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2021 Volume 1
Article Contents
Introduction
Plant phenotyping
Ground-based plant phenotyping platforms
Aerial-based plant phenotyping platforms
Remote sensing technologies
Visible light imaging
Spectral imaging
Infrared thermal imaging
Fluorescence imaging
Other imaging technologies
Conclusion and future perspective
Acknowledgments
Conflict of interest
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References
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China requires region-specific manure treatment and recycling technologies

  • Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, Chinese Academy of Science/Hebei Key Laboratory of Soil Ecology/Key Laboratory of Agricultural Water Resources, Chinese Academy of Science Shijiazhuang 050022, China

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  • Introduction

    Turfgrasses are utilized throughout inhabited regions of the world for home lawns, athletic field and golf course turf, parks and recreational areas, and roadside vegetation[1]. In addition, turfgrass seed and sod production contribute to the significant economic, ecological, and environmental values of the turf industry[2]. It is estimated that maintained turfgrass in the U.S. covers approximately 20 million hectares of managed land[3]. The annual economic value of the turfgrass industry is approximately $60 billion, making a large contribution to the national economy[4]. The value of turfgrass continues to grow due to strong demand for use in landscape, recreation, and sports areas, as well as environmental and aesthetic benefits of turfgrasses such as moderating temperatures, preventing soil erosion, reducing noise and air pollution, and increasing property values[1,5].

    Extensive efforts in turfgrass breeding have resulted in persistent, attractive varieties with improved turf quality characteristics, pests and stress tolerance, and reduced maintenance requirements[6]. Breeding objectives currently focus on improving tolerance to abiotic stress factors such as drought, heat, cold, and salinity and biotic stresses such as diseases and insects[7−10]. In addition, efforts are also aimed at developing grasses that will perform at high levels with limited inputs of fertility, irrigation, pesticides, and mowing[7−9]. Breeders of seed propagated species continue to focus on improving seed yield characteristics and identifying grasses with resistance to seed production diseases such as stem rust, caused by Puccinia graminis, while for sod propagated species, breeders focus on developing varieties with improved sod-forming ability[7−9]. In addition to the aforementioned objectives, breeders are working to maintain the high turf quality characteristics that have been bred into all major turfgrass species to date[9].

    Modern breeding programs strive to incorporate advanced breeding strategies such as DNA sequencing and high-throughput phenotyping with traditional breeding strategies to identify and select germplasm and genes of interest[11−14]. In recent years, DNA sequencing and molecular biology methods have rapidly increased, and, as a result, plant phenotyping is currently a bottleneck in the process of advancing breeding programs[15−17]. Recent advances in remote sensing have offered improved, non-destructive plant phenotyping approaches[18−20]. These advancements have been coupled with improvements in robotics and unmanned aerial systems (UAS) technology to provide mobile, time efficient platforms for remote sensors that have contributed to high-throughput plant phenotyping applications across large fields (Fig. 1).

    Figure 1.  Overview of high-throughput phenotyping tools for modern turfgrass breeding programs. 1: UAS for remote sensing data collection on mowed turf plot trials; 2: ground robot for proximal sensing data collection on mowed turf plot trials; 3: turfgrass breeder for visual assessment and oversight of various data collection practices on mowed turf plot trials; 4: ground vehicle for proximal sensing data collection on mowed turf plot trials; 5: ground vehicle for proximal sensing data collection on turfgrass nursery trials; 6: turfgrass breeder for visual assessment and oversight of various data collection practices on turfgrass nursery trials; 7: ground robot for proximal sensing data collection on turfgrass nursery trials; 8: UAS for remote sensing data collection on turfgrass nursery trial; 9: weather station for environmental data collection. All data is stored and processed via cloud computing services.
    DownLoad: Full-Size Img PowerPoint

    The use of ground- and aerial-based platforms and imaging technologies for high-throughput phenotyping applications have been thoroughly reviewed previously[13,16,17,19,21−26]. This review provides an overview of ground- and aerial-based plant phenotyping platforms, with particular emphasis placed on applications to turfgrass breeding practices. Similarly, imaging technologies that have been used in various plant breeding programs are discussed, with indications as to how those technologies could be applicable to turfgrass breeding programs.

    Plant phenotyping

    A phenotype is the physical appearance of a plant; this includes complex traits related to architecture, growth, development, physiology, ecology, yield, and tolerance to abiotic and biotic stresses[23,27]. Plant phenotyping is the act of assessing phenotypic plant traits in order to rank or compare germplasm to identify elite lines for breeding purposes[28]. Traditionally, plant phenotyping has involved the use of manual and visual assessments, which are labor intensive, time consuming, and variable due to observational bias and preference[29]. These limitations, in light of genotyping advancements[30−32], have led to a phenotyping bottleneck in plant breeding programs[15, 33−36]. However, many breeding programs have combined efforts from biological science, computer science, mathematics, physics, data science, and statistics to develop more efficient phenotyping methods, which is an area of research commonly known as high-throughput plant phenotyping[37]. The high-throughput phenotyping approaches employed in breeding programs consist of both ground- and aerial-based platforms that are equipped with various remote sensors to efficiently collect quantitative and geospatial data across large geographic areas[38].

    Ground-based plant phenotyping platforms

    Ground-based plant phenotyping involves the assessment of plant phenotypes using proximal sensors, which are located close to the plants of interest[19]. For this application, sensors may be handheld or mounted on phenotyping platforms such as stationary towers, cable suspensions, and ground vehicles[39,40]. Handheld sensors are convenient to use but require long periods of time to phenotype large fields, which can result in significant environmental variation during the data collection process[23,24,41]. Moreover, data collection is not always consistent among different evaluators using handheld devices, and this adds systematic error to resulting datasets[42]. Another limitation of handheld sensors is that only one sensor may be used at a time, which does not provide the best solution for a high-throughput, time efficient means of plant phenotyping[23]. Stationary towers and cable suspensions are also acceptable for certain phenotyping applications, but their use is limited by aspects such as inability to cover large field areas and angle distortion issues that arise from having a single viewpoint and collecting data across large fields[22].

