Jinwei Guo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jinwei Guo has a total of 27 co-authors. Here are the top co-authors by number of shared publications:
- Yanbing Qi
College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, China
5 shared publications - Jingyan Sun
College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, China
3 shared publications - Xin Wei
College of Humanities and Social Development, Northwest A&F University, Yangling 712100, China
2 shared publications Yuchun Yang
College of Natural Resources and Environment Northwest A&F University Yangling China
2 shared publicationsAwais Shakoor
Hawkesbury Institute for the Environment Western Sydney University Penrith New South Wales Australia
1 shared publication- Guohong Du
College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, China
1 shared publication
Latest Publications
- 24 June 2026
- Published by Wiley in Land Degradation & Development
Biochar Application Improves Chemical Properties and Nutrient Availability in Saline–Alkali Soils: A Meta‐Analysis
Saline–alkali soils are a major global challenge because they degrade soil chemistry, reduce nutrient availability, and limit crop productivity. Despite increasing interest in biochar as a sustainable soil amendment, its overall effects on soil chemical properties under saline–alkali conditions remain inconsistent. Therefore, we conducted a meta‐analysis of 113 peer‐reviewed publications and 714 paired comparisons published between 2014 and 2025 to evaluate the effects of biochar on soil chemical properties in saline–alkali soils. Biochar significantly ( p < 0.05) increased soil organic carbon (SOC) (90.4%), available nitrogen (AN) (47.9%), available phosphorus (AP) (51.1%), available potassium (AK) (101.8%) and cation exchange capacity (34.1%) compared to the control. Biochar also reduced soil pH (1.5%), electrical conductivity (7.7%), and exchangeable sodium percentage (32.7%). In addition, it decreased soluble sodium (3.2%) and chloride (41.7%), while increasing soluble calcium (41.8%) and magnesium (24.0%). Furthermore, stalk‐derived biochar applied at high rates (≥ 3%) showed the best performance, increasing SOC by 123%. Stalk‐ and wood‐derived biochar feedstocks produced the greatest improvements in AK (165.9%), AP (66.1%), and AN (54.2%). Correlation analysis revealed that SOC was positively correlated with calcium (Ca 2+ ) ( r 2 = 0.038), magnesium (Mg 2+ ) ( r 2 = 0.298), and AP ( r 2 = 0.061), while negatively correlated with AN ( r 2 = −0.034). In summary, medium‐temperature pyrolyzed biochar derived from stalk and wood feedstocks, when applied at higher rates, represents an effective and sustainable strategy for improving chemical properties and nutrient status in saline–alkali soils. These findings provide valuable insights for developing evidence‐based biochar application protocols and formulating ecological restoration strategies for large‐scale saline–alkali soil remediation.
- 24 June 2026
- Published by Wiley in Land Degradation & Development
Biochar Application Improves Chemical Properties and Nutrient Availability in Saline–Alkali Soils: A Meta‐Analysis
Saline–alkali soils are a major global challenge because they degrade soil chemistry, reduce nutrient availability, and limit crop productivity. Despite increasing interest in biochar as a sustainable soil amendment, its overall effects on soil chemical properties under saline–alkali conditions remain inconsistent. Therefore, we conducted a meta‐analysis of 113 peer‐reviewed publications and 714 paired comparisons published between 2014 and 2025 to evaluate the effects of biochar on soil chemical properties in saline–alkali soils. Biochar significantly ( p < 0.05) increased soil organic carbon (SOC) (90.4%), available nitrogen (AN) (47.9%), available phosphorus (AP) (51.1%), available potassium (AK) (101.8%) and cation exchange capacity (34.1%) compared to the control. Biochar also reduced soil pH (1.5%), electrical conductivity (7.7%), and exchangeable sodium percentage (32.7%). In addition, it decreased soluble sodium (3.2%) and chloride (41.7%), while increasing soluble calcium (41.8%) and magnesium (24.0%). Furthermore, stalk‐derived biochar applied at high rates (≥ 3%) showed the best performance, increasing SOC by 123%. Stalk‐ and wood‐derived biochar feedstocks produced the greatest improvements in AK (165.9%), AP (66.1%), and AN (54.2%). Correlation analysis revealed that SOC was positively correlated with calcium (Ca 2+ ) ( r 2 = 0.038), magnesium (Mg 2+ ) ( r 2 = 0.298), and AP ( r 2 = 0.061), while negatively correlated with AN ( r 2 = −0.034). In summary, medium‐temperature pyrolyzed biochar derived from stalk and wood feedstocks, when applied at higher rates, represents an effective and sustainable strategy for improving chemical properties and nutrient status in saline–alkali soils. These findings provide valuable insights for developing evidence‐based biochar application protocols and formulating ecological restoration strategies for large‐scale saline–alkali soil remediation.
