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2024 Volume 4
Article Contents
Abstract
Introduction
Materials and methods
Plant materials
Protein extraction
Trypsin digestion
Affinity enrichment of ubiquitinated peptides
LC-MS/MS and data analysis
Bioinformatics methods
Results
Overview of the ubiquitomic analysis
Properties of ubiquitinated proteins and motifs
A temperature-regulated rice endosperm ubiquitome
Functions for differentially expressed ubiquitinated proteins
Discussion
Ubiquitination of heat shock proteins and transcription factors under high-temperature stress
Sucrose and starch metabolism are modified by ubiquitination under high-temperature stress
Ubiquitination affects sucrose hydrolysis
Ubiquitination of ADP-glucose pyrophosphorylase (AGPase)
Ubiquitination regulates amylose biosynthesis
Ubiquitination regulates amylopectin biosynthesis
Ubiquitination affects ribosomal systems under high-temperature stress
Author contributions
Data availability
Acknowledgments
Conflict of interest
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References
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ARTICLE   Open Access    

Interacting effects of water and compound fertilizer on the resource use efficiencies and fruit yield of drip-fertigated Chinese wolfberry (Lycium barbarum L.)

  • 1.

    College of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021, China

  • 2.

    College of Information Engineering, Ningxia University, Yinchuan 750021, China

  • 3.

    Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA

  • 4.

    Ningxia Research Center of Technology on Water-saving Irrigation and Water Resources Regulation, Yinchuan 750021, China

  • 5.

    Engineering Research Center for Efficient Utilization of Water Resources in Modern Agriculture in Arid Regions, Yinchuan 750021, China

  • 6.

    Department of Agronomy, Faculty of Agriculture, University of Jaffna, Ariviyal Nagar, Kilinochchi 44000, Sri Lanka

  • 7.

    School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand

  • 8.

    Department of Export Agriculture, Uva Wellassa University, Badulla 90000, Sri Lanka

More Information
  • Chinese wolfberry (Lycium barbarum L.) is an important cash crop in the Ningxia region of China, but water scarcity, low water use efficiency (WUE) and fertilizer use efficiency (FUE) have limited the growth of its production. Field experiments were conducted in central Ningxia (China) during 2018−2019 to investigate the interaction effects of irrigation and fertilizer levels on agronomic performances (AP), WUE, partial fertilizer productivity (PFP), and economic benefits (EB). The optimal range of irrigation and fertilizer inputs was determined using multiple regression, the entropy weight method, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) coupling comprehensive evaluation method. Three drip irrigation levels were designated as a percentage of reference crop evapotranspiration (ETo); low (65% ET0: W1), medium (85% ET0: W2) and high (105% ET0: W3). Three N-P2O5-K2O compound fertilization levels (kg·ha−1) were selected as low (135-45-90: F1), medium (180-60-120: F2) and high (225-75-150: F3). Results showed that AP, WUE, PFP, and EB increased initially and then decreased with increasing levels of irrigation under the same fertilization levels. The PFP decreased with increasing fertilization levels and the lowest PFP was observed at high fertilizer (F3) application level. The above parameters reached the maximum value under medium irrigation. By establishing the multi-objective optimization model, it was found that 252−262 mm of irrigation and 185-62-123~200-67-133 kg·ha−1 of N-P2O5-K2O fertilization level offers more than 90% of yield, WUE, PFP, and EB simultaneously. The present results provide scientific insights into the resource optimization under drip-fertigation for Chinese wolfberry.

  • Introduction

    Rice (Oryza sativa L.) is a world staple crop that feeds over half of the world's population[1,2]. In recent years, high-temperature events are becoming more frequent and intensive as a result of global warming, which can severely affect rice grain yield and quality[3,4]. During flowering and grain filling stages, high-temperature stress can result in a significant reduction in seed setting rate and influence amylose content, starch fine structure, functional properties and chalkiness degree of rice[5−7]. Transcriptome and proteome analysis in rice endosperm have also been used to demonstrate the differences in high-temperature environments at gene and protein expression levels[8−10]. In addition, as an important post-translational modification, protein phosphorylation has proven to be involved in the regulation of starch metabolism in response to high-temperature stress[11]. However, little is known about whether protein ubiquitination regulates seed development under high-temperature stress.

    Ubiquitination is another form of post-translational modification that plays key roles in diverse cellular processes[12]. Several reports have described the functions of ubiquitination in rice defense responses based on ubiquitome analysis. Liu et al.[13] investigated relationships between ubiquitination and salt-stress responses in rice seedlings using a gel-based shotgun proteomic analysis and revealed the potential important role of protein ubiquitination in salt tolerance in rice. Xie et al.[14] identified 861 peptides with ubiquitinated lysines in 464 proteins in rice leaf cells by combining highly sensitive immune affinity purification and high resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). These ubiquitinated proteins regulated a wide range of processes, including response to stress stimuli. A later study revealed the relationships between ubiquitination status and activation of rice defense responses, and generated an in-depth quantitative proteomic catalog of ubiquitination in rice seedlings after chitin and flg22 treatments, providing useful information for understanding how the ubiquitination system regulates the defense responses upon pathogen attack[15]. Although many studies have shown that ubiquitination plays improtant roles in the heat response of plant[16,17], there has been little systematic discussion on the ubiquitome of rice endosperm in the context of global climate change.

    In this study, we examine the high-temperature induced ubiquitination change in two rice varieties with different starch qualities, through a label-free quantitative ubiquitome analysis. This study provides a comprehensive view of the function of ubiquitination in high-temperature response of rice developing seed, which will shed new light on the improvement of rice grain quality under heat stress.

    Materials and methods

    Plant materials

    Two indica rice varieties with different starch quality, 9311 and Guangluai4 (GLA4), were used as materials. 9311 is a heat-sensitive variety, which displays low amylose content with good starch quality; while GLA4 is known to be the parental variety of HT54, an indica breeding line with heat tolerance, and thus GLA4 is possibly heat tolerant, which shows high amylose content with poor starch quality[18,19]. Rice growth conditions, sample treatment and collection were conducted as previously described[11].

    Protein extraction

    Husk, pericarp and embryo were detached from immature rice grains on ice[20]. Rice endosperm was then ground with liquid nitrogen, and the cell powder was sonicated 10 rounds of 10 s sonication and 15 s off-sonication on ice in lysis buffer (6 M Guanidine hydrochloride, pH 7.8−8.0, 0.1% Protease Inhibitor Cocktail) using a high intensity ultrasonic processor. Following lysis, the suspension was centrifuged at 14,000 g for 40 min at 4 °C to remove the debris. The supernatant was collected, and the protein concentration was estimated using BCA assay (Pierce BCA Protein assay kit, Thermo Fisher Scientific, Waltham, MA, USA) before further analysis.

    Trypsin digestion

    The protein mixture was reduced by DTT with the final concentration of 10 mM at 37 °C for 1 h, alkylated by iodoacetamide with a final concentration of 50 mM at room temperature in the dark for 0.5 h, and digested by trypsin (1:50) at 37 °C for 16 h. Then the sample was diluted by adding trifluoroacetic acid (TFA) to the final concentration of 0.1%. The enzymatic peptides were desalted on a Sep-Pak C18 cartridge (Waters, Milford, MA, USA), concentrated by lyophilization and reconstituted in precooled IAP buffer (50 mM MOPS-NaOH PH 7.2, 10 mM Na2HPO4, 50 mM NaCl) for further analysis.

    Affinity enrichment of ubiquitinated peptides

    The peptides solution was incubated with prewashed K-ε-GG antibody beads (PTMScan Ubiquitin Remnant Motif (K-ε-GG) Kit), and gently shaken at 4 °C for 1.5 h. The suspension was centrifuged at 2,000 g for 30 s, and the supernatant was removed. The Anti-K-ε-GG antibody beads were washed with IAP Buffer three times and with ddH2O three times. The peptides were eluted from the beads with 0.15% trifluoroacetic acid (TFA). Finally, the eluted fractions were combined and desalted with C18 Stage Tips.

    LC-MS/MS and data analysis

    LC-MS/MS analysis were performed using the methods of Pang et al.[11]. Raw mass spectrometric data were analyzed with MaxQuant software (version 1.3.0.5) and were compared with the indica rice protein sequence database (Oryza sativa subsp. indica-ASM465v1). Parameters were set according to Pang et al.[11]. All measurements were obtained from three separate biological replicates.

    Bioinformatics methods

    Quantification of the modified peptides was performed using the label-free quantification (LFQ) algorithm[11]. Differentially ubiquitinated sites (proteins) in response to high-temperature were identified by Student's t-test (p < 0.05, log2(fold-change) > 1) with at least two valid values in any condition or the ubiquitination sites that exhibited valid values in one condition (at least two of three replicates) and none in the other.

    Subcellular localization was performed using CELLO database (http://cello.life.nctu.edu.tw). Gene Ontology (GO) annotation proteome was derived from the AgriGO (http://bioinfo.cau.edu.cn/agriGO/). The differential metabolic profiles were visualized with MapMan software (version 3.6.0RC1). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation was performed by using KEGG Automatic Annotation Server (KAAS) software. A p-value of < 0.05 was used as the threshold of significant enrichment. SWISS-MODEL was used to generate the tertiary structure of GBSSI (SWISS-MODEL, http://swissmodel.expasy.org/). The figures were annotated with Adobe Illustrator (Adobe Systems, San Jose, CA, USA).

    Results

    Overview of the ubiquitomic analysis

    To elucidate how high-temperature stress influences rice developing endosperm at the ubiquitination level, a label-free analysis was performed to quantify ubiquitome from two indica rice varieties under normal (9311-C and GLA4-C) and high-temperature conditions (9311-H and GLA4-H). The distribution of mass error of all the identified peptides was near zero and most of them (71.5%) were between -1 and 1 ppm, suggesting that the mass accuracy of the MS data fits the requirement (Fig. 1a). Meanwhile, the length of most peptides distributed between 8 and 42, ensuring that sample preparation reached standard conditions (Fig. 1b).

