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Open AccessArticle

Optimized Water and Fertilizer Management of Mature Jujube in Xinjiang Arid Area Using Drip Irrigation

by 1,2,*, 1,2, 1,2 and 3,*
1
College of Water and Architectural Engineering, Shihezi University, Shihezi 832000, Xinjiang, China
2
Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production and Construction Corps, Shihezi University, Shihezi 832000, Xinjiang, China
3
College of Agricultural and Life Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
*
Authors to whom correspondence should be addressed.
Water 2018, 10(10), 1467; https://doi.org/10.3390/w10101467
Received: 7 September 2018 / Revised: 12 October 2018 / Accepted: 15 October 2018 / Published: 17 October 2018
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)

Abstract

Studying water–fertilizer coupling effects in a drip irrigation system is critical for sustainable agricultural development in arid areas, such as that of Xinjiang in China, to find out the optimized water and fertilizer management. Therefore, a two-year field experiment was conducted to find out how the combination of three levels of irrigation quotas and three levels of fertilizer amounts would affect the physiological and growth indexes of jujube, as well as ascertain the differences between drip irrigation and flood irrigation. The results showed that the interacted and coupled effects of irrigation and fertilization influenced most of the physiological indicators and growth indexes. On the other hand, the physiological and growth indexes were increased after transferring flood irrigation to drip irrigation, as the maximum chlorophyll content (CC) and photosynthetic nitrogen use efficiency (PNUE) values increased on average by 6.00%, and 11.39% in 2016, and 1.47% and 6.83% in 2017, respectively. Undoubtedly, inappropriate water and fertilizer management had negative impacts on jujube growth and yield. Based on the treatments and results in this paper, low fertilizer and moderate irrigation would be the best choice. The crop yield, irrigation water use efficiency (iWUE), and fertilizer partial productivity (PFP) increased by 6.77%, 29.48%, and 193.62% in 2016, and similar increments were also found in 2017 of 6.17%, 78.72%, and 133.06%, respectively. This indicated that fertilizer efficiency was promoted along with water use, and in turn, the water amounts were adjusted by the amount of fertilizer that was applied. Based on a comprehensive consideration of the physiological and growth indexes, a mathematical model was established, and the optimized irrigation and fertilizer amounts of jujube in northern Xinjiang area were found to be 815 mm and 400 kg ha−1 (with N–P2O5–K2O proportioned at 2–1–1.5). The results that were obtained in this paper would provide theoretical reference to the sustainable development of jujube plantation using drip irrigation in the arid areas.
Keywords: mature Jujube; water and fertilizer management; northern Xinjiang arid area mature Jujube; water and fertilizer management; northern Xinjiang arid area

1. Introduction

Jujube (Zizyphus jujube mill) is one of the most important economical crops in the northern desert of Xinjiang area in China, and it has become one of the Chinese’s favorite fruits due to its good taste, sufficient nutrition, and medical value [1]. The planting area of jujube in Xinjiang has reached 4.3 × 105 hm2, which has made it the most planted fruit in China. However, the majority of jujube are still using flood irrigation, and the irrigation quota exceeds 1000 m3/mu, leaving merely 9.5% applying drip irrigation. The traditional flood irrigation applied to a jujube plantation has disadvantages in its water-usage efficiency (WUE), production efficiency, and effects on the ecological environment [2]. On the contrary, drip irrigation is a high-efficiency water-saving irrigation method that directly supplies water and nutrients to the root of crops. As an extreme water shortage has always been the key barrier of agriculture in this area, scholars have increasingly paid more and more attention to drip irrigation technology [3].
Some related results indicated that different crops and soil types all showed positive responses to drip irrigation, compared with the traditional flood irrigation, as the crop yield increased to some extent [4]. Fertigation is the other critical factor that affects water and nutrient supplements, and thus affects crop yield and WUE [5]. In general, the rational management of water–fertilizer showed positive impacts on improving water and fertilizer management [6,7]. The effects of water and fertilizer on crop growth are not isolated, but instead interact with each other, and show a coupling effect [8]. Some scholars have explored multiple crops using drip irrigation and fertilization, such as wheat [9], cotton [10], and fruit trees [11,12]. Although the results were different as the irrigation quota and fertilizer requirement were obviously different for each type of crop studied, these studies determined that reasonable irrigation and fertilization was beneficial to the improvement of the physiological characteristics and yield of crops. Although several scholars studied the effects of irrigation or fertilizer on the jujube plantation, limited results were related to deficit irrigation [13,14] and drip irrigation [15], and no direct results were reported on the water–fertilizer interaction using the drip irrigation system, especially in extremely arid areas, such as Xinjiang province in China [1]. The difference between drip irrigation and flood irrigation were also unknown.
Based on these, a two-year field experiment was conducted to study the effects of different water and fertilizer treatments on the physiological growth and yield of jujube in Xinjiang, China. The study aims to find out the optimized water–fertilizer management of jujube in northern Xinjiang area, and thus provide guidance for the sustainable development of the jujube plantation using drip irrigation in the arid areas.

