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Article

Dynamic Changes and Driving Mechanisms of Net Primary Production (NPP) in a Semi-Arid Region of China

by
Dandan Zhao
,
Wenyue Jia
and
Jiping Liu
*
School of Geographical Science and Tourism, Jilin Normal University, Siping 136000, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(15), 11829; https://doi.org/10.3390/su151511829
Submission received: 23 June 2023 / Revised: 19 July 2023 / Accepted: 25 July 2023 / Published: 1 August 2023
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Abstract

:
The objective of this study is to analyze the spatiotemporal dynamics of net primary production (NPP) change combined with land use and to further explore the driving factors of NPP change, allowing us to provide a scientific reference point for optimizing the land-use structure and improving regional carbon sequestration capacity. The average annual NPP ranged from 200 to 300 gC/m2•a in the period of 2001–2020 in our study area. We used trend analysis and linear regression analysis to explore the spatial and temporal dynamic changes in annual NPP and analyzed the driving mechanism in a semiarid region (western Jilin Province) of China in the period of 2001–2020. The results showed that NPP presented a trend of fluctuating growth, and the spatial distribution of NPP showed that NPP values of cultivated land, forest and grassland were generally higher than those of other land-use types. The high value in the southeastern region and low value in the northwestern region were identified because there were large areas of cultivated land distributed in the southeastern part of the study area in the period of 2001–2020. The main driving factors that affected NPP were annual precipitation, CO2 emissions, GDP and hours of sunshine. NPP was positively correlated with annual precipitation, CO2 emissions and GDP, and it had a significant negative correlation with hours of sunshine. Our study provides important support for research into land-use structure and improvements to the regional carbon sequestration capacity, making an important contribution to regional sustainable development.

