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MAP  >  Tropical Plants
2023 Volume 2
Article Contents
Abstract
Introduction
Materials and methods
Plant materials and growth conditions
Experimental treatments and design
Measurements
Soil water content and plant growth and physiological traits
Hyperspectral imaging of phenotypic responses to drought stress
Multispectral imaging of phenotypic responses to drought stress
Chlorophyll fluorescence imaging of physiological response to drought stress
Statistical analysis
Results
Changes in soil water content, leaf relative water content, and visual turf quality during drought stress
Hyperspectral and multispectral imaging of phenotypic responses to drought stress
Chlorophyll fluorescence imaging of photosynthetic responses to drought stress
Correlation among individual imaging indices associated with phenotypic and photosynthetic responses to drought stress
Prediction of turf quality and leaf relative water content using integrated indices from hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence
Discussion
Conclusions
Acknowledgments
Conflict of interest
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References
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ARTICLE   Open Access    

Artificial pollination can improve fruit set and quality in the ice cream tree (Casimiroa edulis)

  • 1.

    Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China

  • 2.

    Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, School of Tropical Crops, Hainan University, Haikou 570228, China

  • 3.

    School of Health, Medical & Applied Sciences, CQ University Australia, Bruce Hwy, North Rockhampton, QLD 4701, Australia

  • 4.

    Hainan Shengda Modern Agriculture Development Co., Ltd., Qionghai 571425, China

More Information
  • Ice cream fruit (Casimiroa edulis La Llave) is a member of the Rutaceae family native to South America, which has recently been introduced to China. In order to improve the yield of ice cream fruit, we carried out a hybridization experiment with paired pollination. Pollen was collected in the morning and carefully stored, before it was applied to the stigmata of sprouted flowers, using a cotton swab or tweezers. This artificial pollination is repeated at different times of the day. This was continued until the stamens fall off the flowers, the stigmata withered, and the fruit began to develop. Fruit yield and quality were recorded as the principal metrics of the success of each hybrid. At the Hainan Tropical Fruit Window Agricultural Company, nine different ice cream fruit varieties were selected and divided into three groups (abundant pollen, little pollen and no pollen) according to the amount of pollen that each variety naturally produced. Four experimental conditions were investigated: 1. No artificial pollination (natural self-pollination); 2. Artificial self-pollination; 3. Cross-pollination (no emasculation); 4. Cross-pollination (with emasculation). The results indicated that the fruit from cultivars producing abundant pollen generally had poor quality, smaller size, average sweetness, and a medium rate of natural fruit set. In contrast, cultivars producing little pollen showed better fruit quality, higher fruit fertility and increased success rates for artificial pollination. The N2 ice cream fruit cultivar showed the best characteristics as the paternal line (pollen source), while the 4P cultivar showed the best characteristics as the maternal line (flower source). The New varieties (N2 × 4P) performs well in fruit quality, fruit size, fruit sweetness and fruit setting rate. Consequently, this cross-pollinated cultivar is recommended for agricultural production and planting.

  • Introduction

    Drought is a major abiotic stress affecting plant growth which becomes even more intensified as water availability for irrigation is limited with current climate changes[1]. Timely detection and identification of drought symptoms are critically important to develop efficient and water-saving irrigation programs and drought-tolerance turfgrasses. However, turfgrass assessments of stress damages have been mainly using the visual rating of turf quality which is subjective in nature and inclined to individual differences in light perception that drives inconsistency in estimating color, texture, and pattern of stress symptoms in grass species[2−4]. Remote sensing with appropriate imaging technology provides an objective, consistent, and rapid method of detecting and monitoring drought stress in large-scale turfgrass areas, which can be useful for developing precision irrigation programs and high-throughput phenotyping of drought-tolerance species and cultivars in breeding selection[5].

    Spectral reflectance and chlorophyll fluorescence imaging are emerging tools for rapid and non-destructive monitoring of drought effects in crops. These tools combine imaging and spectroscopy modalities to rigorously dissect the structural and physiological status of plants[6,7]. Spectral reflectance imaging captures reflected light (one out of three fates of light: reflect, absorb and transmit when striking leaf) at different wavelengths ranging from visible to near-infrared regions to characterize vegetation traits[8,9]. Within spectral reflectance imaging, multispectral imaging on one hand measures reflected light in three to ten different broad spectral bands in individual pixels[10,11]. Hyperspectral imaging on the other hand captures reflected light in narrow and more than 200 contiguous spectral bands. Some absorbed light by leaf is re-radiated back in the form of fluorescence and fluorescence imaging utilizes those lights in red and far-red regions to capture plant physiological status[12]. When drought progresses, plants start to develop various symptoms (physiological modifications) gradually over time[13]. Some of those symptoms include stomata closure, impediment in gas exchange, change in pigment composition and distribution which result in wilting and associated morphological alteration in leaf color (senescence), shape (leaf curling) and overall plant architecture. As different plant components or properties reflect light differently at different wavelengths and patterns of reflectance and fluorescence change along with plant stress and related symptoms development, spectral reflectance and fluorescence imaging provide accurate, reliable and detailed information for crop drought monitoring. Fluorescence imaging primarily based on fluorescing plant components or chlorophyll complex in photosynthetic antenna and reaction centers and therefore it mainly monitors stress development by tracking changes in overall photosynthetic performance or other metabolism that interfere with photosynthetic operation[9,14]. Multispectral imaging, hyperspectral imaging, or chlorophyll fluorescence has been used in different studies for plant responses to drought stress in various plant species[10,15−17]. The comparative approach of multiple imaging technologies could help to find the efficient methods for the evaluation of plant responses and tolerance to drought[18].

    Vegetation indices derived from multispectral or hyperspectral imaging and fluorescence parameters typically are ratio or linear combinations of reflectance and fluorescence emissions from leaves or canopy of plants, respectively[19,20]. Canopy reflectance at different wavelengths and chlorophyll fluorescence varies with canopy color and density and changes with environmental conditions that affect plant growth, including drought stress[14,20,21]. These variations in reflectance and fluorescence are captured by vegetation indices, such as normalized difference vegetation index (NDVI) and fluorescence parameters including the ratio of variable fluorescence to maximum fluorescence (Fv/m) which are commonly used to evaluate environmental impact on plant growth. Other indices reflect physiological health of plants, such as photochemical reflectance index (PRI) has recently been reported to be useful for drought stress assessment in crops[19]. Previous research identified varying sensitivity of PRI and NDVI to detect water stress; for example, Sun et al.[22] found PRI to be a prominent indicator of drought stress whereas Kim et al.[20] discovered NDVI had greater correlation with drought stress development. There are also several conflicting findings on the responsiveness of fluorescence parameters to drought stress. Photochemical efficiency of PSII (Fv/Fm) was found to be greatly related to drought stress by Panigada et al.[23] but Jansen et al.[24] reported Fv/Fm to be relatively insensitive to drought progression. Lu & Zhang[25] identified that coefficient of photochemical quenching (qP) was insensitive to drought stress whereas Moustakas et al.[26] reported that (qP) being the most sensitive indicator of such stress conditions. There is a need for a comprehensive study that examines multiple vegetation indices (both hyperspectral and multispectral indices) and fluorescence parameters, and parallelly assess their sensitivities to reflect plant growth and physiological status during drought stress.

    The objectives of the current study were: (1) to perform comparative analysis of drought responses of vegetation and photosynthetic indices using multispectral, hyperspectral and chlorophyll fluorescence imaging for Kentucky bluegrass (Poa pratensis L.), a cool-season perennial grass species widely used as turfgrass; (2) identify major vegetation and photosynthetic indices from the imaging technologies and correlated to visual turf quality and leaf relative water content from the destructive measurement; and (3) determine the major vegetation and photosynthetic indices that are most responsive or sensitive to the progression of drought stress that may be useful to early detection and monitoring the level of drought stress causing growth and physiological damages in cool-season grass species.

    Materials and methods

    Plant materials and growth conditions

    Sod strips of Kentucky bluegrass cultivar 'Dauntless' were collected from established field plots at the Rutgers Plant Science Research and Extension Farm, Adelphia, NJ, USA. Sods were planted in plastic pots of 18 cm diameter and 20 cm length filled with a mixture of soil (sandy loam, semi-active, mesic Typic Hapludult; pH 6.55; 260 kg·P·ha−1, 300 kg·K·ha−1) and sand in the ratio of 2/1 (v/v). Plants were established for 50-d in a greenhouse with 24/22 °C day/night average temperatures, 12-h average photoperiod and 750 μmol·m−2·s−1 average photosynthetically active radiation (PAR) with natural sunlight and supplemental lightings. Plants were well-watered, trimmed weekly to 100 mm and fertilized weekly with a 24–3.5–10 (N–P–K) fertilizer (Scotts Miracle-Gro) at the rate of 2.6 g·N·m−2 during the establishment period in the greenhouse. Once plants were well-established, they were moved to the controlled environmental growth chamber (GC72, Environmental Growth Chambers, Chagrin Falls, OH, USA). The growth chamber was controlled at 22/18 °C day/night temperature, 60% relative humidity, 12-h photoperiod and 650 μmol·m−2·s−1 PAR at the canopy level. Plants were allowed to acclimate for a week within the growth chamber conditions and then treatments were initiated.