2. Materials and Methods

2.1. Experimental Site

The experiment was conducted at Alar agricultural irrigation experiment station, Xinjiang, China (40°34′28′′ N, 81°14′12′′ E, altitude 1015 m, Figure 1), which has a typical extreme continental arid climate. The experiment was carried out in 2016 and 2017, the average annual rainfall was 67 mm (May–October), and the average sunshine hours and temperature were 2865 h and 10.7 °C, separately. Local groundwater was below 3.5 m, and the atmospheric pressure, ground temperature, effective radiation, minimum relative humidity, and solar radiation were automatically monitored and recorded using a meteorological station (TRM-ZS2). Figure 2 represents the rainfall and reference evapotranspiration during the experiment. Besides, the organic matter, total nitrogen, available P, and available K, were 3.81 g·kg−1, 0.47 mg·kg−1, 9.4 mg·kg−1, and 68 mg·kg−1 in the upper 0.2 m soil layer, respectively, and the other soil properties are shown in Table 1.

2.2. Field Experiment Layout

Eight-year-old mature jujube trees (Zizyphus Jujube Mill) were selected for the experiment. They were planted in 2008 and grafted in 2009, and flood irrigation was applied until 2015. The annual irrigation and fertilization amounts (N–P–K) were 1020 mm and 600–300–450 kg ha−1, respectively. Their water and fertilizer management transferred from flood irrigation to drip irrigation on 2016 (Figure 3). The wide–narrow row-planting mode was applied, which means that the distance between jujube rows were 200 cm (wide row) and 80 cm (narrow row) by turns. Each row of jujube was irrigated with two driplines centered in narrow rows with 40 cm on each side. Jujube was planted at constant intervals of 80 cm in each row, and the emitter spacing was 30 cm. The overall layout of the experiment was displayed in Figure 4. The drip irrigation system was operated under the constant working pressure of 0.10 MPa and was 20 m in length, and the rated outflow of each emitter was 2 L h−1 with an internal diameter of 12 mm.

2.3. Experiment Design

To find out the optimized water and fertilizer management of jujube when changing from traditional flood irrigation to drip irrigation, two factors and three levels of treatments were set up for two years. The irrigation quota and fertilizer amount (N:P:K = 2:1:1.5 based on local agricultural management) were defined as the two factors. In 2016, three levels were included in irrigation amounts as 620 mm (W1), 820 mm (W2), and 1020 mm (W3), and the fertilizer treatments were 450 kg ha−1 (F1), 900 kg ha−1 (F2), and 1350 kg ha−1 (F3). In 2017, irrigation amounts were 570 mm (W1), 770 mm (W2), and 970 mm (W3), and the fertilizer treatments were 410 kg ha−1 (F1), 860 kg ha−1 (F2), and 1310 kg ha−1 (F3), respectively. These values were based on the comprehensive consideration of local evaporation, seeding time, and the flood irrigation references. The traditional flood irrigation was treated as control check (CK), and thus, there were 10 treatments in total for the orthogonal experiment, and three replications for each treatment. The detailed water and fertilizer managements during the experiment are shown in Table 2. During the experiment, fertilization was conducted both for 30 min after operation began and 30 min before irrigation stopped.

