Net Primary Production Trends Research Articles

Quantifying the impacts of natural and human factors on changes in NPP using an optimal parameters-based geographical detector

Investigating the driving mechanisms behind fluctuations in vegetation net primary productivity (NPP) has the potential to enhance our comprehension of ecosystem dynamics and their response to environmental changes. However, identifying the nonlinear and spatiotemporal heterogeneity of factors contributing to NPP variation remains a challenge. This research employed the Theil-Sen trend, Hurst index, and nonparametric Mann-Kendall test methodologies through the utilization of Google Earth Engine (GEE) to detect monotonic trends in NPP, distinguishing between upward and downward trends. The influence of individual factors and their interactive effects on NPP changes were quantified using the optimized parameter-based geographical detector (OPGD) model. The findings revealed a general upward trend in NPP, exhibiting an average growth rate of around 85.06 gC m−2.a-1. Nevertheless, these rates of growth were not uniform, leading to noteworthy fluctuations spanning from 447.44 to 543.69 gC m−2.a-1. The eastern and central regions showcased relatively elevated NPP, whereas the western and southern regions demonstrated comparatively reduced levels. Roughly 51.66% of the areas displayed a rising trend in NPP, with 44.81% of the entire area indicating a noteworthy increase (p < 0.01), including both a substantial increase (p < 0.01) and a moderate increase (0.01 ≤ p < 0.05). The areas witnessing heightened NPP were predominantly situated in the northeastern, southeastern, northwestern sections, and the southwestern portion of Sichuan. The fluctuations in NPP trends displayed mild persistence or slight antipersistence traits, with regions where 0 < H < 0.5 constituting 80.26% of the overall area. Natural factors (such as elevation, mean annual temperature, NDVI, and topographic relief) along with human influences (changes in land use type) were identified as effectively accounting for the fluctuations in NPP. These factors demonstrated interactive effects on NPP, with the synergistic effect resulting in nonlinear enhancement and bilinear enhancement effects. The interaction between these two factors strengthened the influence of each individual factor. Identifying the optimal characteristics or ranges of these factors can facilitate ecological conservation and vegetation restoration. These factors exhibited interactive effects on NPP, with the synergistic effect resulting in nonlinear enhancement and bilinear enhancement effects. The interplay between these two factors heightened the influence of each separate element. Determining the optimal attributes or ranges of these factors can contribute to the facilitation of ecological preservation and the restoration of vegetation.

Quantifying the Spatiotemporal Variation of NPP of Different Land Cover Types and the Contribution of Its Associated Factors in the Songnen Plain

Net primary productivity (NPP) of vegetation is considered an important indicator for ecological stability and is the main object for analyzing the factors influencing the terrestrial carbon cycle. Recent studies have made clear the changes in the NPP of vegetation and its influencing factors at various scales. However, the variations in NPP based on different land cover types under various natural conditions, along with their driving factors, remain not well understood. In this study, spatial overlay analysis was used to investigate the link among climatic, soil moisture (SM), and topographic parameters and NPP of various land cover types after analyzing the spatial and temporal trends of NPP in the Songnen Plain from 2001 to 2020. Additionally, the contribution of each influence factor to the NPP of different land cover types was calculated using the elastic net regression model. The elastic net regression model eliminates the multicollinearity among the influencing factors while maintaining the model stability, and the R2 of all lands is greater than 0.62, which can effectively quantify the contribution of each influencing factor to NPP. The results show a continuously increasing trend of the overall NPP in the research area over the selected 20 years, and NPP increased most significantly in forest land (FOR). Precipitation (PRE) and NPP showed high correlations in all the different land cover types, while the correlations between NPP and other influencing factors were significantly different. In addition, we found that perennials led to a more significant degree of NPP enhancement, and the effect of topographic conditions on NPP was mainly reflected in differences in moisture conditions due to surface runoff. From the results of the modeling calculations, the cumulative contribution of PRE to NPP ranks first in all land types and is the most vital influencing factor of NPP in the Songnen Plain. SM was an important influence, but the contribution of NPP was greater in land classes with shallow root systems. The results of the study revealed the positive transformation relationship of NPP among land cover types in ecologically fragile areas, which provides a reference for ecological restoration and rationalization of land use structure in zones such as intertwined agricultural and pastoral zones.