1. Introduction

Net primary productivity (NPP) refers to the total amount of organic matter accumulated by vegetation in terrestrial ecosystems per unit of time and space [1], which is calculated as the difference between the carbon absorbed through photosynthesis and the carbon released through respiration [2]. This concept reflects the production capacity of vegetation under comprehensive influence (climate, terrain, human factors, pathogen, etc.) in the natural environment. NPP is considered to be a major factor involved in determining ecosystem carbon accumulation and regulating ecological processes [3,4], because it not only reflects regional vegetation productivity but is also crucial for surface carbon cycling and ecosystems [5]. Land use land cover change (LULCC) identifies links between human social and economic activities and natural ecological processes [6]. However, LULCC has an extremely disadvantageous influence on regional biodiversity, water quality, soil and carbon cycles [7] and the ability of ecosystems to support human needs [8]. LULCC directly changes the type, structure and function of the ecosystem, and it thus affects the NPP of the ecosystem.
Humans affect natural ecosystems through the disturbance of LULCC, thus changing the global balance of income and expenditure in both direct and indirect ways [9]. In this study, NPP reflected the carbon sequestration capacity of plant communities in the natural environment, and the quantity of its changes also reflected the fact that the response of vegetation to land-use change is an important indicator of ecosystem health [10]. Zhao et al. (2023) explored the effect of land-use change on regional NPP change based on the area of each land-use type, and the results showed that the cultivated land area decreased, while the NPP increased; the forest and grassland area also increased, and the NPP correspondingly increased in the Beijing–Tianjin–Hebei region of China in the period of 2000–2020 [11]. Research into NPP changes was not only conducive to improving our understanding of the carbon cycle of terrestrial ecosystems and its dynamic regulation mechanism [12], which was vital to carrying out corresponding management of different environmental types, but it also provided scientific guidance that enabled scientific development of ecological resources [13]. Therefore, an accurate assessment of the impact of LULCC on NPP was crucial to understanding the regional carbon cycle.
Many scholars have carried out studies of NPP changes in combination with LULCC [13,14]. The net flux of carbon from LULCC accounted for 12.5% of anthropogenic carbon emissions in the global carbon budget in the period of 1990–2010 [15]. Global urban expansion offset the improvements in land net primary productivity driven by climate using a high-resolution data set related to global LULCC [16]. The shift from carbon sinks to carbon sources was associated with LULCC due to net primary productivity (NPP) reduction in tropical peat lands [17]. LULCC contributed 34% of NPP growth in the period of 2000–2006; however, the change in NPP hindered growth by 27% in the period of 2006–2012 in Beijing, China [18]. The contribution rates of vegetation land-use type area change to NPP change in the periods 2000–2010 and 2010–2020 were 18% and 34%, respectively, in the Beijing–Tianjin–Hebei region of China [11]. The relative contributions of climate change and land-use change to NPP change were 85.60% and 10.85 at the regional scale, respectively. The NPP of cultivated land and grassland ecosystems were dominated by land-use change, which accounted for 54.99% and 56.91%, respectively, in the middle and lower reaches of the Yangtze River, China, at the grid scale [19]. Therefore, the study of NPP changes should be combined with LULCC.
Researchers have analyzed the long-term trend changes in NPP. For example, scholars used trend analysis and correlation analysis methods to explore the trend of change in NPP in the Fenhe River Basin, which showed that the NPP increased in tandem with an annual growth rate of 6.62 g C·m−2•a−1 in the period of 2000–2015 [20]. Liang et al. (2023) analyzed the NPP of vegetation using the methods of trend analysis, coefficient of variation and geographical detection in urban agglomerations in Guanzhong Plain, China, in the period of 2000–2019, finding that the average annual growth rate was 9.1 g C/m2, which represented an overall fluctuating upward trend [21]. He et al. (2023) studied the temporal and spatial changes in NPP in the Manas River Basin, and the results showed that the mean NPP of vegetation in the basin showed an increasing trend, with the high values concentrated in the low hilly zone and the middle of the oasis part of the mountain area, while the low values were distributed in the desert ecological zone, in the period of 2001–2020 [22]. Fan et al. (2022) explored the spatial–temporal variation in NPP in Tibet, China, and the results indicated that the NPP increased at an average rate of 137.36 g/(m2·a) in the period of 2000–2020 [23]. Few studies discuss the trend analysis of NPP in semi-arid regions.
Additionally, many studies considered the driving mechanism of NPP. LULCC and management measures contributed 62.78% and 37.22% to the increase in grassland NPP, respectively, in the Qinghai–Tibet Plateau, China [12]. NPP showed a significant positive correlation with precipitation, while it showed a less general correlation with air temperature in the Shiyang River Basin, Northwestern China [24]. Change in precipitation was a major factor involved in the degradation of the northern grasslands through NPP changes [25]. NPP was more sensitive to precipitation than temperature, and NPP changes were bimodal with altitude in Shanxi Province, China [26]. Temperature, precipitation and solar radiation contributed the most to NPP-related changes in wetland vegetation, and solar radiation contributed most significantly to NPP changes in the Dongting Lake wetlands, China [27]. NPP was moderately positively correlated with temperature, accumulated temperature (10 °C) and sunshine, while NPP was negatively correlated with precipitation in the Hengduan Mountain area of China during 2000–2015 [28]. Peng et al. (2020) found that the main driving forces of NPP change are NDVI and population density in debris-flow-prone areas in Southwest China [29] At present, the driving mechanism of NPP mainly focuses on land-use types, such as wetland and grassland, while less attention has been paid to NPP for land use in semi-arid regions.
As the ultimate criterion and approach to the success of regional sustainable development, ecological sustainability has always been the focus of the international community and an important part of sustainable development research [30,31]. The biological production of green plants is the nutritional basis and material energy source of all heterotrophic life on earth, which organically links man with nature [32]. Western Jilin Province is located in the agricultural and pastoral transitional zone in northeastern China, which belongs to a typical semi-arid region. An unreasonable land-use structure has led to the extremely fragile ecological environment in the region, which is an important ecological barrier in Jilin Province, China [33]. In particular, continuous LULCC inevitably affected ecosystem productivity under the background of climate change and human activities [34]. Quantitative assessment of the impact and occupation of human activities on green plant productivity and exploration of the supporting ability of plant production and self-sustaining ability of the human ecosystem are the measurement methods and approaches for regional sustainable development ecological assessment [35]. Therefore, the objective of this study is to analyze the spatiotemporal dynamics of NPP change combined with land use and to further explore the driving factors of NPP change, so as to provide scientific reference for optimizing the land-use structure and improving the regional carbon sequestration capacity.

2. Materials and Methods

2.1. Study Area

Our study area is located in northeast China (western Jilin Province), 43°22′~46°18′ N, 121°38′~126°11′ E, with a total area of 46,794 km2. There are two prefecture-level cities: Baicheng and Songyuan (including FY-Fuyu City, NJ-Ningjiang District, QG-Qianguorulos Mongolian Autonomous County, Changling (CL) County, Qian ’an (QA) County, Taobei (TB) District, Taonan (TN) City, Zhenlai (ZL) County, Da’an (DA) City, Tongyu (TY) County) (Figure 1). Western Jilin Province belongs to the arid and semi-arid temperate monsoon climate zone, with an annual mean temperature of 4–5 °C. Annual precipitation of 400–500 mm decreased from the eastern part of the plain to the western part, which was far less than evaporation. Our study area is located in the transitional zone of the humid eastern monsoon and arid and semi-arid inland areas of China with obvious seasonal changes, and it is also a typical ecologically fragile zone of agriculture and animal husbandry [26]. NPP changed significantly in recent years, which threatened regional ecological security and sustainable development.