2.4. Measurements and Methods Applied

2.4.1. Crop Growth

The ISL were measured on 5 May 2016 for the first time, and continued until 10 July with the constant interval of seven days. In 2017, the two indicators were first measured on 10 May, and continued until 15 July with same interval. During each measurement, five jujube trees were selected, and ISL was measured with the tapeline.

2.4.2. Photosynthetic Characteristics

Leaf photosynthetic characteristics, including Pn, Gs, Ci, and Tr were measured on 30 July 2016 and 25 July 2017, respectively. Validation experiment was conducted between 10:00 and 20:00 on 31 July 2016 and 25 July 2017, on three expanded upper canopy leaves of each tree (five trees for each treatment) with the Li-6400 Portable Photosynthetic System (manufactured by Li-Cor Inc., Lincoln, NE, USA). This system was equipped with a red/blue light source (6400-02B LED).

2.4.3. Chlorophyll and Nitrogen Contents

In this experiment, the chlorophyll content (CC) was measured by Minolta SPAD 502 m. Three jujube trees were selected for each treatment, and the upper, middle, and lower leaves of each tree were measured. Each treatment totaled nine leaves and took the average. Then, the CC was separated from the plant at 105 °C for 30 min, and then dried at 80 °C to constant quality. Then, the leaf nitrogen content (NC) was measured by a semi-micro Kjeldahl method [16]. Photosynthetic nitrogen use efficiency (PNUE) was calculated by Pn/NC [17].

2.4.4. Water and Fertilizer Efficiency and Crop Yield

The irrigation water use efficiency (iWUE) (unit: kg·m−3) was calculated according to the method used by Kuslu et al. [18]. The fertilizer partial productivity (PFP) (unit: kg·kg−1) referred to the method that was used by Ierna et al. [19]. The jujube yield was measured with five trees and took their average value. Then, its EI was calculated using the method applied by Badr et al. [20], which was based on the difference between the yield of treatments and CK.

2.4.5. Water and Fertilizer Optimization Method

The binary quadratic regression equation was applied for optimized water and fertilizer management. Y was regarded as the dependent variable (Y), and the irrigation amount (W) and fertilizer amount (F) were treated as the independent variables.

3. Results and Analysis

3.1. Effects of Water–Fertilizer Treatments on the Physiological Indicators

The effects of the different water and fertilizer treatments on the physiological indicators (Pn, Tr, Gs, Ci CC, and PNUE) are shown in Table 3.
Seen from Table 3, the Pn, Tr, Gs, and Ci values of different water and fertilizer treatments ranged within 7.26~8.94 μmol·m−2·s−1, 3.15~3.86 mmol·m−2·s−1, 0.31~0.49 μmol·m−2·s−1, and 447.5~707.41 μmol·m−2·s−1, respectively, in 2016. Similar values were acquired in 2017 as 7.54~8.51 μmol·m−2·s−1, 3.24~3.68 mmol·m−2·s−1, 0.36~0.48 μmol·m−2·s−1, and 568.48~678.22 μmol·m−2·s−1, respectively. The maximum values were all observed in W2F1 treatment for two years, and they were larger than those of CK by 3.65~12.61%, 3.67~17.32%, 6.67~8.89%, and 10.66~19.30%, respectively. These results indicated that the medium irrigation quota was helpful to improve the efficiency of lower-level fertilizer and resulted in the most positive effects on these four indexes. Generally speaking, the Pn, Tr, Gs, and Ci values were significantly affect by an irrigation quota of (p < 0.01), but their dynamic responses to fertilizer amounts failed to reach a significant level (p < 0.05). As the effects of irrigation were higher than fertilization, their coupling effects also reached the significant level (p < 0.05).
Meanwhile, the CC of different water and fertilizer treatments ranged from 43.17 mg·g−1 (W1F3) to 53.00 mg·g−1 (W3F3) in 2016, and from 44.2 mg·g−1 (W1F3) to 51.81 mg·g−1 (W3F3) in 2017. The minimum and maximum values were obtained in the same treatments. Compared with W1F3 and CK, the CC value under W3F3 treatment was increased by 17.22~22.77% and 1.47~6.00%, respectively. The CC was not affected by the fertilizer level, as the effects of the irrigation quota was much more significant. However, the maximum and minimum PUNE were obtained in different treatments, which were W1F2 (1.95 μmolCO2·g−1·Ns−1) and W2F2 (2.25 μmolCO2·g−1·Ns−1) in 2016, and turned out to be W1F1 (1.96 μmolCO2·g−1·Ns−1) and W2F1 (2.19 μmolCO2·g−1·Ns−1) in 2017. A small irrigation quota seemed to be beneficial for improving the PUNE. However, their effects were combined with those of different fertilizer treatments. According to the significant test, the effect of merely irrigation or fertilization on these three indicators that were mentioned above was not significant (p < 0.05), but their interactions on CC and PUNE reached significant values (p < 0.01).

