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2023 Volume 3
Article Contents
Abstract
Introduction
Materials and methods
Plant materials and growth conditions
Experimental treatments and design
Measurements
Soil water content and plant growth and physiological traits
Hyperspectral imaging of phenotypic responses to drought stress
Multispectral imaging of phenotypic responses to drought stress
Chlorophyll fluorescence imaging of physiological response to drought stress
Statistical analysis
Results
Changes in soil water content, leaf relative water content, and visual turf quality during drought stress
Hyperspectral and multispectral imaging of phenotypic responses to drought stress
Chlorophyll fluorescence imaging of photosynthetic responses to drought stress
Correlation among individual imaging indices associated with phenotypic and photosynthetic responses to drought stress
Prediction of turf quality and leaf relative water content using integrated indices from hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence
Discussion
Conclusions
Acknowledgments
Conflict of interest
Supplementary information
Rights and permissions
References
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ARTICLE   Open Access    

Effects of long-term irrigation on soil phosphorus fractions and microbial communities in Populus euphratica plantations

  • 1.

    School of Forestry & Landscape Architecture, Anhui Agricultural University, Hefei 230036, China

  • 2.

    Teaching Center, Zhejiang Open University, Hangzhou 310012, China

More Information
  • Irrigation has been demonstrated to be effective in managing Populus euphratica plantations, but its impacts on phosphorus (P) availability and the soil microbiome have not been fully elucidated. In this study, we compared soil properties, P fractions, phosphatase activities, and microbial communities in the surface soil (0–20 cm) of P. euphratica plantations under both drought and irrigation conditions. We found that total P, labile P and moderately labile P all increased significantly under irrigation by 12.3%, 70.1%, and 3.0%, respectively. The increased levels of labile P were primarily driven by higher levels of NaHCO3-Pi, which increased from 1.9 to 12.3 mg·kg−1. Furthermore, irrigation markedly altered labile P composition and the relative levels of resin P, NaHCO3-Pi, and NaHCO3-Po were all impacted. Improved soil moisture increased soil phosphatase activity, suggesting that soil organic P (Po) mineralization was positively affected by irrigation. Moreover, we observed that bacterial diversity, fungal diversity, and alkaline phosphatase gene communities, rather than total microbial biomass carbon or total phospholipid fatty acids, were most explained in the dynamics of soil P fractions. Furthermore, we found positive correlations among inorganic P (Pi) and Bradyrhizobiaceae, Nocardiaceae, and Sphingomonadaceae, whereas negative correlations were found between Burkholderiaceae and Pi, highlighting the diverse functional bacteria involved in P cycling. Our study demonstrates that irrigation can increase soil P availability and supply capacity, with shifts in P composition closely linked to changes in soil microbial characteristics. Water management strategies that target the restoration of soil microbial communities may therefore improve soil quality and enhance soil P cycling.

  • Introduction

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

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

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

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

    Materials and methods

    Plant materials and growth conditions

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

    Experimental treatments and design

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

    Measurements

    Soil water content and plant growth and physiological traits

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

    Hyperspectral imaging of phenotypic responses to drought stress

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

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

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

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

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

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

    Statistical analysis

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

    Results

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

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

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

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

    Hyperspectral and multispectral imaging of phenotypic responses to drought stress

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

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

    Chlorophyll fluorescence imaging of photosynthetic responses to drought stress

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Conclusions

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

    Acknowledgments

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

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

  • Supplemental Material S1 Soil DNA extraction, high-throughput sequencing.
    Supplemental Fig. S1 Redundancy analysis of soil P fractions impacted by soil properties.
    Supplemental Fig. S2 Comparisons of alpha and beta diversity of alkaline phosphatase gene communities under different water management treatments.
    Supplemental Fig. S3 Relative abundance of dominant alkaline phosphatase gene communities (phoD and phoX) at the family and genus level under different water management treatments.
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  • Cite this article

    He Y, Lin X, Wang L, Ma X, Fang L, et al. 2023. Effects of long-term irrigation on soil phosphorus fractions and microbial communities in Populus euphratica plantations. Forestry Research 3:17 doi: 10.48130/FR-2023-0017
    He Y, Lin X, Wang L, Ma X, Fang L, et al. 2023. Effects of long-term irrigation on soil phosphorus fractions and microbial communities in Populus euphratica plantations. Forestry Research 3:17 doi: 10.48130/FR-2023-0017
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Effects of long-term irrigation on soil phosphorus fractions and microbial communities in Populus euphratica plantations

  • 1.

    School of Forestry & Landscape Architecture, Anhui Agricultural University, Hefei 230036, China

  • 2.

