Changes In Net Primary Productivity Research Articles

Evaluating net primary productivity dynamics and their response to land-use change in the loess plateau after the 'Grain for Green' program

Assessing net primary productivity (NPP) dynamics and the contribution of land-use change (LUC) to NPP can help guide scientific policy to better restore and control the ecological environment. Since 1999, the “Green for Grain” Program (GGP) has strongly affected the spatial and temporal pattern of NPP on the Loess Plateau (LP); however, the multifaceted impact of phased vegetation engineering measures on NPP dynamics remains unclear. In this study, the Carnegie-Ames-Stanford Approach (CASA) model was used to simulate NPP dynamics and quantify the relative contributions of LUC and climate change (CC) to NPP under two different scenarios. The results showed that the average NPP on the LP increased from 240.7 gC·m−2 to 422.5 gC·m−2 from 2001 to 2020, with 67.43% of the areas showing a significant increasing trend. LUC was the main contributor to NPP increases during the study period, and precipitation was the most important climatic factor affecting NPP dynamics. The cumulative amount of NPP change caused by LUC (ΔNPPLUC) showed a fluctuating growth trend (from 46.23 gC·m−2 to 127.25 gC·m−2), with a higher growth rate in period ΙΙ (2010–2020) than in period Ι (2001–2010), which may be related to the accumulation of vegetation biomass and the delayed effect of the GGP on NPP. The contribution rate of LUC to increased NPP in periods Ι and ΙΙ was 101.2% and 51.2%, respectively. Regarding the transformation mode, the transformation of grassland to forest had the greatest influence on ΔNPPLUC. Regarding land-use type, the increased efficiency of NPP was improved in cropland, grassland, and forest. This study provides a scientific basis for the scientific management and development of vegetation engineering measures and regional sustainable development.

Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Tibetan Plateau from 1979 to 2018

The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net Primary Production (NPP) and the effects of meteorological factors on the NPP change on the Tibetan Plateau (TP) using version 5.0 of the Community Land Model. The results showed that the average NPP was 256 (g C·m2·yr−1) over the past 40 years, with a continuously increasing trend of 2.38 (g C·m2·yr−1). Precipitation was the main factor affecting NPP changes, temperature had no significant effect on NPP changes, while radiation showed a negative trend. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively. Based on grass coverage, we categorized alpine grasslands into three types: high, medium, and low coverage. Our findings indicate the NPP change of the high-coverage grasslands was mainly affected by precipitation, and then the temperature and radiation. Comparatively, the precipitation change is the driving factor of the increased NPP of low-coverage grasslands, but the temperature increase is the negative factor. Our studies have implications for assessing and predicting vegetation responses to future climate change.

Analysis of spatio-temporal changes in net primary productivity and their driving factors in ecological functional areas of the Yellow River Basin

The Yellow River Basin (YRB) is an important region for both ecological and economic growth in China, and a healthy and stable ecological environment is fundamental to achieving high-quality development. Investigating the spatio-temporal changs of vegetation carbon sinks in the YRB is necessary to improve understanding of the regional carbon cycle. This study analyzed the YRB using the Google Earth Engine (GEE) platform. The basin was divided into sections based on ecological function regionalization schemes. We applied Theil-Sen median trend analysis and Spearman correlation analysis, along with the lasso regression model, to examine the NPP's temporal and spatial patterns and its driving factors in these regions. The objective was to examine the NPP’ s temporal and spatial patterns and its driving factors in these regions, identifying commonalities within regions and differences between regions, while analyzing in a targeted manner the lack of vegetation carbon sequestration in ecological areas. This study highlighted the importance of ecological engineering in ecological civilization and carbon neutrality, providing scientific theoretical support and a decision-making basis for the realization of carbon neutrality. The research results indicate significant upward trends (p<0.001) in both the average annual NPP (AANPP) growth rate (57.98%) and the total annual NPP (TANPP) growth rate (41.9%) in the YRB from 2001 to 2020. The multi-year average NPP (MYANPP) exhibit a spatial distribution with high values in the south and low values in the north. The spatial distribution of NPP exhibits notable spatial aggregation and heterogeneity, where regions demonstrating improvement trends constitute 93.01% of the area, and approximately 79.73% of this region displays low volatility. A high-value area is concentrated in the Agroecological Zone of the Fenwei Basin (I-11), the Loess Plateau Agriculture and Grassland Ecological Zone (I-12), and the Qinba Mountains Deciduous and Evergreen Broad-leaved Forest Ecological Zone (I-15), with significant spatial differences. Temperature, precipitation, and forests are the foremost elements affecting NPP in the YRB, exhibiting positive correlations. Conversely, farmland displays negative correlations. Overall, NPP shows an increasing trend in all ecological function areas, and the NPP of the YRB shows a trend of improvement as a whole, as the ecological environment of the YRB has improved significantly.

