and HUMICAP®180R sensor (R.M Young Company, Traverse City, MI, USA). Photosynthetically active radiation (PAR, J m − 2 s − 1 ) was measured using a PAR Quantum Sensor (Kipp & Zonen). All the data were averaged every 30 minutes and recorded by a CR1000 data logger (Campbell Scientific, Inc.) (Fig. 2). The photo-thermal products (PTP) represents the cumulative photo-thermal products during grape growth (MJ m − 2 ). PTP serves as an indicator of grape maturity, integrating both temperature and radiation factors to reflect the influence of the climatic environment on grape growth. PTP is calculated using the following equations (Eq. 1, 2, 3): $$RPTP=\sum\limits_{{i=1}}^{{24}} {(RTE(i) \times PAR(i) \times 3600/{{10}^6})}$$ 2 $$PTP=\sum {(RPTP)}$$ 3 where, RTE (i) T represents the relative thermal effectiveness of the crop during the i th hour at temperature T ; T m , T b , T ob , T ou denote the upper growth limit temperature (38°C), lower growth limit temperature (5°C), lower growth optimum temperature (20°C), and upper growth optimum temperature (25°C), respectively (Omazić et al., 2023; Su et al., 2022); PAR represents the average photosynthetically active radiation in 1 h (J m − 2 s − 1 ); The daily relative photo-thermal product is calculated as RPTP (MJ m − 2 d − 1 ); RTE (i) is the average RTE during i th hour in 1 day; PAR(i) is the average PAR during i th hour in 1 day (J m − 2 s − 1 ). The unit conversion factor for converting J m − 2 s − 1 to J m − 2 h − 1 is 3600, while the unit conversion factor for converting J m − 2 s − 1 to MJ m − 2 s − 1 is 10 6 . 2.3.2 Grape aboveground dry matter and yield During the experiment, grapes were randomly sampled from each plot at 5 to 7 day intervals, with one vine sampled per plot. After measuring the fresh weight, the vines were separated into stems (G), leaves (Y), and fruits (M). The separated parts were subjected to enzyme deactivation in an oven at 105°C for 30 minutes, followed by drying to a constant weight at 80°C. The dry weights were then recorded. Grapes yield was estimated based on planting density. 2.3.3 Dry matter allocation index and harvest index The index of dry matter allocation to each organ was calculated as the proportion of organ weight relative to total aboveground dry matter. Harvest index (HI) was the ratio of fruit dry matter to total aboveground dry matter accumulation (Li et al., 2020b; Zhang et al., 2019): 2.3.4 WUE and RUE Water consumption was calculated using the water balance equation (Li et al., 2020a; Zheng et al., 2024b): $$ET{\text{=}}\sum\limits_{{i=1}}^{m} {E{T_i}}$$ 4 $$E{T_i}=10\sum\limits_{{j=1}}^{n} {{r_j}} {H_j}({\theta _{ji}} - {\theta _{j(i+1)}})+{M_i}+{P_i}+{K_i}$$ 5 where, ET represents the water consumption of the whole reproductive period (mm); ET i represents the water consumption of each phenological stage (mm); m represents the number of grape phenological stage; n represents the number of soil layers; r j represents the soil capacity of the j th layer (g cm − 3 ); H j represents the soil thickness of the j th layer (cm); θ ji , θ j(i+1) represent the water content at the beginning and the end of the calculation time period, %; M i represents irrigation amount during the time period (mm); P i represents precipitation during the time period (mm); equal to zero inside the greenhouse); K i represents groundwater recharge during the time period (mm). The water table in this region was deeper than 5m, thus, K i was set to zero. Water use efficiency (WUE, kg m − 3 ) was determined as per Li et al. (2024) : $$WUE{\text{=}}\frac{{Yield}}{{ET}}$$ 6 Radiation use efficiency (RUE, g MJ − 1 ) was calculated at biomass levels (Liu et al., 2023): $$RUE{\text{=}}\frac{{DDMA}}{{IPAR}}$$ 7 The intercepted photosynthetically active radiation ( IPAR , MJ m − 2 ) of the plant canopy was determined as follows (Liu et al., 2023): $$IPAR{\text{=}}\sum {PAR(1 - {e^{ - k \times LAI}})}$$ 8 where, PAR is photosynthetically active radiation (J m − 2 s − 1 ); k is the extinction coefficient and DDMA is aboveground dry matter (g m − 2 ). 2.4 Logistic model To investigate the response of the dry matter accumulation process to different irrigation treatments, Eq. 9 was employed to fit the dry matter accumulation process. The photo-thermal products during grape growth (PTP, MJ m -2 ) was used as the independent variable, while the dry matter (g m -2 , g plant -1 ) was used as the dependent variable (Yan et al., 2022a). $$y=\frac{A}{{(1+{e^{B - Kx}})}}$$ 9 where, A is the upper limit of maximum dry matter accumulation; B and K are constants. By deriving Eq. 9, the accumulation rate of aboveground dry matter ( V , g m − 2 MJ − 1 m 2 ) is obtained as follows: $$V(t)=\frac{{dy}}{{dt}}=\frac{{AK{e^{B - Kx}}}}{{{{(1+{e^{B - Kx}})}^2}}}$$ 10 when Eq. 10 is equal to zero, the time required to achieve the maximum the aboveground dry matter rate ( X max , MJ m − 2 ) can be obtained, as shown in Eq. 12; when\({X_{\hbox{max} }}=\frac{B}{K}\), the maximum accumulation rate of aboveground dry matter ( V max , g m − 2 MJ − 1 m 2 ) is obtained, as shown (Eq. 11). $${V_{\hbox{max} }}=\frac{{AK}}{4}$$ 11 $${X_{\hbox{max} }}=\frac{B}{K}$$ 12 Yv max represented the dry matter when the rate of dry matter accumulation reached maximum (g m − 2 ): $$Y{v_{\hbox{max} }}=\frac{A}{{\text{2}}}$$ 13 $${V_{{\text{avg}}}}=\frac{{AK}}{{\text{6}}}$$ 14 where, V avg is the average accumulation rate of dry matter (g m − 2 MJ − 1 m 2 ). To divide the growth processes into the gradual, rapid and slow growth stages, two inflection points X 1 (MJ m − 2 ) and X 2 (MJ m − 2 ) were determined using Eq. 15. The time at which the aboveground dry matter approached its maximum value was expressed as X 3 (MJ m − 2 ). $${X_1}=\frac{{ - \ln (\frac{{2+\sqrt 3 }}{{{e^B}}})}}{K},{X_2}=\frac{{ - \ln (\frac{{2 - \sqrt 3 }}{{{e^B}}})}}{K}$$ 15 The dry matter accumulation during the gradually growth stage, rapid growth stage and slow growth stage were denoted as y 1 (g m − 2 ), y 2 (g m − 2 ) and y 3 (g m − 2 ), respectively: $${y_{\text{1}}}=\frac{A}{{(1+{e^{B - K{x_1}}})}},{y_2}=\frac{A}{{(1+{e^{B - K{x_2}}})}} - {y_1},{y_3}=\frac{A}{{(1+{e^{PT{P_{\hbox{max} }}}})}} - \frac{A}{{1+{e^{B - K{x_2}}}}}$$ 16 2.5 Statistical analysis The analysis of variance (ANOVA) and multiple comparisons ( p < 0.05) were conducted using SPSS 26.0. Random forest was employed to assess the relative significance of the characteristic parameters of aboveground dry matter accumulation. Data visualization was performed using Origin 2021 and R Studio 2023. 2.6 Evaluation of the model accuracy The accuracy of the models was evaluated using the coefficient of determination ( R 2 ; Eq. 17), normalized root mean square error ( NRMSE ; Eq. 18, 19), and the index of agreement ( IA ; Eq. 20) (Wang et al., 2022b). $${R^2}=\frac{{[\sum\nolimits_{1}^{n} {({X_{obs}} - {{\overline {X} }_{obs}})({X_{sim}} - {{\overline {X} }_{sim}}){]^2}} }}{{\sum\nolimits_{1}^{n} {{{({X_{obs}} - {{\overline {X} }_{obs}})}^2}\sum\nolimits_{1}^{n} {{{({X_{sim}} - {{\overline {X} }_{sim}})}^2}} } }}$$ 17 $$RMSE=\sqrt {\frac{1}{n} \times \sum\limits_{{i=1}}^{n} {{{({X_{obs}} - {X_{sim}})}^2}} }$$ 18 $$NRMSE={\text{100}} \times \frac{{RMSE}}{{{{\overline {X} }_{obs}}}}$$ 19 $$IA={\text{1-}}\frac{{\sum\limits_{{i=1}}^{n} {{{({X_{obs}} - {X_{sim}})}^2}} }}{{\sum\limits_{{i=1}}^{n} {{{(\left| {{X_{obs}} - {{\overline {X} }_{obs}}} \right|+\left| {{X_{sim}} - {{\overline {X} }_{obs}}} \right|)}^2}} }}$$ 20 where, X sim is the simulated value; X obs is the observed value; n is the number of values; \(\:\stackrel{-}{X}\) obs is the average observed value. The coefficient of determination ( R 2 ) indicates a linear relationship between the simulated and the actual values. NRMSE represents the relative difference from the mean, expressed as an unbounded percentage. Specifically, NRMSE < 15% indicates "good" agreement, 15% ≤ NRMSE < 30% indicates "moderate" agreement, and NRMSE ≥ 30% indicates "poor" agreement. The index of agreement (0 ≤ IA ≤ 1) serves as a descriptive, relative, and bounded measure of measure performance. The closer the IA value and R 2 value are to 1, the better the model performance. 3 Results analysis 3.1 Dry matter accumulation As the reproductive period progressed, the dry matter accumulation in each organ and aboveground of grapes under different irrigation treatments showed a characteristic S-shaped growth curve. The growth was characterized by an initial slow increase in the early stages, followed by rapid growth in the middle stages, and eventual stabilization after reaching a peak (Fig. 3). Except for the I3 treatment in 2023, no significant differences were observed in stem, leaf, fruit, and aboveground dry matter accumulation among the different irrigation treatments during Stages I and II ( p > 0.05). Differences in the effects of different irrigation treatments on dry matter accumulation gradually became significant during Stage III and Stage IV. Moreover, the final stem, leaf, fruit and aboveground biomass of I3 treatment in Stage IV were 109.46 and 118.64 g plant − 1 , 40.87 and 43.73 g plant − 1 , 94.76 and 100.8 g plant − 1 , and 1206.65 and 1295.59 g m − 2 in 2023 and 2024, respectively. These values were only 58.0 and 64.84%, 87.20 and 86.27%, 74.25 and 80.34%, 67.52% and 73.28% of those in the CK treatment. In addition, significant differences in stem, leaf, and aboveground dry matter accumulation were observed among different irrigation treatments at Stage Ⅳ (except I1 and I2 treatments in 2024) ( p < 0.05). However, the effect of different irrigation treatments on the fruit dry weight was not significant at this stage, except for I3 treatment. This showed that grape stem and leaf dry weights were more sensitive to water stress than fruit dry weight. Mild and medium water stress treatments effective reduced redundant growth of grape stems and leaves ( p 0.05). The relationship between dry matter and the photo-thermal products (PTP) in grape stem, leave, fruit, and aboveground under different irrigation amount was described using the logistic Eq. 1, 2, 3 The fitted curves and key parameters of the curves are shown in Fig. 3 and Table 3. As shown in Fig. 4, the fitted models described the different irrigation treatments well ( R 2 ≥ 0.97, NRMSE ≤ 15%, IA ≥ 0.99). The model provided the best fit for fruit dry matter accumulation, followed by leaf dry matter accumulation, while the simulation accuracy for stem and aboveground dry matter accumulation was relatively lower. Specifically, the aboveground dry matter accumulation in I3 treatment was the worst simulated. However, R 2 , NRMSE , and IA values reached 0.98 and 0.98, 10.23 and 10.53%, and 0.99 and 0.99 in the two years. As shown in Table 3, parameter A (the upper limit of maximum dry matter accumulation) was significantly affected by irrigation amount ( p 0.05). I1 treatment significantly affected only the stem A values ( p < 0.05), while the I2 and I3 treatments significantly affected on the A values of stem, leaf, fruit, and aboveground ( p < 0.05). Particularly, in I3 treatment, A values for the stem, leaf, fruit and aboveground dry matter were 104.22 and 115.83 g plant -1 , 39.90 and 44.04 g plant -1 , 107.68 and 102.02 g plant -1 , and 1169.76 and 1334.82 g m -2 in the two years. Compared with other irrigation treatments, the A values for stem, leaf, fruit, and aboveground dry matter in the I3 treatment were decreased by 12.41–43.04%, 3.80-15.09%, 3.87–26.45% and 8.23–35.27%, respectively. Table 3 Parameters of model curves for dry matter accumulation in grapes under different irrigation treatments Year Organ Curve parameter Year Curve parameter A B K*10^ −2 A B K*10^ −2 2023 GDMA CK 182.97a 2.80a 1.33b 2024 180.84a 3.03a 1.45a I1 157.87b 2.91a 1.51ab 150.66b 3.02ab 1.51a I2 131.32c 2.87a 1.62ab 132.24bc 3.01ab 1.52a I3 104.22d 3.01a 1.81a 115.83c 2.75c 1.34a YDMA CK 46.99a 2.86a 2.55a 50.90a 2.79a 2.11a I1 44.94ab 2.83a 2.54a 47.84ab 2.77ab 2.07a I2 42.79bc 2.82a 2.61a 45.78ab 2.59b 1.85ab I3 39.90c 2.65a 2.37a 44.04b 2.45a 1.71a MDMA CK 146.41a 4.85a 1.16a 123.66a 6.80a 2.09a I1 133.75ab 4.63a 1.14a 112.42ab 6.65a 2.05a I2 116.86bc 4.56a 1.17a 106.13bc 6.42a 1.98a I3 107.68c 4.72a 1.18a 102.02c 6.09a 1.84a DDMA CK 1807.02a 2.70a 1.01a 1808.34a 2.90a 1.15a I1 1605.76ab 2.65a 1.05a 1607.23ab 2.88a 1.17a I2 1397.71c 2.57a 1.07a 1454.54bc 2.86a 1.21ab I3 1169.76c 2.66a 1.14a 1334.82c 2.90b 1.22b The curves of aboveground dry matter accumulation rate under different irrigation treatments are shown in Fig. 5, while the characteristic parameters of the aboveground dry matter accumulation are shown in Table 4. The dry matter accumulation and its rate decreased with increasing water stress. Concurrently, water stress hastened the occurrence of maximum aboveground dry matter ( X max ). Specifically, the peak accumulation rate in the I3 treatment occurred at PTP values of 232.67 and 237.12 MJ m − 2 in the two years, which was 26.21 and 21.24% earlier than those under the CK treatment. However, no significant differences were observed in the timing of peak occurrence among the different irrigation treatments ( p < 0.05). The maximum aboveground dry matter rate ( V max ) occurred during Stage Ⅲ, corresponding to PTP values ( X max ) ranging from 230 to 250 MJ m − 2 . Both V max and average dry matter accumulated rate ( V avg ) decreased with reduced irrigation amount. I3 treatment had the lowest V max and V avg for all treatments, 3.35 and 4.08 g m − 2 MJ − 1 m 2 , and 2.23 and 2.72 g m − 2 MJ − 1 m 2 in the two years, respectively. Significant differences in V max and V avg were observed among the different irrigation treatments in 2024 ( p < 0.05). Compared with the rest of the treatments, V max decreased by 21.24–26.21%, 12.82–20.24% and 6.85–9.95%, while V avg decreased by 21.16–26.40%, 12.82–20.36% and 6.85–10.08%. The rapid growth stage of dry matter accumulation usually occurred around Stage II, corresponding to a PTP of approximately 130 MJ m − 2 , and ended in late Stage III and pre-Stage IV with a PTP of approximately 360 MJ m − 2 . The duration of the rapid growth stage ( T 2 ) shortened with decreasing irrigation amount, with the I3 treatment being significantly shorter by 1.55–12.06% compared with other treatments ( p < 0.05). In the two years, the proportion of dry matter accumulation during the rapid growth stage ( y 2 ) accounted for as high as 57.74% of the total dry matter accumulation throughout the entire reproductive period. Extending T 2 is beneficial for the dry matter accumulation in grapes. Table 4 Characteristic parameters of aboveground dry matter accumulation in grapes under different irrigation treatments Characteristic parameters 2023 2024 CK I1 I2 I3 CK I1 I2 I3 X max 268.14a 253.42a 241.56a 232.67a 253.27a 247.21a 237.34a 237.12a V max 4.54a 4.20a 3.72a 3.35a 5.18a 4.68b 4.38c 4.08d Yvmax 903.51a 802.88ab 698.85b 584.88b 904.17a 803.62b 727.27bc 667.41c X 1 137.23a 127.52a 117.90a 117.55a 138.35a 134.17a 128.05a 129.52a X 2 399.05a 379.33a 365.22a 347.79a 368.19a 360.25a 346.64a 344.71a T 1 137.23a 127.52a 117.90a 117.55a 138.35a 134.17a 128.05a 129.52a T 2 261.82a 251.81ab 247.32ab 230.24b 229.84a 226.09ab 218.58ab 215.19b T 3 199.57a 219.29a 233.40a 250.83a 166.30a 174.23a 187.85a 189.7a y 1 381.87a 339.34ab 295.37b 247.20b 382.15a 339.65b 307.38bc 282.08c y 2 1043.28a 927.09ab 806.97b 675.36b 1044.05a 927.94b 839.78 bc 770.66c y 3 319.10a 297.07ab 264.87ab 229.69b 348.25a 313.29b 288.91c 266.28c V avg 3.03a 2.80a 2.48a 2.23a 3.45a 3.12b 2.92c 2.72d Note: X max represents the PTP when grapes reach the maximum rate of aboveground dry matter accumulation, MJ m − 2 ; V max represents the maximum rate of aboveground dry matter accumulation in grapes, g m − 2 MJ − 1 m 2 ; Yvmax represents the amount of aboveground dry matter accumulation in grapes when V = V max , g m − 2 ; X 1 and X 2 represent the start times of the rapid growth stage and the slow stage, respectively, MJ m − 2 ; T 1 , T 2 , T 3 represent the duration of the gradual, rapid, and slow growth periods, respectively, MJ m − 2 ; y 1 , y 2 , and y 3 represent the accumulation of aboveground dry matter during the gradual, rapid, and slow growth periods, respectively, g m − 2 ; V avg represents the average accumulation rate, g m − 2 MJ − 1 m 2 . 3.2 Dry matter allocation The fitted curves of the allocation indexes of stem, leave and fruit with PTP under different irrigation treatments and the fitting accuracy indexes are shown in Figs. 6, 7 and Supplementary Table 1. The stem allocation index exhibited a rapid increase with increasing PTP during Stage Ⅰ and Stage Ⅱ, followed by a significant decrease in Stage III, and stabilized in Stage IV. Irrigation increased the stem allocation index, which reached the highest values in CK treatment at 0.64 and 0.65 in the two years. The Leaf allocation index and fruit allocation index showed opposite trends with increasing PTP. The leaf allocation index monotonically decreased, with the minimum values occurring in Stage Ⅳ, and the I3 treatment had the highest leaf allocation index of 0.17 in both years. Grape fruit allocation index increased monotonically, with the I3 treatment reaching a final allocation index as high as 0.39 in 2023 and 0.38 in 2024 at Stage IV. Different irrigation treatments had no significant effects of on stem, leaf, and fruit allocation indexes in the first three reproductive stages (except for the stem allocation index at Stage Ⅲ in 2023) ( p > 0.05). However, significant differences were observed in Stage IV ( p < 0.05). Compared with CK treatment, the I1, I2, and I3 treatments decreased the final stem allocation index during the harvesting period by 3.85 and 5.88%, 9.62 and 7.84%, and 13.46 and 11.76% in the two years, respectively. The leaf allocation index increased by 7.69 and 7.15%, 15.38 and 14.29%, and 30.77 and 21.43%. The fruit allocation index increased by 2.86 and 2.86%, 5.71 and 5.71%, and 11.43 and 8.57%, respectively. The equations fitted well for stem, leaf, and fruit allocation indexes for different irrigation treatments (Fig. 6), with R² ≥ 0.93, NRMSE ≤ 15%, and IA ≥ 0.98. As shown in Fig. 7, the fitting accuracy was in the order of fruit > leaf > stem. Among all treatments, the I3 treatment for fruit had the best fit, with R ² values of 0.99 and 0.96, NRMSE values of 12.54 and 5.51%, and IA values of 0.99 and 0.99 in the two years. 