Spatiotemporal Characteristics Prediction and Driving Factors Analysis of NPP in Shanxi Province Covering the Period 2001–2020

The advent of a range of high-precision NPP products, including MODIS NPP, MOD17 NPP, and GIMMS NPP, has sparked growing interest in the study of Earth’s ecosystems. In order to enhance comprehension of ecosystem health, in order to facilitate the development of rational resource management and environmental conservation policies, this investigation employs the MOD17A3 dataset to analyze historical variations in Net Primary Productivity (NPP) within Shanxi Province from 2001 to 2020, while also exploring future trends. The Theil–Sen median trend analysis and Mann–Kendall test are commonly used methods for analyzing time series data, employed to study the spatiotemporal trends and variations in NPP. The Grey Wolf Optimization–Support Vector Machine (GWO–SVM) model combines optimization algorithms and machine learning methods, enhancing the predictive capacity of the model for future NPP time series changes. Conversely, the Hurst exponent utilizes historical NPP trends to assess the persistence characteristics of NPP and predict future spatial variations in NPP. This study additionally investigates the natural driving factors of NPP using the Geographic Detector approach. The key findings of this study are as follows. (1) Overall, NPP in Shanxi Province exhibits a fluctuating upward trend from 2001 to 2020, with an average value of 206.278 gCm−2a−1. Spatially, NPP exhibits a northwest–low and southeast–high pattern, with significant spatial heterogeneity and considerable variability. (2) The average Hurst exponent is 0.86, indicating a characteristic of strong persistence in growth in future NPP. Regions with strong or higher persistent growth account for 95.54% of the total area. (3) According to the CMIP6 climate scenarios, NPP is projected to gradually increase from 2025 to 2030. (4) The interactive effects between natural factors contribute more to NPP variations than individual factors, with the rainfall–elevation interaction having the highest contribution percentage.

Temporal and Spatial Characteristics of Soil Salinization and Its Impact on Cultivated Land Productivity in the BOHAI Rim Region

Soil salinization can have an inhibitory effect on crop productivity and pose a serious threat to regional agricultural resource utilization and sustainable development. However, there are scarce studies on the quantitative impact of soil salinization on crop productivity. The purpose of this study is to identify the spatial and temporal characteristics of the cultivated land, the soil salinization degree, and the net primary productivity (NPP) of vegetation in the Shandong region around the Bohai Sea and to couple the quantitative relationship between soil salinization and the productivity of the cultivated land. We found that the amount of cultivated land in the study area gradually decreased from 2001 to 2019. The degree of soil salinization in the cultivated land in the north of the study area increased slightly from 2001 to 2005 and decreased continuously after 2011. The NPP value of the cultivated land increased, fluctuating from 2001 to 2019. The spatial distribution of the NPP value was generally lower in the north than in the south, with Dongying District as the boundary. In addition, in different soil salt content (g/kg) intervals, the change trends in cultivated land NPP varied. When the salt content of cultivated soil was in the range of 0–3 g/kg, 3–7 g/kg, or 7–15 g/kg, for every 1 g/kg increase in salt content, the NPP value decreased by 43.62 gC m−2a−1, 30.36 gC m−2a−1, and 44.19 gC m−2a−1, respectively. These results are expected to provide decision-making support for strengthening the dynamic monitoring and regulation of cultivated land salinization and promoting sustainable utilization of salinized cultivated land in the coastal areas to alleviate the food crisis.

Spatiotemporal variation of marsh vegetation productivity and climatic effects in Inner Mongolia, China

