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Prospects and challenges of elite coconut varieties in China: a case study of makapuno

  • # Authors contributed equally: Zhihua Mu, Zhuang Yang, Hang Xu

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  • Received: 06 May 2024
    Revised: 24 June 2024
    Accepted: 01 July 2024
    Published online: 04 September 2024
    Tropical Plants  3 Article number: e029 (2024)  |  Cite this article
  • Makapuno, one of the rarest and economically valuable coconut, boasting superior qualities like soft, jelly-like endosperm, creamy texture, and special flavor, presents significant opportunities for the Chinese market. Through a comprehensive analysis of available evidence in coconut-producing countries, this study evaluates the potential for makapuno cultivation in China, considering factors such as the application of embryo culture techniques, economic acceptance, market demand, and agricultural and climate suitability. Furthermore, this paper identifies the key challenges hindering the widespread adoption of biotechnologies involved in makapuno cultivation, including embryo culture, acclimatization of in vitro seedlings, limited germplasm resources, inadequate technical expertise, and regulatory constraints. By leveraging makapuno as a case study, this paper provides insights into the broader issues surrounding the development of elite coconut varieties in China. It offers recommendations for researchers, policymakers, and industry stakeholders to overcome these challenges and unlock the full potential of makapuno cultivation in the country.
  • 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[24]. 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[10,1517]. 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.

    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.

    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.

    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.

    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

    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 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

    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.

    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.

    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).

    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. 3cf).

    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.
    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.

    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.
    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.

    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.

    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).

    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[3840]. 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.

    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.

    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

    Mu Z, Yang Z, Xu H, Khongmaluan M, Arikit S, et al. 2024. Prospects and challenges of elite coconut varieties in China: a case study of makapuno. Tropical Plants 3: e029 doi: 10.48130/tp-0024-0028
    Mu Z, Yang Z, Xu H, Khongmaluan M, Arikit S, et al. 2024. Prospects and challenges of elite coconut varieties in China: a case study of makapuno. Tropical Plants 3: e029 doi: 10.48130/tp-0024-0028

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Prospects and challenges of elite coconut varieties in China: a case study of makapuno

Tropical Plants  3 Article number: e029  (2024)  |  Cite this article

Abstract: Makapuno, one of the rarest and economically valuable coconut, boasting superior qualities like soft, jelly-like endosperm, creamy texture, and special flavor, presents significant opportunities for the Chinese market. Through a comprehensive analysis of available evidence in coconut-producing countries, this study evaluates the potential for makapuno cultivation in China, considering factors such as the application of embryo culture techniques, economic acceptance, market demand, and agricultural and climate suitability. Furthermore, this paper identifies the key challenges hindering the widespread adoption of biotechnologies involved in makapuno cultivation, including embryo culture, acclimatization of in vitro seedlings, limited germplasm resources, inadequate technical expertise, and regulatory constraints. By leveraging makapuno as a case study, this paper provides insights into the broader issues surrounding the development of elite coconut varieties in China. It offers recommendations for researchers, policymakers, and industry stakeholders to overcome these challenges and unlock the full potential of makapuno cultivation in the country.

    • As one of the most important palm species in the world, the coconut (Cocos nucifera L.) belongs to the Cocoideae subfamily within the Arecaceae (palm) family and stands as the sole species in the Cocos genus[1]. Its origin is believed to be traced back to Polynesia or the Indian Archipelago[2]. However, in the present day, coconut palms thrive in over 90 countries worldwide[3]. Referred to as the 'tree of life', the coconut palm is highly versatile[4], with almost every part of the tree serving a purpose. It not only meets essential daily needs but also caters to various luxuries in life. Coconut products are diverse, including coconut oil, coconut milk, coconut water, coconut sugar, coconut mats, coconut fiber, coconut shell activated carbon, building materials, furniture, and coconut carving handicrafts[5].

      The variant characterized by a jelly-like endosperm in its fruit is referred to as 'makapuno' (or macapuno), a term originating from Filipino, meaning 'tends to fullness'[6]. This particular variety was initially identified in the Philippines, as documented by Gonzalez in 1914, who described the fruit as having a cavity that is either completely or partially filled with a white, gelatinous endosperm[7]. It was observed that not every fruit from a makapuno-bearing palm displayed these characteristics[8]. Intriguingly, similar varieties with different local names are found in various countries, such as Dừa Sáp (Vietnam), Dikiri Pol (Sri Lanka), Kopyor (Indonesia), Maphrao Kathi (Thailand), Dahi Nariyel (Myanmar), Thairu Thengai (India), Dong Kathy (Cambodia), and Niu Garuk (Papua New Guinea)[9]. Makapuno coconut cannot germinate in natural conditions. Makapuno embryos exhibit morphological normalcy, but its inability to germinate stems from biochemical and physical characteristics of the exceptional endosperm, which fails to support germination[10]. The non-germination of the makapuno embryo is attributed to the haustorium's failure to develop, resulting in the loss of connection between the viable embryo and the abnormal endosperm[11]. Makapuno is the most economically valuable coconut variety worldwide and deeply loved by tropical people, sometimes makapuno serves as a gift for its costliness in producing countries[12].