    Teaching Center, Zhejiang Open University, Hangzhou 310012, China

  • Received: 03 May 2023
  • Accepted: 10 July 2023
  • Published online: 26 July 2023
Forestry Research  3,  Article number: 17  (2023)  |  Cite this article

Abstract: Irrigation has been demonstrated to be effective in managing Populus euphratica plantations, but its impacts on phosphorus (P) availability and the soil microbiome have not been fully elucidated. In this study, we compared soil properties, P fractions, phosphatase activities, and microbial communities in the surface soil (0–20 cm) of P. euphratica plantations under both drought and irrigation conditions. We found that total P, labile P and moderately labile P all increased significantly under irrigation by 12.3%, 70.1%, and 3.0%, respectively. The increased levels of labile P were primarily driven by higher levels of NaHCO3-Pi, which increased from 1.9 to 12.3 mg·kg−1. Furthermore, irrigation markedly altered labile P composition and the relative levels of resin P, NaHCO3-Pi, and NaHCO3-Po were all impacted. Improved soil moisture increased soil phosphatase activity, suggesting that soil organic P (Po) mineralization was positively affected by irrigation. Moreover, we observed that bacterial diversity, fungal diversity, and alkaline phosphatase gene communities, rather than total microbial biomass carbon or total phospholipid fatty acids, were most explained in the dynamics of soil P fractions. Furthermore, we found positive correlations among inorganic P (Pi) and Bradyrhizobiaceae, Nocardiaceae, and Sphingomonadaceae, whereas negative correlations were found between Burkholderiaceae and Pi, highlighting the diverse functional bacteria involved in P cycling. Our study demonstrates that irrigation can increase soil P availability and supply capacity, with shifts in P composition closely linked to changes in soil microbial characteristics. Water management strategies that target the restoration of soil microbial communities may therefore improve soil quality and enhance soil P cycling.

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    Introduction

    • Ecologically vulnerable regions are more susceptible to the impacts of global climate change[1]. Extreme droughts are likely to be more frequent in arid regions[2], and soil moisture is a critical limiting factor for many ecosystems. Soil properties and microbiomes are detrimentally affected by drought, leading to changes in pH levels, organic matter content, microbial diversity, and nutrient dynamics, with the essential macronutrient phosphorus (P) often seriously impacted[3−6]. P enters the soil via weathering of P-bearing primary minerals. Following release into the soil, P undergoes complex geochemical and biological transformations[7], resulting in a diversity of coexisting organic and inorganic forms. The turnover rates and bioavailability of these various forms of P to plants and microbes vary significantly in the soil[8]. Despite studies indicating that increasing frequency of droughts will profoundly impact soil P cycling, few studies fully consider soil P cycling in ongoing drought manipulation experiments in forest ecosystems.

      Plants assimilate P from the soil via their root systems. Root exudates, such as organic acids and phosphatase enzymes, facilitate the solubilization and mobilization of soil P[9], and root architecture significantly influences P uptake efficiency[9]. Organic P (Po) in the soil is predominantly mineralized by plant roots and soil microorganisms, which produce a variety of enzymes that impact this process, primarily extracellular acid phosphatases (ACPs) and alkaline phosphatases (ALPs)[10]. While plant roots primarily produce ACPs, soil microorganisms, particularly bacteria, are the primary producers of ALPs[11]. Soil ACP and ALP activities have been shown to be directly dependent on soil water availability[12]. Drought can alter the composition and function of microbial communities in the soil, thereby influencing soil nutrient cycling[13].

      The relationship between soil P fractions and microbial communities remains unclear and largely depends on which aspects of microbial communities are evaluated. Microbial groups, such as bacteria and fungi, display highly variable capacity to utilize soil P[14]. The abundance, composition, and functional diversity of soil microorganisms can be assessed using microbial biomass (measured through phospholipid fatty acid [PLFA] biomarkers and microbial biomass carbon [MBC]), composition and diversity of taxonomic communities (taxonomic profiles measured using 16S rRNA or ITS genes and their amplicon sequencing or PLFA biomarkers), or potential functions (profiles of functional genes measured using qPCR). Secreted ALP activity is primarily driven by phoD and phoX[10,15]. ALP activity and the prevalence of the phoD gene have also been shown to be directly correlated[16], and the abundance and diversity of the phoD genes are affected by soil pH[17] as well as fertilizer inputs[18]. However, research aimed at explicitly elucidating the association between microbial communities and soil P components is still lacking. Clarifying these relationships will be beneficial to integrating soil microbial processes into soil P cycling models.