    Experimental treatments and design

    There were two different treatments: well-watered control and drought stress. For the well-watered control, plants were irrigated once every two days with sufficient water until drainage occurred from the pot bottom or when soil water content reached the field capacity. Drought stress was imposed by withholding irrigation from each pot throughout the experiment period. Each treatment had five replicates. The experimental treatments were arranged as a complete randomized design with plants of both treatments randomly placed and relocated in the chamber twice each week to minimize effects of potential microenvironment variations in the growth chamber.

    Measurements

    Soil water content and plant growth and physiological traits

    A time-domain reflectometry system (Model 6050 × 1; Soil Moisture Equipment, Santa Barbara, CA, USA) installed with 20 cm soil moisture probe was used to measure soil volumetric water content. Volumetric water content was measured every two days in each pot to track soil moisture dynamics in control and drought stress treatments. To assess plant responses at different soil moisture levels, turfgrass quality (TQ) and leaf relative water content (RWC) were evaluated. Turfgrass quality was visually rated on a scale of 1-9 depending upon canopy color, uniformity and density[27]. A rating of 1 indicates discolored and completely dead plants, 9 indicates lush green colored healthy plants and 6 indicates the minimum acceptable turfgrass quality. Leaf RWC was measured by soaking 0.2 g fresh leaves in distilled water overnight at 4 °C[28]. Turgid leaves after overnight soaking were oven dried at 70 °C to a constant dry weight. Leaf RWC was calculated as [(fresh weight – dry weight)/ (turgid weight – dry weight)] × 100.

    Hyperspectral imaging of phenotypic responses to drought stress

    Control and drought stress pots were scanned using a close-range benchtop hyperspectral imaging system (Resonon Inc., Bozeman, MT, USA) containing Pika XC2 camera equipped with 23 mm lens. This camera took images in spectral range of 400–1,000 nm with much detailed spectral resolution of 1.9 nm in 447 different spectral channels. The camera provided 1600 spatial pixels and maximum frame rate of 165 frames per second. It had 23.1° field of view and 0.52 milli-radians instantaneous field of view. Resonon hyperspectral imaging systems are line-scan imagers (also referred to as push-broom imagers) that collect spectral data from each pixel on one line at a time. Multiple lines are imaged when an object or pot kept in scanning stage of linear stage assembly underneath the camera is moved by a stage motor. Those line images are assembled to form a complete image. The systems had regulated lights placed above the linear stage assembly to create optimal conditions for performing the scans. Lights were at the same level as the lens on a parallel plane. Distance between lens and the top of grass canopy was maintained at 0.4 m for capturing the best representation of drought progression. All scans were performed using spectronon pro (Resonon Inc., Bozeman, MT, USA) software connected to the camera using a USB cable. Before performing a scan, the lens was appropriately focused, dark current noise was removed and the system was calibrated for reflectance measurement using a white tile provided by the manufacturer. To ensure distortion-free hyperspectral datacube with a unit-aspect-ratio image, benchtop system's swatch settings were adjusted using pixel aspect ratio calibration sheet also provided by the manufacturer. Once the system was ready, controlled- and stressed-pots were scanned individually every two days throughout the experiment. As the lens was focused centrally, obtained images were of the central grass area and were processed using spectronon pro data analysis software. The entire grass image was selected using a selection tool and the spectrum was generated. From each spectrum, vegetation indices were calculated either using built-in plugins or by manually creating algorithms. The list of vegetation indices calculated using image analysis is mentioned in Table 1.

    Table 1.  List of vegetation indices calculated using hyperspectral and multispectral image analysis for drought stress monitoring in Kentucky bluegrass. Name and number in subscript following the letter R in each formula represent the reflectance at individual light and particular wavelength.
    Vegetation indexIndex abbreviation and formula
    Hyperspectral analysisMultispectral analysis
    Structure Independent Pigment IndexSIPI = (R800 – R445) / (R800 + R680)SIPIm = (RNIR840 – RBlue444) / (RNIR840 + RRed668)
    Simple Ratio IndexSRI = R800 / R675SRIm = RNIR840 / RRed668
    Plant Senescence Reflectance IndexPSRI = (R680 –R500) / R750PSRIm = (RRed668 – RBlue475) / RRededge740
    Photochemical Reflectance IndexPRI = (R570 – R531) / (R570 + R531)PRI = (RGreen560 – RGreen531) / (RGreen560 + RGreen531)
    Normalized Difference Vegetation IndexNDVI = (R800 – R680) / (R800 + R680)NDVIm = (RNIR840 – RRed668) / (RNIR840 + RRed668)
    Normalized Difference Red EdgeNDRE = (R750 – R705) / (R750 + R705)NDREm = (RRededge717 – RRed668) / (RRededge717 + RRed668)
     | Show Table
    DownLoad: CSV
    Multispectral imaging of phenotypic responses to drought stress

    Micasense Rededge-MX dual camera system (AgEagle Sensor Systems Inc., Wichita, KS, USA) was used to collect multispectral images of controlled- and drought stressed-pots placed within a light box (1.2 m × 0.6 m × 0.6 m). The multispectral camera system had 1,280 × 960 resolution, 47.2° field of view and 5.4 mm focal length. The camera captured ten different spectral bands simultaneously on a command (Table 2). To allow the multispectral camera system, which was designed for aerial operation, to work in the light box settings, a downwelling light sensor (DLS) module provided by the manufacturer was installed to the camera system. Images were captured manually through WIFI connection from mobile devices or computer to the multispectral camera system. The sensor layout of the dual camera system, while causing negligible error in aerial condition, led to mismatching between spectral bands in a close distance, therefore, spectral bands needed to be overlapped during post-processing. The captured images of individual spectral bands were stored as separate .jpgf image files and then were used to calculate the relevant vegetation indices. Multispectral image analysis was executed using Python (Version 3.10) code by Rublee et al.[29]. Image analysis aligned ten spectral bands using Oriented FAST and Rotated BRIEF algorithm to achieve complete overlap between spectral band images. The reflectance correction panel provided by the manufacturer was used to account for the illumination condition in light box environment and the correction was reflected in pixel value adjustment for each band in python code; vegetation indices based on the aligned images were then calculated using the corresponding formula (Table 1). Images that included background noise were excluded from analysis.

    Table 2.  Spectral band details (center wavelength and band width) for Micasense Rededge-MX dual camera system.
    Band nameCentral wavelength (nm)Band width (nm)
    Blue44444428
    Blue47547532
    Green53153114
    Green56056027
    Red65065016
    Red66866814
    RE70570510
    RE71771712
    RE74074018
    NIR84084257
     | Show Table
    DownLoad: CSV
    Chlorophyll fluorescence imaging of physiological response to drought stress

    Chlorophyll fluorescence images were taken using a pulse amplitude modulated fluorescence imaging system (FC 800-O/1010, Photon System Instruments, Drasov, Czech Republic). A high-speed charge-coupled device (CCD) camera was mounted on a robotic arm placed in the middle of LED light panels. The camera had 720 × 560 pixels spatial resolution, 50 frames per second frame rate and 12-bit depth. Four different LED light panels each of 20 cm × 20 cm size were equipped with 64 orange-red (617 nm) LEDs in three panels and 64 cool-white LEDs (6,500 k) in the rest of one panel. Before making measurements, plants were dark-adapted for 25 min in a dark room to open all PSII reaction centers. The distance between camera and the top of the grass canopy was maintained at 0.3 m while taking images to ensure optimum quality. Images were acquired following the Kautsky effect measured in a pulse amplitude modulated mode[30,31]. Briefly, dark-adapted plants were first exposed to non-actinic measuring light for 5 s to measure minimum fluorescence at the dark-adapted state (Fo). Plants were immediately exposed to 800 ms saturation pulse of 3,350 µmol·m−2·s−1 to measure maximum fluorescence after dark adaptation (Fm). They were kept under dark relaxation for 17 s and then exposed to actinic light 750 µmol·m−2·s−1 for 70 s. Plants were exposed to a series of saturating pulses at 8 s, 18 s, 28 s, 48 s and 68 s during their exposure to actinic light conditions and maximum fluorescence at different light levels and steady state were measured. They were kept under dark relaxation again for 100 s and irradiated with saturating pulses at 28 s, 58 s and 88 s during dark relaxation for measuring maximum fluorescence during the relaxation. Selected durations for each light and dark relaxation state were preset in default quenching-act2 protocol of the fluorescence imaging system. Fluorescence at different light levels and steady states were used to calculate several fluorescence parameters (Table 3).