2.2. Data Source and Processing

We collected data including land use, NPP and driving factors (average annual temperature, annual precipitation, hours of sunshine, average annual wind speed, carbon dioxide emission, population and GDP) (Table 1). The data source is shown in Figure 1. Land use land cover (LULC) data were reclassified into six primary land classes: cultivated land, forest, grassland, waters, building land, and unused land. The accuracy of land-use data has been verified to be more than 90%, which basically met the requirements of our study. NPP data were derived from the United States National Space Administration (NASA) and the resolution was 500 m. We zeroed pixels with original data greater than 30,000 (the original data unit was kgC/m2•a) and transformed to obtain new NPP data (gC/m2•a) through a series of mask processing and related processes based on ArcGIS 10.3 and ENVI 5.2 software.

2.3. Methodology

We used unitary linear regression analysis to simulate the interannual trend of each pixel by fitting a data trend line based on the software MatlabR2016a. The advantage of this method is that it can eliminate the influence of abnormal factors on the variation trend of NPP in a specific year and reflect the evolution trend of NPP [36,37]. The calculation formula is as follows:
θ s l o p e = n × ∑ i = 1 n i × N P P i − ( ∑ i = 1 n i ∑ i = 1 n N P P i ) n × ∑ i = 1 n i 2 − ( ∑ i = 1 n i ) 2
where  θ  slope refers to the fitting annual NPP value change rate. Negative values indicate that NPP shows a decreasing trend, while positive values indicate that NPP shows an increasing trend. n is the length of the studied time series, and NPPi is the NPP value of the first year. According to Slope and significance test results (p value), the spatial change trend of NPP was divided into five grades: extremely significant increase; significant increase; insignificant; significant decrease and extremely significant decrease.
Our research took this semi-arid area in the west of Jilin Province, China, as the research object. We studied spatial and temporal distribution characteristics of average annual NPP during 2001–2020 in different land-use types based on the spatial mapping function on ArcGIS10.3. We used trend analysis to study spatial characteristics of annual average NPP and trend analysis during 2001–2020 to analyze the interannual variation in NPP. We used correlation analysis to examine the driving mechanism analysis of average NPP changes during 2001–2020, used to explore the driving factors of NPP changes. Net primary productivity (NPP) is an important indicator of terrestrial ecosystem productivity, which is used to represent the potential of vegetation growth to offset anthropogenic carbon emissions, and it is of great significance for maintaining global carbon balance, mitigating climate change and sustainable development of resources and the environment.

3. Results

3.1. Spatial Distribution Characteristics of Average Annual NPP during 2001–2020

3.1.1. Average Annual NPP Distribution during 2001–2020

Average annual NPP showed spatial heterogeneity and decreased from east to west in the study area during 2001–2020. The average annual NPP was higher than 300 gC/m2•a in the eastern region, and the lowest NPP was lower than 100 gC/m2•a in the central region. The maximum NPP was 427 gC/m2•a in Songyuan City (Figure 2).
The average annual NPP of Songyuan City, in descending order, was FY, NJ, CL, QG and QA during 2001–2020 (Figure 2 and Figure 3). FY had the highest average annual NPP during 2001–2020, reaching 311.7 gC/m2•a. The average annual NPP of NJ, CL, QG and QA were 298.5 gC/m2•a, 273.8 gC/m2•a, 290.7 gC/m2•a and 246.5 gC/m2•a, respectively, during 2001–2020. The average annual NPP of Baicheng city from high to low wereTB, TN, ZL, DA and TY, with the values of 231.9 gC/m2•a, 224.2 gC/m2•a, 218.1 gC/m2•a and 206 gC/m2•a, respectively (Figure 2 and Figure 3).

Average Annual NPP in Different Land-Use Types during 2001–2020

We calculated the maximum, mean and minimum values of NPP for different land-use types. The average annual NPP varied according to different land-use types, and the highest NPP values, ranked from high to low, were woodland > cultivated land > grassland > waters > unused land > building land during 2001–2020 (Figure 4). The maximum annual NPP values of forest land, cultivated land and grassland were 427.64 gC/m2•a, 423.93 gC/m2•a and 422.73 gC/m2•a, respectively. The average annual NPP was ranked from high to low as woodland > cultivated land > building land > grassland > waters > unused land. The highest average annual NPP of woodland was 277.81 gC/m2•a and the minimum average annual NPP of unused land was 35.9 gC/m2•a (Figure 4).

3.2. Temporal Variation Characteristics of Average Annual NPP during 2001–2020

3.2.1. Trend Analysis of Annual Average NPP Changes during 2001–2020

We analyzed the NPP changes using a trend analysis of the 2001–2020 data. The average annual NPP ranged from 200–300 gC/m2•a during 2001–2020 in the study area, and a small number of areas were lower than 200 gC/m2•a. The highest average annual NPP was about 290 gC/m2•a in 2013, and the lowest average annual NPP was 167 gC/m2•a in 2001 (Figure 5). The annual variations in NPP in the study area were divided into three stages: the annual average NPP increased during 2001–2003, the average annual NPP decreased during 2004–2007 and the average annual NPP increased volatility during 2007–2020 (Figure 5).