    Figure 1.  Characteristics of the ubiquitinated proteome of rice endosperm and QC validation of MS data. (a) Mass error distribution of all identified ubiquitinated peptides. (b) Peptide length distribution. (c) Frequency distribution of ubiquitinated proteins according to the number of ubiquitination sites identified.
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    In all endosperm samples, a total of 437 ubiquitinated peptides were identified from 246 ubiquitinated proteins, covering 488 quantifiable ubiquitinated sites (Supplemental Table S1). Among the ubiquitinated proteins, 60.6% had only one ubiquitinated lysine site, and 18.7%, 8.1%, 5.3%, or 7.3% had two, three, four, or five and more ubiquitinated sites, respectively. In addition, four proteins (1.6%, BGIOSGA004052, BGIOSGA006533, BGIOSGA006780, BGIOSGA022241) were ubiquitinated at 10 or more lysine sites (Fig. 1c, Supplemental Table S1). The proteins BGIOSGA008317 had the most ubiquitination sites with the number of 16. It was noted that besides ubiquitin, two related ubiquitin-like proteins NEDD8 and ISG15 also contain C-terminal di-Gly motifs generated by trypsin cleavage, and the modifications of these three proteins cannot be distinguished by MS[21]. Here, the di-Gly-modified proteome therefore represents a composite of proteins modified by these three proteins. However, the sites from NEDD8 or ISG15 modifications were limited because they mediate only a few reactions in cells[21].

    Properties of ubiquitinated proteins and motifs

    To better understand the lysine ubiquitome changes in rice endosperm induced by high-temperature, we performed a Gene Ontology (GO) functional annotation analysis on all identified ubiquitinated proteins (Fig. 2a). In the biological process GO category, 'metabolic process' and 'cellular process' were mainly enriched, accounting for 75.1% and 74.1% of ubiquitinated proteins, respectively. In addition, 34.6% proteins were associated with 'response to stimulu', emphasizing the regulatory role of ubiquitination modification in response to high-temperature stress. From the cellular component perspective, ubiquitinated proteins were mainly associated with 'cellular anatomical entity' (99.4%), 'intracellular' (84.4%) and 'protein-containing complex' (29.9%). The molecular function category result suggested that these proteins were largely involved in 'binding' (62.7%), 'catalytic activity' (43.4%) and 'structural molecule activity' (16.5%). Furthermore, subcellular location annotation information indicated that 34.7%−39.4% proteins were located in the cytoplasm, and other were mostly located in the nucleus (23.5%−27.7%), plasma membrane (9.4%−11.4%), and chloroplast (9.6%−12.8%) (Fig. 2b). It is noteworthy that the ubiquitinated proteins located in the cytoplasm were decreased in high-temperature environments in both varieties.

    Figure 2.  Analysis of ubiquitinated proteins and motifs. (a) Gene ontology (GO) functional characterization of ubiquitinated proteins. (b) Subcellular localization of ubiquitinated proteins. From the inside out, the ring represents 9311-C, 9311-H, GLA4-C and GLA4-H, respectively. (c) Motif enrichment analysis of ubiquitinated proteins.
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    The following two significantly enriched motifs from all of the identified ubiquitinated sites were identified using MoDL analysis: [A/S]xKub and Kubxx[E/Q/R/V]x[E/G/L/P/Q/R/Y], which covered 84 and 100 sequences, respectively (Fig. 2c). Further analysis showed that the conserved alanine (A) and glutamic acid (E) were included in upstream and downstream of the ubiquitinated lysine sites in rice endosperm. A similar phenomenon also occurred in rice leaf[14], wheat leaf[22], and petunia[23], indicating that alanine (A) and glutamic acid (E) were likely to be the specific amino acids in conserved ubiquitination motifs in plants. Additionally, serine (S) was enriched at the position -2 (upstream) of the ubiquitinated lysine, while various amino acids such as arginine (R), glutamic acid (E), glutamine (Q), valine (V) were found at positions +3 and +5 (downstream).

    A temperature-regulated rice endosperm ubiquitome

    To detect possible changes in rice endosperm ubiquitome attributable to high-temperature stress, we then performed LFQ analysis on all quantifiable ubiquitination sites within our dataset. As shown in Fig. 3a, more ubiquitinated proteins, peptides and sites were detected in the treatment groups (9311-H and GLA4-H), suggesting that exposure to high-temperature stress may increase the ubiquitination events in rice endosperm. Only 282 common ubiquitinated sites in 158 proteins were quantifiable for all sample groups due to reversible ubiquitination induced by high-temperature (Fig. 3b). Principal component analysis (PCA) showed that three repeats of each sample clustered together, and four groups were clearly separated (Fig. 3c). Furthermore, the differentially expression profiles of ubiquitination sites (proteins) in 9311 and GLA4 under high-temperature stress were depicted to further understand the possible changes (Fig. 3d). Where LFQ values were missing, the data were filtered to identify those ubiquitination sites with a consistent presence/absence expression pattern. These analyses yielded 89 ubiquitination sites that were only present in 9311-H and six that were only present in 9311-C (Fig. 3d, Supplemental Table S2). Similarly, 51 differentially expressed ubiquitination sites were present in GLA4-H and 13 ubiquitination sites only occurred in GLA4-C (Fig. 3d & Supplemental Table S3). Beyond that, a total of 113 and 50 significantly changed ubiquitination sites (p < 0.05, log2(fold-change) >1) were screened out in 9311 and GLA4, respectively (Fig. 3d, Supplemental Tables S4 & S5). For subsequent comparative analysis, the ubiquitination expression profiles with consistent presence/absence and ubiquitination sites with significant differences in statistical testing were combined and named as 9311-Up, 9311-Down, GLA4-Up, and GLA4-Down, respectively (Fig. 3d). The number of significantly up-regulated ubiquitination sites was far greater than down-regulated ubiquitination sites in both 9311 and GLA4 varieties. These findings indicated that high temperature not only induced the occurrence of ubiquitination sites, but also significantly upregulated the intensity of ubiquitination. Beyond that, the magnitude of the up-regulation in 9311 was higher than that in GLA4 (Fig. 3b & d), indicating that the ubiquitination modification of heat-sensitive varieties was more active than heat-resistant varieties in response to high-temperature stress.

    Figure 3.  A temperature regulated rice endosperm ubiquitome. (a) The number of ubiquitinated proteins, peptides and sites detected in four group samples. (b) Venn diagram of ubiquitination sites (proteins) detected in four group samples. (c) PCA based on ubiquitination intensity across all four sample groups with three biological repetitions. (d) Differentially expression profiles of ubiquitination sites (proteins) in 9311 and GLA4 under high-temperature stress. The expression profiles of selected ubiquitination sites (p < 0.05, log2(fold-change) >1) were normalized using the Z-score and presented in a heatmap.
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    To further investigate the ubiquitination regulatory pattern under high temperature stress in two varieties, four groups with significantly regulated sites were analyzed. There were 37 ubiquitination sites showed the same regulatory trend in 9311 and GLA4, accounting for 17.8% and 32.5% of the total differentially expressed sites in 9311 and GLA4, respectively. Among them, 36 ubiquitination sites were upregulated and one site was downregulated (Fig. 4a). In addition, 159 upregulated ubiquitination sites and three downregulated sites were only present in 9311, while 53 upregulated sites and 15 downregulated sites were only present in GLA4. Moreover, nine ubiquitination sites showed opposite regulatory trends in 9311 and GLA4. A similar regulatory trend of ubiquitination proteins is shown in Fig. 4b. It is noted that some proteins had both upregulated and downregulated ubiquitination sites (Supplemental Tables S6 & S7), indicating that significant differences in ubiquitination were, to some extent, independent of protein abundance.

    Figure 4.  Comparison of differentially ubiquitinated sites and proteins in 9311 and GLA4 under high-temperature stress.
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    Functions for differentially expressed ubiquitinated proteins

    To understand the function of ubiquitination in response to the high-temperature stress of rice endosperm, we conducted GO enrichment-based clustering analysis of the differentially ubiquitinated proteins in 9311 and GLA4 at high temperature, respectively (Fig. 5). In the biological process category of 9311, proteins were relatively enriched in the carbohydrate metabolic process, polysaccharide metabolic process, starch biosynthetic process, cellular macromolecule localization, protein localization, intracellular transport, and phosphorylation (Fig. 5). For the molecular function analysis, we found that the proteins related to kinase activity, nucleotidyltransferase activity, phosphotransferase activity, and nutrient reservoir activity were enriched (Fig. 5). The two principal cellular components were intrinsic component of membrane and integral component of membrane (Fig. 5). There was no significantly enriched GO term in the GLA4 group due to the dataset containing relatively few proteins, and thus, further enrichment analysis was conducted on the proteins that were common to both varieties. The results showed that proteins were over-represented in carbon metabolism, including starch biosynthesis and metabolism, glucan biosynthesis and metabolism, and polysaccharide biosynthesis and metabolism (Fig. 5), indicating the importance of carbohydrate synthesis and metabolism in the ubiquitination regulatory network.

    Figure 5.  Enrichment analysis of differentially expressed ubiquitinated proteins based on Gene Ontology (GO) terms.
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    To identify pathways which were differentially ubiquitinated under high-temperature stress, the KEGG pathway-based clustering analysis was conducted. The results showed that the differentially ubiquitinated proteins in both 9311 and GLA4 were mostly abundant in the pathways of carbohydrate metabolism, starch and sucrose metabolism, folding, sorting and degradation, translation, ribosome, and protein processing in endoplasmic reticulum (Fig. 6a). In the 9311 group, the pathways of carbohydrate metabolism, starch and sucrose metabolism, glycosyltransferases, glycolysis, and energy metabolism were enriched in the differentially ubiquitinated proteins (Fig. 6b); while there was no significantly enriched KEGG pathway in the GLA4 group. We further found the proteins that were common to both varieties were only significantly enriched in the starch and sucrose metabolism pathways (p = 0.04). The ubiquitination proteins involved in the starch and sucrose metabolism mainly include: sucrose hydrolysis (SUS, FK, UGPase), and starch synthesis (AGPase, GBSSI, BEI, BEIIb, PUL, PHO1), which are discussed below.

    Figure 6.  KEGG classification and enrichment analysis of differentially ubiquitinated proteins. (a) Number of differentially ubiquitinated proteins based on KEGG classification in 9311 and GLA4. (b) KEGG enrichment analysis of differentially ubiquitinated proteins in 9311.
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    Discussion

    Although many reports have described specific examples of ubiquitination in rice defense responses[13,15,16], our knowledge on global changes in the developing endosperm ubiquitome under high-temperature stress is still lacking. In this study, a label-free quantitative proteomic analysis of ubiquitination was applied to examine the high-temperature induced ubiquitination change of two indica rice varieties (9311 and GLA4) with distinct starch quality. We identified many new lysine modification sites on proteins involved in various pathways, highlighting the complexity of the ubiquitination-mediated regulatory system in high-temperature stress responses in rice.

