Primary Productivity In China Research Articles

Explainable deep learning insights into the history and future of net primary productivity in China

Net primary productivity (NPP) is a crucial component of the terrestrial carbon cycle and plays a crucial role in assessing ecological security. Although machine learning models have been widely used in research on ecosystem modelling, however, the performance of deep learning models with interpretability in predicting NPP are still very limited. In this study, we developed an interpretable deep learning predictive model to uncover the historical drivers and future changes of NPP in China using remote sensing data, climate data and topography. The results show that more than three quarters (88 %) of China’s regional NPP have shown an increasing trend over the past 20 years, with an overall trend of 2.46 gC/m2/yr. This extensive range of dynamic processes is a nonlinear response to climatic conditions and topographic factors. The constructed convolutional neural network (CNN) model showed good predictive skill for historical NPP, and revealed that temperature is the main controlling factor, highlighting its importance in vegetation growth in China. Compared to the historical period (2001–2020), in the context of future climate change, the risk of carbon sinks decreasing in China is highly likely in the imminent near term (2021–2039), especially under high emission scenarios. These results suggest that interpretable deep learning method is a useful tool for revealing vegetation productivity drivers and estimating vegetation productivity under a nonstationary climate background.

The impact of drought time scales and characteristics on gross primary productivity in China from 2001 to 2020

ABSTRACT Drought events have occurred frequently in the past and have had profound effects on Gross Primary Productivity (GPP). However, the role of different drought event characteristics in the response of GPP to drought is not well understood. To address this issue, we investigated the spatiotemporal response patterns of Chinese terrestrial ecosystem GPP to different time scales of meteorological droughts, and explored the difference of GPP response to drought events under various drought characteristics. Our research revealed that: Different time scales of meteorological droughts significantly impacted the GPP of 17.3%–25.6% area of China. Among them, the impact range of 1-month and 12-month meteorological drought was the smallest and largest respectively. GPP along the 400 mm isohyet line was more susceptible to drought. Second, grasslands and shrubs are more susceptible to drought than forested areas. Among the characteristics of drought events, drought severity had the greatest impact on GPP changes, while drought frequency had the least impact. An increase in drought frequency intensified the impact of 1-month meteorological drought on GPP. Increased drought duration and severity weakened the impact of 3-month meteorological drought on GPP and intensified the impact of 1-month and 12-month meteorological droughts on GPP. Our study reveals the impact of different time scales drought and drought characteristics on plant productivity, and provides more possibilities for understanding the effect of drought on plant productivity.

Changing Climate Threatens Irrigation Benefits of Maize Gross Primary Productivity in China

AbstractIntensive irrigation has been proven to profoundly impact climate through the surface energy budget. However, the impacts of irrigation and climate interactions on gross primary productivity (GPP) in maize cultivated areas remain uncertain. Here we quantified the irrigation effects on maize GPP (∆GPP) across China by combining a land surface model and a light‐use efficiency model and using satellite‐based irrigation water use. We show that irrigation significantly contributed to an increase in maize GPP by an average of 430 gC · m−2 · yr−1, equivalent to 28% of the irrigated maize GPP in China. These benefits (∆GPP) were attributed to irrigation effects (water supply, surface cooling) and climate interactions based on a machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). Irrigation water supply and surface cooling explained 54% ± 19% and 23% ± 20% of ∆GPP respectively, the rest being due to strong climate interactions with irrigation through water and energy balance. Assuming business‐as‐usual irrigation levels, changing climate both increases and decreases ΔGPP over different regions, driven primarily by temperature changes. The irrigation benefits in those areas under heat stress are greatly threatened due to changing climate. The roles of climate change on ∆GPP are reversed from beneficial to detrimental in the North China Plain, dominated by different warming levels of future scenarios. Our analysis provides new insights into assessing irrigation potential with the climate interactions and future irrigation priority regions.