    • Elite coconuts like makapuno and aromatic varieties are not just prized for their scarcity. The delicious makapuno endosperm (as shown in Fig. 1), with its melt-in-your-mouth texture and rich, nutty flavor, makes them highly sought-after for fresh consumption and a range of attractive processed products[13]. When Sri Lanka is used as an example, it becomes evident that this elite variety commands a higher price due to its distinctive flavor. Sri Lankan dikiri coconut is gaining a reputation as a high-potential coconut type in the coconut sector because of its high composition of pectin which physico-chemical properties are comparable with commercial fruit-grade pectin. The dietary fiber components available in dikiri coconut are pectin and hemicellulose while normal coconut contains cellulose and lignin. Lignin is comparatively very low in dikiri coconuts. The high amount of methoxyl pectin indicates the potential in food products like shakes, jam, bread spread and ice cream without adding extra pectin. Dikiri kernel-based ice cream provides a substitute for dairy ice cream which is preferred by consumers with lactose intolerance[14]. The ice cream prepared from dikiri kernel is rich in fat and pectin with quality attributes like appearance, taste, texture, mouth feel and overall acceptability, not significantly different from those of dairy ice cream[15]. Dikiri kernel has a lower composition of fat and a higher composition of carbohydrates, than the ordinary coconut which is a favorable characteristic in food processing, as risk of rancidity in storage is reduced[16].

      Figure 1. 

      The comparison between normal coconut and makapuno coconut. (a) Fruit of Thai makapuno coconut with jelly-like liquid endosperm, (b) fruit from normal tall coconut variety, (c) special makapuno variety from Indonesia called kopyor.

    • The nutritional composition analysis of makapuno was initiated early and conducted comprehensively, revealing its rich nutritional profile (as illustrated in Fig. 2). Some nutritional components present in makapuno are even absent in regular coconuts. Makapuno meat primarily consists of carbohydrates, followed by lipids, which is in contrast to the normal mature meat that serves as a source of oil[17]. They also noted that the main cell wall material of normal mature coconut meat is hemicellulose, while that of makapuno is pectin[18]. Previous studies indicated that the polysaccharides in coconut meat are primarily cellulose, with high amounts of galactomannan and mannan[19]. The vitamin content of makapuno meat is also similar to that of normal mature meat but exhibits significantly higher levels of vitamin C and α-tocopherol[17]. The mineral composition of makapuno meat is comparable to that of young or normal mature meat, making it a good source of dietary minerals, with potassium being the chief mineral[20]. The sweet taste of normal coconut is largely attributed to sucrose, and makapuno meat has higher contents of sucrose, glucose, and fructose compared to normal mature meat[20,21].

      Figure 2. 

      Nutritional value of makapuno coconut.

      Makapuno type coconut is deficient in the function of α-D-galactosidase, an enzyme that converts galactomannan into mannan during nut maturation. The absence of α-D-galactosidase activity results in the accumulation of galactomannan, whereas in the normal coconut, it converts galactomannan to mannan[22]. Accumulated galactomannan makes the solid endosperm soft, fluffy, and gelatinous and the liquid endosperm highly viscous in makapuno type coconut[23]. Galactomannan functions as a dietary fiber since the molecules are not digested by digestive secretions in the small intestine of humans[24]. Galactomannan and citrus pectin are considered 'super fibers' known for altering gut microbiota composition and improving glucose and lipid metabolism. The dietary fiber of makapuno meat is comparable to that of oat bran, chickpea, or other legumes. Makapuno contains high dietary fiber in its meat which was found to be hemicellulose, whereas in young meat, it is cellulose[20]. Therefore, due to the higher dietary fiber content makapuno type coconut has the potential to be used as a functional food as well as in prebiotics to support gut health[25]. It is well known that the intake of dietary fiber is associated with health benefits. Furthermore, soluble dietary fiber, like galactomannan is fermented by gut microbiota and it has important health benefits due to its hypo-cholesterolemic properties. Due to these nutritional and health properties, dietary fiber is widely used as a functional ingredient in the food industry[26]. Makapuno meat contained about 3-fold lower fat, than what has been reported for mature coconut. This is reflected in the calories of makapuno meat, which was approximately 3-fold lower than that of mature coconut[25]. According to Gunathilake, dikiri kernel has a significantly lower fat content (34.45% dry basis) compared to that of the normal coconut kernel (66.11% dry basis)[14]. Therefore, makapuno meat has high potential in foods such as ice cream, pastries, cakes, candies, and beverages like smoothies and shakes with authentic coconut taste. Makapuno galactomannan can be exploited as a plant-based biopolymer and a hydrocolloid material, having potential as a new commercial source of galactomannan[23]. Gunathilake also reported that dikiri contains a considerably high amount of pectin (22.36% ± 1.2%) compared to other commercial pectin sources[14].

    • Kopyor is an Indonesian type of coconut with a soft and friable endosperm, and it is said to be distinct from makapuno coconut. Two main areas have been known as central production areaa, i.e. Pati District of Central Java Province which mostly produces about 3 to 5 thousand kopyor nuts per week and South Lampung, Sumatera which produces less than a thousand nuts per week[27]. With the high demand for kopyor nuts in Indonesia and the limited number of nut production per year, the price of kopyor nuts increases rapidly every year. In 2014, the price of a kopyor nut was about 20 to 30 thousand rupiahs (USD$ 1.5–2.5) depending on the nut size[28], while in 2023, the price increased almost twice to about 30 to 60 thousand rupiahs (USD$ 2.5–5) at the farmer level. This is more than 10 times higher compared to the price of normal nuts. To date, kopyor nuts in Indonesia are mostly produced from natural palm in which only 20% to 30% of the nuts are kopyor while the rest are normal nuts. Remain a small number of coconut farmers planting kopyor using seedlings produced from embryo culture. By using the last method, the nut production will increase almost four times to about 80%–97% of the nuts are kopyor. This means that the income of coconut farmers will also increase four times.