    Ubiquitination of heat shock proteins and transcription factors under high-temperature stress

    Heat shock proteins accumulate under various stresses and play important roles in plant defenses against abiotic stresses[24,25]. Research has shown that a number of heat shock proteins were prominent in the rice ubiquitome network, of which OsHSP71.1, and OsHSP82A showed increased ubiquitination levels under chitin and flg22 treatment[15]. Here, seven lysine residues on five heat shock proteins possessed ubiquitination modification in rice endosperm. Three sites (BGIOSGA011420-K78, BGIOSGA026764-K99, BGIOSGA029594-K106) showed significant up-regulation in 9311 under high-temperature stress, while in GLA4, the ubiquitination level of BGIOSGA011420-K78 was down-regulated. This differential ubiquitination of heat-tolerant and heat-sensitive varieties provided a basis for studying the regulation of post-translational modification of heat shock proteins under high-temperature stress, despite the regulatory role of those heat shock proteins being still unclear.

    Transcription factors (TFs) play an essential role in the regulation of gene expression. A total of three transcription factors were identified in the ubiquitination dataset, of which two were NAC family members. As one of the largest plant-specific TF families, NAC is involved in the responses to abiotic and biotic stresses[26]. The ubiquitination modification of K173 in BGIOSGA018048 was specifically expressed in the 9311-H group, which may affect the stress resistance level in high-temperature environments. In addition, two sites K148 and K149 of ERF TF family member BGIOSGA024035 was downregulated in GLA4 under high-temperature stress. This differential ubiquitination was likely to affect the expression of related genes regulated by this transcription factor.

    Sucrose and starch metabolism are modified by ubiquitination under high-temperature stress

    Ubiquitination affects sucrose hydrolysis

    The results of GO and KEGG enrichment analysis of differential ubiquitination proteins indicated that the sucrose and starch metabolic pathway was largely affected by ubiquitination regulation under high-temperature stress (Figs 5 & 6). The ubiquitination sites involved in sucrose and starch metabolism are listed in Table 1. To assess how high-temperature stress affects the crucial pathway, the significantly differential ubiquitination sites in 9311 and GLA4 were displayed in the heatmap of specific proteins (Fig. 7).

    Table 1.  Ubiquitination sites related to sucrose and starch metabolism in rice endosperm.
    Gene nameAnnotationProtein entryModification site(s)
    SUS1Sucrose synthase 1BGIOSGA010570K172, K177
    SUS2Sucrose synthase 2BGIOSGA021739K160, K165, K176, K804
    SUS3Sucrose synthase 3BGIOSGA026140K172, K177, K541, K544, K588
    FKFructokinaseBGIOSGA027875K143
    UGPaseUDP-glucose pyrophosphorylaseBGIOSGA031231K27, K150, K303, K306
    AGPS1ADP-glucose pyrophosphorylase small subunit 1BGIOSGA030039K94, K464, K484
    AGPS2ADP-glucose pyrophosphorylase small subunit 2BGIOSGA027135K106, K132, K385, K403, K406, K476, K496
    AGPL2ADP-glucose pyrophosphorylase large subunit 2BGIOSGA004052K41, K78, K134, K191, K227, K254, K316, K338, K394, K396, K463, K508, K513
    AGPL3ADP-glucose pyrophosphorylase large subunit 3BGIOSGA017490K509
    GBSSIGranule bound starch synthase IBGIOSGA022241K130, K173, K177, K181, K192, K258, K371, K381, K385, K399, K462, K517, K530, K549, K571, K575
    BEIStarch branching enzyme IBGIOSGA020506K103, K108, K122
    BEIIbStarch branching enzyme IIbBGIOSGA006344K134
    PULStarch debranching enzyme:PullulanaseBGIOSGA015875K230, K330, K431, K736, K884
    PHO1Plastidial phosphorylaseBGIOSGA009780K277, K445, K941
     | Show Table
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    Figure 7.  Sucrose and starch pathway at the ubiquitination levels in rice endosperm under high-temperature stress.
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    In cereal endosperm, sucrose is the substrate for the biosynthesis of starch. The formation of glucose 1-phosphate (G1P, used in starch synthesis, see below) from sucrose requires a series of enzymes[27]. Here, we found that sucrose synthase 1 (SUS1), SUS2, SUS3, fructokinase (FK), and UDP-glucose pyrophosphorylase (UGPase) were ubiquitinated among all sample groups (Table 1, Fig. 7). In the ubiquitome of seedling and leaf in japonica rice, ubiquitination sites have been found in SUS1, SUS2, UGPase, and FK, which were related to sucrose hydrolysis[14,15]. SUS catalyzed the process of cleaving sucrose into UDP-glucose (UDPG) and fructose. Two ubiquitination sites, K172 and K177, were identified in SUS1 in rice endosperm, which were also found in rice leaves[14]. A total of four ubiquitination sites were identified in SUS2, two of which were also reported in rice seedling and leaf, indicating the conservation of the lysine residues in different rice tissues. It was noted that all four ubiquitination sites in SUS2 were upregulated in high-temperature environments, although the regulated sites of 9311 and GLA4 were different. In 9311, the ubiquitination levels of K160, K174, and K804 were increased, while GLA4 was only upregulated in K176. The ubiquitination sites K541, K544, and K588 in SUS3 were screened from developing rice seeds for the first time. In addition, SUS3 had two completely overlapping sites K172 and K177 with SUS1, and it was difficult to determine which enzymes the two sites belonged to. The ubiquitination levels of SUS3-K541 and SUS3-K544 in 9311 significantly increased in high-temperature environments, while there was no significant difference in the ubiquitination level of SUS3 in GLA4. Overall, the ubiquitination sites of SUS in rice endosperm were located in the functional domain except for SUS2-K804, reflecting the importance of ubiquitination regulation in SUS.

    UGPase catalyses the conversion of glucose 1-phosphate and UTP into UDPG[28]. Research has shown that the mutation of UGPase gene lead to chalky endosperm[29]. As shown in Table 1, four ubiquitination sites K27, K150, K303, and K306 were identified in rice endosperm, which were completely inconsistent with the seven ubiquitination sites in rice seedlings and two in leaves[14,15], reflecting the tissue specificity. We speculated that the UGPase with different modification sites may play different regulatory roles in metabolic pathways in different tissues. Under high-temperature stress, the ubiquitination level of UGPase-K27 was 8.1-fold up-regulated. Liao et al.[10] demonstrated that the expression of UDPase was down-regulated in both heat-tolerant and heat-sensitive rice lines under high temperature conditions, which could reasonably explain the significant up-regulation of UGPase-K27 ubiquitination level. The ubiquitination site K143 of FK was also reported in seedling tissues[15].

    Ubiquitination of ADP-glucose pyrophosphorylase (AGPase)

    The AGPase reaction represents the first committed step of starch biosynthesis[27]. A total of 22 lysine ubiquitination sites were identified in four AGPase subunits (AGPL2, AGPL3, AGPS1, AGPS2). AGPL2 had 13 ubiquitination sites, of which six were located in NTP_transferase domain, including K254, K338, K191, K134, K227, and K316. High-temperature stress resulted in an increase in the ubiquitination level of K254 in both 9311 and GLA4, and significant upregulation of K508 and K513 in 9311, as well as K191, K227, and K316 in GLA4. In contrast, AGPL2-K394 were significantly downregulated in GLA4. AGPL3 contained one ubiquitination site K509, and the modification level of AGPL3-K509 was up-regulated in high-temperature environments in 9311. AGPS1 had one specific ubiquitination site K464 and another two sites K94 and K484 that completely overlapped with AGPS2-K106 and AGPS2-K496, respectively. The modification levels of K464 and K484 significantly increased in high-temperature environments in 9311, and K94 was significantly up-regulated in both varieties. There were seven ubiquitination sites in AGPS2 in rice endosperm, which were different with the sites found in rice leaves[14]. In addition to the two sites that overlapped with AGPS1, AGPS2 had another two ubiquitination sites (K406 and K132) that upregulated in high-temperature environments.

    Ubiquitination regulates amylose biosynthesis

    Amylose content is one of the key determinants that strongly influence rice grain quality[30]. The biosynthesis of amylose requires the catalytic effect of granule-bound starch synthase I (GBSSI)[30,31]. Here, a total of 16 ubiquitination sites were identified in GBSSI (Table 1, Fig. 8a). Among these ubiquitination sites, six lysine residues (K130, K173, K177, K181, K192, K258) were located in glycosyltransferase 5 (GT5) domain, and three sites (K399, K462, K517) were located in GT1 domain (Fig. 6), indicating the important role of ubiquitination regulation of GBSSI. Under high-temperature stress, the ubiquitination levels of six sites (K130, K177, K399, K381, K385, K549) increased in two indica rice varieties, while one sites (K258) showed downregulation in 9311 (Fig. 8a). Numerous studies had described that the amylose content was reduced under high-temperature stress in rice[5,7], which might be due to the degradation of GBSSI proteins caused by the increased significantly up-regulated ubiquitination sites. These ubiquitination sites identified in rice GBSSI with significant differences under high-temperature stress were expected to become a new breakthrough point for the improvement of starch quality.

    Figure 8.  Structure of GBSSI. (a) Domain structure of GBSSI and ubiquitination sites with significant differences in response to high-temperature stress. (b) 3D model of GBSSI and the relationship between ubiquitination sites K462 and ADP, SO4 (salt bridge or hydrogen bond).
    DownLoad: Full-Size Img PowerPoint

    To further determine the regulatory role of the ubiquitination sites in GBSSI, SWISS-MODEL was used to predict 3D structural model. As shown in Fig. 8b, GBSSI had three SO4 (sulfate ions) and one ADP ligand. These ligands interact with GBSSI through hydrogen bonds and salt bridges. Three sites, K447, R458, and K462, were associated with SO4 through salt bridges, while G100, N265, Q412, K462, and Q493 interact with the hydrogen bonds of ADP in GBSSI[32,33]. Based on this finding, it can be reasonably inferred that the K462 site with ubiquitination modification located in the GT1 domain played an important role in the interaction between GBSSI, SO4, and ADP. An in-depth investigation was necessary to gain a more comprehensive understanding of the regulatory function of ubiquitination modification at GBSSI-K462, although there was no significant difference in the ubiquitination level under high-temperature stress.