3.2. Effects of Water–Fertilizer Treatments on the Growth Indexes and Crop Yield

The effects of different water and fertilizer treatments on the growth indexes are shown in Table 4.
As shown in Table 4, ISL values were 48~49 cm (W1F1) to 62~64 cm (W3F2) in 2016 and 2017, and ISL values under W3F2 treatment were 26.53~51.32% and 3.33~21.58% larger than those of W1F1 and CK. Seen from the results obtained, the irrigation quota was more related to ISL, as more irrigated water were helpful to obtain larger ISL values, and the effects of irrigation quotas on the differences of ISL values reached a significant level (p < 0.05), while those of fertilizer amounts failed. However, their coupling effects were enhanced to significant impacts under the significant level of p < 0.01. On the other hand, Ymax and Ymin were acquired in W2F1 (7256 kg·ha−1) and W1F2 (4661 kg·ha−1), while that of YCK was 6976 kg·ha−1 in 2016. Similar results were obtained in 2017. Besides, the highest PFP values were in both W2F1 treatments for two years, and it was 133.06~193.62% higher than that of CK (5.49 kg·kg−1). However, the highest iWUE value was obtained under W1F3 treatment in both 2016 and 2017 (8.17 kg·m−3 and 14.86kg·m−3), which increased by 29.48% and 78.72% when compared with CK (6.31 kg·m−3 and 8.31 kg·m−3). The improving effect (EI) of W1F2, W3F2, and W3F3 treatments were negative values, and the EImax was acquired in W2F1 treatment (33.14% and 24.86%). Based on the two-year data analysis, the iWUE was significantly affected by irrigation quotas (p < 0.05), the PFP was significantly influenced by fertilizer amounts (p < 0.01), and they were both significantly affected by the coupling effects of water–fertilizer (p < 0.01). Besides, the crop yield was significantly affected neither by the irrigation quota nor the fertilizer amount; only their interaction reached the significant level (p < 0.01).

3.3. Optimized Water and Fertilizer Managements of Jujube in the Xinjiang Area

Based on the results mentioned above, the impact of water and fertilizer managements on physiological indicators and growth indexes varied among different treatments. The corresponding yield effect model was obtained by regression in Equation (1), and the optimized results of the yield model were shown in Figure 5. According to Equation (1), the interaction coefficient of water and fertilizer was a positive value, which meant that the positive impacts were increasing. Seen from Figure 5, the effect of the water and fertilizer coupling model showed a bimodal surface; the relatively higher yield was acquired with either low fertilizer or high fertilizer amount when irrigated with a moderate quota. Low fertilizer and moderate irrigation would be a better choice, as the maximum yield was acquired as 6.87 Mg ha−1, and the optimized irrigation quota should be 815 mm while applying fertilizer with the amount of 400 kg ha−1.
Y = 4.8 × 10 6 × F 2 3.1 × 10 5 × W 2 + 1.4 × 10 6 × W × F + 5.0 × 10 2 × W 1.0 × 10 2 × F 10.0   ( R 2 = 0.85 )
where Y represents the jujube yield (kg ha−1); W is the irrigation quota (mm), and F is the fertilizer amount (kg ha−1).