Spatiotemporal variation and prediction of NPP in Beijing-Tianjin-Hebei region by coupling PLUS and CASA models

Vegetation productivity is crucial for human production and livelihoods. Understanding net primary productivity (NPP) in historical contexts and predicting its future fluctuations is imperative for assessing the environmental sustainability of a region. However, relatively few researches have been conducted on predicting NPP, requiring further development and refinement of NPP prediction methods and models. This study introduces a novel approach that discretely couples the PLUS and CASA models for NPP prediction, and it validates the applicability of this approach in the research area. The objective of our study is to analyze the spatiotemporal patterns of NPP change in the Beijing-Tianjin-Hebei (BTH) region from 2001 to 2020, predict NPP under three different climate scenarios (SSP 1-2.6, SSP 2-4.5, SSP 5-8.5) in 2030, and identify an appropriate path for the future development of this region. The results indicate:(1) From 2001 to 2020, NPP in the research area has shown a gradual improvement trend and maintained a certain spatial distribution pattern in general. (2) The study discovered a correlation coefficient of 0.83 and an RMSE of 102.86 between predicted and actual NPP for 2020. This suggests that the method introduced in our study is suitable for predicting NPP in the research area. (3) NPP in the study area is predicted to decline in 2030 compared with 2020 under all three scenarios. Moreover, the SSP 1-2.6 scenario, representing a low-emission scenario, is suitable for the BTH region compared with other climate scenarios. This research sheds light on NPP variations in the BTH region over the past 20 years and the next 10 years, offering a scientific basis for relevant departments to formulate future policies.

Spatio-Temporal Variation and Future Sustainability of Net Primary Productivity from 2001 to 2021 in Hetao Irrigation District, Inner Mongolia

The Hetao Irrigation District in Inner Mongolia, a vital grain-producing region in northern China, faces growing environmental challenges. Studying net primary productivity (NPP) is essential for understanding spatiotemporal vegetation shifts and guiding locally adapted restoration and management efforts. Utilizing MOD17A3/NPP data, this study applies the Theil–Sen median trend, Mann–Kendall significance, and the Hurst index to scrutinize the spatiotemporal distribution patterns of NPP from 2001 to 2021 and forecast future changes in the area. The findings reveal cyclic temporal trends, forming a “∧” shape with initial increases followed by decreases, notably during the July to August period each year. The multi-year average NPP exhibits a slight upward fluctuation trend, averaging 172.40 gCm−2a−1. Peaks occur approximately every three years, reaching the highest average in 2012 at 218.96 gCm−2a−1. Spatially, NPP distribution stays consistent over the years, influenced by various land cover types, especially cropland, shaping the spatial patterns. Monthly and yearly NPP trends over the 21 years indicate a significant decrease in May and June, with other months mostly showing a non-significant increase. The Hurst index for monthly and yearly NPP changes over 21 years shows relatively high weak anti-persistence. In summary, over the past 21 years, the NPP trend in the study area has not significantly improved and is expected to decline in the future. This study offers data support and a scientific foundation for refining the carbon cycle model, quantifying vegetation carbon sequestration capacity, addressing climate change policies, and striving for carbon peak and neutrality in the Hetao Irrigation District.

