Grassland Net Primary Productivity Research Articles

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.

Productivity response characteristics of different grasslands to flash drought and their relationship with drought tolerance

Flash droughts have attracted worldwide attention because of their rapid outbreak and extensive influence. However, studies regarding the characteristics and effects of flash droughts in grassland ecosystems are insufficient. In this study, the frequency and intensity characteristics of flash droughts in the Xilinguole Grassland in China were studied. The response characteristics of the productivity of different types of grassland to flash droughts and the relationship between these characteristics and the drought tolerance of grassland were revealed. The results show that (1) flash droughts had the greatest impact on grassland net primary productivity (NPP) and rain use efficiency (RUE) in summer and spring, respectively, with a level of intensity above that of moderate drought. Strong evapotranspiration flash droughts (SEFD) require more attention from decision-makers than heat wave flash droughts (HWFD). A higher frequency and intensity of flash droughts had a greater impact on vegetation. (2) Flash droughts caused moderate negative anomalies in the NPP and RUE indices in more than 90 % of the grasslands. The longest lag time of the NPP response to flash droughts was 2 months, and NPP anomalies were affected by flash droughts for nearly 2 months. RUE was more sensitive to flash droughts than NPP. RUE responded to flash droughts within 10 days, with a decrease of more than 80 % in magnitude, which was 30 % higher than that of the NPP, and the duration of the anomaly was half that of the NPP. (3) Grasslands with a high sensitivity to flash droughts had shorter response durations, fewer abnormalities, better recovery abilities and better drought tolerance. The drought tolerance of grasslands did not increase in association with large NPP and RUE values. Desert grasslands were the most drought tolerant, while meadow grasslands were the least drought tolerant, with the highest risk of flash droughts. This study provides theoretical support for improving the ability of an ecosystem to cope with flash drought risk and scientific grassland management.

Impacts of Extreme Precipitation and Diurnal Temperature Events on Grassland Productivity at Different Elevations on the Plateau

The impact of extreme climate events on vegetation growth and ecosystem function has garnered widespread attention, particularly in plateau regions, which are facing increasingly severe environmental pressures. This study employs the Events Coincidence Analysis (ECA) method to examine the impacts of extreme climate events on the Net Primary Productivity (NPP) of vegetation in plateau regions. Specifically, we focus on the unique phenomenon of asymmetric daytime and nighttime warming and evaluate the compounding effect of extreme precipitation with extreme temperature events. The results indicate that grassland NPP has higher overall sensitivity and Coincidence Rates (CR) to extreme precipitation events compared to extreme temperature events. Specifically, extreme drought events significantly negatively impact grassland NPP, and the sensitivity of NPP increases with the severity of extreme drought events. In lower elevations (<3200 m), grassland NPP shows a strong response to extreme precipitation events, with sensitivity decreasing with increasing elevation, especially for alpine meadows. Extreme high-temperature events contributed significantly to NPP in mid to high elevations (3000–5000 m). Compound extreme climate events amplify the average coincidence degree with NPP anomalies, with extreme precipitation events playing a major role in compound effects. The CR of compound climate events can reach above 0.6, reflected in the anomaly increase of NPP in temperate grasslands at higher elevations caused by compound events of extremely wet and extremely high temperatures.

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.

The role of climatic factor timing on grassland net primary productivity in Altay, Xinjiang

Grassland, as highly vulnerable ecosystem, requires a comprehensive understanding of its dynamics and response patterns to climate factors in response to climate change challenges. While previous research has primarily centered on the influence of interannual climate variability on grassland Net Primary Productivity (NPP), knowledge of the impacts of seasonal or monthly climate variations on annual net primary productivity (ANPP) remains limited. This study investigated the climatic drivers of grassland NPP dynamics in Xinjiang's Altay region from 2000 to 2022 using the Carnegie-Ames-Stanford approach (CASA) model and the random forest regression model. The study examined the significance and patterns of precipitation, solar radiation, temperature, soil moisture, and snowmelt water on NPP at three temporal scales. The results revealed the following key findings: (1) Grassland NPP declined significantly from 2000 to 2009 but showed a gradual increase from 2009 to 2022. Spatially, higher values were observed in the northern region and lower values were observed in the southern region. (2) Precipitation was the primary influential climate factor affecting grassland NPP, followed by solar radiation, temperature, soil moisture, and snowmelt water. In determining the significance of climate factor timing on ANPP, June played a critical role particularly for precipitation, temperature, and soil moisture, while August was essential for solar radiation. Moreover, the importance of snowmelt water had a bimodal distribution, with peaks in April and October. (3) Grassland NPP exhibited diverse nonlinear response dynamics and spatial heterogeneity to various climate factors at different temporal scales. These findings highlight the importance of considering both the magnitude and the local conditions, as well as the timing of climate variations when studying grassland dynamic responses to climate change and predicting future impacts. These insights enhance the comprehension of the intricate dynamics of grassland ecosystems and enhance predictions of their responses to future climate change.