4. Discussion

Water and fertilizer management is critical to crop production, especially for agriculture in arid areas [21]. When the coupling effects of water and fertilizer are optimized, the crop yield is significantly increased [20]. The key of water and fertilizer coupling is “using water to promote fertilizer [and] using fertilizer to adjust water”. In recent years, water–fertilizer management has become a hot research topic, especially when applying water-saving technology [22,23]. However, the related studying results related to jujube plantations are lacking.
In this paper, water and fertilizer interaction showed significant effects on jujube CC, NC, and PNUE, which indicated that fertilizer efficiency was promoted by the irrigation water, and in turn the amount of water that was needed was adjusted by the fertilizer applied, on the condition that neither water nor fertilizer showed significant impacts. We failed to find results on jujube CC, NC, and PNUE in the arid area, but the trend that we observed was consistent with the results of the wheat photosynthetic characteristics under water and fertilizer coupling conditions that were reported by Wang et al. [24]. After applying the drip irrigation technology in 2016, the maximum CC, NC, and PUNE values increased on average by 6.00%, 9.49%, and 11.39%, respectively, upon comparing those using flood irrigation in 2015, which indicated the great improvement due to changing the irrigation method. Although these contents continued to increase after application in the second year of 2017, the increasing rates decreased to 1.47%, 3.37%, and 6.83%, respectively, as they were compared with those using drip irrigation. Meanwhile, neither excessive irrigation nor deficit irrigation was beneficial for photosynthetic characteristics, and the best effects occurred at the low fertilization level, which was similar to the results of Li et al. [25] and Zhou et al. [26]. Compared with flood irrigation, fertigation with drip irrigation could not only promote the nutrient absorption of crops, it could also improve the utilization rate of water fertilizer [21,27]. The results in this paper also showed that the highest yield was obtained with moderate irrigation and low fertigation treatment (W2F1) using drip irrigation, when the iWUE and PFP reached higher levels.
On the other hand, some scholars argued whether the net photosynthetic rate could increase crop yield significantly, especially under water stress conditions [28]. Actually, photosynthesis and the transpiration of jujube were directly affected by stomatal conductance, as leaves could control the jujube net photosynthetic rate and transpiration rate through the opening size of stomatal conductance. Therefore, it is important to study the relations between Pn and other indicators. In this paper, we studied the quantitative relations between physiological indicators and growth indexes (Figure 6). The results showed that Pn, Tr, and Gs were closely related (R2 > 0.84 **, p < 0.01). Meanwhile, a significant linear relation was found between Pn and ISL (R2 = 0.52*, p < 0.05), while those of Pn and ISL failed (R2 = 0.26, p < 0.05). On the other hand, the realization of photosynthesis depends on the absorption of light energy obtained by chlorophyll. Seen from Figure 6, Pn had linear relations with CC and NC (R2 > 0.39 *, p < 0.05). Among them, CC was not only the most important pigment for the photosynthesis of crops, it was also the basic substance of the photosynthesis of plants [29]. We also found linear relations between leaf CC and NC (R2 = 0.97 **, p < 0.01).
Through the experiment, we acquired some meaningful results on how water–fertilizer management would affect jujube growth and yield. However, there were still some issues to study in the future: (1) the experiment was conducted with three irrigation quotas and three fertilizer amounts according to the local planting mode. More water and fertilizer gradients are needed, especially with higher irrigation and fertilization levels, in order to identify the most appropriate water–fertilizer management; (2) long-term experiment verifications are needed to find out the water–fertilizer coupling treatments after we turned flood irrigation to drip irrigation in the Xinjiang area. Besides, their effects on a healthy soil and microorganism community are also unknown.

5. Conclusions

When planting jujube in the Xinjiang area using drip irrigation and integrating fertilization, most of the physiological indicators and growth indexes were not significantly affected merely by the irrigation quota nor the fertilizer amounts; only the water–fertilizer interaction showed significant effects. The physiological indicators and growth indexes were increased when transferring flood irrigation to drip irrigation. After applying drip irrigation technology, fertilizer efficiency was promoted with the irrigation water input, and in turn, the water requirements were adjusted by the fertilizer applied. However, inappropriate water and fertilizer management would show negative impacts on jujube growth and yield. Among the treatments in this paper, low fertilizer and moderate irrigation would be the best choice. Therefore, the optimized irrigation and fertilizer amounts of jujube in northern Xinjiang area should be 815 mm and 400 kg ha−1, on the basis of a comprehensive consideration of physiological indicators and growth indexes.