    Several ground vehicle plant phenotyping platforms have been developed for various breeding applications in crops such as cotton (Gossypium barbadense L.)[43], maize (Zea mays L.)[44], triticale (× Triticosecale Wittmack L.)[45], and wheat (Triticum aestivum L.)[46,47]. These ground vehicle platforms range from simple pushcart designs to more sophisticated motor-driven buggies and are capable of accommodating multiple sensors and other data recording devices[43,45,46,48−50]. For turfgrass applications, there are different types of ground-based platforms with various sensors and cameras to be used in field phenotyping (Fig. 2). Researchers have demonstrated the usability of ground-based mobile platforms to accurately and precisely monitor characteristics such as soil moisture[51−53], turfgrass health[51−54], and turfgrass disease symptoms[54]. A major benefit of ground-based platforms is that they generate high spatial resolution data, which is required for plant science research and breeding programs. However, field-scale applications of these ground-based approaches are limited by the time to phenotype large areas and the fact that soil conditions immediately following irrigation or precipitation events can limit access to ground-based platforms[22−24,41].

    Figure 2.  Examples of ground-based phenotyping devices used in turfgrass breeding and research. Left: pushcart with multispectral sensor; middle: light box with digital camera; right: hand-held NDVI meter. (Photo credit: Brian Schwartz).
    DownLoad: Full-Size Img PowerPoint

    Aerial-based plant phenotyping platforms

    Aerial-based plant phenotyping involves the assessment of plant phenotypes using aerial, remote sensors, which are located farther away from the plants of interest than proximal sensors[26]. Aerial-based plant phenotyping efforts began by using traditional, manned vehicles such as small airplanes, blimps, and parachutes, which all remain useful for certain phenotyping applications today[23]. However, advancements in UASs have increased rapidly in recent years, and these platforms have become routinely used for remote sensing-based plant phenotyping applications.

    Traditional aerial vehicles such as small airplanes and blimps require a person to be onboard for operational purposes[23]. These vehicles have higher payloads than UASs but generally require relatively higher operational altitudes and speeds. Such limitations have given rise to a widespread use of UAS technology. By definition, a UAS consists of a vehicle that can travel through the air without a person onboard for operation[55]. The UASs are typically categorized as either fixed wing or multicopter aircrafts. The selection of one platform over the other is dependent upon a specific application and available resources, as these platforms vary widely in terms of maneuverability, initial costs, maintenance costs, run time, and payload[23].

    Fixed wing UASs, compared to multicopter UASs, have faster flight speeds, longer flight times, and can carry a heavier payload[56]. This means that fixed wing systems can cover more land area and can accommodate more sensors and other data recording devices onboard. The limitations to fixed wing UASs are also attributed to the fast travel speeds; operators must be aware of image blurring risks and ensure onboard sensors are compatible with the fast speeds of travel[56]. In addition, fixed wing aircrafts cannot hover, and, with exception to some fixed wing aircrafts that have vertical takeoff and landing capabilities, they require relatively large areas for takeoff and landing[56]. Multicopter UASs, on the other hand, have slower flight speeds, shorter flight times, and cannot carry as heavy of a payload as fixed wing systems[57]. The ability of multicopter platforms to maintain stable positions at slower travel speeds and lower altitudes gives them an advantage for use in plant science research and breeding programs[58].

    Remote sensing technologies

    Various remote sensing technologies have been explored for plant phenotyping applications. Many of these technologies are based on plant interactions with light at wavelengths that span much of the electromagnetic spectrum (Fig. 3). The following sections provide detailed descriptions of visible light imaging, spectral imaging, infrared thermal imaging, and fluorescence imaging technologies with particular emphasis on their usefulness in high-throughput plant phenotyping for turfgrass breeding applications. These remote sensing approaches are primarily used to assess two-dimensional plant characteristics but can be used to assess limited three-dimensional plant traits as well. However, light ranging and detection (LiDAR) and ultrasonic sensors represent much more appropriate options for assessing three-dimensional plant architecture and are also discussed herein.

    Figure 3.  Plant light reflectance curve at wavelengths ranging from 300 nm to 2,500 nm. Chlorophyll absorption, red edge, spongy mesophyll reflectance, and water absorption regions are shown[23,169].
    DownLoad: Full-Size Img PowerPoint

    Visible light imaging

    Visible light imaging is based on plant interactions with light intensities in the 400 nm to 700 nm wavelength range (Fig. 4) and is meant to mimic human perception[23]. For phenotyping purposes, visible light imaging is primarily used to capture plant characteristics such as color, morphology, and architecture[23,25]. This is an affordable and convenient imaging solution and has been extensively used for plant phenotyping applications among various crop species[13,26]. Standard digital cameras are typically used for visible light imaging to capture raw data that correspond with photon fluxes in the red (~650 nm), green (~550 nm), and blue (~450 nm) spectral bands (Fig. 4); for this reason, these images are often called RGB images.

    Figure 4.  Light wavelengths along the electromagnetic spectrum captured by various optical sensors. Visible light imaging sensors for 400 nm to 700 nm, spectral imaging sensors for 400 nm to 2,500 nm, and infrared thermal imaging sensors for 7,500 nm to 13,000 nm[17].
    DownLoad: Full-Size Img PowerPoint

    Once RGB data are captured, there are different approaches that can be taken to process the data depending on the objectives of a given project. One approach for analyzing these data is to convert RGB images into color indices such as excess green index, green index, green leaf index, greenblue, normalized difference index, or visible atmospherically resistant index, which can be done using gray-scale, single band data (Table 1). This approach can also be used to obtain measurements of percentage green cover by thresholding, which is a pixel classification procedure whereby pixels with values above a threshold are classified as green and pixels below a threshold are classified as non-green[59]. A second approach is to convert RGB pixel values to hue, saturation, and brightness (HSB) pixel values, which can subsequently be used to generate measurements including percentage ground cover[60] and plant color[61]. The HSB data can also be used to calculate the dark green color index (Table 1). In addition to plant characteristics such as green cover and plant color, plant breeders can also obtain plant height information using the Structure-from-Motion technique, which combines computing algorithms, digital cameras, and aerial vehicles to reconstruct a three-dimensional digital surface model of the target[62,63]. This approach is challenging to use in mowed turfgrass research because of the low canopy height (< 10 cm) but does offer some promise in estimating yield for seed production research and breeding programs.