The spatiotemporal pattern of grassland NPP in Inner Mongolia was more sensitive to moisture and human activities than that in the Qinghai-Tibetan Plateau

The Qinghai-Tibet Plateau (QTP) and the Inner Mongolia Plateau (IMP) are important ecological barriers in China, and their grassland ecosystems are extremely fragile. Recently, the grassland ecosystems in both regions have been at risk of degradation owing to climate change combined with anthropogenic disturbance. However, comparative studies on how the driving factors of natural and human activities affect grassland spatiotemporal patterns on the QTP and IMP have been limited. Therefore, net primary productivity (NPP) was chosen as an indicator to understand its spatiotemporal patterns and driving mechanisms in this study. The spatiotemporal variation and spatial autocorrelation of NPP in the QTP and IMP grasslands NPP from 2000 to 2018 were analyzed. The partial least squares structural equation model and geographical time-weighted regression model were used to develop a research framework to explore the influence of natural factors, human activities and their interactions on the grassland NPP of the QTP and the IMP. Overall, the results showed that the spatiotemporal variations in grassland NPP of the QTP and IMP increased, with localized degradation from 2000 to 2018, with a faster NPP recovery in the IMP grassland NPP than in the QTP grassland NPP. Both the QTP and IMP showed high degrees of spatial autocorrelation. The driving factors explained 78.2 % and 80.6 % of the QTP and IMP grassland NPP, respectively. Moisture was the dominant driver but had a greater impact on the IMP than on the QTP. Among the human activity factors, atmospheric acid deposition had a negative influence on the QTP grassland and a positive impact on the IMP grassland. Socioeconomic activities presented stronger direct negative impact on IMP grassland. The indirect effects of human factors on the QTP through terrain and climate were greater. The grassland NPP exhibited significant spatiotemporal heterogeneity in response to the main driving factors of the QTP and IMP. This study explores the divergent mechanisms driven by natural and anthropogenic attributes of grassland NPP and provides information to scientifically guide the restoration of grassland ecosystems.

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 < 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.

Trends and contribution of different grassland types in restoring the Three River Headwater Region, China, 1988–2012

Rapid greening of the Qinghai–Tibet Plateau had been confirmed, but the contributions to the overall change and its causes in various grassland types has been less studied. Previous research has focused on exogenous factors such as climate change and human activities, rather than on endogenous factors, such as grassland types. Using net primary productivity (NPP), precipitation and temperature data, we applied trend, contribution and pull contribution analysis to understand the spatiotemporal evolution and driving factors of six different grassland types at a pixel scale in the Three River Headwater Region (TRHR) of China from 1988 to 2012. The results showed that grassland NPP in the TRHR increased at an average growth amount of 3.46 gC m−2 yr−1 and an average growth rate of 2.26 %. The average growth amount of alpine desert and alpine steppe (0.42 gC m−2 yr−1, 1.74 gC m−2 yr−1, respectively) showed great potential improvement. The average growth rate (1.27 %, 1.87 %) of montane meadow and alpine meadow, respectively, presented a high potential to increase (P < 0.05). Alpine meadow, montane meadow and temperate steppe were positive pullers to the average growth amount. Alpine steppe and alpine desert were positive pullers to the average growth rate. In general, alpine meadow had the highest growth amount contribution (84.86 %), while alpine meadow and alpine steppe had the highest contribution to the growth rate (62.16 %, 34.24 %, respectively). The study implied that, in addition to external factors, differences in internal factors such as the community composition and structure of different grassland types could also affected the grassland recovery process. These results contribute to understanding the specific differences in the contribution of regional grassland restoration processes from vegetation composition. Assessing grasslands with the potential to increase productivity, we can provide scientific reference for implementing more precise and efficient measures in future grassland management restoration.