      Embryo culture has been applied to coconut seedlings production for several reasons such as germplasm collection and exchange[29,30], germplasm storage[3138], physiological study on coconut including biotic and abiotic stresses[39]. In Indonesia, embryo culture has been applied widely on producing true-to-type of certain coconut cultivars such as makapuno in the Philippines[13,40,41] or kopyor in Indonesia[4246]. In general, kopyor seedling production using the embryo culture approach consists of two steps, in vitro and ex vitro steps (Fig. 3). In vitro step mostly needs about 6 to 9 months for culture initiation, embryo germination, and seedling development. After the coconut seedlings produce more than two open leaves then culture can be moved to the ex vitro step which consists of a rooting and acclimatization process, followed by transplanting in nursery. This ex vitro step needs another 9 months before the plants reach about 1 m height and are ready for field planting.

      Figure 3. 

      Makapuno coconut propagation methods (Sri Lanka). (a) Mature dikiri nuts at 11−12 months postpollination, (b) dikiri kernel, (c) extraction of dikiri embryo, (d) dikiri embryos taken from kernel, (e) dikiri plantlets from embryo culture at different stages, (f) plantlets at growth stages 7−8-month in vitro period, (g) initial acclimatization in polybags, (h) potted plants, (i) fully acclimatized plants ready for selling, (j) field establishment of dikiri plants in Coconut Research Institute, Sri Lanka.

      The most common in vitro culture medium used for kopyor seedling production is hybrid embryo culture (HEC) medium in which Y3 macro- and micro-nutrients are combined with UPLB vitamins and high sucrose concentration (60 g/L), without addition of plant growth regulators[47]. However, minor changes have been reported such as the HEC medium with added coconut water[42,45], indole butyric acid (IBA), and kinetin during seedling development[44], or added with IBA during rooting and acclimatization process[43]. In several Indonesian tissue culture laboratories, in vitro rooting steps have been reported[42,45]. However, a more efficient protocol has been reported by combining rooting and acclimatization through ex vitro rooting[43]. To date, there are several laboratories in Indonesia commercially producing true-to-type kopyor seedlings using embryo culture with the potential to produce more than 30,000 seedlings per year.

    • A makapuno-type coconut called dikiri with a gelatinous kernel is originally found in some villages of southern Sri Lanka. In dikiri coconut, the solid endosperm is a soft translucent gelatinous pulp and amorphous which fills almost the entire central cavity of the coconut, and the liquid endosperm is viscous and colorless. The thickness of meat differs among the nuts, from filled nuts with no or very little liquid endosperm to thick and soft but not amorphous kernels, with a considerable amount of thick liquid endosperm. Only a few tall-type palms were identified initially with a potential of bearing about 2%−5% of dikiri nuts from the harvest. Dwarf varieties bearing similar nuts were not reported in Sri Lanka. Dikiri nuts were collected from these palms and the extracted embryos were in vitro raised, acclimatized, and field planted in two experimental plots in research centers for conservation. Since 2006, a limited number of embryo-culture dikiri plants have been distributed among interested growers for domestic use. In Sri Lanka, dikiri kernel is consumed as fresh with sugar, treacle, or jaggary. Since the nuts are scarce, these nuts are sold at higher prices (approximately USD$ 2−3 ), mostly three times higher price as the price of a normal nut. However no value-added products are available in the market due to the scarcity of nuts[14].

      To propagate true-to-type plants dikiri is propagated through embryo culture technology (Fig. 3). The embryo of the dikiri nuts is excised from the kernel and cultured aseptically in solid (with 0.3% gelrite) Y3 nutrient medium[48] supplemented with activated charcoal (0.1%) and sucrose (6%) for germination. The germination percentage is above 70%. The germinated plants with roots are transferred to Y3 liquid medium. Sometimes rooting hormones (naphthaleneacetic acid, 200 μM) are supplemented to acquire well developed root system. In vitro raised plantlets with 2−3 leaves and a good root system are transferred to the potting media (sand : coir pith : compost : paddy husk charcoal, 1:1:1:1) after 6−8 months of culture initiation. After the hardening process, the plants are ready for field planting. The Coconut Research Institute of Sri Lanka collects the dikiri nuts from the fields established at research centers and raises the plants in vitro at the Tissue Culture Laboratory. An acclimatized poly-bagged plant is sold at about USD$ 3.0 for interested growers.

    • In Thailand, the makapuno coconut, also known as 'maphrao kathi', stands out with its unique soft and fluffy meat, earning it favor among consumers and a premium price in the market due to its rarity. The makapuno market in Thailand features two primary varieties: the landrace makapuno and the aromatic makapuno[25]. The landrace type is characterized by its coconut oil-like aroma, large fruit size, and tall plant height, with a harvesting cycle of approximately six years. Conversely, the aromatic makapuno emits a delightful pandan scent, matures faster at around three years, and can be harvested at 7−8 months for consumption as young aromatic coconuts or, if delayed, as aromatic makapuno at 12 months.

      Makapuno cultivation is widespread across various provinces of Thailand, particularly in central regions like Samut Songkhram and Prachuap Khiri Khan, as well as southern regions such as Chumphon, Surat Thani, and Nakhon Si Thammarat. In Thai cuisine, makapuno is a prized ingredient in traditional desserts, thanks to its creamy texture and flavor. Moreover, its frozen flesh is popularly used in ice creams, enriching the consumer experience. Due to its rarity and unique characteristics, makapuno commands a significantly higher price compared to regular coconuts, sometimes reaching up to 50 times more expensive[49]. This has led to a growing consumer interest and increased demand, with local farmers selling makapuno at prices ranging from USD$ 1.7 to 2.8 , while online platforms facilitate access at prices between USD$ 3.7 and 8.4 per fruit.