3.2.2. Spatial Characteristics of Annual Average NPP Trend Analysis during 2001–2020

We conducted trend analysis and a significance test on NPP, and the results showed that the average NPP showed an increasing trend during 2001–2020. The average NPP in Baicheng City (TN, TY and ZL) increased significantly, and the increasing trend was obvious (Figure 6). Average NPP changes were significant, showing sporadic distribution in the study area. The average NPP changes with no significance were mainly scattered in the western part and most of the eastern part. The average NPP decreased significantly and clearly changed with a small scope (Figure 6).

3.3. Driving Mechanism Analysis of Average NPP Changes during 2001–2020

Based on the previous studies related to NPP changes [11], we selected meteorological factors such as the average annual temperature, annual precipitation, hours of sunshine, average wind speed, CO2 emission, population and GDP to conduct a correlation analysis with NPP using SPSS 20.0 software. NPP was significantly positively correlated with annual precipitation, CO2 emissions and GDP and negatively correlated with hours of sunshine, but it did not correlate with average annual temperature, wind speed or population (Table 2). Therefore, annual precipitation, hours of sunshine, CO2 emissions and GDP were selected as the main driving factors in our study.
We further analyzed the relationship between the changes in average annual temperature, hours of sunshine, annual precipitation and NPP during 2001–2020 in the study area (Table 2). The statistical analysis results are as follows:
Results showed that annual precipitation and NPP had a significant positive correlation. The correlation coefficient was 0.744 and was significant at the level of 0.01. Generally, the fluctuation amplitude of annual precipitation changes and NPP changes was slightly different during 2001–2020. Specifically, the annual precipitation curve was the most similar to the average annual NPP curve, and the two peaks appeared almost synchronously during 2001–2007. However, annual precipitation increased sharply while NPP only showed a slight increase during 2010–2012 (Figure 7). Hours of sunshine were negatively correlated with average NPP. The correlation coefficient was −0.502 and was significant at the level of 0.05. Specifically, the maximum and minimum hours of sunshine occurred in 2019 and 2010, respectively, in the study area. Hours of sunshine showed an overall downward trend, fluctuated during 2001–2010 and slightly increased during 2010–2019 (Figure 7).
CO2 emissions were positively correlated with annual NPP, and the correlation coefficient was 0.739, which was significant at the level of 0.01. CO2 emissions showed a clear upward trend during 2001–2020 and was closely matched with the annual NPP during 2009–2020. There was a significant positive correlation between GDP and annual NPP, and the correlation coefficient was 0.645, which was significant at the level of 0.01. GDP showed a trend of significant increase and fluctuation decrease during 2001–2013 and 2013–2020, respectively. The highest values of both GDP and NPP occurred in 2013.

4. Discussion

4.1. Temporal and Spatial Variations in NPP

Presently, many studies have focused on NPP of specific land-use types, such as grassland and wetland, etc. Previous research studied the estimation and temporal and spatial characteristics of grassland NPP in Da’an City, China, and the results showed that the total amount of NPP showed a gradual increasing trend during 2005–2016 [25]. Scholars studied the fluctuation of NPP in the western Songnen Plain of China, which showed an obvious upward trend during 2000–2009 [38]. Previous research studied the annual average NPP and the total amount of regional NPP of wetlands in western Jilin Province, China, which showed a gradually increasing trend during 2000–2016 [27]. Our results showed that average annual NPP showed an increasing trend that was basically consistent with previous studies during 2001–2020. The NPP distribution showed obvious spatial differences in Jilin Province during 2000–2019 [26]. NPP showed an increasing trend in most regions, and the overall variability was stable, but the spatial variability of NPP was large in the northwest region.
Research on the impact of land use on NPP has been carried out to protect the regional ecological environment and provide a reference for vegetation resource management [19]. Previous studies showed that the cultivated land area decreased, but the NPP increased, and the forest and grassland area increased, and the NPP increased correspondingly in the Beijing–Tianjin–Hebei region of China during 2000–2020 [11]. The NPP of farmland, grassland and forest ecosystems all showed an upward trend, but the NPP of farmland ecosystems showed the most significant upward trend in the southwest of China during 2000–2021 [19]. In our study, we found that the NPP values of woodland and grassland were the largest, which was basically consistent with the previous results. However, we only made the NPP corresponding to the land-use type in 2020 and did not explore the impact of land-use area change on NPP in the diachronic period, which will continue to be strengthened in the future.