Spatial–Temporal Dynamics of Grassland Net Primary Productivity and Its Driving Mechanisms in Northern Shaanxi, China

Grasslands, a vital ecosystem and component of the global carbon cycle, play a significant role in evaluating ecosystem health and monitoring the global carbon balance. In this study, based on the Carnegie–Ames–Stanford Approach (CASA) model, we estimated the Net Primary Productivity (NPP) of grasslands in northern Shaanxi from 2000 to 2020. Employing trend analysis, stability analysis, multiple regression analysis, and residual analysis, the research examined the dynamic changes of grassland NPP and its response to climatic and human factors. Key findings include: (1) Grassland NPP showed a significant increasing trend during 2000–2020, with high-coverage grasslands showing a higher rate of increase than medium and low-coverage grasslands. (2) Most grasslands (&gt;90%) exhibited unstable growth and high NPP fluctuation. (3) While temperature, precipitation, and radiation undulate, the trends were not significant. Rainfall and radiation emerged as dominant factors affecting NPP, with temperature suppressing NPP increase to some extent. (4) Policies like returning farmland to grassland had a positive impact on grassland recovery, vegetation productivity, and regional ecosystem health.

Carbon budget response to climate change varies with grassland type in Qilian Mountains, China

Carbon budget estimation is crucial for scientifically assessing the ecosystem’s carbon sequestration capacity. Grasslands in the Qilian Mountains are widely recognized as vulnerable and sensitive to ongoing climate change. However, it is still unclear how the carbon budget of different grassland types responds to climate change in this area. Here, we evaluated the net primary productivity (NPP), soil heterotrophic respiration (RH), and net ecosystem productivity (NEP) of grassland ecosystems in the Qilian Mountains. Correlation analysis, linear regression, and standardized coefficient methodologies were employed to explore the response of NPP, RH, and NEP in various grassland types to changes in temperature, precipitation, and solar radiation over the past two decades. The results showed that the alpine meadow has the highest NPP and NEP while the temperate typical steppe has the highest RH. Grassland NPP, RH, and NEP increased significantly from 2001 to 2020, with a distinct spatial pattern of being low in the northwest and high in the southeast of the Qilian Mountains. NPP and NEP correlated with precipitation and solar radiation more closely than with temperature. Precipitation and solar radiation, respectively, explained 43.7% and 52.9% of grassland NEP alterations, and there was a threshold effect on the response of NEP and NPP to climate factors. Specifically, the NPP and NEP increased at first and then decreased as the precipitation, temperature, and solar radiation increased. The highest NEP and NPP occurred when precipitation ranged from 415 to 455 mm, along with temperatures between − 0.1–1.5 ℃ and solar radiation reaching 7685–7905MJ/m2. The warming and humidification of climate is conducive to the increase of NPP and NEP in alpine meadows and alpine steppes, but when the precipitation exceeds 415 mm and the temperature is greater than 1.5 ℃, the NPP and NEP begin to decrease. Solar radiation dominated changes in NPP and NEP of alpine meadows, alpine steppes, and temperate desert steppes. With the increase of solar radiation, NPP and NEP of these three grasslands first increased and then decreased. However, the changes in NPP and NEP of the temperate typical steppe were most affected by precipitation, and NPP and NEP first increase and then decrease with the increase of precipitation The higher the temperature, the higher the NPP, RH, and NEP of the temperate typical steppe. This study helps better understand how climate change impacts the carbon budget components of different grassland types in the Qilian Mountains and provides more theoretical thoughts on managing local grasslands sustainably.