      Populus euphratica trees play a crucial role in desert ecosystems but are susceptible to the impacts of global climate change. As a dominant tree species in arid areas, P. euphratica can be used for windbreaking, sand fixation, and soil water conservation[19]. It provides multiple ecosystem services as a natural barrier to the expansion of deserts[20]. Given the projected increase in the frequency and severity of droughts, P. euphratica trees will likely face increasing environmental stresses[9,21−23]. The Chinese government has implemented projects aimed at restoring P. euphratica populations, including groundwater irrigation near the Tarim River, which has successfully facilitated the recovery of P. euphratica[24]. Nevertheless, the impacts of long-term irrigation on soil P cycling processes in P. euphratica plantations, along with the relationships between soil P fractions and soil microorganisms, are currently unknown. In this study, we assessed the impacts of irrigation on soil P pools and the soil microbiome to test three hypotheses: (1) irrigation significantly affects soil P status, particularly labile P fractions; (2) irrigation significantly increases soil phosphatase activity, an outcome that may be correlated with soil properties and microbial changes; (3) the association between P fractions and soil microbes may depend on the metrics used to evaluate microbiome characteristics.

    Materials and methods

      Field sites

    • This study was performed on the northwestern border of the Tarim Basin, located in China's Xinjiang Autonomous Region. The climate in the region is characterized by an annual mean air temperature of 10.8 °C and a mean annual precipitation of 50 mm. The soil type in the area is classified as calcic xerosol.

      A factorial experiment was conducted using two water management conditions, with six replicates each. A total of 12 plots, each measuring 15 m × 10 m, were selected along the upper reaches of the Tarim River (81°17′ E, 40°32′N – 40°81′ N). Populus euphratica trees were planted in 2003 as part of a vegetation restoration project.

      For the initial five years, all plots received irrigation. From 2009 to 2021, half of the plots were randomly selected to be irrigated for half a month in March and April (irrigation treatment), while the other half only received ambient precipitation (drought treatment). Irrigation was maintained for eight hours per day at approximately 50 m3·h−1, and the relative soil moisture content was kept at 90% to a depth of 60 cm during the irrigation period[9].

      Soil sampling

    • Six 2 m × 2 m sub-plots were randomly selected for each water treatment to ensure representative sampling. In mid-August 2021, three samples were collected from the top 20 cm of soil in each plot and combined to form six composite samples. The composite samples were placed in sterile sealed bags and kept on ice during transportation to the laboratory for processing. Subsequently, the samples were thoroughly mixed and passed through a 2-mm sieve. Portions of each fresh soil sample were stored at 4 °C and −80 °C for further analysis, while the remaining portion was air-dried and divided into an archival sample and a sample used to determine soil properties and P fractions.

      Soil properties

    • Soil pH was determined using a pH meter, with a soil-to-CaCl2 solution ratio of 1:2.5 (v/v), following standard protocols. Soil organic carbon (SOC) was quantified using the electric sand bath potassium dichromate titration method, as described by Bremner and Jenkinson[25]. Total nitrogen (TN) content was measured using the micro-Kjeldahl method. NH4+-N and NO3−-N concentrations were extracted from the soil using 1M KCl at a ratio of 1:5 and analyzed using a Continuous-Flow Auto Analyzer (Bran+Luebbe AA3, Germany). Exchangeable K+, Na+, Ca2+, and Mg2+ were extracted using acetamide and analyzed using an inductively coupled plasma emission spectrometer (ICP).

      Soil P fractions

    • Soil P was fractionated using the continuous extraction method originally developed by Hedley et al.[26] and modified by Tiessen[27]. The following protocol was used to extract P fractions from 0.5 g of air-dried soil deposited in a 50 mL centrifuge tube: (a) each centrifuge tube received two 9 mm × 62 mm resin strips along with 30 mL distilled water, followed by stirring at 160 rpm for 16 h to extract P from the resin strips using 0.5 M HCl (referred to as resin-Pi); (b) following the removal of the aqueous solution, 30 mL of 0.5 M NaHCO3 at pH 8.5 was added and the tubes were shaken for 16 h to extract NaHCO3-P; (c) NaOH-P was extracted by adding 30 mL of 0.1 M NaOH and rotating the tubes for 16 h; (d) 30 mL of 1 M HCl was added to each centrifuge tube, and the tubes were shaken for 16 h to extract 1 M HCl-Pi; (e) 15 mL of concentrated HCl was used to further extract soil residue at 80 °C (conc. HCl-P); (f) P was obtained by boiling the soil residue in 8 mL of concentrated H2SO4 with 10 drops of HClO4, to obtain residual P.