Spatial patterns of light response parameters and their regulation on gross primary productivity in China

Light response parameters are used to model the photosynthetic processes of ecosystems, but they are usually considered as fixed or empirical values for specific vegetation type and their geographical variation and influence mechanisms at ecosystem scale remain unclear. In this study, we analyzed the light response parameters (photosynthetic quantum yields, α; maximum gross photosynthetic rate, Amax) at 64 typical ecosystems of ChinaFLUX over 20 years’ measurement, to explore their spatial patterns and controlling factors. The results showed that the light response parameters significantly varied with ecosystem types: in grassland and desert ecosystems, both α and Amax were significantly lower than other ecosystems, and in cropland ecosystems, the Amax was significantly higher than that of other ecosystems. The light response parameters significantly decreased with increasing altitude. Vegetation index and photosynthetically active radiation were the dominant factor for the spatial variation of α, while the spatial variation of Amax was mainly controlled by moisture conditions. Compared to Amax, α was more stable which varied less with hydrothermal conditions. The dominant factors of light response parameters also differed among ecosystem types. Hydrothermal conditions regulated vegetation indexes and further modulate light response parameters and GPP. This study suggested that the quantitative analysis and simulation prediction of photosynthesis should consider the spatial heterogeneity of light response parameters and their different environmental responses at ecosystem scale in future studies.

Maximum Gross Primary Productivity Dominates the Trend in Gross Primary Productivity in China’s Deciduous Forest Ecosystems

The terrestrial gross primary productivity (GPP) has increased over the past two decades. However, the climatic attribution and the physiological and phenological processes that control the trends in the GPP are still unclear. Here, we used remote-sensing-based vegetation GPP and phenology datasets, analyzed the spatial and temporal variation in the GPP, investigated the influence of the growing season length (GSL) and the maximum value of gross primary productivity (GPPmax) on the annual GPP, and quantified the effect of climate variables on the annual GPP. Our results identified a significant increase in the annual GPP (11.97 gC/m2/yr) during 2001–2020 in China’s deciduous forest. The GPPmax trend dominated the trends in the GPP, when compared with the GSL. Moreover, climate warming in summer contributes to the increase in the GPP and the GPPmax, while the extension of the GSL is primarily due to the temperature rise in spring. The annual GPP of the planted forest showed a higher increasing rate than the natural forest, due to the significant enhancement of the GPPmax and the high sensitivity of the GSL to climatic factors in the planted forest. Our findings provide a new perspective on the phenological and physiological causes of the trends in the GPP, and emphasize the importance of capturing the variability in the GPPmax when modeling the GPP.

Spatiotemporal patterns and drivers of phytoplankton primary productivity in China's lakes and reservoirs at a national scale

Spatiotemporal patterns and drivers of phytoplankton primary productivity in China's lakes and reservoirs at a national scale

Accelerated vegetative growth measured by gross primary productivity in China from 1980 to 2018

Vegetation response to climate change can be characterized as long-term trends, but with periodic oscillations. Revealing vegetation response trends, with oscillations, is essential for formulating climate change mitigation and adaptation planning, as well as adopting ecological protection and restoration measures. Here, the long-term trend and periodic oscillations in vegetation changes were explored through a measure of gross primary production (GPP), an indicator of vegetation photosynthesis, using data of both high spatial resolution (1 km2) and a long-term of nearly 4 decades, from 1980 to 2018. The GPP data were estimated for China’s terrestrial ecosystem and validated against the eddy covariance-based observations of 70 site-year in China and compared to other GPP data products. The results showed the estimated GPP can explain 76% of spatial–temporal variance with the same data accuracy as other products, but with the advantage of both high spatial resolution and long time periods that other products do not have. From the estimated GPP data, Chinese terrestrial vegetation growth was found to have a periodic oscillation of close to 2.79 years and a long-term increasing trend of 59.8 Mt C per year over 92% of all vegetated land in China from 1980 to 2018. Moreover, it was found that vegetation growth accelerated significantly in the two recent decades over the previous two decades. The growth rate was 83.5 Mt C a−1 from 2000 to 2018, which was 2.31 times faster than the rate from 1980 to 2000. The underlying mechanism of the vegetation responses was analyzed by ridge regression, a method to decrease the effects of multicollinearity. Long-term variability and trends in vegetation could be attributed to a warming minimum air temperature when examined with precipitation, maximum air temperature, and solar shortwave radiation. Though more influences, such as those from freezing and thawing and human activities, should be explored in the future, this study offers insight that warming, with increasing daily minimum temperatures, is likely to be a vital contributor to ecosystem changes, and will be essential knowledge for mitigation of effects and adaptation to future climate.