Spatiotemporal pattern of NPP and its response to climatic factors in the Yangtze River Economic Belt

Net primary productivity (NPP) of terrestrial ecosystems serves as a crucial carbon sink, playing a significant role in mitigating climate change. This study, focusing on the Yangtze River Economic Belt (YREB), a key area for both economic development and ecological conservation in China, evaluates the temporal and spatial changes in regional NPP and its responses to climatic factors against the backdrop of global change. Utilizing long-term NPP and meteorological grid data from 1981 to 2017, we analyzed the annual change patterns of NPP and its linear and nonlinear responses to climatic factors using copula functions. Our findings indicate that 57.00 % of the YREB has experienced a significant increase in NPP, with the most substantial decreases observed in Shanghai and its surrounding areas (P < 0.05). Temperature emerged as the primary climatic driver, positively correlating with NPP changes in over half of the study area (P < 0.05). Moreover, the nonlinear response of NPP to temperature and precipitation underscores the complex interactions between these factors, revealing a sensitivity to temperature variations that surpasses that to precipitation. The study also highlights the influence of solar radiation and topographic factors, with a significant positive correlation between NPP and solar radiation observed in 15.85 % of the area (P < 0.05). The practical significance of these findings lies in their implications for regional ecological management and climate resilience strategies. The positive correlation between temperature and NPP underscores the potential for targeted temperature management to optimize carbon sequestration across the YREB. Furthermore, the identified nonlinear responses and the role of solar radiation and topography in influencing NPP offer valuable insights for the formulation of adaptive strategies that enhance ecosystem productivity and contribute to achieving carbon neutrality goals. By elucidating the dynamics of NPP in response to a range of environmental factors, this study provides a foundation for informed decision-making in ecological conservation and climate change mitigation efforts within the YREB and similar regions globally.

Spatiotemporal Dynamics and Influencing Factors of Vegetation Net Primary Productivity in the Yangtze River Delta Region, China

Vegetation Net Primary Productivity (NPP) plays a crucial role in terrestrial carbon sinks and the global carbon cycle. Investigating the spatiotemporal dynamics and influencing factors in the Yangtze River Delta (YRD) region can furnish a solid scientific foundation for green, low-carbon, and sustainable development in China, as well as a reference for other rapidly urbanizing regions. This study focuses on the YRD region as an illustration and utilizes the Carnegie–Ames–Stanford Approach (CASA model) to quantify NPP in this region from 2000 to 2018. Investigation into the spatiotemporal dynamics and influencing factors was conducted using Theil–Sen median trend analysis and scenario analysis. The results indicate that the NPP in the YRD region from 2000 to 2018 exhibited pronounced spatial differentiation characteristics, typically exhibiting a spatial distribution pattern of being high in the south and low in the north, high in the west and low in the east. Additionally, the expansion of built-up areas and the reduction in cultivated land have the potential to reduce NPP in the YRD region. Moreover, the influence of land-use and land-cover change (LULCC) is anticipated to be relatively limited compared to that of climate change. Furthermore, changes in precipitation were found to be positively correlated with changes in NPP, with the effect being relatively more pronounced. The correlation between temperature and NPP demonstrated spatial differentiation, with a mainly positive correlation in the central and southern parts of the YRD and a mainly negative correlation in the northern part. Changes in solar radiation had a negative correlation with changes in NPP. Based on these results, it is recommended that local governments strictly enforce urban development boundaries and manage the disorderly expansion of built-up areas, enhance the regional irrigation infrastructure, and address air pollution, so as to ensure the necessary conditions for the growth of vegetation, reduce greenhouse gas emissions, and control regional temperature rises. This study can provide stronger evidence for revealing the influencing mechanisms of NPP through the control of impact conditions and the exclusion of confounding factors via scenario analysis. The policy implications can offer insights into NPP enhancement and environmental management for the YRD and other rapidly urbanizing regions.

Analysis of change process of NPP dominated by human activities in Northwest Hubei, China, from 2000 to 2020.