      After extraction, the supernatant was partitioned into two aliquots for Pi and Po determination. The quantification of Pi was carried out using the molybdate-ascorbic acid procedure originally proposed by Murphy & Riley[28]. Total P (TP) was determined by incubating the supernatant with acidified ammonium persulfate at 121 °C for 1 h. TP and Pi were directly measured from the extracts, and Po was calculated by subtracting TP from Pi[29]. To determine soil microbial biomass P (MBP), chloroform fumigation and NaHCO3 extraction were carried out over a 24-h period, as described by Brookes et al.[30]. Separate non-fumigated samples were spiked with 25 mg·L−1 of P to evaluate the efficacy of P recovery during the fumigation process.

      Activities of ACP and ALP

    • ACP and ALP activities in soil samples were determined via the method described by Tabatabai[31]. Briefly, fresh soil samples (1 g) were incubated at pH 6.5 (for ACP) and pH 11.0 (for ALP) at 37 °C for 1 h, using p-nitrophenyl phosphate (pNPP) and disodium phenyl phosphate as substrates. ACP and ALP activities were recorded as mg p-nitrophenol and phenol·kg−1 soil (dry weight)·h−1, respectively.

      Soil microbial characteristics

    • We determined microbial biomass through analysis of MBC and PLFAs. Extraction of MBC was performed by adding 0.5 M K2SO4 to both chloroform-fumigated and unfumigated soil samples, followed by measurement with a computerized total organic carbon analyzer (Analytikjena, Germany). MBC was quantified by calculating the variation in organic carbon extracted between the fumigated and unfumigated soils. PLFAs were obtained from freeze-dried soil (5 g) following the procedure described by Frostegård et al.[32]. PLFAs were further classified into respective microbial functional groups in accordance with the procedure created by Ruess & Chamberlain[33]. Total PLFAs were determined by summing the biomass of all microbial functional groups. The bacterial to fungal ratio (B: F) was calculated by dividing the sum of all bacterial biomarkers by the sum of all fungal biomarkers.

      Soil DNA was extracted from freeze-dried soil samples using the PowerSoil® DNA Isolation Kit (MO BIO Laboratories Inc., Carlsbad, CA, US), following the manufacturer's instructions. Amplifications of bacterial 16S (V4-V5) and fungal ITS rRNA genes, as well as quantitative polymerase chain reaction (qPCR) and amplicon sequencing of phoD and phoX, were performed following the protocols described by Xia et al.[34] and are provided in Supplemental Material S1.

      Statistical analysis

    • T-tests were employed to determine variations in soil properties, P fractions, ACP and ALP, total PLFAs, MBC, MBP, bacterial and fungal Shannon-diversity, copy numbers of phoD and phoX genes, and alpha diversity (richness and Shannon-diversity) of alkaline phosphatase gene communities between the drought and irrigation treatment groups. The Wilcoxon test was used to detect variations in the relative abundance of phoD and phoX bacteria at the family and genus levels between the two treatments. The 'ggvegan' R package was used to conduct a covariance analysis for soil properties and P fractions. To investigate the associations between soil properties and soil P fractions, as well as between soil microbial characteristics and soil P fractions, we employed redundancy analysis (RDA). Additionally, the 'vegan' R package was employed to conduct principal co-ordinates analysis (PCoA) for phoD and phoX community composition and procrustean analysis among the phoD and phoX community with soil P fractions. The correlations between soil P fractions with the phoD and phoX community, as well as MBP, ACP, and ALP, were performed using the 'psych' and 'pheatmap' R packages. All box plots were generated utilizing the 'ggplot2' package in R (https://cran.r-project.org/package=ggplot2).

    Results

      Soil properties, P fractions, and their associations

    • Compared to the drought treatment, irrigation significantly increased several soil physical and chemical attributes, including WC, pH, TN, SOC, NH4+-N, TP, and TPi (Table 1). The labile P fraction represented approximately 1%−3% of TP, while the moderately labile P fraction accounted for 84%−89% and decreased under irrigation (Fig. 1a). The relative content of resin-Pi decreased slightly, whereas the amount of NaHCO3-Pi in the labile P fraction increased significantly with irrigation (Fig. 1b). The NaHCO3-Pi fraction accounted for approximately 56% and 28% of labile P under irrigation and drought treatments, respectively (Fig. 1b). The components comprising moderately labile P were minimally altered by irrigation (Fig. 1a). The ratio of NaOH-extracted P in the moderately labile P fraction increased by 2% comparing drought to irrigation treatment, while the fraction of 1 M HCl-Pi was reduced by 2% (Fig. 1c). Irrigation led to a slight variation in the relative concentration of conc. HCl-Pi, which varied between 74%−76% under the two water treatments (Fig. 1d).