- Open Access
- 7 January 2026
- Published by Springer Nature in Carbon Balance and Management
- Vol. 21 (1)
Quantifying erosion-induced carbon emissions from SOC decomposition across sediment pathways in the yellow river basin
Background: Soil erosion not only leads to soil loss but also redistributes soil organic carbon (SOC) and releases carbon dioxide (CO 2 ) that contributes significantly to regional carbon emissions. Great efforts have been made to prevent soil erosion in the Yellow River Basin (YRB) in China in the past decades. Only few studies have paid attention to carbon emissions from soil loss. This study integrates the China Soil Loss Equation (CSLE) with a transport-limited sediment delivery (TLSD) model to quantify sediment redistribution and associated carbon emissions across five depositional processes (slope, reservoir, plain, river channel, and regional output) in the YRB. Results: The CSLE-TLSD model calibrated to a significantly improved Nash-Sutcliffe efficiency of 0.5690, compared to 0.5628 for the CSLE model. Results reveal that 28.50 ± 4.43% of eroded SOC was decomposed during transport, releasing 2.48 ± 0.11 × 10 8 t CO 2 in the YRB from 1990 to 2020. Striking spatial disparities emerged in different regions: the upper reaches exhibited a SOC decomposition ratio of 49.66 ± 4.40%, in sharp contrast to 22.96 ± 10.35% in the middle reaches. The five provinces with the highest carbon emission rate from 1990 to 2020 were Shanxi (15.45 t CO 2 /km 2 ), Shaanxi (14.23 t CO 2 /km 2 ), Shandong (13.10 t CO 2 /km 2 ), Qinghai (11.98 t CO 2 /km 2 ), and Gansu (11.25 t CO 2 /km 2 ). Conclusion: These findings underscore the necessity of incorporating erosion-driven carbon flux dynamics into terrestrial carbon accounting frameworks, particularly in basins undergoing intensive anthropogenic modification.
- 7 January 2026
- Published by Springer Nature in Carbon Balance and Management
- Vol. 21 (1)
Open AccessQuantifying erosion-induced carbon emissions from SOC decomposition across sediment pathways in the yellow river basin
Background: Soil erosion not only leads to soil loss but also redistributes soil organic carbon (SOC) and releases carbon dioxide (CO 2 ) that contributes significantly to regional carbon emissions. Great efforts have been made to prevent soil erosion in the Yellow River Basin (YRB) in China in the past decades. Only few studies have paid attention to carbon emissions from soil loss. This study integrates the China Soil Loss Equation (CSLE) with a transport-limited sediment delivery (TLSD) model to quantify sediment redistribution and associated carbon emissions across five depositional processes (slope, reservoir, plain, river channel, and regional output) in the YRB. Results: The CSLE-TLSD model calibrated to a significantly improved Nash-Sutcliffe efficiency of 0.5690, compared to 0.5628 for the CSLE model. Results reveal that 28.50 ± 4.43% of eroded SOC was decomposed during transport, releasing 2.48 ± 0.11 × 10 8 t CO 2 in the YRB from 1990 to 2020. Striking spatial disparities emerged in different regions: the upper reaches exhibited a SOC decomposition ratio of 49.66 ± 4.40%, in sharp contrast to 22.96 ± 10.35% in the middle reaches. The five provinces with the highest carbon emission rate from 1990 to 2020 were Shanxi (15.45 t CO 2 /km 2 ), Shaanxi (14.23 t CO 2 /km 2 ), Shandong (13.10 t CO 2 /km 2 ), Qinghai (11.98 t CO 2 /km 2 ), and Gansu (11.25 t CO 2 /km 2 ). Conclusion: These findings underscore the necessity of incorporating erosion-driven carbon flux dynamics into terrestrial carbon accounting frameworks, particularly in basins undergoing intensive anthropogenic modification.