Net primary productivity (NPP) is a vital ecological index that reflects the ecological function and carbon sequestration of marsh ecosystem. Inner Mongolia has a large area of marshes, which play a crucial role in the East Asian carbon cycle. Under the influence of climate change, the NPP of Inner Mongolian marsh has changed significantly in the past few decades, but the spatiotemporal variation in marsh vegetation NPP and how climate change affects marsh NPP remain unclear. This study explores, for the first time, the spatiotemporal variation of marsh NPP and its response to climatic change in Inner Mongolia based on the MODIS-NPP and climate datasets. We find that the long-term average annual NPP of marsh is 339.85 g⋅C/m2 and the marsh NPP shows a significantly increasing trend (4.44 g⋅C/m2/a; p &amp;lt; 0.01) over Inner Mongolia during 2000–2020. Spatially, the most prominent increase trend of NPP is mainly distributed in the northeast of the region (Greater Khingan Mountains). The partial correlation results show that increasing autumn and summer precipitation can increase the NPP of marsh vegetation over Inner Mongolia. Regarding the temperature effects, we observe a strong asymmetric effect of maximum (Tmax) and minimum (Tmin) temperature on annual NPP. A high spring Tmax can markedly increase marsh NPP in Inner Mongolia, whereas a high Tmin can significantly reduce it. In contrast to spring temperature effects on NPP, a high summer Tmax can decrease NPP, whereas a high Tmin can increase it. Our results suggest different effects of seasonal climate conditions on marsh vegetation productivity and highlight the influences of day-time and night-time temperatures. This should be considered in simulating and predicting marsh carbon sequestration in global arid and semi-arid regions.

Optimization of Green Spaces in Plain Urban Areas to Enhance Carbon Sequestration

Global climate change and rapid urbanization have placed enormous pressure on the urban ecological environment worldwide. Urban green spaces, which are an important component of urban ecosystems, can maintain ecological and environmental sustainability and benefits, including biodiversity conservation and carbon sequestration. However, land use changes across urban landscapes, especially in plain urban areas with high development pressure, have significantly impacted the carbon sequestration efficiency of urban green spaces. Nevertheless, research examining the impact of land use change and development pressure on urban green spaces and carbon sequestration is relatively scarce. Understanding the carbon sequestration efficiency of urban green spaces and its determining factors will help predict future carbon capture trends within urban ecosystems and formulate more targeted sustainable urban planning and management strategies to improve urban carbon sink efficiency and achieve the goal of carbon neutrality. Therefore, to understand the factors affecting the carbon sequestration efficiency of urban green spaces, this paper used an integrated framework that combined the Carnegie–Ames–Stanford approach (CASA) model, landscape pattern index, multiple linear regression, and Markov–FLUS model. The study explored the impact of urban land use and land cover changes on carbon sequestration within the plain urban areas of Beijing at street scale. The results showed that, at street scale, there was a significant positive and negative correlation between the landscape pattern index and net primary productivity (NPP). In addition, the green spaces located in areas with more complex landscape structures had better carbon sequestration benefits. In addition, multiscenario carbon sequestration efficiency prediction suggested that the sustainable development (SD) scenario could achieve a positive increment of overall NPP. In contrast, the business-as-usual development (BD), the fast development (FD), and the low development (LD) scenarios showed a downward trend in NPP. This paper also proposed strategies for optimizing and enhancing green spaces within urban plain areas. Based on the strategies, the results guide decision making for sustainable urban green space planning that maintains the ecological, economic, and social integrity of urban landscapes during urbanization.

Drivers of Net Primary Productivity Spatio-Temporal Variation in Ningxia, China

The drivers and spatial distribution trends for net primary productivity (NPP) in Ningxia were studied to determine the priority vegetation restoration areas. NPP data from MOD17 A3 were used to determine the future NPP trends through slope trend analysis and the Hurst index. Spatial drivers were defined by a geographic detector and correlation analysis. Results indicate that NPP positively fluctuated from 2000 to 2020 with an average range between 119.98 and 249.66 gC/m2a, and a multi-year average of 190.15 gC/m2a. The spatial distribution has more obvious divergent characteristics, showing distribution characteristics of low in the central and northern sides and high in the southern and northern middle. Superimposed on the analysis of slope and Hurst indices, the future vegetation NPP in Ningxia will show four scenarios of continuous increase, continuous decrease, change from increase to decrease and change from decrease to increase, accounting for 22.35%, 1.36%, 71.42% and 2.86% of the area of the region, respectively. Driving factor influence can be divided into dominant factors and important factors. The interaction between the two factors is positive, and the maximum q value under the interaction of precipitation and temperature is 0.687. NPP is mainly driven by climatic factors in 50.92% of the area and is mainly distributed in the central, western and southern parts of Ningxia. The non-climatic-factor-driven areas can be used as priority vegetation restoration areas, which accounting for 47.08%, are mainly concentrated in the northern Yellow River irrigation area, the desert steppe in the central and eastern parts, and a small part in the southern Liupan Mountains.