Clarifying the spatial distribution of the impact of different human disturbance activities on the net primary productivity (NPP) in regions with single climatic conditions is of considerable importance to ecological protection. Time-series NPP from 2000 to 2020 was simulated in Northwest Hubei, China, and the effects of the climate and human activities on the NPP changes were separated. Research results showed that from 2000 to 2020, the NPP change with an area of 10,166.63 km2 in Northwest Hubei is influenced by climate and human activities. Among them, human activities account for as high as 84.53%. From 2000 to 2020, the NPP in Northwest Hubei showed a slight upward trend at a rate of 1.61g C m-2year-1. The significantly increased NPP accounted for 21.4% of the total, which was mainly distributed in north of Northwest Hubei. And the farming of cultivated land led to the increase of NPP in west as well as the reduced human distribution in cultivated land, which was scattered in forests. Only 6.67% of the total area demonstrated a significantly decreased NPP, which was distributed mainly in the central affected by the expansion of rural-urban land and change of broad-leaved forests to shrubs and in southeast regions of Northwest Hubei caused by the increase in potential evapotranspiration. This study refined the driving factors of spatial heterogeneity of NPP changes in Northwest Hubei, which is conducive to rational planning of terrestrial ecosystem protection measures.

Climate change causes spatial shifts in the productivity of agricultural long-term field experiments

Long-term field experiments (LTE) are highly valuable infrastructures in agricultural- and soil sciences for understanding the long-term impacts of climate and management practices. While they are designed to run under constant conditions, climate change is expected to affect site conditions considerably. This needs to be quantified when interpreting experimental results and when redesigning the experimental setup. One way to achieve this is by utilizing vegetation growth and carbon dynamics, specifically the Net Primary Productivity (NPP), as a spatially explicit indicator. NPP facilitates the assessment and interpretation of yield performance in LTEs under future climatic conditions. Our study estimated the changes in NPP for 271 LTE sites in Germany, comparing a baseline (2000–2020) with two scenarios (2081–2100) that were based on the Shared Socioeconomic Pathways (SSPs) (SSP245) and SSP585) by the Intergovernmental Panel on Climate Change (IPCC). We used the NASA-CASA biogeochemical model to calculate NPP in baseline and IPCC scenarios using Germany as a test case. LTEs were grouped by land use (crop types) and soil information (soil type, texture), drawing on the geodata infrastructure “BonaRes Repository”. The total annual terrestrial NPP for the baseline was calculated as 202.4 Mt C (sum of forests, grasslands, and arable lands) in Germany, while total NPP was up to 56.0 Mt C for different land use types. For both scenarios, NPP was projected to increase in LTEs located in southern Germany, indicating increased crop productivity, while a decrease was projected for the central Germany. The decrease in NPP of numerous LTEs in central Germany was estimated to extend to the LTEs in the eastern part corresponding to the worst-case scenario SSP585. Explicitly, the use of the multi-model ensemble mean as the climate driver in modelling may overestimate projected NPP by reducing inter-annual variability, highlighting the importance of methodological choices for accurate future projections. Besides, the results indicated that poor soils are projected to experience a further decline in productivity, primarily attributed to escalating water scarcity. Conversely, soils with high quality are likely to witness enhanced productivity, largely driven by the extension of the growing seasons. The outcomes of this study provide a basis for considering the future conditions of German LTEs and facilitate distinguishing between the effects of climate change and the impact of agricultural management on productivity at the regional level. These outputs enable planning and developing research strategies for selecting future LTE sites and redesigning existing or newly planned experiments. Moreover, the integrated modelling framework presented here highlights the potential of LTE data for large-scale modelling studies of ecosystem functions.

NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)

The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only −0.37%, while the multi-model average process-based model (MMEM) strongly underestimated −15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982–2010 and a significant positive correlation (r = 0.795, p &amp;lt; 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP.

Quantifying the contributions of climatic and human factors to vegetation net primary productivity dynamics in East Africa