    Ubiquitination regulates amylopectin biosynthesis

    Amylopectin, the major component of starch, is synthesized by the coordinated action of multiple enzymes including soluble starch synthase (SSs), starch branching enzyme (BEs), starch debranching enzyme (DBEs), and phosphorylases (PHOs or Phos) with ADPG as a substrate. In this study, ubiquitination sites were detected in BEs, DBEs, and Phos.

    BEs, covering two isoforms, BEI and BEII, are responsible for catalyzing the formation of α-1,6-glucosidic linkages of amylopectin[34]. There were three ubiquitination sites (K103, K108, and K122) identified in BEI (Fig. 9a). K122 was the first amino acid in the carbohydrate-binding module 48 (CBM48) domain. Sequence alignment analyses of BEs from eight plants revealed K122 was conserved among all plants' BEI (Fig. 9a), suggesting a high probability of the functional effects of ubiquitination modification at this site. In high-temperature environments, ubiquitination levels of K108 and K122 were significantly up-regulated in 9311, while no significantly regulated ubiquitination sites of BEI were observed in GLA4. Only one ubiquitination site, K134, was found in BEIIb (Fig. 9a). The ubiquitination levels showed a slightly upward trend with no significant differences in high-temperature environments in both varieties. These changes could be one of the reasons for increased gelatinization temperature and relative crystallinity of rice starch in response to high-temperature[5].

    Figure 9.  Domain structure of (a) BEs, (b) PUL and (c) Pho1 as well as their ubiquitination sites with significant differences in response to heat stress. Residues in red indicate the ubiquitination site. Non-ubiquitinated residues are shown in dark grey.
    DownLoad: Full-Size Img PowerPoint

    DBEs consists of isoamylase (ISA) and pullulanase (PUL) with catalytic function for hydrolyzing α-1,6-glucosic linkages[35]. In the present study, we found that only PUL was ubiquitinated in rice endosperm (Fig. 9b). Among five ubiquitination sites (K230, K330, K432, K736, and K884) identified in PUL, K230 was located in the PULN2 domain, while K330 was in the CBM48 domain. Under high-temperature stress, K330 showed completely opposite regulatory trends in two cultivars. In addition, the ubiquitination level of K884, located in the DUF3372 domain, was significantly up-regulated in 9311. Previous study has reported that the expression of PUL was significantly up-regulated in 9311 under high-temperature stress, while GLA4 showed down-regulation in PUL abundance[11]. Consequently, there might be two possible functions of these ubiquitination sites. One possibility is that ubiquitination sites were unrelated to protein degradation; instead, they regulated the biosynthesis of amylopectin by affecting other functions of the protein. Secondly, ubiquitination sites were associated with protein degradation, and the levels of ubiquitination modification were based on protein abundance, resulting in a completely consistent regulation of ubiquitination modification and protein abundance under high-temperature stress.

    PHOs, including two types, Pho1/PHO1 and Pho2/PHO2, are responsible for the transfer of glucosyl units from Glc-1-P to the non-reducing end of a-1,4-linked glucan chains[36]. Pho1 is a temperature-dependent enzyme and considered crucial not only during the maturation of amylopectin but also in the initiation process of starch synthesis[37,38]. The three ubiquitination sites (K277, K445, K941) identified in Pho1 were located in two phosphorylase domains. We found that two sites, Pho1-K277 and Pho1-K445, were only ubiquitinated in high-temperature environments in 9311 and GLA4, respectively. Pang et al.[11] has demonstrated that the protein abundance of Pho1 decreased under high-temperature stress, especially in GLA4. Satoh et al.[38] reported that the functional activity of Pho1 was weakened under conditions of high temperature and its function might be complement by one or more other factors. Hence, these ubiquitination modifications that specifically occurred in high-temperature environments might be related to the degradation of Pho1 proteins.

    Ubiquitination affects ribosomal systems under high-temperature stress

    As a factory for protein synthesis in cells, the ribosome is an extremely crucial structure in the cell[39]. It has been proven that multiple ribosomal subunits were abundantly ubiquitinated in Arabidopsis and wheat[22]. In the present study, 57 ubiquitination sites involving 33 ribosome subunits were identified in 40S and 60S ribosome complexes in rice. Under high-temperature stress, the ubiquitination levels of some sites were significantly upregulated or downregulated, implying that ubiquitination of ribosomal proteins is likely to be an important regulatory mechanism in high-temperature response in rice endosperm. The results of GO and KEGG enrichment analysis indicated that the ribosome system was one of the most active systems for ubiquitination regulation under high-temperature stress. We speculated that the ubiquitin-proteasome system might be involved in the removal of subunits or entire ribosomes that were improperly folded in high-temperature environments. As shown in Fig. 10, the S10e, L18Ae, S27Ae, L9e, S3e, S28e, S20e, and S2e subunits were significantly up-regulated in 9311, while L13e subunits showed a completely opposite regulatory trend at the ubiquitination sites K81 and K88. In GLA4, the ubiquitination levels of S10e, S27Ae, L10Ae, L9e, S3e, S2e, and L4e showed a significant increase, while the ubiquitination level of L17e was significantly down-regulated under high-temperature stress. A total of seven ubiquitination sites involving S10e, S27Ae, L9e, S3e, and S2e subunits were jointly up-regulated in both two varieties. These sites might be related to the degradation of improperly folded ribosome subunits under high-temperature stress, while other ubiquitination sites with variety specificity might be associated with ribosomal function.

    Figure 10.  Ribosome system at the ubiquitination levels in rice endosperm under high-temperature stress. Grey shadings represent ubiquitinated proteins with no significant differences under heat stress. Red and blue shadings indicate up-regulated and down-regulated ubiquitinated proteins, respectively. Orange shading displays a combination of up- and down-regulated ubiquitinated sites in the same ubiquitinated protein.
    DownLoad: Full-Size Img PowerPoint

    In conclusion, this study provides the first comprehensive view of the ubiquitome in rice developing endosperm, and demonstrated that ubiquitination has diverse functions in the high-temperature response of rice endosperm by modulating various cellular processes, especially the sucrose and starch metabolism. Comparative analysis of the temperature-induced ubiquitination status revealed some similarities and more interesting differences between 9311 and GLA4. These differences might be the reason for the different qualities formation of the two indica rice varieties, which could provide potential genetic resources for the improvement of the heat resistance in rice. Considering the diversity of ubiquitination modification, it is worthwhile to further validate and explore the function and regulatory mechanism of the key targets and key pathways. The findings provide valuable insights into the role of ubiquitination in response to high-temperature stress and lay a foundation for further functional analysis of lysine ubiquitination in rice.

    Author contributions

    The authors confirm contribution to the paper as follows: study conception and design: Bao J, Pang Y; data collection: Pang Y; analysis and interpretation of results: Pang Y; draft manuscript preparation: Ying Y; Revised manuscript preparation: Ying Y, Pang Y, Bao J. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

    Acknowledgments

    This work was financially supported by the AgroST Project (NK2022050102) and Zhejiang Provincial Natural Science Foundation (LZ21C130003).

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Deng Z, Yin J, Eeswaran R, Gunaratnam A, Wu J, et al. 2024. Interacting effects of water and compound fertilizer on the resource use efficiencies and fruit yield of drip-fertigated Chinese wolfberry (Lycium barbarum L.). Technology in Horticulture 4: e019 doi: 10.48130/tihort-0024-0016
    Deng Z, Yin J, Eeswaran R, Gunaratnam A, Wu J, et al. 2024. Interacting effects of water and compound fertilizer on the resource use efficiencies and fruit yield of drip-fertigated Chinese wolfberry (Lycium barbarum L.). Technology in Horticulture 4: e019 doi: 10.48130/tihort-0024-0016
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Interacting effects of water and compound fertilizer on the resource use efficiencies and fruit yield of drip-fertigated Chinese wolfberry (Lycium barbarum L.)

  • 1.

    College of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021, China

  • 2.

    College of Information Engineering, Ningxia University, Yinchuan 750021, China

  • 3.

    Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA

  • 4.

    Ningxia Research Center of Technology on Water-saving Irrigation and Water Resources Regulation, Yinchuan 750021, China

  • 5.

    Engineering Research Center for Efficient Utilization of Water Resources in Modern Agriculture in Arid Regions, Yinchuan 750021, China

  • 6.

    Department of Agronomy, Faculty of Agriculture, University of Jaffna, Ariviyal Nagar, Kilinochchi 44000, Sri Lanka

  • 7.

    School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand

  • 8.

    Department of Export Agriculture, Uva Wellassa University, Badulla 90000, Sri Lanka

  • Received: 11 June 2024
  • Revised: 01 August 2024
  • Accepted: 05 August 2024
  • Published online: 28 August 2024
Technology in Horticulture  4,  Article number: e019  (2024)  |  Cite this article

Abstract: Chinese wolfberry (Lycium barbarum L.) is an important cash crop in the Ningxia region of China, but water scarcity, low water use efficiency (WUE) and fertilizer use efficiency (FUE) have limited the growth of its production. Field experiments were conducted in central Ningxia (China) during 2018−2019 to investigate the interaction effects of irrigation and fertilizer levels on agronomic performances (AP), WUE, partial fertilizer productivity (PFP), and economic benefits (EB). The optimal range of irrigation and fertilizer inputs was determined using multiple regression, the entropy weight method, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) coupling comprehensive evaluation method. Three drip irrigation levels were designated as a percentage of reference crop evapotranspiration (ETo); low (65% ET0: W1), medium (85% ET0: W2) and high (105% ET0: W3). Three N-P2O5-K2O compound fertilization levels (kg·ha−1) were selected as low (135-45-90: F1), medium (180-60-120: F2) and high (225-75-150: F3). Results showed that AP, WUE, PFP, and EB increased initially and then decreased with increasing levels of irrigation under the same fertilization levels. The PFP decreased with increasing fertilization levels and the lowest PFP was observed at high fertilizer (F3) application level. The above parameters reached the maximum value under medium irrigation. By establishing the multi-objective optimization model, it was found that 252−262 mm of irrigation and 185-62-123~200-67-133 kg·ha−1 of N-P2O5-K2O fertilization level offers more than 90% of yield, WUE, PFP, and EB simultaneously. The present results provide scientific insights into the resource optimization under drip-fertigation for Chinese wolfberry.