Quantifying effects of different types of droughts on gross primary production in China

AbstractThe increasing impact of drought stress on ecosystems with regard to climate change has received considerable attention, which has been demonstrated to have a time‐lag effect on ecosystems. However, the time‐lag effect on different types of droughts and gross primary production (GPP) at large regional scales remains unclear. Thus, using the GOSIF GPP product data of solar‐induced chlorophyll fluorescence, the meteorological drought index (standardized precipitation evapotranspiration index [SPEI]), and the agricultural drought index (soil moisture deficit index [SMDI]) based on the soil moisture data obtained from the Global Land Data Assimilation System, the time‐lag effect of regional drought and GPP and the contribution of drought to GPP variation were analysed in China from 2000 to 2018. Results showed that (1) China's annual GPP increased at an average rate of 9.26 g C m−2 y−1. With regard to the average rate for each climate zone, the largest increase of 30.44 g C m−2 y−1 was observed in the southern subtropical region, and the smallest increase (4.01 g C m−2 y−1) was observed in the Qinghai–Tibetan plateau, with about 53.11% of the regions showing a significant upward trend. In addition, the significantly declining regions were primarily concentrated in urban areas, accounting for about 2.26% of China. (2) SPEI and GPP had an average lag time of 6.28 months, whereas SMDI and GPP had an average lag time of 7.17 months. The comparison demonstrated that SMDI had a greater influence on GPP than SPEI. (3) The average contribution rate of meteorological drought and agricultural drought to the decrease of GPP was 79.37% and 87.06%, and their average contribution rate to the increase of GPP was 39.11% and 50.57%, respectively. This study will provide comprehensive understanding of the effects of drought on carbon and water cycles in terrestrial ecosystems with regard to climate change.

Estimation and Climate Impact Analysis of Terrestrial Vegetation Net Primary Productivity in China from 2001 to 2020

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments.

Changes in dryland areas and net primary productivity in China from 1980 to 2020

Changes in dryland areas and net primary productivity in China from 1980 to 2020

The Forest Fire Dynamic Change Influencing Factors and the Impacts on Gross Primary Productivity in China

Forest fire as a common disturbance has an important role in the terrestrial ecosystem carbon cycling. However, the causes and impacts of longtime burned areas on carbon cycling need further exploration. In this study, we exploit Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large-scale forest fire dynamic monitoring in China. Band 2, band 4, band 6, and band 7 of MOD09A1 were selected as the most sensitive bands for calculating the Normalized Difference Fire Index (NDFI) to effectively estimate fire burned area. The Convergent Cross Mapping (CCM) algorithm was used to analyze the causes of the forest fire. A trend analysis was used to explore the impacts of forest fire on Gross Primary Productivity (GPP). The results show that the burned area has an increased tendency from 2009 to 2018. Forest fire is greatly influenced by natural factors compared with human factors in China. But only 30% of the forest fire causes GPP loss. The loss is mainly concentrated in the northeast forest region. The results of this study have important theoretical significance for vegetation restoration of the burned area.