4.2. Driving Mechanism of NPP Changes

Results showed that the annual precipitation and NPP had a significant positive correlation; proper precipitation is conducive to plant growth, which contributes to an increase in NPP. Hours of sunshine were negatively correlated with average NPP; suitable sunshine hours are beneficial to plant growth and development. CO2 emissions and GDP were positively correlated with annual NPP; the economic source of some residents was agriculture as an important agricultural production base in China, which was basically consistent with the second place in the average annual NPP of cultivated land in our study. Our results were consistent with previous studies: solar radiation was the most important factor affecting the actual value of the regional average annual NPP [39]. This might be because the fluctuation in solar radiation during the growing season directly affects the accumulation process of organic matter in plants, and it then affects the changes in NPP [40]. Spatial variation in NPP in the northwest of Jilin Province was large, which was related to the implementation of returning farmland to forests and the control of grassland degradation, desertification and salinization, and the vegetation recovery was obvious [41,42]. Meanwhile, NPP had a positive correlation with the average annual temperature, while the correlation with annual precipitation was opposite to the average annual temperature in most areas of Jilin Province [41], which was inconsistent with the results of our study, possibly because it was related to the scope of the study.
Relevant policies were also the reasons for the NPP changes. Vigorous economic development led to a fragile ecological environment [43] and relatively low NPP value in the early 19th century. In recent years, China has attached great importance to the sustainable development of the ecological environment and implemented relevant laws and regulations (such as returning farmland to forest, etc.) to prevent the destruction of land use [36,37]. Therefore, the severely damaged land recovered well during this period, and the net land productivity increased, making the average annual NPP show a gradual upward trend. Additionally, combined with the LULC, the annual NPP significantly decreased, mainly in the areas near river channels with decreased vegetation coverage. Second, in areas where urban expansion turned the land into building land, NPP decreased significantly. Our study revealed the annual mean variation in NPP during 2001–2020. A limitation of our study was that we analyzed the average annual NPP without considering the difference in seasonal variation. Therefore, it failed to fully study the NPP variation trend of different seasons; we will strengthen this aspect in future research.

5. Conclusions

The spatial distribution of the annual average NPP was high in the southeast and low in the northwest during 2001–2020 in the study area. The average annual NPP value was different according to different land use land cover; from high to low, they were forest land, cultivated land, grassland and water area, respectively. The average annual NPP showed a gradual upward trend during 2001–2020 and decreased slightly during 2004–2007 in the semi-arid region of China. NPP was positively correlated with annual precipitation, CO2 emissions and GDP, and it had a significant negative correlation with the hours of sunshine. Our research is significant for the further implementation of appropriate ecological restoration policies to increase NPP and enhance the carbon sequestration potential of terrestrial ecosystems.