3.3 Aboveground dry matter accumulation, yield and harvest index The accumulation and allocation of aboveground dry matter were key factors of yield formation. The correlation analyses of aboveground dry matter accumulation, yield, and harvest index (HI) of greenhouse grapes under different irrigation treatments are shown in Fig. 8 Aboveground dry matter accumulation, yield, and HI fluctuated within the ranges of 15.04 ± 2.36 and 15.09 ± 1.87 t hm − 2 , 30.79 ± 3.67 and 32.87 ± 2.92 t hm − 2 , and 0.37 ± 0.02 and 0.41 ± 0.05 in the two years, respectively. There were no significant differences in HI, yield, and aboveground dry matter accumulation between the two years ( p > 0.05), and these parameters were closely related. HI and aboveground dry matter accumulation explained for 91 and 88% of the variation in yield in 2023, respectively. Yield and aboveground dry matter accumulation explained for 93 and 81% of the variation in HI in 2024, respectively. The effects of different irrigation treatments on aboveground dry matter accumulation and yield of greenhouse grapes are shown in Fig. 9. As the irrigation amount decreased, the yield and aboveground dry matter accumulation of greenhouse grapes were in the order of CK > I1 > I2 > I3. Specially, the reductions in yield and aboveground dry matter accumulation in the I2 and I3 treatments reached significant levels ( p I2 > I1 > CK. It could be seen that reducing irrigation increased the HI of the crop, although it decreased the yield and aboveground dry matter accumulation. The yield and aboveground dry matter accumulation of I1 treatment in this study were 32.46 and 31.65 t hm − 2 , and 1611.91 and 1547.66 g m − 2 in the two years, respectively. The I1 treatment increased HI without significantly decreasing yield and aboveground dry matter accumulation, making it an ideal irrigation treatment. In addition, the HI of I3 treatment increased by an average of 2.69%, 5.03% and 9.03% in the two years compared with I2, I1, and CK treatments, respectively. However, this treatment had the lowest yield and aboveground dry matter accumulation in the two years. This showed that increasing crop HI might not necessarily lead to increased yield. 3.4 Correlation of characteristic parameters of aboveground dry matter accumulation with yield, WUE and RUE Radiation use efficiency (RUE) and water use efficiency (WUE) were key factors in determining yield. There were different degrees of reduction in RUE with decreasing irrigation amount. Specifically, the I3 treatment experienced a significant decrease in RUE ( p < 0.05), which was 20.91 and 12.02%, 18.21 and 5.15%, and 10.31 and 2.09% lower than those in CK, I1, and I2 treatments in the two years, respectively. The water consumption of grapes in different treatments at each fertility stage and at full fertility stage are shown in Fig. 10a, e. WUE initially increased and then decreased with decreasing irrigation amount. The I2 treatment had the highest WUE, followed by I1 and I3 treatments. The CK treatment exhibited a significant decrease in WUE compared with the other treatments ( p < 0.05). The I2 treatment was identified as the optimal strategy, significantly enhancing WUE ( p 0.05). The I1 treatment was the suboptimal treatment, significantly increasing WUE ( p 0.05) (Fig. 10). The correlations between the characteristic parameters of aboveground dry matter accumulation with yield, RUE, and WUE are shown in Fig. 11. Combined with the data in Table 4, yield and RUE were significantly and positively correlated with V max , Yvmax , T 2 , y 1 , y 2 , y 3 , and V avg in both years ( p 0.05, r < 0.2). In 2024, WUE exhibited a significant correlation with V max , Yvmax , V avg , y 1 , y 2 , y 3 , and V avg ( p < 0.05, r ≥ 0.2). Data from both years revealed a significant positive correlation between Yvmax and y 2 . This showed that during the rapid growth stage, Yvmax accounted for a larger proportion of y 2 when the maximum dry matter accumulation rate was reached, and the two were mutually reinforcing. A significant positive correlation was also observed between T 2 and y 2 . y 2 accounted for more than 57.74% of the total aboveground dry matter accumulation throughout the entire reproductive period. A random forest algorithm was employed to rank the importance of indicators that were significantly correlated with yield and RUE in both 2023 and 2024 (no indicators were significantly correlated with WUE in both 2023 and 2024). The results showed that the characteristic parameters of aboveground dry matter accumulation, specifically y 3 , y 1 , Y vmax , and y 2 , exhibited the most significant effect on yield and RUE. The duration of the rapid growth stage ( T 2 ) was more critical for the formation of yield and RUE than the other two stages. The relative importance of V avg was the lowest. 4 Discussion 4.1 Effects of irrigation on yield, aboveground dry matter accumulation, and HI in grape In this study, the irrigation amount had a significant effect on grape yield, which gradually decreased with increasing water stress. Medium water stress (I2) and severe water stress (I3) significantly inhibited yield (Fig. 9 ). Over the two-year study period, yield reductions of 4.65–24.60% were observed in the treatments compared with the CK treatment. This finding is consistent with the study by Pech et al. ( 2008 ), who found that sustained decreases in irrigation amount led to grape yield reductions of 9–31%. Numerous studies have shown that reduced irrigation results in yield losses in grapes, partly due to the inhibition of cell expansion and the diminution of the inner mesocarp cell sap, which subsequently reduces berry quality (Torres et al., 2021 ). Mild stress irrigation can improve fruit quality without significantly reducing the yield (Jiang et al., 2021 ). In addition, severe water stress may lead to undesirable fruit development. Similar to the findings of Martínez-Moreno et al. ( 2022 ), water that failed to meet crop growth requirements could lead to significant yield reductions. Studies by Han et al. ( 2023 ) on wine grapes in North China and Wang et al. ( 2022a ) on sweet peppers in Northwest China showed that the yields of grapes and sweet peppers increased and then decreased with increasing water stress, which inconsistent with the results of the present study. Their research indicated that mild water stress irrigation did not decrease yield but increased the potential yield of the crop, while over-irrigation or under-irrigation limited yield. This analysis may be supported by the following factors: 1) increasing the irrigation amount is favorable for yield improvement, but there is a certain threshold range. Yield can be decreased by exceeding or falling below the irrigation threshold. The irrigation threshold can be influenced by a variety of factors, such as crop variety and the climate of the growing region; 2) excessive soil moisture supply leads to redundant crop growth and excessive stem and leaf growth. Increased competition for nutrient and water between nutrient organs and fruits may reduce crop yield (Du et al., 2022 ; Lodhi, 2014 ); 3) deficit irrigation of grapes reduces the amount of nutritive growth hormones, such as cytokinins, and increases the amount of reproductive growth hormones, such as abscisic acid. Optimizing the growth state of the bud promotes the accumulation of carbohydrate reserves, improves berry development, and encourages crop yield (Conesa et al., 2022 ). In a study of vineyards in the Xinjiang region, Ren et al. ( 2022 ) similarly found that mild water stress was more conducive to grape yield formation. Instead of surface drip irrigation, they employed root-zone irrigation. Root-zone irrigation directly supplies water to the grape root system, thereby reducing unnecessary evaporation from the soil surface and enhancing WUE. This approach is particularly advantageous for water conservation and yield improvement under the extremely arid conditions of Xinjiang. Additionally, root-zone irrigation helps minimize redundant nutrient growth in fruit trees, thereby conserving photosynthetic products and promoting fruit development. In the present study, mild water stress (I1 treatment) reduced aboveground dry matter accumulation by 9.80 and 12.46% compared with fully irrigated treatment (CK treatment) in the two years, while these reductions reached an insignificant level ( p > 0.05) (Fig. 9 ). Medium water stress (I2 treatment) and severe water stress (I3 treatment) significantly reduced aboveground dry matter accumulation by 20.99 and 19.21%, 32.48 and 26.72% in the two years, respectively (Fig. 9 ). These findings differ from those of Wang et al. ( 2022a ), who reported that aboveground dry matter accumulation in sweet peppers in Northwest China initially increased and then decreased with decreasing irrigation. This discrepancy may be caused by the different irrigation gradient settings. In their experiment, the highest irrigation level was 105% the crop evapotranspiration (ETc), and over-irrigation appeared to inhibit dry matter accumulation. Additionally, meteorological factors can influence crop dry matter accumulation (Ronga et al., 2017 ; Wang et al., 2018 ; Yan et al., 2022c ). This study was conducted in a solar greenhouse in the cold region of Northeast China, characterized by a temperate monsoon climate. Their study conducted in a solar greenhouse in Northwest China, characterized by a temperate continental climate. The complexity of the greenhouse photothermal environment and the significant differences in climatic characteristics lead to differences in radiation and temperature. However, both studies observed that dry matter accumulation was hindered under excessive drought conditions, likely due to the reduced stomatal conductance of leaves, which weakened photosynthesis (Deng et al., 2021 ; Li et al., 2023c ; Pérez-Álvarez et al., 2021 ; Weiler et al., 2019 ). In addition, their study found that 75% ETc dry matter accumulation was still greater than 90% ETc, indicating that medium water stress treatment was more favorable for dry matter accumulation. In contrast, this study concluded that mild water stress was more favorable for dry matter accumulation in grapes. Therefore, cultivar differences also affect dry matter accumulation (Lin et al., 2011 ; Wang et al., 2018 ; Yan et al., 2022b ). In the study by Chen et al. ( 2021a ), it was observed that the total dry matter of potted grapes was higher under 50% irrigation than under 100% irrigation, which was different from the findings of the present study. This discrepancy can be attributed to the use of alternate partial root-zone drip irrigation at 50% irrigation, compared to conventional irrigation at 100% irrigation. Alternating partial root-zone drip irrigation minimizes inefficient water evaporation and enhances WUE. This efficient water management enables plants to utilize water more effectively, thereby promoting dry matter accumulation. Thus, irrigation practice is a significant factor influencing dry matter accumulation. In this study, the logistic equation effectively fitted the process of dry matter accumulation in various organs and aboveground dry matter of grapes under different irrigation treatments, with the best fit under no water stress condition (Fig. 3 , 4 ). Dou et al. ( 2024 ) also obtained excellent simulations using this equation to fit aboveground dry matter, stem, leaf, and spike weights during the growth period of summer maize under different water stress conditions. In this study, the model A values were sensitive to changes in irrigation amount (Table 3 ). In 2024, irrigation amount significantly affected on V avg and V max , but not on X max (Table 4 ). Yan et al. ( 2022a ) showed that irrigation amount significantly influenced on the model A values of winter wheat, as well as on X max , V max and V avg . This discrepancy may arise from differences in experimental regions and subject materials, as their study was conducted in a large field trial in Northwest China. Numerous studies shown that irrigation influences the cycle and rate of aboveground dry matter accumulation (Liu et al., 2023 ; Ma et al., 2021 ). However, dry matter accumulation characteristics are influenced by many factors, such as variety, climatic condition, planting and management pattern (Ali et al., 2018 ; Ma et al., 2021 ; Mao et al., 2018 ). Ma et al. ( 2021 ) concluded that both V max and V avg of maize increased with increasing irrigation levels, which aligned with the results of this study. However, their study concluded that irrigation shortened X max , contrary to the results of this study. Meanwhile, this study suggested that irrigation prolonged the entry time of the rapid growth stage in favor of dry matter accumulation in grapes. In contrast, Zhang et al. ( 2019 ) pointed out that reasonable irrigation could induce maize dry matter to enter the rapid growth stage and prolong the slow growth stage of dry matter at the same time, which was beneficial for increasing maize dry matter accumulation. However, this was also contrary to the findings of this study. This difference may be due to the fact that the experimental irrigation setups are different. In addition, the test subjects are changed from wheat and maize to grapes, which are more sensitive to water change (Chaves et al., 2007 ). The early phase of berry growth, characterized by active cell division, is highly sensitive to water stress (Caruso et al., 2022 ). Water stress induces physiological drought in grapes, resulting in reduced growth potential and an accelerated attainment of the peak dry matter accumulation rate. Consequently, the rapid growth stage is initiated earlier. Notably, when the aboveground dry matter accumulation rate reached V max for each irrigation treatment condition in grapes, Yvmax was approximately 50% of its corresponding maximum theoretical dry matter accumulation (Table 3 , 4 ). In addition, the rapid growth stage was mainly concentrated in Stage Ⅲ, during which the aboveground dry matter accumulation accounted for more than 50% of the total. This pattern was similar to the results obtained by Wei et al. ( 2021 ) for the percentage of dry matter accumulation in rice during the rapid growth stage. In this study, as water stress decreased, HI declined, although aboveground dry matter accumulation and yield increased. The HI values fluctuated in the range of 0.34–0.43 (Fig. 8 , 9 ), which was similar to the findings of Li et al. ( 2020b ), who concluded that grape HI ranged from 0.36 to 0.46 under different irrigation treatments. While increased water inputs can improve dry matter and yield of grapes, a high yield does not necessarily imply a high HI. However, the overall enhancement in harvest index (HI) observed across different irrigation treatments in the study by Li et al. ( 2020b ) contrasts with the findings of the present study. Notably, Li et al. ( 2020b ) employed mulched drip irrigation (MDI) in their experiment, which was conducted in the arid region of Xinjiang. MDI is known for its capacity to significantly diminish soil surface evaporation, augment soil water storage and moisture retention, and thereby elevate WUE (Vishwakarma et al., 2023 ; Yang et al., 2023 ). This irrigation method can effectively redirect resources away from nutrient growth, thereby enhancing grape yield and fruit dry matter accumulation (Wang et al., 2024 ). Additionally, the more stable greenhouse environment, under equivalent water conditions, may foster more vigorous nutrient growth. In contrast, field trials, which are often subject to natural environmental stresses, tend to prompt plants to allocate a greater proportion of photosynthetic products to the fruit, resulting in higher HI. Du et al. ( 2022 ) showed that in rice, the yield and the conversion rate of the stem and sheath from the heading stage to the maturity stage decreased with increasing irrigation amount, thus reducing the rice HI. However, adopting alternative dry–wet irrigation mode could increase HI by improving the transport capacity of non-structural carbohydrates. Wang et al. ( 2018 ) and Wang et al. ( 2022a ) revealed that HI of tomato and pepper showed an initial increase and followed by a decrease with increasing irrigation amount. These variations in HI are based on changes in crop yield and aboveground dry matter accumulation under different irrigation conditions, reflecting the ability of crops to convert photosynthetic products into economic products (Yang and Zhang, 2023 ). It has been shown that HI varies depending on crop variety, growing light and temperature condition, and water and environmental condition, such as soil pH and fertility (Fleisher et al., 2022 ; Su et al., 2022 ). 4.2 Effects of irrigation on the allocation of aboveground dry matter of grape This study concluded that increased water stress promoted the allocation of grape photosynthetic assimilation products to fruits and leaves while reducing the stem dry matter allocation index (Fig. 6 ). The sensitivity of grape organs to water stress was in the order of stem > leaf > fruit. Reducing the amount of water first led to a significant reduction in stem dry matter, followed by leaves, and finally fruits. Among the treatments, I1 treatment increased the fruit allocation index without significantly reducing aboveground dry matter accumulation and yield. The fruit allocation index reached 0.36 in the two years, which were 2.86% higher than those of the fully irrigated treatment (CK treatment) (Fig. 6 ). Similarly, Gao et al. ( 2024 ) and Dou et al. ( 2024 ) suggested that plant biomass was preferentially allocated to spikes under water stress. Golzardi et al. ( 2017 ) and Wang et al. ( 2021b ) demonstrated that not all of the photosynthates produced by over-irrigated leaves were transferred to the grains but were stored in the stem. However, Madani et al. ( 2010 ) and Yan et al. ( 2022a ) indicated that water stress significantly increased both grain dry matter allocation index and the stem allocation index in winter wheat, which was inconsistent with our findings. This discrepancy may be attributed to the different physiological characteristics of grapes and wheat. Wheat stems and spike reserves can be converted into nutrients for grain filling as they enter the reproductive growth stage, thereby maintaining and enhancing grain filling capacity in response to environmental stress. In addition, Zhang et al. ( 2019 ) and Yan et al. ( 2022c ) found that a longer period of high temperature and drought during the maize grain filling stage sharply reduced the root water absorption function, leading to the inactivation of nutrient organs and decreased dry matter allocation to grain. In the present study, no decrease in fruit dry matter allocation index was observed under water stress conditions. This may be due to the fact that the lowest irrigation levels are sufficient to maintain the normal growth in this study. Moreover, the greenhouse is ventilated and equipped with a cooling device during the high summer temperatures, which likely mitigates the effects of water stress. In their study on local grape varieties in New Zealand vineyards, Zhu et al. ( 2021 ) found that fruit dry matter accounted for the largest proportion of dry matter allocation among various grape organs, followed by stems and then leaves. This finding was different from the result of the present study. There may be notable differences in the dry matter allocation patterns between grapes grown in field and greenhouse settings. Thus, the experimental environment and grape variety appear to be significant factors influencing the pattern of dry matter allocation. 