Estimation of aerosol and cloud radiative effects on terrestrial net primary productivity over northeast Qinghai-Tibet plateau

Processes of plant growth are affected by solar radiation reaching the surface, which is regulated by atmospheric components such as aerosols and clouds. However, there are still large uncertainties in evaluating the impact of aerosol radiative effects (ARE) and cloud radiative effects (CRE) on ecosystems. This study estimated the ARE and CRE on vegetation productivity from 2001 to 2020 over the Northeast Qinghai-Tibet Plateau (QTP) based on MODIS satellite data coupled with the validated Radiative Transfer model (RTM) libRadtran and CASA (Carnegie-Ames-Stanford Approach)model. The results showed that both ARE and CRE have restrained vegetation growth in the last 20 years. The net primary productivity (NPP) of vegetation attenuation by ARE was strongest in summer with an increase of temperature and humidity which caused enhanced air particle transformation and hygroscopic growth, followed by spring and autumn, which were extremely affected by dust, and NPP attenuation by ARE was weakest in winter, accounting for 5.2% of the annual NPP. CRE plays an attenuating role on NPP with increasing cloudiness. The ARE reduced NPP by 18.8 g C/m2/yr, and CRE reduced NPP by 406.9 g C/m2/yr. NPP attenuation by CRE was more than ten times that of NPP attenuation by ARE, which indicated that the CRE has a much stronger weakening of NPP than ARE. The area where the trend of NPP at all-sky conditions increases/decreases had a good match with the areas where the trend of NPP by CRE increases/decreases with R2 being 0.89, indicating that CRE plays a dominant role in attenuating NPP. The increase in NPP is mainly attributed to the decrease in NPP attenuation by CRE over the past two decades. The study results could help to comprehensively understand the ARE and CRE on the terrestrial carbon cycle, and to further access and analyze vegetation ecosystem conditions.

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.

Impact of ocean warming on net primary production in the northern Indian Ocean: role of aerosols and freshening of surface ocean.

Warming due to climate change stratifies the upper ocean and reduces nutrient input to the photic zone resulting in a decline in net primary production (NPP). On the other hand, climate change increases both anthropogenic aerosol input into the atmosphere and the river discharge due to the melting of glaciers on land resulting in enhanced nutrient inputs to the surface ocean and NPP. To examine the balance between these two processes, spatial and temporal variations in the rate of warming, NPP, aerosol optical depth (AOD), and sea surface salinity (SSS) were studied between 2001 and 2020 in the northern Indian Ocean. Strong heterogeneity in the warming of the sea surface was observed in the northern Indian Ocean with significant warming in the south of 12°N. Insignificant trends in warming were observed in the northern Arabian Sea (AS), north of 12°N, during winter and fall, and western Bay of Bengal (BoB) during winter, spring, and fall associated with higher levels of anthropogenic AOD (AAOD) due to a reduction in incoming solar radiation. The decline in NPP was observed in the south of 12°N in both AS and BoB and correlated inversely with SST suggesting that a weak supply of nutrients due to upper ocean stratification controlled NPP. Despite warming, the weak trends in NPP in the north of 12°N were associated with higher AAOD levels and their rate of increase suggesting that the deposition of nutrients from the aerosols seems to be compensating for declining trends due to warming. The decrease in sea surface salinity confirmed an increase in river discharge, and nutrient supply led to weak NPP trends in the northern BoB. This study suggests that the enhanced atmospheric aerosols and river discharge played a significant role in warming and changes in NPP in the northern Indian Ocean, and these parameters must be included in the ocean biogeochemical models for accurate prediction of possible changes in the upper ocean biogeochemistry in the future due to climate change.