Author Contributions

Z.W. and B.Z. provided the idea of the study and writing of the manuscript, Q.B. conducted the data analysis, and J.Z. provided important advice on the concept of the methodology.

Acknowledgments

We are grateful for the financial support from the National Key Technology Support Program (2014BAC14B01) and the National Natural Science Fund of China (51669032).

Conflicts of Interest

The authors declare that there is no conflict of interest.

Abbreviations

Pn, net photosynthetic rate; Gs, stomatal conductance; Ci, intercellular CO2 concentration; Tr, transpiration rate; ISL, length increment; PFP, partial fertilizer productivity; PNUE, photosynthetic nitrogen use efficiency; iWUE, irrigation water use efficiency; NC, nitrogen content; CC, chlorophyll content; EI, improving effect.

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Figure 1. The location of the experimental site.
Figure 1. The location of the experimental site.
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Figure 2. Reference evapotranspiration and rainfall during the experiment.
Figure 2. Reference evapotranspiration and rainfall during the experiment.
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Figure 3. Different irrigation methods of jujube.
Figure 3. Different irrigation methods of jujube.
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Figure 4. Planting patterns of jujube.
Figure 4. Planting patterns of jujube.
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Figure 5. Water–fertilizer optimization based on maximum jujube yield.
Figure 5. Water–fertilizer optimization based on maximum jujube yield.
Water 10 01467 g005
Figure 6. Quantitative relations between physiological indicators and growth indexes. (a) The relationship between Pn and Tr, Gs; (b) The relationship between Pn and ISD, ISL; (c) The relationship between Pn and CC, NC; (d) The relationship between CC and NC, LWUE.
Figure 6. Quantitative relations between physiological indicators and growth indexes. (a) The relationship between Pn and Tr, Gs; (b) The relationship between Pn and ISD, ISL; (c) The relationship between Pn and CC, NC; (d) The relationship between CC and NC, LWUE.
Water 10 01467 g006aWater 10 01467 g006b
Table 1. Soil properties used in the experiment.
Table 1. Soil properties used in the experiment.
Soil Layer/cmSoil TypeField Moisture Capacity/(g·g−1)Soil Bulk Density/(g·cm−3)Soil Density/(g·cm−3)Soil Total Porosity/%Capillary Porosity/%
0–20loamy sand soil0.191.51 ± 0.032.05 ± 0.0847.6 ± 1.3545.28 ± 0.78
20–40sand soil0.201.57 ± 0.031.89 ± 0.0542.11 ± 1.4540.12 ± 0.98
40–60loamy sand soil0.191.53 ± 0.041.43 ± 0.0643.27 ± 0.4241.02 ± 0.87