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    Introduction

    • Chinese wolfberry (Lycium barbarum L.) has been cultivated in China for more than 500 years. Fruit, root bark, and young leaves of this plant have both medicinal and nutritional values[1,2]. As of 2020, more than half of the commercial production of Chinese wolfberry in China comes from the Ningxia region. Furthermore, Chinese wolfberry is an important cash crop in the Ningxia region and generates substantial amounts of household income. Irrigation and fertilizer are two key factors that determine the quantity and quality of the yield of Chinese wolfberry[3]. The average irrigation water utilization efficiency in Ningxia region is only 0.47 which is lower than the country average[4]. The central region of Ningxia in the northwestern China is a typical arid region with an average annual evaporation of 7−8 times the annual precipitation. Therefore, water shortage for agriculture is a huge challenge for developing agricultural production in this region.

      With the expansion of Chinese wolfberry cultivation in Ningxia and a decreasing amount of water from the Yellow River, optimization of irrigation, and fertilizer management schemes for Chinese wolfberry production became decisive. Further, improving the irrigation and fertilizer use efficiency is a key issue that needs to be urgently addressed to sustain Chinese wolfberry production. To this end, drip irrigation along with plastic mulching is beneficial to conserving soil moisture, reducing evapotranspiration, and effectively saving water in the arid regions such as central Ningxia[5]. Several studies showed that drip irrigation coupled with plastic mulching increased the yield and water use efficiency of Chinese wolfberry compared to drip irrigation without mulching[6,7].

      Water is an essential input for Chinese wolfberry production and many studies showed that sustainable irrigation methods could increase the production and water use efficiency[8,9]. Moreover, Du et al.[10] reported that drip irrigation combined with mulching saved 42% to 60% of the water consumption of Chinese wolfberry compared to traditional border irrigation. When the irrigation quota was 277.5 mm during the growing season of Chinese wolfberry, water use efficiency and dry fruit yield could reach an optimal value. The irrigation quota above 229 mm has decreased the yield of Chinese wolfberry which expresses that excessive irrigation is not conducive to the growth and development of Chinese wolfberry[11]. Ma & Tian[5] reported that the plant height, crown width, chlorophyll, and photosynthetic rate were the highest, and the yield per hectare was 11.6% higher for treatment with film mulching than without mulching for a Chinese wolfberry variety. Sun et al.[12] concluded that drip irrigation under mulch decreased the water and fertilizer consumption by 35%~42% and 20%, respectively, while increasing the yield of Chinese wolfberry by 11.5% compared to border irrigation. Zhang et al.[13] further reported that saving direct labor economy reached CNY¥900 ha−1, and the comprehensive benefit increased by CNY¥3,000 ha−1. In addition, reducing the irrigation quota could minimize the soil salinization which is an important issue in arid regions.

      Fertilizer is another key factor which determines the growth and yield of Chinese wolfberry[14]. Chinese wolfberry is a fertilizer-responsive crop and its yield increases with the application of synthetic fertilizers. Dry fruit yield of Chinese wolfberry was within the range of 1,847 to 2,575 kg·ha−1 when the N, P, and K fertilization ratio was 6.6:1.8:3.5[15]. Wu & Zhang[16] reported that under three different soil fertility conditions, fertilizer formulations increased leaf dry weight, leaf area, and chlorophyll content, as well as improved the yield and quality of Chinese wolfberry. Among them, fertilizer formulation with high nitrogen, medium phosphorus, and low potassium had the highest yield and 100-grain weight of Chinese wolfberry. Similarly, Zhang et al.[17] also found that formula mixed chemical fertilizers (N : P2O5 : K2O = 1:0.75:0.5) could improve the yield and quality of Chinese wolfberry, and soil quality compared to conventional chemical fertilizers. Similar findings were reported for similar crops such as tomato, brinjal, black pepper, and strawberry[18−22].

      The interaction of irrigation and fertilizer could have better effects on agronomic performances of Chinese wolfberry and resource use efficiencies than the individual effect[23,24]. A combination of irrigation and fertilization could effectively improve the water and fertilizer utilization rates in crops[17,25]. Similar interaction effect of irrigation and fertilizer applications were reported for other horticultural crops namely, apple[26], cherry tomato[27], and mango[8]. Liu & Li[28] established a water and fertilizer production function to predict the yield of Chinese wolfberry using a binary quadratic polynomial regression model and found that medium irrigation (5,010 m3·ha−1) and medium fertilizer (607.50 kg·ha−1) is the optimal application level of irrigation and fertilizer, respectively. This study showed that the influence of irrigation amount on the yield of Chinese wolfberry was greater than the amount of fertilizer, but an excessive amount of fertilizer and irrigation was not conducive to the increase of the yield of Chinese wolfberry. All these studies showed that only the correct combination of irrigation and fertilization could ensure the yield and quality of Chinese wolfberry.

      The effect of a single factor such as irrigation, fertilization, and mulching method on agronomic performances of Chinese wolfberry and resource use efficiency was investigated in previous studies. Nonetheless, studies on the interaction effect of water and fertilizer on the growth, yield, and resource use efficiency of Chinese wolfberry are very limited. However, the optimum level of water and fertilizer would enhance productivity and resource use efficiency in Chinese wolfberry, especially in the resource-poor arid regions, and the optimum level could only be quantified by evaluating the interaction effects. Hence, the objective of this study was to determine the optimum level of water and compound fertilizer (i.e., N-P-K inclusive) by evaluating the interaction effects of water and fertilizers on the resource use efficiencies and fruit yield of drip-fertigated Chinese wolfberry. Further, this study aims to provide a reference for optimal resource allocation for effective water and nutrient management of Chinese wolfberry in arid regions.

    Materials and methods

      Experimental location and environmental data

    • Field experiments were conducted during the Chinese wolfberry growing seasons (April−September) in 2018 and 2019. The experiments were located at the RunDe Chinese wolfberry plantation in Hexi town, Tongxin County, WuZhong City, Ningxia Province, China (36°58'48" N, 105°54'24" E, altitude 1,240 m amsl). This region belongs to an arid zone with a typical continental monsoon climate. The average annual precipitation is around 145−280 mm which is received mostly in July through September. The average annual temperature is recorded at 8.8 °C, while the mean annual sunshine duration amounts to 2,983 h. The frost-free period spans approximately 150 d, with an effective accumulated temperature (calculated by summing the daily temperatures when the daily mean temperature exceeds 10 °C) reaching around 3,397 °C. The drought index is measured at 8.4, and the groundwater depth is determined to be more than 30 m. A decagon micro meteorological monitoring station was installed in an open place 10 m away from the experimental location to monitor meteorological variables. The effective rainfall (≥ 5 mm) during the experimental period was 149 and 155 mm in 2018 and 2019, respectively. The changes in weather variables of daily mean air temperature, rainfall, and reference crop evapotranspiration during the growth period of Chinese wolfberry from 2018 to 2019 are shown in Fig. 1. During the whole growth period of the crop, the temperature and precipitation reached a peak in June to July, and the precipitation was mainly confined to June–September (Fig. 1a). In addition, the variation of reference crop evapotranspiration was similar to that of the temperature (Fig. 1b). In the same period, the reference crop evapotranspiration in 2019 exceeded that of 2018, and the inter-annual variation was inconsistent or irregular.

      Figure 1. 

      (a) Daily rainfall and daily mean temperature, and (b) reference crop evapotranspiration (ET0) during the study period in 2018 and 2019.

      The physicochemical properties of soil in the experimental field are shown in Table 1. The soil in this region is generally silt loam in texture and most of them are saline-alkaline soils. There were no substantial variations in the measured soil chemical properties across the experimental years. The soil was low in terms of soil carbon and other nutrients, representing most of the marginal soils in the arid regions.

      Table 1.  Soil physicochemical properties of the experimental site during the study period.

      Year pH Organic
      matter
      (g·kg−1)      		 Total
      N
      (g·kg−1)      		 Available
      N
      (mg·kg−1)      		 Available
      P
      (mg·kg−1)      		 Available
      K
      (mg·kg−1)      		 Total
      salt
      (g·kg−1)
      2018 8.27 9.77 0.41 13.7 4.87 112 2.22
      2019 8.25 9.95 0.47 14.2 5.64 91 2.09

      Experimental design and treatment combinations

    • A popular variety 'Ningqi No.7' of Chinese wolfberry crop at the 4-year maturity stage was selected for this study and the crops were already established in a 75 and 300 cm spacing (Fig. 2). A 60 cm wide plastic film strip was laid on the cropping line to mulch the soil. Nearly 240 cm of intercrop space was uncovered and exposed to the environment (Fig. 2). A drip irrigation pipe with 16 mm inner diameter was used for irrigation and it was kept 5 cm away from the Chinese wolfberry tree (Fig. 2). The average discharge rate of the pipe was 3.0 L·h−1 and the amount of irrigation is controlled by an electronic water meter mounted on a drip irrigation pipe. Spring irrigation and winter irrigations were 300 and 450 m3·ha−1, respectively.

      Figure 2. 

      The layout of the plants, spacing, and drip irrigation used in the field experiments.

      Three levels of drip irrigation and three levels of fertilization were arranged in a randomized complete two-factor factorial block design and each treatment was replicated three times. The irrigation levels were selected considering the historical precipitation and evapotranspiration of the study area. Three levels of drip irrigation were applied based on reference crop evapotranspiration (ET0), which were low irrigation (65% ET0, W1), medium irrigation (85% ET0, W2), and high irrigation (105% ET0, W3) as presented in Table 2. In this study, the application of fertilizer treatments involved the application of a compound fertilizer which consisted of a combination of all three N-P-K fertilizers. Three levels of N-P2O5-K2O fertilizer treatments were 135-45-90 (F1), 180-60-120 (F2), and 225-75-150 (F3) kg·ha−1. Each treatment plot had a row of ten Chinese wolfberry trees.

      Table 2.  Irrigation scheduling of Chinese wolfberry during the two years of experiments.