Author Contributions

Methodology, W.J. and D.Z.; resources, W.J. and J.L.; software, W.J.; writing—original draft, D.Z.; writing—review and editing, D.Z., W.J. and J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was jointly supported by the Science and Technology Department Program of Jilin Province of China (YDZJ202201ZYTS474) and the National Key R&D Program of China (2022YFF1300900).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We sincerely acknowledge the constructive comments of the anonymous reviewers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liu, Y.; Han, X.; Weng, F.; Xu, Y.; Zhang, Y.; Tang, S. Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data. J. Meteorol. Res. 2022, 36, 401–416. [Google Scholar] [CrossRef]
  2. Chan, P. Discrepancies between Global Forest Net Primary Productivity Estimates Derived from MODIS and Forest Inventory Data and Underlying Factors. Remote Sens. 2021, 13, 1441. [Google Scholar]
  3. Ahmad, S.; Pandey, A.C.; Kumar, A.; Lele, N.V.; Bhattacharya, B.K. Primary productivity estimation of forest based on in-situ biophysical parameters and sentinel satellite data using vegetation photosynthesis model in an eastern Indian tropical dry deciduous forest. Trop. Ecol. 2022, 63, 409–422. [Google Scholar] [CrossRef]
  4. Kato, S. Ten-Year Estimation of Net Primary Productivity in a Mangrove Forest under a Tropical Monsoon Climate in Eastern Thailand: Significance of the Temperature Environment in the Dry Season. Forests 2020, 11, 987. [Google Scholar]
  5. Yu, R. An improved estimation of net primary productivity of grassland in the Qinghai-Tibet region using light use efficiency with vegetation photosynthesis model. Ecol. Model. 2020, 431, 109121. [Google Scholar] [CrossRef]
  6. Dong, Y.; Ren, Z.; Fu, Y.; Yang, R.; Sun, H.; He, X. Land Use/Cover Change and Its Policy Implications in Typical Agriculture-forest Ecotone of Cen-tral Jilin Province, China. Chin. Geogr. Sci. 2021, 31, 261–275. [Google Scholar] [CrossRef]
  7. Li, F.; Meng, J.; Zhu, L.; You, N. Spatial pattern and temporal trend of land degradation in the Heihe River Basin of China using local net primary production scaling. Land Degrad. Dev. 2019, 31, 518–530. [Google Scholar] [CrossRef]
  8. Vadrevu, K.P.; Justice, C.; Prasad, T.; Prasad, N.; Gutman, G. Land cover/land use change and impacts on environment in South Asia. J. Environ. Manag. 2015, 148, 1–3. [Google Scholar] [CrossRef]
  9. Zhang, Y.; Wang, Q.; Wang, Z.; Yang, Y.; Li, J. Impact of human activities and climate change on the grassland dynamics under different regime policies in the Mongolian Plateau. Sci. Total Environ. 2019, 698, 134304. [Google Scholar] [CrossRef]
  10. Sun, G.; Mu, M. Understanding variations and seasonal characteristics of net primary production under two types of climate change scenarios in China using the LPJ model. Clim. Chang. 2013, 120, 755–769. [Google Scholar] [CrossRef]
  11. Zhao, Y.; Ju, Z.; Wu, L. Effects of land-use change on net primary productivity. Sci. Surv. Mapp. 2023, 48, 166–175. [Google Scholar]
  12. Liu, Y.; Ren, H.; Zheng, C.; Zhou, R.; Hu, T.; Yang, P.; Zhang, W.; Wang, Z.; Li, Y.; Zhang, Z.; et al. Untangling the effects of management measures, climate and land use cover change on grassland dy-namics in the Qinghai–Tibet Plateau, China. Land Degrad. Dev. 2021, 32, 4974–4987. [Google Scholar] [CrossRef]
  13. Yang, H.; Zhong, X.; Deng, S.; Xu, H. Assessment of the impact of LUCC on NPP and its influencing factors in the Yangtze River basin, China. Catena 2021, 206, 105542. [Google Scholar] [CrossRef]
  14. Luo, Q.; Zhou, J.; Li, Z.; Yu, B. Spatial differences of ecosystem services and their driving factors: A comparation analysis among three urban agglomerations in China’s Yangtze River Economic Belt. Sci. Total Environ. 2020, 725, 138452. [Google Scholar] [CrossRef]
  15. Houghton, R.A.; House, J.I.; Pongratz, J.; Van Der Werf, G.R.; DeFries, R.S.; Hansen, M.C.; Le Quéré, C.; Ramankutty, N. Carbon emissions from land use and land-cover change. Biogeosciences 2012, 9, 5125–5142. [Google Scholar] [CrossRef] [Green Version]
  16. Liu, X.; Pei, F.; Wen, Y.; Li, X.; Wang, S.; Wu, C.; Cai, Y.; Wu, J.; Chen, J.; Feng, K.; et al. Global urban expansion offsets climate-driven increases in terrestrial net primary productivity. Nat. Commun. 2019, 10, 5558. [Google Scholar] [CrossRef] [Green Version]
  17. Basuki, I.; Kauffman, J.B.; Peterson, J.T.; Anshari, G.Z.; Murdiyarso, D. Land Cover and Land Use Change Decreases Net Ecosystem Production in Tropical Peatlands of West Kalimantan, Indonesia. Forests 2021, 12, 1587. [Google Scholar] [CrossRef]
  18. Cheng, F.; Liu, S.; Zhang, Y.; Yin, Y.J.; Hou, X.Y. Effects of land-use change on net primary productivity in Beijing based on the MODIS series. Acta Ecol. Sin. 2017, 37, 5924–5934. [Google Scholar]