    Table 3.  Chlorophyll fluorescence parameters calculated from pulse amplitude modulated fluorescence imaging system.
    Chlorophyll fluorescence parameterFormula
    Maximum photochemical efficiency of PSII (Fv / Fm)(Fm-Fo) / Fm
    Photochemical efficiency of open PSII centers
    (F'v / F'm)    		(F'm – F'o) / F'm
    Actual photochemical quantum yield of PSII centers Y(PSII)(F'm – Fs) / F'm
    Photochemical quenching coefficient (Puddle model; qP)(F'm – Fs) / (F'm – F'o)
    Photochemical quenching coefficient (Lake model; qL)qP × F'o / Fs
    Non-photochemical quenching coefficient (qN)(Fm-F'm) / Fm
    Non-photochemical quenching (NPQ)(Fm-F'm) / F'm
    Chlorophyll fluorescence decrease ratio (Rfd)(Fm-Fs) / Fs
     | Show Table
    DownLoad: CSV

    Statistical analysis

    The two-way repeated measure analysis of variance was performed to determine treatment effects and t-test was performed to compare control and drought stress treatments at a given day of measurement. Correlation analysis using all individual observations (five replications for each control and drought stress treatments) was performed to determine the relationship among all measured traits, vegetation indices and fluorescence parameters. Partial least square regression (PLSR) models were developed in SAS JMP (version 13.2; SAS Institute, Cary, NC, USA) for comparing hyperspectral, multispectral and chlorophyll fluorescence imaging in their overall associations with physiological assessments of drought stress. Vegetation indices and fluorescence parameters from individual imaging technologies were predictor variables, and turfgrass quality and leaf relative water content were response variables. A leave one out cross validation approach was used to develop the best performing partial least square model for each imaging technology. A model was first established with all predictor variables and the variable with the lowest importance was removed from the dataset and the model was rebuilt with the remaining variables. The rebuilt model was re-validated using leave one out cross validation and assessed checking root mean PRESS and percent variation explained for cumulative Y values. From each loop of operation, one variable was removed, and a new model was developed. The whole process ended when the last variable was removed and thus no more models could be developed. Finally, a series of models was obtained, and they were compared to identify a model with the highest accuracy for individual imaging technologies. The best performing model from each imaging technology was used to estimate turfgrass quality and leaf relative water content.

    Results

    Changes in soil water content, leaf relative water content, and visual turf quality during drought stress

    The initial soil water content prior to drought stress was maintained at the field capacity of 29% and remained at this level in the well-watered control treatment during the entire experimental period (20 d) (Fig. 1a). SWC in the drought treatment significantly decreased to below the well-watered treatment, beginning at 4 d, and declined to 5.8% by 20 d.

    Figure 1.  Drought stress affected turf quality, leaf relative water content and soil volumetric water content during 20 d of stress period in Kentucky bluegrass. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint

    Leaf RWC was ≥ 93% in all plants prior to drought stress and declined to a significantly lower level than that of the control plants, beginning at 10 d of treatment when SWC declined to 16% (Fig. 1b). TQ began to decrease to a significantly lower level than the that of the well-watered plants at 10 d of drought stress at RWC of 87% and SWC of 16%, and further declined to the minimally acceptable level of 6.0 at 16 d of drought stress when RWC decreased to 66% and SWC dropped to 8% during drought stress (Fig. 1c).

    Hyperspectral and multispectral imaging of phenotypic responses to drought stress

    Most hyperspectral imaging indices, including SIPI (Fig. 2a), SRI (Fig. 2b), PRI (Fig. 2d), NDVI (Fig. 2e) and NDRE (Fig. 2f) exhibited a declining trend during 20-d drought stress while PSRI (Fig. 2C) showed increases during drought stress. The index value of drought-stressed plants became significantly lower than that of the well-watered plants, beginning at 14 d for SIPI and SRI, 16 d for PRI and PSRI, and 18 d for NDVI and NDRE. The multispectral SIPIm and SRIm did not differ between drought-stressed plants from the control plants until 18 d of treatment (Fig. 3a, b) while NDVIm, NDREm , PRIm , and PSRIm values were significantly lower than those of well-watered control plants at 16 d of drought stress (Fig. 3c−f).

    Figure 2.  Vegetation indices generated by hyperspectral sensing and sensitivity of these indices in monitoring drought in Kentucky bluegrass exposed to 20 d of drought stress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint
    Figure 3.  Vegetation indices generated by multispectral image analysis and sensitivity of these indices in monitoring drought in Kentucky bluegrass exposed to 20 d of drought tress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint

    Chlorophyll fluorescence imaging of photosynthetic responses to drought stress

    Chlorophyll fluorescence indices detected drought damages in leaf photosynthesis systems, as shown by declines in different indices during drought stress (Fig. 4). Drought-stressed plants exhibited significant lower chlorophyll fluorescence levels than that of the well-watered plants, beginning at 12 d for NPQ (Fig. 4a), 16 d for Fv/Fm (Fig. 4b), and 18 d for F'V/F'm (Fig. 4c), Y(PSII) (Fig. 4d), qP (Fig. 4e), and qL (Fig. 4f). Separation between drought-stressed and well-watered plants were also evident in index- or parameter- generated images (Fig. 5).

    Figure 4.  Chlorophyll fluorescence parameters measured by pulse amplitude modulated fluorescence imaging system and detection of drought by these parameters in Kentucky bluegrass exposed to 20 d of drought stress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points. NPQ, Non-photochemical quenching; Fv /Fm, Maximum photochemical efficiency of PSII; F'v/F'm, Photochemical efficiency of open PSII centers; Y(PSII), Actual photochemical quantum yield of PSII centers; qP, Photochemical quenching coefficient (Puddle model); qL, Photochemical quenching coefficient (Lake model); qN, Non-photochemical quenching coefficient; Rfd, Chlorophyll fluorescence decrease ratio.
    DownLoad: Full-Size Img PowerPoint
    Figure 5.  Maps generated by the three most drought sensitive indices and parameters [hyperspectral structure independent pigment index (SIPI), multispectral normalized difference vegetation index (NDVIm) and chlorophyll fluorescence NPQ]. These maps clearly separated control and drought stress after 18 d of treatment when majorities of indices and parameters detected drought stress.
    DownLoad: Full-Size Img PowerPoint

    Correlation among individual imaging indices associated with phenotypic and photosynthetic responses to drought stress

    Leaf RWC and TQ had significant correlation with most of indices and parameters calculated using three different imaging sensors (hyperspectral, multispectral and chlorophyll fluorescence) (Table 4). Among the indices, RWC had the strongest correlations with chlorophyll fluorescence NPQ (r = 0.88) and qL (r = 0.89), hyperspectral PRI (r = 0.94), and multispectral PSRIm (−0.92). TQ was most correlated to chlorophyll fluorescence NPQ (r = 0.89), hyperspectral PSRI (r = −0.90), and multispectral PSRIm (r = −0.85).

    Table 4.  Correlations among several physiological traits, vegetation indices and chlorophyll fluorescence parameters.
    RWCTQFV/FmF'v/F'mY(PSII)NPQqNqPqLRfdSIPISRIPSRIPRINDVINDREWBISIPImPSRImPRImNDVImNDREm
    RWC1.00
    TQ0.95*1.00
    FV/Fm0.87*0.85*1.00
    F'v/F'm0.81*0.77*0.95*1.00
    Y(PSII)0.85*0.74*0.80*0.74*1.00
    NPQ0.88*0.89*0.95*0.84*0.75*1.00
    qN0.84*0.83*0.96*0.84*0.77*0.96*1.00
    qP0.82*0.70*0.73*0.66*0.99*0.69*0.72*1.00
    qL0.89*0.81*0.90*0.86*0.97*0.83*0.86*0.95*1.00
    Rfd0.84*0.82*0.89*0.83*0.77*0.92*0.86*0.72*0.83*1.00
    SIPI0.84*0.71*0.63*0.58*0.51*0.57*0.69*0.48*0.60*0.46*1.00
    SRI0.57*0.62*0.44*0.45*0.330.41*0.45*0.300.400.330.83*1.00
    PSRI−0.83*−0.90*−0.90*−0.86*−0.76*−0.83*−0.87*−0.71*−0.86*−0.76*−0.75*−0.57*1.00
    PRI0.94*0.82*0.80*0.76*0.71*0.79*0.71*0.66*0.77*0.78*0.260.17−0.78*1.00
    NDVI0.53*0.65*0.41*0.43*0.41*0.42*0.400.380.43*0.42*0.50*0.42*−0.54*0.311.00
    NDRE0.64*0.73*0.64*0.63*0.45*0.54*0.64*0.400.56*0.44*0.92*0.85*−0.75*0.330.50*1.00
    SIPIm0.52*0.50*0.56*0.58*0.47*0.52*0.49*0.43*0.52*0.51*0.330.28−0.58*0.61*0.270.39−0.281.00
    PSRIm−0.92*−0.85*−0.85*−0.85*−0.83*−0.80*−0.77*−0.79*−0.88*−0.77*−0.40−0.230.77*−0.82*−0.41−0.400.32−0.52*1.00
    PRIm0.20−0.030.06−0.010.280.140.110.310.200.180.050.100.01−0.040.000.090.060.09−0.041.00
    NDVIm0.75*0.74*0.77*0.78*0.67*0.72*0.68*0.62*0.73*0.70*0.43*0.33−0.76*0.81*0.370.47*−0.350.93*−0.76*−0.051.00
    NDREm0.90*0.89*0.89*0.89*0.81*0.83*0.81*0.76*0.88*0.81*0.52*0.41*−0.87*0.87*0.45*0.53*−0.320.62*−0.87*−0.040.85*1.00
    Details for individual abbreviations of vegetation indices and fluorescence parameters used in this table were previously mentioned in Tables 1 & 3. Some other abbreviations are: RWC, leaf relative water content; and TQ, turfgrass quality. Values followed by * indicate significant correlation at p ≤ 0.05. Correlation analysis was performed using all individual data points (five replications for each control and drought stress treatments).
     | Show Table
    DownLoad: CSV

    Correlations among different vegetation indices and parameters were also significant in many cases. Hyperspectral indices such as PSRI and PRI were significantly correlated with all multispectral indices except PRIm. Multispectral NDVIm and NDREm were significantly correlated with all hyperspectral indices. When hyperspectral and multispectral indices were correlated with chlorophyll fluorescence parameters, majorities of these indices significantly associated with fluorescence parameters with exceptions of multispectral PRIm which had weak and positive (r ranges 0.06 to 0.31) associations with fluorescence parameters.