As an important part of the terrestrial ecosystem, vegetation dynamics are subject to impacts from both climate change and human activities. Clarifying the driving mechanisms of vegetation variation is of great significance for regional ecological protection and achieving sustainable development goals. Here, net primary productivity (NPP) was used to investigate the spatiotemporal variability of vegetation dynamics from 2000 to 2020 in East Africa, and its correlations with climate factors. Furthermore, we utilized partial derivatives analysis and set up different scenarios to distinguish the relative contributions of climatic and human factors to NPP changes. The results revealed that NPP exhibited a significant increase with 4.16 g C/m2/a from 2000 to 2020 in East Africa, and an upward trend was detected across 71.06% of the study area. The average contributions of precipitation, temperature, and solar radiation to the NPP inter-annual variations in East Africa were 2.02, −1.09, and 0.31 gC⋅m–2⋅a–1, respectively. Precipitation made the greatest positive contribution among all of the climatic factors, while temperature made strong negative contributions. The contributions of climate change and human activities to NPP changes were 1.24 and 2.34 gC⋅m–2⋅a–1, respectively. Moreover, the contribution rate of human activities to NPP increase was larger than that of climate change, while the role of climate change in NPP decrease was larger than that of human activities. The findings of the study can provide new evidence for a deeper understanding of ecosystem stability and carbon cycling in East Africa, as well as a reference for decision-making and scientific support for ecological environmental protection.

Analysis of Spatiotemporal Evolution and Influencing Factors of Vegetation Net Primary Productivity in the Yellow River Basin from 2000 to 2022

The Yellow River Basin (YRB) plays a very important role in China’s economic and social development and ecological security, so studying the spatiotemporal variation characteristics of net primary productivity (NPP) and its influencing factors is of great significance for protecting the stable development of its ecological environment. This article takes the YRB as the research area, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, climate data, terrain data, land data, social data, and the gravity recovery and climate experiment (GRACE) data. The spatiotemporal evolution characteristics of vegetation NPP in the YRB from 2000 to 2022 were explored using methods such as trend analysis, correlation analysis, and geographic detectors, and the correlation characteristics of NPP with meteorological factors, social factors, and total water storage (TWS) were evaluated. The results indicate that the NPP of vegetation in the YRB showed an increasing trend (4.989 gC·m−2·a−1) from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The correlation coefficient indicates that temperature and accumulated temperature have a significant positive impact on the change of NPP, while TWS has a significant negative impact. In the study of the factors affecting vegetation NPP in the YRB, the most influential factors are soil type (0.48), precipitation (0.46), and temperature (0.32). The strong correlation between TWS and vegetation NPP in the YRB is about 39%, with a contribution rate of about 0.12, which is a factor that cannot be ignored in studying vegetation NPP changes in the YRB.

The impact of climate change and human activities on the change in the net primary productivity of vegetation-taking Sichuan Province as an example.

Vegetation is an essential component of terrestrial ecosystems, influenced by climate change and human activities. Quantifying the relative contributions of climate change and human activities to vegetation dynamics is crucial for addressing global climate change. Sichuan Province is one of the essential ecological functional areas in the upper reaches of the Yangtze River, and its vegetation change is of great significance to the environmental function and ecological security of the Yangtze River Basin and southwest China. In this paper, the modified Carnegie-Ames-Stanford Approach(CASA) model was used to estimate the monthly NPP (Net Primary Productivity) of vegetation in Sichuan Province from 2000 to 2018, and the univariate linear regression analysis was used to analyze the temporal and spatial variation of vegetation NPP in Sichuan Province from 2000 to 2018. In addition, taking vegetation NPP as an index, Pearson correlation analysis, partial correlation analysis, and second-order partial correlation analysis were carried out to quantitatively analyze the contribution of climate change and human activities to vegetation NPP. Finally, the Hurst index and nonparametric Man-Kendall significance test were used to predict the future change trend of vegetation NPP in Sichuan Province. The results show that (1) from 2000 to 2018, the NPP of vegetation in Sichuan Province has a significant increasing trend (Slope = 6.09gC·m-2·a-1), with a multi-year average of 438.72 gC·m-2·a-1, showing a trend of low in the east and high in the middle. The response of vegetation NPP to altitude is different at different elevations; (2) the contribution rates of climate change and human activities to vegetation NPP change are 4.12gC·m-2·a-1 and 1.97gC·m-2·a-1, respectively. In contrast, the impact of human activities on NPP is more significant than climate change. Human activities are the main factors affecting vegetation restoration and degradation in Sichuan Province. However, the positive contribution to NPP change is less than climate change; (3) the future vegetation NPP change trend in Sichuan Province is mainly rising, and the same direction change trend is much larger than the reverse change trend. The areas with an increasing trend in the future account for 89.187% of the total area. This research helps understand the impact of climate change and human activities on vegetation change in Sichuan Province. It offers scientific bases for vegetation restoration and ecosystem management in Sichuan and the surrounding areas.