      Table 1.  Effects of irrigation on soil properties (mean ± standard error) in P. euphratica plantations.

      Soil propertiesIrrigationDroughtp value
      WC (%)25.26 ± 0.546.93 ± 0.95< 0.01
      pH8.41 ± 0.058.72 ± 0.02< 0.01
      TN (g·kg−1)1.18 ± 0.050.76 ± 0.020.03
      SOC (g·kg−1)40.13 ± 0.2232.12 ± 0.29< 0.01
      NO3−-N (mg·kg−1)6.35 ± 1.824.55 ± 0.900.16
      NH4+-N (mg·kg−1)2.15 ± 0.231.25 ± 0.11< 0.01
      TP (g kg−1)0.65 ± 0.010.57 ± 0.010.01
      DON (mg·kg−1)7.96 ± 0.479.65 ± 0.270.05
      AK (g·kg−1)0.41 ± 0.090.32 ± 0.050.12
      DOC (g·kg−1)0.26 ± 0.030.39 ± 0.070.17
      Na+ (g·kg−1)1.85 ± 0.061.82 ± 0.160.37
      Ca2+ (g·kg−1)15.75 ± 0.5113.95 ± 0.340.86
      Mg2+ (g·kg−1)0.83 ± 0.080.61 ± 0.030.06
      TPi (g·kg−1)0.57 ± 0.010.53 ± 0.010.01
      Pi/Pt (%)88.15 ± 0.9192.29 ± 0.300.68
      Po/Pt (%)11.85 ± 0.917.71 ± 0.300.16
      Bold numbers indicate significant differences (p < 0.05) between treatments. WC: water content, TN: total nitrogen, SOC: soil organic carbon, DON: dissolved organic nitrogen, AK: available K, DOC: dissolved organic carbon. Total P is the sum of all P fractions; total Pi is the sum of Resin-Pi, NaHCO3-Pi, NaOH-Pi, 1 M HCl-Pi, and conc. HCl-Pi; total Po is the sum of NaHCO3-Po, NaOH-Po, and conc. HCl-Po.

      Figure 1. 

      Percentage of each phosphorus (P) fraction under different water management treatments. (a) Total P. (b) Labile P. (c) Moderately labile P. (d) Sparingly labile P.

      The concentrations of labile and moderately labile P increased markedly under irrigation (Table 2). The Pi and Po fractions in the labile P and moderately labile P fractions also increased under irrigation. Although sparingly labile P values were not significantly different between irrigation and drought treatments, irrigation had significantly higher conc. HCl-Po and conc. HCl-Pi than the drought treatment (Table 2).

      Table 2.  Soil phosphorus (P) sequential fractionation under different water management treatments.

      P fraction (mg·kg−1)IrrigationDroughtp value
      Labile P
      Resin-Pi1.15 ± 0.270.50 ± 0.060.04
      NaHCO3-Pi12.31 ± 2.401.88 ± 0.51< 0.01
      NaHCO3-Po8.69 ± 0.824.25 ± 0.490.03
      ΣLabile P22.15 ± 1.676.62 ± 0.54< 0.01
      Moderately labile P
      NaOH-Pi7.02 ± 0.792.07 ± 0.12< 0.01
      NaOH-Po16.41 ± 1.836.98 ± 0.17< 0.01
      1 M HCl-Pi521.10 ± 12.37505.52 ± 1.560.03
      ΣModerately labile P544.53 ± 13.51511.58 ± 1.540.02
      Sparingly labile P
      Conc. HCl-Pi27.50 ± 1.8421.35 ± 1.730.01
      Conc. HCl-Po9.79 ± 1.386.68 ± 1.500.02
      ΣSparingly labile P37.29 ± 2.7528.04 ± 1.950.17
      Nonlabile P
      Residual P41.76 ± 5.1126.22 ± 0.820.49
      Bold numbers indicate significant differences (p < 0.05) between treatments.

      Redundancy analysis (RDA) identified variations in soil P fractions between drought and irrigation (Supplemental Fig. S1a), and soil properties explained almost 98% of the variation observed in soil P fractions (Supplemental Fig. S1a). Of the soil physicochemical parameters evaluated, WC and pH had the most explanatory power for the observed variations in soil P fractions. Except for 1M HCl Pi, all soil P parameters were positively correlated with higher WC, NH4+-N, and AK and negatively associated with lower pH values (Supplemental Fig. S1b).