Quantifying the contributions of climate factors and human activities to variations of net primary productivity in China from 2000 to 2020

Net primary productivity (NPP) plays a vital role in the globe carbon cycle. Quantitative assessment of the effects of climate changes and human activities on net primary productivity dynamics is vital for understanding the driving mechanisms of vegetation change and sustainable development of ecosystems. This study investigates the contributions of climatic factors and human activities to vegetation productivity changes in China from 2000 to 2020 based on the residual trend analysis (RESTREND) method. The results showed that the annual average net primary productivity in China was 325.11 g C/m2/year from 2000 to 2020 and net primary productivity showed a significantly increasing trend (p<0.05) at a rate of 2.32 g C/m2/year. Net primary productivity increased significantly (p<0.05) across 40.90% of China over the study period, while only 1.79% showed a significantly declining trend (p<0.05). The contributions of climatic factors and human activities to net primary productivity increase were 1.169 g C/m2/year and 1.142 g C/m2/year, respectively. Climate factors contributed positively mainly in Sichuan Basin, the Loess Plateau, the Mongolian Plateau, and Northeast China Plain. Positive contributions of human activities to net primary productivity mainly occurred in the Loess Plateau, Central China, and the Greater Khingan Mountains. The effects of climatic factors and human activities on net primary productivity changes varied among sub-regions. In Tropical Monsoon Climate Region and Subtropical Monsoon Climate Region, human activities had greater impacts on net primary productivity increase than climate factors, while climate factors were the dominant factor for net primary productivity recovery in other sub-regions. In addition, during 2000–2020, net primary productivity was dominated by both climate factors and human activities in 49.84% of China, while areas dominated solely by climate factors and human activities accounted for 13.67% and 10.92%, respectively. Compared to changed land cover types, the total net primary productivity as well as the increase of total net primary productivity in China was mostly contributed by unchanged land cover types, which contributed more than 90%.

A dataset of annual gross primary productivity in China’s terrestrial ecosystems during 2000-2020

The annual gross primary productivity (AGPP) is the basis of food production and carbon sequestration in terrestrial ecosystems. An accurate assessment of regional AGPP can provide a theoretical basis for analyzing the spatiotemporal variation of AGPP and ensuring regional food security and mitigating climate change trends. Based on Chinese Flux Observation and Research Network (ChinaFLUX) measurements and public datasets, we produced a dataset of annual gross primary productivity over China’s terrestrial ecosystems was constructed. In combination with biological, climatic, and soil factors, we used the random forest regression tree to construct the assessment model of China AGPP by simulating the AGPP of unit leaf area. The dataset of annual gross primary productivity over China’s terrestrial ecosystems during 2000-2020 was generated with a spatial resolution of 30arcsecond and a data format of tiff. The dataset can provide validation data for model simulation, as well as data support for regional productivity, ecological quality, and assessment and management of terrestrial carbon sinks.

Spatio-temporal dynamics of gross primary productivity in China from 1982 to 2017 based on different datasets

Understanding the spatio-temporal variations of gross primary productivity (GPP) of terrestrial ecosystem and its relationship with climatic factors can provide important basis for vegetation restoration and protection. Based on meteorological data and three public GPP datasets (EC-LUE GPP, GLASS GPP, and NIRv GPP), we syste-matically analyzed the spatial-temporal variations of GPP and its response to climate change in China during 1982-2017. All the results based on the three GPP datasets showed that the annual and seasonal GPP in China increased annually from 1982 to 2017, with that in 1998 and 2002 significantly being higher than the average level during the study period, and that in 1989 and 1992 significantly being lower than the average annual GPP. From 1982 to 2017, GPP showed a significant upward trend in most regions of China, with the regions with significant increases accounting for 75.7%, 73.0%, and 69.6% of the whole study area, respectively. There was a significant positive correlation between annual GPP and precipitation and temperature, but spatial heterogeneity was strong. Among them, the regions with positive correlation between GPP and temperature were mainly distributed in Northwest and Central China, while the regions with positive correlation between GPP and precipitation were mainly distributed in North China. There was obvious spatial-temporal heterogeneity in regions that GPP being affected by temperature and precipitation in different seasons. Temperature was the limiting factor of GPP in spring, autumn and winter, while summer GPP was mainly affected by precipitation.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Stand carbon storage and net primary production in China’s subtropical secondary forests are predicted to increase by 2060

