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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 green manure cultivation for aboveground carbon store and returning to the field to ameliorate soil quality in saline alkali soil

  • 1.

    College of Agro-grassland Science, Nanjing Agricultural University, Nanjing 210095, China

  • 2.

    Jiangsu Coast Development (Dongtai) Co., Ltd., Yancheng 224237, China

More Information
  • The utilization of saline-alkali land together with the consideration of the productive value (improving soil productivity) and ecological value (increasing carbon store ability) has rarely been reported. We conducted a field experiment to investigate the impact of green manure cultivation for aboveground carbon (C) store and then returning this to field to improve soil quality in saline alkali soil. The biomass in Lolium multiflorum cultivation treatment was significantly (P < 0.05) higher than that of Medicago sativa and Brassica campestris cultivation. A similar tendency was observed in aboveground C store. Green manure cultivation resulted in largely different physicochemical properties at time of harvest. Returning the green manure to the field could significantly (P < 0.001) improve soil fertility. Moreover, the soil fertility index of Lolium multiflorum treatment was significantly (P < 0.05) enhanced by 55.56% and 33.33%, as compared with Medicago sativa and Brassica campestris treatments. Based on PLS-PM analysis, both fast-changing soil properties and biomass exhibited the greatest positive impacts (0.72 of the total effects) on soil fertility improvement after aboveground returning to the field. Our research provides evidence that Lolium multiflorum is the best potential variety to improve saline alkali soil fertility. Additionally, green manure cultivation in saline-alkali soil is an important way to store carbon in plants, then returning to the field is a feasible approach to improve saline alkali soil quality, which is beneficial for the green and sustainable development of saline-alkali agriculture.

  • Introduction

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

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

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

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

    Materials and methods

    Plant materials and growth conditions

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

    Experimental treatments and design

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

    Measurements

    Soil water content and plant growth and physiological traits

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

    Hyperspectral imaging of phenotypic responses to drought stress

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

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

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

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

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

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

    Statistical analysis

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

    Results

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

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

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

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

    Hyperspectral and multispectral imaging of phenotypic responses to drought stress

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

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

    Chlorophyll fluorescence imaging of photosynthetic responses to drought stress

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

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

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

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

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

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

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

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

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

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

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

    Discussion

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

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

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

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

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

    Conclusions

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

    Acknowledgments

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

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

  • Supplemental Table S1 ANOVA F-values of soil properties measured under different treatment at green manure harvest in saline alkali soil field experiment
    Supplemental Table S2 ANOVA F-values of Soil properties measured under different treatment after green manure returning to saline alkali soil field for 30 days
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  • Cite this article

    Zhang F, Han Y, Shang H, Ding Y. 2023. Effects of green manure cultivation for aboveground carbon store and returning to the field to ameliorate soil quality in saline alkali soil. Grass Research 3:1 doi: 10.48130/GR-2023-0001
    Zhang F, Han Y, Shang H, Ding Y. 2023. Effects of green manure cultivation for aboveground carbon store and returning to the field to ameliorate soil quality in saline alkali soil. Grass Research 3:1 doi: 10.48130/GR-2023-0001
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Effects of green manure cultivation for aboveground carbon store and returning to the field to ameliorate soil quality in saline alkali soil

  • 1.

    College of Agro-grassland Science, Nanjing Agricultural University, Nanjing 210095, China

  • 2.

    Jiangsu Coast Development (Dongtai) Co., Ltd., Yancheng 224237, China

  • Received: 15 November 2022
  • Accepted: 27 December 2022
  • Published online: 18 January 2023
Grass Research  3,  Article number: 1  (2023)  |  Cite this article

Abstract: The utilization of saline-alkali land together with the consideration of the productive value (improving soil productivity) and ecological value (increasing carbon store ability) has rarely been reported. We conducted a field experiment to investigate the impact of green manure cultivation for aboveground carbon (C) store and then returning this to field to improve soil quality in saline alkali soil. The biomass in Lolium multiflorum cultivation treatment was significantly (P < 0.05) higher than that of Medicago sativa and Brassica campestris cultivation. A similar tendency was observed in aboveground C store. Green manure cultivation resulted in largely different physicochemical properties at time of harvest. Returning the green manure to the field could significantly (P < 0.001) improve soil fertility. Moreover, the soil fertility index of Lolium multiflorum treatment was significantly (P < 0.05) enhanced by 55.56% and 33.33%, as compared with Medicago sativa and Brassica campestris treatments. Based on PLS-PM analysis, both fast-changing soil properties and biomass exhibited the greatest positive impacts (0.72 of the total effects) on soil fertility improvement after aboveground returning to the field. Our research provides evidence that Lolium multiflorum is the best potential variety to improve saline alkali soil fertility. Additionally, green manure cultivation in saline-alkali soil is an important way to store carbon in plants, then returning to the field is a feasible approach to improve saline alkali soil quality, which is beneficial for the green and sustainable development of saline-alkali agriculture.