      Soil phosphatase enzymes and microbial characteristics

    • Under irrigation, there was a significant increase in MBP, ACP, and ALP relative to the drought treatment (Fig. 2a−c). Notably, irrigation increased ACP more strongly than ALP (Fig. 2a & b). Significant and positive relationships were identified between labile P and moderately labile P, as well as between residual P and MBP, ACP, and ALP. However, HCl-P did not exhibit noteworthy associations with MBP, ACP, or ALP, with the exception of MBP and conc. HCl Po (Fig. 3). To assess the impact of water management on soil microbial characteristics, microbial analyses were conducted using three metrics: biomass changes, taxonomic profiles, and functional changes (Fig. 4a−g). This analysis showed that soil microbial biomass was greater under irrigation than under drought, regardless of whether it was measured using MBC or total PLFAs (Fig. 4a & b). While there were no substantial differences between the two treatments in the relative proportions of bacteria and fungi based on PLFA classification, the diversity of bacteria and fungi under irrigation was higher than under drought when it was assessed using bacterial 16S and fungal ITS rRNA gene amplifications (Fig. 4c−e). Additionally, phoD and phoX copy numbers were higher under irrigation than under drought (Fig. 4f & g).

      Figure 2. 

      Soil phosphatase and microbial biomass P in P. euphratica plantations under different water management treatments. (a) Acid phosphatase activity. (b) Alkaline phosphatase activity. (c) Microbial biomass P. The error bars indicate the SE of the mean (n = 6). Asterisks indicate the level of significance: ** p < 0.01, *** p < 0.001.

      Figure 3. 

      Spearman's correlation analysis among soil P fractions and soil phosphatase (acid and alkaline phosphatase) activity and microbial biomass P. Significance of changes in each P fraction: * p < 0.05; ** p < 0.01.

      Figure 4. 

      Effects of water management on soil microbial characteristics (biomass change, taxonomic profile and functional profile). (a) Total microbial biomass C. (b) Total PLFAs. (c) Bacteria to fungi ratio. (d) Shannon diversity of bacteria. (e) Shannon diversity of fungi. (f) phoD copies. (g) phoX copies. Significance levels were standardized across the panels (* p < 0.05; ** p < 0.01 and *** p < 0.001).

      Notably, all soil microbial parameters except the bacteria-to-fungi ratio were positively correlated with elevated levels of soil P (Fig. 5a). Among the evaluated soil microbial characteristics, microorganism composition and functional levels exhibited better explanatory power for the variations in soil P fractions (Fig. 5b).

      Figure 5. 

      Redundancy analysis of soil P fractions impacted by soil microbial characteristics. (a) RDA across all experimental units. (b) The variation in soil microbial characteristics explaining soil P fractions. Red arrows represent soil microbial characteristics. Blue crosses represent soil P fractions. Significance is indicated by ** p < 0.01; * p < 0.05.

      The richness and diversity of the bacterial phoD genes were significantly higher under irrigation than under drought (Supplemental Fig. S2a & b). In contrast, no major variations were observed between treatments in the richness and diversity of bacterial phoX genes (Supplemental Fig. S2b). Principal coordinate analysis (PCoA), conducted using the Bray-Curtis distance matrix, indicated significant variations in phoD and phoX gene communities between treatments (Supplemental Fig. S2c & d).

      The taxonomic composition of phoD and phoX gene bacterial communities was assessed at the family and genus levels, where the relative abundances exceeded 0.01% (Supplemental Fig. S3a−d). Specifically, phoD gene reads were primarily classified into 14 families and 14 genera, while phoX gene reads were classified into 14 families and 13 genera. Analysis of phoD gene community composition at the family level revealed that irrigation had a considerable impact on the relative abundance of Bradyrhizobiaceae, Nocardiaceae, Sphingomonadaceae, and Burkholderiaceae (Supplemental Fig. S3a). Furthermore, at the genus level, irrigation increased the relative abundance of Bradyrhizobium and Rhodococcus (Supplemental Fig. S3b). Similarly, phoX gene community composition analysis demonstrated that irrigation significantly affected the relative abundance of Phyllobacteriaceae and Xanthomonadaceae at the family level. In contrast, at the genus level, the relative abundance of Halomonas and Rhodopirellula reduced and increased during irrigation, respectively (Supplemental Fig. S3c & d).

      Procrustean analysis confirmed a strong relationship between the structure of the phoD gene community at Operational Taxonomic Unit (OTU) level and soil P fractions across treatment type (Fig. 6a). However, soil P fractions were not affected by the composition of the phoX gene community (Fig. 6b).

      Figure 6. 