      Year Growth stage Irrigation
      date (m/d)      		 Number of irrigation Irrigation (mm)
      Low (W1) Medium (W2) High (W3)
      2018 Spring slightly growing stage 5/4 1 17.8 23.3 28.8
      Flowering stage 5/17 2 22.6 29.6 36.5
      6/2 3 26.3 34.5 42.6
      Fruit ripening stage 6/19 4 39.3 51.4 63.5
      7/5 5 30.6 40.0 49.5
      7/21 6 26.7 34.9 43.1
      Deciduous stage 8/4 7 24.3 31.7 39.2
      Total 187.6 245.4 303.2
      2019 Spring slightly growing stage 5/5 1 18.3 24.0 29.6
      Flowering stage 5/19 2 24.5 32.1 39.2
      6/4 3 38.7 50.7 62.6
      Fruit ripening stage 6/20 4 35.2 46.1 56.9
      7/3 5 30.3 39.6 48.9
      7/13 6 27.4 35.8 44.2
      Deciduous stage 8/5 7 25.7 33.6 41.5
      Total 200.1 261.9 322.9

      Fertilizers namely urea (N 46%), superphosphate (P2O5 44%), and potassium chloride (K2O 60%) were applied a total of seven times to the fields at different growth stages of the crop. The fertilizer was fertigated with drip irrigation at the middle stage in each irrigation event. The supply of fertilizer for different growth stages were; 20% at the spring slightly growing stage (one time), 20% at the flowering stage (two times equal application), 50% at the fruit ripening stage (three times equal application), and 10% at the deciduous stage (one time). Separate differential pressure tanks with 13 L capacity were used to set up fertigation of each treatment plot.

      Measurements and calculations

      Growth measurements

    • The plant height and leaf area of Chinese wolfberry were measured for three trees from each plot which were randomly selected in each measurement. The plant height was measured using a meter stick for three replicates and the average value of each growth stage was calculated. A portable leaf area meter (CI-202, CID Bioscience, Camas, WA, USA) was used to measure the leaf area. Three sample plants were calibrated in each plot, and the maximum leaf area of the sample plants at each growth stage was taken as the leaf area value of the plot.

      Yield measurements

    • Chinese wolfberry crops bear fruit for two seasons namely summer and autumn. Generally, the quality and yield of autumn fruits are relatively low and therefore, the yield of summer fruits was only considered in this study. The yield can be categorized into dry fruit yield and fresh fruit yield, with dry fruit being more convenient for preservation and transportation compared to fresh fruit. Hence, this study adopts dry fruit yield as the standard for evaluation. Summer fruits were harvested in late June (first pick), early July (second pick), mid-July (third pick), late July (fourth pick), and early August (fifth pick). A total of 10 Chinese wolfberry trees were harvested from each treatment plot in both years. The harvested fruits were subjected to gradient drying under the following combinations of temperature and time; 40 °C - 2 h, 45 °C - 15 h, 55 °C - 15 h and 65 °C - 6 h. The dried weight of 100 grains for a plot was repeated and the maximum value of the mean was taken as the weight of 100-grain Chinese wolfberry.

      Water consumption and water use efficiency

    • Water consumption was calculated based on the water balance equation (Eqn 1)[29].

      ET=I+P+U−R−D−ΔW (1)

      where, ET is evapotranspiration (mm), I is irrigation amount (mm), P is rainfall (mm), U is groundwater recharge (mm), R is runoff (mm), D is deep percolation (mm), and ΔW is the change in soil moisture between the onset and end of the study (mm). The groundwater recharge, runoff, and deep percolation were negligible due to the prevailing conditions of the experimental site during the experiment period. Therefore, the Eqn (1) could thus be simplified as,

      ET=I+P−ΔW (2)

      The irrigation amount was calculated based on the reference crop evapotranspiration (ET0) using the Penman-Monteith equation[30].

      Water use efficiency (WUE) was calculated based on Badr et al.[25] as follows,

      WUE=Y/ET (3)

      where, WUE is water use efficiency (kg·m−3), Y is dry fruit yield (kg·ha−1) and ET is evapotranspiration (mm).

      Partial factor productivity of fertilizer (PFP)

    • The partial factor productivity of fertilizer was calculated as proposed by Ierna et al.[31] using the following formula,

      PFP=Y/FT (4)

      where, PFP is partial factor productivity of fertilizer (kg·kg−1), Y is yield (kg·ha−1) and FT is the total amount of N-P2O5-K2O fertilizer (kg·ha−1).

      Economic benefit (E)

    • The economic benefit was calculated using a simple benefit-cost analysis as shown in Eqn 5.

      E=Gw−Ww−Fw−Hw−Ow (5)

      where, E is Economic benefits (CNY¥·ha−1), Gw is the gross profit, Ww is the water fee, Fw is the fertilizer cost, Hw is the harvesting cost, and Ow is other costs (pesticides, weeding, etc.).

      Data analysis

    • The data were analyzed using the analysis of variance (ANOVA) procedure for the factorial experiments and mean separation was performed using least significance differences (LSD) at the 5% level. The SPSS 19.0 software (Chicago, IL, USA) was used in statistical analysis and the Matlab (Version 2016b, Natick, MA, USA) was used to calculate the evaluation values. The Origin (Version 2018, Irvine, CA, USA) was used for graphical visualization.

    Results

      Growth indicators and yield

    • In both years, plant height was significantly (p < 0.05) affected by irrigation, but not significantly influenced by fertilization. Although the interaction of irrigation and fertilizer was not significant on plant height in 2018, it was significant (p < 0.05) in 2019 (Table 3). The plant height showed an unclear relationship with fertilization rate under the same level of irrigation in both years (Fig 3). Similarly, the relationship between plant height and irrigation level was random at the same fertilizer application level for both years (Fig 3). It is because of the synergistic effect of water and fertilization on plant height from the measured data, as shown in Table 3. Under the same irrigation and fertilization level, the average plant height in 2018 was 2%−12% higher than that in 2019.

      Table 3.  Level of significance of growth parameters and yield under different irrigation and fertilizer treatments in 2018 and 2019.

      Treatment Plant height Leaf area Yield
      2018 2019 2018 2019 2018 2019
      Level of significance
      Irrigation * * * ** * *
      Fertilization ns ns ns * * ns
      Irrigation × fertilization ns * ns ns ** **
      * means significant at the 0.05 probability level, ** means significant at the 0.01 probability level, and ns means non-significant.

      Figure 3. 

      Effects of different irrigation and fertilizer treatments on plant height, leaf area, and yield in 2018 and 2019. Error bars show the standard error (n = 3). Different letters on top of the bar indicate a significant difference for the means at p < 0.05 according to the LSD test.

      The interaction effect of irrigation and fertilization was not significant on the leaf area in both years. Irrigation exhibited a significant effect (p < 0.05) on the leaf area in 2018 and it was strongly significant (p < 0.01) in 2019. Fertilization did not significantly influence leaf area in 2018 but it was significant in 2019 (Table 3). Generally, the leaf area was smaller in 2019 than the previous year (Fig. 3). This could be due to dryer weather in 2019 compared to the year 2018, which appears to decrease the leaf area.

      In both years, irrigation and fertilization had a strong significant interaction effect on yield (p < 0.01) (Table 3). At low-level irrigation (65% ET0, W1), the yield of Chinese wolfberry significantly (p < 0.05) increased with the increasing fertilization rate in 2018. The lowest yield (1,506 kg·ha−1) in 2018 was recorded for W1F1 treatment whereas the highest yield (2,056 kg·ha−1) was observed for W2F2 treatment. At the irrigation level of W2, the yield increased first and then decreased with increasing fertilizer application, and the highest yield (2,356 kg·ha−1) was received for W2F2 treatment in 2018 (Fig. 3). At the W1 irrigation level, the yield was not significantly different between different fertilizer treatments for 2019. The W3F3 treatment provided the lowest yield (1,325 kg·ha−1) while the highest was observed in the W2F3 treatment (1,954 kg·ha−1) in 2019. Under the high irrigation regime (105% ET0, W3), increasing fertilizer levels decreased the yield significantly (p < 0.05) (Fig. 3). For F1 and F2 fertilization levels, the yield significantly increased (p < 0.05) initially and declined thereafter with increasing irrigation levels in 2018 (Fig. 3). Nevertheless, this trend was not seen in the F3 treatment. For F2 and F3 fertilizer application levels, increasing irrigation levels significantly (p < 0.05) increased the yield initially and then significantly (p < 0.05) decreased during the year 2019 (Fig. 3). For the same year, yield significantly (p < 0.05) increased with increasing irrigation levels for F1 fertilizer treatment.

      In general, the W3F1 treatment showed the highest plant height in both years and the leaf area was highest for W1F2, W1F3, W2F1, W2F2, and W2F3 treatment combinations over the two years. However, the highest yield was obtained with W2F2 and W2F3 treatments in 2018 and 1019, respectively (Fig. 3).

      Overall, under the same irrigation and fertilization regime, the changes in leaf area and yield were similar. However, the changes in plant height of Chinese wolfberry were not uniform. In 2018, the yield of Chinese wolfberry reached the highest under the medium irrigation-fertilizer regime (W2F2), while in 2019, the highest yield was obtained under the medium irrigation and high fertilization (W2F3). Accordingly, the medium irrigation level could be the key to obtaining high yield in Chinese wolfberry. Furthermore, the interaction effect of irrigation and fertilization was highly significant on yield than plant height and leaf area (Table 3).

      Water use efficiency (WUE) and partial factor productivity (PFP) of fertilizer

    • Water use efficiency (WUE) was significantly (p < 0.05) influenced by irrigation in 2019 and it was strongly significant (p < 0.01) in 2018 (Table 4). Fertilization had no significant effect on WUE in 2019, and conversely, it showed a significant effect in 2018 (p < 0.05). The interaction of irrigation and fertilization had a significant effect on WUE in both years. The highest WUE (0.55 kg·m−3) was attained for W2F2 treatment, and it was 40%−41% higher than the lowest values (W3F1 and W3F3) in 2018. The highest WUE value in 2019 was recorded for the W2F3 treatment (0.39 kg·m−3) and it was 41 % greater than the lowest value obtained for the W3F3 treatment (Table 4).

      Table 4.  Treatment effects on water use efficiency (kg·m−3) and partial factor productivity of fertilizer (kg·kg−1).

      Treatment Water use efficiency
      (kg·m−3)      		 Partial factor
      productivity of
      fertilizer (kg·kg−1)
      2018 2019 2018 2019
      W1F1 0.42d 0.34b 5.58b 4.89b
      W1F2 0.43c 0.31cd 4.40cd 3.36cd
      W1F3 0.47b 0.33bc 4.34d 3.24d
      W2F1 0.42d 0.32c 6.59a 5.2a
      W2F2 0.55a 0.31cd 6.55a 3.82c
      W2F3 0.44c 0.39a 4.78c 4.5b
      W3F1 0.37ef 0.32c 6.52a 5.76a
      W3F2 0.42d 0.27d 5.55b 3.63c
      W3F3 0.39e 0.23e 4.75c 2.89e
      Level of significance
      Irrigation ** * * *
      Fertilization * ns ** **
      Irrigation × fertilization * * * *
      Means with different letters are significantly different (p < 0.05) based on the LSD test. * Means significant at the 0.05 probability level, ** means significant at the 0.01 probability level, and ns means non-significant.