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    INTRODUCTION

    • Saline-alkali soil is widely distributed, and is generally characterized by high salt content, poor structure and nutrients, suppresses plant growth, even causes plant death and seriously threatens agricultural production[1, 2]. Saline-alkali stress is the most significant factor influencing the sustainable development of agriculture, which urgently needs improvement and utilization[3, 4]. Extensive studies have been carried out on salinized soil from different perspectives and progress has been made[5, 6]. In general, the improvements of saline-alkali land mainly include physical, chemical and biological measures[7−9]. The physical improvements, which are generally based on irrigation or soil change, require a large amount of capital investment, thus are not suitable for large-scale promotion. Chemical improvement measures[10], usually using gypsum, zeolite, sulfuric acid, citric acid and other chemicals to offset the salt in soil, can achieve enhancement effects in the short term. However, the inorganic amendments are complex and costly, and can also cause secondary pollution to the water and soil environments. Biological fertility improvement measures are mainly adopted to reduce soil salt content by planting highly salt-tolerant plants, which are characterized by strong operability, sustainable development and broad application prospects[11].

      The cultivation of salt-tolerant green manure, with both productive and ecological value, is considered to be an effective measure for utilization and sustainable development of saline-alkali land[12]. For productive value, green manure cultivation could largely improve the buffering of alkaline soil, prevent dramatic changes in soil pH value due to excessive alkaline substances, and reduce the accumulation of soluble salt in the soil surface. Besides, the huge plant roots can effectively improve the soil structure, enhance the ability to retain water and fertilizer and improve yields[13]. A large number of studies have demonstrated the advantages of planting forage legumes on improving soil properties[14−16]. For ecological value, the introducing of salt-tolerant plants is a good approach to develop the carbon (C) sequestration potential of saline-alkali land, which can rapidly increase the vegetation cover on the surface, improve greenhouse gas absorption and store up aboveground C, contributing to modification of the global C cycle[17].

      In recent decades, more attention has been paid to the cultivation of saline-tolerant forage species. Alfalfa, ryegrass, forage rape, sweet sorghum and sesbania are the potential forages that grow in saline soil[18,19]. Brassica campestris can accumulate more soluble sugars, amino acids and other similar compounds to improve the concentration of cell fluid, thus normally absorbing water and nutrients from the saline soil solution with higher concentration and avoiding salt-alkali damage[2]. Previous studies have demonstrated that Medicago sativa, an excellent perennial leguminous forage, has strong adaptability and can withstand a certain degree of saline-alkali stress[20]. Italian ryegrass has a large and developed root system, along with strong reproduction, wide distribution, rich germplasm resources, and good stress resistance[21]. Feng et al.[22] investigated the salt tolerance ability of two Italian ryegrass cultivars, and demonstrated that high salt tolerance was partly due to the prevention of plants from ionic homeostasis disruption. Therefore, it is of great significance to improve soil quality and agricultural productivity by using salt-tolerant green manure in saline-alkali land.

      It is well known that utilization of green manure, to a large extent, is helpful for reducing the dependence on mineral fertilizers[23, 24]. The green manure returned to the field contributes to the generation of a good growth environment, with increase of soil organic matter and soil nutrient content, as well as improvement of soil aggregate structure, soil microbial community, and micro-ecological environment[25,26]. Accumulated evidence has demonstrated that green manure application could increase soil nutrient cycling and utilization efficiency, depending on the positive effects of soil microbes in the decomposition process[27,28]. Mwafulirwa et al.[29] reported that ryegrass shoot residue addition resulted in higher residue C mineralization rates, accelerated soil microbial activity, and increased soil organic matter priming, as compared with that of root residues, particularly in the early days. Understanding the impacts of different green manure returning to the field on saline alkali soil quality improvement is of benefit to the sustainability of agricultural production.

      Soil fertility quality is an essential component of soil quality, which directly affects plant growth, agricultural production structure, distribution and benefits[30]. Appropriate evaluation methods and appropriate soil fertility indicators should be the most important considerations, which have a significant impact on soil fertility quality outcomes[31]. The calculation of soil fertility quality index is the core issue of soil quality evaluation, which is a widely employed way to evaluate the relationship between certain soil factors and soil productivity using fuzzy mathematical methods[32,33]. In this study, we employed different green manure species (Medicago sativa, Lolium multiflorum and Brassica campestris) with eight varieties, using a field experiment, aiming to measure their effects of cultivation for aboveground carbon store and returning to the field to improve saline alkali soil quality.