      Procrustean analyses and spearman's correlations between soil P fractions and alkaline phosphatase gene communities. (a) Procrustean analyses of phoD community composition and soil P fractions across samples. (b) Procrustean analyses of phoX community composition and soil P fractions across samples. (c) Spearman’s correlations of soil P fractions and the relative abundance of phoD at family level. (d) Spearman's correlations of soil P fractions and the relative abundance of phoX at family level. (e) Spearman's correlations of soil P fractions and the relative abundance of phoD at genus level. (f) Spearman's correlations of soil P fractions and the relative abundance of phoX at genus level. Significance of each bacteria taxa: * p < 0.05; ** p < 0.01.

      We next conducted a correlation analysis between the phoD and phoX gene communities and soil P fractions. At the family level of the phoD gene community, relative abundances of Bradyrhizobiaceae, Nocardiaceae, and Sphingomonadaceae were positively correlated with resin-Pi, NaHCO3-Pi, NaHCO3-Po, NaOH-Pi, and NaOH-Po. Meanwhile, Burkholderiaceae was negatively correlated with all P fractions except NaHCO3-Po (Fig. 6c). Spearman’s correlation analysis revealed a significant positive correlation between NaHCO3-Po and Methylobacteriaceae, conc. HCl-Pi with Oxalobacteraceae, Acetobacteraceae, and Methylobacteriaceae, and residual-P with Bradyrhizobiaceae, Sphingomonadaceae, and Methylobacteriaceae (Fig. 6c). At the genus level of the phoD community, the relative abundance of Bradyrhizobium and Rhodococcus was positively correlated with resin-Pi, NaHCO3-Pi, NaHCO3-Po, NaOH-Pi, and NaOH-Po. Bradyrhizobium was also positively correlated with residual-P (Fig. 6e). Further, Methylobacterium was positively correlated with resin-Pi, NaHCO3-Po, conc. HCl-Pi, and residual-P (Fig. 6e). In contrast, the relative abundance of Mesorhizobium was negatively correlated with NaHCO3-Po and conc. HCl-Pi (Fig. 6e).

    Discussion

    • Compared to drought, irrigation increased TP and TPi due to significant increases in labile and moderately labile P. Labile P, which serves as the primary source of P for plant growth, increased substantially by 2.34 fold (Table 2)[35,36]. However, labile P only accounted for 3% of TP under the irrigation treatment (Fig. 1a), indicating that available P deficiency was still an important limiting factor in this ecosystem's productivity. Nevertheless, improving soil moisture increased litter production and soil coverage, thereby reducing Pi leaching from the soil surface[37]. Additionally, higher litter input and the proliferation of roots have been shown to increase Po, and the partial decomposition of Po by plants and microorganisms can increase the availability of resin P[38,39]. NaHCO3-P increased significantly under irrigation and altered labile P composition, with NaHCO3-Pi gradually coming to dominate this fraction (Fig. 1b). Activated soil phosphatase promoted the release of inorganic P from NaHCO3-Po, which represents a labile form of Po that can rapidly dissolve and mineralize. These characteristics act to supplement P in deficient soils, mitigating declines in resin-Pi and NaHCO3-Pi.

      NaOH-P is a component of moderately labile P pool in soils and requires long-term mineralization before it is available to plants[40]. Its presence therefore reflects the soil's future potential to supply P. Irrigation can increase NaOH-P content, likely as a result of the influx of external sources of carbon[38,41]. P represented by HCl-P is associated with calcium and is likely derived from primary minerals[42,43]. We found that 1 M HCl-Pi was the predominant form at our study location, accounting for approximately 70% of TP (Fig. 1c). Recent studies have shown that the mean residence time of HCl-P can range from years to millennia and that the average turnover rate of Ca-phosphate is 0.00088 g·g−1·d−1[44,45], indicating that it is highly stable. However, it should be noted that the stability of HCl-P is significantly influenced by the soil pH[45]. In alkaline soils, the HCl-P pool is typically comprised of highly stable calcium-P minerals. Long-term irrigation can lead to increased soil moisture and decreased pH (Supplemental Fig. S1) and may facilitate the weathering of primary minerals and the desorption of Ca-associated P. Residual P, bound by secondary minerals, represents the most stable P fraction in soils. Although it is typically unavailable to plants and soil microorganisms, desorption and weathering can eventually mobilize it for plant uptake[46].

      The mineralization of Po to Pi is believed to be heavily influenced by phosphatases[47,48]. A substantial increase in both ACP and ALP activities in response to irrigation was observed in our study (Fig. 2a & b) and may be linked to increased litter input and higher soil water content. Irrigation stimulates enzyme activity because water increases the connectivity between soil pores and the availability of nutrient resources to meet the physiological needs of microorganisms[6]. Soil microbial biomass is increasingly recognized as a critical driver of soil P dynamics and we found a positive correlation between soil MBP and most soil P fractions (Fig. 3)[49,50]. Furthermore, irrigation increased MBP (Fig. 2c), while drought has been shown to inhibit microbial growth and cause the release of significant quantities of P, resulting in a reduction of MBP[51].