    Prediction of turf quality and leaf relative water content using integrated indices from hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence

    Partial least square regression models were developed by integrating all indices from individual imaging technologies which identified the most reliable imaging systems to detect and monitor plant responses to drought stress. The PLSR model developed using hyperspectral imaging indices had improved predictability (root mean PRESS ≤ 0.38 and percent variation explained ≥ 87) compared to such models developed using other imaging systems and associated indices (Table 5). Comparing multispectral imaging with chlorophyll fluorescence imaging, multispectral imaging had slightly better predictability [root mean PRESS = 0.40 (RWC) and 0.44 (TQ) and percent variation explained = 86 (RWC) and 83 (TQ)] considering similar number of predictor variables used for estimating TQ and RWC in all imaging systems.

    Table 5.  Summary of partial least square model showing predictability of individual models using specific numbers of predictor variables (identified by leave one out cross validation) generated by different sensing technologies. Details of individual abbreviations are mentioned in previous tables. Partial least square was performed using all individual data points (five replications for each control and drought stress treatments).
    Sensing technology used for predictionPredicted
    variable    		No. of predictors usedPredictor variablesRoot mean
    PRESS    		Percent variation explained
    for cumulative Y    		Cumulative Q2
    HyperspectralTQ4PRI, PSRI, NDRE, SIPI0.36870.99
    RWC4PRI, PSRI, NDRE, SIPI0.38890.99
    MultispectralTQ3PSRIm, NDVIm, NDREm0.44850.97
    RWC3PSRIm, NDVIm, NDREm0.40860.97
    Chlorophyll fluorescenceTQ4Fv/Fm, NPQ, qN, qL0.46830.95
    RWC3Fv/Fm, NPQ, qL0.59840.93
     | Show Table
    DownLoad: CSV

    The integrated indices from each of the three imaging systems were highly correlated to TQ, with R2 of 0.90, 0.85, and 0.83 for hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence, respectively (Fig. 6). For RWC, the correlation R2 was 0.88, 0.84, and 0.80, respectively with hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence. The hyperspectral imaging was better be able to predict TQ and RWC compared to other imaging systems (Fig. 6).

    Figure 6.  Comparison of predicted turfgrass quality (TQ) and leaf relative water content (RWC) versus their measured values using partial least square regression model. Turfgrass quality and relative water contents were predicted using various indices generated by hyperspectral, multispectral and chlorophyll fluorescence sensing technologies. The dashed line represents the I:I line. Regression analysis was performed using all individual data points (five replications for each control and drought stress treatments).
    DownLoad: Full-Size Img PowerPoint

    Discussion

    Leaf RWC and TQ are the two most widely used parameters or traits to evaluate turfgrass responses to drought stress[28,32,33]. In this study, RWC detected water deficit in leaves at 10 d of drought stress when SWC declined to 16% and TQ declined to below the minimal acceptable level of 6.0 at 16 d of drought stress when RWC decreased to 66% and SWC dropped to 8% during drought stress. These results suggested that RWC was a sensitive trait to detect water stress in plants, which is in agreement with previous research[34,35]. However, leaf RWC is a destructive measurement and TQ is a subjective estimate. Nondestructive and quantitative detection of stress symptoms in plants through assessing changes in phenotypic and physiological responses of plants to drought stress is critical for developing water-saving irrigation programs and breeding selection traits to increase water use efficiency and improve plant tolerance to drought stress. In this study, some of the phenotypic traits assessed by hyperspectral and multispectral imaging analysis and photosynthetic parameters measured by chlorophyll fluorescence were highly correlated to leaf RWC or visual TQ, as discussed in detail below, which could be used as non-destructive indicators or predictors for the level of drought stress in Kentucky bluegrass and other cool-season turfgrass species.

    The strong correlation of integrated indices from each of the three imaging systems with TQ (R2 of 0.90, 0.85, and 0.83, respectively) and RWC (R2 of 0.88, 0.84, and 0.80, respectively) for hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence suggested that all three non-destructive imaging systems could be used as a non-destructive technique to detect and monitor water stress in Kentucky bluegrass. However, the hyperspectral imaging indices had higher predictability to RWC and visual TQ compared to the indices from multispectral imaging and chlorophyll fluorescence based on the PLSR models. Hyperspectral imaging used in this study captured images in 447 different spectral bands and gathered much more details about individual components of entire vegetation as each component has its own spectral signature. Multispectral imaging captures images with ten spectral bands and chlorophyll fluorescence imaging used only emitted red and far-red lights to snap images. Nevertheless, our results suggested that the PLSR models by integrating all indices from each individual imaging technologies identified the most reliable imaging systems to detect and monitor plant responses to drought stress in this study.

    The indices derived from the three imaging systems varied in their correlation to RWC or TQ in Kentucky bluegrass in this study. Among the indices, RWC had the strongest correlations with chlorophyll fluorescence NPQ (r = 0.88) and qL (r = 0.89), hyperspectral PRI (r = 0.94), and multispectral PSRIm (r = −0.92). TQ was most correlated to chlorophyll fluorescence NPQ (r = 0.89), hyperspectral PSRI (r = −0.90), and multispectral PSRIm (r = −0.85). The indices also varied in their sensitivity to drought stress for Kentucky bluegrass, and therefore they detected drought stress in plants at different times of treatment. The hyperspectral SIPI and SRI were the most responsive to drought stress with significant decline at 14 d followed by PRI and PSRI at 16 d while NDVI and NDRE were slowest showing decline (18 d) in response to drought. Multispectral indices exhibited decline later during drought at 16 d of drought stress for NDVIm, NDREm , PRIm , and PSRIm and 18 d for SIPIm and SRIm. Indices SIPI and SRI are related to leaf carotenoid composition and vegetation density and high spectral resolution of hyperspectral system was able to capture subtle changes in pigment concentration and canopy (slight leaf shrinking and rolling) at early phase of drought progression[36,37]. Index PSRI is indicative of the ratio of bulk carotenoids including α- and β-carotenes to chlorophylls and PRI is sensitive to xanthophyll cycle particularly de-epoxidation of zeaxanthin that releases excess energy as heat in order to photoprotection[38−40]. Activation of photoprotective mechanisms including xanthophyl cycle require a certain level of stress severity depending on type of abiotic stress and plant species[41]. The PSRI calculated using both hyperspectral and multispectral imaging systems exhibited similar trends, and PSRI and PRI from either imaging system detected drought stress after 16 days of treatment applications. In agreement with our results, Das & Seshasai[42] found that PSRI showed similar trends when its value > −0.2 regardless of whether measured using hyperspectral or multispectral imaging. Both PSRI and PRI were also highly correlated to leaf RWC or TQ in Kentucky bluegrass exposed to drought stress in this study, suggesting that these two indices could be useful parameters to detect and monitor plant responses to drought stress.

    Vegetation index of NDVI has been the most widely used vegetation index in several crops such as wheat (Triticum aestivum L.)[43], cool- and warm-season turfgrass species including perennial ryegrass (Lolium perenne L.), tall fescue (Festuca arundinacea Schreb.), seashore paspalum (Paspalum vaginatum Sw.) and hybrid bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt-Davy][2, 44, 45]. For example, Bhandari et al.[43] and Badzmierowski et al.[14] found NDVI was correlated to overall turfgrass quality and chlorophyll content under nitrogen and drought stresses in tall fescue and citrus (Citrus spp.) plants. In this study, NDVI and NDRE were also correlated to leaf RWC and TQ, both NDVI and NDRE calculated from hyperspectral or multispectral imaging were least responsive to drought stress or detected drought stress later than other indices. Hong et al.[46] reported that NDVI being a better indicator than NDRE for early drought stress detection in turfgrasses when these indices were measured by handheld multispectral sensor. Eitel et al.[47] utilized broadband satellite images to estimate NDVI and NDRE and identified NDRE being a better option for early detection of stress condition in woodland area. Either NDVI or NDRE could be used as indices for vegetation density, but not sensitive indicators for plant responses to drought stress or for detecting drought damages in plants.