      For farmers, cultivating makapuno yields substantial profits, with estimated returns per hectare surpassing those from aromatic coconuts and normal coconuts; an estimated USD$ 26,430 ha−1 per year from makapuno, surpassing returns from aromatic coconuts (USD$ 12,334 ha−1 per year) and normal coconuts (USD$ 1,409 ha−1 per year). However, the propagation of makapuno seedlings pose challenges, requiring the use of tissue culture techniques known as 'embryo rescue' (Fig. 4) due to their inability to grow naturally[50]. Although the demand for makapuno is on the rise, there is a limited supply of seedlings, resulting in significantly higher prices compared to normal coconut seedlings. Consequently, makapuno seedlings produced by tissue culture can fetch even higher prices in the market, reflecting the ongoing prominence and profitability of makapuno cultivation. Notably, the distribution of makapuno seedlings are predominantly through private companies using popular social platforms such as Facebook, TikTok, Shopee, and Lazada. The increasing demand and accessibility contribute to the ongoing prominence of makapuno in the market.

      Figure 4. 

      Makapuno coconuts and the products derived from them. (a) Freshly cracked makapuno coconut, (b) packed makapuno flesh from Thailand, (c) makapuno sweetmeat from Vietnam, (d) jelly made from makapuno liquid endosperm, (e) homemade makapuno ice cream, (f) kopyor cake from Indonesia, (g) frozen kopyor endosperm from Indonesia.

    • In the dynamic landscape of the Vietnamese agriculture, an unusual coconut variety with a distinct type of endosperm, resembling the renowned makapuno variety in the Philippines, was discovered in Tra Vinh province in the early 1900s and garnered significant attention for scientific research and agronomic cultivation. Albeit its morphological and physiological similarities to other native tall coconuts, this exceptional coconut, thriving specific soil and climate conditions in the region exhibits unique traits with exceptionally thick, spongy, butter-like endosperm which occupies the majority of space within the nut while remaining only a small amount of glutinous coconut water with a slight fragrance. Due to its distinctive properties, this novel variety is commonly referred to as 'Dừa Sáp' (wax coconut), 'Dừa Kem' (cream coconut), or the 'Vietnamese Makapuno'[51,52].

      For years, Vietnamese makapuno coconut has become a famous specialty of Tra Vinh province with significantly beneficial values. Its unique, butter-like, and highly nutritious endosperm is widely preferred for direct consumption, preparation of tasteful desserts and beverages, as well as the production of numerous processed products such as makapuno jams, sweetmeats, candies, nutrition bars, packed yogurts, and ice cream. These products have been strategically positioned in nationwide supermarkets, e-commerce platforms, and have even been exported to global markets[53,54]. Furthermore, makapuno coconut serves as the key ingredient for the production of low-calorie cream powder and purified galactomannan which contribute significant values in the cosmetic and pharmaceutical industries[55]. Additionally, recent studies have also discovered the potential of biodiesel production from makapuno coconuts, suggesting a promising approach for renewable energy development in the Mekong Delta[56].

      Under high demand of fruit production, makapuno coconut is highly promoted for cultivation in Vietnam. Due to gemination failure in natural conditions, the key approach for seedling production of this elite variety is through embryo culture (Fig. 4). Since the early 2000's, the Institute for Oil and Oil Plants (Vietnam Ministry of Industry and Trade) and Tra Vinh University have proceeded as the leading institutes for researching and supplying embryo-cultured makapuno coconuts for planting purposes[57,58]. To date, seedling production via this technology has been widely expanded to industrial scale by various agricultural companies that are capable of producing 10,000–20,000 seedlings annually for each facility with an average price of USD$ 25–30 per seedling[58,59]. By employing embryo-cultured seedlings and providing technical training for farmers, makapuno cultivation has been efficiently established across more than 722 hectares in Tra Vinh province with approximately 75,000 individuals and an overall yield of 3.3 million fruits per year[51,58].

      Furthermore another approach for coconut cloning, via somatic embryogenesis, has recently been developed in Vietnam[6062]. This advanced technique enables the propagation of makapuno seedlings with a superior multiplication rate compared to embryo culture. As this innovative technology is transitioning to commercialization, the mass production of elite makapuno seedlings could be notably facilitated with outstanding efficiency and significantly lower prices in the near future, providing a large potential supply of high-quality planting materials for the cultivation of Vietnamese makapuno coconuts.

      Driven by substantial consumption potential, the price of makapuno coconuts in Vietnam have experienced a steady increase, reaching up to USD$ 8–10 per fruit (roughly 10–20 times higher than regular coconuts) and can even surge further during tourist months or festival seasons[51,63,64]. As a result, converting to makapuno cultivation can elevate farmers' annual profit to USD$ 12,000 ha−1 with conventionally propagated seedlings, and even up to USD$ 42,000 ha−1 with tissue cultured materials. This represents a superior increase of 5 to 18 fold compared to other coconut varieties, which typically yield an average of USD$ 2,300 ha−1[64]. With these outstanding results, Vietnamese makapuno coconut now serves as the primary income for numerous households, underscoring undoubtful benefits with great developing potential for not only Tra Vinh province but also the Vietnam national agriculture and economics.