    MATERIALS AND METHODS

      Green manure materials

    • We employed four Medicago sativa varieties (Aurora, Sanditi, Eureka+, Sardi), Lolium multiflorum, and three Brassica campestris varieties (Huayouza 82, Huayouza 158, Huayouza 62) in our experiment. The Medicago sativa and Lolium multiflorum seeds were provided by Zhengzhou kaiyuan Grass Industry Technology Co., Ltd (China), and Brassica campestris seeds were provided by Zhengzhou Huafeng Grass Industry Technology Co., Ltd (China), with all germination rate > 85%.

      Field site description

    • Our field experiment was carried out in Tiaozini, Dongtai, Jiangsu Province, China (32°51' N, 120°56' E). This region is a transition zone between subtropical and warm temperate zone with distinct seasons. The average annual temperature in this area is 15 °C, with the coldest month being January (mean monthly temperature 0.8 °C). July is the hottest month with an average monthly temperature of 27 °C. The average annual rainfall is 1,061 mm. The annual rainfall from June to September accounts for 63% of the whole year. The soil properties were as follows: pH 8.20, salt content 1.83 ‰, soil organic matter 7.92 g·kg−1, total N 0.48 g·kg−1, total P 0.65 g·kg−1, total K 18.76 g·kg−1, soil available N 45.92 mg·kg−1, available P 18.31 mg·kg−1, available K 187.07 mg·kg−1, Ca2+ content 41.84 g·kg−1, Mg2+ content 13.64 g·kg−1.

      Experimental design

    • The field experiment, with a random design, included eight green manure varieties. We defined each variety as a treatment and a non-cultivation as control (CK). Each treatment had three replicates (plots), and the size of each plot was 2 m × 2 m. The cultivation was performed in late October, 2021. The sowing density of Medicago sativa, Lolium multiflorum and Brassica campestris was 2.5 g·m−2, 2.5 g·m−2 and 1 g·m−2, respectively. The sowing depth was 3 cm and row spacing was 25 cm. No fertilization was applied during the growth of green manure. Irrigation and weeding were the same as in routine management.

      Collection of aboveground and soil samples

    • The green manure biomass was recorded at harvest (May 5, 2022). We took random subplots (0.5 × 0.5 m) within each plot and destructively harvested, and the shoots were cut at the soil surface. At the same time, we collected soil samples. Five cores (5 cm diameter and 0−15 cm depth), on the rows, were randomly sampled and sufficiently mixed to yield one representative sample. After sampling, the rest of the green manure in each plot were crushed and returned to the field with the returning depth of 20 cm. Thirty days later, soil samples were collected as previously. All collected soil samples (54 samples in total: 9 treatments × 3 replications × 2) were sieved (2.0 mm mesh) and homogenized for soil physicochemical property analysis.

      Determination of aboveground samples

    • The collected green manure aboveground samples were oven dried at 65 °C for 72 h to a constant weight before weighing. The shoot dry weights were expressed as total aboveground biomass per m2. Then the dry shoots were ground through 20 mesh in a Wiley mill. The prepared aboveground samples were measured by an Elementar Analyzer (Vario EL III, Germany) for total carbon (C).

      Determination of soil physicochemical properties and soil fertility evaluation

    • Soil pH was determined with soil-water slurry (1:5, w/v) by a PB-10 pH meter (Sartorius, Germany). Soil electrical conductivity (EC) was measured by a conductivity meter (B-173; HORIBA, Kyoto, Japan). Soil organic C (SOC) was measured by an Elementar Analyzer (Vario EL III, Germany). The total N was determined using a Kjeltec Analyser (FOSS Tecator, Hoganas, Sweden). The determination of soil available nitrogen (N) was measured according to Shi[34]. The soil fertility evaluation was calculated according to previously described methods[35, 36].

      Statistical analyses

    • The data in our study were log-transformed when necessary to meet the criteria for a normal distribution. We employed SPSS 22.0 (IBM, Armonk, NY, USA) software for statistical analysis of all parameters. The data from each treatment were analyzed using one-way analysis of variance (ANOVA), and Duncan’s multiple range tests (P < 0.05) were performed for multiple comparisons. The Mann-Whitney U test method was used to test soil fertility index differences between non-cultivation and cultivation groups after returning to the field.

      For soil fertility evaluation, we employed five soil fertility evaluation parameters, including pH, EC, SOM, TN and AN. Then we chose appropriate function curves and turning points to determine values of each soil fertility parameter, according to our data characteristics. The 'optimum' curve equation is employed for pH and EC, while the 'more is better' curve equation is used for SOM, TN and AN[33, 35, 36]. The equation for the scoring curve as follows:

      (a) The 'optimum' curve equation:

      f(x)={0.1x⩽L,x⩾U0.1+0.9(x−L)/(O1−L)L<x<O11.0O1⩽x⩽O21.0−0.9(x−O2)/(U−O2)O2<x<U

      (b) The 'more is better' curve equation:

      f(x)={1.0x⩾U0.1+0.9(x−L)/(U−L)L<x<U0.1x⩽L

      where x is the monitoring value of the parameter; f(x) is the score of the parameters ranging between 0.1 and 1.0; U and L are the upper and the lower threshold values of the parameters, respectively. O1 and O2 are the best values of the variables.