    Chlorophyll fluorescence reflects the integrity and functionality of photosystems in the light reactions of photosynthesis and serves as a good indicator for photochemical activity and efficiency[48]. The Y(PSII) is an effective quantum yield of photochemical energy conversion and estimates the actual proportion of absorbed light that is used for electron transport[49]. The ratio of F'v/F'm is maximum proportion of absorbed light that can be used for electron transport when all possible PSII reaction centers are open under light adapted state. Parameters qP and qL estimate the fraction of open PSII centers based on 'puddle' and 'lake or connected unit' models of photosynthetic antenna complex, respectively[50]. Rfd is an indicator for photosynthetic quantum conversion associated with functionality of the photosynthetic core unit. Overall, these parameters revolve around the operation status and functioning of PSII reaction centers or the core unit that possesses chlorophyll a-P680 in a matrix of proteins[51]. Parameter NPQ indicates non-photochemical quenching of fluorescence via heat dissipation involving xanthophyll cycle and state transition of photosystems[52]. This parameter is mostly associated with xanthophylls and other pigments in light harvesting antenna complex of photosystems but not with the PSII core unit[53]. Li et al.[9] reported that chlorophyll fluorescence imaging parameters including F'V/F'm have a limitation of late drought detection in plants. Shin et al.[54] reported F'V/F'm, Y(PSII), qP, and qL detected stress effects under severe drought when leaves were completely wilted and fresh weights declined in lettuce (Lactuca sativa L.) seedings. In this study, NPQ and Fv/Fm exhibited significant decline earlier (12−16 d of stress treatment) when drought was in mild to moderate level (> 60% leaf water content) compared to other chlorophyll fluorescence indices. The NPQ was strongly correlated to leaf RWC (r = 0.88) and TQ (r = 0.89) for Kentucky bluegrass exposed to drought stress. These results suggested that NPQ is a sensitive indicator of photosynthetic responses to drought stress and could be a useful parameter for evaluating plant tolerance to drought stress and monitoring drought responses.

    Conclusions

    The comparative analysis of phenotypic and photosynthetic responses to drought stress using three imaging technologies (hyperspectral, multispectral and chlorophyll fluorescence) using the partial least square modeling demonstrated that the integrated vegetation indices from hyperspectral imaging had higher predictability for detecting turfgrass responses to drought stress relative to those from multispectral imaging and chlorophyll fluorescence. Among individual indices, SIPI and SRI from hyperspectral imaging were able to detect drought stress sooner than others while PSRI and PRI from both hyperspectral and multispectral imaging were also highly correlated to leaf RWC or TQ responses to drought stress, suggesting these indices could be useful parameters to detect and monitor drought stress in cool-season turfgrass. While NDVI or NDRE from both hyperspectral and multispectral imaging could be used as indices for vegetation density, but not sensitive indicators for plant responses to drought stress. Among chlorophyll fluorescence indices, NPQ and Fv/Fm were more closely correlated to RWC or TQ while NPQ was most responsive to drought stress, and therefore NPQ could be a useful indicator for detecting and monitoring cool-season turfgrass response to drought stress. The sensitivity and effectiveness of these indices associated with drought responses in this study could be further testified in other cool-season and warm-season turfgrass species under field conditions. As each imaging technology used in this experiment comes with bulky accessories such as LED panels, mounting tower and support system, capturing images within limited space of controlled environmental chambers are challenging. Future research should be in developing multimodal imaging integrating major features of all three technologies and reducing size and space requirement that would deliver improved decision support for drought monitoring and irrigation management in turfgrasses. Development of advanced algorithms that could incorporate broader spectral details or band reflectance for calculating novel vegetation indices are warranted.

    Acknowledgments

    The research presented in this paper was funded by the United State Department of Agriculture - National Institute of Food and Agriculture (2021-51181-35855).

  • The authors declare that they have no conflict of interest. Bingru Huang is the Editorial Board member of Journal Grass Research who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial Board member and her research groups.

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  • Cite this article

    Lv W, Johnson JB, Zaman QU, Zhu M, Liu H, et al. 2023. Artificial pollination can improve fruit set and quality in the ice cream tree (Casimiroa edulis). Tropical Plants 2:12 doi: 10.48130/TP-2023-0012
    Lv W, Johnson JB, Zaman QU, Zhu M, Liu H, et al. 2023. Artificial pollination can improve fruit set and quality in the ice cream tree (Casimiroa edulis). Tropical Plants 2:12 doi: 10.48130/TP-2023-0012
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Artificial pollination can improve fruit set and quality in the ice cream tree (Casimiroa edulis)

  • 1.

    Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China

  • 2.

    Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, School of Tropical Crops, Hainan University, Haikou 570228, China

  • 3.

    School of Health, Medical & Applied Sciences, CQ University Australia, Bruce Hwy, North Rockhampton, QLD 4701, Australia

  • 4.

    Hainan Shengda Modern Agriculture Development Co., Ltd., Qionghai 571425, China

  • Received: 19 September 2022
  • Accepted: 19 July 2023
  • Published online: 10 August 2023
Tropical Plants  2,  Article number: 12  (2023)  |  Cite this article

Abstract: Ice cream fruit (Casimiroa edulis La Llave) is a member of the Rutaceae family native to South America, which has recently been introduced to China. In order to improve the yield of ice cream fruit, we carried out a hybridization experiment with paired pollination. Pollen was collected in the morning and carefully stored, before it was applied to the stigmata of sprouted flowers, using a cotton swab or tweezers. This artificial pollination is repeated at different times of the day. This was continued until the stamens fall off the flowers, the stigmata withered, and the fruit began to develop. Fruit yield and quality were recorded as the principal metrics of the success of each hybrid. At the Hainan Tropical Fruit Window Agricultural Company, nine different ice cream fruit varieties were selected and divided into three groups (abundant pollen, little pollen and no pollen) according to the amount of pollen that each variety naturally produced. Four experimental conditions were investigated: 1. No artificial pollination (natural self-pollination); 2. Artificial self-pollination; 3. Cross-pollination (no emasculation); 4. Cross-pollination (with emasculation). The results indicated that the fruit from cultivars producing abundant pollen generally had poor quality, smaller size, average sweetness, and a medium rate of natural fruit set. In contrast, cultivars producing little pollen showed better fruit quality, higher fruit fertility and increased success rates for artificial pollination. The N2 ice cream fruit cultivar showed the best characteristics as the paternal line (pollen source), while the 4P cultivar showed the best characteristics as the maternal line (flower source). The New varieties (N2 × 4P) performs well in fruit quality, fruit size, fruit sweetness and fruit setting rate. Consequently, this cross-pollinated cultivar is recommended for agricultural production and planting.

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    Introduction

      Origin and development of ice cream fruit

    • Ice cream fruit (Casimiroa edulis La Llave) is a tropical fruit tree from the Rutaceae family, also known by the common names white persimmon, white sapote, Mexican apple and fragrant meat fruit. It is a monoecious species, bearing male and female parts in the same flowers (existing simultaneously)[1]. It is called ice cream fruit in China because it tastes like vanilla ice cream after freezing. Ice cream fruit originated in Mexico and Central America, but is currently distributed across a range of tropical and subtropical regions[2]. The tree shows high levels of resistance against biotic and abiotic stresses[3]. The fruit is rich in flavor, with soft flesh, minimal fruit juice, a milk-like fragrance, and a sweet flavor reminiscent of ice cream[1]. This combination of characteristics makes them highly sought after by consumers.

      At present, the commercial planting area of domestic ice cream fruit is not large, and the output is limited, resulting in a relatively high market price of around 50 Euros/kilogram (390 Chinese Yuan/kg). Similarly, it is not widely planted in China, only being introduced in Hainan, Yunnan and a few other places. However, as a tropical fruit with high economic value, there is considerable promise to expand its production as a high-end tropical fruit variety in the domestic market. Therefore, it is necessary to further study the cultivation technology of ice cream fruit - cross pollination.

      Morphology and floral biological characteristics of ice cream fruit

      Morphological characteristics

    • In the Hainan region, ice cream fruit trees can grow to 5−6 m, with gray-brown bark. The leaves are oval and lanceolate when young, with 3−5 leaflets typically comprising each leaf. They range between 6–12 cm long and 4–6 cm wide, with short-pointed or rounded leaf tips. The leaf margin has fine blunt teeth, with short hairs on the petiole, leaf shaft. The fruit begins green, turning more spherical and yellow and spherical as it ripens[3].

      The flowers have five petals, with a yellow-green corolla borne at the leaf base, around 8−10 mm in size. The stamens are 2−3 mm long.

      The medium-sized (5−10 cm diameter) fruit is shaped like a persimmon, with thin skin, white or yellowish flesh, and a smooth texture (Fig. 1). When refrigerated, it tastes like ice cream, with a sweetness of 17%−25%. A mature tree can produce 500 kg of fruit in one fruit period.

      Figure 1. 

      (a) Ice cream fruit. (b) Peeled ice cream fruit. (c) Ice cream tree flower.

      It is generally easy to cultivate, growing in a range of conditions (light, shaded, dry, wet, barren soil). It shows good cold resistance, withstanding temperatures as low as −2 to −4 °C.