Machine learning-based prediction and assessment of recent dynamics of forest net primary productivity in Romania

While the analysis of spatio-temporal changes in the net primary productivity (NPP) of forests can provide critical information on carbon cycle and climate change, these ecological trends have remained unclear in many countries worldwide, including Romania. By using complex (satellite, forest and climate) data, many sophisticated (machine learning) algorithms and some widely applied (the Mann-Kendall test and Sen's slope estimator) statistical procedures, this study investigates, for the first time, recent forest NPP trends (1987–2018) that occurred in Romania, in relation to climate change that affected the country over the past decades. Following the modelling, mapping and assessment of NPP dynamics, results showed almost exclusively positive trends for this ecological parameter, which accounts for ∼99% of all forest NPP changes that occurred throughout the country, after 1987. Interestingly, almost three quarters (∼73%) of all NPP increasing trends are statistically significant, which indicates that Romania's forests have recently experienced a large-scale improvement in carbon fluxes and stocks. Investigations of eco-climatic relationships suggest that climate change has partially contributed to these surprising NPP dynamics observed in recent decades. All these findings can provide valuable information for forest management and for many stakeholders and policymakers who operate in the forestry and climate fields in Romania.

Spatio-Temporal Development of Vegetation Carbon Sinks and Sources in the Arid Region of Northwest China.

Drylands, which account for 41% of Earth's land surface and are home to more than two billion people, play an important role in the global carbon balance. This study analyzes the spatio-temporal patterns of vegetation carbon sinks and sources in the arid region of northwest China (NWC), using the net ecosystem production (NEP) through the Carnegie-Ames-Stanford approach (CASA). It quantitatively evaluates regional ecological security over a 20-year period (2000-2020) via a remote sensing ecological index (RSEI) and other ecological indexes, such as the Normalized Difference Vegetation Index (NDVI), fraction of vegetation cover (FVC), net primary productivity (NPP), and land use. The results show that the annual average carbon capacity of vegetation in NWC changed from carbon sources to carbon sinks, and the vegetation NEP increased at a rate of 1.98 gC m-2 yr-1 from 2000 to 2020. Spatially, the annual NEP in northern Xinjiang (NXJ), southern Xinjiang (SXJ) and Hexi Corridor (HX) increased at even faster rates of 2.11, 2.22, and 1.98 gC m-2 yr-1, respectively. Obvious geographically heterogeneous distributions and changes occurred in vegetation carbon sinks and carbon sources. Some 65.78% of the vegetation areas in NWC were carbon sources during 2000-2020, which were concentrated in the plains, and SXJ, the majority carbon sink areas are located in the mountains. The vegetation NEP in the plains exhibited a positive trend (1.21 gC m-2 yr-1) during 2000-2020, but this speed has slowed since 2010. The vegetation NEP in the mountain exhibited only intermittent changes (2.55 gC m-2 yr-1) during 2000-2020; it exhibited a negative trend during 2000-2010, but this trend has reversed strongly since 2010. The entire ecological security of NWC was enhanced during the study period. Specifically, the RSEI increased from 0.34 to 0.49, the NDVI increased by 0.03 (17.65%), the FVC expanded by 19.56%, and the NPP increased by 27.44%. Recent positive trends in NDVI, FVC and NPP have enhanced the capacity of vegetation carbon sinks, and improved the eco-environment of NWC. The scientific outcomes of this study are of great importance for maintaining ecological stability and sustainable economic development along China's Silk Road Economic Belt.

NPP Variability Associated with Natural and Anthropogenic Factors in the Tropic of Cancer Transect, China

The regions near the Tropic of Cancer are a latitudinal geographical zone with typical climatic, topographic, and human landscape features. It is necessary to explore the region’s net primary productivity (NPP) dynamics as it combines complex topography, various vegetation types, and intense human activities. The study sets the transect near the Tropic of Cancer (TCT) and uses the Carnegie–Ames–Stanford (CASA) model to estimate the NPP from 2000 to 2020. After using the RESTREND method, the paper calculates and compares the relative contributions of climate variability and anthropogenic activities to NPP changes. Finally, the geographical detector (Geodetector) model is applied to evaluate how anthropogenic and natural factors affect spatial distribution patterns and NPP changes. The results indicated that the average annual NPP is 820.39 gC·m−2·yr−1 during the 21 years. In addition, when the NPP varies, it increases over the entire study area, with a slope of 4.81 gC·m−2·yr−1, particularly in the western region. Across the entire research area, 63.39% and 77.44% of the total pixels positively contribute to climate variability and human activities in NPP, with a contribution of 0.90 and 3.91 gC·m−2·yr−1, respectively. Within the western, central, and eastern regions, anthropogenic activities have a stronger impact on NPP than climate variability, particularly pronounced in the eastern region. Furthermore, vegetation cover is the dominant factor in the spatial patterns and NPP trends across the TCT and the three regions. In contrast, climate factors are shown to be less influential in NPP distribution than in the western region. The results also demonstrated that the effect of population density and the GDP on NPP gradually rises. Two-factor interaction is much larger than any individual factor, with the dominant interaction factor being vegetation cover with climatic factors. Lastly, the findings revealed that anthropogenic activities positively promote NPP accumulation across the TCT, thus highlighting the importance of human activity-led ecological restoration and ecological protection measures that contribute to regional carbon sequestration and carbon balance.