      We employed partial least squares path modeling (PLS-PM), based on 'plspm' (1000 bootstraps) package in R software (v.4.0.0), to determine the complex multivariable relationships among green manure varieties, edaphic variables, plant C content, biomass and soil fertility. Then we tested the model architectures from simple to complex (direct and indirect links, previous effects)[37]. Based on the determination coefficient (R2) of the explained latent variables and goodness of fit (GoF), we selected the corresponding architecture.

    RESULTS

      Green manure biomass of different varieties cultivation

    • There were significant (P < 0.05) differences among Medicago sativa, Lolium multiflorum and Brassica campestris cultivation treatments for biomass at harvest (Fig. 1). In total, the largest green manure biomass was observed in Lolium multiflorum cultivation treatment (895.11 g·m−2). Lolium multiflorum cultivation treatment increased the biomass by 2.48 and 0.79 times compared with Medicago sativa and Brassica campestris cultivation treatments, respectively. Additionally, we found significant (P < 0.05) differences among the varieties of each green manure. For Medicago sativa, the biomass of Eureka+ was significantly (P < 0.05) increased by 10.38%, 8.29% and 14.88%, compared with that of Aurora, Sanditi, Sardi, respectively. For the biomass of Brassica campestris, there was no significant difference between Huayouza 82 and Huayouza 158, while they were both significantly (P < 0.05) higher than that of Huayouza 62, with increasing by 60.66% and 71.29%, respectively.

      Figure 1. 

      The green manure biomass of different varieties cultivation at harvest in saline alkali soil field experiment. 'M1, M2, M3 and M4' represent Aurora, Sanditi, Eureka+, Sardi, respectively, which all belong to Medicago sativa. 'L' refers to Lolium multiflorum. 'B1, B2 and B3' represent Huayouza 82, Huayouza 158, Huayouza 62, respectively, which all belong to Brassica campestris. One-way analysis of variance (ANOVA) was used to assess the differences among treatments. Bars represent the mean values of three replicates ± SD. Values that do not share the same lower case letter are significantly different (P < 0.05) among green manure varieties. Values that do not share the same uppercase letter are significantly different (P < 0.05) at different green manure species (Medicago sativa, Lolium multiflorum, Brassica campestris). *** refers to P < 0.001.

      Green manure carbon sequestration of different varieties cultivation

    • The carbon content was lowest in Medicago sativa cultivation treatment (401.58 g·kg−1), however, no significant difference was observed among Medicago sativa, Lolium multiflorum and Brassica campestris cultivation treatments (Fig. 2a). For Medicago sativa, the carbon content of Aurora (364.25 g kg−1) was significantly (P < 0.05) lower than that of Eureka+. For Brassica campestris, the carbon content of Huayouza 82 was significantly (P < 0.05) higher than that of Huayouza 158 and Huayouza 62 by 12.55% and 12.09%, respectively. In addition, different green manure cultivation led to a significant (P < 0.05) difference in aboveground carbon store (Fig. 2b). The carbon store in Lolium multiflorum cultivation (400.58 g·m−2) was significantly (P < 0.05) higher than that of Medicago sativa and Brassica campestris cultivation treatments by 2.87 and 0.91 times, respectively. For Medicago sativa, the carbon store of Eureka+ was significantly (P < 0.05) higher by 33.88%, 25.71% and 20.36% than that of Aurora, Sanditi and Sardi, respectively. Additionally, for Brassica campestris, the carbon store of Huayouza 62 (139.41 g·m−2) was largely significantly (P < 0.05) lower than that of Huayouza 82 (251.31 g·m−2) and Huayouza 158 (237.78 g·m−2), respectively.

      Figure 2. 

      (a) Carbon content and (b) carbon store of different varieties cultivated at harvest in saline alkali soil field experiment. 'M1, M2, M3 and M4' represent Aurora, Sanditi, Eureka+, Sardi, respectively, which all belong to Medicago sativa. 'L' refers to Lolium multiflorum. 'B1, B2 and B3' represent Huayouza 82, Huayouza 158, Huayouza 62, respectively, which all belong to Brassica campestris. One-way analysis of variance (ANOVA) was used to assess the differences among treatments. Bars represent the mean values of three replicates ± SD. Values that do not share the same lower case letter are significantly different (P < 0.05) among green manure varieties. Values that do not share the same uppercase letter are significantly different (P < 0.05) at different green manure species (Medicago sativa, Lolium multiflorum, Brassica campestris). * refers to P < 0.05; ** refers to P < 0.01; *** refers to P < 0.001.