    Table 1.  Color, temperature, and vegetation indices used in plant remote sensing research and breeding applications.
    IndexFormulaReference
    Canopy-Air Temperature Difference (CATD)TL – TA[162]
    Canopy Temperature Variability (CTV)σTC[163]
    Crop Water Stress Index (CWSI)(TC−TA)−(TC−TA)ll(TC−TA)ul−(TC−TA)ll[164]
    Dark Green Color Index (DGCI)(Hue−6060+(1−Saturation)+(1−Brightness))3[61]
    Difference Vegetation Index (DVI)Near Infrared − Red[108]
    Enhanced Vegetation Index (EVI)2.5NearInfrared−RedNearInfrared+(6Red)−(7.5Blue)+1[110]
    Excess Green Index (ExG)2Green – Red – Blue[165]
    Green Chlorophyll Index (GCI)NearInfraredGreen−1[111]
    Green Difference Vegetation Index (GDVI)Near Infrared – Green[113]
    Green Index (GI)GreenRed[166]
    Green Leaf Index (GLI)2Green−Red−Blue2Green+Red+Blue[83]
    GreenBlue (GB)Green−BlueGreen+Blue[85]
    Normalized Difference Index (NDI)Green−RedGreen+Red[167]
    Normalized Difference Red Edge (NDRE)NearInfrared−RedEdgeNearInfrared+RedEdge[112]
    Normalized Difference Vegetation Index (NDVI)NearInfrared−RedNearInfrared+Red[106]
    Optimized Soil Adjusted Vegetation Index (OSAVI)NearInfrared−RedNearInfrared+Red+0.16[109]
    Ratio Vegetation Index (RVI)RedNearInfrared[104]
    Simple Ratio (SR)NearInfraredRed[105]
    Temperature Stress Day (TSD)Tstress – Tnon-stress[168]
    Transformed Vegetation Index (TVI)NearInfrared−RedNearInfrared+Red[107]
    Visible atmospherically resistant index (VARI)
    		Green−RedGreen+Red−Blue[84]
     | Show Table
    DownLoad: CSV

    Visible light imaging has been widely used in turfgrass science research to date[64]. Since the early 2000s, researchers have routinely used RGB digital imagers attached to ground-based, enclosed lighting systems (Fig. 2) to collect phenotypic data for turf plot trials. Percentage ground cover measurements have been used to evaluate important turfgrass characteristics such as establishment rate[65−68] and turf performance during periods of drought[69−73] and traffic[74,75] stress, for example. Turfgrass color measurements, indicated by the dark green color index (Table 1), have been used to monitor turfgrass diseases[76−78] and seasonal turf performance[79,80].

    In recent years, studies have been conducted to assess the potential applications for RGB imagers mounted to aerial platforms. The first study to use a UAS-mounted RGB camera in turfgrass science research found only a 1.5% difference between digital image data and ground survey data when studying turfgrass response 40 d after herbicide application using an unmanned helicopter[81]. More recently, Zhang et al.[82] compared ground- and aerial-based measurements on small plot bermudagrass (Cynodon spp.) and zoysiagrass (Zoysia spp.) research field trials and found that both UAS-based green leaf index and visible atmospherically resistant index, introduced by Louhaichi et al.[83] and Gitelson et al.[84], respectively, adequately predicted ground-based percent green cover ratings. Hong et al.[85] evaluated the ability of UAS-based RGB imagery to detect early drought stress in creeping bentgrass (Agrostis stolonifera L.) and reported that the greenblue color index (Table 1) enabled drought stress detection 5 d before decreases in visual turf quality were observed. These studies offer foundational evidence that RGB digital imagery is an affordable, entry-level plant phenotyping tool, and it is anticipated that additional studies of UAS-based visible light imaging will be reported in the future to further characterize the usefulness and limitations of this technology for turfgrass breeding applications.

    Based on prior research in turfgrasses and other crops, some limitations of UAS-based RGB imagery have been identified. Examples of current concerns include the difficulties in differentiating various plant stresses, processing datasets when sun and shade irregularities exist within the plant canopies at time of data collection, and challenges in distinguishing soil from vegetation in noncontinuous plant canopies. These and other issues are being further studied to search for solutions and enhance the usability of this technology for phenotyping applications. On a positive note, commercial UASs, fully integrated with RGB cameras and software for mapping missions, are available for plant breeders, requiring minimal technical training to operate compared to earlier developed platforms.

    Spectral imaging

    Spectral imaging sensors, also known as imaging spectrophotometers, collect data from the interaction of plants with light intensities that span much of the electromagnetic spectrum[28]. There are several key wavelengths (Fig. 3) along the spectrum that have been extensively studied in prior research. Light reflection from plant leaves is limited within the visible light range, as much of the light is absorbed by leaf pigments, particularly the chlorophyll; there is a notably high reflectance at approximately 550 nm in the green region and low reflectance at approximately 450 nm and 680 nm in the blue and red regions, respectively[86]. As wavelengths extend into the near-infrared range (690 nm to 730 nm), there is a marked increase in light emittance due primarily to light scattering within leaf cells[87]. This region has proven useful for assessing various plant characteristics, and because of the drastic increase in reflection at this region, it is commonly called the 'red edge'[88]. Just beyond this region, there is a water absorbing band at 970 nm that has been used as an indirect assessment of plant leaf water content[89−91]. There are also additional regions of interest as wavelengths progress into the short-wave infrared region (1,000 nm to 2,500 nm). For example, strong water absorbing bands exist at 1,200 nm, 1,450 nm, 1,930 nm, and 2,500 nm, which could potentially be used for remote assessment of leaf water content[92−95].

    Spectral imaging can be further classified into multispectral imaging and hyperspectral imaging (Fig. 5). Multispectral imaging collects discrete light reflectance data from approximately 3 to 10 bands, where the bands are typically broader than those in hyperspectral sensing[96]. These bands are typically well characterized and often assigned descriptive titles. Hyperspectral imaging, on the other hand, collects continuous light reflectance data from tens to thousands of bands. In this case, the bands are much narrower than those in multispectral sensing, and they do not typically have descriptive titles.

    Figure 5.  Comparison of multispectral imaging and hyperspectral imaging. Discrete light reflectance data is generated from multispectral sensors whereas continuous light reflectance data is generated from hyperspectral sensors[170].
    DownLoad: Full-Size Img PowerPoint

    Arguably the most noteworthy work to come from plant spectral imaging research to date has been the derivation of various vegetation indices (Table 1), which are calculated based on simple mathematical functions such as differences or ratios between spectral reflectance at two or more spectral bands[97]. Vegetation indices are found to be useful in assessing chlorophyll and biomass production[98], plant stress and health[99−101], and nutritional status[102] in plants.