Detecting Influencing Factor of Vegetation NPP in Southwest China Based on Spatial Scale Effect

Vegetation net primary productivity (NPP) is an important parameter for evaluating the quality of terrestrial ecosystems. It is of great importance to study the spatio-temporal evolution of vegetation NPP and its driving force for regional ecological environment protection and sustainable development. On the basis of MODIS NPP data, meteorological data, DEM data, population density data, GDP data, and land use type data, this study used linear regression analysis, R/S analysis, and a Geodetector model to analyze the spatio-temporal variation in vegetation NPP and its future changing trend on both regional and landform scales and to detect the influencing factors that affect the spatial differentiation of vegetation NPP. The results showed that the vegetation NPP exhibited an extremely significant upward trend in southwest China from 2000 to 2020. On the landform scale, the vegetation NPP had showed an upward trend in all landforms, except for the southern Tibet Plateau; among them, the vegetation NPP in the Sichuan Basin showed the most obvious upward trend. The variation in vegetation NPP exhibited obvious spatial heterogeneity in southwest China, with the changing rate of "high in the east and low in the west." The areas with an upward trend of vegetation NPP were greater than the areas with a downward trend, but the changing trend was dominated by a decreasing trend in the future, both in southwest China and each landform unit. The Geodetector results showed that elevation was the dominant factor controlling the spatial differentiation of vegetation NPP in southwest China and all landform units, except for the Yunan-Guizhou Plateau, in which the spatial differentiation of vegetation NPP was mostly dominated by temperature. The interaction detection results showed that the interaction between the influencing factors was manifested as two-factor enhancement or nonlinear enhancement. The interaction between elevation and temperature showed the highest impact on vegetation NPP distribution. On the landform scale, the spatial differential of vegetation NPP was dominated by the interaction between elevation and climate factors or elevation and GDP in the Guangxi Hills, Sichuan Basin, Zoige Plateau, Hengduan Mountains, and southern Tibet Plateau and between climate factors in the Yunan-Guizhou Plateau. The above results indicated that vegetation NPP variation and the influencing factors that dominate its spatial differential in southwest China showed obvious scale effects. Therefore, exploring the dynamic variation in vegetation NPP and its influencing factors at different spatial scales has practical significance for a comprehensive understanding of the vegetation cover situation and formulating regional ecological restoration plans in southwest China.

Impacts of the world’s largest afforestation program (Three-North Afforestation Program) on desertification control in sandy land of China

ABSTRACT In order to control the desertification, large-scale afforestation programs have been attempted worldwide. Among them, China initiated the world’s largest afforestation program, Three-North Afforestation Program (TNAP, 1978–2050), in which the afforestation in sandy land has been questioned during the first 40 years. In fact, the contribution of the TNAP to vegetation establishment and its effectiveness in desertification control still remain unclear, which limited the further construction of the program. To answer the questions, we detected the dynamics of vegetation distribution (forest, shrubland, and grassland) and desertification status (slight, moderate, severe, and extremely severe) during 1978–2017 in the sandy land (45.5 million ha), by visual interpretation of 5-period remote sensing images with validation based on 3,100 sample plots from field surveys and 15,175 sample plots from the National Forest Resource Inventory. Vegetation degradation was identified by analysis combining the trends of net primary productivity and precipitation use efficiency. By Geographical Detector model, the driving forces of vegetation degradation (climate change, human activities, vegetation type, and sandy land type) were ranked and the contributions of the influential factors (climate change, human activities, and vegetation dynamics) to desertification changes were estimated. The results showed that for the 40 years, vegetation coverage increased by 0.5%, with increasing 113.8% and 338.8% of forest and shrubland, but decreasing 9.0% of grassland. Desertification area had little change while the overall intensity decreased. The TNAP contributed to desertification dynamics by 10.3%, which is lower than expected. Vegetation type was the dominant factor of vegetation degradation in general. Forest is less suitable for afforestation in sandy land than shrubland and grassland because of its lower stand establishment rate, higher degradation rate, and less contribution to desertification control. Thus, adjusting vegetation type to match local conditions (e.g., use shrub-land, grassland, and native species) and improving the vegetation resistance (e.g., transform monoculture forests into mixed forests, and make proper proportion for forest, shrubland, and grassland) was suggested. Our study provided specific and feasible strategies for further planning and implementation of TNAP, and references for vegetation restoration of sandy lands worldwide.