    • Interestingly, makapuno coconuts cannot germinate in nature due to their special structure (as illustrated in Fig. 3). To propagate makapuno, scientists devised coconut embryo culture technology. The main steps of coconut embryo culture are illustrated in Fig. 4. Serving as the most crucial, if not the sole, method for cultivating makapuno, coconut embryo culture technology has undergone significant development over the past few decades[13]. The recalcitrance of coconut to in vitro manipulations is well-known. To overcome that, various plant growth regulators (PGRs) could influence the initial in vitro response of coconut embryos in Y3 media[65]. Various embryo culture systems for coconut propagation have been documented. The journey towards successfully isolating and culturing coconut embryos began with Cutter & Wilson in 1954[66], and it took another decade for Guzman & Rosario to achieve successful plant regeneration from makapuno embryos[67]. Subsequent modifications were made by Philippines Coconut Authority (PCA), Coconut Research Institute of Sri Lanka (CRISL), the University of Queensland (UQ) and other industry organizations such as the Food and Agriculture Organization (FAO) of the United Nations, the International Plant Genetic Resources Institute (IPGRI), the Australian Centre for International Agricultural Research (ACIAR), and Bioversity International[68]. Towards the conclusion of the previous century, embryo culture technology had evolved into a method facilitating the transfer of germplasm among various research laboratories, though its adoption within the industry was not commonplace. Leveraging this well-established technology allows for the collection, preservation, and sterile transportation of valuable and distinctive germplasm, which can subsequently be cultured in any laboratory worldwide[69].

      Products from elite coconut are becoming more popular and valuable, which could help coconut farmers and the industry[13]. Special types of coconuts that taste good and smell nice are in high demand and can be sold for much more than regular coconuts. A technique called embryo culture is used to grow these special coconuts, and scientists are working on improving this process[68]. Scientists successfully cultured makapuno embryos in the 1960s using a special nutrient mixture[70]. Their efforts bore fruit two years later when De Guzman and Del Rosario utilized a basal medium developed[67], using medium compositions described by White[71] and Nitsch[72]. These studies demonstrated favorable shoot development in the medium, albeit with limited root growth[73]. Subsequent advancements have been achieved since the 1960s, including enhancements in surface sterilization techniques[74], consideration of embryo age during culture[75], and the implementation of additional methods to foster root growth and seedling adaptation[76].

      Cutter & Wilson were the pioneers in documenting the process of coconut embryo culture[66]. The embryo culture technique initially developed for other coconut varieties has proven successful for makapuno[77]. Reports of success extend to similar varieties like 'Dahi Nariyel' in the Andaman Islands (India) and Kopyor in Indonesia[78]. In the latter case, excised embryos were first cultured on White's basal medium to encourage germination[79]. An embryo culture procedure typically involves four main stages: (i) initiating embryo germination; (ii) inducing shoot formation; (iii) promoting root development; and (iv) facilitating acclimatization[13]. The commonly followed procedure involves extracting the embryo from a small portion of solid endosperm, often referred to as a cylindrical plug or core, using a cork borer[80]. Research comparing different embryo ages has determined that the 11-month-old makapuno embryo is most suitable for in vitro growth[75]. Surface sterilization typically consists of two stages. Initially, the cylindrical plug undergoes surface sterilization in the field using a concentrated commercial bleach solution (approximately 5% sodium hypochlorite), followed by triple rinsing in sterile water. After transportation to the laboratory and placement in a laminar flow cabinet, the embryos are isolated and subjected to surface sterilization using a milder bleach solution (ca. 0.5% sodium hypochlorite) and 70% ethanol, followed by at least three rinses with sterile water[13]. Although various basal media formulations, such as those by White[71], Nitsch[72], and Murashige & Skoog[81], have been experimented with, the Y3 medium by Eeuwens[48] has emerged as the most advantageous and is now widely utilized for makapuno embryo culture[74]. This medium offers a higher concentration of micro-elements essential for tissue growth, including cobalt, copper, and notably iodine. Optimal germination and the attainment of a desirable shoot-to-root ratio have been linked to a high sucrose level (6%)[82]. To prevent tissue browning, activated charcoal (0.10%−0.25% w/v) is commonly incorporated into the medium[83,84]. Furthermore, studies have indicated that the addition of gibberellic acid to the liquid medium can enhance germination rate[82]. Autoclaved coconut water and high concentrations of indole acetic acid (IAA) and other IAA-like plant growth regulators[85], such as naphthalene acetic acid (NAA) and indole-3-butyric acid (IBA), have been found to significantly enhance root formation[86,87].

      Recently, a novel method has emerged involving the longitudinal division of the kopyor embryo, followed by culturing both halves in a medium supplemented with plant growth regulators. This approach has shown success in generating two kopyor plantlets in most cases[43]. However, its effectiveness hinges greatly on precise embryo cutting, making it unsuitable for large-scale plantlet production. Additionally, an innovative embryo transplantation technique has been proposed, wherein a zygotic embryo from a non-germinable fruit is aseptically transferred to a viable fruit[88]. This alternative to embryo culture allows for the nurturing of non-viable fruits without the need for artificial support, resulting in a considerably more cost-effective outcome[13]. Automation technology has been employed in coconut embryo culture techniques and has yielded excellent results, enhancing the efficiency of embryo culture and seedling propagation and acclimatization[89].

    • China's economy has been growing at a rapid pace in recent years. This has led to an increase in disposable income for many Chinese consumers[90]. As a result, they are now more willing to spend money on premium products. China's fruit production accounts for 29.16% of the world's total, but the average per capita consumption of fruit in China is less than 50 kg, which is still significantly lower than the average of 80 kg or more in developed countries[91]. In recent years, with the change of consumption habits of urban residents, especially those in large and medium-sized cities, are increasingly consuming imported fruits[91]. More and more high-quality imported fruits are pouring into the Chinese market. Blueberries, strawberries, and raspberries, representing small berries, clementines representing citrus fruits, and apples representing temperate fruits, have become the representatives of high-end fruits in China[92]. China's rapid economic growth and the growing demand for green organic fruits have led to a sustained increase in the scale of China's sustainable agriculture[93]. China has invested heavily in the development and utilization of fruit germplasm resources. The protection and utilization technology of tropical economic crop germplasm resources has also reached the world-leading level, among which tropical fruits lychee, longan, and loquat are all ranked first in the world[94].