    Trenholm et al.[103] used a hand-held Cropscan multispectral radiometer to measure turfgrass reflectance at seven wavelengths, which were subsequently used to calculate four vegetation indices as indicators of turfgrass visual quality, shoot density, and shoot tissue injury from traffic wear. This was one of the earlier studies to correlate turfgrass reflectance data with traditional visual qualitative estimates. Fitz–Rodríguez and Choi[97] found that normalized difference vegetation index, ratio vegetation index, and difference vegetation index (Table 1) correlated well with turfgrass visual quality under different irrigation treatments. Developing new and improved vegetation indices has been the focus for research projects for many years, and those types of studies are still actively being conducted at present[104−113]. However, the normalized difference vegetation index, which was first introduced by Rouse et al.[106], has been extensively studied and remains one of the most widely used vegetation indices of plant health across various plant species, including turfgrasses[100,101,114,115].

    Spectral imaging is a promising technology for high-throughput plant phenotyping applications. As mentioned above, this technology is adaptable to ground- or aerial-based platforms and offers the ability to investigate plant interactions with light intensities beyond the visible light range. Many plant responses are more active outside the visible light range; therefore, spectral imaging in the near-infrared and short-wave infrared regions offer insights to many plant behaviors that are not detectable with visible light imaging platforms. Widespread implementation of spectral imaging technologies in plant science research and breeding programs has been slowed by a few difficulties that are being addressed in current research projects. Two of the most notable limitations to spectral imaging are the large quantities of data that are generated and the startup costs associated with purchasing these instruments. However, research and advancements in fields such as computer science and data science are offering solutions to these issues.

    Infrared thermal imaging

    Infrared thermal imaging, also known as long-wave infrared imaging, thermal long-wave infrared imaging, or forward-looking infrared imaging, collects reflectance data in the far-infrared and long-wave infrared range of wavelengths, which span from 7,500 nm to 13,000 nm (Fig. 4). Over the last few decades, there has been mounting interest in using infrared thermometers to characterize drought- and heat-induced plant water stress based on the concept that water-stressed canopies have higher temperatures than well-watered canopies[116]. However, other than plant physiological status, canopy temperature measurements can also be affected by other factors such as surface soil exposure, solar radiation, and air temperature at the time of observation. Indices have been developed to normalize canopy temperature measurements to account for these types of environmental factors (Table 1).

    Among the indices listed in Table 1, crop water stress index is one of the most commonly used indices in studies on turfgrass irrigation scheduling. Jalali-Farahani et al.[117] reported that midday estimates of crop water stress index in bermudagrass were related to soil percent available extractable water. Bijanzadeh et al.[118] monitored crop water stress index of bermudagrass subjected to deficit irrigation on a monthly basis in southern Iran and concluded that turfgrass quality can be maintained with seasonal crop water stress index being kept at a value of approximately 0.15. However, one of the challenges is to accurately measure the upper and lower limit of temperature difference between canopy and air; such types of baseline values vary across different soil and environmental conditions[119] and could be dynamic during the day[120]. A model was developed to predict those baselines in tall fescue with meteorological factors such as air temperature, solar radiation, vapor pressure deficit, and wind speed[121].

    Another limitation regarding the application of canopy temperature is the dynamic nature of the measurement, which is highly variable if the time of data collection stretches too long. More valuable information can be derived regarding the water status of the plants if the data collection can be done within a few minutes. Infrared thermal cameras mounted on UASs would provide an option for thermal imagery to be collected across turfgrass breeding trials within minutes. Moreover, exposed soil among vegetation could potentially be removed if combined with RGB and multispectral imagery. Several hurdles need to be overcome to use UAS-based thermal imagery including temperature calibration, canopy temperature extraction, and establishment of canopy temperature-based crop water stress indicator[122]. Early exploration was reported using UAS-based thermal imagery to detect early drought stress in creeping bentgrass[85]. The researchers detected a rise of canopy temperature under 15% and 30% evapotranspiration replacements before visible decline of turf compared to 100% ET plots. More studies are needed to address these limitations associated with using UAS-based thermal imagers to detect drought stress in turfgrass.

    Fluorescence imaging

    Fluorescence is the emitted light generated during the absorption of short wavelength radiation, and in plants, the chlorophyll complex is the most common fluorescing machinery. As chloroplasts are irradiated with actinic or blue light, a portion of the light absorbed by chlorophyll will be reemitted as fluorescence[123]. The proportion of absorbed light that gets reemitted varies due to the plant's light metabolic capacity[124]. This fluorescence is a valuable indication of the plant's ability to assimilate actinic light[125]. Moreover, adding brief pulses of saturating blue light to the actinic light is useful to assess plant status for physiological parameters such as non-photochemical quenching and photo-assimilation[23].

    Fluorescence imaging, also known as chlorophyll fluorescence imaging, is the procedure of capturing images of fluorescence emitted by plants upon illumination with visible or ultraviolet light[126]. This technique commonly uses charge-coupled device cameras that are sensitive to fluorescence signals generated by light-emitting diodes, pulsed flashlights, or pulsed lasers[127]. Fluorescence imaging provides an efficient means for in vivo assessment of the electron transport rate, the extent of non-photochemical quenching, and the effective and potential quantum efficiency of photosystem II[128−130]. Many uses of chlorophyll fluorescence imaging have been investigated including early detection of pathogen attack[131−135], herbicide injury[136,137], and other abiotic and biotic stress factors[134,138−140].

    Although fluorescence imaging is a promising technique for assessing plant health status, there are several limitations that have inhibited its implementation for high-throughput plant phenotyping in field settings. Fluorescence imaging requires that plants be dark-adapted prior to light excitation, meaning data collection for each plant sample will take multiple minutes[126]. In addition, currently available fluorescence imaging systems are only capable of measuring fluorescence from single leaves; for high-throughput applications, the technology must be developed to assess multiple plants at once. Another complication is that substantial power sources are needed to operate various light and sensor components of fluorescence imaging systems[141]. For this technology to be applicable for high-throughput plant phenotyping, concerns around robustness, reproducibility, and fluorescence image processing must be addressed.