4.3 Effects of irrigation on WUE and RUE In this study, the effects of irrigation treatments on the WUE and RUE of grapes were thoroughly investigated. As water stress increased, WUE initially increased and then decreased. The highest WUE was observed in the I2 treatment, which was significantly higher than that in the CK treatment. However, this increase in WUE was associated with a significant reduction in grape yield. The I1 treatment did not cause a significant decrease in yield ( p > 0.05) and significantly increased WUE. In 2023, I1 treatment decreased water consumption by 14.56%, decreased yield by only 5.38%, and increased WUE by 9.95%. In 2024, water consumption decreased by 13.21%, yield decreased by only 4.65%, and WUE increased by 9.87% (Fig. 2 , 9 , 10 ). These findings are consistent with those of Ma et al. ( 2019 ), who showed that an average 35% decrease in water use could increase the WUE of grapes by 14–23% and decrease the grape yield by only 15–18%, without causing significant yield reductions. Moreover, Han et al. ( 2023 ) showed that 60% irrigation level in North China decreased grape yield by only 10.3% and increased WUE, which was significantly less than the 19.9% decrease in actual water consumption. However, their study observed an increase in both WUE and grape yield at an 80% irrigation level. Lodhi ( 2014 ) and Ozbahce and Tari ( 2010 ) concluded that appropriate water stress could increase crop yield and WUE. This study did not observe yield improvement under water stress, likely due to differences in the degree of water regulation and underlying mechanisms. The present study was conducted in a solar greenhouse, whereas the study by Han et al. ( 2023 ) was carried out in a vineyard. Therefore, different crop variety, crop type, and hydrothermal environmental factor in the growing region lead to different capacities for crop root water uptake and water use. However, Numerous studies have shown that water stress can increase WUE without affecting yield (Liao et al., 2022 ; Wang et al., 2021b ). This study concluded that RUE of grapes decreased with increasing water stress. Mild water stress (I1 treatment) and medium water stress (I2 treatment) did not significantly decrease aboveground dry matter accumulation, while severe water stress was detrimental to intercepted radiation (Fig. 10 ). Numerous studies have shown that irrigation can affect canopy structure, photosynthetic rate and source-store relationships in crops. This enhances the ability of canopy to intercept photosynthetically active radiation, increasing RUE and ultimately boosting yield (Gu et al., 2021 ; Hou et al., 2019 ). Moreover, excessive leaf growth may cause mutual shading within the cotton canopy, reducing RUE and dry matter accumulation (Bai et al., 2024 ; Wu et al., 2023 ). Buesa et al. ( 2020a ) found that decreasing water consumption in grapes might change their canopy architecture and decrease the interception of photosynthetically active radiation, thereby affecting RUE. However, this did not necessarily have a negative impact on the yield and dry matter accumulation. In addition, a moderate decrease in water consumption was effective in improving WUE. Similarly, this study concluded that grape plants had a limited capacity to use radiation and water. Mild water stress (I1 treatment) increased WUE while maintaining high yields, aboveground dry matter accumulation and RUE. Subsequently, the problem of excess resources was effectively resolved. Furthermore, under fully irrigated conditions, the RUE of grapes reported by Prats-Llinàs et al. ( 2020 ) in Spanish vineyards varied within the range of 1–2 g MJ − 1 , which was significantly lower than that observed in the present study. This discrepancy may be attributed to the more stable light and temperature conditions within the greenhouse, coupled with more refined management practices that enable grapes to utilize light radiation more efficiently, thereby enhancing RUE. 4.4 Research limitations and future prospects Despite achieving some results, this experiment has several limitations. As perennial vines, grapes will continue to grow in age, and the same irrigation pattern may induce an inter-annual lag effect (Ibba et al., 2024 ). The diversity of internal greenhouse environments across different years further exacerbated the fluctuations in grape yield, aboveground dry matter accumulation, and resource use efficiency. Differences in the characteristic parameters of aboveground dry matter accumulation were particularly pronounced among the different irrigation treatments, especially in 2024. WUE also showed higher levels. Therefore, it is crucial to account for inter-annual variation factors and develop a long-term irrigation program and an integrated environmental control system. It is necessary to conduct multi-year sentinel experiments to provide a scientific basis for green and efficient production of solar greenhouse grapes. With the advancement of computers, descriptions of crop growth processes have become increasingly sophisticated. Functional structural plant modelling (FSPM), an emerging simulation approach in recent years (Gu et al., 2024 ), is capable of modelling the interaction of environmental effects on plant architectures at the organ scale. It has also been widely applied to various crops, such as tomatoes (Zhang et al., 2020 ), grapes (Zhu et al., 2018 ), and cotton (Gu et al., 2024 ). In this study, there were limitations in the interface between the crop structural and functional models, which failed to fully exploit the potential of crop growth data. Additionally, the greenhouse environment exhibited spatial heterogeneity. Therefore, there is a need to develop a three-dimensional structural functional model of grapes and further enhance the mechanistic aspects of the model. This would facilitate the guidance of grape growth and development, light energy use, water and fertilizer regulation, and other specific production practices. These areas represent urgent priorities for future in-depth exploration. 5 Conclusion Mild and medium water stress (I1 and I2 treatments) significantly affected stem, leaf, and aboveground dry matter accumulation ( p 0.05). The Logistic model parameter A was the most sensitive to changes in irrigation amount. Increasing water stress could promote dry matter allocation to leaves and fruits, but this came at the expense of yield and aboveground dry matter accumulation. The Mantel test showed that V max , Yv max , T 2 , y 1 , y 2 , y 3 , and V avg were significantly positively correlated with yield and RUE ( p 0.05, r ≤ 0.2). The random forest model further determined that y 3 and y 1 were the most important parameters influencing the yield and RUE. Increasing V max and V avg and extending T 2 significantly promoted dry matter accumulation and yield. In this study, the yield, aboveground dry matter accumulation and RUE of I1 treatment for the two years were 32.79 and 34.24 t hm − 2 , 1611.91 and 1547.66 g m − 2 , and 3.90 and 3.63 g MJ − 1 . None of these values were significantly lower than those in CK treatment. However, I1 treatment increased the WUE (by 9.95% in 2023 and 9.87% in 2024) and fruit allocation index (by 2.86% in 2023 and 2.86% in 2024). Mild water stress (I1 treatment) was the optimal treatment. This study provides robust theoretical support for the cultivation and management of solar greenhouse grapes in Northeast China, guiding the optimal adjustment of irrigation strategies in actual production to achieve efficient green production and sustainable development of grapes. Declarations Acknowledgement This work was supported by the Natural Science Foundation of Liaoning Province, China (No.2021-MS-233), the Natural Science Foundation of Liaoning Province, China (JYTMS20231272). Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships. 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Trans Chin Soc Agric Eng 40:91-100. http://dx.doi.org/10.11975/j.issn.1002-6819.202403089 Wei YX, Cao XQ, Ji JC (2021) Effects of different irrigation methods on photosynthetic characteristicsand dry matter accumulation dynamics of dry direct seeding rice. J Agric Mach 52:358-368. http://dx.doi.org/10.6041/j.issn.1000-1298.2021.10.037 Weiler CS, Merkt N, Hartung J, Graeff-Hönninger S (2019) Variability among young table grape cultivars in response to water deficit and water use efficiency. Agronomy 9:9030135. http://dx.doi.org/10.3390/agronomy9030135 Wen LW, Meng ML, Liu KY, Zhang QL, Zhang TT, Chen YZ, Liang HW (2024) Effect of Photoperiod on dry matter accumulation and partitioning in potato. Agriculture 14:14071156. http://dx.doi.org/10.3390/agriculture14071156 Wu BJ, Zuo W, Yang PQ, Zhang WF (2023) Optimal water and nitrogen management increases cotton yield through improving leaf number and canopy light environment. 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Agric Water Manag 262:107332. http://dx.doi.org/10.1016/j.agwat.2021.107332 Yan ZN, Cheng J, Wan Z, Wang BB, Lin D, Yang YJ (2023) Prediction model of pumpkin rootstock seedlings based on temperature and light responses. Agronomy 13:13020516. http://dx.doi.org/10.3390/agronomy13020516 Yang JC, Zhang JH (2023) Simultaneously improving grain yield and water and nutrient use efficiencies by enhancing the harvest index in rice. Crop Environ 2:157-164. http://dx.doi.org/10.1016/j.crope.2023.07.001 Yang WC, Parsons D, Mao XM (2023) Exploring limiting factors for maize growth in Northeast China and potential coping strategies. Irrig Sci 41:321-335. http://dx.doi.org/10.1007/s00271-022-00813-y Yu Z, Wang JX, Liu SR, Rentch JS, Sun PS, Lu CQ (2017) Global gross primary productivity and water use efficiency changes under drought stress. Environ Res Lett 12:01406 http://dx.doi.org/10.1088/1748-9326/aa5258 Zhang DT, Sun ZX, Feng LS, Bai W, Yang N, Zhang Z, Du GJ, Feng C, Cai Q, Wang Q, Zhang Y, Wang RN, Arshad AN, Hao XY, Sun M, Gao ZQ, Zhang LZ (2020) Maize plant density affects yield, growth and source-sink relationship of crops in maize/peanut intercropping. Field Crop Res 257:107926. http://dx.doi.org/10.1016/j.fcr.2020.107926 Zhang T, Tang YY, Shan BX, Xu MZ, Cong N, Chen N, Ji XM, Zhao G, Zheng ZT, Zhu JT, Zhang YZ (2023) Drought-induced resource use efficiency responses in an alpine meadow ecosystem of northern Tibet. Agr for Meteorol 342:109745. http://dx.doi.org/10.1016/j.agrformet.2023.109745 Zhang ZX, Yu KL, Jin XL, Nan ZB, Wang JF, Niu XL, Whish JPM, Bell LW, Siddique KHM (2019) Above and belowground dry matter partitioning of four warm-season annual crops sown on different dates in a semiarid region. Eur J Agron 109:125918. http://dx.doi.org/10.1016/j.eja.2019.125918 Zhao H, Yang H, Avenson TJ, Sassenrath GF, Kirkham MB, Welch SW, Zhang L, Wan N, Nelson AM, Gowda P, Lin X (2024) Nonlinear contributions of surface solar brightening to maize yield gains in the US Corn Belt. Agr for Meteorol 356:110169. http://dx.doi.org/10.1016/j.agrformet.2024.110169 Zheng HY, Zhang L, Sun HB, Zheng A, Harrison MT, Li WJ, Zou J, Zhang DT, Chen F, Yin XG (2024a) Optimal sowing time to adapt soybean production to global warming with different cultivars in the Huanghuaihai Farming Region of China. Field Crop Res 312:109386. http://dx.doi.org/10.1016/j.fcr.2024.109386 Zheng SY, Cui NB, Wei XG, Wang TL, Bai YK, Pei DJ, Fu SN, Plauborg F, Jiao P (2024b) Dynamics, plant physiological and environmental controls of energy exchange in a grapevine greenhouse in Northeast China. J. Hydrol 637:131395. http://dx.doi.org/10.1016/j.jhydrol.2024.131395 Zhou YP, He J, Liu YZ, Liu HS, Wang TZ, Liu YX, Chen WJ, Muhammad T, Li YK (2023) Aerated drip irrigation improves watermelon yield, quality, water and fertilizer use efficiency by changing plant biomass and nutrient partitioning. Irrig Sci 41:739-748. http://dx.doi.org/10.1007/s00271-023-00853-y Zhou YF, Mahmoud Ali HS, Xi JS, Yao D, Zhang H, Li XJ, Yu K, Zhao FY (2024) Response of photosynthetic characteristics and yield of grape to different CO2 concentrations in a greenhouse. Front Plant Sci 15:1378749. http://dx.doi.org/10.3389/fpls.2024.1378749 Zhu JQ, Dai ZW, Vivin P, Gambetta GA, Henke M, Peccoux A, Ollat N, Delrot S (2018) A 3-D functional–structural grapevine model that couples the dynamics of water transport with leaf gas exchange. Ann Bot 121:833-848. http://dx.doi.org/10.1093/aob/mcx141 Zhu JQ, Parker A, Fang G, Agnew R, Yang LL (2021) Developing perennial fruit crop models in APSIM Next Generation using grapevine as an example. In Silico Plants 3:diab021. http://dx.doi.org/10.1093/insilicoplants/diab021 Additional Declarations No competing interests reported. Supplementary Files Highlights.docx Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Irrigation Science → Version 1 posted Editorial decision: Revision requested 30 Aug, 2025 Reviews received at journal 10 Jun, 2025 Reviews received at journal 05 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 30 May, 2025 Reviewers agreed at journal 30 May, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 27 Feb, 2025 Submission checks completed at journal 27 Feb, 2025 First submitted to journal 24 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6100579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":422375943,"identity":"d3739aae-4840-484e-bfd3-c8010b890770","order_by":0,"name":"Dantong Wang","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dantong","middleName":"","lastName":"Wang","suffix":""},{"id":422375944,"identity":"77d89df2-f9b2-4b09-87b4-13929cecd799","order_by":1,"name":"Kewei Zhu","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kewei","middleName":"","lastName":"Zhu","suffix":""},{"id":422375945,"identity":"8f5e1d2a-ce72-446d-aee2-0a18db6e9809","order_by":2,"name":"Xinguang Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYFACxgYGhgoom4d4LWdI0wLS1UaKFoPjzW2SX+fVJfbPSGB88LaNQd6coJYzB5uNZbcdTpxxI4HZcG4bg+HOBgJazG4kNj6W3HYgseFGAps0bxtDgsEBQlruP2w4LDmnLnH+jQT238RpucHY+PBjA3PiBqAtzERpsT+T2GzMcOyw8cYzD5sl55yTMNxASItk+/Fnkj9q6mTnHU8++OFNmY08QVtAgBkYHY4N4DhlkCBCPRAw/gA6kDilo2AUjIJRMCIBAMjbRgJKJPm5AAAAAElFTkSuQmCC","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Xinguang","middleName":"","lastName":"Wei","suffix":""},{"id":422375946,"identity":"6dadf501-4022-4b4f-842b-233fb76ef975","order_by":3,"name":"Yikui Bai","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yikui","middleName":"","lastName":"Bai","suffix":""},{"id":422375947,"identity":"ee61cc0a-40cf-46e5-9fb5-6bc8d59eb3cc","order_by":4,"name":"Tieliang Wang","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Tieliang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-02-25 02:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6100579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6100579/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00271-025-01065-2","type":"published","date":"2025-11-28T15:58:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77595274,"identity":"ca487492-d9b2-434a-a6bb-f1a554dc07ce","added_by":"auto","created_at":"2025-03-03 12:03:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1198827,"visible":true,"origin":"","legend":"\u003cp\u003eThe location, boundary, and geomorphic features of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/8b604cfd3a54fc6d95ef99e0.png"},{"id":77596189,"identity":"f45ac5ff-d4ac-4d45-8ec0-5b4a486bd5d7","added_by":"auto","created_at":"2025-03-03 12:11:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":275974,"visible":true,"origin":"","legend":"\u003cp\u003eThe seasonal variations in the greenhouse environmental variables during the grape\u0026nbsp;\u003ca href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/growing-season\" title=\"Learn more about growing season from ScienceDirect's AI-generated Topic Pages\"\u003egrowing season\u003c/a\u003e\u0026nbsp;of 2023–2024. (a,b) represent photosynthetically active radiation (PAR), (c,d) represent air temperature (T\u003csub\u003ea\u003c/sub\u003e) and\u0026nbsp;photo-thermal products (PTP), (e,f) represent soil volumetric water content (SWC) and cumulative irrigation amount (WC)\u003c/p\u003e\n\u003cp\u003eNote: The experimental was configured with radiation sensors positioned above the canopy and at multiple depths (upper, middle, and lower) within a fully irrigated control plot (CK treatment). The grape trellises were Y-shaped, exhibiting significant variation in extinction coefficients across different height layers. These sensors measured radiation at various locations and were employed to calibrate the hourly mean values of extinction coefficients for three height layer and to quantify the photosynthetically active radiation (PAR) intercepted by other treatments.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/e7b443260e30413f08da424d.png"},{"id":77596598,"identity":"413dc9e8-9929-4011-ae2b-b21316a17499","added_by":"auto","created_at":"2025-03-03 12:19:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":275228,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic growth model fitting for dry matter accumulation in grapes under different irrigation treatments\u003c/p\u003e\n\u003cp\u003eNote: The shaded blank area in the figure delineates the four fertility periods of the grapes sequentially. The embedded table indicates significant differences in the mean values of the measurements within each fertility period at \u003cem\u003ep\u003c/em\u003e = 0.05. DDMA represents aboveground dry matter accumulation (g m\u003csup\u003e-2\u003c/sup\u003e); GDMA represents stem dry matter mass (g plant\u003csup\u003e-1\u003c/sup\u003e); YDMA represents leaf dry matter mass (g plant\u003csup\u003e-1\u003c/sup\u003e); MDMA represents fruit dry matter mass (g plant\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/2befbab239b795614dd8e70b.png"},{"id":77594991,"identity":"2fb91ee5-4700-4271-bc19-c93adbf0dd43","added_by":"auto","created_at":"2025-03-03 11:55:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":224856,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of dry matter accumulation model performance\u003c/p\u003e\n\u003cp\u003eNote: CKD-I3D, CKG-I3G, CKY-I3Y, and CKM-I3M represent the model simulation performance of dry matter accumulation in the aboveground part, stem, leave, and fruit under different irrigation treatments.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/55db889d86deac27d3d71258.png"},{"id":77596187,"identity":"e467310a-bea6-4d73-acf0-58fd58783882","added_by":"auto","created_at":"2025-03-03 12:11:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":192846,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of irrigation treatments on the rate of aboveground dry matter accumulation in grapes\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/845c4a1aec84c48eb58cb740.png"},{"id":77594993,"identity":"f1753e62-ff85-4068-a5e2-0858e5c0c060","added_by":"auto","created_at":"2025-03-03 11:55:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":210906,"visible":true,"origin":"","legend":"\u003cp\u003eModel fitting of grape dry matter allocation index under different irrigation treatments\u003c/p\u003e\n\u003cp\u003eNote: The shaded blank areas in the figure delineate the four fertility stages of the grapes. The embedded table presents the analysis of significant differences at the \u003cem\u003ep\u003c/em\u003e = 0.05 level for the mean values of the measurements within each reproductive period. GAI, YAI, and MAI represent the allocation indexes for grape stem, leaf, and fruit.