      Coconut products, especially high-value items like coconut oil, have become true luxury goods in China, enjoying popularity among the middle class[95]. Coconut products are a popular choice for Chinese consumers, as an entirely new concept to Chinese enthusiasts, and have entered the coconut market in China (as shown in Fig. 4). Coconut is a healthy and nutritious food source, and they are also considered to be a medicine for certain infections and diseases[96]. China is now the world's largest importer of coconut products[97], and the growth of China's coconut industry is having a positive impact on the economy of many coconut-producing countries. This trend is expected to continue to grow in the coming years[98,99]. Thus, coconut producers are seeing increased sales and profits. As per data provided by China customs, the period from January to July 2022 saw a notable rise in coconut imports to China, totaling 566,000 tonnes. This marked a significant increase of 35.3% when compared to the corresponding period in 2021. Indonesia, Thailand, Vietnam, Philippines, and Malaysia, being the key origins of China's coconut imports in 2022. Indonesia contributes over 40% of China's annual coconut imports (ca. 17.16 million metric tons)[100]. The major contributors to China's coconut supply were primarily Thailand, Indonesia, and Vietnam, constituting 48.6%, 32.5%, and 18.4%, respectively[101]. Indonesia contributes to almost 30% of the global coconut production and serves as a significant provider of raw materials for Chinese coconut processing industry[102]. Vietnamese coconut growers and industries are enthusiastic about scaling up production to meet the rising domestic and international demand. The director of Mekong Fruit Import-Export Ltd. Co. in Ben Tre highlighted that China stands as the world's largest coconut importer, importing approximately thousands of containers of fresh coconuts annually from Vietnam[103]. According to the 2020 China Coconut Oil Import Source Countries Statistics from the Philippine Embassy in Beijing, the Philippines is the second-largest provider of coconuts to China, holding a 27% share of the overall market[104].

      The development of the coconut industry is encouraged at both the national and provincial levels in China. As an important oil crop, the central government of China places great emphasis on the development of the coconut industry[105]. The Hainan provincial government is encouraging farmers to plant more coconuts and develop coconut forest tourism. This will help to increase farmers' income by selling coconuts and generating tourism revenue. The government is providing financial incentives to farmers who plant coconuts, and it is also working to promote coconut forest tourism. This includes developing infrastructure for the coconut industry, such as roads, trails, and restaurants, and promoting the region's coconut culture[106]. Given the favorable climate, soil conditions, and diverse resources in Hainan, coconut cultivation is well-suited to the region. Despite the imbalance in coconut processing and planting sectors, the Chinese coconut industry is currently presented with significant opportunities arising from the establishment of the Hainan free trade zone and the implementation of the national rural revitalization strategy[107]. As home to 99% of China's coconut farms, Hainan Island of China boasts a 2000-year legacy of cultivating this versatile palm. It also takes center stage in China's burgeoning coconut industry; an annual production value has reached CNY 20 billion in 2022[108]. With an annual coconut yield of 56.3 billion across 12 million hectares, the potential for coconut-based products is vast, drawing stakeholders together to harness this tropical treasure's economic and health benefits[109]. Despite producing 99% of China's coconuts, Hainan Island's booming coconut processing industry relies heavily on imported raw materials (CNY 20 billion coconut annually)[110]. Recognizing this, the Chinese Academy of Tropical Agricultural Sciences (CATAS) held the 2023 China (Hainan) International Coconut Industry Forum and mobilized local stakeholders to promote integrated development and reduce reliance on imports[111].

      The explosive growth of the coconut market in China became one of the hot topics of news in 2023. The skyrocketing prices of coconut water catalyzed the booming Chinese coconut market[85]. Previously relegated to a processing byproduct, coconut water surged through China's summer beverage market, achieving explosive popularity and transitioning from obscurity to a coveted blockbuster[112]. After coconut water became a hit in the European and American markets, some coconut brands tried to enter the Chinese market, but all failed[113]. Consumers, increasingly attentive to ingredient provenance and natural attributes embraced coconut water's inherent goodness: its refreshing taste, natural appeal, and rich vitamins and potassium content, solidifying its image as a health beverage leader[114]. A remarkable 92.5% (37 out of 40) of China's top new beverage brands incorporated coconut-based drinks into their portfolios, which led to a staggering 66% year-on-year increase in the number of coconut-themed beverage stores in 2022[115]. Beyond coconut water, the coconut's versatility thrives with coconut milk, dense coconut cream, powdered forms, functional blends, and coconut-infused fruit and vegetable juices, solidifying its dominance as a top ingredient in 40 Chinese freshly made beverage brands, according to the Report on Beverage Industry of China 2022[115]. Because macapuno is not legally imported from China, only a small amount of macapuno is currently circulating in the Chinese market, which leads to a very high price in China, as shown in Fig. 5. This rapid proliferation underscores the immense potential of coconut within China's dynamic beverage market and suggests a sustained consumer appetite for its health and taste attributes[116].

      Figure 5. 

      The price of makapuno in different Asian countries.