    Other imaging technologies

    Plant traits related to height and canopy architecture are highly prioritized in breeding goals and can be obtained through three-dimensional reconstruction of plant canopies[28]. LiDAR and ultrasonic sensors are both classified as ranging sensors, which means they measure the distance to the nearest object by emitting an electromagnetic signal and calculating the time difference between emitting and receiving the signal to indicate distance to the target[142]. For LiDAR, a laser beam is emitted to the target and the reflected light is analyzed[143]. One of the advantages of using LiDAR is being able to supply structural information of plants with high accuracy compared to other sensors due to view-obscuration from nadir view. In theory, LiDAR-based plant phenotyping can provide information from the leaf level to the canopy level, potentially helping diagnosis of plant status and crop management[143]. Growing literature reported the use of LiDAR-based plant phenotyping in row crops such as maize[144], sorghum [Sorghum bicolor (L.) Moench.][145], soybean [Glycine max (L.) Merr.][146], and cotton[147], focusing on traits including plant height, row spacing, and biomass.

    Given the high cost and availability of the integrated platform, LiDAR is less explored for plant phenotyping than other technologies. Limited studies investigated the use of LiDAR-based phenotyping in turfgrass. Nguyen et al.[148] reported using an unmanned ground vehicle (DairyBioBot) and LiDAR pipeline for the high-throughput phenotyping of biomass in forage perennial ryegrass (Lolium perenne L.) with R2 = 0.73 at the plot level when correlating with fresh mass basis observation. Nonetheless, the application of LiDAR in individual-plant-level phenotyping is promising in the future as this technology continues to be developed and becomes more affordable and integrated in user-friendly platforms.

    Ultrasonic sensors are generally more affordable compared to LiDAR. Similar to LiDAR, ultrasonic sensors can be used to estimate geometrical parameters of plants (for instance, plant height and canopy volume) if appropriate acquisition and data processing is applied. Studies were carried out to use ultrasonic sensors to estimate plant height in cotton[43], alfalfa (Medicago sativa L.) and bermudagrass[149], and wheat[150]. Yuan et al.[151] compared LiDAR, ultrasonic sensor, and RGB camera mounted on UAS in estimating plant height in wheat and concluded that LiDAR and UAS-mounted RGB camera provided the best results. Therefore, the strength of ultrasonic is not prominent but it provides an alternative for LiDAR in estimating plant height on the ground level when the target plants are too small for UAS applications.

    Conclusion and future perspective

    Plant breeding programs have greatly benefited from recent advancements in DNA genotyping technologies. However, plant phenotype assessment has become the limiting factor in screening large numbers of plants in current plant breeding programs. Advancements in remote and proximal sensing technologies have led to the development and implementation of high-throughput plant phenotyping practices, which is beginning to increase the efficiency of plant phenotyping. Visible light imaging has been the most widely used remote sensing approach. This is a relatively inexpensive phenotyping solution for assessing plant traits such as ground cover and canopy architecture. Future research efforts of visible light imaging for plant phenotyping applications should emphasize the need for improved analysis approaches to account for shading issues and light variation as well as alleviating difficulties associated with distinguishing soil from plant tissues.

    Spectral imaging technologies have expanded in recent years and are becoming increasingly more prevalent in plant science research efforts. This technology is expected to continue to expand for additional plant phenotyping applications. Turfgrass breeders have already begun experimenting with this technology and have found promising results thus far. As the technology advances, it is expected that the initial costs associated with purchasing equipment will reduce; this will enable more plant breeding programs to utilize this technology. Research efforts should continue in developing improved data handling and processing options to better accommodate the large datasets generated using this imaging technology.

    Thermal imaging and fluorescence imaging are two technologies that are also being adapted to field applications. Although these technologies are not currently suited for in-field breeding applications, researchers are experimenting with these technologies to determine their usefulness in monitoring plant health and growth characteristics. As these technologies continue to be developed, it is anticipated that they will be more readily used in turfgrass breeding applications. Additionally, range sensors such as LiDAR will be further developed for use in assessing morphological characteristics such as leaf texture, leaf width, and plant height for turfgrass breeding programs.

    In addition to the phenotyping tools mentioned in this review, various other technologies are being explored to efficiently assess plant root phenotypes both in controlled environment and field conditions. Programs such as EZ Rhizo[152], IJ Rhizo[153], Root System Analyzer[154], Root Trace[155], Smart Root[156], and WhinRhizo[157] have been widely used for image-based analysis of root architecture. However, these approaches do not offer in situ root analyses, as they require roots to be cleaned of soil. Options for in situ root assessment include the use of mini-rhizotrons equipped with cameras or scanners to periodically gather root architecture data[158]. This approach is not well-suited for high-throughput applications and can only accommodate limited numbers of genotypes[159]. Other promising approaches currently being investigated include non-destructive methods such as magnetic resonance imaging and X-ray computed tomography[160,161]. The development of high-throughput phenotyping tools for characterizing root performance under stresses such as drought, insect feeding, and disease will be valuable resources for plant breeding programs in the future.

    Modern turfgrass breeding programs will continue to research, develop, and implement remote sensing technologies for high-throughput plant phenotyping applications. These technologies will enable turfgrass breeders to assess larger numbers of genotypes to efficiently identify elite germplasm. All together, these efforts will improve cultivar development efficiency and aid plant breeders in developing improved turfgrass cultivars to meet current and future demands of the turfgrass industry.

    Acknowledgments

    The authors would like to acknowledge the Rutgers Center for Turfgrass Science and the USDA – NIFA Specialty Crop Research Initiative (grant number: 2019-51181-30472) for partial funding of this effort.

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

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  • Cite this article

    Bai Z, Wang X, Wu X, Wang W, Liu L, et al. 2021. China requires region-specific manure treatment and recycling technologies. Circular Agricultural Systems 1: 1 doi: 10.48130/CAS-2021-0001
    Bai Z, Wang X, Wu X, Wang W, Liu L, et al. 2021. China requires region-specific manure treatment and recycling technologies. Circular Agricultural Systems 1: 1 doi: 10.48130/CAS-2021-0001
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China requires region-specific manure treatment and recycling technologies

  • Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, Chinese Academy of Science/Hebei Key Laboratory of Soil Ecology/Key Laboratory of Agricultural Water Resources, Chinese Academy of Science Shijiazhuang 050022, China

  • Received: 28 December 2020
  • Accepted: 04 January 2021
  • Published online: 22 February 2021
Circular Agricultural Systems  1,  Article number: 1  (2021)  |  Cite this article

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  • Heterogenous distribution of crops, feed and livestock across China has halted the circulation of nutrients within the agricultural system and is responsible for massive nutrient losses[1, 2]. Generated livestock manure exceeded optimal crop requirements in 30% and 50% of over 2 300 studied counties when there was improved recycling of nitrogen (N) and phosphorus (P) in the food chain, repectively[2]. Most of these counties are located in southern and coastal areas, whereas there is a deficit of livestock manure in northern and western China. Such heterogenous distribution of crop-livestock production led to 4.0 Tg manure N and 0.9 Tg manure P[2], which are economically impossible to recycle and will end up in the surrounding environment. In addition, about 40% of feed protein consumed by domestic livestock production relied on importation, putting China’s livestock production supply at high risk in the post pandemic world[3]. Hence, China is facing the twin issues of too many manure nutrients but too little feed nutrients simultaneously. Such mismatch of feed protein demand and manure nutrient production is more severe at the regional level due to the heterogenous distribution of crops, feed and livestock within China, which may further impact sustainable livestock production.