      The interaction effect of irrigation and fertilization was significant (p < 0.05) in PFP during both years (Table 4). The maximum values for PFP were recorded with W2F1, W2F2, and W3F1 treatments in 2018 and the corresponding PFP values were 6.59, 6.55, and 6.52 kg·kg−1, respectively. The lowest values in 2018 were observed for W1F2, and W1F3 treatments which were 4.40 and 4.34 kg·kg−1, respectively. At a higher level of irrigation (W3), PFP decreased with increasing fertilizer application rate in 2018 (Table 4).

      In 2019, the maximum values for PFP were 5.2 and 5.76 kg·kg−1 for W2F1 and W3F1 treatments, respectively. The W1F3 treatment exhibited the lowest PFP value (3.24 kg·kg−1) in 2019. In the same year, the irrigation levels W1 and W3 showed a similar trend on PFP to that of 2018 with increasing fertilization levels (Table 4).

      In general, under W1 and W2 irrigation levels, PFP decreased with increasing fertilizer application rates. Furthermore, under the low fertilization level (F1), PFP increased with increasing level of irrigation. The PFP reached the minimum value at W3F3 for the year 2019, which could be an indication that the yield of Chinese wolfberry can be retarded under the high level of irrigation and fertilization.

      Economic benefits

    • At present, Chinese wolfberry cultivation provides an annual comprehensive output value of 13 billion RMB and an average annual income of CNY¥13,500 to 195,000 ha−1[32]. The effect of different irrigation and fertilization treatments on economic benefits in 2018 and 2019 were estimated and presented in Table 5. The economic benefits in 2018 and 2019 were between CNY¥155,596 ha−1 (W1F1) to CNY¥218,001 ha−1 (W2F2), and CNY¥132,423 ha−1 (W3F3) to CNY¥205,199 ha−1 (W2F3), respectively. In 2018 and 2019, the highest economic benefits were higher by 28.5% and 35.5% compared to the lowest economic benefits, respectively. This result indicates that a higher level of irrigation and fertilization do not always maximize the economic benefits, thus emphasizing the requirement for an optimum level of irrigation and fertilizer management for Chinese wolfberry production.

      Table 5.  Effects of different irrigation and fertilization treatments on economic benefits.

      Treatment Water fee
      (CNY¥ ha−1)      		 Fertilizer cost
      (CNY¥ ha−1)      		 Harvesting cost
      (CNY¥ ha−1)      		 Other costs
      (CNY¥ ha−1)      		 Gross profit
      (CNY¥ ha−1)      		 Economic benefits
      (CNY¥ ha−1)
      2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019
      W1F1 500 534 2,878 6,778 6,847 15,000 180,752 182,584 155,596 157,325
      W1F2 500 534 3,838 7,027 6,344 15,000 187,375 169,178 161,010 143,462
      W1F3 500 534 4,797 7,706 6,606 15,000 205,499 176,150 177,496 149,213
      W2F1 654 698 2,878 8,010 7,218 15,000 213,607 192,474 187,065 166,680
      W2F2 654 698 3838 9,253 7,536 15,000 246,746 200,947 218,001 173,875
      W2F3 654 698 4,797 8,397 8,793 15,000 223,925 234,487 195,077 205,199
      W3F1 808 862 2,878 7,917 7,900 15,000 211,108 210,656 184,505 184,016
      W3F2 808 862 3,838 8,958 6,785 15,000 238,883 180,940 210,279 154,455
      W3F3 808 862 4,797 8,330 5,964 15,000 222,120 159,046 193,185 132,423

      The water fee is the smallest proportion of the total expenditure and the cost difference of the water fee between treatments is also small. The low cost of water fees and considerable economic losses in cutting down irrigation levels are the major reasons for the lack of interest by farmers in water saving. Suboptimal or super-optimal application of water and fertilizer not only affect the economic return but also waste a very competitive resource like water.

      Interaction effect of water and fertilizer on yield, WUE, PFP, and economic benefits

    • Farmers cultivating Chinese wolfberry aim at high economic return and it is usually considered that a high water and fertilizer input would increase the economic return. However, the results of this study showed that higher irrigation and fertilization levels increased the yield of Chinese wolfberry only up to a certain extent, usually referred to as an optimum level of input. Application beyond this level has led to economic loss, and reduction of water use efficiency and PFP. Moreover, excessive use of chemical fertilizer deteriorates the soil health, increases fertilizer loss to the environment, causing soil and water pollution, and eventually affecting the sustainability of agriculture[14]. Water use efficiency, economic benefits and ecologically sound crop production are the keys to sustainable agricultural development in arid regions. Therefore, the Chinese wolfberry yield, WUE, PFP, and economic benefits were selected as targeting variables for the optimization process of relevant inputs.

      Based on the least square method, four binary quadratic regression equations were established, considering irrigation and fertilizer levels as the independent variables and Chinese wolfberry yield, WUE, PFP, and economic benefits as the dependent variables (Table 6). In addition, the amount of irrigation and fertilization were calculated when the above dependent variables were maximized (Table 7).

      Table 6.  Regression equations between irrigation and fertilization inputs and yield, WUE, PFP and economic benefits.

      Dependent variable/Y Regression equation R2 P
      Yield/Y1

      Y1 = −4120.2737 + 37.5905I + 5.7081Y − 0.0628I2 − 0.0031F2 − 0.0129IF

      		 0.67 * (0.037)
      WUE/Y2

      Y2 = −0.7415 + 0.007I + 0.0018F − 0.000013I2 − 0.00000144F2 − 0.000003IF

      		 0.63 * (0.043)
      PFP/Y3

      Y3 = −3.233 + 0.122I − 0.0325F − 0.0002I2 + 0.000047F2 − 0.000043IF

      		 0.74 * (0.029)
      Economic benefits/Y4

      Y4 = −490877.3168 + 4339.0072I + 648.5543F − 7.2545I2 − 0.3537F2 − 1.4897IF

      		 0.67 * (0.038)
      I and F represent the amounts of irrigation and fertilization, respectively. * Means significant at the 0.05 probability level.

      Table 7.  The optimum level of irrigation and fertilization for maximum yield, WUE, PFP, and economic benefits.

      Dependent variable/Y Maximum value of dependent variable Irrigation
      amount
      (mm)      		 Fertilization
      (N-P2O5-K2O)
      (kg·ha−1)
      Yield/Y1 1859.74 259.7 192-64-128
      WUE/Y2 0.42 225.5 204-68-136
      PFP/Y3 6.31 269.5 135-45-90
      Economic benefits/Y4 195,101.33 261.5 183-61-122

      It is difficult to obtain the maximum yield, WUE, PFP, and economic benefits simultaneously. When the amount of irrigation and fertilization (N-P2O5-K2O) were 259.7 mm and 192-64-128 kg·ha−1, respectively, the Chinese wolfberry yield reached the maximum of 1,859.74 kg·ha−1. The WUE reached the maximum of 0.42 kg·m−3 at the amount of irrigation and fertilization (N-P2O5-K2O) of 225.5 mm and 204-68-136 kg·ha−1, respectively. The greatest PFP (6.3 kg·kg−1) was achieved at 269.5 mm and 135-45-90 kg·ha−1 irrigation and fertilization (N-P2O5-K2O) levels, respectively. The maximum economic benefit of CNY¥195,101 ha−1 was achieved with the irrigation and fertilization application of 261.5 mm and 183-61-122 kg·ha−1 of (N-P2O5-K2O), respectively. The irrigation amount at the time of the highest economic benefit was 0.67% higher than that at the time of the highest yield, and the corresponding fertilizer application amount was 4.86% lower than that at the time of the highest yield.

      The WUE reached the maximum at a 13.8% lower irrigation amount and 10 % higher fertilization rate than the maximum economic benefit point. The amount of irrigation and fertilization rate was higher than 3% and 26.3%, respectively, for the highest PFP compared to the highest economic benefits.

      The interaction effect of irrigation and fertilization inputs on yield, WUE, and economic benefits showed a downward convex shape, while the PFP decreased with increasing fertilization application (Fig. 4). The maxima of yield, WUE, and economic benefits were reached at similar levels of irrigation and fertilization, however, input values to maximize the PFP differs greatly from the other three indicators. Ecological sustainability, water and fertilizer savings are the goals of our multi-objective optimization problem to achieve high yield and high economic benefits. A comprehensive evaluation method by combining the entropy weight method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was established to evaluate each irrigation and fertilization treatment in 2018 and 2019, as shown in Fig. 5.

      Figure 4. 

      Relationship of (a) yield, (b) water use efficiency (WUE), (c) partial factor productivity (PFP) and (d) economic benefits with the amount of irrigation and fertilization (N-P2O5-K2O) during the two years. The red dots in the figure represent the measured experimental data during 2018 to 2019.

      Figure 5. 

      Effects of irrigation and fertilization on comprehensive evaluation index for (a) 2018, and (b) 2019.

      It can be found that the maximum index value appeared in the medium level of irrigation and fertilization region in 2018 and a medium level of irrigation and high level of fertilization region in 2019. This observation is consistent with the irrigation and fertilization level reflected by the measured data in these two years. To have an overlapping area in the maximum value of comprehensive evaluation indicators in both years, 90% of the maximum value of comprehensive evaluation indicators was determined as acceptable regions. According to this, when the irrigation range was 252 to 262 mm and the fertilization range was 185-62-123 to 200-67-133 kg·ha−1, the Chinese wolfberry yield, WUE, PFP, and economic benefits reached above 90% of their maxima concurrently.

    Discussion

      Effects of different levels of irrigation and fertilizers on the growth indicators and yield

    • Plant height and leaf area are commonly used as growth parameters of Chinese wolfberry[33]. Yin et al.[34] reported that plant height was significantly affected by irrigation, thus increasing irrigation level was beneficial for the growth of Chinese wolfberry. However, the effect of fertilizer on plant height was not significant as it also accumulated with tree age, and therefore, fertilizer amount in two years may not change the plant height significantly[35]. The results of this study are consistent with previous findings where irrigation had a significant effect on plant height, while fertilizer application had no significant effect on the plant height of Chinese wolfberry.

      Leaf plays an important role in photosynthesis and transpiration, thus the leaf size has a great influence on the growth and the yield formation[4]. It was reported that the leaf area increased first and then decreased with the increasing irrigation and fertilizer levels[36,37] which is similar to the findings of this study. The interaction effect of irrigation and fertilization did not significantly influence the leaf area. Irrigation levels significantly influenced the leaf area which was also noticed in other studies[3,38,39].

      It was found that the interaction effect of water and fertilizer considerably influences the Chinese wolfberry yield. In both years, suboptimal and excess application of irrigation reduced the yield whereas a high level of fertilization did not result in the highest yield. This could be because Chinese wolfberry is a perennial plant that used up a larger portion of the absorbed nutrients for vegetative growth rather than for the conversion of reproductive growth[40]. Under high irrigation levels, nutrient leaching beyond the root zone may have decreased plant available nutrients and eventually results in a lower yield[5]. However, insufficient irrigation retards the plant growth decreases the leaf area and lowers the photosynthetic efficiency which is not conducive to high yield. Dai et al.[41] also confirmed that water shortage reduced the production of the crop. The results showed that Chinese wolfberry yield was lower in 2019 (1,954 kg·ha−1) than in 2018 (2,056 kg·ha−1). The possible reason for this is that the evapotranspiration in 2019 was higher than that in 2018 while the precipitation remains almost the same, which could have decreased the soil moisture availability to the plants and ultimately reduced the yield[42]. Therefore, the optimum level of irrigation and fertilization could increase the agronomic performances and provide the highest yield[43].

      Effects of different levels of irrigation, and fertilizers on WUE, PFP and economic benefits

    • The interaction of irrigation and fertilizer was significant in WUE which showed a good agreement with previous studies[44,45]. In general, the WUE showed a parabolic relationship with increasing irrigation and fertilization wherein irrigation had a stronger relationship than fertilization. The maximum WUE was achieved with medium level of irrigation and medium fertilizer treatment in 2018 whereas under medium level irrigation and high fertilizer level in 2019. At a high level of irrigation, the WUE decreased with increasing fertilization. High irrigation level often induces the leaching losses of nutrients, especially N, possibly the reason for this observation. At the same level of irrigation, the WUE of high fertilization level was generally higher than that of a low fertilization level[46]. Likewise, Eissa et al.[47] reported that 28%−42% increase in WUE with higher levels of N (240 kg·ha−1) as compared to the lower level (120 kg·ha−1) in wheat. This is because fertilizer improves growth and yield in some crops and improving WUE, while excessive fertilization will affect the absorption of nutrients by Chinese wolfberry, resulting in excessive soil nutrients and reduced WUE. Improved WUE could be achieved through the proper application of N and P fertilizer was documented in several studies such as Li et al.[48] and Wei et al.[49].

      Meanwhile, previous studies showed that PFP decreases with the increase of fertilization, and increases initially and then decreases with the increase of irrigation[35,50] which is in agreement with the results of this study as well. In this experiment, the PFP values corresponding to the highest yield treatments (i.e., W2F2 in 2018, W2F3 in 2019) were 0.61% lower than the highest PFP values in 2018, and 21.88% lower than in 2019. These results showed that a low level of fertilization yielded higher PFP, but didn't meet the production requirements. However, excessive nitrogen fertilization promotes vegetative growth and impedes the supply of nutrients to reproductive components of the crop, leading to yield reduction[51]. Several studies showed that higher levels of nutrient application failed to support high yield. For example, Okebalama et a.l[52] pointed out that P fertilizer had a greater effect on corn grain yield than N fertilizer and P fertilizer should be supplied not exceeding the critical level of 60 kg·ha−1 (in Plinthic Acrisol) and 90 kg·ha−1 (in Gleyic Plinthic Acrisol) for optimum maize yield. Trujillo Marín et al.[27] reported that a 30% N application rate increased the yield of fresh fruit by 32.9%, and increased nitrogen accumulation by 9.0% compared to a 70% N application rate in tomato. Moradi et al.[53] found that 60 kg·ha−1 gave the highest yield of rice than the other two levels of N application rates; 40 and 60 kg·ha−1. All these findings support the results of this study that either low or high nutrient application is not conducive for high yield.

      The ultimate aim of the farmers is to gain high economic return which influences the viability of the farming. The economic benefits of medium level irrigation (W2) 17.7% (2018), 17.6% (2019), and 2% (2018), 13.7% (2019) times higher than low and high irrigation levels, respectively. At the same level of irrigation, economic benefits increased initially and then decreased with increasing fertilization. Therefore, this study emphasizes that increasing either irrigation or fertilization beyond the optimal level decreased the economic benefits[41,42,54,55]. Considering the cost of inputs, cutting down the fertilizer cost is more beneficial than reducing the expenditure on water. However, saving water is also equally important on the basis of environmental protection. Therefore, it is necessary to seek an irrigation and fertilization management scheme that can ensure not only the efficient management of irrigation and fertilizer but also take into account economic benefits in both water-deficient and non-water-deficient areas.

      Interaction effects of irrigation and fertilizer on Chinese wolfberry production and optimum management strategy

    • The interaction effect of irrigation and fertilizer was significant on WUE and PFP and was strongly significant on the yield. Obviously, the interaction of water and fertilizer is the effective method to improve the comprehensive benefits of Chinese wolfberry[5]. This study developed appropriate relationship models between inputs (irrigation and fertilization) and yield, WUE, PFP, and economic benefits by combining the quadratic polynomial stepwise regression, and spatial analysis method. The solution of the models showed that no irrigation and fertilizer management scheme maximized all indicators. Similar observations were reported in other studies[56,57]. In addition, the entropy weight method was combined with TOPSIS to comprehensively evaluate all the treatments for the two years of experiment. Few studies have shown that appropriate adjustment of the confidence interval can solve the problem of comprehensive benefits[56,58].

      Therefore, a 90% confidence interval was set as an acceptable range in this study to maximize the yield, WUE, PFP, and economic benefits. More than 90% of the maximum values were achieved at the irrigation range of 252−262 mm and the N-P2O5-K2O fertilization range of 185-62-123 to 200-67-133 kg·ha−1 without spring and winter irrigation. The irrigation and N-P-K fertilizer application amount of local Chinese wolfberry park are 300 mm and 396-166-225 kg·ha−1 respectively, and the annual income is CNY¥13,000 ha−1. If the irrigation and fertilizer management scheme proposed in this study is adopted, it could save water by 13%−16%, N-P2O5-K2O fertilizer by 50%-60%-41% to 53%-63%-45% and increase economic benefits by about 8%.

    Conclusions

    • Lack of appropriate irrigation and fertilizer management is one of the deeply rooted issues in Chinese Wolfberry cultivation in northwest China. This study attempts to find the optimal irrigation and fertilization level based on yield, WUE, PFP, and economic benefits for Chinese Wolfberry over a two-year field experiment. None of the treatment combinations provided the maximum values for yield, WUE, PFP, and economic benefits. The WUE decreased with increasing irrigation level. The WUE with low irrigation level (65% ET0) and medium irrigation level (85% ET0) were all higher than that of high irrigation levels (105% ET0) in both years. With increasing fertilization, PFP showed a decreasing trend. Both low (65% ET0), and high (105% ET0) irrigation levels were not conducive to the effective utilization of fertilizer. The irrigation and fertilizer schemes corresponding to the maximum yield and economic benefits in 2018 and 2019 were medium irrigation levels (85% ET0) with medium and high fertilizer treatments, respectively.

      The least square method, multiple regression, and comprehensive evaluation of a multi-objective optimization problem revealed that the yield and economic benefits do not decrease, when the irrigation range was 252−262 mm and the N-P2O5-K2O fertilization range was 185-62-123~200-67-133 kg·ha−1. At this application level, yield, WUE, PFP, and economic benefits of Chinese wolfberry reached 90% of the maximum value, which would maximize the comprehensive benefit. The finding of this study is of importance in providing the baseline of irrigation and fertilization levels for farmers cultivating Chinese wolfberry in the northwest China and other regions with similar soil and climate characteristics. Nevertheless, further studies may be required to validate the findings of this research across different geographical regions.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Deng Z, Yin J; data collection: Deng Z, Wu J, Zhang H; data curation: Deng Z, Yin J, Eeswaran R, Abhiram G; analysis and interpretation of results: Deng Z, Yin J, Eeswaran R, Abhiram G; draft manuscript preparation: Deng Z; writing – review & editing: Yin J, Eeswaran R, Abhiram G; fund acquisition & supervision: Yin J. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • All data generated or analyzed during this study are included in this published article.

      Acknowledgments

      • This work was financially supported by the First-class Subject Project at Ningxia University (Grant No. NXYLXK2017A03), and the talent plan of Ningxia youth 'support project' in 2017.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (5)  Table (7) References (58)
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    Cite this article
    Deng Z, Yin J, Eeswaran R, Gunaratnam A, Wu J, et al. 2024. Interacting effects of water and compound fertilizer on the resource use efficiencies and fruit yield of drip-fertigated Chinese wolfberry (Lycium barbarum L.). Technology in Horticulture 4: e019 doi: 10.48130/tihort-0024-0016
    Deng Z, Yin J, Eeswaran R, Gunaratnam A, Wu J, et al. 2024. Interacting effects of water and compound fertilizer on the resource use efficiencies and fruit yield of drip-fertigated Chinese wolfberry (Lycium barbarum L.). Technology in Horticulture 4: e019 doi: 10.48130/tihort-0024-0016
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  • Introduction
  • Materials and methods

  • Experimental location and environmental data

  • Experimental design and treatment combinations

  • Measurements and calculations

  • Growth measurements
  • Yield measurements
  • Water consumption and water use efficiency
  • Partial factor productivity of fertilizer (PFP)
  • Economic benefit (E)

  • Data analysis

  • Results

  • Growth indicators and yield

  • Water use efficiency (WUE) and partial factor productivity (PFP) of fertilizer

  • Economic benefits

  • Interaction effect of water and fertilizer on yield, WUE, PFP, and economic benefits

  • Discussion

  • Effects of different levels of irrigation and fertilizers on the growth indicators and yield

  • Effects of different levels of irrigation, and fertilizers on WUE, PFP and economic benefits

  • Interaction effects of irrigation and fertilizer on Chinese wolfberry production and optimum management strategy

  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgments
  • Conflict of interest
  • Rights and permissions
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