60–80sand soil0.201.58 ± 0.072.09 ± 0.0541.74 ± 1.0539.24 ± 0.66
Table 2. Irrigation and fertilizer managements in jujube.
Table 2. Irrigation and fertilizer managements in jujube.
YearGrowth StagePeriodIrrigation Treatment/mmFertilization Treatment/kg ha−1Flood IrrigationIrrigation TimesFertilization Times
W1W2W3F1F2F3WF
2016Budding and shooting04/30–06/20701101509018027015027011
Flowering06/21–07/2016020024012024036024036022
Fruiting07/21–08/1716020024012024036024036022
White maturity08/18–09/1816020024012024036024036022
Fully maturity09/19–10/307011015015010
2017Budding and shooting05/05–06/15601001408017026014026011
Flowering06/16–07/1515019023011023035023035022
Fruiting07/16–08/0615019023011023035023035022
White maturity08/07–09/1615019023011023035023035022
Fully maturity09/17–11/056010014014010
Table 3. Effects of water–fertilizer treatments on photosynthetic characteristics.
Table 3. Effects of water–fertilizer treatments on photosynthetic characteristics.
YearsTreatmentsPn/μmol·m−2·s−1Tr/mmol·m−2·s−1Gs/μmol·m−2·s−1Ci/μmol·m−2·s−1CC/mg·g−1PNUE/μmol·CO2·g−1 Ns−1
2016W1F17.26 ± 0.07 f3.15 ± 0.06 b0.31 ± 0.01 c447.50 ± 2.78 f49.2 ± 0.85 b1.96 ± 0.16 c
W1F27.82 ± 0.12 e3.17 ± 0.02 b0.33 ± 0.02 c519.89 ± 3.38 e44.5 ± 1.25 c1.96 ± 0.08 c
W1F37.73 ± 0.04 e3.19 ± 0.05 b0.35 ± 0.10 c365.07 ± 5.11 g43.2 ± 0.38 c2.15 ± 0.02 b
W2F18.93 ± 0.03 a3.86 ± 0.01 a0.49 ± 0.02 a707.41 ± 4.48 a52.2 ± 0.51 ab2.20 ± 0.04 a
W2F28.34 ± 0.08 cd3.68 ± 0.02 a0.47 ± 0.04 ab639.25 ± 3.45 b44.9 ± 0.58 c2.25 ± 0.04 a
W2F38.43 ± 0.03 c3.76 ± 0.02 a0.45 ± 0.02 b710.42 ± 2.79 a52.6 ± 1.77 ab1.99 ± 0.07 c
W3F18.24 ± 0.05 d3.27 ± 0.06 b0.35 ± 0.02 c631.01 ± 2.52 b51.9 ± 0.58 ab2.03 ± 0.02 c
W3F28.67 ± 0.13 b3.40 ± 0.06 b0.36 ± 0.02 c592.06 ± 2.04 c43.8 ± 0.77 c2.19 ± 0.03 a
W3F38.41 ± 0.09 cd3.28 ± 0.05 b0.44 ± 0.03 b536.53 ± 1.44 d53.0 ± 0.69 a1.97 ± 0.03 c
CK7.93 ± 0.15 e3.29 ± 0.04 b0.45 ± 0.27 b639.26 ± 2.56 b50.0 ± 0.53 ab2.03 ± 0.06 b
2017W1F17.54 ± 0.05 c3.24 ± 0.05 bc0.36 ± 0.01 c568.48 ± 2.78 d44.2 ± 0.74 d1.96 ± 0.05 c
W1F27.78 ± 0.09 c3.28 ± 0.04 bc0.32 ± 0.02 c597.89 ± 4.31 c46.5 ± 1.17 c1.99 ± 0.07 c
W1F38.05 ± 0.06 bc3.09 ± 0.03 c0.42 ± 0.10 bc604.19 ± 6.48 c46.9 ± 0.98 c2.06 ± 0.03 b
W2F18.51 ± 0.04 a3.68 ± 0.06 a0.48 ± 0.02 a678.22 ± 4.48 a51.5 ± 0.74 a2.19 ± 0.05 a
W2F28.49 ± 0.07 ab3.57 ± 0.07 b0.41 ± 0.04 ab659.69 ± 3.45 ab46.6 ± 0.78 c2.14 ± 0.03 ab
W2F38.36 ± 0.06 cb3.49 ± 0.06 b0.45 ± 0.02 b654.42 ± 6.74 ab51.6 ± 1.47 ab1.90 ± 0.05 c
W3F18.41 ± 0.08 b3.54 ± 0.06 b0.46 ± 0.02 ab648.27 ± 3.75 b50.9 ± 0.14 b2.11 ± 0.04 ab
W3F28.39 ± 0.08 b3.48 ± 0.03 b0.45 ± 0.02 b652.34 ± 3.48 ab44.4 ± 0.87 cd2.12 ± 0.06 ab
W3F38.35 ± 0.10 b3.39 ± 0.02 b0.44 ± 0.03 b649.26 ± 5.10 b51.8 ± 0.79 a2.06 ± 0.04 b