      Soil physicochemical properties in different cultivation treatment at green manure harvest

    • The green manure cultivation generated significantly different physicochemical properties in saline alkali soil (Table 1, Supplemental Table S1). The cultivation of Medicago sativa (Aurora, Sanditi, Eureka+) and Lolium multiflorum resulted in a significant (P < 0.05) lower pH by 6.32%, 3.67%, 1.69% and 4.03% than that of CK. Moreover, compared with CK, the EC in Medicago sativa (except for Sanditi) and Brassica campestris were also significantly (P < 0.05) declined. For soil organic matter, only Eureka+ and Huayouza 62 were significantly (P < 0.05) higher than that of CK. Additionally, soil total N in Sardi and Huayouza 62 were significantly (P < 0.05) lower than that of CK. For available N, Aurora, Sanditi, Sardi and Lolium multiflorum were significantly (P < 0.05) higher by 33.08%, 27.53%, 29.70%, 38.49%, when compared with CK.

      Table 1.  Soil properties measured under different treatment at green manure harvest in saline alkali soil field experiment.

      Green manure cultivationTreatmentpHEC
      (us·cm−1)      		Soil organic matter
      (g·kg−1)      		Total N
      (g·kg−1)      		Available N
      (mg·kg−1)
      No-cultivationCK9.04 ± 0.16abc496.33 ± 19.50b5.27 ± 0.14bc0.58 ± 0.05abc38.76 ± 2.03bc
      Medicago sativaM18.50 ± 0.13f429.00 ± 26.21cd4.41 ± 0.78c0.58 ± 0.01ab51.58 ± 2.66a
      M28.72 ± 0.02e470.33 ± 13.32bc5.11 ± 0.42bc0.54 ± 0.01cd49.43 ± 4.35a
      M38.89 ± 0.03d356.67 ± 6.43ef5.63 ± 0.66a0.54 ± 0.02bcd41.89 ± 4.57b
      M48.93 ± 0.03cd391.00 ± 4.36de4.33 ± 0.24c0.53 ± 0.02d50.27 ± 1.61a
      Lolium multiflorumL8.69 ± 0.04e567.67 ± 52.88a5.33 ± 0.46bc0.60 ± 0.01a53.68 ± 3.79a
      Brassica campestrisB19.02 ± 0.04bc363.00 ± 26.91ef5.13 ± 0.11bc0.51 ± 0.01de37.53 ± 3.16bc
      B29.11 ± 0.02ab330.67 ± 12.66f5.07 ± 1.32bc0.51 ± 0.02de38.87 ± 2.33bc
      B39.17 ± 0.01a328.00 ± 28.69f6.00 ± 0.21a0.49 ± 0.01e34.97 ± 5.50c
      CK: no cultivation. 'M1, M2, M3 and M4' represent Aurora, Sanditi, Eureka+, Sardi, respectively, which all belong to Medicago sativa. 'L' refers to Lolium multiflorum. 'B1, B2 and B3' represent Huayouza 82, Huayouza 158, Huayouza 62, respectively, which all belong to Brassica campestris. Data are the mean values of three replicates. Numbers followed by '±' are the standard deviations (SDs). Within a column, values that do not share the same letter are significantly different (P < 0.05).

      Variation of soil physicochemical properties in different treatment after green manure returning to field for 30 days

    • The soil properties were measured after green manure was returned to the saline alkali soil field for 30 d. In general, the soil properties after green manure return (Table 2, Supplemental Table S2) were generally improved when compared with previous (Table 1). The returning of Medicago sativa (only Sardi), Lolium multiflorum and Brassica campestris (Huayouza 82, Huayouza 158, Huayouza 62) had significant (P < 0.05) reduction effects on soil pH, which reduced by 2.04%, 8.30%, 4.90%, 4.77% and 4.17%, respectively, when compared to no-returning (CK). Moreover, as compared with CK, only returning of Sanditi and Eureka+ significantly (P < 0.05) decreased soil EC content. In addition, the green manure return led to significant (P < 0.05) improvement on soil organic matter. Similarly, significant (P < 0.05) promoting effects on soil total N were also observed, expect for Sanditi, as compared to CK. The soil available N in Sardi, Lolium multiflorum, and Brassica campestris (Huayouza 82) were significantly (P < 0.05) increased by 30.55%, 57.24%, 23.22% than that of CK.

      Table 2.  Soil properties measured under different treatment after green manure return to saline alkali soil field for 30 d.