  19. Xu, Y.; Lv, Y.; Dai, Q.; Zhao, C.; Huang, W.; Chen, T.; Zhang, Y. Assessment of the Relative Contribution of Climate Change and Land Use Change on Net Primary Productivity Variation in the Middle and Lower Reaches of the Yangtze River Basin. China Environ. Sci. 2023. [Google Scholar] [CrossRef]
  20. Tian, H.; Bi, R.; Zhu, H.; Yan, J. Driving factors and gradient effect of net primary productivity in Fenhe River Basin. Chin. J. Ecol. 2019, 38, 3066. [Google Scholar]
  21. Liang, C.; Zhai, G.; Fu, M.; Sun, C. Spatiotemporal Changes and Influencing Facotrs of Net Primary Productivity of Guanzhong Plain City Cluster from 2000 to 2019. Res. Soil Water Conserv. 2023, 30, 293–300. [Google Scholar]
  22. Ying, H.; Yang, J.; Xia, G.; Lin, T. Spatio-temporal change of ner primary productivity and the evaluation of the importance of biodi-versity maintenance functionas in Manas River Basin. Acta Ecol. Sin. 2023, 43, 4664–4673. [Google Scholar]
  23. Fan, W.; Wu, H.; Fan, F. Spatial-temporal variation of net primary productivity and its influencing factors in Tibet over past 20 years. Bull. Soil Water Conserv. 2022, 42, 378–386. [Google Scholar]
  24. Li, C.; Zhao, K. Spatiotemporal variations of vegetation NPP and related driving factors in Shiyang River basin of Northwest China in 2000–2010. Chin. J. Ecol. 2013, 32, 712–718. [Google Scholar]
  25. Wang, Y.; Yue, H.; Peng, Q.; He, C.; Hong, S.; Bryan, B.A. Recent responses of grassland net primary productivity to climatic and anthropogenic factors in Kyrgyzstan. Land Degrad. Dev. 2020, 31, 2490–2506. [Google Scholar] [CrossRef]
  26. Wei, X.; Yang, J.; Luo, P.; Lin, L.; Lin, K.; Guan, J. Assessment of the variation and influencing factors of vegetation NPP and carbon sink capacity un-der different natural conditions. Ecol. Indic. 2022, 138, 108834. [Google Scholar] [CrossRef]
  27. Zhang, M.; Yuan, N.; Lin, H.; Liu, Y.; Zhang, H. Quantitative estimation of the factors impacting spatiotemporal variation in NPP in the Dong-ting Lake wetlands using Landsat time series data for the last two decades. Ecol. Indic. 2022, 135, 108544. [Google Scholar] [CrossRef]
  28. Chen, S.; Guo, B.; Zhang, R.; Zang, W.; Wei, C.; Wu, H.; Yang, X.; Zhen, X.; Li, X.; Zhang, D.; et al. Quantitatively determine the dominant driving factors of the spatial–temporal changes of vegetation NPP in the Hengduan Mountain area during 2000–2015. J. Mt. Sci. 2021, 18, 19. [Google Scholar] [CrossRef]
  29. Zhao, P.; Wang, D.; He, S.; Lan, H.; Chen, W.; Qi, Y. Driving forces of NPP change in debris flow prone area: A case study of a typical region in SW China. Ecol. Indic. 2020, 119, 106811. [Google Scholar] [CrossRef]
  30. Zhang, S.; Li, F.; Zhou, Y.; Hu, Z.; Zhang, R.; Xiang, X.; Zhang, Y. Using Net Primary Productivity to Characterize the Spatio-Temporal Dynamics of Ecological Footprint for a Resource-Based City, Panzhihua in China. Sustainability 2022, 14, 3067. [Google Scholar] [CrossRef]
  31. Zhuang, Q.; Shao, Z.; Li, D.; Huang, X.; Cai, B.; Altan, O.; Wu, S. Unequal weakening of urbanization and soil salinization on vegetation production capacity. Geoderma 2022, 411, 115712. [Google Scholar] [CrossRef]
  32. Duarte, C.M.; Gattuso, J.; Hancke, K.; Gundersen, H.; Filbee-Dexter, K.; Pedersen, M.F.; Middelburg, J.J.; Burrows, M.T.; Krumhansl, K.A.; Wernberg, T.; et al. Global estimates of the extent and production of macroalgal forests. Glob. Ecol. Biogeogr. 2022, 31, 1422–1439. [Google Scholar] [CrossRef]
  33. Liu, Y.; Liu, L.; Sheng, L. Scenario Simulation on Changing Pattern of Land Use for Wetland in the West of Jilin Province. J. Jilin Univ. (Earth Sci. Ed.) 2016, 46, 865–875. [Google Scholar]
  34. Liu, T.; Yu, L.; Zhang, S. Land Surface Temperature Response to Irrigated Paddy Field Expansion: A Case Study of Semi-arid Western Jilin Province, China. Sci. Rep. 2019, 9, 5278. [Google Scholar] [CrossRef] [Green Version]
  35. Li, C.; Dou, T.; Wang, Y.; Zhu, T.; Yin, H.; Zhou, M.; Liu, L.; Wu, X. A Method for Quantifying the Impacts of Human Activities on Net Primary Production of Grasslands in Northwest China. Remote Sens. 2021, 13, 2479. [Google Scholar] [CrossRef]
  36. Cheng, Z.; Zhang, Y.; Wang, L.; Wei, L.; Wu, X. An Analysis of Land-Use Conflict Potential Based on the Perspective of Production–Living–Ecological Function. Sustainability 2022, 14, 5936. [Google Scholar] [CrossRef]
  37. Zou, T.; Chang, Y.; Chen, P.; Liu, J. Spatial-temporal variations of ecological vulnerability in Jilin Province (China), 2000 to 2018. Ecol. Indic. 2021, 133, 108429. [Google Scholar] [CrossRef]
  38. Luo, L.; Wang, Z.; Mao, D.; Lou, Y.; Ren, C.; Song, K. Temporal and Spatial Pattern of Grassland Net Primary Productivity in Westerrn Songnen Plain. Chin. J. Grassl. 2012, 34, 5–11. [Google Scholar]