CK8.21 ± 0.09 b3.45 ± 0.06 b0.45 ± 0.03 b635.19 ± 3.07 b51.1 ± 0.93 ab2.05 ± 0.05 b
Significant Analysis
2016W (irrigation)10.35 *63.20 **13.90 **13.43 **1.930.79
F (fertilization)0.390.100.150.170.200.12
W × F77.55 **10.52 **10.60 **1251.19 *10.87 **7.89 **
2017W (irrigation)11.72 *54.69 **25.49 **19.48 **0.860.99
F (fertilization)0.450.690.750.480.220.24
W × F100.55 **25.44 **16.46 **69.13 *14.59 **10.01 **
Note: The data in the Table 3 is mean value ± standard error, different letters indicate significant difference between treatments (p < 0.05). * and ** in the significance test represent reached significant values under p < 0.05 and p < 0.01, respectively.
Table 4. Effects of water–fertilizer treatments on yield indexes.
Table 4. Effects of water–fertilizer treatments on yield indexes.
Cropping YearTreatmentsISL/mmY/kg ha−1iWUE/kg·m−3PFP/kg·kg−1EI/%
2016W1F148 ± 2.08 c5450 ± 104.58 d7.57 ± 0.15 b12.11 ± 0.54 b
W1F251 ± 2.08 bc4661 ± 85.58 e6.47 ± 0.12 c5.53 ± 0.25 d−14.48
W1F353 ± 1.53 bc5879 ± 84.79 c8.17 ± 0.25 a4.75 ± 0.36 d7.87
W2F154 ± 2.08 b6956 ± 125.79 a7.87 ± 0.14 ab16.12 ± 0.58 a33.14
W2F258 ± 1.53 b6803 ± 84.89 b7.38 ± 0.19 b8.06 ± 0.44 c24.83
W2F356 ± 1.53 b5799 ± 55.65 c6.29 ± 0.16 c4.69 ± 0.35 d6.40
W3F157 ± 1.53 b5603 ± 89.81 cd5.19 ± 0.15 d12.45 ± 0.66 b2.81
W3F264 ± 1.00 a4894 ± 95.73 e4.53 ± 0.14 e5.81 ± 0.45 d−10.20
W3F359 ± 1.53 ab5414 ± 105.23 d5.01 ± 0.21 d4.37 ± 0.22 d−0.66
CK61 ± 1.46 ab6796 ± 89.78 b6.31 ± 0.12 c5.49 ± 0.12 d24.70
2017W1F149 ± 1.52 c5651 ± 97.85 d13.89 ± 0.15 b13.78 ± 0.54 b
W1F251 ± 1.70 c5798 ± 106.46 e14.25 ± 0.12 c6.74 ± 0.25 d2.60
W1F350 ± 2.03 c6047 ± 88.69 c14.86 ± 0.25 a4.62 ± 0.36 d7.01
W2F155 ± 1.25 b7056 ± 111.45 a13.74 ± 0.14 ab17.20 ± 0.58 a24.86
W2F257 ± 1.26 b6803 ± 109.48 b13.25 ± 0.19 b7.91 ± 0.44 c20.38
W2F356 ± 1.73 b6716 ± 78.95 c13.07 ± 0.16 c5.12 ± 0.35 d18.84
W3F160 ± 1.26 a6841 ± 97.58 cd10.57 ± 0.15 d16.68 ± 0.66 b21.06
W3F262 ± 2.00 a5984 ± 108.44 e9.24 ± 0.14 e6.96 ± 0.45 d5.89
W3F358 ± 1.25 ab6144 ± 97.52 d9.50 ± 0.21 d4.69 ± 0.22 d8.72
CK61 ± 2.00 a6648 ± 91.42 b8.31 ± 0.12 c7.38 ± 0.12 d24.70
Significant Analysis
2016W (irrigation)8.46 *4.7111.32 **0.20/
F (fertilization)0.700.410.1729.01 **/
W × F78.12 **78.01 **155.82 **758.71 **/
2017W (irrigation)10.11 *1.599.48 *0.22/
F (fertilization)0.410.130.1418.75 **/
W × F56.89 **98.46 *205.49 **544.17 **/
Note: The data in the Table 4 is the mean value ± standard error; different letters indicate significant difference between treatments (p < 0.05). * and ** in the significance test represent reaching significant values under p < 0.05 and p < 0.01, respectively.
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