      Green manure returnTreatmentpHEC
      (us·cm−1)      		Soil organic matter
      (g·kg−1)      		Total N
      (g·kg−1)      		Available N
      (mg·kg−1)
      No-returningCK9.00 ± 0.14ab591.00 ± 56.45bcd5.84 ± 0.74b0.52 ± 0.01c39.80 ± 1.88d
      Medicago sativaM18.89 ± 0.07bc536.33 ± 32.59d7.92 ± 1.24a0.60 ± 0.01b41.99 ± 4.37cd
      M29.11 ± 0.03a407.33 ± 43.25e8.55 ± 0.80a0.54 ± 0.05c45.70 ± 4.68bcd
      M39.00 ± 0.02ab438.33 ± 49.08e8.36 ± 0.59a0.60 ± 0.02b47.24 ± 4.26bcd
      M48.82 ± 0.02c549.67 ± 15.28cd7.76 ± 0.55a0.63 ± 0.02b51.96 ± 1.87b
      Lolium multiflorumL8.31 ± 0.12e519.00 ± 14.53d7.72 ± 0.88a0.71 ± 0.02a62.58 ± 5.98a
      Brassica campestrisB18.58 ± 0.05d621.67 ± 5.13bc7.75 ± 0.44a0.62 ± 0.02b49.04 ± 8.57bc
      B28.59 ± 0.07d701.33 ± 71.44a7.30 ± 0.46a0.61 ± 0.01b42.43 ± 3.22cd
      B38.64 ± 0.01d627.67 ± 35.53b7.61 ± 0.28a0.68 ± 0.03a48.57 ± 3.73bcd
      CK: no cultivation. 'M1, M2, M3 and M4' represent Aurora, Sanditi, Eureka+, Sardi, respectively, which all belong to Medicago sativa. 'L' refers to Lolium multiflorum. 'B1, B2 and B3' represent Huayouza 82, Huayouza 158, Huayouza 62, respectively, which all belong to Brassica campestris. Data are the mean values of three replicates. Numbers followed by '±' are the standard deviations (SDs). Within a column, values that do not share the same letter are significantly different (P < 0.05).

      Effects of different returning treatment to improve soil quality in saline alkali field experiment

    • The descriptions of detailed scoring function values and weights assigned to the selected soil fertility parameters are available in Table 3. The weights of pH, EC, soil organic matter, total N and available N were 0.25, 0.23. 0.07, 0.23 and 0.22, respectively. Based on these, the soil fertility index in each returning treatment was calculated. As shown in Fig. 3, we observed significant (P < 0.001) difference between the non-cultivation and cultivation group. In the cultivation group, returning Lolium multiflorum to the field had the best soil fertility enhancing effect (0.56), which significantly (P < 0.05) improved soil fertility by 55.56% and 33.33% compared with Medicago sativa and Brassica campestris. For Medicago sativa, the soil fertility index of Sardi was significantly (P < 0.05) higher than that of the other three varieties. However, there was no significant difference among Brassica campestris varieties.

      Table 3.  Scoring function values and weights assigned to selected soil fertility parameters.

      pH (H2O)EC
      (us·cm−1)      		Soil organic matter
      (SOM, g·kg−1)      		Total nitrogen
      (TN, g·kg−1)      		Available nitrogen
      (AN, mg·kg−1)
      Scoring curve#aabbb
      Turning pointU91,500151.2120
      L6.010050.530
      O16.5300
      O28400
      weight0.250.230.070.230.22
      'a' Refers to the 'optimum' curve equation; 'b' refers to the 'more is better' curve equation.

      Figure 3. 

      Soil fertility index of different treatment that returning to the field in saline alkali soil experiment. One-way analysis of variance (ANOVA) was used to assess the differences among treatments. Bars represent the mean values of three replicates ± SD. Values that do not share the same lower case letter are significantly different (P < 0.05) among green manure varieties. Values that do not share the same uppercase letter are significantly different (P < 0.05) at different green manure species (Medicago sativa, Lolium multiflorum, Brassica campestris). ** refers to P < 0.01; *** refers to P < 0.001.

      Contributions of green manure varieties, soil physicochemical properties, plant C, green manure biomass on soil fertility improvement

    • PLS-PM analysis was employed to identify direct and indirect effects of different green manure cultivation and returning to the field on saline alkali soil fertility improvement (Fig. 4a). Green manure varieties significantly (P < 0.05) positively affected biomass (0.76 of the direct effects) and then positively (P < 0.001) affected soil fertility (0.72). Similarly, green manure varieties significantly (P < 0.001) positively affected slow-changing soil properties (0.78), then positively (P < 0.05) affected plant C (0.81), followed with affecting biomass (0.55), and finally affected soil fertility. Furthermore, we observed fast-changing soil properties could significantly (P < 0.01) and directly affect biomass (0.91), while slow-changing soil properties had no significant effect on biomass.