      Irrigation affected several soil microbiome characteristics, including microbial biomass, taxonomy, and functional profiles. Irrigation has been shown to increase both total and active microbial biomass in soil, as well as bacterial and fungal diversity (Fig. 4a & b). This suggests that adequate water availability facilitates the metabolic activities of soil microorganisms, promoting their growth and propagation. Moreover, water availability can influence soil microbial function[52,53]. Irrigation increased the number of phoD and phoX bacteria (Fig. 4c) and the diversity of phoD (Supplemental Fig. S2a & b), a gene encoding an important phosphatase enzyme involved in P cycling. This indicates that water availability enhances the ability of soil microbes to perform essential functions, such as nutrient cycling. Therefore, irrigation can significantly impact soil microbial function, ultimately affecting the overall health of soil ecosystems.

      We also found that the composition and function of microorganisms, rather than total microbial biomass, significantly influenced soil P fractions, as demonstrated by variations in the diversity of bacteria and fungi and the copy numbers of phoD and phoX bacteria, which is involved in P mineralization (Fig. 5). Furthermore, a notable increase in the relative abundance of phosphorus solubilizing bacteria ([PSB] Bradyrhizobiaceae, Nocardiaceae, Sphingomonadaceae, Phyllobacteriaceae and Xanthomonadaceae) under irrigation was observed (Supplemental Fig. S3). Bradyrhizobiaceae and Sphingomonadaceae were also positively correlated with various P forms, including resin-Pi, NaHCO3-Pi, NaHCO3-Po, NaOH-Pi, and NaOH-Po (Fig. 6c). Bradyrhizobiaceae, which belong to the Proteobacteria phylum, are known to release organic acids that help solubilize different forms of P[54], improving plant growth by increasing P availability in nutrient-poor forest soils[55]. Similarly, Sphingomonadaceae have been found to have a beneficial connection with ALP activity in phosphite-treated soil[56]. It is also known that Xanthomonadaceae promote organic P mineralization[57,58]. A significant positive correlation between NaHCO3-Po and Methylobacteriaceae was also found. Methylobacterium are copiotrophic bacteria that are widespread in nutrient-rich environments and are known to be correlated with SOC[59,60]. Due to its capacity to tolerate hostile environments, Bacillus is among the most abundant PSB categories[61,62]. However, the relative abundance of Bacillus in our study was uncorrelated with soil P fractions and did not significantly vary between irrigation and drought treatments. These results suggest that the irrigation status of soil had little effect on Bacillus, likely due to its high drought tolerance.

    Conclusions

    • This field study provided empirical evidence that irrigating P. euphratica plantations increase soil P availability and supply capacity and causes significant reallocation within soil P fractions. The enhanced mineralization of organic P was linked to variations in soil moisture and pH and to changes in the composition and functional profiles of soil microorganisms, mainly bacteria possessing phoD genes. However, it will be necessary to fully characterize the allocation of foliar-P fractions of P. euphratica and its relationship with soil-P fractions in the future. These findings underscore the potential impacts of water management on soil P dynamics.

      Acknowledgments

      • This work was supported by the Start-up Foundation for Advanced Talents of Anhui Agricultural University (rc372210). We would like to thank A&L Scientific Editing (www.alpublish.com) for its linguistic assistance during the revision of this manuscript.

      Conflict of interest

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

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      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    He Y, Lin X, Wang L, Ma X, Fang L, et al. 2023. Effects of long-term irrigation on soil phosphorus fractions and microbial communities in Populus euphratica plantations. Forestry Research 3:17 doi: 10.48130/FR-2023-0017
    He Y, Lin X, Wang L, Ma X, Fang L, et al. 2023. Effects of long-term irrigation on soil phosphorus fractions and microbial communities in Populus euphratica plantations. Forestry Research 3:17 doi: 10.48130/FR-2023-0017
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Catalog

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  • Introduction
  • Materials and methods

  • Field sites

  • Soil sampling

  • Soil properties

  • Soil P fractions

  • Activities of ACP and ALP

  • Soil microbial characteristics

  • Statistical analysis

  • Results

  • Soil properties, P fractions, and their associations

  • Soil phosphatase enzymes and microbial characteristics

  • Discussion
  • Conclusions
  • Acknowledgments
  • Conflict of interest
  • Supplementary information
  • Rights and permissions
  • About this article
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