Untangling the effects of climate variation and human interference on grassland dynamics in North China

AbstractClimatic and anthropogenic disturbances play pivotal roles in shaping the dynamics of grassland. Quantifying their impacts on grassland variation is essential to ensure sustainable grassland management. In this study, we employed the Thornthwaite Memorial and Carnegie–Ames–Stanford‐approach (CASA) models to investigate the spatiotemporal effects of these two variables on grassland variation in northern China from 2000 to 2016, using the net primary productivity (NPP) as a measure. Our findings reveal that approximately 25.92% of the grassland in northern China experienced degradation, while restored grasslands occupied 45% of the total grassland area. The average grassland actual NPP (ANPP) and human‐induced NPP decreased at rates of −0.60 and −5.62 gC m−2 a−1, respectively. Conversely, potential NPP exhibited an upward trend with an average increase of 2.27 gC m−2 a−1. Furthermore, grassland ANPP showed a projected increase in most parts of northern China. Climate change emerged as the primary driver for grassland restoration in Xinjiang, Qinghai, and Inner Mongolia, leading to an increase of 21582.79 Gg C in grassland NPP. In contrast, human activities were the dominant catalysts for grassland degradation, resulting in a reduction of 51932.3 Gg C in grassland NPP. Human‐induced grassland degradation was ubiquitous in northwest and northeast China. With the exception of slope grassland, climate change primarily influenced the restoration of most grassland types, while human activities were the primary cause of degradation. Our analysis indicated a strong correlation between temperature and grassland degradation, while precipitation played a pivotal role in grassland restoration in northern China. Human interference demonstrated both positive and negative impacts on grassland changes. In conclusion, the increase in precipitation and the implementation of ecological restoration plans have effectively promoted the restoration of grasslands in northern China.

Vegetation Dynamics and Its Trends Associated with Extreme Climate Events in the Yellow River Basin, China

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China’s national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants’ growth and extreme climate, together with the reaction of plants’ growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants’ growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin.

Cumulative effects of drought have an impact on net primary productivity stability in Central Asian grasslands

Global warming has exacerbated the threat of drought in Central Asia, amplifying its ecological implications within the region's grassland ecosystems. This has become an increasingly prominent issue that requires attention and action. The temporal link between grassland development and drought is asymmetric. However, a quantitative assessment of the temporal effects of multiscale drought on Central Asian grasslands has yet to be explored. Based on correlation analysis and the coefficient of variation method, this study analysed the cumulative and lag effects of multitimescale drought on grassland NPP (net primary productivity) under different climatic zones, altitudes and water availabilities in Central Asia from 1982 to 2018, and discussed the impact of temporal effects on grassland NPP stability. Our results on the cumulative effects of drought on grasslands indicate the 6.72 months preceding NPP measurement was the duration for which, on average, drought was most strongly correlated with NPP. Additionally, we found a mean lagged effect of 5.36 months, meaning that the monthly drought 5.36 months prior to NPP measurement was, on average, most strongly correlated with NPP. The degree to which grassland NPP was affected by cumulative drought at a given level of water availability was inversely proportional to the number of cumulative drought months. Under different water availabilities, the lagged effect of grassland NPP was stronger in dry areas than in wet areas, and the number of lag months tended to decrease and then increase as the water availability increased. The percentage of areas where grassland NPP was dominated by the cumulative and lagging effects of drought was 30.02% and 69.98%, respectively. The stability of grassland NPP was adversely affected by the drought accumulation effect. The findings of this study contribute to a deeper understanding of the long-term effects of drought on grassland ecosystems. Additionally, it will aid in the development of strategies for mitigating and adapting to drought events, thereby minimizing their negative impacts on agriculture, livestock, and ecosystems.

An integrated method to quantify human appropriation of net primary production in grasslands of the Qinghai-Tibet Plateau

Human appropriation of net primary production (HANPP) is an important indicator for measuring anthropogenic influences on ecosystems. However, it is a challenge to accurately delineate the spatial heterogeneity of the HANPP, mainly due to great uncertainties in the simulation of potential net primary production (NPPp). Here, we established a novel method for simulating grassland NPPp by integrating ecoregion delineation and a random forest (RF) model and derived HANPP calculated by the NPPp minus NPP remaining in the ecosystem after harvest (NPPeco). In a case study of the Qinghai-Tibet Plateau (QTP), we evaluated the proposed method via validation and comparison with the NPPp and HANPP results in 2010 based on the Thornthwaite Memorial (TM) model and LPJ-GUESS model. The results indicated that the grassland NPPp values simulated by our method were closer to the observed field data in the two ecoregions, while the TM model generally overestimated NPPp. Moreover, the spatial distribution of the grassland HANPP based on the proposed method showed a high correlation coefficient (0.704) with grazing intensity, which was 0.669 higher than that of the TM model and 0.21 higher than that of the LPJ-GUESS model. The proposed method can provide a valuable basis for grassland sustainability assessment and disaggregating livestock data from administrative units into grid cells.