    Heterogenous distribution of crop-livestock production sites has also led to region-specific conservation activities. For example, southern China has suffered from severe water pollution, resulting from intensive watercourse and livestock production, leading to lower capacities for crop nutrient uptake[4]. Hence, the central government initiated the south-to-north pig transfer project in which southern farms were closed and northern ones established to effectively manage water pollution[5]. This will, however, increase ammonia emissions in northern China, a region already suffering from high PM2.5 levels that are in part due to ammonia emissions generated from livestock production[6, 7].

    Recently, treatment and the recycling of manure have received greater attention from the public and policymakers. The central government has tightened environmental regulations on livestock manure management, aiming to promote the recycling of manure to reduce losses[8]. New regulations require manure to be treated for parasites, e. coli, flies and mosquitos prior to field application. However, this policy has overlooked the possible increase of ammonia emissions from this mandatory manure processing. Increasing ammonia emissions may have large impacts on the quality and biodiversity richness of plants in some protected and ecologically sensitive regions, contradicting the newly released ecological protection and restoration polices by the National Development and Reform Commission[9].

    Current environmental regulations on livestock manure treatment overlook environmental risk as well as region-specific requirements and conditions. Treatment and circular manure systems across different regions are both necessary, but China lacks technologies and relevant system designs, despite a long history of manure application. Lessons learned abroad, where there is oftentimes less heterogeneity of crop-livestock production, may be difficult to adapt to China. For example, in the Netherlands, a country with high livestock density and a surplus of nutrients, manure recycling and processing is far-reaching and well developed. Around 25% of its annually produced manure is exported to neighboring countries after being heated at 70 °C for one hour[10]. However, such transregional transportation costs could contribute up to 10% of total production costs in livestock farms in the Netherlands[11]. In China, the lower profitability of livestock production and longer transportation distances limit the possibility of transnational or trans-provincial transportation of manure, especially when large surpluses of manure are located in South China with deficits more common in the Northeast[2]. Lessons and technology systems from the Netherlands are difficult to adapt to China, particularly given the recent controversial ban of agricultural production due to ammonia emissions in protected regions in the Netherlands[12].

Requirement for region-specific manure treatment strategies and technologies

  • We argue the need for designing new region-specific manure recycling and treatment strategies to combat environmental pollution, reduce reliance on imported feed and protect vulnerable ecosystems in China. The criteria for the design of such a system should seriously consider the following elements: (i) recycling nutrients in the food chain; (ii) local livestock feed self-sufficiency; and (iii) county-level human health and ecosystem vulnerability to ammonia emission.

    All regions are categorized into three groups according their status: regions (Region I) with an excess of manure nutrients, increased sensitivity to ammonia emissions and lack of feed that should implement new modern technologies to treat manure with low ammonia emissions, high efficiency and rapid manure dewatering, liquid manure nutrient concentration and feed protein production technologies, which will allow exporting excess manure nutrients to other regions while increasing internal recycling of manure nutrients as feed protein (Fig 1, 2); regions (Region II) with moderate soil nutrient carrying capacities featuring sensitivity to ammonia emissions that should more focus on solid-liquid manure separation and solid manure dewatering to allow exporting solid manure and in-situ application of liquid manure within region (Fig 1, 3); and regions (Region III) with a deficit of manure nutrients featuring no sensitivity to ammonia emissions and feed protein supply that should implement little or no treatment technologies to allow the full potential recycling of manure nutrients on farms (Fig 1, 4).

    Figure 1. 

    Flow chart dividing all cities into three different manure treatment and recycle regions.

    Figure 2. 

    Technical model diagram of region I, including: reactor composting, liquid manure concentration technologies and freed protein production.

    Figure 3. 

    Technical model diagram of region II, including: efficient solid-liquid separation and liquid returned to field.

    Figure 4. 

    Technical model diagram of region III, including: manure storage and in-situ field application.

    Through using information from over 2,300 counties, including crop nutrient uptake, whole food chain nutrient management, ammonia emissions and self-sufficiency of feed protein, we have developed the first region-specific designation of manure treatment technologies in China (Fig 1). Categorization was carried out at the city level with over 360 cities included due to the strong role of city-level governments in providing subsidies to technology companies and farms. This illustrative example shows that the total area of Region I covers around 14.3 million ha cropland (12% of the manure N production), and Region II and III cover the remaining 97% and 88% of cropland and manure N production, respectively (Fig 1).

Strategies and technology systems for Region I

  • Region I is generally located in southeast China, mainly in Guangdong, Fujian and Hainan Provinces as well as the cities of Beijing and Tianjin (Fig 1). Technology system concepts are shown in Fig 2. Generally, livestock farms in Region I need to implement strict ammonia mitigation options in animal housing, such as frequent cleaning, air purification and filtration. Region I has a lower capacity to recycle manure, hence there is less need for storing large amounts of manure as most of it is regularly treated. Solid manure should be dewatered via advanced reactor composting technology, most of which is either directly exported outside of the region or used to produce insect feed to feed animals (Fig 2). The liquid part of manure needs to be treated via reverse osmosis technology to concentrate nutrients, allowing for long-distance transportation. Summarized, the three main proper treatments of manure in Region I are as follows: i) advanced reactor composting technology; ii) efficient liquid manure nutrient concentration technologies; and iii) feed protein production technologies.

    Reactor composting. Reactor composting technology is an effective and environmentally friendly method[13]. The well-controlled temperature and aeration in closed vessels achieves the elimination of pathogens, parasites and weed seeds within 7−10 days, with more than 90% of antibiotics and their resistance genes undergoing degradation[13]. Combined with a serial exhaust gas bio-filtration system, zero emission of ammonia, GHG and odor could be achieved[14, 15]. Meanwhile, fresh manure generated in livestock farms could be feeding into reactors continuously, combined with manure cleaning systems in animal housing. Closed composting reactors play the role of both storage and treatment for manure that shortens the manure management chain. Its high efficiency and small size make it an ideal in-situ manure treatment method for intensive livestock farms. It could thus be used both in Region I and II to achieve NH3 mitigation and convert manure into high-quality organic fertilizer that benefits manure nutrient transportation and epidemic prevention.

    Liquid manure concentration. Reversed osmosis (RO) is based on the ability of RO-membranes to let water pass and block salt ions. The technique is widely used for the desalination of sea water. Recently, the application of selective electrodialysis with monovalent exchange membranes on the recovery phosphate or ammonium from sewage water and livestock slurry has been investigated as a promising technique, and the technology has performed well in the Netherlands. Recovery efficiency of ammonia could reach 78% and 75% of phosphate and 87% of volatile fatty acids via using a bipolar membrane electro-dialysis system[16]. After treatment, 20% of mass was retained in the solid fraction, while 30% and 50% was retained in the concentrate and permeate material. The volume of concentrate, which contains higher concentration of ammonium-N, K and other elements, could be further reduced via use ventilating heat[17]. The effectiveness of nutrients in the concentrate was comparable to granulate chemical fertilizer (calcium-ammonium-nitrate), a common fertilizer in European countries. The Netherlands began pioneering experiments in 2009, and in total, eight large pilot plants have successfully treated slurry[17]. These could serve as good examples for Region I in China as nutrients in liquid slurry are effectively concentrated, allowing long-distance transportation to get rid of excess manure nutrients.

    Feed protein production. The use of insects for animal manure management is a sustainable and low-cost technology that effectively reduces the volume and nutrient concentration of manure residue, thereby reducing potential pollution. The black soldier fly larva (BSFL), one of the most powerful recyclers, can reduce the bulk of manure residue by 56% and nutrient concentrations by 40%−55% within 14 days of manure breeding[18]. Besides effectively degrading antibiotics in the manure[19], BSFL can also greatly reduce the abundance of pathogenic bacteria[20], decrease the offensive odor[21] and inhibit the breeding of house flies[22]. Few pilot plants have initiated commercial level production. Henan Enzyme Company has developed a pilot-scale automated BSFL breeding facility to treat pig manure and produce insect proteins[23]. Through cloud technology, the device can upload data in real-time, and the controller can adjust and monitor the parameters of the breeding workshop through mobile application or computer. The facility can completely decompose 3.24 tonnes (78% moisture) of pig manure per day and produce 480 kg of insect biomass. This effectively reduces the volume and nutrient concentration of the manure residue, thereby reducing potential pollution by at least 50%−60%[18]. BSFL biomass, containing about 40% protein and 30% fat[24], can be used as a substitute for soybean and fish meal for feeding poultry and fish without causing adverse reactions[25]. Through the recycling of insects, the twin problems of excessive manure nutrients and shortage of feed in Region I can be addressed simultaneously. An alternative choice is microalgae protein production. Recently, microalgae have been considered as a promising solution for wastewater management owing to their high capacity to deplete inorganic nutrients (N and P) from a wide range of wastewater[26]. Microalgae could use wastewater effluent as a source of carbon and nitrogen to support their rapid growth and be converted to microbial protein as animal feed after proper filtration, concentration and drying.

Strategies and technology systems for Region II

  • Region II covers major cropland production areas in China, excluding the northeast and Region I (Fig 1). These area shows a moderate level of ammonia emission intensity, manure loading capacity and feed self-sufficiency; accordingly, attention should focus on the trans-regional recycling of manure with less emphasis on complex and high-cost treatment technologies. The core recommended technology system is solid-liquid separation in which solid manure is dewatered via closed reactor composting, transported between regions and liquid manure injected to fields adjacent livestock farms.

    Efficient solid-liquid separation system. Solid-liquid separation can be divided into sedimentation, drainage, centrifugation and pressure filtration (Fig 3). Centrifugal separation is the most effective method for slurry separation, with the highest removal rate of total solids reaching up to 60%[27]. However, centrifugal equipment is used relatively less in China due to its high price, high energy consumption and demanding maintenance. Pressurized filtration, including screw press and press auger, is usually more efficient than screening technology. Improving pressure can increase the removal rate of solids and accelerate the removal of nitrogen, phosphorus, potassium and other nutrient elements. This technology is suitable for widespread application in China due to lower costs[28]. In addition, flocculent can be used before separation to improve separation efficiency.

    Liquid manure injection or irrigation. Liquid manure is rich in N after separation, which can be connected to the irrigation system through the pipe network or transported to the farmland and applied with supporting agricultural machinery as liquid fertilizer. Liquid fertilizer can be diluted directly into irrigation water for field application, but the spread of nutrients on the field surface may be uneven[29]. Liquid fertilizers can be applied to fields by spraying vehicles, but it may cause the evaporation of ammonia and odors[30]. Deep soil injection could reduce ammonia emissions. Fecal injection could be combined with seeding, reducing costs and improving seeding rates, especially in dry regions and seasons. In the Netherlands, strict regulations of manure application have promoted the development and manufacturing of equipment related to liquid fertilizer. The disc-type fertilizer applicator is an injection machine with disc harrow capable of simultaneous stubble crushing, land overturning and manure injecting[31, 32]. In China, use of the liquid injection machine is relatively unexplored. However, injecting liquid fertilizer can greatly improve the utilization rate of fertilizer and reduce pollution. Li et al. (2020) showed that integrating seedings with liquid manure injection could replace 50% of mineral N fertilizer, reduce ammonia emissions by 27%−49% and increase corn grain yield by 17%−33%[33].

Strategies and technology systems for Region III

  • Region III, largely found across Xinjiang, Inner Mongolia and Northeast China, has an abundance of feed resources, with higher manure nutrient loading capacity and lower sensitivity to ammonia emissions. These regions usually feature larger farms, which require fewer manure treatment technologies but better manure storage facilities. This is because these regions have a single cropping system that is different from Region I and II. This indicates manure could only be applied once per year in these regions. An ideal model for Region III is shown in Fig 4. This model is characterized by manure storage and in-situ field applications. Manure needs to be stored in belowground concrete tanks for almost one year. Coverage, which reduces the timing for manure exposure in the air and resulted in reduced emissions of NH3, odour[34, 35] as well as surface acidification, further decreasing NH3 emissions with lower cost[36], are two favorable technologies to preserve N in manure during long-term storage. Applying the slurry in accordance with the principles of 4R nutrient stewardship, that is, applying the slurry at the right place, rate, time and type[37, 38], could also be used to help treat storage manure.