- Open Access
Carbon Emission Status and Regional Differences of China: High-Resolution Estimation of Spatially Explicit Carbon Emissions at the Prefecture Level
There are disagreements regarding the accuracy of estimation and spatial distribution of carbon emissions in China. It is of great significance to estimate a more detailed carbon emission inventory for China and analyze the carbon emission characteristics of different regions. This study comprehensively estimated carbon dioxide and methane emissions (and their spatial distributions) across eight carbon-emitting sectors in 360 prefecture-level cities in China in 2020. The results indicated that total carbon emissions in China amounted to 146.00 × 108 t, with carbon dioxide and methane accounting for 95.87% and 4.13%, respectively. The industrial sector was the main source of carbon emissions, accounting for 75.42% of the total. The North China Plain, the Northeast Plain, and the Sichuan Basin were identified as the carbon emission hotspot areas with the most intensive carbon emission densities. Among the clustered four carbon emission zones based on carbon emission density and economic carbon intensity, the High Carbon Emission Density and High Economic Carbon Intensity zones accounted for 41.73% of total carbon emissions. To achieve carbon neutrality, it is essential to devise emission reduction strategies for specific areas by thoroughly considering spatially explicit variation at the prefecture level, with a focus on primary carbon-emitting cities and sectors.
Open AccessCarbon Emission Status and Regional Differences of China: High-Resolution Estimation of Spatially Explicit Carbon Emissions at the Prefecture Level
There are disagreements regarding the accuracy of estimation and spatial distribution of carbon emissions in China. It is of great significance to estimate a more detailed carbon emission inventory for China and analyze the carbon emission characteristics of different regions. This study comprehensively estimated carbon dioxide and methane emissions (and their spatial distributions) across eight carbon-emitting sectors in 360 prefecture-level cities in China in 2020. The results indicated that total carbon emissions in China amounted to 146.00 × 108 t, with carbon dioxide and methane accounting for 95.87% and 4.13%, respectively. The industrial sector was the main source of carbon emissions, accounting for 75.42% of the total. The North China Plain, the Northeast Plain, and the Sichuan Basin were identified as the carbon emission hotspot areas with the most intensive carbon emission densities. Among the clustered four carbon emission zones based on carbon emission density and economic carbon intensity, the High Carbon Emission Density and High Economic Carbon Intensity zones accounted for 41.73% of total carbon emissions. To achieve carbon neutrality, it is essential to devise emission reduction strategies for specific areas by thoroughly considering spatially explicit variation at the prefecture level, with a focus on primary carbon-emitting cities and sectors.
- Open Access
Identifying and Mapping the Spatial Factors That Control Soil Erosion Changes in the Yellow River Basin of China
The Yellow River Basin has been considered to have the most serious soil erosion in the world, and identifying and mapping the spatial controlling factors would be of great help in adopting targeting strategies for soil erosion prevention. This study used the Universal Soil Loss Equation (USLE) to estimate the spatial and temporal changes in soil erosion from 1985 to 2020 and analyzed the controlling factors. The results indicated that from 1985 to 2020, the average erosion modulus in the Yellow River Basin was 1160.97 t∙km−2∙yr−1, and the erosion modulus in the middle reach was significantly greater than in the lower and upper reaches. Changes in vegetation coverage, rainfall and land use controlled 38.95%, 40.87% and 9.21% of soil erosion changes, respectively. Among them, the area in which soil erosion was decreased due to increased vegetation coverage accounted for 70.77% of the area controlled by vegetation coverage, while the area in which soil erosion was increased due to increased rainfall accounted for 86.62% of the area controlled by rainfall. These results prove the effectiveness of vegetation restoration projects in controlling soil erosion in the Yellow River Basin, but more attention needs to be paid to the impact of rainfall on soil erosion in the future.