Impacts of Climate Change and Anthropogenic Activities on the Net Primary Productivity of Grassland in the Southeast Tibetan Plateau

Climate change and anthropogenic activities have had a profound effect on the variation in grassland productivity in the Tibetan Plateau in recent decades. Quantifying the impacts of climatic and anthropogenic variables on grassland productivity is a necessary step in making the management policies of a sustainable grassland ecosystem. Net primary productivity (NPP) is an important part of the terrestrial carbon cycle and can be used to assess vegetation growth. Based on the Carnegie–Ames–Stanford Approach model and statistical analysis method, in this study we estimated the variations in grassland potential NPP (PNPP), actual NPP (ANPP) and human-induced NPP (HNPP) in the Northwest Sichuan Plateau (NWSP) of the Southeast Tibetan Plateau from 2001 to 2020. Also, we assessed the contribution of climatic change and anthropogenic activities to grassland ANPP. The results showed that the average values of grassland ANPP, PNPP and HNPP in the whole NWSP increased at the rates of 3.81, 9.14 and 7.18 g C m−2 a−1, respectively. Grassland ANPP increased in 91.7% of the total area. Climate-oriented impacts led grassland ANPP to increase in 82.6% of the area, and temperature increase was the dominant factor. Additionally, anthropogenic activity was the major reason for the grassland ANPP’s decline (5.4% of the total area). Overall, our findings are beneficial for the formulation of practical countermeasures regarding climate change adaption and damaged grassland recovering in the plateau.

Effects of a changing climate and anthropogenic impacts on net primary production in Yinshanbeilu, Inner Mongolia, China

Inappropriate long-term reclamation has reduced ecological services, including carbon sequestration, in Inner Mongolia's interlocking agricultural and pastoral lands near the northern foot of Yinshanbeilu, making this one of China's most environmentally fragile areas. Regional ecosystem changes and sustainable development depend on the understanding of how climate change and anthropogenic activities affect the net primary production (NPP) of vegetation. (1) Fluctuations in vegetation NPP tended to increase at a ratio of 2.974 g·m−2·a−1. The vegetation NPP increased in 99.49% of the research area; 77.97% of this production was significant. (2) Precipitation and temperature restricted vegetation growth, and the impact of atmospheric precipitation on vegetation NPP exceeded that of both the spatial and temporal dimensions. Precipitation affected grassland NPP more than arable NPP. However, the direct and indirect effects of temperature were greater on arable NPP than on grassland NPP. (3) Changes in climate and anthropogenic activities changed the vegetation NPP by 46.23% and 53.77%, respectively, with the values showing significant spatial variability. Climate change dominated vegetation changes in sparsely populated and underdeveloped areas, whereas human activity was dominant in populated and economically developed areas. (4) Vegetation NPP loss in Yinsanbeilu occurred mostly when the land was changed from grassland to other land use types, whereas growth occurred mostly when the land was changed from arable land to grassland. These results improve the understanding of how climate change and anthropogenic activities affect vegetation dynamics and provide a foundation for vegetation restoration and quality enhancement.

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.

Different nitrogen saturation thresholds for above-, below-, and total net primary productivity in a temperate steppe.

Identifying the thresholds for the positive responses of total net primary productivity (NPP) to nitrogen (N) enrichment is an essential prerequisite for predicting the benefits of N deposition on ecosystem carbon sequestration. However, the responses of below-ground NPP (BNPP) to N enrichment are unknown in many ecosystems, which limits our ability to understand the carbon cycling under the scenario of increasing N availability. We examined the changes in above-ground NPP (ANPP), BNPP, and NPP of a temperate meadow steppe across a wide-ranging N addition gradient (0, 2, 5, 10, 20, and 50 g N m-2 year-1 ) during 5 years. Both ANPP and NPP increased nonlinearly with N addition rates. The N saturation threshold for ANPP (TA ) and NPP (TN ) was at the rate of 13.11 and 6.70 g N m-2 year-1 , respectively. BNPP decreased with increasing N addition when N addition rates ˃5 g N m-2 year-1 , resulting in much lower TN than TA . Soil N enrichment played a key role in driving the negative impacts of high N addition rates on BNPP, and consequently on the earlier occurrence of N saturation threshold for NPP. Our results highlight the negative effects of soil N enrichment on NPP in natural grasslands super-saturated with N. Furthermore, by considering ANPP and BNPP simultaneously, our results indicate that previous findings from above-ground might have over-estimated the positive effects of N deposition on primary productivity.