Policy implications

  • We used current data to demonstrate an approach to designing region-specific technology systems for the treatment and recycling of manure in China, and we provided an illustrative example in Fig 1. This is a preliminary analysis based on limited data and broad assumptions, and more research is needed to improve the granularity of such designs by quantifying water and airborne pollution as well as landscape and transportation costs at the regional level. Furthermore, region-specific manure management designs could become part of the recently implemented rural revitalization[39] and Blue-Sky actions[40].

    The ultimate goal of manure treatment is to reduce environment impacts by recycling it as either fertilizer for crop production or feed protein for livestock production. However, in China, livestock has been largely decoupled from crop production in terms of exchange between feed and manure at both the farm household and regional scale. The share of rural households with both crop and livestock production has declined from 71% to 12% in the period between 1986 to 2017[41]. A recent nation-wide survey revealed that only 1/3 of farmers were willing to use manure as fertilizer in cereal crop production[42] due to concerns of cost, odor, antibiotics and heavy metal issues. Recycling of manure-based insect feed protein to livestock production is also a controversial issue in terms of consumer acceptance. These obstacles could be alleviated via strict feed quality control regulations and public education campaigns.

MATERIAL AND METHODS

  • The criteria used in this study included soil-bearing capacity, local livestock feed self-sufficiency rate and ecosystem vulnerability. The soil-bearing rate refers to the ratio of total excretion of N by livestock and humans as well as the N withdrawal of harvested crops. In the present study, the soil-bearing capacity was estimated based on the NUFER (NUtrient flow in Food chains, Environment and Resources use) model, which calculates all nutrients for each city. The equation used to calculate soil bearing capacity was:

    Nsoil=Nhumanmanure+NlivestockmanureNplantuptake+Ngrass (1)

    Where N soil is soil bearing capacity, N human manure is the N content from human manure (tonnes N yr−1), N livestock manure is the N content from livestock manure (tonnes N yr−1), N plant uptake is N content taken by plants (tonnes N yr−1) and N grass is the N content taken by grass. The estimated soil bearing capacity was summarized in Fig 1. Jin et al. (2020) claimed that 2 was the threshold value for the soil-bearing capacity in China, which means areas with values higher than 2 were considered low soil-bearing capacity[2]. Across China, 19% of the total area was lower than 2 and considered as having high soil-bearing capacity. Conversely, areas higher than 2 were considered as having low soil-bearing capacity.

    The feed self-sufficiency rate refers to the ratio of domestically consumed feed supplied by domestic producers. Local livestock feed self-sufficiency rate was estimated based on livestock consumption and feed production. The equation used was:

    Nratio=Ntotalcons−Nimported+NexportedNtotalcons (2)

    Where N ratio is livestock feed self-sufficiency rate and N total cons is the total N consumption for each category of livestock (sheep, cattle, pig, poultry, horse, rabbit, mule and donkey). N imported is feed N imported from other areas. N exported is feed N exported to other areas. The distribution of livestock feed self-sufficiency rate across China is shown in Fig 2. The present study designated 0.7 as the threshold value, and values higher than 0.7 were defined as high livestock feed self-sufficiency rates. As seen in Fig 2, high livestock feed self-sufficiency rates (exceeding 0.7) were mainly located in northern and western China China.

    Ammonia emissions considered here include NH3 from crop and animal production. The amount of total ammonia emissions was estimated by the NUFER model (Fig 3). The SDGs report, EU SDG index scores and ammonia data are reported for each country. Using this data, the relationship between SDG index scores and ammonia emissions data was established through multiple linear regression analysis. The statistical model (R2 = 0.91, n = 23) used was:

    Ammonia=−1.4Score+104.9 (3)

    Where Ammonia is ammonia emission per agricultural land (kg ha−1) and Score is the SDG index score. SDGs report designated a score of 60 as the threshold for European countries, and this present study assumes the European standard as the corresponding limit for China. Therefore, this statistical modeling can provide the ammonia threshold value (31 kg ha−1) for the designated 60 score. As seen in Fig 3, higher ammonia emissions (higher than 31 kg ha−1) were found in southeastern regions.

    The total land area of China was divided into 3 regions, each of which in turn contains two or more ecosystem statuses (Fig 4): regions (Region I) with high ammonia emissions, low soil-bearing capacity and low livestock feed self-sufficiency rates; regions (Region II) with low ammonia emissions, low soil-bearing capacity and low livestock feed self-sufficiency rates; and regions (Region III) with low ammonia emissions and high soil-bearing capacity. This process was compiled in ArcGIS 10.6 in which areas were selected by overlaying different criteria layer (ecosystem status layers).

    Acknowledgments

    • This work was supported by the National Key R&D Program of China (2016YFD0800106); the National Natural Science Foundation of China (31572210, 31872403, 71961137011); Key Research Program of Frontier Sciences-CAS (QYZDY-SSW-SMC014); Key Laboratory of Agricultural Water Resources-CAS (ZD201802); the Key Research Program-CAS (KFJ-STS-ZDTP-053); Hebei Dairy Cattle Innovation Team of Modern Agro-industry Technology Research System, China (HBCT2018120206); the Youth Innovation Promotion Association, CAS (2019101) and Outstanding Young Scientists Project of Natural Science Foundation of Hebei (C2019503054).

    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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    Cite this article
    Bai Z, Wang X, Wu X, Wang W, Liu L, et al. 2021. China requires region-specific manure treatment and recycling technologies. Circular Agricultural Systems 1: 1 doi: 10.48130/CAS-2021-0001
    Bai Z, Wang X, Wu X, Wang W, Liu L, et al. 2021. China requires region-specific manure treatment and recycling technologies. Circular Agricultural Systems 1: 1 doi: 10.48130/CAS-2021-0001
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    • Requirement for region-specific manure treatment strategies and technologies
    • Strategies and technology systems for region i
    • Strategies and technology systems for region ii
    • Strategies and technology systems for region iii
    • Policy implications
    • Material and methods
    • Acknowledgments
    • Conflict of interest
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