Open AccessIdentifying and Mapping the Spatial Factors That Control Soil Erosion Changes in the Yellow River Basin of China
The Yellow River Basin has been considered to have the most serious soil erosion in the world, and identifying and mapping the spatial controlling factors would be of great help in adopting targeting strategies for soil erosion prevention. This study used the Universal Soil Loss Equation (USLE) to estimate the spatial and temporal changes in soil erosion from 1985 to 2020 and analyzed the controlling factors. The results indicated that from 1985 to 2020, the average erosion modulus in the Yellow River Basin was 1160.97 t∙km−2∙yr−1, and the erosion modulus in the middle reach was significantly greater than in the lower and upper reaches. Changes in vegetation coverage, rainfall and land use controlled 38.95%, 40.87% and 9.21% of soil erosion changes, respectively. Among them, the area in which soil erosion was decreased due to increased vegetation coverage accounted for 70.77% of the area controlled by vegetation coverage, while the area in which soil erosion was increased due to increased rainfall accounted for 86.62% of the area controlled by rainfall. These results prove the effectiveness of vegetation restoration projects in controlling soil erosion in the Yellow River Basin, but more attention needs to be paid to the impact of rainfall on soil erosion in the future.
- Open Access
- 21 October 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (20) , 11532
Spatiotemporal Variation of Fractional Vegetation Cover and Its Response to Climate Change and Topography Characteristics in Shaanxi Province, China
Since the beginning of the 21st century in Shaanxi Province, China, ecological restoration has increased fractional vegetation cover (FVC) and decreased soil and water erosion. The climate and topography will be critical factors for maintaining vegetation coverage in the future. Based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, we monitored FVC variations in Shaanxi Province, China, as well as in three subregions of the Loess Plateau (LOP), Qinling–Bashan Mountain (QBM), and Guanzhong Plain (GZP). Using Sen+Mann–Kendall, correlation analysis, and geodetector methods, we detected trends and responses to climate change and topographical characteristics in Shaanxi Province from 2000 to 2018. The results indicated that 73.86% of the area in Shaanxi Province exhibited an increasing FVC with a growth rate of 0.0026 year−1 from 2000 to 2018. The FVC in the three subregions varied, as QBM (87.24–91.47%) > GZP (47.45–66.93%) > LOP (36.33–49.74%), which displayed a significant increase, slight increase, and slight decrease, respectively. The variation of FVC was significantly positively correlated with climate factors (precipitation, temperature, sunshine duration) at monthly and seasonal scales. The time-lag duration between FVC and climate factors was 1–3 months except for the conjunctional areas of GZP with the LOP and QBM, which exhibited a time-lag of 5–6 months. Topographically, the landform of hills had the highest FVC increase at an altitude of 500–1500 m and a slope of 2°–6°. The dominant driving factors affecting FVC variation in Shaanxi Province and LOP area were climatic factors. In the QBM area, the dominant factors were related to topography (relief, elevation, slope), whereas in the GZP area, they were relief and sunshine duration. We can conclude that local topography characteristics are important in implementing revegetation projects because they strongly influence water, temperature, and sunshine redistribution.
- 21 October 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (20) , 11532
Open AccessSpatiotemporal Variation of Fractional Vegetation Cover and Its Response to Climate Change and Topography Characteristics in Shaanxi Province, China
Since the beginning of the 21st century in Shaanxi Province, China, ecological restoration has increased fractional vegetation cover (FVC) and decreased soil and water erosion. The climate and topography will be critical factors for maintaining vegetation coverage in the future. Based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, we monitored FVC variations in Shaanxi Province, China, as well as in three subregions of the Loess Plateau (LOP), Qinling–Bashan Mountain (QBM), and Guanzhong Plain (GZP). Using Sen+Mann–Kendall, correlation analysis, and geodetector methods, we detected trends and responses to climate change and topographical characteristics in Shaanxi Province from 2000 to 2018. The results indicated that 73.86% of the area in Shaanxi Province exhibited an increasing FVC with a growth rate of 0.0026 year−1 from 2000 to 2018. The FVC in the three subregions varied, as QBM (87.24–91.47%) > GZP (47.45–66.93%) > LOP (36.33–49.74%), which displayed a significant increase, slight increase, and slight decrease, respectively. The variation of FVC was significantly positively correlated with climate factors (precipitation, temperature, sunshine duration) at monthly and seasonal scales. The time-lag duration between FVC and climate factors was 1–3 months except for the conjunctional areas of GZP with the LOP and QBM, which exhibited a time-lag of 5–6 months. Topographically, the landform of hills had the highest FVC increase at an altitude of 500–1500 m and a slope of 2°–6°. The dominant driving factors affecting FVC variation in Shaanxi Province and LOP area were climatic factors. In the QBM area, the dominant factors were related to topography (relief, elevation, slope), whereas in the GZP area, they were relief and sunshine duration. We can conclude that local topography characteristics are important in implementing revegetation projects because they strongly influence water, temperature, and sunshine redistribution.