    • The main challenges facing the development of the makapuno industry in China are twofold: technological obstacle (including embryo culture and cultivation techniques) and sourcing of germplasm resources. It has been demonstrated that embryo culture is the cornerstone coconut tissue culture technique due to its many applications (propagation, conservation, and genetic transformation). In China, this technology has already been successfully applied by the Chinese Academy of Tropical Agricultural Sciences (CATAS)[117] and Hainan University[89]. Although the technique has been widely documented, embryo culture is being underused for germplasm movement and conservation. In addition, it has been found that most protocols have room for improvement. Key enhancements which are needed include ensuring consistently low contamination rates and high survival rates for a wide range of genotypes, in several laboratories. Protocols for in vitro steps during coconut seedling production using embryo culture technique have been established successfully with a high success rate for the conversion of embryos to plantlets. Losses are still particularly high during acclimatization steps and when plants are transferred from tissue culture facilities into field conditions. The ex-vitro steps especially during the transfer of in vitro plantlets into the nursery remain a major bottleneck for most laboratories[76,118,119].

      The rooting step is the most crucial among these processes, bearing significant importance for the survival of embryo-cultured seedlings[87]. Several approaches have been applied to improve in vitro rooting process such as increasing sucrose content in the medium[120], exposure for plant growth regulators especially IBA[121], adding polyethylene glycol into culture medium[122] or by removing haustorium during culture process[123]. However, the success rate during in vitro rooting remains low. Moreover, several protocols have also been developed during the acclimatization process, i.e. increasing humidity around the seedlings by using a plastic bag cover or humidity tent[41], using a wooden box chamber[122] or increasing the photosynthetic level by pumping carbon dioxide into the box chamber in a photoautotrophic system[76]. However, the success rate after acclimatization during those approaches remains low. Ex vitro rooting, a more efficient protocol by smart combining of in vitro rooting and acclimatization, then showed a better result on the conversion of in vitro plantlets into the nursery[43]. The last method is quite simple, seedlings with two or three opened leaves (about 6–8 months old) were taken from an in vitro system, washed gently with tap water then dipped into 2% fungicide for 20 min, then planted into small plastic pots containing a combined cocopeat and rice charcoal medium. The pots were then put into a glass box (70 cm × 40 cm × 40 cm), flooded with 12 L macro- and micro-nutrient liquid medium added with 1 μM IBA then closed the lid tightly for about a month. To maintain the air exchange and relative humidity inside the box, the medium was aerated using an air pump. A month later the lid was opened gradually every week to decrease the relative humidity until fully opened in the following months. The seedlings were kept in the glass box for 3 months before transferring the seedlings into bigger pots and growing in the nursery for another 6 months. This method was efficient in producing kopyor seedlings to the field (90% success rate), with less contamination risk and lower labor cost[43]. This method has been successfully applied in several coconut in vitro laboratories in Indonesia, but its application in other countries may not result in the same success rate of ex vitro transplanted seedlings. Several climate factors such as humidity and light intensity and also the vigor of plantlets produced from in vitro steps have to be considered aside from this ex-vitro rooting protocol.

      Coconut biotechnology, such as embryo culture, is anticipated to offer significant assistance in the identification and development of novel coconut genotypes like makapuno[124]. Further refinement is necessary to fully utilize the potential of embryo culture, innovative approaches like marker-assisted breeding should be explored. The recent increase in genetic information for marker gene identification enables more efficient molecular screening and identification of candidate cultivars compared to the past[125]. Hybridization of recognized coconut varieties is currently underway to enhance fruit production and harvest index. In addition to increasing the production of improved planting materials, meticulous quality control measures for products are warranted. The limitation of market reach due to the shelf life of these fruit types remains a challenge, impeding the full realization of their market potential. Enhancements in pertinent food processing techniques will further facilitate the access of these uniquely captivating coconut varieties to markets beyond tropical regions[126].

      Reports on makapuno from China are scarce, thus China is considered to be severely lacking in makapuno germplasm resources. Thus, major coconut producers near China may be strong competitors in the makapuno industry, for instance, Thailand, the Philippines, Vietnam, and Indonesia[127]. The coconut market in China also faces competition from imported products from other countries, which may exert pressure on domestic producers. Finally, with increasing attention to sustainable development and environmental protection, the coconut industry in China may need to address challenges related to land use, water resource management, and ecological conservation. In addition, climate conditions that coconuts typically thrive in tropical or subtropical regions, and certain parts of China may not have sufficiently suitable climates, limiting the range for coconut cultivation and distribution. Hainan island is the southmost province in China, and it accounts for 99% of coconut cropping area in China[128]. However, only 42.5% land area of this island are tropical and subtropical land. The population of the province is predominantly rural, at around 68.5% of the total population. The annual average temperature is recorded as 23−25 °C, with mean annual rainfall of above 1,600 mm[129]. Coconuts require specific types of soil and land conditions for healthy growth, and some areas in China may lack these conditions. Furthermore, yield and quality control must be considered. Coconut cultivation in China may be affected by factors such as farming techniques, management practices, and quality control measures, potentially impacting market competitiveness and product quality.

      As with other plantation crops, it is recommended to use good agriculture practices (GAP) to enhance the yield and quality of makapuno. GAP on makapuno (kopyor) farming mostly include site selection and management, sourcing and selecting of plant materials, farm establishment, farm maintenance, harvesting and post-harvest handling, and waste management. Makapuno farming should be in areas suitable for food production and market. For optimum growth, it is also preferable that makapuno farming is better within an altitude of not more than 600 m above sea level. The types of makapuno varieties to be grown should also be selected based on the market requirements, adaptability to local conditions, and farmer preferences. Several makapuno varieties (e.g. kopyor) have been known as water-sensitive varieties while other varieties have been known as fertilizer-sensitive varieties. Makapuno market has its own requirements. Indeed, the selection of the correct variety with superior quality become key factors for the success of makapuno farming. As makapuno has been known as a recessive trait, the use of true-to-type makapuno seedlings coming from embryo culture or other vegetative tissue culture approaches as planting materials becomes the main key for the success of makapuno farming. Mother palms used for embryo culture should be selected from the homogenous bearing kopyor areas with average of at least 10 nuts per bunch for the tall varieties and 15 nuts per bunch for the dwarf varieties.