      Figure 4. 

      Cascading relationships of soil fertility with green manure and soil physicochemical properties. (a) Partial least squares path modelling (PLS-PM) disentangling major pathways of the influences of green manure varieties, soil physicochemical properties, plant C, green manure biomass on soil fertility. Red and blue arrows indicate positive and negative flows of causality, respectively. Solid and dashed lines indicate significant (*, P < 0.05; **, P < 0.01; ***, P < 0.001) and nonsignificant (P > 0.05) levels, respectively. Values on arrows indicate significant standardized path coefficients. R2 indicates the variance of dependent variable explained by the model. (b) Total effects of soil fertility in the PLSPM models for green manure varieties, fast-changing soil properties, slow-changing soil properties, plant C, and green manure biomass.

      Overall, we found that all variates (green manure varieties, slow-changing soil properties, fast-changing soil properties, plant C and biomass) had positive impacts on soil fertility improvement. Among these, both fast-changing soil properties and biomass exhibited the greatest positive impacts (0.72 of the total effects). In addition, the total effects of fast-changing soil properties (0.552) was greater than slow-changing soil properties (0.55). Plant C also largely contribute to saline alkali soil fertility improvement (0.60). Green manure varieties had the lowest but also positive impact (0.07) (Fig. 4b).

    DISCUSSION

    • Previous studies[38−41] have demonstrated that green manure, with positive effects on soil improvement, is a main and feasible approach for sustainable crop production. In the present study, we conducted green manure varieties cultivation experiments in saline-alkali soil. Here we observed that the biomass of different salt-tolerant green manure was significantly different, and there were also certain differences among different varieties of the same green manure, which indicated that both green manure species and cultivars contributed to biomass. Son[42] revealed that the biomass of green manure crop was the highest in ryegrass, which was consistent with our result. Monirifar el al.[43] assessed the effects of cultivar on alfalfa yield in saline conditions, and found that cultivar selections could largely influence the yield. Meza et al.[44] also demonstrated that total plant biomass, aboveground biomass, root biomass were all influenced by forage species. These all supported our results. Establishment of plants provides an important opportunity to store C in both plant biomass and the soil to mitigate climate change[45]. Thus, the plant carbon store was closely correlated with biomass and C content. It is a good way to improve carbon sequestration by increasing plant biomass, especially when there is no difference among plant C contents. Besides, biomass also significantly affected the deposition of photosynthetically-fixed C into the plant-soil system[16]. Although aboveground C store largely contribute to the whole carbon pool, it’s better to evaluate the aboveground and belowground together. We should pay attention to carbon sequestration from a more holistic perspective in our further research.

      Soluble salt content and pH value can reflect the degree of soil salinization, which are important evaluation indexes of saline-alkali soil[46]. Gelaye et al.[47] employed rhodes grass, alfalfa, sudangrass and blue panicgrass to cultivate them in saline field plots, and found they affected pH and mitigated soil salinity and soluble saline ion concentrations. Our study showed that the green manure cultivation reduced soil pH, among which, Medicago sativa and Lolium multiflorum had a better effect on reducing soil pH than Brassica campestris. Additionally, except Lolium multiflorum, salt-tolerant plant cultivation could effectively reduce soil EC. The planting of salt-tolerant green manure had an obvious inhibition effect on the surface soil salinity of soil, which might be due to the planting reducing the surface evaporation and keeping the salt in the subsoil or some salt was taken up and carried out of the soil by salt-tolerant grasses. This is consistent with the results obtained by Liu et al.[48] that planting salt-tolerant forage grass can reduce soil salinity and pH value. For soil nutrients, certain cultivation treatments increased soil organic matter (Eureka+, Huayouza 62) and available nitrogen contents (Aurora, Sanditi, Sardi, Lolium multiflorum), which was consistent with Jing et al.[49] that the planting of M. sativa, S. sudanense, S. bicolor, and P. frumentum had significant enhancement on soil organic matter and available nutrient contents in inland saline soil. Astier et al.[50] also demonstrated that when vetch and oat were planted, soil organic matter and total N increased. The growth of legumes does not always increase the content of total nitrogen in the soil[25], which supported our result that the total soil nitrogen after Medicago sativa and Brassica campestris cultivation decreased to different degrees, as compared with non-cultivation, mainly due to the fact that green manure needs to absorb a large amount of a variety of nutrients from soil during its growth and development, and its consumption of nitrogen increases, resulting in a decrease in total nitrogen content.