Changes in the Spatiotemporal of Net Primary Productivity in the Conventional Lake Chad Basin between 2001 and 2020 Based on CASA Model

Accurate estimation of vegetation Net Primary Productivity (NPP) has important theoretical and practical significance for ecological environment governance, carbon cycle research, and the rational development and utilization of natural resources. In this study, the spatial characteristics, temporal changes, and driving factors of NPP in the Conventional Lake Chad Basin (CLCB) were based on MODIS data by constructing a Carnegie Ames Stanford Approach (CASA) model and using a combination of Residual trends (RESTREND) and correlation analysis. The results showed that from 2001 to 2020, the NPP of the CLCB decreased annually (1.14 g C/m2), mainly because of overgrazing, deforestation, and large-scale irrigation. We conducted a driving factor analysis and found that the main influencing factor of the NPP of the CLCB is high-intensity human activities, including farmland reclamation and animal husbandry. Although the impact of climate change on NPP is not obvious in the short term, climate change may help recover NPP in the long term. The continued reduction in NPP has greatly increased the difficulty of regreening the Sahel; the increase in population density and rapid urbanization have led are major contributing factors to this. Our findings have important implications for the continued implementation of stringent revegetation policies. However, owing to limited data and methods, only the overall change trend of NPP was obtained, and comprehensive follow-up studies are needed.

Multiple effects of climate changes and human activities on NPP increase in the Three-north Shelter Forest Program area

In the framework of ecological engineering, vegetation net primary productivity (NPP) has grown dramatically in the Three-north Shelter Forest Program area (TSFPA), which is influenced by both human activities and climate change, as well as their interaction. However, it remains unclear how the individual (pure effects) and interactive (shared and coupling effects) impacts affect and maintain the growing vegetation NPP trend. In this study, the monotonically increasing NPP trend from nonlinear trends was detected using Ensemble empirical mode decomposition (EEMD), and its persistence was investigated using boosted regression tree (BRT) models. We investigated the relative contributions of environmental factors and variable interactions, as well as spatially identified the multiple effects of climate change and human activities on the increasing trend and its persistence over TSFPA. The results indicated that: (1) The vegetation NPP was dominated by the monotonically increasing trend (41.08 %), and 30.04 % of all pixels can still maintain NPP increment. (2) Although soil moisture exhibited the highest interaction with the precipitation seasonality (32.21 %) on the monotonically increasing NPP trend, land-use change substantially interacted with soil moisture (26.97 %), snowmelt (23.34 %), and net solar radiation (13.43 %). (3) The monotonically increasing vegetation NPP was caused not only by the pure effects of climate change (23.57 %) and human activities (11.69 %) directly but also by their coupling effects (26.64 %) or shared effects (38.10 %) interactively. (4) The shared effects (50.94 %) dominated the persistently increasing NPP trend, whereas the coupling effects (78.78 %) dominated the non-persistently increasing NPP trend. The findings highlight the importance of the pure, shared, and coupling effects of human activities and climate change interactively for elucidating the driving of the vegetation NPP increment and its persistence over ecological engineering areas.