      Floral characteristics

    • Ice cream fruit is widely cultivated in the tropics and subtropics, and can also flower in temperate regions. Ice cream fruit can bear fruit more than once per year, with flowering occurring twice a year between January to February, and June to July[2]. The time from flower germination until flowering is around 15−20 d[1], but fertilisation rates and fruit survival vary widely among cultivars. Varieties producing little pollen or no pollen seem to have a lower fruit yield but a higher fruit survival rate; Varieties with more pollen have lower fruit survival rate.

      Ice cream fruit nutritional composition

    • Ice cream fruit contains a lot of trace elements beneficial to the human body, such as potassium, calcium, iron and other minerals. It also contains high levels of several vitamins, including vitamin C (62 mg vitamin C/100 g); in some areas this fruit is colloquially known as the 'king of vitamin C'. Ice cream fruit is high in sugar, including fructose, glucose and other sugars, with a total sugar content as high as 16%[4].

      Research status of ice cream fruit

    • Practical research into any crop is inseparable from the collection of germplasm resources, breeding techniques and the investigation of biotic and abiotic factors affecting it. At present, few research groups are conducting experiments on ice cream fruit in China, thus much basic experimental research and investigation is still required.

      In China, ice cream fruit is currently cultivated on a large scale in Yunnan Xishuangbanna Botanical Garden and in the Hainan Shanda agricultural tropical fruit window. This survey uses ice-cream fruit grown by Hainan Qionghai Shanda agricultural company. The imported ice cream fruit came from the United States. This is one of the few only plantings areas in China.

      Study purpose and significance

    • At the present time, commercial ice cream fruit plantings suffer from low to moderate fruit set rates following artificial pollination, as well as inconsistencies in fruit quality. In order to improve the yield and quality of ice cream fruit, we investigated different artificial pollination methods in this study. Different ice cream fruit varieties were crossed using the artificial hybrid method for paired pollination experiments, with pollen taken from one variety used to pollinate another variety. The resultant fruit yield and quality were used as metrics to determine the best-performing crosses.

      Main content of the study

    • Ice cream fruit is a monoecious species, and pollen counts vary depending on the variety. A total of nine varieties were investigated in this work, being divided into three groups: little pollen (varieties AU4, 4P, C1), abundant pollen (N2, AU1, AU2) and no pollen (N6, N7, W4). The varieties with abundant pollen or little pollen were used as male parents, while the varieties with little pollen or no pollen were used as the female parents. In addition to cross-pollination, natural hanging fruit and artificial self-pollination were also investigated in each variety. After pollination, the flowers were bagged to avoid interference from external factors.

    Results

      Observation of natural self-pollination and artificial self-pollination

    • The different ice cream fruit varieties showed significant morphological differences in their stamens, as shown in Fig. 2.

      Figure 2. 

      (a) W4 variety – no pollen. (b) 4P variety – little pollen. (c) AU2 variety – abundant pollen.

      The varieties with no pollen showed large ovaries, with a visible, yellow pistil stigma. The stamens and yellow and black, with dry and infertile anthers.

      The varieties with little pollen stigma is pink or yellow-pink, and the ovary and stigma are abnormally large. Five stamens are very small, the anthers are yellow-white or yellow-black, and the pollen content is very little.

      The varieties with abundant pollen pistil is very small, and the stigma is not obviously light green, the capital part is also very small, but the five stamens are very thick, especially the anther flower sac is also very large, showing bright yellow, the pollen content is very high.

      In Fig. 3, the development process of naturally self-pollinated fruit is recorded, with the corresponding data for this experimental group shown in Table 1.

      Figure 3. 

      The natural development process of self-pollinated fruit.

      Table 1.  Statistics of the naturally self-pollinated fruit.

      BreedRate of fruit set
      (%)      		Width
      (cm)      		Length
      (cm)      		Sugar
      (%)
      Abundant pollenAU10%−−−
      AU225%4.1 ± 04 ± 010.7 ± 0
      N250%4.7 ± 0.23.95 ± 0.259.1 ± 0.1
      Little pollen4P66%7.5 ± 0.26.9 ± 0.111.7 ± 0.5
      AU450%8.3 ± 0.27.6 ± 0.310.5 ± 05
      C1100%6.6 ± 0.25 ± 0.110.9 ± 0.1

      Comparing the two groups (abundant pollen and little pollen) shows that the fruit set rate, fruit size and sweetness were all higher in varieties with little pollen, compared to those with more abundant pollen. Within each group, variety N2 showed the best performance for the abundant pollen varieties, while variety 4 showed the highest sweetness and acceptable size and fruit set amongst the varieties with little pollen. However, variety C1 did show the highest rate of fruit set.

      To compare the effects of natural and artificial pollination, another experiment was carried out with artificial self-pollination conducted at different times every day. The bag surrounding each flower was gently shaken each time to ensure that the pollen was transferred from the stamens to the stigma; this process was repeated until the stamens and stigma withered, and the fruit began to grow, indicating that pollination was successfully completed.

      As shown in Table 2, the fruit set rate of ice cream fruit varieties with abundant pollen increased in the case of artificial pollination. However, there was no significant change in the size or sweetness of the fruit quality. It is known that artificial pollination can slightly improve the fruit set rate of abundant pollen varieties, but it has no significant effect on fruit quality.

      Table 2.  Statistics of experimental data of artificial self pollination.

      BreedFruit set
      (%)      		Width
      (cm)      		Length
      (cm)      		Sugar
      (%)
      Abundant pollenAU133%5.9 ± 05.5 ± 010.0 ± 0
      AU250%4.4 ± 0.54.0 ± 0.19.5 ± 0.5
      N240%6.0 ± 04.5 ± 08.6 ± 0
      Little pollen4P66%7.7 ± 0.16.4 ± 0.811.0 ± 1.0
      AU450%8.9 ± 0.17.5 ± 0.110.2 ± 0.2
      C1100%7.8 ± 0.16.9 ± 0.19.5 ± 0.1

      Therefore, artificial cross-pollination and hybridization studies were conducted to find possible combinations which could provide improved fruit quality compared to self-pollination.

      Artificial cross-pollination (male/female)

    • Tables 3−8 show the experimental results from the artificial cross-pollinations, broken down by each female parent. Additionally, each table shows the results with and without emasculation.

      Table 3.  Experimental data for the cross-pollination with AU4 as the maternal parent, with and without emasculation.

      GroupMother treeFather treeFruit set (%)Length (cm)Width (cm)Sugar (%)
      FemaleAU4 (little pollen)Little pollenC133%7.5 ± 0.17.4 ± 0.18.9 ± 0.1
      4P50%8.8 ± 0.27.9 ± 0.210.4 ± 0.1
      Abundant pollenAU140%8.15 ± 0.17.75 ± 0.110.4 ± 0.2
      AU250%9.2 ± 0.17.95 ± 0.19.5 ± 0.1
      N275%9.47 ± 0.18.47 ± 0.110.5 ± 0.5
      MaleLittle pollenC10%−−−
      4P50%8.05 ± 0.17.95 ± 0.28.5 ± 1
      Abundant pollenAU133%8.7 ± 0.28.5 ± 0.18.0 ± 2
      AU233%8.5 ± 0.37.9 ± 0.18.1 ± 0.2
      N250%9.2 ± 0.38.04 ± 18.5 ± 1

      Table 4.  Experimental data for the cross-pollination with 4P as the maternal parent, with and without emasculation.

      GroupMother treeFather treeFruit set (%)Length (cm)Width (cm)Sugar (%)
      Female4P (little pollen)Little pollenC150%7.8 ± 0.67.0 ± 0.29.8 ± 1.2
      AU466%6.9 ± 0.16.3 ± 0.28.78 ± 1.0
      Abundant pollenAU150%8.7 ± 0.66.57 ± 1.09.8 ± 0.5
      AU250%7.35 ± 0.26.45 ± 2.08.5 ± 1.5
      N280%7.7 ± 1.07.075 ± 1.011.0 ± 1.0
      MaleLittle pollenC142%8.5 ± 0.36.9 ± 0.210.5 ± 0.5
      AU450%8.0 ± 2.07.95 ± 2.010.0 ± 0.8
      Abundant pollenAU150%7.15 ± 2.06.55 ± 3.09.2 ± 0.6
      AU242%6.8 ± 0.46.1 ± 0.58.87 ± 0.4
      N262%7.5 ± 0.37.4 ± 0.69.84 ± 0.3

      Table 5.  Experimental data for the cross-pollination with C1 as the maternal parent, with and without emasculation.

      GroupMother treeFather treeFruit set (%)Length (cm)Width (cm)Sugar (%)
      FemaleC1 (little pollen)Little pollenAU460%7.27 ± 1.06.76 ± 1.010.3 ± 1.0
      4P33%6.9 ± 0.45.9 ± 0.310.0 ± 0.5
      Abundant pollenAU125%6.8 ± 0.56.2 ± 0.68.0 ± 0.4
      AU233%5.9 ± 0.45.9 ± 0.310.2 ± 1.0
      N250%6.8 ± 0.66.8 ± 0.311.0 ± 0.5
      MaleLittle pollenAU450%6.9 ± 0.26.8 ± 0.410.3 ± 1.0
      4P0%−−−
      Abundant pollenAU10%−−−
      AU233%6.8 ± 0.45.9 ± 0.110.0 ± 0.5
      N250%7.1 ± 0.55.9 ± 0.29.16 ± 1.0

      Table 6.  Experimental data for the cross-pollination with N6 as the maternal parent, with and without emasculation.