      Farm establishment is another important factor for optimum growth for makapuno, including land preparation, proper field layout and planting of either square or triangular systems with recommended distance between palms being about 8 to 10 m. Farm maintenance including soil conservation such as minimum tillage, contour planting, cover cropping, fertilizer management, and water management are important growth factors. The use of fertilizer materials at different stages of growth such as nitrogen, phosphorus, and potassium are highly recommended for optimum growth and production of nuts. In some areas of Indonesia, Oryctes rhinoceros and red weevil (Rhincoporus) remain the main insect pest for makapuno farming. Integrated pest management including various strategies to control insect pests, mites, pathogens, and rodents are also important in maintaining makapuno farming. Makapuno fruits should be harvested at a certain time, mostly 10−11 months after fertilization. During this time, the solid endosperm is at the optimum stage, the taste is also fresh. The younger fruits will give a better taste, but less solid endosperm can be harvested, meanwhile the older fruits will give non-consumable endosperm. Similar results will also be found for longer fruit storage, more than 3 weeks storage. Furthermore, time of harvesting and postharvest handling are also important to enhance the quality of makapuno fruits. As the expertise of the tissue culture practitioner influences the success of embryo culture, the key to further coconut embryo culture development and widespread adoption is likely to involve the adequate training and perpetual support of genebank staff in the use of the technique[68]. These valuable experiences from coconut-producing countries will have a positive impact on the localization of makapuno in China.

      Furthermore, as sequencing technology has advanced, various omics techniques have emerged, enabling scientists to delve deeply into the genetic foundations and regulatory mechanisms behind unique plant phenotypes. In the case of makpunuo, which displays distinct characteristics from regular coconuts, employing a range of omics approaches such as genomics, metabolomics, transcriptomics, variant omics, epigenomics, etc., can reveal the metabolite composition and levels in makpunuo's special coconut meat and water. This analysis can also pinpoint the genes responsible for these compounds and identify crucial mutation sites. Subsequently, the development of molecular markers linked to beneficial allelic variations will facilitate the cultivation of more flavorful and favorable makpunuo coconuts, supported by data and theoretical insights.

    • Makapuno coconut stands out as one of the most economically valuable coconut varieties globally. Currently, this special type of coconut has great popularity worldwide. Amidst China's rapid economic development, there exists a growing demand for high-end coconut varieties like makapuno. Despite its significance, the makapuno industry in China has yet to gain substantial traction. Leveraging biotechnological methods such as embryo culture presents a promising avenue to establish a thriving makapuno coconut industry in China. By harnessing the potential of biotechnology (embryo culture, cryopreservation, and micropropagation), we can overcome existing challenges and capitalize on the opportunities presented by the burgeoning market for elite coconut varieties. With concerted efforts and strategic investments, China can position itself as a significant player in the makapuno coconut industry, contributing to both its economic growth and agricultural diversification.

    • The authors confirm contribution to the paper as follows: study designing and writing: Mu Z, Yang Z, Xu H (contributed equally); draft manuscript preparation: Khongmaluan M, Tran B, Vidhanaarachchi V, Sisunandar S; figure and table modification: Xu H, Yang Z, Xia W, Yang S; manuscript review and editing: Xia W, Luo J, Mu Z. All authors reviewed and approved the final version of the manuscript.

    • All data used in this review paper were derived from the Hainan University institutional repository, the Kasetsart University institutional repository, the Coconut Research Institute of Sri Lanka institutional repository, the University of Muhammadiyah Purwokerto institutional repository and the Vietnam National University of Ho Chi Minh City institutional repository. All data were freely available and accessible without restrictions.

      • This research was supported by Hainan Seed Industry Laboratory and sponsored by the Hainan Province Science, Hainan Yazhou Bay Seed Lab. (JBGS + B21HJ0903), Technology and Innovation Project for Talent (KJRC2023L09 and KJRC2023D16), Postdoctoral Research Funding Program of Hainan Province (No. B23B10015), Science and Technology Innovation Special Project of Sanya City (2022KJCX53), PhD Scientific Research and Innovation Foundation of Sanya Yazhou Bay Science and Technology City (HSPHDSRF-2023-12-002) and '111' Project (No. D20024).

      • The authors declare that they have no conflict of interest. Jie Luo and Siwaret Arikit are the Editorial Board members of Tropical Plants who were 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 these Editorial Board members and the research groups.

      • Received 6 May 2024; Accepted 1 July 2024; Published online 4 September 2024

      • # Authors contributed equally: Zhihua Mu, Zhuang Yang, Hang Xu

      • Copyright: © 2024 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)  References (129)
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    Mu Z, Yang Z, Xu H, Khongmaluan M, Arikit S, et al. 2024. Prospects and challenges of elite coconut varieties in China: a case study of makapuno. Tropical Plants 3: e029 doi: 10.48130/tp-0024-0028
    Mu Z, Yang Z, Xu H, Khongmaluan M, Arikit S, et al. 2024. Prospects and challenges of elite coconut varieties in China: a case study of makapuno. Tropical Plants 3: e029 doi: 10.48130/tp-0024-0028

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