BackgroundForest ecosystems play an important role in carbon sequestration, climate change mitigation, and achieving China's target to become carbon (C) neutral by 2060. However, changes in C storage and net primary production (NPP) in natural secondary forests stemming from tree growth and future climate change have not yet been investigated in subtropical areas in China. Here, we used data from 290 inventory plots in four secondary forests [evergreen broad-leaved forest (EBF), deciduous and evergreen broad-leaved mixed forest (DEF), deciduous broad-leaved forest (DBF), and coniferous and broad-leaved mixed forest (CDF)] at different restoration stages and run a hybrid model (TRIPLEX 1.6) to predict changes in stand carbon storage and NPP under two future climate change scenarios (RCP4.5 and RCP8.5).ResultsThe runs of the hybrid model calibrated and validated by using the data from the inventory plots suggest significant increase in the carbon storage by 2060 under the current climate conditions, and even higher increase under the RCP4.5 and RCP8.5 climate change scenarios. In contrast to the carbon storage, the simulated EBF and DEF NPP declines slightly over the period from 2014 to 2060.ConclusionsThe obtained results lead to conclusion that proper management of China’s subtropical secondary forests could be considered as one of the steps towards achieving China’s target to become carbon neutral by 2060.

Change Trend and Restoration Potential of Vegetation Net Primary Productivity in China over the Past 20 Years

As an important vegetation parameter and ecological index, vegetation net primary productivity (NPP) can intuitively reflect changes in the ecological environment and the level of the carbon budget. However, the change trend of NPP and its recovery potential in China over the past 20 years remain unclear. Here, we used trend analysis, multiple regression analysis and residual analysis methods to analyse the change trend in the NPP of China’s terrestrial ecosystems from 2000 to 2019, as well as the climax background, restoration status and restoration potential of the NPP of forest, grassland and desert ecosystems. The results showed that (1) the change in vegetation NPP in China from 2000 to 2019 showed a continuous upward trend, with a change slope of 2.39 gC/m2/a2, and the area with a positive slope of change accounted for 68.10% of the country’s land area. The contribution rates of meteorological conditions and human activities to vegetation NPP changes were 85.41% and 14.59%, respectively. (2) The results obtained by the regression analysis method of meteorological conditions based on nature reserves could reflect the zonal climax vegetation status to a large extent, and the obtained values had a smooth transition within each ecogeographical division and between each ecogeographical division, which truly reflected the law of gradual change in climate, vegetation and natural conditions. The annual total NPP of the climax background vegetation in China’s forest, grassland and desert ecosystems was approximately 2.76 ± 0.28 PgC, and the annual total NPP of the three ecosystems was 1.90 ± 0.2 PgC, 0.80 ± 0.07 PgC and 0.009 ± 0.0005 PgC, respectively. (3) The annual total vegetation NPP of the restoration status of China’s forest, grassland and desert ecosystems was 2.24 PgC, and the annual total vegetation NPP of the three was 1.54 PgC, 0.65 PgC and 0.007 PgC, respectively. Benefiting from the effective implementation of climate warming and humidification and ecological engineering, the agro-pastoral zone, the Loess Plateau, the eastern Sichuan Basin and the Greater Khingan Range had the most significant increases in the past 20 years. (4) The annual total vegetation NPP of China’s forest, grassland and desert ecosystem restoration potential was approximately 0.52 ± 0.28 PgC, which accounted for approximately 19.05% of the annual total NPP of the climax background vegetation. The annual total vegetation NPP of forest, grassland and desert ecosystems restoration status was 0.36 ± 0.2 PgC, 0.16 ± 0.07 PgC and 0.002 ± 0.0005 PgC, respectively; the restoration potential accounted for 18.80%, 9.67% and 23.95% of the climax background vegetation NPP, respectively. The deployment of ecological projects should fully consider the restrictive climate conditions for decision makers and ecological scholars, and the benefits and costs of the projects should be considered comprehensively.