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/35951090913aee3ca88590c8.png"},{"id":77595276,"identity":"6e26006a-50ef-4230-a59f-a49573df9533","added_by":"auto","created_at":"2025-03-03 12:03:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":251996,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of dry matter allocation model performance\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/c71e728a05d695577e51406e.png"},{"id":77596599,"identity":"ac04875b-ebd7-4d2c-ab53-df246e045c8e","added_by":"auto","created_at":"2025-03-03 12:19:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":165138,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between aboveground dry matter accumulation, yield and harvest index in\u003c/p\u003e\n\u003cp\u003egrapes under different irrigation treatments\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/7006cfb7de819cfed741e1c0.png"},{"id":77594956,"identity":"9f7e94e5-5f89-4132-8d2d-9b855340cd29","added_by":"auto","created_at":"2025-03-03 11:55:40","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":207850,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different irrigation treatments on aboveground dry matter accumulation and yield in grapes\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/0af33fbcb321262d4ba6c764.png"},{"id":77595277,"identity":"ff7c7ce1-db2e-4c49-b9fe-700858516eac","added_by":"auto","created_at":"2025-03-03 12:03:40","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":237760,"visible":true,"origin":"","legend":"\u003cp\u003eRadiation use efficiency and water use efficiency in grapes under different irrigation treatments\u003c/p\u003e","description":"","filename":"floatimage21.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/36fd8ae033c82a31710f9db4.png"},{"id":77594953,"identity":"c6babff5-8406-4ebc-ac7e-3da937ea7cb5","added_by":"auto","created_at":"2025-03-03 11:55:40","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":370274,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of dry matter characterization parameters with grape yield, FUE and WUE and ranking of characterization parameters\u003c/p\u003e\n\u003cp\u003eNote: The order of relative importance of parameters significantly correlated with Yield and RUE in both years (c and d).\u003c/p\u003e","description":"","filename":"floatimage22.png","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/a2c7f1a19431dab7a8d8d1b7.png"},{"id":97178803,"identity":"88ccab65-455d-496a-9eaa-10cfe980a219","added_by":"auto","created_at":"2025-12-01 16:13:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5440580,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/8c53e1f1-34e1-4958-b53e-04b20204ccf7.pdf"},{"id":77596190,"identity":"a5184dc1-1b34-48d4-86df-a38232ec3fe3","added_by":"auto","created_at":"2025-03-03 12:11:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12512,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/9c038950a2960770883a7750.docx"},{"id":77594946,"identity":"288c173b-a8af-47bb-99eb-a1229f463244","added_by":"auto","created_at":"2025-03-03 11:55:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15918,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6100579/v1/ca737b2e96bd3d4a8537d024.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of different irrigation treatments on dry matter accumulation, allocation and yield of grapes in solar greenhouse","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGrapes, as globally significant cash crops, play a pivotal role in Chinese agriculture (Albasha and Bartlett, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Niu et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By the end of 2022, China ranked fourth worldwide in total grape viticultural area and was the largest producer of table grapes (FAO, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Northeastern China, with its high latitude, large diurnal temperature differences, and abundant light resources, is an ideal region for grape cultivation. However, extreme weather conditions are also more frequent (Fu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Approximately two-thirds of China\u0026rsquo;s solar greenhouses are located here, where table grapes are mainly grown under greenhouses conditions to mitigate the impacts of extreme meteorological conditions (Wang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Under greenhouse cultivation, grape production is largely dependent on artificial irrigation. Irrational irrigation amount can result in an imbalance between the nutritional and reproductive growth in greenhouse grapes (Albrizio et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e). Irrational irrigation amount can also cause several problems, including redundant branch growth (Chaves et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e), low grape yield, and poor use of greenhouse water, light, and heat resources (Jiang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These problems have become bottlenecks constraining the development of the local greenhouse grape industry and sustainable use of resources.\u003c/p\u003e \u003cp\u003eTemperature, radiation, and water are central factors influencing dry matter accumulation and yield formation in grapes, playing crucial roles in grape growth and development. Previous studies have shown that both temperature and water significantly affect crop growth, development, and physiological processes. Unsuitable temperature conditions or excessive water stress can severely inhibit the growth and yield formation processes in grapes (Davide et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Valent\u0026iacute;n et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Radiation is also a critical factor influencing crop productivity, with approximately 90% of the dry matter originating from radiation interception (Anda et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Buesa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Droulia and Charalampopoulos, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Greenhouse environments are characterized by their complexity, with environmental factors such as light intensity, air and soil temperature, and humidity being influenced by human-induced irrigation and fertilization practices (Rahimikhoob et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Greenhouse production enhances crop yield and quality. In contrast, field environments are primarily influenced by a single factor, predominantly rain-fed irrigation, with supplementary artificial irrigation. The interplay of multiple environmental factors results in distinct response mechanisms of dry matter accumulation and yield to water between vineyard and greenhouse-grown grapes, even under identical irrigation conditions. Yan et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used photo-thermal products (PTP) to establish a prediction model for pumpkin growth and development based on the quality indicators of pumpkin rootstock seedlings (hypocotyl length, stem diameter, shoot dry weight, root dry weight, root shoot ratio, and seedling quality index), providing theoretical guidance for the regulation of light and temperature environments in the greenhouse cultivation of pumpkin rootstock seedlings. Zheng et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) used growing degree days (GDD) and high temperature degree days (HDD) to investigate the optimal sowing time for different soybean varieties in the Huanghuaihai farming region of China. GDD and HDD ignore the effect of radiation, while the PTP comprehensively considers the combined effects of temperature and radiation. PTP has been widely applied to simulate the growth and development of various crops, such as bell peppers (Diao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and lettuce (Hang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The logistic (Verhulst, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1838\u003c/span\u003e), Richards (Richards, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1959\u003c/span\u003e), and Gompertz (Gompertz, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1825\u003c/span\u003e) equations are common PTP models that have been successfully applied to crops, such as tomato (Teng et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and potato (Wen et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Numerous studies have shown that grapes are subjected to the combined effects of temperature and radiation during their growth and development stages (Hunter et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Prats-Llin\u0026agrave;s et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The use of the PTP to construct crop models provides a powerful tool for exploring the characteristics of grape dry matter accumulation and allocation. However, model parameters and applicability can vary significantly depending on factors such as crop variety, experimental location, and irrigation management (Li et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Despite considerable research on the mechanisms of crop growth responses to varying moisture and light-temperature conditions, few studies have specifically focused on the growth processes (Oliver-Manera et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While some studies have described the effects of moisture on grape growth across different fertility stages, the description of the differences in growth response to moisture at each stage remains insufficiently systematic (Basile et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Junquera et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, the analysis and modelling of the accumulation and allocation processes of dry matter in grapes under temperate monsoon climates with different irrigation treatments remain limited. The application of PTP to simulate dry matter accumulation and allocation processes in grapes, especially in greenhouse with more complex light and temperature environments, is not yet common.\u003c/p\u003e \u003cp\u003eOptimal irrigation strategies are critical for greenhouse viticulture. Excessive irrigation can lead to vigorous branch growth, which may inhibit grape yield and reduce berry exposure and quality. Conversely, inadequate irrigation amount can decrease the stomatal conductance, weaken photosynthesis, and lead to flower and fruit abscission, thereby decreasing yield and dry matter accumulation (Romero et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The application of deficit irrigation strategies to regulate nutritional growth may compromise fruit dry matter accumulation and yield. In numerous crops, nutritional organs exhibit greater sensitivity to water stress than reproductive organs, thereby providing the potential to control canopy development without significantly diminishing yield (Calvo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies have shown that appropriate control of irrigation can help achieve a balance between nutrient and reproductive growth in crops, which is beneficial for yield formation and quality enhancement (Ben-Gal et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Oliver-Manera et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Wang et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) reported that water stress in maize in Xinjiang promoted early maturity and facilitated the transfer of photosynthetic product from stems to grains. In contrast, Yan et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) showed that water stress in winter wheat in northwest China increased the grain-filling capacity and promoted dry matter accumulation in stems and grain. Dry matter allocation patterns are influenced by multiple factors and exhibit diverse response mechanisms to water stress. Therefore, it is essential to further delineate the water stress threshold for regulating the growth of nutritional organs, in accordance with the characteristics of grapes and prevailing environmental conditions, to optimize the accumulation and allocation of dry matter in grapes.\u003c/p\u003e \u003cp\u003eDespite the abundance light resources in Northeast China, greenhouse irrigation mainly relies on manual intervention. Improper management practices have led to common problems of low water use efficiency (WUE) and yield (Yu et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Improving WUE and yield is critical for achieving sustainable agricultural development in the region. Numerous studies have shown that appropriate water stress can reduce water consumption while increasing WUE without significantly affecting yield (Liao et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Some researchers have shown that mild water stress can result in insignificant yield reduction, which can improve WUE and enhance grape quality (Jiang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Soltekin and Altındişli, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, some studies have shown that medium water stress can still satisfy growth and development requirements of grapes, ensuring yield and quality while improving WUE (El-Sayed et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Salazar et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Li et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) reported superior results with mild water stress in tomatoes in the Northwest, and Al-Qthanin et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed similar benefits in orange trees in Egypt. Therefore, the differences in WUE and yield responses to water stress are influenced by crop variety, irrigation level, crop environment, and climatic characteristic (Ali et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCrop productivity is intricately linked to canopy sunlight interception and the ability to utilize light and heat energy. Inadequate water supply can constrain crop canopy growth, reduce photosynthetic capacity, and thereby suppress crop yield and dry matter accumulation (Liu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, excessive irrigation may lead to mutual shading within the canopy, which not only impairs radiation absorption and photosynthetic efficiency of leaves (Gao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but also increases competition among organs for environmental resources. Inadequate or excessive irrigation amount can reduce radiation use efficiency (RUE). Resource competition increases the proportion of dry matter allocated to nutrient organs (Wang et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and decreases fruit dry matter allocation, ultimately lowering the harvest index (HI) (Li et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Water stress can increase HI to varying degrees. However, the response of HI to water stress varies among crops (Diez et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The idea that the combined effects of light, heat, and water significantly influence grape growth was supported by Buesa et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) and Bambach et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Numerous studies on grape yield, dry matter accumulation, and resource use efficiency have been conducted by previous researchers (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Salazar et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, studies focusing on optimizing irrigation strategies based on the potential interlinkages among multiple indicators, such as grape yield, WUE, RUE, and the characteristic parameters of aboveground dry matter accumulation, are still relatively limited.\u003c/p\u003e \u003cp\u003eTherefore, the objectives of this study were (1) to explore the effects of different irrigation amount on grape yield and aboveground dry matter accumulation and allocation, and to construct an aboveground dry matter accumulation and allocation model based on PTP. This model was used to examine the dynamic trends of dry matter accumulation and to analyze the characteristic parameters of aboveground dry matter accumulation in grapes; (2) to analyze the effects of irrigation amount on grape WUE, RUE, and HI; and (3) to explore the correlation response mechanisms between the characteristic parameters of aboveground dry matter accumulation and grape yield, as well as the indicators of RUE and WUE. By integrating yield, dry matter accumulation and allocation characteristics, and greenhouse resource use efficiency indicators, this study aimed to determine the optimal irrigation amount for solar greenhouse grapes in the cold region of Northeast China, thereby providing a theoretical basis for optimizing irrigation strategies in this region.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Experimental site description\u003c/h2\u003e\n \u003cp\u003eThe experiments were conducted between 2023 and 2024 in the solar greenhouse No. 44 at Beishan Scientific Research and Experimental Base (41.28\u0026deg;N, 123.57\u0026deg;E), located Shenyang city, Liaoning Province, China. The region experiences a temperate continental monsoon climate, characterized by hot and rainy summers, as well as cold and dry winters. The greenhouse type belonged to the Liaoshen III style solar energy-saving greenhouse, with steel frame construction. It had an east-west length of 60 m, the north-south width of 8 m, and a height of 4 m. The greenhouse roof was covered with a non-drip polyolefin plastic film, and a rainproof cotton blanket was employed for insulation (Fig. 1). The grape trellis were Y-shaped. The average soil bulk density at a depth of 30 cm was 1.44 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, the soil field capacity was 22.3 g g\u003csup\u003e-1\u003c/sup\u003e, and the permanent wilting point was 9.0 g g\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eIn this study, a 7-year-old Muscat Hamburg grape was used as the research object. The size of the treatment test plot was 10.4 m long and 5.4 m wide, with a planting density of 45714 plants ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (row line spacing was 1.5 m, and planting spacing was 0.43 m). According to the \u0026quot;Irrigation experiment standard\u0026quot;, the growing season was delineated into four phenological stages: the new shoot growth stage (Stage Ⅰ), the flowering and fruit setting stage (Stage Ⅱ), the fruit swelling stage (Stage III) and the maturity stage (Stage IV) (Table 1).