Grassland productivity increase was dominated by climate in Qinghai-Tibet Plateau from 1982 to 2020

Grassland areas occupy 60% of the Qinghai-Tibetan Plateau (QTP) and play a critical role in enhancing the ecological barrier functions of the QTP. Grassland ecosystems, and net primary productivity (NPP) in general, have dramatically changed in time and space since the 1980s, with climate warming and the intensification of human activities. Current research widely believes that climate and human activities are the common driving factors of grassland ecosystem changes on the QTP. However, there is still controversy over their dominant driving factors and their contribution rates, especially during different research periods. Therefore, this study calculated the relative contributions of climate change, human activities (except LUCC) and LUCC to actual NPP (aNPP) changes during the two periods from 1982 to 2000 to 2001–2020. This study simulated three kinds of NPPs, that is, actual NPP, potential NPP, and human-appropriated NPP, using climate productivity, the light use efficiency model, and the residual method, respectively. Moreover, the study area was divided into two parts, and then the contributions of the three factors were calculated objectively. The results showed that (1) the actual NPP (aNPP) of the QTP increased dramatically from 1982 to 2000 but did not change obviously from 2001 to 2020. (2) Comprehensive evaluation showed that climate was the dominant factor for the aNPP net increase, and its contribution rate dramatically increased from 36.3% during 1982–2000 to 224.2% during 2001–2020. Human activities changed from a positive contribution (53.3%) to a negative contribution (−124.8%) to the aNPP net increase. The contribution rate of LUCC to the aNPP increase significantly decreased from 10.4% to 0.6%. Therefore, finer grassland restoration measures and government policies must be implemented to increase NPP against the background of favorable warm-wet climate conditions for increasing grassland NPP in the QTP. These results benefit the scientific management of grassland resources and ecological barrier building on the QTP.

Human activities significantly impact China’s net primary production variation from 2001 to 2020

Formulating ecological restoration strategies requires accurately quantifying how climate and anthropogenic factors influence net primary production (NPP). A Carnegie-Ames-Stanford approach (CASA) model was applied to estimate China’s terrestrial NPP from 2001 to 2020. We adopted a random forest (RF) method to identify the main driving forces for NPP change in China. Total NPP in China increased noticeably with a 24.91 Tg C/yr rate, as shown in our results. The significantly increased NPP was mainly attributed to human activities (64.29 ± 0.17%), chiefly due to human management and ecological projects (afforestation or other) fostered vegetation growth. The primary drivers of NPP variation varied in different geographic regions. Climate dominated the NPP dynamic in north China (52.38 ± 0.91%), where the main factor that restricted the increase of NPP was precipitation. Human activities strongly impacted the NPP variation in the remaining regions. Human management measures increased NPP in northwest and southwest China. In the northeast, east, and south-central China, the NPP change resulted from land use change, primarily grassland, cropland, and forest change. Collectively, our study expands the understanding of the driving forces of NPP change, informing different strategies for achieving ecological restoration and carbon neutrality.

Unraveling the enigma of NPP variation in Chinese vegetation ecosystems: The interplay of climate change and land use change

Global carbon emissions have exacerbated the greenhouse effect, exerting a profound impact on ecosystems worldwide. Gaining an understanding of the fluctuations in vegetation net primary productivity (NPP) is pivotal in the assessment of environmental quality, estimation of carbon source/sink potential, and facilitation of ecological restoration. Employing MODIS and meteorological data, we conducted a comprehensive analysis of NPP evolution in Chinese vegetation ecosystems (VESs), employing Theil–Sen median trend analysis and the Mann–Kendall test. Furthermore, utilizing scenario-based analysis, we quantitatively determined the respective contributions of climate change and land use change to NPP variations across various scales. The overall NPP exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 5.83 gC·m−2·year−1. Forestland ecosystem (FES) displayed the highest rate of increase (9.40 gC·m−2·year−1), followed by cropland ecosystem (CES) (4.00 gC·m−2·year−1) and grassland ecosystem (GES) (3.40 gC·m−2·year−1). Geographically, NPP exhibited a spatial pattern characterized by elevated values in the southeast and diminished values in the northwest. In addition, climate change had elevated 76.39 % of CES NPP, 90.62 % of FES NPP, and 71.78 % of GES NPP. At the national level, climate change accounted for 83.14 % of the NPP changes, while land use change contributed 14.14 %. Notably, climate change emerged as the primary driving force behind NPP variations across all VEGs, with land use change exerting the most pronounced influence on CES. At the grid scale (2 km × 2 km), land use change played a substantial role in all VEGs, contributing 60.01 % in CES, 54.20 % in FES, and 55.61 % in GES of the NPP variations.

Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019

Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China’s foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) &gt; GDP (0.326) &gt; precipitation (0.255) &gt; elevation (0.167) &gt; slope (0.136) &gt; temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world.

Opposing effects of warming on the stability of above- and belowground productivity in facing an extreme drought event.

Climate warming, often accompanied by extreme drought events, could have profound effects on both plant community structure and ecosystem functioning. However, how warming interacts with extreme drought to affect community- and ecosystem-level stability remains a largely open question. Using data from a manipulative experiment with three warming treatments in an alpine meadow that experienced one extreme drought event, we investigated how warming modulates resistance and recovery of community structural and ecosystem functional stability in facing with extreme drought. We found warming decreased resistance and recovery of aboveground net primary productivity (ANPP) and structural resistance but increased resistance and recovery of belowground net primary productivity (BNPP), overall net primary productivity (NPP), and structural recovery. The findings highlight the importance of jointly considering above- and belowground processes when evaluating ecosystem stability under global warming and extreme climate events. The stability of dominant species, rather than species richness and species asynchrony, was identified as a key predictor of ecosystem functional resistance and recovery, except for BNPP recovery. In addition, structural resistance of common species contributed strongly to the resistance changes in BNPP and NPP. Importantly, community structural resistance and recovery dominated the resistance and recovery of BNPP and NPP, but not for ANPP, suggesting the different mechanisms underlie the maintenance of stability of above- versus belowground productivity. This study is among the first to explain that warming modulates ecosystem stability in the face of extreme drought and lay stress on the need to investigate ecological stability at the community level for a more mechanistic understanding of ecosystem stability in response to climate extremes.

Impacts of Human Activities on the Net Primary Productivity of Vegetation in Chengde's Transitional Region from Plateau to Plain in the Context of Climate Change

Chengde's transitional region from plateau to plain is located in the transition zone of agriculture and livestock and is extremely sensitive to climate change and human activities. This study used the net primary productivity(NPP) of vegetation as an evaluation index to quantify the degree impacts of climate change and human activities on vegetation change in the region. The Thornthwaite Memorial model was used to calculate the potential NPP, and the actual NPP was obtained based on MODIS NPP remote sensing images, using the difference between the actual and potential NPPs to express the amount of change in NPP owing to human activities. We used the slope trend and coefficient of variation method to analyze the trend and stability distribution of the actual NPP, potential NPP, and NPP influenced by human activities, and the correlation between actual NPP and annual precipitation and annual average temperature was analyzed using the correlation coefficient method. Finally, we quantified the impact of climate change and human activities on vegetation change in the region. The results showed that 99.87% of the vegetation in the region was improved and changed steadily, and the proportions of the areas showing positive correlation between actual NPP and annual precipitation and annual average temperature were 99.87% and 91.66%, respectively. The potential NPP showed an increasing trend from northwest to southeast, whereas the trend and stability of the potential NPP both showed an increasing trend from west to east. The area where climate change and human activities played a role in vegetation improvement accounted for 99.71%, and that affected by climate change accounted for 0.14%, with the proportion of human activities leading to vegetation degradation being 0.15%.

Spatial–Temporal Variation Characteristics and Driving Factors of Net Primary Production in the Yellow River Basin over Multiple Time Scales

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m2 in the YRB and increased at rates of 4.64 g C/m2/a1 (R2 = 0.86, p &lt; 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts.