Variation In Net Primary Production Research Articles

The spatiotemporal evolution and prediction of vegetation NPP in the Huangshui River Basin of Qilian Mountains

The Qilian Mountains and Huangshui River Basin (HRB) represent significant ecological functional areas and carbon reservoirs within China. The estimation and prediction of vegetation net primary productivity (NPP) in this area is beneficial for the management of China’s terrestrial ecosystems. Nevertheless, the existing estimation methods for vegetation NPP at the local scale are characterised by considerable uncertainty and error, and have not accounted for the influence of multi-factor interactions. Accordingly, this study initially sought to quantify the vegetation NPP data within the HRB from 2000 to 2019 through the implementation of an improved Carnegie-Ames-Stanford Approach (CASA) model. Subsequently, it endeavoured to elucidate the spatiotemporal evolution patterns and influencing factors of vegetation NPP within the HRB over the years. Subsequently, the ConvGRU spatiotemporal prediction model was employed to investigate the prospective trajectory of vegetation NPP in the HRB. The findings revealed a notable upward trajectory in the annual variation of vegetation NPP in the HRB between 2000 and 2019. The majority of regions have demonstrated a notable increase in vegetation NPP, although a few areas have exhibited a decline. Furthermore, the correlation between vegetation NPP and PRE, TEMP, SR, and NDVI exhibits regional disparities. Furthermore, the spatial variation characteristics of vegetation NPP in the HRB in the future also demonstrate an overall increasing trend. Additionally, the vegetation NPP in the HRB exhibits significant spatial distribution characteristics, with evident trends of hot spot contraction or cold spot expansion. This study provides pivotal methods and theoretical support for the assessment of carbon sequestration status in the HRB of the Qilian Mountains and analogous regions.

Analyzing the Spatial Patterns and Impact Factors of Vegetation Net Primary Productivity and Precipitation Utilization Efficiency in Heilongjiang Province Under Climate Change

Understanding the spatial patterns and driving mechanisms of net primary productivity (NPP) and precipitation utilization efficiency (PUE) is crucial for assessing ecosystem services. This study analyzed the variations in NPP and PUE in Heilongjiang Province from 2001 to 2020, using MOD17A3 NPP products and meteorological, topographic, and land use data. The distribution of the NPP and PUE of seven land use categories was determined in the study, namely, cropland, forest, grassland, water, barren, impervious and wetland. The multi-year spatial averages for NPP and PUE were 428.96 gC·m−2·a−1 and 0.74 gC·m−2·mm−1, respectively, with forests showing the highest values and barren lands the lowest. During the study period, 91.4% of the NPP increased at an average rate of 3.36 gC·m−2·a−1, while PUE exhibited a polarized trend. Changes in land use, especially conversions involving cropland and forest, along with climatic factors such as rising precipitation and temperature, significantly influenced NPP and PUE dynamics. These findings provide a scientific basis for ecological restoration and the assessment of ecosystem function under changing climatic conditions.

Spatio-temporal Variation in Monthly Vegetation NPP and Its Responses to Climate and Terrain Factors in Chang-Zhu-Tan Green Heart Area

The Chang-Zhu-Tan ecological Green Heart area is the largest urban agglomeration Green Heart area in China. To clarify the spatiotemporal changes and driving factors of net primary productivity （NPP） of vegetation in the Chang-Zhu-Tan Green Heart area, an improved Carnegie Ames Stanford Approach （CASA） model was used to estimate the monthly vegetation NPP from 2011 to 2020 based on measured and remote sensing data. With the help of ArcGIS 10.2 software, linear regression and spatial partial correlation analysis were used to explore the spatiotemporal variations in monthly vegetation NPP and its responses to climate and terrain factors. The results showed that the monthly vegetation NPP in the Chang-Zhu-Tan Green Heart area showed an overall trend of first increasing and then decreasing, with an average vegetation NPP growth rate （measured in C, the same below） of 5.75 g·（m2·month）-1 from January to July and a monthly decrease rate of 2.64 g·（m2·month）-1 from August to December. The spatial distribution of monthly vegetation NPP in the Green Heart area showed a significant difference, with a coefficient of variation ranging from 34.52% to 48.76%. The high values of vegetation NPP in each month were concentrated in the ecological core areas of the central and eastern parts in the Green Heart area, whereas the low values were mainly distributed along the Xiangjiang River in the western part and in the narrow and elongated areas in the southern part of the Green Heart Area. The impact of climate and terrain factors on the monthly NPP of vegetation in the Green Heart area had both positive and negative inhibitory effects. Among climate factors, solar radiation was the major controlling factor for monthly vegetation NPP changes. The impact of terrain factors on vegetation NPP was relatively weak, with slope orientation having a greater positive impact on vegetation NPP than altitude and slope.

Net Primary Production Simulation and Influencing Factors Analysis of Forest Ecosystem Based on a Process-Based Model

Accurate assessment of net primary production (NPP) can truly reflect the carbon budget balance of the forest ecosystem. In this study, the boreal ecosystem productivity simulation (BEPS) model was used to simulate the NPP of Saihanba mechanized forest farm in 2020, and the influencing factors of NPP were analyzed. The meteorological, forest cover, leaf area index (LAI) and other data required for the model, as well as the data for verifying, were from field surveys or downloaded from different sources. The results showed that: (1) Within the scale of the flux tower, the diurnal variation of NPP reached a maximum in June. The monthly average peak value of latent heat flux was in June, and the sensible heat flux was in March. The temperature of the understory canopy was mostly higher than that of the overstory canopy and air temperature. (2) At the regional scale, the total NPP in the study area in 2020 was 4.25 × 1011 g C a−1, with an average of 564.71 g C m−2 a−1. The annual average NPP of silver birch (Betula platyphylla) was the largest, and the total NPP of northern Chinese larch (Larix principis-ruprechtii) was the largest. (3) NPP was highly sensitive to LAI. Topographic factors had effects on NPP. The average value of NPP was relatively high in the shady slope and the gentle slope.

Variations and influencing factors of vegetation net primary productivity over 31 years in Wuyishan National Park, China

In this study, the Carnegie–Ames–Stanford approach model was used to estimate the net primary productivity (NPP) of Wuyishan National Park from 1990 to 2020. Variation in NPP in parks was examined, and the influencing factors were identified. NPP showed an increasing trend annually with climate change. The value (625.67 gC m−2 a−1) obtained in this study was higher than the regional value along similar latitudes and coastlines. Affected by the spatial distribution characteristics of vegetation, NPP in the study area exhibited significant spatial heterogeneity, with a distribution of higher values in the northwestern region and lower values in the southeastern region. However, the growth rates showed the opposite trend. The higher growth rate of NPP in the ecological restoration zone was attributed to the ecological conservation efforts undertaken in Wuyishan National Park. Among the various environmental factors, the vapor pressure deficit contributed substantially to NPP temporal distribution, accounting for 25.71%. Terrain factors, which are influenced by the spatial distribution characteristics of vegetation and environmental factors, are important variables that affect the spatial distribution of NPP within the study area. The highest vegetation NPP in Wuyishan National Park was observed in regions with an elevation of approximately 1000 m, slopes ranging from 20° to 40°, and an aspect of approximately 150°. Compared with slope and aspect, elevation provides a stronger explanatory power (R2 = 0.93) for the spatial distribution patterns of vegetation NPP in Wuyi Mountain National Park. This study suggests that Wuyishan National Park has considerable carbon sequestration potential. In the context of climate warming, the favorable aspects of climate warming should be considered, and ecological restoration measures tailored to local conditions should be implemented.Article title mismatch between manuscript and publisher provided information, so we have follow the manuscript. Kindly check and confirm.Variations and influencing factors of vegetation net primary productivity over 31 years in Wuyishan National Park, China

The Impact of Seasonal Climate on Dryland Vegetation NPP: The Mediating Role of Phenology

Dryland ecosystems are highly sensitive to climate change, making vegetation monitoring crucial for understanding ecological dynamics in these regions. In recent years, climate change, combined with large-scale ecological restoration efforts, has led significant greening in China’s arid areas. However, the mechanisms through which seasonal climate variations regulate vegetation growth are not yet fully understood. This study hypothesizes that seasonal climate change affects net primary productivity (NPP) of vegetation by influencing phenology. We focused on China’s Windbreak and Sand-Fixation Ecological Function Conservation Areas (WSEFCAs) as representative regions of dryland vegetation. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate vegetation NPP from 2000 to 2020. To extract phenological information, NDVI data were processed using Savitzky–Golay (S–G) filtering and threshold methods to determine the start of season (SOS) and end of season (EOS). The structural equation model (SEM) was constructed to quantitatively assess the contributions of climate change (temperature and precipitation) and phenology to variations in vegetation NPP, identifying the pathways of influence. The results indicate that the average annual NPP in WSEFCAs increased from 55.55 gC/(m2·a) to 75.01 gC/(m2·a), exhibiting uneven spatial distribution. The pathways through which seasonal climate affects vegetation NPP are more complex and uneven. Summer precipitation directly promoted NPP growth (direct effect = 0.243, p < 0.001) while also indirectly enhancing NPP by significantly advancing SOS (0.433, p < 0.001) and delaying EOS (−0.271, p < 0.001), with an indirect effect of 0.133. This finding highlights the critical role of phenology in vegetation growth, particularly in regions with substantial seasonal climate fluctuations. Although the overall ecological environment of WSEFCAs has improved, significant regional disparities remain, especially in northwestern China. This study introduces causal mediation analysis to systematically explore the mechanisms through which seasonal climate change impacts vegetation NPP in WSEFCAs, providing new insights into the broader implications of climate change and offering scientific support for ecological restoration and management strategies in arid regions.

Spatial Characteristics and Driving Factors of Net Primary Productivity of Vegetation in the Upper and Middle Yellow River Basin

The Net Primary Productivity （NPP） of vegetation plays a crucial role in terrestrial ecosystems, and a detailed investigation into the annual average NPP and its driving factors is of significant importance for promoting regional ecological construction and sustainable development. This research utilized MOD17A3 annual average NPP data from 2000 to 2020 and employed methods such as trend analysis, Hurst index, random forest model, partial dependence model, geographic weighted regression, and partial least squares-structural equation model （PLS-SEM） to analyze the annual variation characteristics of NPP and its relationship with driving factors in the upper and middle reaches of the Yellow River. The results showed： ① During the period from 2000 to 2020, the annual average NPP in the upper and middle reaches of the Yellow River generally exhibited a year-on-year increasing trend, with 79.25% of the region showing a significant improvement in annual average NPP, while 0.25% of the region exhibited a severe degradation trend. ② The dominant influencing factors of the annual average NPP in the upper and middle reaches of the Yellow River included precipitation, NDVI, drought, and relative humidity. The suitable range for increasing annual average NPP was 400 mm to 650 mm for precipitation and 40% to 70% for relative humidity, and NDVI showed a significantly linear positive correlation with annual average NPP. Drought index was negatively correlated with annual average NPP. The annual average NPP tended to stabilize and did not decline further when the drought index exceeded the threshold of 8. ③ Extreme rainfall indirectly affected the variation in annual average NPP in the basin by influencing the vegetation growth condition. The impact degree of extreme rainfall indices on the annual average NPP in the basin was in the following order： R10>R95P>PRCPTOT>SDII>Rx5day>R99P. ④ The next steps in ecological construction in the upper and middle reaches of the Yellow River should focus on the arid and severely arid regions in the northern part of the Yellow River, adapting measures to local conditions and enhancing the regional ecological environment.

Quantitative Analysis of Human Activities and Climatic Change in Grassland Ecosystems in the Qinghai–Tibet Plateau

Net primary production (NPP) serves as a critical proxy for monitoring changes in the global capacity for vegetation carbon sequestration. The assessment of the factors (i.e., human activities and climate changes) influencing NPP is of great value for the study of terrestrial systems. To investigate the influence of factors on grassland NPP, the ecologically vulnerable Qinghai–Tibet Plateau region was considered an appropriate study area for the period from 2000 to 2020. We innovated the use of the RICI index to quantitatively represent human activities and analyzed the effects of RICI and climatic factors on grassland NPP using the geographical detector. In addition, the future NPP was predicted through the integration of two modeling approaches: The Patch-Generating Land Use Simulation (PLUS) model and the Carnegie–Ames–Stanford Approach (CASA) model. The assessment revealed that the expanded grassland contributed 7.55 × 104 Gg C (Gg = 109 g) to the total NPP, whereas the deterioration of grassland resulted in a decline of 1.06 × 105 Gg C. The climatic factor was identified as the dominant factor in grassland restoration, representing 70.85% of the total NPP, as well as the dominant factor in grassland degradation, representing 92.54% of the total NPP. By subdividing the climate change and human activity factors into sub-factors and detecting them with a geographical detector, the results show that climate change and anthropogenic factors have significant ability to explain geographic variation in NPP to a considerable extent, and the effect on NPP is greater when the factors interact. The q-values of the Relative Impact Contribution Index (RICI) and the RICI of the land use change NPP are consistently greater than 0.6, with the RICI of the human management practices NPP and the evapotranspiration remaining at approximately 0.5. The analysis of the interaction between climate and human activity factors reveals an average impact of greater than 0.8. By 2030, the NPP of the natural development scenario, economic development scenario (ED), and ecological protection scenario (EP) show a decreasing trend due to climate change, the dominant factor, causing them to decrease. Human activities play a role in the improvement. The EP indicates a positive expansion in the growth rate of forests, water, and wetlands, while the ED reveals rapid urbanization. It is notable that this is accompanied by a temporary suspension of urban greening.

Characteristics of Climate Change in Poyang Lake Basin and Its Impact on Net Primary Productivity

Climate change exerts substantial impacts on human society and the carbon cycle of terrestrial ecosystems. Studying the spatiotemporal characteristics of regional climate change and its impact on carbon sequestration is an important topic in ecology and environmental science. This study utilized meteorological and land use/cover data to explore these dynamics. Statistical methods such as the Mann–Kendall (M-K) test and wavelet analysis were used to simulate the changes in annual average temperature and precipitation in the Poyang Lake Basin from 1980 to 2020. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate the interannual variation in net primary productivity (NPP) in the region over the past 40 years. Additionally, the present study examined the influence of various factors on NPP changes. The main results are as follows: (1) Over the past four decades, the average temperature in the Poyang Lake Basin was 17.85 °C, while the average precipitation was 1621.35 mm. The average annual temperature rises at a rate of 0.27 °C per decade. (2) A significant shift in the average annual temperature occurred in the early 21st century, and annual precipitation exhibited multiple abrupt changes during the mid-to-late 1990s. Both temperature and precipitation changes follow a 25-year cycle, with temperature hotspots located in the south and precipitation hotspots in the northeast. (3) The impact of climate change on the change in NPP in the Poyang Lake Basin is about 70%, with the annual average temperature having a significant effect on the increase in NPP. This study can provide a scientific foundation for formulating policies aimed at mitigating climate-related disasters and enhancing carbon cycling in terrestrial ecosystems.

Atmospheric Nitrogen Deposition Controls Interannual Variability of Net Primary Production in the Bohai Sea

AbstractIncreasing human activities and climate change have resulted in significant changes in net primary production (NPP) of the marginal sea in the past several decades. The Bohai Sea (BHS) is a shallow semi‐enclosed marginal sea of North China, the knowledge of the relative impacts of human activities and climate change on the NPP in the BHS remains limited. This study primarily aimed to elucidate the interannual variability of NPP and its influencing factors in the BHS during the 2002–2021 period by synthesizing four widely‐used global NPP models, namely the vertically generalized production model (VGPM), the Eppley‐VGPM, the size‐fractioned phytoplankton pigment absorption‐based model (SABPM), and the carbon, absorption, and fluorescence euphotic‐resolving model (CAFE). The multi‐model average NPP showed significant interannual variation, with an increasing trend from 2002 to 2012 and a decreasing trend from 2012 to 2021. The concentration of DIN in the BHS was primarily affected by atmospheric nitrogen deposition. The structural equation model demonstrated that chlorophyll‐a (Chl‐a) concentration, influenced by DIN concentration, had an important effect on NPP. In brief, atmospheric nitrogen deposition impacted the DIN concentration and thus controlled the Chl‐a concentration and NPP change in the BHS. Furthermore, atmospheric nitrogen deposition in the BHS have potential ecological impacts on the red tide features. These findings hold valuable implications for future marine resource planning and marine ecological management in the BHS.

Analysis of spatial and temporal variations of vegetation NPP and TWS in the Yangtze River Basin

Net primary productivity (NPP) is an important parameter reflecting vegetation growth, and water is one of the necessary factors for vegetation growth. Investigating the mutual influence between NPP and water is significant for ensuring the stable development of the ecological environment. This study focuses on the Yangtze River Basin (YRB) as the research area, and based on medium-resolution imaging spectrometer (MODIS) data, climate data, and gravity recovery and climate experiment (GRACE) data, the spatiotemporal evolution characteristics of vegetation NPP and terrestrial water storage (TWS) in the YRB from 2000 to 2022 are explored and analyzes the mutual influence of NPP with climate factors and TWS. The results show that vegetation NPP (4.10 gC·m−2·a−1) and TWS (0.55 mm) in the YRB have exhibited an increasing trend from 2000 to 2022, with a strong correlation between the two, which is related to recent environmental policies. Analysis of the impact of climate factors on NPP reveals that temperature and TWS significantly positively impact NPP changes. Furthermore, comparisons between NPP and TWS indicate that changes in TWS substantially promote plant growth. In addition, the comparison between NPP and TWS indicates that changes in TWS have an important promoting effect on plant growth. Surface water (SWS) and soil water (SM) have a significant promoting effect on plant growth, but with a strong lag, while the consumption of groundwater (GWS) has been promoting plant growth without significant lag.

Spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen’s tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate.

Climate Change Drove the Decline in Yangtze Estuary Net Primary Production Over the Past Two Decades.

Net primary productivity (NPP) is highly sensitive to multiple stressors under progressive and intensifying climate change and anthropogenic impacts. The importance of understanding spatiotemporal distribution patterns and the associated driving factors that govern estuary NPP is paramount for regional carbon (C) budget assessments. Using a combined remote sensing and machine learning (ML) approach, the average NPP of the Yangtze Estuarine-offshore continuum (YEOC) was measured at 273.19 ± 21.26 mgC m-2 day-1 over the past two decades. Temporally, NPP exhibited a significant downward trend between 2002 and 2022. Climate factors (climate fluctuations, sea level rise, and discharge) drove phytoplankton biomass (Chl-a) while light conditions (PAR and Kd490) affected photosynthesis rates. Together, they can explain 65% of the NPP variation. Anthropogenic disturbances (i.e., damming and nutrient emissions) were not significant. Additionally, changes in NPP decreased phytoplankton C sequestration rates from 11.9 to 10.4 Tg C year-1, reducing the estuary's C sink capacity, which relies on biological C fixation. This study highlights the climate's influence on the spatiotemporal transformation of YEOC NPP while enhancing our understanding of the response of EOC C budgets to climate change and anthropogenic activities.

Detecting Drought-Related Temporal Effects on Global Net Primary Productivity

Drought has extensive, far-reaching, and long-lasting asymmetric effects on vegetation growth worldwide in the context of global warming. However, to date, few scholars have attempted the systematic quantification of the temporal effects of drought on global vegetation across various vegetation types and diverse climate zones. Addressing this gap, we quantitatively investigated the effects of drought on global vegetation growth under various scenarios, considering lagged and cumulative effects as well as combined effects in the 1982–2018 period. Our investigation was based on long-term net primary productivity (NPP) and two multiple-timescale drought indices: the standardised precipitation index (SPI) and the standardised precipitation and evapotranspiration index (SPEI). Our main findings were the following: (1) SPI and SPEI exhibited lagged effects on 52.08% and 37.05% of global vegetation, leading to average time lags of 2.48 months and 1.76 months, respectively. The cumulative effects of SPI and SPEI were observed in 80.01% and 72.16% of global vegetated areas, respectively, being associated with relatively longer cumulative timescales of 5.60 months and 5.16 months, respectively. (2) Compared to the scenario excluding temporal effects, there were increases in the explanatory powers of SPI and SPEI for variations in vegetation NPP based on the lagged, cumulative, and combined effects of drought: SPI increased by 0.82%, 6.65%, and 6.92%, respectively, whereas SPEI increased by 0.67%, 5.73%, and 6.07%, respectively. The cumulative effects of drought on global vegetation NPP were stronger than the lagged effects in approximately two-thirds (64.95% and 63.52% for SPI and SPEI, respectively) of global vegetated areas. (3) The effects of drought on vegetation NPP varied according to climate zones and vegetation types. Interestingly, vegetation in arid zones was the most sensitive and resilient to drought, as indicated by its rapid response to drought and the longest cumulative timescales. The vegetation NPP in tropical and temperate zones exhibited a relatively stronger response to drought than that in cold and polar zones. The strongest correlation of vegetation NPP with drought occurred in shrubland areas, followed by grassland, cropland, forest, and tundra areas. Moreover, for each vegetation type, the correlations between vegetation NPP and drought differed significantly among most climate zones. (4) The vegetation NPP in warming-induced drought regions displayed a higher correlation to drought than that in non-warming-induced drought regions, with shorter lagged and longer cumulative timescales. Our findings highlight the heterogeneity of the lagged, cumulative, and combined effects of drought across various climate zones and vegetation types; this could enhance our understanding of the coupling relationship between drought and global vegetation.

Dynamic Analysis and Risk Assessment of Vegetation Net Primary Productivity in Xinjiang, China

Vegetation net primary productivity (NPP) is a key indicator for assessing vegetation dynamics and carbon cycle balance. Xinjiang is located in an arid and ecologically fragile region in northwest China, but the current understanding of vegetation dynamics in the region is still limited. This study aims to analyze Xinjiang’s NPP spatial and temporal trends, using random forest regression to quantify the extent to which climate change and human activities affect vegetation productivity. CMIP6 (Coupled Model Intercomparison Project Phase 6) climate scenario data help assess vegetation restoration potential and future risks. Our findings indicate that (1) Xinjiang’s NPP exhibits a significant increasing trend from 2001 to 2020, with three-quarters of the region experiencing an increase, 2.64% of the area showing significant decrease (p &lt; 0.05), and the Ili River Basin showing a nonsignificant decreasing trend; (2) precipitation and radiation are major drivers of NPP variations, with contribution ratios of 35.13% and 30.17%, respectively; (3) noteworthy restoration potential exists on the Tian Shan northern slope and the Irtysh River Basin, where average restoration potentials surpass 80% relative to 2020, while the Ili River Basin has the highest future risk. This study explores the factors influencing the current vegetation dynamics in Xinjiang, aiming to provide references for vegetation restoration and future risk mitigation, thereby promoting sustainable ecological development in Xinjiang.

Monitoring the Net Primary Productivity of Togo’s Ecosystems in Relation to Changes in Precipitation and Temperature

Climate variability significantly impacts plant growth, making it crucial to monitor ecosystem performance for optimal carbon sequestration, especially in the context of rising atmospheric CO2 levels. Net Primary Productivity (NPP), which measures the net carbon flux between the atmosphere and plants, serves as a key indicator. This study uses the CASA (Carnegie–Ames–Stanford Approach) model, a radiation use efficiency method, to assess the spatio-temporal dynamics of NPP in Togo from 1987 to 2022 and its climatic drivers. The average annual NPP over 36 years is 4565.31 Kg C ha−1, with notable extremes in 2017 (6312.26 Kg C ha−1) and 1996 (3394.29 Kg C ha−1). Productivity in natural formations increased between 2000 and 2022. While climate change and land use negatively affect Total Production (PT) from 2000 to 2022, they individually enhance NPP variation (58.28% and 188.63%, respectively). NPP shows a strong positive correlation with light use efficiency (r2 = 0.75) and a moderate one with actual evapotranspiration (r2 = 0.43). Precipitation and potential evapotranspiration have weaker correlations (r2 = 0.20; 0.10), and temperature shows almost none (r2 = 0.05). These findings contribute to understanding ecosystem performance, supporting Togo’s climate commitments.

The Analysis of NPP Changes under Different Climatic Zones and under Different Land Use Types in Henan Province, 2001–2020

Net Primary Productivity (NPP) is a crucial indicator of ecological environment quality. To better understand the carbon absorption and carbon cycling capabilities of Henan Province, this study investigates the trends and driving factors of NPP across different climatic zones and land use types. The Theil–Sen Median trend analysis method and the Mann–Kendall trend test are employed to monitor NPP changes from 2001 to 2020. The average annual NPP in Henan Province during this period was 414.61 gC·m−2·year−1, showing a significant increasing trend with a growth rate of 3.73 gC·m−2·year−1. Spatially, both the annual average NPP and its increase rate were higher in the western part of Henan compared to the eastern part, and NPP variability was more stable in the southern region than in the northern region. By classifying climatic zones and using the Geodetector method to assess NPP sensitivity to natural factors, the results show that climate and vegetation factors jointly influence NPP variations, with annual precipitation being the primary natural factor affecting NPP trends in Henan Province from 2001 to 2020. By analyzing the NPP gain and loss matrix, the impact of land use changes on NPP was evaluated. Forests had the highest average annual NPP at 483.52 gC·m−2·year−1, and the conversion of arable land to urban areas was identified as the primary land change type leading to NPP reductions. In the subtropical zone of Henan, forests, croplands, and grasslands exhibited higher NPP values and increase rates compared to those in the warm belt. This study provides new insights into the spatial variation of NPP caused by changes in climatic zones and land use types.

Land cover dynamics and net primary productivity in Hubei Province, China: an integrative analysis of ecological and landscape transitions

ABSTRACT To better protect the ecological environment, this study investigates the evolution of the landscape in Hubei Province and its impact on Net Primary Production (NPP). By utilizing landscape assessment tools and neighborhood proxy methods, it quantifies the changes in NPP resulting from landscape transformations over periods of 5 and 20 years. The results indicate significant NPP variations, with changes ranging from −406.199 to 507.181 g*C/m2. The geographical detector model identifies key drivers, particularly noting significant changes in landscape features at type boundaries, which affect NPP. Land cover, especially transitions between forests, shrublands, and croplands, is identified as a critical factor. This research highlights the complex relationship between landscape modifications and NPP variations, emphasizing the importance of ecological considerations in landscape management amid urbanization. It offers valuable insights for future landscape management and conservation strategies in Hubei, aiming for ecological preservation alongside development. Additionally, this work fills a theoretical gap by linking changes in landscape features to NPP, proposing new perspectives for landscape protection and ecological capacity enhancement, and providing practical guidance for improving NPP and coordinated landscape management in Hubei.

Spatio-Temporal Changes and Driving Mechanisms of Vegetation Net Primary Productivity in Xinjiang, China from 2001 to 2022

Net primary productivity (NPP), a key indicator of terrestrial ecosystem quality and function, represents the amount of organic matter produced by vegetation per unit area and time. This study utilizes the MOD17A3 NPP dataset (2001–2022) to analyze the spatio-temporal dynamics of NPP in Xinjiang and projects future trends using Theil-Sen trend analysis, the Mann–-Kendall test, and the Hurst Index. By integrating meteorological data, this study employs partial correlation analysis, the Miami model, and residual analysis to explore the driving mechanisms behind NPP changes influenced by climatic factors and human activities. The results indicate that: (1) The average NPP in Xinjiang has increased over the years, displaying a spatial pattern with higher values in the north and west. Regions with increasing NPP outnumber those with declining trends, while 75.18% of the area shows un-certain future trends. (2) Precipitation exhibits a stronger positive correlation with NPP compared to temperature. (3) Climate change accounts for 28.34% of the variation in NPP, while human activities account for 71.66%, making the latter the dominant driving factor. This study aids in monitoring ecological degradation risks in arid regions of China and provides a scientific basis for developing rational coping strategies and ecological restoration initiatives.

Spatiotemporal Variation Characteristics and Driving Factors of Vegetation NPP in the Ulansuhai Nur Basin from 2001 to 2020

Vegetation net primary productivity （NPP） represents the ability of plants to fix ecosystem carbon, which is a key indicator to determine the health status and sustainable development of ecosystems. Its spatial and temporal changes and driving factors play an important role in revealing the status of vegetation restoration and guiding ecological restoration. Based on MODIS17A3 NPP data, land use, and meteorological data from 2001 to 2020, the temporal and spatial variation characteristics and driving factors of vegetation NPP in the Ulansuhai Nur Basin of Inner Mongolia were explored by using the methods of coefficient of variation, Theil-Sen Median trend analysis, Mann-Kendall significance test, Hurst index, and Geodetector. The results showed that： ① From 2001 to 2020, the vegetation NPP in the Ulansuhai Nur Basin showed a fluctuating upward trend, with an average value （in terms of C） of 141.03 g·（m2·a）-1 and an average increase rate of 2.33 g·（m2·a）-1. The vegetation NPP had obvious spatial differentiation, which was characterized by high in the southwest and low in the northeast and high in Hetao Plain and low in sandy land and mountainous areas. ② NPP mainly showed an increasing trend, and the area proportions of increasing, decreasing, and unchanged areas were approximately 80%, 3%, and 17%, respectively. The average coefficient of variation of vegetation NPP was 0.149, which mainly showed low fluctuation change, and the area accounted for approximately 51%. The future change trend of NPP was mainly characterized by anti-persistence, with an area ratio of approximately 75%. ③ Land use, altitude, maximum temperature, and slope were the dominant driving factors of variation NPP change in the Ulansuhai Nur Basin, and the q values were all above 0.200. The interaction between altitude and relative humidity had the greatest explanatory power for the spatial differentiation of vegetation NPP in the Ulansuhai Nur Basin. There were significant differences in the explanatory power of land use and all factors except nighttime light to the spatial differentiation of vegetation NPP in the Ulansuhai Nur Basin. According to the research results, in the future, we should strengthen the ecosystem management of the Ulansuhai Nur Basin； continue to implement strict ecological protection and restoration policies； and comprehensively consider factors such as climate, topography, and human activities to carry out comprehensive ecological management according to local conditions to improve the quality of ecosystem services.