\u003c/p\u003e\n \u003cp\u003eTable 1 Timing of grape phenological growth stages\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 45px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eNew shoot growth stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eFlowering and fruit setting stage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eFruit swelling stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMaturity stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eStage Ⅰ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eStage Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eStage Ⅲ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eStage Ⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eDOY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e81-117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e118-128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e129-197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e198-243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eDOY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e92-116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e117-128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e129-196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e197-244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Experimental design\u003c/h2\u003e\n \u003cp\u003eFour irrigation treatments were conducted based on the soil field capacity (\u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e) during the growing seasons of 2023 and 2024, commencing after budburst and continuing until harvest (Table 2). Each treatment was replicated three times, resulting in a total of 12 experimental plots, each with an area of 4.68 m\u003csup\u003e2\u003c/sup\u003e (1.8 m \u0026times; 2.6m). The water control test was conducted at all stages using an automatic drip irrigation system. The irrigation amount was measured using individual water meters installed at the inlet of the water pipes in each experimental plot. The cumulative irrigation amount in each treatment is shown in Fig. 2. Previous studies have indicated that the root distribution of grape in greenhouses is concentrated within the 0\u0026ndash;60 cm soil layer, with a soil wetting depth of approximately 60 cm. However, the majority of the root system is predominantly located within the 0\u0026ndash;30 cm soil layer (Li et al., 2020a). The TDT moisture probe (CS650, Campbell Scientific Inc.) was positioned at the 30cm soil depth to dynamically monitor soil water content, which was utilized to represent the average soil water content (SWC, %) (Fig. 2). Irrigation was automatically controlled via solenoid valves (Rain Bird Company, Glendora City, USA). Specifically, real-time data monitored by the TDT soil moisture sensors for each treatment were compared against the predefined lower threshold for irrigation. Irrigation was initiated when the monitored value fell below this threshold and ceased upon reaching the upper limit.\u003c/p\u003e\n \u003cp\u003eThe irrigation method employed was drip irrigation, utilizing pressure-compensated drip head with a flow rate of 2.0 L h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. To prevent lateral soil moisture movement, PVC sheets (2 mm thick) were buried 80 cm deep and laid in each plot.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eExperimental treatments applied\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIrrigation levels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater capacity (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull irrigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild water stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium water stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere water stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Measurements\u003c/h2\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.3.1 Greenhouse micro-environment\u003c/h2\u003e\n \u003cp\u003eMeteorological data were measured using an automatic environmental monitoring system located within the greenhouse. Air temperature (Ta, ℃) was measured with a Pt100RTD and HUMICAP\u0026reg;180R sensor (R.M Young Company, Traverse City, MI, USA). Photosynthetically active radiation (PAR, J m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was measured using a PAR Quantum Sensor (Kipp \u0026amp; Zonen). All the data were averaged every 30 minutes and recorded by a CR1000 data logger (Campbell Scientific, Inc.) (Fig. 2). The photo-thermal products (PTP) represents the cumulative photo-thermal products during grape growth (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). PTP serves as an indicator of grape maturity, integrating both temperature and radiation factors to reflect the influence of the climatic environment on grape growth. PTP is calculated using the following equations (Eq. 1, 2, 3):\u003c/p\u003e\n \u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$RPTP=\\sum\\limits_{{i=1}}^{{24}} {(RTE(i) \\times PAR(i) \\times 3600/{{10}^6})}$$\u003c/div\u003e\u003cdiv\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\"\u003e\u003cdiv id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$PTP=\\sum {(RPTP)}$$\u003c/div\u003e\u003cdiv\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere, \u003cem\u003eRTE\u003c/em\u003e(i)\u003csub\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sub\u003e represents the relative thermal effectiveness of the crop during the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e hour at temperature \u003cem\u003eT\u003c/em\u003e; \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eob\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eou\u003c/em\u003e\u003c/sub\u003e denote the upper growth limit temperature (38\u0026deg;C), lower growth limit temperature (5\u0026deg;C), lower growth optimum temperature (20\u0026deg;C), and upper growth optimum temperature (25\u0026deg;C), respectively (Omazić et al., 2023; Su et al., 2022); PAR represents the average photosynthetically active radiation in 1 h (J m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); The daily relative photo-thermal product is calculated as \u003cem\u003eRPTP\u003c/em\u003e (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); \u003cem\u003eRTE\u003c/em\u003e(i) is the average \u003cem\u003eRTE\u003c/em\u003e during \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e hour in 1 day; PAR(i) is the average PAR during \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e hour in 1 day (J m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The unit conversion factor for converting J m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to J m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is 3600, while the unit conversion factor for converting J m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is 10\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\"\u003e\u003ch2\u003e2.3.2 Grape aboveground dry matter and yield\u003c/h2\u003e\u003cp\u003eDuring the experiment, grapes were randomly sampled from each plot at 5 to 7 day intervals, with one vine sampled per plot. After measuring the fresh weight, the vines were separated into stems (G), leaves (Y), and fruits (M). The separated parts were subjected to enzyme deactivation in an oven at 105\u0026deg;C for 30 minutes, followed by drying to a constant weight at 80\u0026deg;C. The dry weights were then recorded. Grapes yield was estimated based on planting density.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\"\u003e\u003ch2\u003e2.3.3 Dry matter allocation index and harvest index\u003c/h2\u003e\u003cp\u003eThe index of dry matter allocation to each organ was calculated as the proportion of organ weight relative to total aboveground dry matter. Harvest index (HI) was the ratio of fruit dry matter to total aboveground dry matter accumulation (Li et al., 2020b; Zhang et al., 2019):\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\"\u003e\u003ch2\u003e2.3.4 WUE and RUE\u003c/h2\u003e\u003cp\u003eWater consumption was calculated using the water balance equation (Li et al., 2020a; Zheng et al., 2024b):\u003c/p\u003e\u003cdiv id=\"Equ4\"\u003e\u003cdiv id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$ET{\\text{=}}\\sum\\limits_{{i=1}}^{m} {E{T_i}}$$\u003c/div\u003e\n \u003cdiv\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ5\"\u003e\n \u003cdiv id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$E{T_i}=10\\sum\\limits_{{j=1}}^{n} {{r_j}} {H_j}({\\theta _{ji}} - {\\theta _{j(i+1)}})+{M_i}+{P_i}+{K_i}$$\u003c/div\u003e\n \u003cdiv\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cem\u003eET\u003c/em\u003e represents the water consumption of the whole reproductive period (mm); \u003cem\u003eET\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the water consumption of each phenological stage (mm); \u003cem\u003em\u003c/em\u003e represents the number of grape phenological stage; \u003cem\u003en\u003c/em\u003e represents the number of soil layers; \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e represents the soil capacity of the \u003cem\u003ej\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e layer (g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e); \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e represents the soil thickness of the \u003cem\u003ej\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e layer (cm); \u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003e\u003cem\u003eji\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003e\u003cem\u003ej(i+1)\u003c/em\u003e\u003c/sub\u003e represent the water content at the beginning and the end of the calculation time period, %; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents irrigation amount during the time period (mm); \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents precipitation during the time period (mm); equal to zero inside the greenhouse); \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents groundwater recharge during the time period (mm). The water table in this region was deeper than 5m, thus, \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e was set to zero.\u003c/p\u003e\n \u003cp\u003eWater use efficiency (WUE, kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) was determined as per Li et al. (2024) :\u003c/p\u003e\n \u003cdiv id=\"Equ6\"\u003e\n \u003cdiv id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$$WUE{\\text{=}}\\frac{{Yield}}{{ET}}$$\u003c/div\u003e\n \u003cdiv\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eRadiation use efficiency (RUE, g MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was calculated at biomass levels (Liu et al., 2023):\u003c/p\u003e\n \u003cdiv id=\"Equ7\"\u003e\n \u003cdiv id=\"FileID_Equ7\" name=\"EquationSource\"\u003e$$RUE{\\text{=}}\\frac{{DDMA}}{{IPAR}}$$\u003c/div\u003e\n \u003cdiv\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe intercepted photosynthetically active radiation (\u003cem\u003eIPAR\u003c/em\u003e, MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) of the plant canopy was determined as follows (Liu et al., 2023):\u003c/p\u003e\n \u003cdiv id=\"Equ8\"\u003e\n \u003cdiv id=\"FileID_Equ8\" name=\"EquationSource\"\u003e$$IPAR{\\text{=}}\\sum {PAR(1 - {e^{ - k \\times LAI}})}$$\u003c/div\u003e\n \u003cdiv\u003e8\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, PAR is photosynthetically active radiation (J m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); \u003cem\u003ek\u003c/em\u003e is the extinction coefficient and \u003cem\u003eDDMA\u003c/em\u003e is aboveground dry matter (g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.4 Logistic model\u003c/h2\u003e\n \u003cp\u003eTo investigate the response of the dry matter accumulation process to different irrigation treatments, Eq. 9 was employed to fit the dry matter accumulation process. The photo-thermal products during grape growth (PTP, MJ m\u003csup\u003e-2\u003c/sup\u003e) was used as the independent variable, while the dry matter (g m\u003csup\u003e-2\u003c/sup\u003e, g plant\u003csup\u003e-1\u003c/sup\u003e) was used as the dependent variable (Yan et al., 2022a).\u003c/p\u003e\n \u003cdiv id=\"Equ9\"\u003e\n \u003cdiv id=\"FileID_Equ9\" name=\"EquationSource\"\u003e$$y=\\frac{A}{{(1+{e^{B - Kx}})}}$$\u003c/div\u003e\n \u003cdiv\u003e9\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, A is the upper limit of maximum dry matter accumulation; B and K are constants.\u003c/p\u003e\n \u003cp\u003eBy deriving Eq. 9, the accumulation rate of aboveground dry matter (\u003cem\u003eV\u003c/em\u003e, g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e) is obtained as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ10\"\u003e\n \u003cdiv id=\"FileID_Equ10\" name=\"EquationSource\"\u003e$$V(t)=\\frac{{dy}}{{dt}}=\\frac{{AK{e^{B - Kx}}}}{{{{(1+{e^{B - Kx}})}^2}}}$$\u003c/div\u003e\n \u003cdiv\u003e10\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhen Eq. 10 is equal to zero, the time required to achieve the maximum the aboveground dry matter rate (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) can be obtained, as shown in Eq. 12; when\\({X_{\\hbox{max} }}=\\frac{B}{K}\\), the maximum accumulation rate of aboveground dry matter (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e) is obtained, as shown (Eq. 11).\u003c/p\u003e\n \u003cdiv id=\"Equ11\"\u003e\n \u003cdiv id=\"FileID_Equ11\" name=\"EquationSource\"\u003e$${V_{\\hbox{max} }}=\\frac{{AK}}{4}$$\u003c/div\u003e\n \u003cdiv\u003e11\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ12\"\u003e\n \u003cdiv id=\"FileID_Equ12\" name=\"EquationSource\"\u003e$${X_{\\hbox{max} }}=\\frac{B}{K}$$\u003c/div\u003e\n \u003cdiv\u003e12\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eYv\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003emax\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e represented the dry matter when the rate of dry matter accumulation reached maximum (g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ13\"\u003e\n \u003cdiv id=\"FileID_Equ13\" name=\"EquationSource\"\u003e$$Y{v_{\\hbox{max} }}=\\frac{A}{{\\text{2}}}$$\u003c/div\u003e\n \u003cdiv\u003e13\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ14\"\u003e\n \u003cdiv id=\"FileID_Equ14\" name=\"EquationSource\"\u003e$${V_{{\\text{avg}}}}=\\frac{{AK}}{{\\text{6}}}$$\u003c/div\u003e\n \u003cdiv\u003e14\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e is the average accumulation rate of dry matter (g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n \u003cp\u003eTo divide the growth processes into the gradual, rapid and slow growth stages, two inflection points \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) were determined using Eq. 15. The time at which the aboveground dry matter approached its maximum value was expressed as \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e).\u003c/p\u003e\n \u003cdiv id=\"Equ15\"\u003e\n \u003cdiv id=\"FileID_Equ15\" name=\"EquationSource\"\u003e$${X_1}=\\frac{{ - \\ln (\\frac{{2+\\sqrt 3 }}{{{e^B}}})}}{K},{X_2}=\\frac{{ - \\ln (\\frac{{2 - \\sqrt 3 }}{{{e^B}}})}}{K}$$\u003c/div\u003e\n \u003cdiv\u003e15\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe dry matter accumulation during the gradually growth stage, rapid growth stage and slow growth stage were denoted as \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e (g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e (g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), respectively:\u003c/p\u003e\n \u003cdiv id=\"Equ16\"\u003e\n \u003cdiv id=\"FileID_Equ16\" name=\"EquationSource\"\u003e$${y_{\\text{1}}}=\\frac{A}{{(1+{e^{B - K{x_1}}})}},{y_2}=\\frac{A}{{(1+{e^{B - K{x_2}}})}} - {y_1},{y_3}=\\frac{A}{{(1+{e^{PT{P_{\\hbox{max} }}}})}} - \\frac{A}{{1+{e^{B - K{x_2}}}}}$$\u003c/div\u003e\n \u003cdiv\u003e16\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eThe analysis of variance (ANOVA) and multiple comparisons (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were conducted using SPSS 26.0. Random forest was employed to assess the relative significance of the characteristic parameters of aboveground dry matter accumulation. Data visualization was performed using Origin 2021 and R Studio 2023.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e2.6 Evaluation of the model accuracy\u003c/h2\u003e\n \u003cp\u003eThe accuracy of the models was evaluated using the coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e; Eq. 17), normalized root mean square error (\u003cem\u003eNRMSE\u003c/em\u003e; Eq. 18, 19), and the index of agreement (\u003cem\u003eIA\u003c/em\u003e; Eq. 20) (Wang et al., 2022b).\u003c/p\u003e\n \u003cdiv id=\"Equ17\"\u003e\n \u003cdiv id=\"FileID_Equ17\" name=\"EquationSource\"\u003e$${R^2}=\\frac{{[\\sum\\nolimits_{1}^{n} {({X_{obs}} - {{\\overline {X} }_{obs}})({X_{sim}} - {{\\overline {X} }_{sim}}){]^2}} }}{{\\sum\\nolimits_{1}^{n} {{{({X_{obs}} - {{\\overline {X} }_{obs}})}^2}\\sum\\nolimits_{1}^{n} {{{({X_{sim}} - {{\\overline {X} }_{sim}})}^2}} } }}$$\u003c/div\u003e\n \u003cdiv\u003e17\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ18\"\u003e\n \u003cdiv id=\"FileID_Equ18\" name=\"EquationSource\"\u003e$$RMSE=\\sqrt {\\frac{1}{n} \\times \\sum\\limits_{{i=1}}^{n} {{{({X_{obs}} - {X_{sim}})}^2}} }$$\u003c/div\u003e\n \u003cdiv\u003e18\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ19\"\u003e\n \u003cdiv id=\"FileID_Equ19\" name=\"EquationSource\"\u003e$$NRMSE={\\text{100}} \\times \\frac{{RMSE}}{{{{\\overline {X} }_{obs}}}}$$\u003c/div\u003e\n \u003cdiv\u003e19\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ20\"\u003e\n \u003cdiv id=\"FileID_Equ20\" name=\"EquationSource\"\u003e$$IA={\\text{1-}}\\frac{{\\sum\\limits_{{i=1}}^{n} {{{({X_{obs}} - {X_{sim}})}^2}} }}{{\\sum\\limits_{{i=1}}^{n} {{{(\\left| {{X_{obs}} - {{\\overline {X} }_{obs}}} \\right|+\\left| {{X_{sim}} - {{\\overline {X} }_{obs}}} \\right|)}^2}} }}$$\u003c/div\u003e\n \u003cdiv\u003e20\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003esim\u003c/em\u003e\u003c/sub\u003e is the simulated value; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eobs\u003c/em\u003e\u003c/sub\u003e is the observed value; \u003cem\u003en\u003c/em\u003e is the number of values; \\(\\:\\stackrel{-}{X}\\)\u003csub\u003eobs\u003c/sub\u003e is the average observed value. The coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) indicates a linear relationship between the simulated and the actual values. \u003cem\u003eNRMSE\u003c/em\u003e represents the relative difference from the mean, expressed as an unbounded percentage. Specifically, \u003cem\u003eNRMSE\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;15% indicates \u0026quot;good\u0026quot; agreement, 15% \u0026le; \u003cem\u003eNRMSE\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;30% indicates \u0026quot;moderate\u0026quot; agreement, and \u003cem\u003eNRMSE\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;30% indicates \u0026quot;poor\u0026quot; agreement. The index of agreement (0\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eIA\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;1) serves as a descriptive, relative, and bounded measure of measure performance. The closer the \u003cem\u003eIA\u003c/em\u003e value and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e value are to 1, the better the model performance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results analysis","content":"\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.1 Dry matter accumulation\u003c/h2\u003e\n \u003cp\u003eAs the reproductive period progressed, the dry matter accumulation in each organ and aboveground of grapes under different irrigation treatments showed a characteristic S-shaped growth curve. The growth was characterized by an initial slow increase in the early stages, followed by rapid growth in the middle stages, and eventual stabilization after reaching a peak (Fig. 3). Except for the I3 treatment in 2023, no significant differences were observed in stem, leaf, fruit, and aboveground dry matter accumulation among the different irrigation treatments during Stages I and II (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). Differences in the effects of different irrigation treatments on dry matter accumulation gradually became significant during Stage III and Stage IV. Moreover, the final stem, leaf, fruit and aboveground biomass of I3 treatment in Stage IV were 109.46 and 118.64 g plant\u003csup\u003e− 1\u003c/sup\u003e, 40.87 and 43.73 g plant\u003csup\u003e− 1\u003c/sup\u003e, 94.76 and 100.8 g plant\u003csup\u003e− 1\u003c/sup\u003e, and 1206.65 and 1295.59 g m\u003csup\u003e− 2\u003c/sup\u003e in 2023 and 2024, respectively. These values were only 58.0 and 64.84%, 87.20 and 86.27%, 74.25 and 80.34%, 67.52% and 73.28% of those in the CK treatment. In addition, significant differences in stem, leaf, and aboveground dry matter accumulation were observed among different irrigation treatments at Stage Ⅳ (except I1 and I2 treatments in 2024) (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). However, the effect of different irrigation treatments on the fruit dry weight was not significant at this stage, except for I3 treatment. This showed that grape stem and leaf dry weights were more sensitive to water stress than fruit dry weight. Mild and medium water stress treatments effective reduced redundant growth of grape stems and leaves (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), but had no significant effect on the fruit yield (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n \u003cp\u003eThe relationship between dry matter and the photo-thermal products (PTP) in grape stem, leave, fruit, and aboveground under different irrigation amount was described using the logistic Eq. 1, 2, 3 The fitted curves and key parameters of the curves are shown in Fig. 3 and Table 3. As shown in Fig. 4, the fitted models described the different irrigation treatments well (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ≥ 0.97, \u003cem\u003eNRMSE\u003c/em\u003e ≤ 15%, \u003cem\u003eIA\u003c/em\u003e ≥ 0.99). The model provided the best fit for fruit dry matter accumulation, followed by leaf dry matter accumulation, while the simulation accuracy for stem and aboveground dry matter accumulation was relatively lower. Specifically, the aboveground dry matter accumulation in I3 treatment was the worst simulated. However, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eNRMSE\u003c/em\u003e, and \u003cem\u003eIA\u003c/em\u003e values reached 0.98 and 0.98, 10.23 and 10.53%, and 0.99 and 0.99 in the two years. As shown in Table 3, parameter A (the upper limit of maximum dry matter accumulation) was significantly affected by irrigation amount (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), while parameters B (initial parameter) and K (dry matter growth rate) were not significantly affected (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). I1 treatment significantly affected only the stem A values (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), while the I2 and I3 treatments significantly affected on the A values of stem, leaf, fruit, and aboveground (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Particularly, in I3 treatment, A values for the stem, leaf, fruit and aboveground dry matter were 104.22 and 115.83 g plant\u003csup\u003e-1\u003c/sup\u003e, 39.90 and 44.04 g plant\u003csup\u003e-1\u003c/sup\u003e, 107.68 and 102.02 g plant\u003csup\u003e-1\u003c/sup\u003e, and 1169.76 and 1334.82 g m\u003csup\u003e-2\u003c/sup\u003e in the two years. Compared with other irrigation treatments, the A values for stem, leaf, fruit, and aboveground dry matter in the I3 treatment were decreased by 12.41–43.04%, 3.80-15.09%, 3.87–26.45% and 8.23–35.27%, respectively.\u003c/p\u003e\n \u003cdiv id=\"15\" name=\"图片 15\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eParameters of model curves for dry matter accumulation in grapes under different irrigation treatments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOrgan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCurve parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCurve parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK*10^\u003csup\u003e−2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK*10^\u003csup\u003e−2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"16\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eGDMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182.97a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"16\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180.84a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157.87b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150.66b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.32c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.24bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.22d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.83c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.75c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eYDMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.99a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.90a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.94ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.84ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.79bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.61a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.78ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.85ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.90c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.37a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.04b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eMDMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.41a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.85a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.66a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.80a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.75ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.42ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.65a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.86bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.13bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.42a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.68c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.02c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eDDMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1807.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1808.34a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1605.76ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1607.23ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1397.71c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1454.54bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1169.76c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.66a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1334.82c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe curves of aboveground dry matter accumulation rate under different irrigation treatments are shown in Fig. 5, while the characteristic parameters of the aboveground dry matter accumulation are shown in Table 4. The dry matter accumulation and its rate decreased with increasing water stress. Concurrently, water stress hastened the occurrence of maximum aboveground dry matter (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e). Specifically, the peak accumulation rate in the I3 treatment occurred at PTP values of 232.67 and 237.12 MJ m\u003csup\u003e− 2\u003c/sup\u003e in the two years, which was 26.21 and 21.24% earlier than those under the CK treatment. However, no significant differences were observed in the timing of peak occurrence among the different irrigation treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The maximum aboveground dry matter rate (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e) occurred during Stage Ⅲ, corresponding to PTP values (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e) ranging from 230 to 250 MJ m\u003csup\u003e− 2\u003c/sup\u003e. Both \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and average dry matter accumulated rate (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e) decreased with reduced irrigation amount. I3 treatment had the lowest \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e for all treatments, 3.35 and 4.08 g m\u003csup\u003e− 2\u003c/sup\u003e MJ\u003csup\u003e− 1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e, and 2.23 and 2.72 g m\u003csup\u003e− 2\u003c/sup\u003e MJ\u003csup\u003e− 1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e in the two years, respectively. Significant differences in \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e were observed among the different irrigation treatments in 2024 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Compared with the rest of the treatments, \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e decreased by 21.24–26.21%, 12.82–20.24% and 6.85–9.95%, while \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e decreased by 21.16–26.40%, 12.82–20.36% and 6.85–10.08%. The rapid growth stage of dry matter accumulation usually occurred around Stage II, corresponding to a PTP of approximately 130 MJ m\u003csup\u003e− 2\u003c/sup\u003e, and ended in late Stage III and pre-Stage IV with a PTP of approximately 360 MJ m\u003csup\u003e− 2\u003c/sup\u003e. The duration of the rapid growth stage (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) shortened with decreasing irrigation amount, with the I3 treatment being significantly shorter by 1.55–12.06% compared with other treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). In the two years, the proportion of dry matter accumulation during the rapid growth stage (\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) accounted for as high as 57.74% of the total dry matter accumulation throughout the entire reproductive period. Extending \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is beneficial for the dry matter accumulation in grapes.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristic parameters of aboveground dry matter accumulation in grapes under different irrigation treatments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eI1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eI2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eI3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eI1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eI2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eI3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253.42a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232.67a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253.27a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247.21a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237.34a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237.12a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.54a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.20a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.18a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.68b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.38c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.08d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYvmax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e903.51a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e802.88ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e698.85b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e584.88b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e904.17a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e803.62b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e727.27bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e667.41c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.23a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.52a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.90a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.55a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.35a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.17a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.52a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365.22a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368.19a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360.25a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346.64a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344.71a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.23a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.52a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.90a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.55a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.35a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.17a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.52a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261.82a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251.81ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247.32ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230.24b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.84a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226.09ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218.58ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215.19b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199.57a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219.29a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.83a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.30a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.23a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.85a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189.7a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381.87a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339.34ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295.37b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247.20b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e382.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339.65b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307.38bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282.08c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1043.28a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e927.09ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e806.97b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e675.36b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1044.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e927.94b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e839.78 bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e770.66c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319.10a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297.07ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264.87ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.69b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e348.25a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313.29b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288.91c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266.28c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.12b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.72d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNote: \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e represents the PTP when grapes reach the maximum rate of aboveground dry matter accumulation, MJ m\u003csup\u003e− 2\u003c/sup\u003e; \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e represents the maximum rate of aboveground dry matter accumulation in grapes, g m\u003csup\u003e− 2\u003c/sup\u003e MJ\u003csup\u003e− 1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003eYvmax\u003c/em\u003e represents the amount of aboveground dry matter accumulation in grapes when \u003cem\u003eV\u003c/em\u003e = \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, g m\u003csup\u003e− 2\u003c/sup\u003e; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e represent the start times of the rapid growth stage and the slow stage, respectively, MJ m\u003csup\u003e− 2\u003c/sup\u003e; \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e represent the duration of the gradual, rapid, and slow growth periods, respectively, MJ m\u003csup\u003e− 2\u003c/sup\u003e; \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e represent the accumulation of aboveground dry matter during the gradual, rapid, and slow growth periods, respectively, g m\u003csup\u003e− 2\u003c/sup\u003e; \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e represents the average accumulation rate, g m\u003csup\u003e− 2\u003c/sup\u003e MJ\u003csup\u003e− 1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.2 Dry matter allocation\u003c/h2\u003e\n \u003cp\u003eThe fitted curves of the allocation indexes of stem, leave and fruit with PTP under different irrigation treatments and the fitting accuracy indexes are shown in Figs. 6, 7 and Supplementary Table 1. The stem allocation index exhibited a rapid increase with increasing PTP during Stage Ⅰ and Stage Ⅱ, followed by a significant decrease in Stage III, and stabilized in Stage IV. Irrigation increased the stem allocation index, which reached the highest values in CK treatment at 0.64 and 0.65 in the two years. The Leaf allocation index and fruit allocation index showed opposite trends with increasing PTP. The leaf allocation index monotonically decreased, with the minimum values occurring in Stage Ⅳ, and the I3 treatment had the highest leaf allocation index of 0.17 in both years. Grape fruit allocation index increased monotonically, with the I3 treatment reaching a final allocation index as high as 0.39 in 2023 and 0.38 in 2024 at Stage IV. Different irrigation treatments had no significant effects of on stem, leaf, and fruit allocation indexes in the first three reproductive stages (except for the stem allocation index at Stage Ⅲ in 2023) (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). However, significant differences were observed in Stage IV (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Compared with CK treatment, the I1, I2, and I3 treatments decreased the final stem allocation index during the harvesting period by 3.85 and 5.88%, 9.62 and 7.84%, and 13.46 and 11.76% in the two years, respectively. The leaf allocation index increased by 7.69 and 7.15%, 15.38 and 14.29%, and 30.77 and 21.43%. The fruit allocation index increased by 2.86 and 2.86%, 5.71 and 5.71%, and 11.43 and 8.57%, respectively. The equations fitted well for stem, leaf, and fruit allocation indexes for different irrigation treatments (Fig. 6), with R² ≥ 0.93, NRMSE ≤ 15%, and IA ≥ 0.98. As shown in Fig. 7, the fitting accuracy was in the order of fruit \u0026gt; leaf \u0026gt; stem. Among all treatments, the I3 treatment for fruit had the best fit, with \u003cem\u003eR\u003c/em\u003e² values of 0.99 and 0.96, \u003cem\u003eNRMSE\u003c/em\u003e values of 12.54 and 5.51%, and \u003cem\u003eIA\u003c/em\u003e values of 0.99 and 0.99 in the two years.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.3 Aboveground dry matter accumulation, yield and harvest index\u003c/h2\u003e\n \u003cp\u003eThe accumulation and allocation of aboveground dry matter were key factors of yield formation. The correlation analyses of aboveground dry matter accumulation, yield, and harvest index (HI) of greenhouse grapes under different irrigation treatments are shown in Fig. 8 Aboveground dry matter accumulation, yield, and HI fluctuated within the ranges of 15.04 ± 2.36 and 15.09 ± 1.87 t hm\u003csup\u003e− 2\u003c/sup\u003e, 30.79 ± 3.67 and 32.87 ± 2.92 t hm\u003csup\u003e− 2\u003c/sup\u003e, and 0.37 ± 0.02 and 0.41 ± 0.05 in the two years, respectively. There were no significant differences in HI, yield, and aboveground dry matter accumulation between the two years (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), and these parameters were closely related. HI and aboveground dry matter accumulation explained for 91 and 88% of the variation in yield in 2023, respectively. Yield and aboveground dry matter accumulation explained for 93 and 81% of the variation in HI in 2024, respectively.\u003c/p\u003e\n \u003cp\u003eThe effects of different irrigation treatments on aboveground dry matter accumulation and yield of greenhouse grapes are shown in Fig. 9. As the irrigation amount decreased, the yield and aboveground dry matter accumulation of greenhouse grapes were in the order of CK \u0026gt; I1 \u0026gt; I2 \u0026gt; I3. Specially, the reductions in yield and aboveground dry matter accumulation in the I2 and I3 treatments reached significant levels (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The HI values followed the order I3 \u0026gt; I2 \u0026gt; I1 \u0026gt; CK. It could be seen that reducing irrigation increased the HI of the crop, although it decreased the yield and aboveground dry matter accumulation. The yield and aboveground dry matter accumulation of I1 treatment in this study were 32.46 and 31.65 t hm\u003csup\u003e− 2\u003c/sup\u003e, and 1611.91 and 1547.66 g m\u003csup\u003e− 2\u003c/sup\u003e in the two years, respectively. The I1 treatment increased HI without significantly decreasing yield and aboveground dry matter accumulation, making it an ideal irrigation treatment. In addition, the HI of I3 treatment increased by an average of 2.69%, 5.03% and 9.03% in the two years compared with I2, I1, and CK treatments, respectively. However, this treatment had the lowest yield and aboveground dry matter accumulation in the two years. This showed that increasing crop HI might not necessarily lead to increased yield.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.4 Correlation of characteristic parameters of aboveground dry matter accumulation with yield, WUE and RUE\u003c/h2\u003e\n \u003cp\u003eRadiation use efficiency (RUE) and water use efficiency (WUE) were key factors in determining yield. There were different degrees of reduction in RUE with decreasing irrigation amount. Specifically, the I3 treatment experienced a significant decrease in RUE (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), which was 20.91 and 12.02%, 18.21 and 5.15%, and 10.31 and 2.09% lower than those in CK, I1, and I2 treatments in the two years, respectively. The water consumption of grapes in different treatments at each fertility stage and at full fertility stage are shown in Fig. 10a, e. WUE initially increased and then decreased with decreasing irrigation amount. The I2 treatment had the highest WUE, followed by I1 and I3 treatments. The CK treatment exhibited a significant decrease in WUE compared with the other treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The I2 treatment was identified as the optimal strategy, significantly enhancing WUE (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) without significantly reducing RUE (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). The I1 treatment was the suboptimal treatment, significantly increasing WUE (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) without significantly reducing RUE (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) (Fig. 10).\u003c/p\u003e\n \u003cp\u003eThe correlations between the characteristic parameters of aboveground dry matter accumulation with yield, RUE, and WUE are shown in Fig. 11. Combined with the data in Table 4, yield and RUE were significantly and positively correlated with \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eYvmax\u003c/em\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e in both years (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, r ≥ 0.2). WUE was insignificantly and negatively correlated with \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05, r \u0026lt; 0.2). In 2024, WUE exhibited a significant correlation with \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eYvmax\u003c/em\u003e, \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, r ≥ 0.2). Data from both years revealed a significant positive correlation between \u003cem\u003eYvmax\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. This showed that during the rapid growth stage, \u003cem\u003eYvmax\u003c/em\u003e accounted for a larger proportion of \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e when the maximum dry matter accumulation rate was reached, and the two were mutually reinforcing. A significant positive correlation was also observed between \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e accounted for more than 57.74% of the total aboveground dry matter accumulation throughout the entire reproductive period. A random forest algorithm was employed to rank the importance of indicators that were significantly correlated with yield and RUE in both 2023 and 2024 (no indicators were significantly correlated with WUE in both 2023 and 2024). The results showed that the characteristic parameters of aboveground dry matter accumulation, specifically \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003evmax\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, exhibited the most significant effect on yield and RUE. The duration of the rapid growth stage (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) was more critical for the formation of yield and RUE than the other two stages. The relative importance of \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e was the lowest.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Effects of irrigation on yield, aboveground dry matter accumulation, and HI in grape\u003c/h2\u003e \u003cp\u003eIn this study, the irrigation amount had a significant effect on grape yield, which gradually decreased with increasing water stress. Medium water stress (I2) and severe water stress (I3) significantly inhibited yield (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Over the two-year study period, yield reductions of 4.65\u0026ndash;24.60% were observed in the treatments compared with the CK treatment. This finding is consistent with the study by Pech et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), who found that sustained decreases in irrigation amount led to grape yield reductions of 9\u0026ndash;31%. Numerous studies have shown that reduced irrigation results in yield losses in grapes, partly due to the inhibition of cell expansion and the diminution of the inner mesocarp cell sap, which subsequently reduces berry quality (Torres et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Mild stress irrigation can improve fruit quality without significantly reducing the yield (Jiang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, severe water stress may lead to undesirable fruit development. Similar to the findings of Mart\u0026iacute;nez-Moreno et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), water that failed to meet crop growth requirements could lead to significant yield reductions. Studies by Han et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) on wine grapes in North China and Wang et al. (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) on sweet peppers in Northwest China showed that the yields of grapes and sweet peppers increased and then decreased with increasing water stress, which inconsistent with the results of the present study. Their research indicated that mild water stress irrigation did not decrease yield but increased the potential yield of the crop, while over-irrigation or under-irrigation limited yield. This analysis may be supported by the following factors: 1) increasing the irrigation amount is favorable for yield improvement, but there is a certain threshold range. Yield can be decreased by exceeding or falling below the irrigation threshold. The irrigation threshold can be influenced by a variety of factors, such as crop variety and the climate of the growing region; 2) excessive soil moisture supply leads to redundant crop growth and excessive stem and leaf growth. Increased competition for nutrient and water between nutrient organs and fruits may reduce crop yield (Du et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lodhi, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); 3) deficit irrigation of grapes reduces the amount of nutritive growth hormones, such as cytokinins, and increases the amount of reproductive growth hormones, such as abscisic acid. Optimizing the growth state of the bud promotes the accumulation of carbohydrate reserves, improves berry development, and encourages crop yield (Conesa et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In a study of vineyards in the Xinjiang region, Ren et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) similarly found that mild water stress was more conducive to grape yield formation. Instead of surface drip irrigation, they employed root-zone irrigation. Root-zone irrigation directly supplies water to the grape root system, thereby reducing unnecessary evaporation from the soil surface and enhancing WUE. This approach is particularly advantageous for water conservation and yield improvement under the extremely arid conditions of Xinjiang. Additionally, root-zone irrigation helps minimize redundant nutrient growth in fruit trees, thereby conserving photosynthetic products and promoting fruit development.\u003c/p\u003e \u003cp\u003eIn the present study, mild water stress (I1 treatment) reduced aboveground dry matter accumulation by 9.80 and 12.46% compared with fully irrigated treatment (CK treatment) in the two years, while these reductions reached an insignificant level (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Medium water stress (I2 treatment) and severe water stress (I3 treatment) significantly reduced aboveground dry matter accumulation by 20.99 and 19.21%, 32.48 and 26.72% in the two years, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003e). These findings differ from those of Wang et al. (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e), who reported that aboveground dry matter accumulation in sweet peppers in Northwest China initially increased and then decreased with decreasing irrigation. This discrepancy may be caused by the different irrigation gradient settings. In their experiment, the highest irrigation level was 105% the crop evapotranspiration (ETc), and over-irrigation appeared to inhibit dry matter accumulation. Additionally, meteorological factors can influence crop dry matter accumulation (Ronga et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e). This study was conducted in a solar greenhouse in the cold region of Northeast China, characterized by a temperate monsoon climate. Their study conducted in a solar greenhouse in Northwest China, characterized by a temperate continental climate. The complexity of the greenhouse photothermal environment and the significant differences in climatic characteristics lead to differences in radiation and temperature. However, both studies observed that dry matter accumulation was hindered under excessive drought conditions, likely due to the reduced stomatal conductance of leaves, which weakened photosynthesis (Deng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023c\u003c/span\u003e; P\u0026eacute;rez-\u0026Aacute;lvarez et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Weiler et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, their study found that 75% ETc dry matter accumulation was still greater than 90% ETc, indicating that medium water stress treatment was more favorable for dry matter accumulation. In contrast, this study concluded that mild water stress was more favorable for dry matter accumulation in grapes. Therefore, cultivar differences also affect dry matter accumulation (Lin et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). In the study by Chen et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), it was observed that the total dry matter of potted grapes was higher under 50% irrigation than under 100% irrigation, which was different from the findings of the present study. This discrepancy can be attributed to the use of alternate partial root-zone drip irrigation at 50% irrigation, compared to conventional irrigation at 100% irrigation. Alternating partial root-zone drip irrigation minimizes inefficient water evaporation and enhances WUE. This efficient water management enables plants to utilize water more effectively, thereby promoting dry matter accumulation. Thus, irrigation practice is a significant factor influencing dry matter accumulation.\u003c/p\u003e \u003cp\u003eIn this study, the logistic equation effectively fitted the process of dry matter accumulation in various organs and aboveground dry matter of grapes under different irrigation treatments, with the best fit under no water stress condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Dou et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also obtained excellent simulations using this equation to fit aboveground dry matter, stem, leaf, and spike weights during the growth period of summer maize under different water stress conditions. In this study, the model A values were sensitive to changes in irrigation amount (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In 2024, irrigation amount significantly affected on \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, but not on \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Yan et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) showed that irrigation amount significantly influenced on the model A values of winter wheat, as well as on \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e. This discrepancy may arise from differences in experimental regions and subject materials, as their study was conducted in a large field trial in Northwest China. Numerous studies shown that irrigation influences the cycle and rate of aboveground dry matter accumulation (Liu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, dry matter accumulation characteristics are influenced by many factors, such as variety, climatic condition, planting and management pattern (Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mao et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ma et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) concluded that both \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e of maize increased with increasing irrigation levels, which aligned with the results of this study. However, their study concluded that irrigation shortened \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, contrary to the results of this study. Meanwhile, this study suggested that irrigation prolonged the entry time of the rapid growth stage in favor of dry matter accumulation in grapes. In contrast, Zhang et al. (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) pointed out that reasonable irrigation could induce maize dry matter to enter the rapid growth stage and prolong the slow growth stage of dry matter at the same time, which was beneficial for increasing maize dry matter accumulation. However, this was also contrary to the findings of this study. This difference may be due to the fact that the experimental irrigation setups are different. In addition, the test subjects are changed from wheat and maize to grapes, which are more sensitive to water change (Chaves et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The early phase of berry growth, characterized by active cell division, is highly sensitive to water stress (Caruso et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Water stress induces physiological drought in grapes, resulting in reduced growth potential and an accelerated attainment of the peak dry matter accumulation rate. Consequently, the rapid growth stage is initiated earlier. Notably, when the aboveground dry matter accumulation rate reached \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e for each irrigation treatment condition in grapes, \u003cem\u003eYvmax\u003c/em\u003e was approximately 50% of its corresponding maximum theoretical dry matter accumulation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In addition, the rapid growth stage was mainly concentrated in Stage Ⅲ, during which the aboveground dry matter accumulation accounted for more than 50% of the total. This pattern was similar to the results obtained by Wei et al. (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the percentage of dry matter accumulation in rice during the rapid growth stage.\u003c/p\u003e \u003cp\u003eIn this study, as water stress decreased, HI declined, although aboveground dry matter accumulation and yield increased. The HI values fluctuated in the range of 0.34\u0026ndash;0.43 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003e), which was similar to the findings of Li et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), who concluded that grape HI ranged from 0.36 to 0.46 under different irrigation treatments. While increased water inputs can improve dry matter and yield of grapes, a high yield does not necessarily imply a high HI. However, the overall enhancement in harvest index (HI) observed across different irrigation treatments in the study by Li et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e) contrasts with the findings of the present study. Notably, Li et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e) employed mulched drip irrigation (MDI) in their experiment, which was conducted in the arid region of Xinjiang. MDI is known for its capacity to significantly diminish soil surface evaporation, augment soil water storage and moisture retention, and thereby elevate WUE (Vishwakarma et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This irrigation method can effectively redirect resources away from nutrient growth, thereby enhancing grape yield and fruit dry matter accumulation (Wang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the more stable greenhouse environment, under equivalent water conditions, may foster more vigorous nutrient growth. In contrast, field trials, which are often subject to natural environmental stresses, tend to prompt plants to allocate a greater proportion of photosynthetic products to the fruit, resulting in higher HI. Du et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that in rice, the yield and the conversion rate of the stem and sheath from the heading stage to the maturity stage decreased with increasing irrigation amount, thus reducing the rice HI. However, adopting alternative dry\u0026ndash;wet irrigation mode could increase HI by improving the transport capacity of non-structural carbohydrates. Wang et al. (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Wang et al. (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) revealed that HI of tomato and pepper showed an initial increase and followed by a decrease with increasing irrigation amount. These variations in HI are based on changes in crop yield and aboveground dry matter accumulation under different irrigation conditions, reflecting the ability of crops to convert photosynthetic products into economic products (Yang and Zhang, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It has been shown that HI varies depending on crop variety, growing light and temperature condition, and water and environmental condition, such as soil pH and fertility (Fleisher et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Effects of irrigation on the allocation of aboveground dry matter of grape\u003c/h2\u003e \u003cp\u003eThis study concluded that increased water stress promoted the allocation of grape photosynthetic assimilation products to fruits and leaves while reducing the stem dry matter allocation index (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The sensitivity of grape organs to water stress was in the order of stem\u0026thinsp;\u0026gt;\u0026thinsp;leaf\u0026thinsp;\u0026gt;\u0026thinsp;fruit. Reducing the amount of water first led to a significant reduction in stem dry matter, followed by leaves, and finally fruits. Among the treatments, I1 treatment increased the fruit allocation index without significantly reducing aboveground dry matter accumulation and yield. The fruit allocation index reached 0.36 in the two years, which were 2.86% higher than those of the fully irrigated treatment (CK treatment) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similarly, Gao et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Dou et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) suggested that plant biomass was preferentially allocated to spikes under water stress. Golzardi et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Wang et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) demonstrated that not all of the photosynthates produced by over-irrigated leaves were transferred to the grains but were stored in the stem. However, Madani et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Yan et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) indicated that water stress significantly increased both grain dry matter allocation index and the stem allocation index in winter wheat, which was inconsistent with our findings. This discrepancy may be attributed to the different physiological characteristics of grapes and wheat. Wheat stems and spike reserves can be converted into nutrients for grain filling as they enter the reproductive growth stage, thereby maintaining and enhancing grain filling capacity in response to environmental stress. In addition, Zhang et al. (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Yan et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e) found that a longer period of high temperature and drought during the maize grain filling stage sharply reduced the root water absorption function, leading to the inactivation of nutrient organs and decreased dry matter allocation to grain. In the present study, no decrease in fruit dry matter allocation index was observed under water stress conditions. This may be due to the fact that the lowest irrigation levels are sufficient to maintain the normal growth in this study. Moreover, the greenhouse is ventilated and equipped with a cooling device during the high summer temperatures, which likely mitigates the effects of water stress. In their study on local grape varieties in New Zealand vineyards, Zhu et al. (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that fruit dry matter accounted for the largest proportion of dry matter allocation among various grape organs, followed by stems and then leaves. This finding was different from the result of the present study. There may be notable differences in the dry matter allocation patterns between grapes grown in field and greenhouse settings. Thus, the experimental environment and grape variety appear to be significant factors influencing the pattern of dry matter allocation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Effects of irrigation on WUE and RUE\u003c/h2\u003e \u003cp\u003eIn this study, the effects of irrigation treatments on the WUE and RUE of grapes were thoroughly investigated. As water stress increased, WUE initially increased and then decreased. The highest WUE was observed in the I2 treatment, which was significantly higher than that in the CK treatment. However, this increase in WUE was associated with a significant reduction in grape yield. The I1 treatment did not cause a significant decrease in yield (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and significantly increased WUE. In 2023, I1 treatment decreased water consumption by 14.56%, decreased yield by only 5.38%, and increased WUE by 9.95%. In 2024, water consumption decreased by 13.21%, yield decreased by only 4.65%, and WUE increased by 9.87% (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e10\u003c/span\u003e). These findings are consistent with those of Ma et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who showed that an average 35% decrease in water use could increase the WUE of grapes by 14\u0026ndash;23% and decrease the grape yield by only 15\u0026ndash;18%, without causing significant yield reductions. Moreover, Han et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) showed that 60% irrigation level in North China decreased grape yield by only 10.3% and increased WUE, which was significantly less than the 19.9% decrease in actual water consumption. However, their study observed an increase in both WUE and grape yield at an 80% irrigation level. Lodhi (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Ozbahce and Tari (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) concluded that appropriate water stress could increase crop yield and WUE. This study did not observe yield improvement under water stress, likely due to differences in the degree of water regulation and underlying mechanisms. The present study was conducted in a solar greenhouse, whereas the study by Han et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) was carried out in a vineyard. Therefore, different crop variety, crop type, and hydrothermal environmental factor in the growing region lead to different capacities for crop root water uptake and water use. However, Numerous studies have shown that water stress can increase WUE without affecting yield (Liao et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study concluded that RUE of grapes decreased with increasing water stress. Mild water stress (I1 treatment) and medium water stress (I2 treatment) did not significantly decrease aboveground dry matter accumulation, while severe water stress was detrimental to intercepted radiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Numerous studies have shown that irrigation can affect canopy structure, photosynthetic rate and source-store relationships in crops. This enhances the ability of canopy to intercept photosynthetically active radiation, increasing RUE and ultimately boosting yield (Gu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hou et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, excessive leaf growth may cause mutual shading within the cotton canopy, reducing RUE and dry matter accumulation (Bai et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Buesa et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) found that decreasing water consumption in grapes might change their canopy architecture and decrease the interception of photosynthetically active radiation, thereby affecting RUE. However, this did not necessarily have a negative impact on the yield and dry matter accumulation. In addition, a moderate decrease in water consumption was effective in improving WUE. Similarly, this study concluded that grape plants had a limited capacity to use radiation and water. Mild water stress (I1 treatment) increased WUE while maintaining high yields, aboveground dry matter accumulation and RUE. Subsequently, the problem of excess resources was effectively resolved. Furthermore, under fully irrigated conditions, the RUE of grapes reported by Prats-Llin\u0026agrave;s et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Spanish vineyards varied within the range of 1\u0026ndash;2 g MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which was significantly lower than that observed in the present study. This discrepancy may be attributed to the more stable light and temperature conditions within the greenhouse, coupled with more refined management practices that enable grapes to utilize light radiation more efficiently, thereby enhancing RUE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Research limitations and future prospects\u003c/h2\u003e \u003cp\u003eDespite achieving some results, this experiment has several limitations. As perennial vines, grapes will continue to grow in age, and the same irrigation pattern may induce an inter-annual lag effect (Ibba et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The diversity of internal greenhouse environments across different years further exacerbated the fluctuations in grape yield, aboveground dry matter accumulation, and resource use efficiency. Differences in the characteristic parameters of aboveground dry matter accumulation were particularly pronounced among the different irrigation treatments, especially in 2024. WUE also showed higher levels. Therefore, it is crucial to account for inter-annual variation factors and develop a long-term irrigation program and an integrated environmental control system. It is necessary to conduct multi-year sentinel experiments to provide a scientific basis for green and efficient production of solar greenhouse grapes.\u003c/p\u003e \u003cp\u003eWith the advancement of computers, descriptions of crop growth processes have become increasingly sophisticated. Functional structural plant modelling (FSPM), an emerging simulation approach in recent years (Gu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), is capable of modelling the interaction of environmental effects on plant architectures at the organ scale. It has also been widely applied to various crops, such as tomatoes (Zhang et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), grapes (Zhu et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and cotton (Gu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, there were limitations in the interface between the crop structural and functional models, which failed to fully exploit the potential of crop growth data. Additionally, the greenhouse environment exhibited spatial heterogeneity. Therefore, there is a need to develop a three-dimensional structural functional model of grapes and further enhance the mechanistic aspects of the model. This would facilitate the guidance of grape growth and development, light energy use, water and fertilizer regulation, and other specific production practices. These areas represent urgent priorities for future in-depth exploration.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eMild and medium water stress (I1 and I2 treatments) significantly affected stem, leaf, and aboveground dry matter accumulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but did not significantly impact fruit dry matter accumulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The Logistic model parameter A was the most sensitive to changes in irrigation amount. Increasing water stress could promote dry matter allocation to leaves and fruits, but this came at the expense of yield and aboveground dry matter accumulation. The Mantel test showed that \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eYv\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e were significantly positively correlated with yield and RUE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, r\u0026thinsp;\u0026ge;\u0026thinsp;0.2), while \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e was insignificantly negatively correlated with yield, RUE or WUE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, r\u0026thinsp;\u0026le;\u0026thinsp;0.2). The random forest model further determined that \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e were the most important parameters influencing the yield and RUE. Increasing \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e and extending \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e significantly promoted dry matter accumulation and yield. In this study, the yield, aboveground dry matter accumulation and RUE of I1 treatment for the two years were 32.79 and 34.24 t hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 1611.91 and 1547.66 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, and 3.90 and 3.63 g MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. None of these values were significantly lower than those in CK treatment. However, I1 treatment increased the WUE (by 9.95% in 2023 and 9.87% in 2024) and fruit allocation index (by 2.86% in 2023 and 2.86% in 2024). Mild water stress (I1 treatment) was the optimal treatment. This study provides robust theoretical support for the cultivation and management of solar greenhouse grapes in Northeast China, guiding the optimal adjustment of irrigation strategies in actual production to achieve efficient green production and sustainable development of grapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Liaoning Province, China (No.2021-MS-233), the Natural Science Foundation of Liaoning Province, China (JYTMS20231272).\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships.\u003cbr\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Natural Science Foundation of Liaoning Province, China (No.2021-MS-233), the Natural Science Foundation of Liaoning Province, China (JYTMS20231272).\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Qthanin RN, AbdAlghafar IM, Mahmoud DS, Fikry AM, AlEnezi NA, Elesawi IE, AbuQamar SF, Gad MM, El-Tarabily KA (2024) Impact of rice straw mulching on water consumption and productivity of orange trees [Citrus sinensis (L.) 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In Silico Plants 3:diab021. http://dx.doi.org/10.1093/insilicoplants/diab021\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"irrigation-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"irsc","sideBox":"Learn more about [Irrigation Science](http://link.springer.com/journal/271)","snPcode":"271","submissionUrl":"https://submission.nature.com/new-submission/271/3","title":"Irrigation Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dry matter accumulation and allocation, Grape yield, Water and radiation use efficiency, Photo-thermal products, Logistic equation","lastPublishedDoi":"10.21203/rs.3.rs-6100579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6100579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExcessive irrigation wastes resources and impairs plant dry matter and yield. The study explored the effects of three irrigation levels (I1: 65\u0026ndash;85% \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e, I2: 60\u0026ndash;80% \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e, I3: 55\u0026ndash;75% \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e) and a fully irrigated control (CK: 70\u0026ndash;90% \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e) on grape dry matter, yield, and resource use efficiency in solar greenhouse from 2023 to 2024. Results showed that irrigation treatments significantly affected dry matter accumulation in organs and aboveground parts, especially during fruit swelling and maturity stages. The logistic model simulated dry matter accumulation, with the maximum theoretical accumulation (A) being most sensitive to water changes. I3 treatment reduced A by 12.4-43.04% in stem, 3.80-15.09% in leaf, 3.87\u0026ndash;26.45% in fruit, and 8.23\u0026ndash;35.27% in aboveground parts. Lower irrigation amount shortened the rapid growth stage duration (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) and decreased the maximum aboveground dry matter rate time (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e) and the dry matter accumulation maximum (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e) and average (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eavg\u003c/em\u003e\u003c/sub\u003e) rates. At maturity, lower irrigation amount promoted dry matter allocation to leaves and fruits but reduced yield. The Mantel test revealed that seven dry matter accumulation characteristic parameters were significantly and positively correlated with yield and radiation use efficiency (RUE) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, r\u0026thinsp;\u0026ge;\u0026thinsp;0.2). The random forest model identified \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e (the dry matter accumulation during the gradually and slow growth stages) as critical parameters influencing yield and RUE. I1 treatment was optimal that increased water use efficiency (WUE) and fruit allocation index by 7.36 and 8.37%, 2.78 and 2.78% in 2023 and 2024, with no significant impact on yield or RUE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e","manuscriptTitle":"Effects of different irrigation treatments on dry matter accumulation, allocation and yield of grapes in solar greenhouse","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-03 11:55:35","doi":"10.21203/rs.3.rs-6100579/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-30T15:17:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-11T03:32:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-05T11:26:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214717136903145732326449584296352599172","date":"2025-06-02T13:43:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51698345820073956967306076278793084955","date":"2025-05-30T09:11:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12608994791655415141602874677002747648","date":"2025-05-30T08:13:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-30T07:55:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-27T18:45:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-27T18:44:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Irrigation Science","date":"2025-02-25T02:02:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"irrigation-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"irsc","sideBox":"Learn more about [Irrigation Science](http://link.springer.com/journal/271)","snPcode":"271","submissionUrl":"https://submission.nature.com/new-submission/271/3","title":"Irrigation Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"760e9e86-9aa4-4142-b32b-f526532ecf72","owner":[],"postedDate":"March 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:07:23+00:00","versionOfRecord":{"articleIdentity":"rs-6100579","link":"https://doi.org/10.1007/s00271-025-01065-2","journal":{"identity":"irrigation-science","isVorOnly":false,"title":"Irrigation Science"},"publishedOn":"2025-11-28 15:58:08","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-03-03 11:55:35","video":"","vorDoi":"10.1007/s00271-025-01065-2","vorDoiUrl":"https://doi.org/10.1007/s00271-025-01065-2","workflowStages":[]},"version":"v1","identity":"rs-6100579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6100579","identity":"rs-6100579","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}