  39. Liu, S. Study on the Spatial and Temporal Dynamic Changes of Wetland and Its NPP in the West of Jilin Province Based on Remote Sensing Techniques. Doctoral Thesis, Jilin University, Changchun, China, 2018. [Google Scholar]
  40. Lee, X. Estimate of Da’an Saline-Alkali Region Grassland NPP in West of Jilin and Spatial-Temporal Characteris-tics Analysis. Master’s Thesis, Jilin University, Changchun, China, 2017. [Google Scholar]
  41. Yan, J.; Hao, H.; Gao, Y.; Wang, T.; Zhang, Y. Estimation and Spatial-Temporal Dynamics of Long-term Sequenced Vegetation Net Primary Productivity in Jilin Provience. J. Soil Water Conserv. 2021, 35, 172–180. [Google Scholar]
  42. Shang, Y.; Wang, D.; Liu, S.; Li, H. Stability of Land-use/Land-cover in National Nature Reserves of Jilin Province, China. Chin. Geogr. Sci. 2022, 32, 324–339. [Google Scholar] [CrossRef]
  43. Zhang, D.; Wang, X.; Bao, G.; Fu, S.; Zhou, X.; Sun, Y.; Wang, D. The land types change and driving factor analysis of western of Jilin province based on landscape index. J. Jilin For. Sci. Technol. 2016, 2016, 41–47. [Google Scholar]
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Figure 1. Study area location (western Jilin Province) in China.
Figure 1. Study area location (western Jilin Province) in China.
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Figure 2. Average annual NPP distribution during 2001–2020 (gC/m2•a).
Figure 2. Average annual NPP distribution during 2001–2020 (gC/m2•a).
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Figure 3. Average annual NPP in the study area during 2001–2020 (gC/m2•a).
Figure 3. Average annual NPP in the study area during 2001–2020 (gC/m2•a).
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Figure 4. Maximum, minimum and average values of annual NPP for different land use.
Figure 4. Maximum, minimum and average values of annual NPP for different land use.
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Figure 5. Average annual NPP during 2001–2020.
Figure 5. Average annual NPP during 2001–2020.
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Figure 6. Spatial distribution of NPP trend analysis during 2001–2020.
Figure 6. Spatial distribution of NPP trend analysis during 2001–2020.
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Figure 7. Trend changes between annual NPP and main driving factors.
Figure 7. Trend changes between annual NPP and main driving factors.
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Table 1. Data source of driving factors for NPP changes.
Table 1. Data source of driving factors for NPP changes.
Source TypeSpecific ContentsData SourcesResolution
Land use/
Land cover		2020Resource and Environment Science and Data Center of China (http://www.resdc.cn (accessed on 5 December 2022)) 30 m
NPP2001–2020National Aeronautics and Space Administration (https://earthdata.nasa.gov (accessed on 30 November 2022))500 m
Driving
factors		Population, GDPGeographical Information Monitoring Cloud Platform (http://www.dsac.cn/
(accessed on 5 December 2022))		prefecture-level city
Average annual temperature, annual precipitation, hours of sunshine, average annual wind speedChina Meteorological Data Sharing Network (http://data.cma.cn/
(accessed on 5 December 2022))		prefecture-level city
Carbon dioxide emissionChina City Greenhouse Gas Working Group (http://www.cityghg.com/ (accessed on 5 December 2022))prefecture-level city
Table 2. The correlation and significance between NPP and driving factors.
Table 2. The correlation and significance between NPP and driving factors.
Average Annual Temperature (°C)Annual
Precipitation (mm)		Average Wind Speed (m/s)CO2 Emission (t)Hours of Sunshine (h)PopulationGDP (105 Million)
Pearson correlation coefficient0.070.744 **−0.3710.739 **−0.502 *−0.280.645 **
Significance0.7700.10700.0240.2320.002
Note: ** The correlation was significant at the 0.01 level; * the correlation was significant at the 0.05 level.
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Zhao, D.; Jia, W.; Liu, J. Dynamic Changes and Driving Mechanisms of Net Primary Production (NPP) in a Semi-Arid Region of China. Sustainability 2023, 15, 11829. https://doi.org/10.3390/su151511829

AMA Style

Zhao D, Jia W, Liu J. Dynamic Changes and Driving Mechanisms of Net Primary Production (NPP) in a Semi-Arid Region of China. Sustainability. 2023; 15(15):11829. https://doi.org/10.3390/su151511829

Chicago/Turabian Style

Zhao, Dandan, Wenyue Jia, and Jiping Liu. 2023. "Dynamic Changes and Driving Mechanisms of Net Primary Production (NPP) in a Semi-Arid Region of China" Sustainability 15, no. 15: 11829. https://doi.org/10.3390/su151511829

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