      GroupMother treeFather treeFruit set (%)Length (cm)Width (cm)Sugar (%)
      FemaleN6 (No pollen)Little pollenC10%−−−
      4P40%7.0 ± 0.56.5 ± 0.28.75 ± 1.0
      AU450%6.8 ± 0.26.3 ± 0.210.0 ± 0.4
      Abundant pollenAU150%7.2 ± 0.25.9 ± 0.38.2 ± 0.2
      AU233%5.8 ± 0.46.2 ± 0.48.0 ± 1.0
      N250%6.9 ± 0.26.7 ± 0.310.0 ± 0.6
      MaleLittle pollenC150%6.6 ± 06.5 ± 08.0 ± 0
      4P33%7.4 ± 06.5 ± 09.5 ± 0
      AU433%6.5 ± 0.156.4 ± 0.48.0 ± 1.0
      Abundant pollenAU166%6.6 ± 0.55.95 ± 0.19.5 ± 0.5
      AU250%6.6 ± 0.16.45 ± 0.10.59 ± 0.5
      N266%6.6 ± 0.65.95 ± 0.110.5 ± 1

      Table 7.  Experimental data for the cross-pollination with N7 as the maternal parent, with and without emasculation.

      GroupMother treeFather treeFruit set (%)Length (cm)Width (cm)Sugar (%)
      FemaleN7 (No pollen)Little pollenC150%6.8 ± 0.26.4 ± 0.210 ± 0.4
      4P33%7.2 ± 0.26.8 ± 0.29.4 ± 0.4
      AU433%6.4 ± 0.26.2 ± 0.49.4 ± 0.4
      Abundant pollenAU133%6.7 ± 0.36.6 ± 0.610.0 ± 0.2
      AU233%7.8 ± 0.46.8 ± 0.59.0 ± 1.0
      N250%8.8 ± 0.47.4 ± 0.19.5 ± 1.0
      MaleLittle pollenC128%5.8 ± 0.15.6 ± 08.5 ± 0.5
      4P50%7.2 ± 06.8 ± 010.0 ± 0
      AU425%6.4 ± 06.3 ± 08.4 ± 0
      Abundant pollenAU10%−−−
      AU240%6.8 ± 06.1 ± 09.0 ± 0
      N250%6.8 ± 0.46.0 ± 0.210.0 ± 1.0

      Table 8.  Experimental data for the cross-pollination with W4 as the maternal parent, with and without emasculation.

      GroupMother treeFather treeFruit set (%)Length (cm)Width (cm)Sugar (%)
      FemaleW4 (No pollen)Little pollenC160%7.2 ± 06.5 ± 016.0 ± 0
      4P50%6.3 ± 0.35.8 ± 0.113.5 ± 0.5
      AU40%−−−
      Abundant pollenAU150%5.6 ± 05.4 ± 0.210.7 ± 0.3
      AU250%7.1 ± 06.2 ± 013.0 ± 0
      N260%6.8 ± 0.26.0 ± 0.114.2
      MaleLittle pollenC125%7.1 ± 0.36.4 ± 0.213.7 ± 1.0
      4P50%7.0 ± 0.16.4 ± 011.7 ± 0.6
      AU40%−−−
      Abundant pollenAU150%6.9 ± 06.8 ± 012.8 ± 0
      AU250%5.8 ± 04.9 ± 013.6 ± 0
      N260%7.1 ± 0.66.0 ± 0.312.8 ± 0.2

      When the AU4 variety was used as the maternal parent (with emasculation), the best performance was found using N2 as the pollen source, which yielded a 75% rate of fruit set and the largest fruit size (9.5 cm × 8.5 cm), as shown in Table 3. It also had a high sugar content (10.5%).

      Similarly, for 4P as the maternal parent, 80% fruit set was found with emasculation and with N2 as the pollen source (Table 4). This cross also showed moderate size and high sugar content. However, the largest fruit were found using AU1 as the pollen source.

      The final little-pollen variety used as a maternal parent was the C1 variety, which generally showed poorer performance than the other two maternal parents previously discussed.

      The best performance was seen using AU4 pollen on emasculated flowers, which gave a 60% rate of fruit set, the largest fruit size and a high sugar content (Table 5). N2 performed acceptably as a pollen source, and was also the best cross on non-emasculated flowers.

      For the maternal parent varieties which did not produce pollen, N7 generally had poorer performance, while N6 and W4 had acceptable performance. Again, N2 was the best pollen source for most maternal varieties.

      With N6 as the maternal parent, N2 and AU1 tied for the highest fruit set rate (66%) under non-emasculated conditions; however, the fruit was only moderate in size (Table 6). N2 also gave the sweetest fruit.

      For N7 as the maternal parent, several combinations gave a 50% fruit set rate, while most other pollen sources gave quite poor rates of fruit set (Table 7). The better-performing pollen sources included C1 and N2 (for emasculated flowers) and 4P and N2 (for non-emasculated flowers). N2 also gave the largest fruit under emasculated conditions (8.8 cm × 7.5 cm).

      Finally, with the W4 maternal parent, N2 again performed well under both emasculated and non-emasculated conditions (60% fruit set rate), with moderately sized fruit (Table 8). C1 also gave 60% fruit set when applied to emasculated flowers and good-sized fruit, as well as an exceptionally high sugar content (16%).

      In general, most crosses showed higher fruit set rates and better fruit quality when the stamen is emasculated. The index of fruit quality as the best paired pollination group is a general method for pollination variety research[5], so we calculated the formula of the best paired parent variety of each parent to obtain the index.

      Therefore, the best matching parent and the fruit quality of each parent in the case of male elimination can be shown as in Table 9. Overall, the best pollination combination between different ice cream fruit varieties was found using variety 4P and N2 as the parents (Table 9).

      Table 9.  Male parent cultivars and index of the best cultivar pairing for each female parent cultivar with emasculation.

      CrossFruit set (%)Length (cm)Width (cm)Sugar
      (%)      		Index of
      fruit quality
      Mother treeFather tree
      C1AU460%7.2 ± 1.06.7 ± 1.010.3 ± 1.010.2
      4PN280%7.7 ± 1.07.1 ± 1.011.0 ± 1.014.4
      AU4N275%9.4 ± 0.18.4 ± 0.110.5 ± 0.514.2
      N6N250%6.6 ± 0.65.9 ± 0.110.5 ± 1.08.3
      N7N250%8.8 ± 0.47.4 ± 0.19.5 ± 1.08.5
      W4C160%7.2 ± 06.5 ± 016.0 ± 013.5

      Hybrid fruit of 4P and N2

    • For the best-performing artificially pollinated hybrid between varieties 4P and N2, the fruit weight was 280 g, with a longitudinal width of 8.1 cm and a transverse diameter of 8.5 cm. This combination showed a 88.9% fruit set rate, with a 62.5% fruit survival rate. The skin of the fruit was green, with golden-yellow flesh, and a sugar content of 12%. This new variety has obvious advantages in both fruit quality and yield, and it shows great potential in the future agricultural production process (Fig 4).

      Figure 4. 

      Hybrid fruit of 4P and N2.

    Discussion

      Ice cream fruit flower differences

    • To gain further insight in the reproductive biology of ice cream fruit, a number of comparative observations on the flower morphology and growth are reported here.

      Ice cream fruit varieties with abundant pollen showed a large number of flowers, although with very small ovaries and stigmas. However, the anthers were large. Each branch can bear up to 12−20 flowers.

      Varieties with little pollen had significantly reduced numbers of flowers, although the ovaries were larger. Each branch bore 8–12 flowers.

      Finally, the ice cream fruit varieties with no pollen had an unusually large ovary, around 2−4 times larger than that of the other varieties flowering at the same period. The anthers are infertile and do not produce pollen. The stigmata on the pistil is also very large, after successful pollination, the stigma withers gradually.

      Pollination of different varieties of ice cream fruit

    • In natural self-pollination experiment, the following conclusions could be drawn:

      (1) Ice cream fruit varieties with abundant pollen generally show lower fruit sizes, average to low sweetness, and low to moderate rates of fruit set.

      (2) The ice cream fruit of little pollen varieties has good fruit quality and the fruit set rate is relatively high.

      Furthermore, we observed that in the absence of artificial intervention, ice cream fruit setting rate, fruit quality and sweetness were worse. This indicates that the ice cream fruit benefits from manual pollination intervention.

      In the experiments with artificial cross-pollination, we observed an increase in fruit set rate but no change in fruit sweetness or quality. This indicates that the ice cream fruit may have the characteristics of self-incompatibility, which limits the development of ice cream fruit related industries. In order to increase the yield, we must cross-pollinate and cross to find the best pair of pollinators. It is suggested that mixed pollen should be used for artificial pollination when growing fruits in the future[6]. The experimental results are consistent with this view.