      After the green manure was returned to the saline alkali field, the pH of the majority treatments were decreased, which might be related to the different dry matter composition of different green manure varieties and their different degradation and transformation rates in soil[51, 52]. Cao et al.[53] also found significant reduction in pH was determined under field conditions of 4 years of alfalfa cultivation in salt-affected soils. The content of soil organic matter, total nitrogen and available nitrogen in cultivation treatments were increased to different degrees compared with the control, indicating that returning green manure to the field had an obvious effect on soil fertility improvement. For Lolium multiflorum and Brassica campestris, their biomass was large, thus the nutrients that carried and were released into the soil were also correspondingly large. For Medicago sativa, except the biomass, a large amount of nitrogen was returned to the soil based on the nitrogen fixation effect to increase the nitrogen content in the soil. Previous studies[46, 54] demonstrated that a variety of soluble organic matter would be produced in the decomposition process, which could promote the efficient cycling of soil nutrients and regulate the soil nutrient balance. The comprehensive soil fertility quality index is a quantifiable index, which indicates a soils ability to perform specific ecological functions[55]. The difference of soil fertility index among different treatments indicates that returning green manure to the field is a good way to improve saline alkali soil quality. Also, the biomass amount of green manure species largely contributed to the soil fertility improvement during the process of returning to the field. Ma et al.[56] generated a comprehensive evaluation of green manure on soil properties based on a meta-analysis and showed their significant improvement effects on soil quality, which was consistent with our research.

      According to our PLS-PM analysis, green manure varieties, slow-changing soil properties, fast-changing soil properties, plant C and biomass make a good explaination for soil fertility improvement after plants are returned to the field. We summarized two ways from the process of cultivation to returning to the field and found plant biomass was the core variate to improve soil fertility. Moreover, plant biomass had the greatest influence on soil fertility, suggesting that the increase of soil fertility mainly depended on the biomass of green fertilizer returned to the field. Thus, we concluded that Lolium multiflorum that has a large aboveground biomass was the best potential variety to improve saline alkali soil fertility. Except for plant biomass, fast-changing soil properties, mainly available N, also had an indirect, but large, influence on soil fertility. Feng et al.[57] found that high soil nutrient availability has a positive effects on the alfalfa biomass. Accumulated evidence suggests that fertilization and biomass are strongly correlated in agricultural ecosystems[58]. Zhang et al.[59] demonstrated that N fertilizer application directly promoted ryegrass yield. Therefore, we should pay more attention to improve aboveground biomass to ameliorate soil quality in saline alkali soil in the future.

    CONCLUSIONS

    • We employed different green manure variates to measure the effects of cultivation for aboveground carbon sequestration and returning to the field to ameliorate soil quality in saline alkali soil, based on a field experiment. We determined that plant carbon store was positively correlated with aboveground biomass. Green manure varieties, slow-changing soil properties, fast-changing soil properties, plant C and biomass all contributed to soil fertility improvement after aboveground returning to the field. The biomass production was a determining factor contributing to soil fertility, and variety with higher biomass production would more effectively improve soil quality in saline alkali soil. Our study can provide crucial theoretical support and a feasible way for green and sustainable development of saline-alkali agriculture.

      Acknowledgments

      • We thank Zhibo Zhou for his assistance in writing and data analysis. This research was financially supported by the Key Research & Development Plan of Jiangsu Province (BE2021365) and Jiangsu Science and Technology Project (SZ-SQ2021060).

      Conflict of interest

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

      Supplementary information
      • Supplemental Table S1 ANOVA F-values of soil properties measured under different treatment at green manure harvest in saline alkali soil field experiment
      • Supplemental Table S2 ANOVA F-values of Soil properties measured under different treatment after green manure returning to saline alkali soil field for 30 days
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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/.
    Figure (4)  Table (3) References (59)
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    Zhang F, Han Y, Shang H, Ding Y. 2023. Effects of green manure cultivation for aboveground carbon store and returning to the field to ameliorate soil quality in saline alkali soil. Grass Research 3:1 doi: 10.48130/GR-2023-0001
    Zhang F, Han Y, Shang H, Ding Y. 2023. Effects of green manure cultivation for aboveground carbon store and returning to the field to ameliorate soil quality in saline alkali soil. Grass Research 3:1 doi: 10.48130/GR-2023-0001
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Catalog

  • Html

  • Introduction
  • Materials and methods

  • Green manure materials

  • Field site description

  • Experimental design

  • Collection of aboveground and soil samples

  • Determination of aboveground samples

  • Determination of soil physicochemical properties and soil fertility evaluation

  • Statistical analyses

  • Results

  • Green manure biomass of different varieties cultivation

  • Green manure carbon sequestration of different varieties cultivation

  • Soil physicochemical properties in different cultivation treatment at green manure harvest

  • Variation of soil physicochemical properties in different treatment after green manure returning to field for 30 days

  • Effects of different returning treatment to improve soil quality in saline alkali field experiment

  • Contributions of green manure varieties, soil physicochemical properties, plant c, green manure biomass on soil fertility improvement

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