Ocean—atmosphere Teleconnections Play a Key Role in the Interannual Variability of Seasonal Gross Primary Production in China

Ocean—atmosphere Teleconnections Play a Key Role in the Interannual Variability of Seasonal Gross Primary Production in China

2000年以来中国潜在植被NPP的时空分布模拟

PDF HTML阅读 XML下载 导出引用 引用提醒 2000年以来中国潜在植被净初级生产力的时空分布模拟 DOI: 10.5846/stxb202111053112 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（42071216）；甘肃省自然科学基金项目（21JR7RA145） Estimation of temporal and spatial distribution of potential vegetation net primary productivity in China since 2000 Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:潜在植被的净初级生产力（PNPP）是在无人类干扰情况下，立地所能发育形成最稳定成熟植被的净初级生产力，PNPP能够直接反映自然生态系统的质量状况，是分离人类活动对生态环境影响的重要指标。研究改进了CASA （Carnegie-Ames-Stanford Approach）模型，引入潜在光合有效辐射吸收比例（PFPAR）模拟了2000-2020年中国植被PNPP的时空分布格局。结果表明：中国PNPP空间分布差异较大，东南沿海和东北大、小兴安岭地区PNPP较高，新疆、西藏等西部干旱、高寒地区PNPP较低，呈现以400 mm等降水量线为界的空间分异格局。2000-2020年，中国PNPP总体上呈现递增趋势，年际波动小，PNPP减少的区域与增加的区域面积基本相等。PNPP波动较大的地区主要位于青藏高原、四川盆地和贵州高原。研究结果量化了气候变化背景下真实的生态状况和潜在生态状况的差异，可分离出人类活动对自然生态系统的直接影响，为制定针对性的生态修复对策提供了科学依据。 Abstract:Potential vegetation net primary productivity (PNPP) is the NPP of the most stable and mature vegetation type without human interference in the current climate conditions. PNPP can directly reflect the quality of natural ecosystems, which is an important index to separate the impact of human activities on ecological environment. This paper improves the CASA model and introduces the potential fraction of photosynthetically active radiation (PFPAR) to simulate the temporal and spatial distribution pattern of PNPP in China's vegetation from 2000 to 2020. The results show that the spatial distribution of China's PNPP is quite different. The PNPP is higher in the coastal areas of southeast China and the Greater and Lesser Khingan Range in northeast China, and lower in the arid and alpine regions of western China such as Xinjiang and Tibet, presenting a spatial differentiation pattern with 400 mm isoprecipitation line as the boundary. From 2000 to 2020, China's PNPP generally showed an increasing trend over time, but the interannual fluctuation was small. The area of PNPP decreased and increased was almost the same. The changes of PNPP in most regions are small, and the areas with large PNPP fluctuation are mainly located on Qinghai-Tibet Plateau, Sichuan Basin and Guizhou. The research results can separate the impact of human activities and climate change on natural ecosystem, and quantify the difference between real ecological status and potential ecological status under external pressure. It provides a scientific basis for formulating differentiated Ecological Restoration Countermeasures. 参考文献 相似文献 引证文献

Resource potential and global warming potential of fruit and vegetable waste in China based on different treatment strategies

Fruit and vegetable waste (FVW) contains rich resources that can be recovered by methods such as incineration, anaerobic digestion to generate heat energy, biogas, and preservation by ensiling. However, a horizontal comparison of the resource potential and environmental impact of different recycling methods employed for FVW has not been conducted. This study quantifies and computes the recycling potential and global warming potential (GWP) of anaerobic digestion, ensiling, and incineration of the FVW generated during primary production in China. First, a gray model was employed to estimate the FVW output in 2030, based on the FVW produced between 2002 and 2017. Next, the resource potential and GWP of anaerobic digestion, incineration, and ensiling were evaluated. Finally, an optimization method was utilized to analyze possible strategies of FVW recycling in 2030. Results indicate that FVW output in China is expected to increase to 170 Mt by 2030, highlighting the need for efficient treatment options. Further, the resource potential and GWP of different waste treatment strategies were notably different. The recycling potential of ensiling was the highest at 1950 MJ/t; while the GWP of anaerobic digestion was the lowest at −31 kg CO2eq. An optimization analysis suggested that the optimal target of 100% would be attained if all FVW is ensiled in 2030. The study provides a basis for informed technical decision-making related to FVW recycling options in the future.