      The experimentation results from artificial cross-pollination (Tables 4−9) showed that the fruit set rate, fruit size and sweetness were all improved. Based on the cross-pollination results from different varieties, we can conclude:

      Firstly, the best-performing variety as the male parent (i.e., the pollen source) is the variety N2, one of the abundant pollen varieties. In general, varieties producing more high pollen showed better performance as the pollen source, and can be used as the pollen source tree in agricultural production.

      Second, the best-performing variety for the female parent was variety 4P, which was one of the varieties bearing little pollen. It showed high bud quality and good fruit characteristics, recommending its use as the mother fruit tree in agricultural production and planting.

      Third, the combination of the pollen-free variety W4 as the mother tree and the little-pollen variety C1 as the pollen source tree showed a higher sweetness in their hybrid fruits than that of any other hybrid. This combination could be used as a special, high-value hybrid to enrich the agricultural production of ice cream fruit.

      Finally, male group and female group contrast. The fruit set rate, fruit quality and fruit sweetness were better in the female group. In the process of agricultural production, artificial cross pollination needs to emasculation. Other research has shown that the average yield of the same maize variety was increased significantly by emasculation than by no emasculation[7]. The experimental results are consistent with this view.

      Selecting appropriate varieties for cross-pollination is one of the key technologies to obtain high quality and high yield[8]. Hybrid technology has played an important role in plant genetics and breeding[9]. In particular, the abundant pollen variety N2 significantly improved fruit quality when used as a pollen source in this study, particularly when combined with variety 4P as the mother fruit tree.

    Materials and methods

      Materials, main instruments and equipment

      Test materials

    • The experimental field work was conducted at the Tuhao field station and Houpo field station, both owned by Hainan Shengda Modern Agricultural Development Co, Ltd, (Hainan, China).

      The ice cream fruit varieties used in this experiment were three varieties producing abundant pollen (N2, AU1, AU2) and three varieties producing little pollen (AU4, 4P, C1) and three varieties producing no pollen (N6, N7, W4). The varieties used in this experiment were randomly selected in the experimental field. All the experimental varieties were planted at appropriate temperature, humidity, sufficient light, with optimum growth conditions and no signs of abnormal flowers and fruits[10,11]. The tree were kept free of diseases and insect outbreaks during the trial.

      Instrument, equipment and appliances

    • The handheld glucose meter I used was the Nohawk HP-TD1.

      Test method

      Observation of natural self-pollination

    • No artificial interventions were made for the natural self-pollination experiment[12,13]; only the selected ice cream fruit trees and the sample flowers in the flower germination period were marked. Observations were made throughout the natural flowering and fruit set process. The fruit set rate and fruit quality data were also collected. In this experimental group, nine flowers were documented from each variety, for three abundant pollen varieties (N2, AU1 and AU2), three little-pollen varieties (AU4, 4P and C1). This gave a total of six experimental groups and 54 data points.

      Artificial self-pollination and cross-pollination (male/female)

    • For the artificial self-pollination work, the selected ice cream fruit trees and the sample flowers were marked, bagged and isolated during the flower germination period. The flowers were then artificially self-pollinated (i.e., between the flowers of the same fruit tree). For this experiment, nine flowers were recorded from each variety, for the three abundant pollen varieties (N2, AU1 and AU2) and the three little-pollen varieties (AU4, 4P and C1). Again, this gave six experimental groups and 54 data points.

      For the artificial cross-pollination work, one fruit tree of either an abundant pollen variety (N2, AU1, AU2) or a little-pollen variety (AU4, 4P and C1) was selected as the 'father' to collect pollen from. The varieties producing no pollen (N6, N7, W4) or little pollen (AU4, 4P and C1) were selected as mother trees (i.e., the flower source).

      For each of the six female trees used as the mother, either five or six other varieties were used as the pollen source to cross-pollinate the parent. Five cross-pollinations were performed where the mother was a little-pollen variety, while six cross-pollinations were performed where the mother was a no-pollen variety. Consequently, there were a total of 15 crosses for the little-pollen mother varieties, and 18 crosses for the no-pollen mother varieties, as shown graphically in Fig. 5. Again, nine flowers were tracked for each cross-pollination experiment.

      Figure 5. 

      Artificial cross-pollination (male/female).

      As a further investigation, this experiment was divided into two groups: male and female. This gave a total of 66 experimental treatments, yielding data on 594 flowers and fruit (Fig. 5).

      Experimental operation procedures

      Selected varieties (fruit trees)

    • For the biological and morphological observations, a total of 24 fruit trees were observed for different purposes (Table 10). The nine ice cream fruit varieties are planted in a large area in Hainan Shengda Agricultural Company, which belongs to the current commercial varieties[14]. The paired pollination completion experiments and experimental results are of great significance to their commercial production[15,16].

      Table 10.  Fruit tree different applications.

      Different varietiesDifferent applications
      Abundant pollen varieties (N2, AU1 and AU2)Observation of natural self-pollination3
      Artificial self-pollination3
      Artificial cross-pollination parent tree3
      Little-pollen varieties (AU4,
      4P and C1)      		Artificial cross-pollination mother tree (male)3
      Artificial cross-pollination mother tree (female)3
      Artificial cross-pollination parent tree3
      No pollen varieties (N6, N7 and W4)Artificial cross-pollination mother tree (male)3
      Artificial cross-pollination mother tree (female)3
      Sum24
      Selection of the flowers

    • From each tree, three flowers were randomly selected and marked for observations[17]. The flowers were bagged in the artificial self-pollination and cross-pollination experiments to avoid external factors[18,19].

      Pollen collection and preservation

    • Ice cream fruit pollen from the required parent was collected at the morning of the crossing, to avoid little pollen quality and quantity[20,21]. Using tweezers, the pollen was gently collected into separate bags for preservation[22]. No pollen was collected on rainy days, as moisture can affect pollen quality[23].

      Artificial pollination

    • Artificial self-pollination was carried out using bagged flowers[24]. Pollen from the required variety was gently shaken onto the stigma; this was repeated at different times every day until the stamens withered or fell off[25−27].

      Artificial cross-pollination was also carried out on bagged flowers; however, the stamens were removed (where required) at the germination stage[28,29]. As with the artificial self-pollination work, either cotton swabs or tweezers were used to transfer pollen from the required variety to the stigma of the mother flower at different times each day. This was repeated until the stamens fell off and the stigmata withered[30−32].

      Emasculation

    • For experiments where emasculation was required, the stamens of the flower were gently cut off at the germination stage. Care was taken to ensure the rest of the pistil and stigma were not harmed during this process[33,34].

      Measurement statistics

    • When the fruit ripened, it was picked and the length and width measured. The sugar content was also measured[35]. Finally, the relevant data was transferred into Microsoft Excel for subsequent processing and analysis[36].

      Acknowledgments

      • This study was funded by Hainan Province Science and Technology Special Fund (ZDYF2022XDNY190), the Project of Sanya Yazhou Bay Science and Technology City (SCKJ-JYRC-2022-83) , University level scientific research project of Hainan University (XTCX2022NYB09), and Hainan Provincial Natural Science Foundation of China (421RC486).

      Conflict of interest

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

      Dates

      • Received 19 September 2022; Accepted 19 July 2023; Published online 10 August 2023

      Rights and permissions
      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press on behalf of Hainan University. 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 (10) References (36)
  • About this article
    Cite this article
    Lv W, Johnson JB, Zaman QU, Zhu M, Liu H, et al. 2023. Artificial pollination can improve fruit set and quality in the ice cream tree (Casimiroa edulis). Tropical Plants 2:12 doi: 10.48130/TP-2023-0012
    Lv W, Johnson JB, Zaman QU, Zhu M, Liu H, et al. 2023. Artificial pollination can improve fruit set and quality in the ice cream tree (Casimiroa edulis). Tropical Plants 2:12 doi: 10.48130/TP-2023-0012
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  • Introduction

  • Origin and development of ice cream fruit

  • Morphology and floral biological characteristics of ice cream fruit

  • Morphological characteristics
  • Floral characteristics

  • Ice cream fruit nutritional composition

  • Research status of ice cream fruit

  • Study purpose and significance

  • Main content of the study

  • Results

  • Observation of natural self-pollination and artificial self-pollination

  • Artificial cross-pollination (male/female)

  • Hybrid fruit of 4P and N2

  • Discussion

  • Ice cream fruit flower differences

  • Pollination of different varieties of ice cream fruit

  • Materials and methods

  • Materials, main instruments and equipment

  • Test materials
  • Instrument, equipment and appliances

  • Test method

  • Observation of natural self-pollination
  • Artificial self-pollination and cross-pollination (male/female)

  • Experimental operation procedures

  • Selected varieties (fruit trees)
  • Selection of the flowers
  • Pollen collection and preservation
  • Artificial pollination
  • Emasculation
  • Measurement statistics
  • Acknowledgments
  • Conflict of interest
  • Dates
  • Rights and permissions
  • About this article
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