- Open Access
- 18 January 2023
- Published by Wiley
Identifying and mapping the spatial factors that control soil erosion changes in the Yellow River Basin of China Running title:Identifying the spatial factors that control soil erosion changes
Soil erosion is a serious environmental degradation controlled by several natural and anthropogenic factors. The Yellow River Basin (YRB) has been considered one of the most serious soil erosion regions in the world. In the past decades, revegetation projects accompanying apparent climate changes in the YRB region have aroused great uncertainty regarding soil erosion. Therefore, identifying and mapping the spatial controlling factors would be of great help in adopting targeting strategies for soil erosion prevention. The spatial-temporal changes of soil erosion from 1985 to 2018 in the YRB region were estimated by using the RUSLE model and its spatial controlling factors were identified and mapped by using the Mann-Kendall test and Sen’s test. The results indicated that the slight erosion intensity accounted for 68.25% to 70.17% of the entire basin from 1985 to 2018, and the average erosion modulus of the entire YRB region was 2009.81 t∙km -2 ∙yr -1 . The erosion modulus in the middle reach (3245. 56 t∙km -2 ∙yr -1 ) was significantly greater than in the lower (1084. 56 t∙km -2 ∙yr -1 ) and upper (1135. 90 t∙km -2 ∙yr -1 ) reaches. Vegetation coverage, rainfall, and land-use changes accounted for 42.07%, 52.23%, and 1.77%, respectively, of the soil erosion change areas. Specifically, 40.19% of the vegetation coverage increase area was responsible for the decrease in soil erosion that was located mainly in the middle and upper reaches, while 42.30% of the rainfall increase area contributed to the increase in soil erosion that was located mainly in the upper and lower reaches.
- 18 January 2023
- Published by Wiley
Open AccessIdentifying and mapping the spatial factors that control soil erosion changes in the Yellow River Basin of China Running title:Identifying the spatial factors that control soil erosion changes
Soil erosion is a serious environmental degradation controlled by several natural and anthropogenic factors. The Yellow River Basin (YRB) has been considered one of the most serious soil erosion regions in the world. In the past decades, revegetation projects accompanying apparent climate changes in the YRB region have aroused great uncertainty regarding soil erosion. Therefore, identifying and mapping the spatial controlling factors would be of great help in adopting targeting strategies for soil erosion prevention. The spatial-temporal changes of soil erosion from 1985 to 2018 in the YRB region were estimated by using the RUSLE model and its spatial controlling factors were identified and mapped by using the Mann-Kendall test and Sen’s test. The results indicated that the slight erosion intensity accounted for 68.25% to 70.17% of the entire basin from 1985 to 2018, and the average erosion modulus of the entire YRB region was 2009.81 t∙km -2 ∙yr -1 . The erosion modulus in the middle reach (3245. 56 t∙km -2 ∙yr -1 ) was significantly greater than in the lower (1084. 56 t∙km -2 ∙yr -1 ) and upper (1135. 90 t∙km -2 ∙yr -1 ) reaches. Vegetation coverage, rainfall, and land-use changes accounted for 42.07%, 52.23%, and 1.77%, respectively, of the soil erosion change areas. Specifically, 40.19% of the vegetation coverage increase area was responsible for the decrease in soil erosion that was located mainly in the middle and upper reaches, while 42.30% of the rainfall increase area contributed to the increase in soil erosion that was located mainly in the upper and lower reaches.