Spatiotemporal evolution and attribution analysis of grassland NPP in the Yellow River source region, China

The protection of the environment and promotion of sustainable development in the Yellow River Basin (YRB) is a major national strategy in China. Madoi County (MC), at the headwaters of the YRB, plays a crucial role in ensuring the ecological security and sustainable development of the middle and lower reaches of the river. Vegetation net primary productivity (NPP) is a critical indicator for assessing the structure and function of the ecosystem. Therefore, studying the spatiotemporal evolutionary characteristics of grassland net primary productivity (GNPP) in MC and its driving mechanisms is of the utmost importance. We employed linear regression analysis, optimal parameters-based geographical detector (OPGD), multiscale geographic weighted regression (MGWR), and residual analysis to investigate the spatiotemporal change of GNPP in MC under the influence of climate change (CC) and policies in different periods based on MOD17A3 NPP data. Additionally, we explored the natural and anthropogenic factors affecting GNPP and their spatiotemporal processes on global and grid scales. Lastly, we quantified the relative contributions of CC and human activity (HA) to the observed changes in GNPP. The results were as follows: 1) GNPP in MC has demonstrated a fluctuating and increasing trend since 2000, with an average annual growth rate of 1.58 gC·m−2·a−1 and a maximum growth rate of 6.29 gC·m−2·a−1 in 2017–2021. 2) Spatial variation in GNPP has been evident since 2000, with a gradual increase in GNPP from the northwest to southeast. Regions with notable increases in grassland were predominantly located in areas with eco-engineering. 3) The OPGD was used to identify key driving factors (DFs), revealing that climate factors had stronger explanatory powers for the GNPP at each phase. MGWR results indicated that the effect of key DFs exhibited clear spatiotemporal heterogeneity. Annual cumulative solar radiation and annual cumulative precipitation had the most significant effect on GNPP. Regions with positive effects (PEs) of grassland area change and grassland cover change were mostly located in eco-engineering zones. The PEs of DFs on GNPP increased. 4) The contribution of CC to GNPP changes and the dominant area share were greater than those of HA. However, areas with PEs of HA increased gradually. The synergies between eco-protection policies and natural regulation are therefore critical to the eco-environmental protection in the YRB.

Concurrent and lagged effects of drought on grassland net primary productivity: a case study in Xinjiang, China

Xinjiang grasslands play a crucial role in regulating the regional carbon cycle and maintaining ecosystem stability, and grassland net primary productivity (NPP) is highly vulnerable to drought. Drought events are frequent in Xinjiang due to the impact of global warming. However, there is a lack of more systematic research results on how Xinjiang grassland NPP responds to drought and how its heterogeneity is characterized. In this study, the CASA (Carnegie Ames Stanford Application) model was used to simulate the 1982–2020 grassland NPP in Xinjiang, and the standardized Precipitation Evapotranspiration Index (SPEI) was calculated using meteorological station data to characterize drought. The spatial and temporal variability of NPP and drought in Xinjiang grasslands from 1982 to 2020 were analyzed by the Sen trend method and the Mann-Kendall test, and the response characteristics of NPP to drought in Xinjiang grasslands were investigated by the correlation analysis method. The results showed that (1) the overall trend of NPP in Xinjiang grassland was increasing, and its value was growing season &amp;gt; summer &amp;gt; spring &amp;gt; autumn. Mild drought occurred most frequently in the growing season and autumn, and moderate drought occurred most frequently in spring. (2) A total of 64.63% of grassland NPP had a mainly concurrent effect on drought, and these grasslands were primarily located in the northern region of Xinjiang. The concurrent effect of drought on NPP was strongest in plain grassland and weakest in alpine subalpine grassland. (3) The lagged effect is mainly in the southern grasslands, the NPP of alpine subalpine meadows, meadows, and alpine subalpine grasslands showed mainly a 1-month time lag effect to drought, and desert grassland NPP showed mainly a 3-month time lag effect to drought. This research can contribute to a reliable theoretical basis for regional sustainable development.