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.)

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.
  • Sweet cherries (Prunus avium L.) are a major focus of agriculture in the Okanagan region of British Columbia (BC), Canada. A large portion of the cherries grown in BC are exported and undergo up to four weeks of storage during transportation before delivery and consumption[1]. In 2022, sweet cherries accounted for 11.6% of the revenue of exported fruit from Canada and have an export value of nearly CAD$130 million[2]. As such, sweet cherry is an important fruit with high commercial importance for Canada. Although the application of cold storage is a necessary postharvest tool to maintain fruit quality up to consumption, there are preharvest factors that impact quality after longer-term storage. The work of Serrano et al.[3] noted that the maturity stage at harvest determined the fruit quality of sweet cherries after storage. For this reason, producers use several parameters to establish the optimum time for harvesting. Producers have long used colour as a marker for maturity, yet the concept of fruit dry matter (DM) at harvest affecting post-storage quality has advanced[46]. In fact, Toivonen et al.[6] developed a predictive model for 'Lapins' sweet cherry DM content using a visible/near-infrared spectrometer and noted its potential application to other cultivars to provide a rapid and non-destructive means of determining DM linked to cherry fruit quality. If sweet cherries are harvested at the wrong time or stored improperly during transit the quality of the cherries at their final destination does not compare to that at the time of harvest. Therefore, it is of the utmost importance to harvest cherries at their optimal time to ensure quality retention. Cherry fruits have minimal reserve carbohydrates so respiration relies primarily upon organic acids[7]. Additionally, cherries have a high susceptibility to physical damage making them highly perishable, so it is imperative to store them properly to maintain their flavour profile and overall quality[811]. Lower respiration rates help to maintain higher titratable acidity (TA) levels, thereby retaining flavour quality[11,12]. Decreased respiration rates are achieved through low-temperature storage and shipping.

    Previous research linking cherry fruit maturity to flavour quality indicated early-harvested cherries with low soluble solids (SS) levels showed low consumer acceptance due to perceived low sweetness, while late-harvested cherries showed low consumer acceptance due to poor texture[13,14]. This information underscores the challenge growers face when determining picking date. Additionally, once cherries are harvested, quality changes occur which include changes in the balance of SS to TA levels. Previous work has shown that SS levels remain relatively constant while malic acid levels (the predominant acid in sweet cherries) decreased by 20% when stored for 4 d at 20 °C[8,14]. Further, SS, TA, and the SS/TA ratio are key parameters in defining flavour quality[15] and consumer acceptance[14] as SS and TA have been reported to be measures of the cherry fruit attributes of sweetness, and sourness, respectively. Additionally, the SS/TA ratio is regarded as an overall taste attribute determining sweet cherry acceptability[14,16,17].

    Depending on cultivar and growing location, SS values for sweet cherries have been reported to range from 12.3 to 23.7 °Brix[14]. Rootstock and storage conditions have also been reported to affect SS, TA, and SS/TA values[18]. Depending on rootstock, for 'Regina' sweet cherries, harvest SS values ranged from 14.8 to 16.6 °Brix and TA values ranged from 5.7 to 7.4 g·L−1, while SS values after storage ranged from 14.6 to 18.2 °Brix and TA values ranged from 4.4 to 6.0 g·L−1. The harvest SS/TA ratio ranged from 2.0 to 2.91 and the SS/TA ratio after storage ranged from 2.47 to 3.77 depending on rootstock. Based on sensory studies using various cherry cultivars and breeding selections to gauge flavour quality and consumer acceptance, the optimal SS/TA ratio was reported to be between 1.5 and 2.0, with SS values ranging between 17 and 19 °Brix[19].

    Unfortunately, members of British Columbia's sweet cherry industry have noted that while their cherries arrive at their export locations with good condition in terms of appearance (i.e. firm, shiny, with green stems), issues have been reported concerning flavour. Poor flavour has been associated with lower levels of TA and lower oxygen in the storage atmosphere. Our previous work noted that BC cherry growers tend to pick their cherries at lighter colours in an attempt to harvest the crop as soon as possible to avoid any weather or pest issues and achieve the highest yield possible[1]. Staccato (SC) is a late maturing economically important cultivar with little research data available. It has been reported that SC cherries have a respiration rate that is negatively correlated with colour when collected between a 2 to 6 colour level as determined with CTFIL (Centre Technique Interprofessionnel des Fruit et Legumes, Paris, France) colour chips[1], which have been typically used as a marker of maturity. For SC cultivars, data showed the lowest respiration levels with cherries harvested at CTFIL colour standards 4-5, and may potentially have better flavour quality retention due to these lower respiration rates[1]. The aim of the work was to: 1) examine indicators of maturity/readiness for harvest (colour, SS, DM, TA, and the ratio of soluble solids to titratable acidity (SS/TA)); and 2) determine whether colour at harvest or other parameters (SS, TA, SS/TA, and DM) better predict flavour quality retention after storage.

    Sweetheart (SH), Staccato (SC), and Sentennial (SL) sweet cherries were sourced from research plots located at the Summerland Research and Development Center (SuRDC, Summerland, BC, Canada) in the Okanagan Valley region of British Columbia over three growing seasons (2018, 2019, and 2021). In the 2018 growing season, two cherry cultivars (SH and SC were collected, in the 2019 growing season, in response to BC Cherry Association interest, three cherry cultivars (SH, SC, and SL) were collected. Due to COVID, data was not collected during the 2020 growing season. In the 2021 growing season, full data (SS, TA, DM, and respiration rate) was only collected on the SC cherry cultivar, while DM and respiration values at harvest were also collected for SH and SL cultivars.

    To collect fruit at different maturity levels, cherry fruits were collected at three different color levels using the CTFIL (Centre Technique Interprofessionnel des Fruit et Legumes, Paris, France) colour standard series at three harvest dates for each cultivar which corresponded to the 3-4, 4-5, and 5-6 colour levels.

    To collect environmental data, two trees were chosen in each orchard block and Onset HOBO (Bourne, MA, USA) temperature and humidity loggers were mounted in these trees as described by Ross et al.[1] and captured data at 10 min intervals. The temperature and humidity data were used to calculate average temperature (AT), average high temperature (AHT), average low temperature (ALT), and average relative humidity (ARH) for 28 d preceding the cherry harvest date.

    Cherries were generally harvested before 11:00 h on each harvest day. Harvested cherries were transported back to the lab for sorting, sampling, and storage. Upon arrival, cherries were placed into a walk-in cooler at 0.5 °C to mimic rapid hydro-cooling capabilities that the industry uses. In the afternoon of each harvest day, cherries were removed from the cooler and sorted following British Columbia Tree Fruits Company protocol: (i) size was greater than 25.4 mm (< 10.5 row size); (ii) stemless cherries were removed; and (iii) cherries with defects such as blemishes, splits, pitting, disease (rot, fungi), hail damage and insect damage were removed. Again, the colour of the cherries was assessed using the CTFIL colour standard series. Based on the harvest period, cherries were separated into different colour levels: 3-4, 4-5, and 5-6. For each colour category, quality assessments (DM, SS, TA, SS/TA ratio, firmness, size, stem pull force, stem shrivel, stem browning, pitting, and pebbling) were performed before and after storage.

    Maintaining the quality of sweet cherries undergoing long distance ocean container shipment (up to 28 d/4 weeks) is important for securing a successful export market for Canadian sweet cherries and for ensuring that current market demand is met[1]. For cherries to be marketable after storage, they must pass several quality attributes. Attributes of firmness, size, stem pull force, stem shrivel, stem browning, pitting, and pebbling were assessed using previously described methods[1,20] to assess fruit quality at harvest and after storage for 28 d at 0.5 °C, ideal refrigerated storage temperature, for all cherry cultivars, and also storage for 28 d at 3 °C, non-ideal refrigerated storage temperature, for SC cherries. The values of these parameters are provided in Supplementary Tables S1S6. As the focus of this work was to examine how maturity level at harvest influenced the flavour quality of SC, SH, and SL sweet cherry cultivars, SS, DM, TA, and respiration rate values were the focus for discussion. Respiration analysis was performed using methods described by Ross et al. on freshly harvested cherries[1]. Rates of CO2 production were expressed as mg CO2 kg−1·h−1.

    DM of the cherries was determined using a Felix F750 handheld spectrometer (Felix Instruments, Inc., Camas, WA, USA) loaded with a valid model developed at the Summerland Research and Development Centre for cherries[6]. DM was measured on 25 fruits that were randomly selected from each sample replicate (i.e. 50 cherries). For SS and TA analyses, the methods of Ross et al.[1,20] were used to test 25 fruits that were randomly selected from each sample replicate (i.e. 50 cherries). Briefly, de-stemmed cherries were transferred into a 15.2 cm × 22.9 cm polyethylene Ziplock bag. The bag was left partially open, and the cherries were pressed by hand to obtain juice. The juice was strained and collected into 60 mL polypropylene screw cap containers. The resulting filtrate was tested for SS (°Brix), and TA (g·L−1 malic acid). For SS determination, the refractive indices of the solutions were observed in °Brix temperature-corrected mode on a digital refractometer (Mettler-Toledo, Refracto 30PX, 13/02, LXC13237, Japan). An automated titrator (Metrohm 848 Tritrino Plus; Mississauga, ON, Canada) was used to measure the TA of 10 mL of the juice with 65 mL distilled water to an endpoint of 8.1 with 0.1 mol·L−1 NaOH.

    At each colour level (3-4, 4-5, or 5-6) 10 kg of cherry samples were cooled to either 0.5 or 3 °C (for SC in 2021) and then packed into cardboard boxes with a polyethylene liner, an absorbent pad, and an iButton (Thermodata, Whitewater, WI, USA), which measured temperatures experienced by the cherries in the cardboard boxes during storage. After 28 d, the same quality assessment tests were performed to see if values varied throughout storage time at each temperature.

    Statistical analysis was conducted using SAS Institute Inc. software version 9.3 (SAS Institute, Cary, NC, USA). Data were subjected to a four-way analysis of variance (ANOVA) using the SAS PROC GLM procedure. The four factors tested were colour level (3-4, 4-5, and 5-6), cultivar (SH, SC, and SL), growing year (2018, 2019, and 2021), and time (harvest or storage (0.5 °C)). The significance of the main effects and interaction of the four factors was determined using Type III sum of squares via the ANOVA test. Additionally, ANOVA using the SAS PROC GLM procedure was performed on data collected for SC cultivar in the 2021 growing year to assess the influence on storage temperature (0.5 and 3 °C) on quality parameters. Statistical significance was determined by least significant difference (LSD) Fisher's test at 5% significance level. Principal Component Analysis (PCA) was performed using SAS version 9.3 PROC PRINCOMP (SAS Institute Inc., Cary, NC, USA) on data collected from the three cultivars over the tested growing years at three colour levels (i.e., up to nine samples per cultivar) and 10 variables for each investigation. Variables included: AT, AHT, ALT, ARH, colour at harvest (ColourH), SS at harvest (SSH), SS after 28 d of storage at 0.5 °C (SS05), TA at harvest (TAH), TA after 28 d of storage at 0.5 °C (TA05), and DM of cherry fruit at harvest (DMH). Microsoft Excel was used to generate PCA plots from the data provided by SAS. Only data available for every growing year were included in the PCA. Correlation coefficients were determined using Pearson's correlation coefficient statistical function in Excel (version 2306, Microsoft, Redmond, WA, USA). Histograms and frequency data were generated using the statistical function in Excel (version 2306, Microsoft, Redmond, WA, USA). Dry matter bin sizes of 1.5% increments were used to present and analyze the histogram and frequency data.

    Tables 14 present data on flavour attributes via values of soluble solids (SS), titratable acidity (TA), SS/TA ratio, and dry matter (DM) as affected by cultivar, growing year, storage and colour level at harvest and after storage, respectively. The most influential parameters in sweet cherry flavour have been found to be SS, TA, and the SS/TA ratio[15]. Additionally, SS and TA values have been found to be related to DM values in kiwis[2124], apples[25], and cherries[6,26]. DM is a measure of solids which includes both soluble sugars and acids along with insoluble structural carbohydrates and starch. Crisosto et al.[4] proposed using DM as an additional quality parameter as DM was determined not to change during cold storage[22]. Toivonen et al.[6] have performed research linking dry matter measurements with sweet cherry quality. As cold storage is also used to maintain cherry quality, assessing DM at harvest and upon storage was of relevance, and a key aspect of this work was to examine how DM values relate to sweet cherry respiration rates and cherry quality parameters.

    Table 1.  Soluble solids values as affected by cultivar, growing year, storage, and colour level at harvest.
    Cultivar Colour level Soluble solids (SS, °Brix)
    2018 2019 2021
    Harvest Storage (0.5 °C) Harvest Storage (0.5 °C) Harvest Storage (0.5 °C) [3 °C]
    Sweetheart 3-4 18.1aA1 17.7aA1 18.1aA1 18.2aA1
    4-5 20.0bA1 19.8bA1 19.4bA1 18.8bA1
    5-6 21.9cA1* 21.1cA1* 19.9bA1* 19.6cA1*
    Staccato 3-4 17.2aA2 17.2aA1 17.1aA2 17.2aA1 18.8aA* 18.9aA*
    18.4a
    4-5 17.8aA2 17.9aA2 18.3bA23 18.1bA1 19.9bA* 18.6aB
    18.9ab
    5-6 20.2bA2 17.9aB2* 20.2cA1 20.3cA13 20.1bA 19.5aA
    19.7b
    Sentennial 3-4 18.1aA1 18.1aA1
    4-5 18.9bA13 18.5aA1
    5-6 20.8cA2 20.6bA23
    Main effects Significance F-value Degrees of freedom
    Cultivar p < 0.0001 36.90 2
    Colour level p < 0.0001 125.76 2
    Year p < 0.0001 9.26 2
    Time (harvest or storage at 0.5 °C) p = 0.0002 13.68 1
    Colour differences: within common time and cultivar, values followed by different lower case letters indicate significant differences (p ≤ 0.05); Cultivar differences: within common colour level and time, values followed by different numbers indicate significant differences (p ≤ 0.05); Growing year differences: within common time, colour level and cultivar, values followed by * indicate significant differences (p ≤ 0.05); Storage differences (Harvest versus 0.5 °C storage): within common cultivar, colour level and growing year, values followed by different uppercase letter indicate significant differences (p ≤ 0.05); Temperature differences: within Staccato cultivar at 28 d storage and at common colour level, bolded values indicate significant differences (p ≤ 0.05) between storage temperatures (0.5 °C vs 3 °C). (N.B. no bolded values appear in this table).
     | Show Table
    DownLoad: CSV
    Table 2.  Titratable acidity values as affected by cultivar, growing year, storage and colour level at harvest.
    Cultivar Colour level Titratable acidity (TA, g·L−1 malic acid)
    2018 2019 2021
    Harvest Storage (0.5 °C) Harvest Storage (0.5 °C) Harvest Storage (0.5 °C) [3 °C]
    Sweetheart 3-4 8.99aA1* 7.11aB1* 7.31aA1* 6.31aB1*
    4-5 9.55bA1* 7.57bB1* 7.14aA1* 6.13aB1*
    5-6 10.56cA1* 8.57cB1* 7.56aA1* 6.50aB1*
    Staccato 3-4 8.53aA2* 6.65aB2* 6.96aA1* 5.51aB2* 12.0aA* 10.5aB*
    9.7a
    4-5 8.87abA2* 7.04bB2* 7.30aA1* 6.21bB1* 12.8aA* 10.2aB*
    9.3a
    5-6 8.91bA2* 6.91abB2 7.18aA1* 6.46bB1 10.6bA* 9.5b*
    8.5b
    Sentennial 3-4 9.21acA2 7.60aB3
    4-5 9.33aA2 7.89aB2
    5-6 8.79bcA2 7.66aB2
    Main effects Significance F-value Degrees of freedom
    Cultivar p < 0.0001 193.58 2
    Colour level p = 0.0023 5.49 2
    Year p < 0.0001 1,007.20 2
    Time (harvest or storage at 0.5 °C) p < 0.0001 474.18 1
    Colour differences: within common time and cultivar, values followed by different lower case letters indicate significant differences (p ≤ 0.05); Cultivar differences: within common colour level and time, values followed by different numbers indicate significant differences (p ≤ 0.05); Growing year differences: within common time, colour level and cultivar, values followed by * indicate significant differences (p ≤ 0.05); Storage differences (Harvest versus 0.5 °C storage): within common cultivar, colour level and growing year, values followed by different uppercase letter indicate significant differences (p ≤ 0.05); Temperature differences: within Staccato cultivar at 28 d storage and at common colour level, bolded values indicate significant differences (p ≤ 0.05) between storage temperatures (0.5 °C vs 3 °C).
     | Show Table
    DownLoad: CSV
    Table 3.  Soluble solids to titratable acidity ratio values as affected by cultivar, growing year, storage and colour level at harvest.
    Cultivar Colour level Soluble solids to titratable acidity ratio (SS/TA)
    2018 2019 2021
    Harvest Storage (0.5 °C) Harvest Storage (0.5 °C) Harvest Storage (0.5 °C) [3 °C]
    Sweetheart 3-4 2.02a1A* 2.49a1B* 2.49a1A* 2.78a1B*
    4-5 2.09a1A* 2.61a1B* 2.71b1A* 3.06b1B*
    5-6 2.07a1A* 2.46a1B* 2.63b1A* 3.02b1B*
    Staccato 3-4 2.01a1A* 2.58a1B* 2.46a1A* 3.11a2B* 1.57aA* 1.79aB*
    1.89a
    4-5 2.0a1A* 2.54a1B* 2.51a2A* 2.91b1B* 1.55aA* 1.83aB*
    2.04a
    5-6 2.27b2A* 2.58a1B* 2.81b1A* 3.14a1B* 1.89bA* 2.06bB*
    2.32b
    Sentennial 3-4 1.97a2A 2.38a3B
    4-5 2.02a3A 2.34a2B
    5-6 2.37b2A 2.69b2B
    Main effects Significance F-value Degrees of freedom
    Cultivar p < 0.0001 94.79 2
    Colour level p < 0.0001 22.04 2
    Year p < 0.0001 361.13 2
    Time (harvest or storage at 0.5 °C) p < 0.0001 134.15 1
    Colour differences: within common time and cultivar, values followed by different lower case letters indicate significant differences (p ≤ 0.05); Cultivar differences: within common colour level and time, values followed by different numbers indicate significant differences (p ≤ 0.05); Growing year differences: within common time, colour level and cultivar, values followed by * indicate significant differences (p ≤ 0.05); Storage differences (Harvest versus 0.5 °C storage): within common cultivar, colour level and growing year, values followed by different uppercase letter indicate significant differences (p ≤ 0.05); Temperature differences: within Staccato cultivar at 28 d storage and at common colour level, bolded values indicate significant differences (p ≤ 0.05) between storage temperatures (0.5 °C vs 3 °C).
     | Show Table
    DownLoad: CSV
    Table 4.  Dry matter values as affected by cultivar, growing year, storage and colour level at harvest.
    Cultivar Colour level Dry matter (DM, %)
    2018 2019 2021
    Harvest Storage (0.5 °C) Harvest Storage (0.5 °C) Harvest Storage (0.5 °C) [3 °C]
    Sweetheart 3-4 20.9aA1* 20.6aA1* 18.6aA1* 18.6aA1* 22.1aA1*
    4-5 21.9bA1 22.0bA1* 19.9bA1* 19.8acA1* 22.5bA1
    5-6 25.2cA1* 23.5cB1* 21.1cA1* 20.6bcA1* 23.6cA1*
    Staccato 3-4 20.2aA1* 19.5aA2* 18.4aA2* 18.1aA23* 21.0aA2* 20.6aA*
    19.8a
    4-5 20.4aA2* 20.8bA2* 19.1aA1* 18.8aA2* 22.0bA2* 21.6bA*
    21.9b
    5-6 22.4bA2 22.3cA2 22.9bA2* 21.6bA23 23.0cA2 22.2bA
    22.4b
    Sentennial 3-4 18.2aA2 18.4aA13 20.5aA2*
    4-5 19.3bA1 18.8bA2 22.6bA12*
    5-6 22.9cA2 20.9bA13 23.5cA12
    Main effects Significance F-value Degrees of freedom
    Cultivar p < 0.0001 11.36 2
    Colour level p < 0.0001 130.99 2
    Year p < 0.0001 56.64 2
    Time (harvest or storage at 0.5 °C) Not significant, p = 0.0922 2.97 1
    Colour differences: within common time and cultivar, values followed by different lower case letters indicate significant differences (p ≤ 0.05); Cultivar differences: within common colour level and time, values followed by different numbers indicate significant differences (p ≤ 0.05); Growing year differences: within common time, colour level and cultivar, values followed by * indicate significant differences (p ≤ 0.05); Storage differences (Harvest versus 0.5 °C storage): within common cultivar, colour level and growing year, values followed by different uppercase letter indicate significant differences (p ≤ 0.05); Temperature differences: within Staccato cultivar at 28 d storage and at common colour level, bolded values indicate significant differences (p ≤ 0.05) between storage temperatures (0.5 °C vs 3 °C). (N.B. no bolded values appear in this table).
     | Show Table
    DownLoad: CSV

    Data in Table 1 shows the SS levels at each colour level for each sweet cherry cultivar. Overall, SH cherries at harvest had ranges of 18.1%−21.9% over the 2018 and 2019 growing seasons. SC cherries at harvest had levels between 17.1%−20.2% over the three growing years (2018, 2019, and 2021), while SL cherries at harvest had levels of 18.1%−20.8% in 2019. Statistical analysis showed that the main effects of Cultivar (p < 0.0001; F-value = 36.90; degrees of freedom (df) = 2), Colour (p < 0.0001; F-value = 125.76, df = 2), Time (p = 0.0007; F-value = 13.68; df = 1), and Year (p < 0.0001; F-value = 9.26; df = 2) were all significant. Additionally, the interactions of Cultivar * Year (p < 0.0001, F-value = 26.37; df = 1), Colour * Year (p < 0.0069; F-value = 4.19; df = 4), Cultivar * Colour * Year (p < 0.0003; F-value = 10.28; df = 2) and Colour * Time * Year (p < 0.0131; F-value = 3.67; df = 4) were all significant.

    With respect to growing year differences, in 2018, SS levels in SH were greater than those of SC at all colour levels. In 2019, again at the 3-4 colour level SC, cherries exhibited lower SS levels than SH and SL cherries. At the 4-5 colour level, SH cherries again showed higher SS levels compared to SC cherries. At the 5-6 colour level, SL cherries showed higher SS levels compared to SC and SH, while SC and SH cherries showed comparable SS levels at the 5-6 colour level. The SS values of SC cherries at 3-4 and 4-5 colour levels from the 2021 growing year were higher than values observed at the same colour levels in 2018 and 2019 growing year samples. There was no difference observed in SS levels of SC cherries at the 5-6 colour level between growing years. The data shows that colour level, cultivar, and growing year all affected SS levels.

    Within cultivars, for SH in the 2018 growing season, as colour level increased, SS level increased. An increase in SS as colour at harvest increased for SH cultivar was reported by Puniran et al.[27]. In the 2019 growing year for SH cherries and in the 2021 growing year for SC cherries, SS levels plateaued at the 4-5 colour level. For SC (2018 and 2019 growing year) and SL cherries (2019 growing year) as colour level increased, SS level increased. Ross et al.[1] reported this same trend for SC cultivar on data collected from 2015, 2016, and 2017 growing years. This data demonstrates the influence of growing conditions on SS levels.

    In general the SS levels at each colour level for each sweet cherry cultivar did not change over the 28 d storage at 0.5 °C. It should be noted that SC cherries from the 2018 growing year at the 5-6 colour level and SC cherries from the 2021 growing year at the 4-5 colour level did show a significant (p ≤ 0.05) decrease in soluble solids after 28 d of storage at 0.5 °C. SS values for 2018 SC cherries at the 5-6 colour level harvest was 20.2 °Brix, while SS values after 28 day storage at 0.5 °C was 17.9 °Brix. SS values for 2021 SC cherries at the 4-5 colour level harvest was 19.9 °Brix, while SS values after 28 d of storage at 0.5 °C was 18.6 °Brix. In 2021 the effect of storage temperature (0.5 °C vs 3 °C) was investigated. SS levels in the SC cherries at the different colour levels were not affected by the different storage temperatures. This result was consistent with other findings[1,8,14], which suggested that under typical low temperature storage conditions and proper shipping conditions, the change in flavour profiles were likely not due to major changes in SS levels. It is important to note that Alique et al.[8] found that while SS values were consistent, the levels of glucose and fructose decreased by 13% and 10%, respectively, after 4 d under ambient conditions. It may be worth investigating if under a lower temperature environment, the levels of key monosaccharides would remain constant or change.

    This moves the focus of flavour retention to TA values. Table 2 shows the TA levels at each colour level for each sweet cherry cultivar. Statistical analysis showed that the main effects of Cultivar (p < 0.0001; F-value = 193.58; df = 2), Colour (p = 0.0023; F-value = 5.49, df = 2), Time (p < 0.0001; F-value = 474.18; df = 1), and Year (p < 0.0001; F-value = 1007.20; df = 2) were all significant. The interactions of Cultivar * Colour (p < 0.001; F-value = 5.81; df = 4); Cultivar * Year (p < 0.0001; F-value = 19.9; df = 1); Colour * Time (p = 0.0431; F-value = 3.44; df = 2); Colour * Year (p < 0.0001; F-value = 18.01; df = 4); Time * Year (p < 0.0001; F-value = 17.21; df = 2); Cultivar * Colour * Year (p = 0.0009; F-value = 8.63; df = 2); Colour * Time * Year (p = 0.0127; F-value = 3.70; df = 4) were all significant.

    SH cherries at harvest had TA value ranges of 8.99−10.56 g·L−1 in 2018, and 7.31−7.56 g·L−1 in 2019. In 2018, the highest TA value was at the 5-6 colour level for SH cherries, while for the 2019 SH cherries there was no difference between colour levels for the TA values. After storage SH cherries at the various colour levels showed TA levels of 7.11−8.57 g·L−1 and 6.31−6.50 g·L−1 for 2018 and 2019, respectively. Again the stored 2018 SH cherries at the 5-6 colour level showed the highest TA values, while the TA values for the stored 2019 SH cherries were not affected by colour level. SC cherries at harvest showed TA value ranges of 8.53−8.91 g·L−1 in 2018, 6.96−7.18 g·L−1 in 2019, and 10.6−12.8 g·L−1 in 2021. After storage SC cherries had lower TA values at all colour levels for all growing years. In 2018, 2019, and 2021, TA values for SC cherries after storage ranged from 6.65–7.04 g·L−1, 5.51−6.46 g·L−1 and 8.5−10.5 g·L−1, respectively. In 2021, for the SC cherries, the higher storage temperature (3 °C) resulted in a greater loss of TA values for all colour levels compared to the lower temperature of 0.5 °C. SL cherries at harvest had TA levels of 8.79−9.33 g·L−1 in 2019 and after storage at 0.5 C, TA values ranged from 7.60−7.89 g·L−1, with the highest value of TA at the 4-5 colour level. Therefore for all cultivars over all growing years, TA values decreased upon storage, which was expected. The data from Table 2 shows the magnitude of the decrease in TA values after storage varied by growing year. Additionally, trends for differences of TA magnitude change with respect to cultivar and colour level were not obvious. The magnitude of the decrease in TA values between harvest and storage was higher for the SC cherries stored at 3 °C compared to those stored at 0.5 °C.

    Comparing the effect of colour level on TA level within cultivar for SH cherries, TA values increased as colour level at harvest increased in 2018, but the trend did not continue in 2019, possibly due to different growing conditions such as orchard temperature and relative humidity values, which will be further discussed. For SC cherries, TA values peaked at the 4-5 colour level for 2018 and 2021 growing years, but were not significantly different between colour levels in 2019 (p ≤ 0.05). In the 2019 growing year, SL cherries also had the highest TA values at the 4-5 colour level. Comparing the effect of cultivar on TA level within comparable colour level, 2018 harvest SH cherries showed higher TA values than SC cherries. In the 2018 growing year, upon storage and at comparable colour level, SH cherries again showed higher TA values than SC cherries. In the 2019 harvest, SC cherries exhibited the same TA values as SH cherries, while SL cherries had higher TA values than SC and SH. In the 2019 growing year SL cherries harvested at the 3-4 colour level showed greater TA values after storage compared to SH and SC cherries; SC cherries had the lowest TA values at this colour level. When harvested at the 4-5 and 5-6 colour levels, SL cherries had higher TA values upon storage compared to SH and SC cherries.

    The data suggests that different cultivars tend to peak TA values, which may be affected by growing conditions/growing year and maturity level/colour level. This is further supported by the work of Miloševic & Miloševic[28] that indicated TA levels of sour cherries are affected by level of ripeness. Puniran et al.[27] also indicted that finding where peak TA values occur at harvest can help negate the decrease in values that occurs during storage and therefore promote flavour quality retention.

    In 2021 the effect of storage temperature (0.5 °C vs 3 °C) was investigated as 0.5 °C represents ideal storage temperature while 3 °C represents a storage temperature where quality deterioration would be promoted, and may be a more realistic temperature experienced during overseas/export shipping (personal communication, Dr. Peter Toivonen, May, 2021). TA levels in the SC cherries harvested at the different colour levels were affected by the different storage temperature as seen in Table 2. At common colour level, SC cherries from the 2021 growing season stored at 3 °C showed lower TA values than samples stored at 0.5 °C. The SC cherries at the 5-6 colour level stored at 3 °C showed the lowest TA values upon storage. As higher TA values are associated with flavour quality[11,12], the SC cherries harvested at the 5-6 colour level and stored at 3 °C would show poorer flavour quality.

    Table 3 shows the SS/TA ratio values as affected by cultivar, growing year, storage and colour level at harvest. Statistical analysis showed that the main effects of Cultivar (p = 0.0001; F-value = 94.79; df = 2), Colour (p < 0.0001; F-value = 22.04; df = 2), Time (p < 0.0001; F-value = 134.15; df = 1), and Year (p < 0.0001; F-value = 361.13; df = 2) were all significant. The interactions of Cultivar * Colour (p = 0.0009; F-value = 5.91; df = 4) and Time * Year (p = 0.0059; F-value = 5.95; df = 2) were significant. The SS/TA ratio values over all cultivars, colour levels and growing years ranged from 1.55 to 2.81 at harvest and 1.79 to 3.14 after storage at 0.5 °C for 28 d. SC cherries in the 2021 growing year were tested to determine the effect of storage temperature (0.5 or 3 °C) on flavour quality. The SS/TA ratio for the 2021 SC cherries stored at 0.5 °C for 28 d ranged from 1.79 to 2.06 while the SS/TA ratio for the 2021 SC cherries stored at 3 °C for 28 d ranged from 1.89 to 2.32. SS/TA ratios for the 3 °C stored cherries at the 4-5 and 5-6 colour levels were significantly higher (p ≤ 0.05) compared to corresponding SS/TA ratios for the 0.5 °C stored cherries at the 4-5 and 5-6 colour levels. Higher SS/TA ratio are due to lower TA values (data in Table 3) and impact flavour quality and therefore lower storage temperatures are preferable, which was expected.

    Comparing between growing years, for SH and SC cherries, the SS/TA ratio at harvest was higher in the 2019 growing year compared to the 2018 growing year. SC cherries from the 2021 growing year showed the lowest at harvest SS/TA ratio. Within cultivar, the SH cherries from the 2018 growing year showed no difference in harvest SS/TA ratio at the different colour levels. SH cherries from the 2019 growing year showed higher harvest SS/TA ratios at the 4-5 and 5-6 colour levels compared to the 3-4 colour level. The SC cherries at all growing years showed highest harvest SS/TA ratio levels at the 5-6 colour level and comparable harvest SS/TA ratio levels at the 3-4 and 4-5 colour levels. SL cherries also showed the highest harvest SS/TA ratio at the 5-6 colour level and comparable harvest SS/TA ratios at the 3-4 and 4-5 colour levels.

    Comparing cultivars at common colour level, in 2018 growing year SC cherries at the 5-6 colour level showed a higher harvest SS/TA ratio compared to SH cherries at the corresponding colour level. In the 2019 growing year SH and SC cherries showed higher harvest SS/TA ratios at the 3-4 colour level compared to SL cherries. At the 4-5 colour level, SH cherries showed the highest harvest SS/TA ratio value and SL cherries showed the lowest harvest SS/TA ratio value. At the 5-6 colour level, SH and SC cherries showed comparable SS/TA ratios while SL cherries showed the lowest SS/TA ratio.

    Overall, this data indicates that ensuring cherry flavour quality is complex as SS/TA ratio varied by growing year, colour level and cultivar. Comparing SS/TA ratio data for all cultivars over all growing years did not show an observable trend between colour level and SS/TA ratio. As such, colour is not a reliable indicator of flavor quality.

    Table 4 shows the DM values as affected by cultivar, growing year, storage and colour level at harvest. Statistical analysis showed that the main effects of Cultivar (p = 0.0001; F-value = 11.36; df = 2), Colour (p < 0.0001; F-value = 130.99; df = 2), and Year (p < 0.0001; F-value = 56.64; df = 2) were all significant. Notably, the main effect of Time (harvest vs stored) was not significant (p = 0.0922). The interactions of Cultivar * Year (p = 0.001; F-value = 6.45; df = 3); Colour * Year (p = 0.0004; F-value = 6.38; df = 4); Cultivar * Colour * Year (p = 0.0018; F-value = 4.64; df = 5) were all significant. Although SS and TA values were not obtained for all three cultivars in 2021, DM values at harvest were obtained for all three cultivars in 2021. Data in Table 4 show, in general, DM levels did not change over 28-d storage, which was expected. Additionally, data in Table 4 shows that in most cases, at common cultivar and colour level, DM values measured in 2021 were greater than DM values measured in 2018 and 2019 for all cultivars (SH, SC, and SL).

    Within each cultivar, over all growing years, the harvest DM values were significantly different (p < 0.05) at each colour level, except for SC cherries in the 2018 and 2019 growing years. 2018 and 2019 DM values were not significantly different from colour level 3-4 to colour level 4-5. At the 5-6 colour level, over all growing years, SH cherries showed higher DM values compared to dry matter values observed for SC cherries (p < 0.05). At the 3-4 colour level, over all growing seasons, the DM values for SL and SC cherries were not significantly different (p < 0.05). At the 4-5 colour level, over all growing seasons, the DM values for SL were not significantly different than the DM values exhibited by SH and SC cherries (p < 0.05). In 2021 the effect of storage temperature (0.5 °C vs 3 °C) was investigated. DM levels in the SC cherries at the different colour levels were not affected by the different storage temperature. Overall, DM levels ranged from 18.1 to 25.2 depending on cultivar, colour level, growing year, and storage.

    Figure 1ac shows the distribution of the three cherry cultivars relative to DM value ranges over all three growing years via histograms. Cherries at the same colour level did not all have the same DM; this was observed both within and between cultivars (Fig. 1ac). DM data, compiled over all available growing years, for each cultivar in the 3-4, 4-5, and 5-6 colour levels had considerable overlap. However, it is noted there is consistent shift to a higher DM at the 5-6 colour level.

    Figure 1.  Histogram representing distribution of dry matter data for: (a) Sweetheart (SH) cherries over the 2018, 2019, and 2021 growing years; (b) Staccato (SC) cherries over the 2018, 2019, and 2021 growing years; and (c) Sentennial (SL) cherries over the 2019 and 2021 growing years. For all parts, the 3-4 colour level is solid black, the 4-5 level is black stripes, and the 5-6 level is solid grey. On the horizontal axis different dry matter value (DM) ranges are shown via bins and on the vertical axis the frequency or proportion (%) of cherries within the DM bins/ranges are shown. Data was generated from two replicates of samples of 25 cherries (i.e. 50 cherries) from all available growing years.

    Over all growing years, DM values for SH cherries ranged from 14%−27%, 14%−28.5%, and 16.5%−33%, at the 3-4, 4-5, and 5-6 colour levels, respectively (Fig. 1a). SH cherries at the 3-4 and 4-5 colour levels showed the highest proportion (26% and 26%, respectively) of cherries resided in the 22.5 and 22.5% DM bins indicating the highest percentage of SH cherries in these colour levels exhibited a DM of 21% to 22.5%, while the highest proportion (21%) of cherries at the 5-6 colour level resided in the 24% DM bin indicating the highest percentage of SH cherries at this colour level exhibited a DM of 22.5% to 24% (Fig. 1a).

    For SC cherries over all growing years, DM values ranged from 14%−25.5%, 14%−30%, and 16.5%−30%, at the 3-4, 4-5, and 5-6 colour levels, respectively (Fig. 1b). SC cherries at the 3-4 colour level showed the highest proportion of cherries (27%) resided in the 19.5% DM bin which indicated the highest percentage of SC cherries at this colour level exhibited a DM of 18 to 19.5%. SC cherries at the 4-5 colour level showed the highest proportion of cherries (25%) resided in the in the 21% DM bin which indicated the highest percentage of SC cherries at this colour level exhibited a DM of 19.5% to 21 %. At the 5-6 colour level, the highest proportion (35%) of SC cherries resided in the 22.5% DM bin which indicated the highest percentage of SC cherries exhibited a DM of 21%−22.5% (Fig. 1b).

    Over the 2019 and 2021 growing years, SL cherries DM values ranged from 15%−25.5%, 15%−28.5%, and 16.5%−33% at the 3-4, 4-5 and 5-6 colour levels, respectively (Fig. 1c). SL cherries at the 3-4 colour level showed the highest proportion of cherries (36%) resided in the 21% DM bin which indicated the highest percentage of SL cherries at this colour level exhibited a DM of 19.5% to 21%. SL cherries at the 4-5 colour level showed the highest proportion of cherries (28%) resided in the in the 21% DM bin which indicated the highest percentage of SL cherries at this colour level exhibited a DM of 19.5% to 21 %. At the 5-6 colour level, the highest proportion (26%) of SC cherries resided in the 22.5% DM bin which indicated the highest percentage of SL cherries exhibited a DM of 21%−22.5% (Fig. 1c).

    In all, the data in Tables 14 and Fig. 1 indicate that colour is not a reliable indicator of maturity or flavor quality. Cherries of the same colour may differ in DM, SS, TA, and SS/TA ratio due to cultivar and growing conditions. The implication of these results are discussed in subsequent sections.

    Temperature, relative humidity and harvest date for the 2018, 2019, and 2021 growing years are detailed in Table 5. Environmental variations between years impacted colour development, which resulted in yearly variations in our harvest dates as cherry picks were based on cherry colour levels: 2018, July 16 to August 9; 2019, July 18 to August 6; and 2021, July 5 to July 27, nearly two weeks earlier than in previous years (Table 5). In terms of environmental data, the average temperature (AT), average high temperature (AHT), and average low temperature (ALT) values measured in 2021 were greater than the values determined in 2018 and 2019, while the average relative humidity (ARH) values determined in 2021 were lower than the values measured in 2018 and 2019 (Table 5). Depending on growing year and harvest date, average temperature values ranged from 17.17 to 24.28 °C, average high temperature values ranged from 28.59 to 50.68 °C, average low temperatures ranged from 7.17 to 10.68 °C, and average relative humidity values ranged from 40.68% to 66.16% (Table 5).

    Table 5.  Temperature and relative humidity environmental data for 2018, 2019, and 2021 growing years.
    Growing year Colour level Harvest date Average
    temperature
    (AT) (°C)
    Average relative humidity (ARH) Average low temperature (ALT) (°C) Average high temperature (AHT) (°C)
    2018 Sweetheart 3-4 July 16 18.96 61.5% 7.17 32.14
    4-5 July 23 18.85 59.8% 7.17 32.13
    5-6 July 30 20.45 56.85% 7.49 32.05
    Staccato 3-4 July 30 19.74 59.17% 7.42 32.04
    4-5 August 9 21.14 54.46% 7.42 32.77
    5-6 August 9 21.14 54.49% 7.42 32.77
    2018 overall average 20.05 57.71% 7.35 32.32
    2019 Sweetheart 3-4 July 18 17.86 66.04% 7.22 28.79
    4-5 July 24 18.39 66.1% 7.22 28.59
    5-6 July 24 18.39 66.1% 7.22 28.59
    Staccato 3-4 July 22 18.03 66.16% 7.22 28.59
    4-5 July 29 19.19 62.92% 8.67 29.39
    5-6 July 31 19.19 62.92% 8.67 29.39
    Sentennial 3-4 July 22 18.03 66.16% 7.22 28.59
    4-5 July 29 18.99 63.77% 8.67 29.39
    5-6 August 6 19.79 59.94% 8.67 29.39
    2019 overall average 18.87 63.65% 8.19 28.96
    2021 Sweetheart 3-4 July 5 24.01 40.68% 8.51 50.68
    4-5 July 12 24.28 40.68% 10.68 49.23
    5-6 July 20 24.10 40.68% 9.66 47.47
    Staccato 3-4 July 13 24.01 40.68% 8.51 50.68
    4-5 July 21 24.28 40.68% 10.68 49.23
    5-6 July 27 24.10 40.68% 9.66 47.47
    Sentennial 3-4 July 12 24.01 40.68% 8.51 50.68
    4-5 July 19 24.28 40.68% 10.68 49.23
    5-6 July 26 24.10 40.68% 9.66 47.47
    2021 overall average 24.12 40.68% 9.62 49.13
     | Show Table
    DownLoad: CSV

    Principal component analysis (PCA) was performed on all cultivars over all growing seasons to best resolve cultivar specific relationships between variables affecting flavour quality parameters: colour, DM, SS and TA (Fig. 2).

    Figure 2.  Principal component analysis (PCA) plot for: Sweetheart (SH) cherries with data from 2018 and 2019 growing years at the 3-4, 4-5, and 5-6 colours levels (SH34-2018, SH45-2018, SH56-2018, SH34-2019, SH45-2019, and SH56-2019; Staccato (SC) cherries with data from 2018, 2019 and 2021 growing years at the 3-4, 4-5, and 5-6 colour levels (SC34-2018, SC45-2018, SC56-2018, SC34-2019, SC45-2019, SC56-2019, SC34-2021, SC45-2021, and SC56-2021); and Sentennial (SL) cherries with data from 2019 growing year (SL34-2019, SL45-2019, and SL56-2019). PC1 and PC2 accounted for 84.75% variation. The variables include: average temperature (AT), average high temperature (AHT), average low temperature (ALT), average relative humidity (ARH), colour at harvest (ColourH), SS at harvest (SSH), SS after 28 d of storage at 0.5 °C (SS05), titratable acidity at harvest (TAH), titratable acidity after 28 d of storage at 0.5 °C (TA05) and dry matter of cherry fruit at harvest (DMH). Orchard growing factors, flavour quality attributes (loading factors), along with sweet cherry cultivars from each growing season (component scores) were presented as lines with arrows, lines with circles, and squares, respectively. Variables close to each other with small angles between them are strongly positively correlated; variables at right angles are likely not correlated; variables at large angles (close to 180°) are strongly negatively correlated.

    Figure 2 shows that principal components 1 and 2 described most of the variation (84.75%) in the model. SS at harvest (SSH) and SS at 28-d storage at 0.5 °C (SS05) along with DM at harvest (DMH) were positively correlated with colour at harvest (ColourH). TA at harvest (TAH) and TA at 28-d storage at 0.5 °C (TA05) were positively correlated with average high temperature (ATH). AHT, average low temperature (ALT) and average temperature (AT). DMH was more strongly correlated with TAH and TA05 compared to ColourH. TAH and TA05 were positively correlated, yet negatively correlated with average relative humidity (ARH). The 2021 SC samples at the 3-4, 4-5, and 5-6 colour levels (SC34-2021, SC45-2021, and SC56-2021) were clustered with TAH, TA05, ALT, AHT, and AT variables and located in a quadrant opposite of ARH. In late June 2021 a heatwave of unprecedented magnitude impacted the Pacific Northwest region of Canada and the United States; the Canadian national temperature record was broken with a new record temperature of 49.6 °C[29]. Also, the relative humidity levels during this period were also extremely low[30]. As the location of this study was impacted by this heatwave, the data shown in Table 6 shows higher temperatures and lower relative humidity values for the 2021 growing year. The TA values measured in the 2021 growing year were nearly two times the levels measured in the 2018 and 2019 growing years (Table 2). This shows an impact of growing conditions on flavor quality; both the negative correlation between ARH and TA and positive correlations of AT, AHT, and ALT with TA are notable. However, it is noted that correlation does not mean causation. The SH and SC cultivars from the 2018 growing year at the 5-6 colour level were clustered together, and were located in the same quadrant as ColourH, SSH, SS05, and DMH variables (Fig. 2). This indicates these samples were characterized by high values of SSH, SS05, and DMH. All cultivars at the 3-4 colour level from the 2018 (SH and SC), and 2019 (SH, SC, and SL) growing years along with all cultivars at the 4-5 colour level from the 2019 (SH, SC, and SL) growing year were clustered in quadrants opposite of the SSH, SS05, DMH, ColourH, TAH, and TA05 variables while near the ARH variable. The clustering of the samples indicates similarity and lower levels of SSH, SS05, DMH, TAH, and TA05.

    Table 6.  Average colour level and respiration rate of sweet cherries at harvest.
    Growing year Colour
    level
    Average colour measured at harvest
    [average dry matter at harvest]
    Dry matter bin (%), highest
    proportions of cherries
    Respiration rate (mg CO2 kg−1·h−1)
    assessed at 0.5, 5 or 10 °C
    0.5 °C 5 °C 10 °C
    2018 Sweetheart 3-4 3.74a1 [20.9%] 21, 38% 2.87a1 * 5.99a1* 9.75a1*
    4-5 4.66b1 [21.9%] 22.5, 38% 3.57b1* 5.53b1* 9.06a1*
    5-6 5.54c1 [25.2%] 25.5, 26% 3.50b1* 5.18b1* 9.58a1*
    Staccato 3-4 3.58a1 [20.2%] 19.5, 44% 4.43a2* 7.08a2* 9.90a1*
    4-5 4.62b1 [20.4%] 21, 36% 4.58a2* 8.05b2* 11.22b2*
    5-6 5.64c1 [22.4%] 22.5, 50% 4.12b2* 6.43c2* 9.78a1*
    2019 Sweetheart 3-4 3.76a1 [18.6%] 18, 26%; 19.5, 26% Nd 6.04 13.7
    4-5 4.42b1 [19.9%] 21, 32% Nd Nd 8.3
    5-6 5.42c1 [21.1%] 22.5, 28% Nd 6.95 13.5
    Staccato 3-4 3.46a1 [18.4%] 19.5, 32% Nd 5.8 12.47
    4-5 4.40b12 [19.1%] 19.5, 40% Nd 6.14 Nd
    5-6 5.40c1 [22.9%] 22.5, 26% Nd Nd Nd
    Sentennial 3-4 3.62a1 [18.2%] 18, 28% Nd 6.5 12.80
    4-5 4.22b2 [19.3%] 21, 40% Nd 3.76 Nd
    5-6 5.20c2 [22.9%] 22.5, 24% Nd Nd Nd
    2021 Sweetheart 3-4 Nd [22.1%] 22.5, 40% 3.18a1* 5.86a1* 8.8a1*
    4-5 Nd [22.5%] 22.5, 28% 2.78a1* 5.08b1* 8.1a1*
    5-6 Nd [23.6%] 24, 30% 2.65a1* 4.32c1* 9.19a1*
    Staccato 3-4 3.50a1 [21.0%] 19.5, 28% 2.72a1 4.65ab2 10.94a2*
    4-5 4.80b1 [22.0%] 21, 24%; 22.5, 20% 3.27a1* 4.38a2* 8.22b1*
    5-6 5.60c1 [23.0%] 24, 30% 3.05a1* 5.07b2* 9.57ab1*
    Sentennial 3-4 Nd [20.5%] 21, 48% 2.71a1* 4.77a2* 7.84a1*
    4-5 Nd [22.6%] 22.5, 28%; 24, 24% 2.78a1* 4.81a12* 8.28ab1*
    5-6 Nd [23.5%] 22.5, 28%; 24, 24% 4.23b2 4.47a12 9.88b1*
    Within common cultivar and growing year, values followed by different letters indicate significant differences (p ≤ 0.05)-shows colour differences; Within common colour level and growing year, values followed by different numbers indicate significant differences (p ≤ 0.05)-shows cultivar differences; Within common cultivar, growing year and colour level, values followed by * indicate significant differences (p ≤ 0.05)-shows respiration rate differences at the different temperatures. Due to incomplete data, statistical analysis was not performed on 2019 data. Nd = Actual colour was not calculated for Sweetheart and Sentennial cherries in 2021 although cherries were collected 3-4, 4-5, 5-6 colour levels as in previous years and can be considered to have colour levels of approximately 3.5, 4.5, and 5.5, respectively.
     | Show Table
    DownLoad: CSV

    High respiration rates have long been associated with rapid fruit quality deterioration[11]. Lower respiration rates help to maintain higher TA levels, thereby retaining flavour quality[11,12]. This work aimed to provide information on assessing whether rapid and non-invasive dry matter measurements can serve as a surrogate for respiration rate measurements and/or TA measurements to predict fruit quality as this information is essential for developing recommendations to optimize cherry quality retention upon long distance transport. Although it is well known that quality deteriorates more quickly in fruit with higher respiration rates, the respiration rate data for the SH, SC, and SL cherries was collected and analyzed with respect to the different colour levels and corresponding DM values to investigate a link between respiration rate and DM value. Examining the data with this perspective is very novel and additionally very little information on respiration rates is available for SC and SL sweet cherries cultivars in the literature.

    Table 6 shows the respiration rates for a) SH and b) SC cherries at different colour levels for the 2018 growing year and how they were affected by respiration rate assessment temperature (0.5, 5, and 10 °C). Table 6 also shows the respiration rates for a) SH, b) SC, and c) SL cherries at different colour levels for the 2021 growing year and how they are affected by respiration rate assessment temperature. Please note respiration data is incomplete for the 2019 growing year because of data constraints due to equipment difficulties and therefore no statistical analysis was performed on the available 2019 respiration data. As little information exists in the literature for SC and SL cherries, we have included the incomplete respiration data. Table 6 also shows average color data and average DM data for the cherries collected at the different colour levels. Additionally, Table 6 shows data on the DM (%) bins containing the highest proportion of cherries for each cultivar in each growing year at each colour level (histograms for each cultivar for individual growing year not shown).

    Table 6 shows the average colour measured for the cherries harvested at the 3-4, 4-5, and 5-6 colour levels all varied slightly depending on growing year and cultivar. In both the 2018 and 2019 growing years there was no difference in color levels between SH and SC at harvest. In the 2019 growing year, the SL cherries showed lower average colour values at the 4-5 and 5-6 colour levels compared to the average colour levels of SH and SC cherries at the same level. Nevertheless, results indicated the cherries were harvested and sorted to the desired colour levels. In 2021, average colour was not calculated for SH and SL cherries, although cherries were collected at the 3-4, 4-5, and 5-6 colour levels, as in previous years, and can be considered to have been within range. The average colour level was calculated for SC cherries in 2021, as SC cultivar received comprehensive study over all growing years (2018, 2019 and 2021). The calculated colour level values for SC cherries in the 2021 growing year at the 3-4, 4-5, and 5-6 colour levels were 3.5, 4.8, and 5.6, respectively.

    Table 6 shows the lower the respiration rate assessment temperature, the lower the respiration rate, which was expected as cherries are recommended to be stored at 0.5 °C to ensure quality retention due to this fact[1]. In 2018, SH respiration rate values were consistently lower than SC cherries at 0.5 and 5 °C. However, at 10 °C the respiration rate values become comparable between cultivars (Table 6). Comparing between colour level, SH at colour level 3-4 showed the lowest respiration rate at 0.5 °C but had the highest respiration rate when assessed at 5 °C (Table 6). While SC 5-6 cherries at the 5 °C respiration rate assessment temperature showed a significantly lower respiration rate compared to respiration rates measured for SC 3-4 and SC 4-5 cherries at 5 °C (Table 6).

    In 2021, SH and SC respiration rates were more comparable at 0.5 and 10 °C, but at 5 °C, SH respiration rates were higher than SC at the 3-4 and 4-5 colour levels while SC cherries showed a higher respiration rate when assessed at 5 °C compared to the SH cherries at the 5-6 colour level (Table 6). SH respiration rates in 2021 were consistent at 0.5 and 10 °C for all colour levels (Table 6). However, at 5 °C SH cherries at the 5-6 colour level had the lowest respiration rate for all three of the colour levels and the SH cherries at the 3-4 colour level showed the highest respiration rate. This occurred in both 2018 and 2021 (Table 6). The respiration rates of SC cherries were not affected by colour level when assessed at 0.5 °C, but at the 5 °C respiration rate assessment temperature, 2018 SC cherries at 5-6 colour level had lower respiration values (Table 6), while 2021 SC cherries at the 4-5 colour level showed a lower respiration rate value (Table 6). Comparing colour levels, SL cherries at the 5-6 colour level had the highest respiration rates at 0.5 and 10 °C (Table 6). However, at 5 °C, all SL colour levels had comparable respiration values (Table 6). Further, in 2021, all cultivars at 0.5 °C assessment temperature showed respiration rates that were comparable between all colour levels except for SL 5-6. This respiration rate value was significantly higher than the respiration rates measured for the SL 3-4 and 4-5 colour levels and was also higher than the respiration rates determined for SH and SC at the 5-6 colour level. Interestingly, the respiration rate assessed at 0.5 °C for SL at the 5-6 colour level was not significantly different than the respiration rate assessed at 5 °C for SL at the 5-6 colour level (Table 6).

    To further discuss the results presented above, a main source of decreasing TA values in cherries is high respiratory activity[11]. Therefore, linking flavour quality, which is affected by TA levels, to differences in respiration rates is reasonable. Higher respiration rate assessment temperatures were related to higher respiration rates of cherries as seen in Table 6, which was not unexpected and again points to the importance of keeping temperature near 0.5 °C during storage. Additionally, the temperature cherries experience during a growing season affects the respiration rate of the harvested fruit, as Ross et al.[1] found the average temperature and the average high temperature measured in an orchard was positively correlated with the cherry respiration rate at both 5 and 10 °C. Therefore, understanding factors that impact respiration rate, and ensuring cherries are harvested under conditions that ensure a low respiration rate is of significant importance. Table 6 (2021 data) suggests the colour level with the lowest respiration rates for SC cherries is 4-5, which is supported by previous work[1]. While Table 6 (2018 data) suggests the 5-6 colour level gives the lowest respiration rates for SC cherries. When this information is combined with the TA value data, the peak TA values for SC cherries occurs at the 4-5 level and 5-6 colour levels. SH cherries showed that for respiration rate assessed at 0.5 °C, the colour level with the consistently lowest respiration rate was 3-4, but at the 5 °C respiration rate assessment temperature, which is a more abusive temperature, the 5-6 colour level showed the lowest respiration rate in both 2018 and 2021. The highest TA values were seen at the 5-6 colour level for SH cherries. SL cherries show highest TA values at the 3-4 and 4-5 colour level in the 2019 growing year, but insufficient respiration rate data is available in 2019 to comment further. However, the data in Tables 14 indicate that colour is not a reliable indicator of maturity and/or flavor quality. Cherries of the same colour may differ in DM, SS and TA due to cultivar and growing conditions. Figure 1 shows that not all cherries at the same colour level are at the same DM both within and between cultivars. There is a range of DM values for each cultivar in the 3-4, 4-5, and 5-6 colour ranges. However, it is noted the distribution of DM shifted to the right (higher levels) in the 5-6 colour cherries. The work of Palmer et al.[25] and Toivonen et al.[6] have indicated the importance of DM as a fruit quality metric. The implications of colour, DM, and respiration rate results on flavour quality and DM standards are discussed below.

    Associations between colour and DM at harvest with sweet cherry flavour quality attributes and respiration rates were statistically examined using Pearson's correlation coefficient from all available data over all growing years to investigate whether there may be colour/DM levels that are associated with lower respiration rates (Table 7) and could be indicative of when harvest should be performed (i.e. maturity). It was found that colour at harvest was positively correlated with SS at harvest and SS after storage (r = 0.845, p ≤ 0.0005, and r = 0.684, p ≤ 0.005, respectively). Colour at harvest was also positively correlated with DM at harvest (r = 0.768, p ≤ 0.0005). This was expected as darker cherries of a certain cultivar are generally more developed or mature; sugar content (main contributor to DM) and TA increases upon fruit development[1,6,22]. Neither colour or DM at harvest were correlated with SS/TA ratio at harvest or after storage (Table 7). Over all cultivars, no correlation was seen between respiration rate at any assessment temperature and colour level (Table 7). No significant correlations were found between respiration rate assessed at 0.5 and 10 °C and DM (Table 7). A significant negative correlation (r = −0.514, p ≤ 0.025) was found between respiration rate at 5 °C and DM. It was speculated that a correlation between respiration rate and DM was not observed when assessed at 0.5 °C, as this temperature is very low and effectively slows metabolic activity regardless of physiological status of the cherry. No observed correlation between respiration rate assessed at 10 °C and DM was speculated to be due to 10 °C being such an abusive temperature that even physiologically healthy cherries show elevated respiration rates when stored at 10 °C, and likely experienced increased flavour quality deterioration, which could be tested by measuring SS and TA values. Over all cultivars at 5 °C, cherries with lower DM tended to have higher respiration rates, and may be susceptible to more rapid quality deterioration at non-ideal temperatures such as 3−5 °C, which can be encountered in the cherry industry, particularly during export shipping (personal communication, Dr. Peter Toivonen, May, 2021). This points to the importance of good temperature control during storage and diverting lower DM cherries to the domestic and/or rapid consumption market vs export market. Colour was not correlated with TA, while DM at harvest was positively correlated with TA at harvest and upon storage (Table 7), which is relevant for flavour quality. These results indicate that DM has a greater influence on flavour quality attributes than cherry colour.

    Table 7.  Correlations between colour and dry matter at harvest with sweet cherry flavour quality attributes and respiration rate.
    Relationship assessed for Sweetheart*,
    Staccato, and Sentennial**
    cultivars over 2018, 2019,
    and 2021 growing seasons
    Pearson's
    correlation
    coefficient
    Significance
    level
    (p value)
    Colour correlated with
    Soluble solids at harvest r = +0.845 p ≤ 0.0005
    Soluble solids at 28-d storage r = +0.684 p ≤ 0.005
    Dry matter at harvest r = +0.768 p ≤ 0.0005
    Dry matter correlated with
    Soluble solids at harvest r = +0.871 p ≤ 0.0005
    Soluble solids at 28-d storage r = +0.776 p ≤ 0.0005
    Colour at harvest r = +0.769 p ≤ 0.0005
    Titratable acidity at harvest r = +0.439 p ≤ 0.05
    Titratable acidity at 28 d storage r = +0.398 p ≤ 0.10
    Respiration rate at 5 °C r = −0.514 p ≤ 0.025
    Insignificant correlations
    Colour and titratable acidity at harvest r = +0.099 p = 0.696
    Colour and titratable acidity at 28 d storage r = +0.100 p = 0.692
    Colour and soluble solids to titratable
    acidity ratio at harvest
    r = +0.218 p = 0.383
    Colour and soluble solids to titratable
    acidity ratio at 28 d storage
    r = +0.073 p = 0.774
    Colour and respiration rate at 0.5 °C r = +0.252 p = 0.364
    Colour and respiration rate at 5 °C r = −0.206 p = 0.462
    Colour and respiration rate at 10 °C r = +0.084 p = 0.766
    Dry matter and soluble solids to titratable
    acidity ratio at harvest
    r = −0.135 p = 0.595
    Dry matter and soluble solids to titratable
    acidity ratio at 28 d storage
    r = −0.227 p = 0.365
    Dry matter and respiration rate at 0.5 °C r =−0.125 p = 0.657
    Dry matter and respiration rate at 10 °C r = −0.181 p = 0.519
    *Only 2018 and 2019 growing season data available; **only 2019 growing season data available.
     | Show Table
    DownLoad: CSV

    Further, specific cultivar respiration rate and DM relationships were also examined (data not shown). For SH cherries, a negative correlation was determined between respiration rate assessed at 5 °C and DM (p ≤ 0.1), yet this correlation was not seen for SL cherries. Based on statistical parameters SC cherries only showed a negative correlation between respiration rate assessed at 5 °C and DM if a higher p value > 0.1 was used which signifies evidence is not strong enough to suggest a relationship exists. Nevertheless, the statistically significant negative correlation between respiration rate assessed at 5 °C and DM over all cultivars was identified (Table 7).

    Although colour was positively correlated with DM (Table 7), a higher colour may not necessarily indicate a low respiration rate as no significant correlation was observed between colour and respiration rate at any assessment temperature when examined over all cultivars over the growing years tested. Although this highlights the importance of DM in overall quality rather than colour, the data presented thus far suggests certain cultivars achieve different optimal DM values or ranges at maturity that are related to quality retention. In general, higher DM is positive, but is there an upper limit/threshold in terms of higher respiration rate. The lack of correlation between respiration rate assessed at 5 °C and DM for SC and SL cherries seems to indicate a lower optimal DM level for these cultivars compared to SH cherries, as a negative correlation between respiration rate assessed at 5 °C and DM was observed for SH cherries. Again, non-ideal temperatures such as 3−5 °C, can be encountered in the cherry industry, particularly during export shipping, which makes these results extremely relevant to help ensure cherry growers deliver high quality fruit for the export market.

    In 2018, the respiration rate at 5 °C was lowest at the 4-5 and 5-6 colour levels for SH cherries, and the average DM values were ~22% and 25%, respectively. Information from Table 6 shows that 38% of SH fruit were in the 22.5% DM bin indicating the highest percentage of SH cherries at the 4-5 colour level had a DM of 21% to 22.5% while 26% of SH cherries at the 5-6 colour level resided in the 25.5% DM bin indicating the highest percentage of cherries at this colour level had a DM of 24% to 25.5%. For SH cherries in 2021, the lowest respiration rate at 5 °C was at the 5-6 colour level and average DM value was ~23.6%. Information from Table 6 shows that 30% of SH at the 5-6 colour level resided in 24% DM bin indicating the highest percentage of SH cherries at this colour level had a dry mater of 22.5% to 24%. For 2018 SH cherries, titratable acidity, which is important for flavour quality, was highest at the 5-6 colour level where respiration rate assessed at 5 °C was lower. These results imply, that at maturity, cherries tend to a certain DM value or range that corresponds to reduced respiratory activity and promotes quality retention, and would therefore be considered optimal. Based on this data (lower respiration rate (2018 and 2021) and peak TA (2018)), an optimal DM range for SH may be between 22.5%−25% DM or around 23% DM. This optimal DM corresponded to cherries in the 4-5 colour level (21.9%) in 2018, and in the 5-6 colour level (25.2%, 23.6% respectively) in 2018 and 2021.

    2018 respiration rate (assessed at 5 °C) was lowest at the 5-6 colour level for SC cherries and the average DM value was ~22%, respectively. Information from Table 6 shows that 50% of SC cherries resided in the 22.5% DM bin indicating the highest percentage of SC cherries at the 5-6 colour level had a DM of 21% to 22.5% The 2021 respiration rate (assessed at 5 °C) was lowest for SC cherries at the 4-5 colour level and average DM value was ~22% while the highest respiration rate was measured at the 5-6 colour level and the average DM value was ~23.0%. Information from Table 6 shows that 24% and 20% of SC cherries resided in each the 21% and 22.5% DM bins, respectively, indicating the highest percentage of SC cherries at the 4-5 colour level had a DM of 19.5% to 22.5%. For 2021 SC cherries, TA was higher at the 4-5 colour levels, where respiration rate assessed at 5 °C was lowest. Again, these results imply that at maturity cherries tends to a certain DM value or range (optimal) that corresponds to reduced respiratory activity and promotes quality retention. Based on this data (lower respiration rate (2018 and 2021) and peak TA (2018 and 2021)) an optimal DM range for SC may be between 19.5%−22.5% DM or around 22% DM. This optimal DM corresponded to cherries in the 4-5 colour level (22.0%) in 2021, and in the 5-6 colour level (22.4%) in 2018. The data also shows the optimal DM range for SH cherries is higher than the optimal DM range for SC cherries.

    In 2021, for the SL cherries, all colour levels showed the same respiration rate (assessed at 5 °C) and average DM values were 20.5%, 22.6% and 23.5% for the 3-4, 4-5 and 5-6 colour levels, respectively. Information from Table 6 shows that SL cherries at both the 4-5 and 5-6 colour levels, 28% and 24% of the cherries resided in the 22.5% and 24% DM bins indicting the highest percentage of cherries at these colour stages ranged from 21% to 24% DM. For SL, based on the one year of respiration data (2021), determining optimum DM was not as clear. At lower storage temperature (0.5 °C), lower average DM (20.5%−22.6% vs 23.5%) maintained lower respiration rates, but at higher storage temperature (5 and 10 °C) respiration rate did not appear to be affected by DM level. These lower DM values occurred at the 3-4 and 4-5 colour levels.

    Available DM, TA and respiration rate data as discussed suggests the optimal DM range for SH may be between 22.5%−25% DM or around 23% and an optimal dry matter range for SC may be between 19.5%−22.5% DM or around 22% DM, The histogram data over all growing years does further strengthen the justification for suggesting different optimal DM values for different cultivars (Fig. 1). The data over all growing years shows higher proportions of cherries in the 24 % DM bin for SH cherries at the 5-6 colour level which indicated highest proportion of cherries with a DM of 22.5% to 24%. The SC cherries at the 5-6 colour level showed the highest proportion of cherries in the 22.5% DM bin, which indicates the highest percentage of cherries have a DM of 21% to 22.5%; again these DM ranges have been suggested as optimal DM values based on previously discussed respiration data.

    In psychology self-actualization is a concept regarding the process by which an individual reaches their full potential[31]. The data suggests that sweet cherries self-actualize, with the majority of cherries reaching maturity with a DM range that promotes quality retention during storage when growing conditions are favourable. This optimal DM range is different for different cultivars, and growing conditions would be expected to influence the rate and/or ability of this 'self-actualization', as this work and our previous research has shown that environmental factors are correlated with these important quality characteristics[1]. Placing highest importance on distribution of DM levels at the 5-6 colour levels (Fig. 1) is justified as the cherries are the most physiologically mature and will likely exhibit the highest proportion of cherries with optimal DM; however, the optimal DM can occur at other colour ranges and should be used as the primary indicator of maturity. Maturity at harvest is the most important factor that determines storage-life and final fruit quality assweet cherries produce very small quantities of ethylene and do not respond to ethylene treatment; they need to be picked when fully ripe to ensure good flavour quality[32]. It is noted the CTFIL colour standard series goes up to colour level 7, yet a balance needs to be reached between flavour quality optimization vs other quality parameters such as firmness and stem pull force. Supplementary Tables S1S6 provide values of the quality parameters of firmness, stem pull force, stem shrivel, stem browning, pebbling and pitting levels at harvest and after storage for the SH, SC and SL cherries. All of these cherry cultivars exhibited good quality attributes at the 5-6 colour level, as well as lighter colour ranges.

    It is noted that the development of DM standards for different cultivars is a novel concept. This work was positioned as field work that collected cherry data for three cultivars over three growing years in adjacent orchards using the same management practices. Equipment constraints limited respiration rate data collection. As such absolute optimal DM values and/or ranges could not be determined nor was the goal of this study. The goal of this work was to further the concept that different cultivars may reach maturity at different DM levels, which would result in lower respiration rates and higher TA levels at harvest, and after storage, achieving enhanced quality retention. In this regard, absolute optimal DM would be the DM achievable by a cherry cultivar that maximizes flavour attributes and minimizes respiration under ideal conditions. In reality, the absolute optimal DM may or may not be reached during a growing season depending on environmental conditions and orchard management practices; however, cherry cultivars will reach a DM that will be optimal for the growing conditions at harvest maturity, as the present research demonstrates. Developing definitive DM standards to determine optimal harvest points for different cultivars under different environmental conditions should be a direction of research to be further pursued to ensure cherry quality, particularly for cherries subjected to longer term storage and/or cherries destined for the overseas export market.

    The present work is not the first to point to the importance of DM and cherry flavour retention, as an anecdotal report[33] indicated that SH cherries should be harvested at a DM of no higher than 20% or the fruit will lose both sugar and acidity more rapidly during shipping storage. Although this DM value is lower than the DM recommended for SH in the current work, it indicates the importance of DM level and flavour quality in relation to a specific cultivar. The differences between optimal DM for flavour retention in our work and the anecdotal report signifies the complexity of determining DM standards and the impact of growing year/environmental conditions and orchard management practices on optimal DM. Growers will continue to face these complex issues but the present work provides valuable information to growers regarding DM standards for three cultivars.

    It must be noted again that development of absolute DM standards for different cherry cultivars requires more study under rigorous controlled environmental conditions. Additionally, given the impact of environmental conditions on optimal DM, cultivars of interest must be studied over many growing years and orchard conditions to collect a robust data set. Nevertheless, the present research indicates that SH, SC, and SL cultivars have different dry matter values at maturity. The data also shows the DM range for SH cherries at maturity is higher than the DM range for SC cherries at maturity and that for SL, lower DM levels maintained lower respiration rates at lower temperatures, potentially improving ability to maintain quality after harvest.

    Overall, this research showed that DM was a better indicator of flavour quality than colour, as DM was related to both sugars and TA, while colour was only related to sugar. Therefore, this work identified that colour may not be a reliable indicator of maturity and/or flavor quality. This work indicated that cherries of the same colour may differ in DM, SS, and TA due to cultivar type and growing conditions as influenced by growing year. Relative humidity encountered by cherries during the growing season was negatively correlated with TA and higher growing temperatures were positively correlated with TA. This work discovered that sweet cherries may self-actualize, in that when growing conditions are favourable, DM levels may tend towards a certain level at maturity (optimal DM) resulting in superior flavour quality attributes and lower respiration, allowing cherries to reach their full quality potential and ensure quality retention in storage. Therefore, optimal DM can be reached at maturity despite colour. Remaining challenges include development of DM standards for various sweet cherry cultivars and further understanding the impact of growing conditions to better allow for self-actualization and prediction of the optimal DM under those conditions to optimize timing of harvest from year-to-year. Nevertheless, this research based on field work for three sweet cherry cultivars over three growing years, indicated an optimal DM range for SH between 22.5%−25% DM and an optimal DM range for SC between 19.5%−22.5% DM. Interestingly, under the same field conditions, the optimal DM range for SH cherries was higher than the optimal DM range for SC cherries. More analysis is required for determining optimal DM for SL, but the initial data indicates DM in the range of 20.5% to 22.6% maintained lower respiration rates at lower temperature potentially improving ability to maintain quality after harvest.

    The authors confirm contribution to the paper as follows: study conception and design: Ross KA, DeLury NC, Fukumoto L; data collection: Ross KA, DeLury NC, Fukumoto L; analysis and interpretation of results: Ross KA, DeLury NC, Fukumoto L; draft manuscript preparation: Ross KA, DeLury N, Fukumoto L, Forsyth JA. All authors reviewed the results and approved the final version of the manuscript.

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

    We are grateful to the BC Cherry Association for financial support of this research project. We acknowledge Manon Gentes, Gillian Beaudry, and Duncan Robinson for their technical assistance. We are grateful to Brenda Lannard for providing her expertise on using the Felix F750 handheld spectrometer (Felix Instruments, Inc.) to obtain dry matter values and obtaining respiration data. Kelly A. Ross would like to thank and acknowledge Dr. Peter Toivonen for his mentorship, valuable scientific discussions and consistently kind support.

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

  • [1]

    Bai X, Tang Y, Li Q, Chen Y, Liu D, et al. 2021. Network pharmacology integrated molecular docking reveals the bioactive components and potential targets of Morinda officinalis–Lycium barbarum coupled-herbs against oligoasthenozoospermia. Scientific Reports 11:2220

    doi: 10.1038/s41598-020-80780-6

    CrossRef   Google Scholar

    [2]

    Toh DWK, Xia X, Sutanto CN, Low JHM, Poh KK, et al. 2021. Enhancing the cardiovascular protective effects of a healthy dietary pattern with wolfberry (Lycium barbarum): a randomized controlled trial. The American Journal of Clinical Nutrition 114:80−89

    doi: 10.1093/ajcn/nqab062

    CrossRef   Google Scholar

    [3]

    Zheng C, Wang R, Zhou X, Li C, Dou X. 2022. Photosynthetic and growth characteristics of apple and soybean in an intercropping system under different mulch and irrigation regimes in the Loess Plateau of China. Agricultural Water Management 266:107595

    doi: 10.1016/j.agwat.2022.107595

    CrossRef   Google Scholar

    [4]

    Hooshmand M, Albaji M, Boroomand nasab S, Alam zadeh Ansari N. 2019. The effect of deficit irrigation on yield and yield components of greenhouse tomato (Solanum lycopersicum) in hydroponic culture in Ahvaz region, Iran. Scientia Horticulturae 254:84−90

    doi: 10.1016/j.scienta.2019.04.084

    CrossRef   Google Scholar

    [5]

    Ma B, Tian J. 2020. Advance in research on water and fertilizer effect on yield and quality of Lycium barbarum L. Water Saving Irrigation 11(11):6−11

    doi: 10.3969/j.issn.1007-4929.2020.11.002

    CrossRef   Google Scholar

    [6]

    da Silva JR, Rodrigues WP, Ferreira LS, de Paula Bernado W, Paixão JS, et al. 2018. Deficit irrigation and transparent plastic covers can save water and improve grapevine cultivation in the tropics. Agricultural Water Management 202:66−80

    doi: 10.1016/j.agwat.2018.02.013

    CrossRef   Google Scholar

    [7]

    Yuan ZQ, Zhang R, Wang BX, Gao BQ, Ayana G, et al. 2019. Film mulch with irrigation and rainfed cultivations improves maize production and water use efficiency in Ethiopia. Annals of Applied Biology 175:215−27

    doi: 10.1111/aab.12531

    CrossRef   Google Scholar

    [8]

    Peng Y, Fei L, Liu X, Sun G, Hao K, et al. 2023. Coupling of regulated deficit irrigation at maturity stage and moderate fertilization to improve soil quality, mango yield and water-fertilizer use efficiency. Scientia Horticulturae 307:111492

    doi: 10.1016/j.scienta.2022.111492

    CrossRef   Google Scholar

    [9]

    Zhao S, Gao H, Jia X, Zhou K, Wang H, et al. 2022. MdHB7-like confers drought tolerance and improves water-use efficiency through modulating stomatal density in apple (Malus domestica). Scientia Horticulturae 294:110758

    doi: 10.1016/j.scienta.2021.110758

    CrossRef   Google Scholar

    [10]

    Du Y, Wang J, Bao Z, Zhang Y, Zhao Y. 2015. Study on water consumption characteristics of Chinese wolfberry under mulch drip irrigation. Journal of China Institute of Water Resources and Hydropower Research 13:166−70

    doi: 10.13244/j.cnki.jiwhr.2015.03.002

    CrossRef   Google Scholar

    [11]

    Zhao Y, Yin J, Cheng L, Wu J. 2018. Influence of irrigation quota on growth index and yield of Lycium under different planting modes. Water Saving Irrigation 6:35−40

    doi: 10.3969/j.issn.1007-4929.2018.06.009

    CrossRef   Google Scholar

    [12]

    Sun H, Wang D, Zhao M. 2020. Drip irrigation experiment under plastic mulching of Lycium barbarum. Forest Science and Technology 12:75−76

    Google Scholar

    [13]

    Zhang T, Dong Q, Zhan X, He J, Feng H. 2019. Moving salts in an impermeable saline-sodic soil with drip irrigation to permit wolfberry production. Agricultural Water Management 213:636−45

    doi: 10.1016/j.agwat.2018.11.011

    CrossRef   Google Scholar

    [14]

    Zhou Y, Gao X, Wang J, Robinson BH, Zhao X. 2021. Water-use patterns of Chinese wolfberry (Lycium barbarum L.) on the Tibetan Plateau. Agricultural Water Management 255:107010

    doi: 10.1016/j.agwat.2021.107010

    CrossRef   Google Scholar

    [15]

    Qin G. 1982. Chinese wolfberry research. China: Ningxia People's Publishing House

    [16]

    Wu L, Zhang G. 2017. Effects of NPK fertilization on fruit quality of Lycium barbarum L. under different soil fertility. Gansu Forestry 6:43−44

    Google Scholar

    [17]

    Zhang M, Dong B, Qiao Y, Shi C, Hong Y, et al. 2018. Yield and water use responses of winter wheat to irrigation and nitrogen application in the North China Plain. Journal of Integrative Agriculture 17:1194−206

    doi: 10.1016/S2095-3119(17)61883-5

    CrossRef   Google Scholar

    [18]

    de França AA, von Tucher S, Schmidhalter U. 2021. Effects of combined application of acidified biogas slurry and chemical fertilizer on crop production and N soil fertility. European Journal of Agronomy 123:126224

    doi: 10.1016/j.eja.2020.126224

    CrossRef   Google Scholar

    [19]

    Liu H, Wang G, Li X, Jiang S. 2019. Effect of combined application of bio-organic fertilizer and chemical fertilizer on growth and fertilizer utilization rate of forage corn. Journal of Shanxi Agricultural Sciences 47:1564−68

    doi: 10.3969/j.issn.1002-2481.2019.09.18

    CrossRef   Google Scholar

    [20]

    Sun Y, Zhang Y, Wang Y. 2019. Effects of chemical fertilizers replaced by organic manure on soil fertility, tea yield and quality in machine-picked tea garden. Chinese Agricultural Science Bulletin 35:43−49

    Google Scholar

    [21]

    Liu PZ, Wang YH, Wang LX, Li MY, Liu H, et al. 2024. Current situation and future outlook of production, processing and marketing in the celery industry. Technology in Horticulture 4:e13

    doi: 10.48130/tihort-0024-0010

    CrossRef   Google Scholar

    [22]

    Lalk GT, Bi G, Stafne ET, Li T. 2023. Fertilizer type and irrigation frequency affect plant growth, yield, and gas exchange of containerized strawberry cultivars. Technology in Horticulture 3:3

    doi: 10.48130/TIH-2023-0003

    CrossRef   Google Scholar

    [23]

    Fasina AS, Shittu OS, Ogunleye KS, Ilori AOA, Babalola TS. 2021. Effect of drip irrigation frequency, N-fertilization, and mulching on yield, nitrogen, and water use efficiencies of cucumber (Cucumis sativus L.) in Ikole-Ekiti, Nigeria. Asian Journal of Agriculture and Rural Development 11:184−91

    doi: 10.18488/journal.ajard.2021.112.184.191

    CrossRef   Google Scholar

    [24]

    Liu H, Gao Y, Sun J, Wu X, Jha SK, et al. 2017. Responses of yield, water use efficiency and quality of short-season cotton to irrigation management: interactive effects of irrigation methods and deficit irrigation. Irrigation Science 35:125−39

    doi: 10.1007/s00271-016-0526-4

    CrossRef   Google Scholar

    [25]