Dynamics of forest net primary productivity based on tree ring reconstruction in the Tianshan Mountains

The lack of long-term high-resolution data makes it difficult to determine historical and future trends in net primary productivity (NPP). This study used tree rings as a proxy to investigate the dynamics of NPP in Tianshan forests where coniferous forests are the major species and the other species are deficient. All trees and some tree cores from five sample plots in different geographic locations in the western Tianshan Mountains were selected to reconstruct forest NPP data from 1950 to 2020. Multiple historical events that resulted in large-scale terrestrial carbon fluxes were identified and the existence of 28a and 17a time-scale cycles of historical forest NPP was observed. We discovered that the reconstructed forest NPP in the western Tianshan Mountains did not significantly correlate with satellite-based products (e.g., MODIS NPP, solar-induced chlorophyll fluorescence data). This result was attributed to the lag of forest growth for climate, the accuracy of the satellite-based products and statistical errors due to the short overlap time. We analysed the uncertainties in reconstructing historical forest NPP using tree ring widths and proposed corresponding solutions. We concluded that the reconstructed data remain the ideal proxy for regions lacking long-term empirical data and exhibit a high degree of confidence for expressing trends in forest productivity change over long time series.

Impact of Farmland Change on Vegetation NPP in the One River and Two Streams Region of Tibet

Studies on the impacts of a particular land use type change are relatively rare, especially in the Tibetan Plateau region (TP). This study focused on the impacts of farmland use change on grain supply and ecosystem stability in the Yarlung Zangbo river and its two tributaries (also known as One River and Two Streams, ORTS), using net primary productivity (NPP), known as the total amount of organic matter left after removal of carbon absorbed from the atmosphere by vegetation through photosynthesis, as a common proxy for farmland productivity and ecosystem stability. The trend analysis method was applied to measure the inter-annual change of NPP, and an ecological impact index was constructed to quantify the impact of farmland use change on the NPP change in the ORTS region. The results showed that: (1) The total area of farmland decreased by 6.09% from 2000 to 2018. Built-up land occupation and ecological restoration were the main reasons for the decrease of farmland area, while there was also new reclaimed farmland, transferred from ecological land. (2) The NPP in the ORTS region was roughly on an increasing trend, while the trends of NPP in different farmland change areas were not the same. Specifically, the NPP of ecological restoration, newly reclaimed farmland, and unchanged farmland areas all showed a significant increasing trend, while the NPP in the area of farmland occupied by built-up land showed a significant decreasing trend. (3) The impact of farmland changes from 2000 to 2018 contributed 1.22% to the increase of NPP in the ORTS region. This study not only provides a research paradigm in quantifying the production and ecological impacts of a particular land use type change that can be applied in related studies in other regions, but at the same time, the results of the empirical analysis in the ORTS region can also provide suggestions for the rational use and conservation of farmland and the stability and sustainable development of ecosystems for the region and even the TP.

Response of Terrestrial Net Primary Production to Quadrupled CO2 Forcing: A Comparison between the CAS-ESM2 and CMIP6 Models.

Terrestrial net primary production (NPP) is a key carbon flux that changes with rising atmospheric CO2 and CO2-induced climate change. Earth system models are commonly used to investigate these NPP changes because of their fundamentally trustworthy ability to simulate physical climate systems and terrestrial biogeochemical processes. However, many uncertainties remain in projecting NPP responses, due to their complex processes and divergent model characteristics. This study estimated NPP responses to elevated CO2 and CO2-induced climate change using the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM2), as well as 22 CMIP6 models. Based on CMIP6 pre-industrial and abruptly quadrupled CO2 experiments, the analysis focused on a comparison of the CAS-ESM2 with the multi-model ensemble (MME), and on a detection of underlying causes of their differences. We found that all of the models showed an overall enhancement in NPP, and that CAS-ESM2 projected a slightly weaker NPP enhancement than MME. This weaker NPP enhancement was the net result of much weaker NPP enhancement over the tropics, and a little stronger NPP enhancement over northern high latitudes. We further report that these differences in NPP responses between the CAS-ESM2 and MME resulted from their different behaviors in simulating NPP trends with modeling time, and are attributed to their different projections of CO2-induced climatic anomalies and different climate sensitivities. These results are favorable for understanding and further improving the performance of the CAS-ESM2 in projecting the terrestrial carbon cycle, and point towards a need for greater understanding and improvements for both physical climatic processes and the terrestrial carbon cycle.