    Badr MA, Abou-Hussein SD, El-Tohamy W. 2016. Tomato yield, nitrogen uptake and water use efficiency as affected by planting geometry and level of nitrogen in an arid region. Agricultural Water Management 169:90−97

    doi: 10.1016/j.agwat.2016.02.012

    CrossRef   Google Scholar

    [26]

    Zhou H, Zhang F, Wu L, Fam J, Xiang Y. 2015. Effect of water-fertilizer coupling on yield, quality and utilization of water and fertilizer in young apple trees. Transactions of the Chinese Society for Agricultural Machinery 46:173−83

    doi: 10.6041/j.issn.1000-1298.2015.12.024

    CrossRef   Google Scholar

    [27]

    Trujillo Marín EE, Wang C, Singha A, Bloem E, Zandi P, et al. 2022. Reduced nitrogen proportion during the vegetative growth stage improved fruit yield and nitrogen uptake of cherry tomato plants under sufficient soil water regime. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 72:700−08

    doi: 10.1080/09064710.2022.2060855

    CrossRef   Google Scholar

    [28]

    Liu D, Li R. 2016. Study on coupling effect of water and fertilizer on Lycium chincnse Mill. under drip fertigation in Chaidamu Balong Area. Journal of Anhui Agricultural Sciences 44:117−18,199

    doi: 10.13989/j.cnki.0517-6611.2016.32.040

    CrossRef   Google Scholar

    [29]

    Abhiram G, McCurdy M, Davies CE, Grafton M, Jeyakumar P, et al. 2023. An innovative lysimeter system for controlled climate studies. Biosystems Engineering 228:105−19

    doi: 10.1016/j.biosystemseng.2023.03.005

    CrossRef   Google Scholar

    [30]

    Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration - guidelines for computing crop water requirements - FAO irrigation and drainage paper 56. M-56, Food and Agriculture Organization of the United Nations, Rome, Italy.

    [31]

    Ierna A, Pandino G, Lombardo S, Mauromicale G. 2011. Tuber yield, water and fertilizer productivity in early potato as affected by a combination of irrigation and fertilization. Agricultural Water Management 101:35−41

    doi: 10.1016/j.agwat.2011.08.024

    CrossRef   Google Scholar

    [32]

    Zhang D. 2017. Annual production value of wolfberry hits 13 billion yuan in NW China. (Accessed on 1 Aug. 2024). www.xinhuanet.com/english/2017-07/19/c_136456445.htm.

    [33]

    Wang X, Sun Z, El-Sway SM, Wang F. 2018. Effect of ridges and furrows plant of wolfberry on alkalized solonchak. Ekoloji 27:975−83

    Google Scholar

    [34]

    Yin Z, Lei J, Gui L, Zhang X. 2018. Impact of drip irrigationamount on growth, yield and quality of different varieties of wolfberry. Journal of Irrigation and Drainage 37:28−34

    doi: 10.13522/j.cnki.ggps.2017.0515

    CrossRef   Google Scholar

    [35]

    Zhang F, Gao Y, Jiao W, Hu W. 2017. Effects of water and fertilizer supply on growth, water and nutrient use efficiencies of potato in sandy soil of Yulin area. Transactions from the Chinese Society of Agricultural Engineering 48:270−78

    doi: 10.6041/j.issn.1000-1298.2017.03.034

    CrossRef   Google Scholar

    [36]

    Deng Z, Yin J, Wu J, Zhang H. 2021. Comprehensive evaluation of water and fertilizer application forLycium barbarum L. based on AHP and entropy weight method. Journal of Drainage and Irrigation Machinery Engineering 39:712−19

    doi: 10.3969/j.issn.1674-8530.20.0319

    CrossRef   Google Scholar

    [37]

    Liu Y, Yin J, Geng H, Wu J. 2019. Effects of water and fertilizer coupling on growth characteristics and photosynthesis of Lycium barbarum. Water Saving Irrigation 3:34−37,42

    doi: 10.3969/j.issn.1007-4929.2019.03.008

    CrossRef   Google Scholar

    [38]

    Sarıdaş MA, Kapur B, Çeliktopuz E, Şahiner Y, Kargı SP. 2021. Land productivity, irrigation water use efficiency and fruit quality under various plastic mulch colors and irrigation regimes of strawberry in the eastern Mediterranean region of Turkey. Agricultural Water Management 245:106568

    doi: 10.1016/j.agwat.2020.106568

    CrossRef   Google Scholar

    [39]

    Abhiram G, Grafton M, Jeyakumar P, Bishop P, Davies CE, et al. 2022. The nitrogen dynamics of newly developed lignite-based controlled-release fertilisers in the soil-plant cycle. Plants 11:3288

    doi: 10.3390/plants11233288

    CrossRef   Google Scholar

    [40]

    Zhu L, He J, Tian Y, Li X, Li Y, et al. 2022. Intercropping Wolfberry with Gramineae plants improves productivity and soil quality. Scientia Horticulturae 292:110632

    doi: 10.1016/j.scienta.2021.110632

    CrossRef   Google Scholar

    [41]

    Dai Z, Fei L, Huang D, Zeng J, Chen L, et al. 2019. Coupling effects of irrigation and nitrogen levels on yield, water and nitrogen use efficiency of surge-root irrigated jujube in a semiarid region. Agricultural Water Management 213:146−54

    doi: 10.1016/j.agwat.2018.09.035

    CrossRef   Google Scholar

    [42]

    Li Y, Chen X, Li F, Zhang X. 2018. Effect of formula fertilization on yield and quality of Lycium barbarum based on fertigation. Nothern Horticulture 22:161−68

    Google Scholar

    [43]

    Tang J, Xiao D, Wang J, Fang Q, Zhang J, et al. 2021. Optimizing water and nitrogen managements for potato production in the agro-pastoral ecotone in North China. Agricultural Water Management 253:106945

    doi: 10.1016/j.agwat.2021.106945

    CrossRef   Google Scholar

    [44]

    Aziz O, Hussain S, Rizwan M, Riaz M, Bashir S, et al. 2018. Increasing water productivity, nitrogen economy, and grain yield of rice by water saving irrigation and fertilizer-N management. Environmental Science and Pollution Research 25:16601−15

    doi: 10.1007/s11356-018-1855-z

    CrossRef   Google Scholar

    [45]

    Yang K, Wang F, Shock CC, Kang S, Huo Z, et al. 2017. Potato performance as influenced by the proportion of wetted soil volume and nitrogen under drip irrigation with plastic mulch. Agricultural Water Management 179:260−70

    doi: 10.1016/j.agwat.2016.04.014

    CrossRef   Google Scholar

    [46]

    Zhang Y, Wei Y, Zheng G, Wang X, Liu G, et al. 2018. Effects of Different Fertilization Amounts on Growth, Yield and Appearance Quality of Lycium barbarum in Southern Xinjiang. Xinjiang Agricultural Sciences 55:2203−11

    Google Scholar

    [47]

    Eissa MA, Rekaby SA, Hegab SA, Ragheb HM. 2018. Optimum rate of nitrogen fertilization for drip-irrigated wheat under semi-arid conditions. Journal of Plant Nutrition 41:1414−24

    doi: 10.1080/01904167.2018.1454956

    CrossRef   Google Scholar

    [48]

    Li X, Xie X, Zhang J, Wang J, Xie Y. 2016. Effect of water and nitrogen fertilizer coupling on water use efficiency of Chuzhou Chrysanthemum morifolium. Journal of Chinese Medicinal Materials 39:245−49

    doi: 10.13863/j.issn1001-4454.2016.02.003

    CrossRef   Google Scholar

    [49]

    Wei Z, Du T, Li X, Fang L, Liu F. 2018. Interactive effects of CO2 concentration elevation and nitrogen fertilization on water and nitrogen use efficiency of tomato grown under reduced irrigation regimes. Agricultural Water Management 202:174−82

    doi: 10.1016/j.agwat.2018.02.027

    CrossRef   Google Scholar

    [50]

    Wu D, Xu X, Chen Y, Shao H, Sokolowski E, et al. 2019. Effect of different drip fertigation methods on maize yield, nutrient and water productivity in two-soils in Northeast China. Agricultural Water Management 213:200−11

    doi: 10.1016/j.agwat.2018.10.018

    CrossRef   Google Scholar

    [51]

    Yao H, Zhang Y, Yi X, Hu Y, Luo H, et al. 2015. Plant density alters nitrogen partitioning among photosynthetic components, leaf photosynthetic capacity and photosynthetic nitrogen use efficiency in field-grown cotton. Field Crops Research 184:39−49

    doi: 10.1016/j.fcr.2015.09.005

    CrossRef   Google Scholar

    [52]

    Okebalama CB, Safo EY, Yeboah E, Abaidoo RC, Logah V. 2019. Vegetative and reproductive performance of maize to nitrogen and phosphorus fertilizers in Plinthic Acrisol and Gleyic Plinthic Acrisol. Journal of Plant Nutrition 42:559−79

    doi: 10.1080/01904167.2019.1567775

    CrossRef   Google Scholar

    [53]

    Moradi A, Roshan NM, Amiri E, Ashouri M, Rezaei M. 2021. Effects of interval irrigation and nitrogen fertilizer at different stages of growth on yield and yield components of rice. Romanian Agricultural Research 38:281−89

    doi: 10.59665/rar3830

    CrossRef   Google Scholar

    [54]

    Geng Y, Bashir MA, Zhao Y, Luo J, Liu X, et al. 2022. Long-term fertilizer reduction in greenhouse tomato-cucumber rotation system to assess N utilization, leaching, and cost efficiency. Sustainability 14:4647

    doi: 10.3390/su14084647

    CrossRef   Google Scholar

    [55]

    Wu C, Zhu K, Bo X, Wang W. 2016. Effects of different fertilization dosages of N, P and K on the yield, economic benefit and quality of greenhouse tomato. Journal of Anhui Agricultural Sciences 44:156−58

    doi: 10.3969/j.issn.0517-6611.2016.11.052

    CrossRef   Google Scholar

    [56]

    Li X, Liu H, Li J, He X, Gong P, et al. 2020. Experimental study and multi–objective optimization for drip irrigation of grapes in arid areas of northwest China. Agricultural Water Management 232:106039

    doi: 10.1016/j.agwat.2020.106039

    CrossRef   Google Scholar

    [57]

    Li T, Zhou X, Liu C, Zhang Y, Wang M. 2021. Effect of water-nitrogen coupling on yield and water use efficiency of kidney bean. Fresenius Environmental Bulletin 30:6516−29

    Google Scholar

    [58]

    Wang H, Wu L, Wang X, Zhang S, Cheng M, et al. 2021. Optimization of water and fertilizer management improves yield, water, nitrogen, phosphorus and potassium uptake and use efficiency of cotton under drip fertigation. Agricultural Water Management 245:106662

    doi: 10.1016/j.agwat.2020.106662

    CrossRef   Google Scholar

  • 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

Figures(5)  /  Tables(7)

Article Metrics

Article views(1718) PDF downloads(181)

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.)

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.

    • 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[1822].

      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.

    • 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
    • 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.

    • 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.

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

      ET=I+P+URDΔW (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+PΔW (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).

    • 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).

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

      E=GwWwFwHwOw (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.).

    • 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.

    • 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) 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.

    • 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.

    • 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.

    • 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].

    • 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.

    • 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%.

    • 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.

    • 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.

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

      • 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.

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

      • 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)
  • About this article
    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

Catalog

  • About this article

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return