Modelling and managing critical zone relationships between soil, water and ecosystem processes across the Loess Plateau
Lead Research Organisation:
Rothamsted Research
Department Name: Sustainable Agriculture Sciences-NW
Abstract
The Loess Plateau of China covers an area 2.5x the size of the UK (some 640,000 square km) in the upper and middle reaches of China's Yellow River and is renowned for having the most severe soil erosion in the world; deforestation, over-grazing and poor agricultural practice have resulted in degenerated ecosystems, desertification and unproductive agriculture in the region. To control severe soil erosion on the Loess Plateau, the Chinese government imposed a series of policies for fragile ecosystems, such as the 1999 state-funded "Grain-for-Green" project, which has resulted in significant land use changes. Related programmes have produced beneficial effects on soil erosion and water cycles. However, the impact of these changes in soil and water processes on related ecosystem services is unknown and demands further study. The proposed research will focus on three spatial scales: slope, watershed, and region. It uses a combination of A) experiments to collect environmental, biological and agronomic data; B) remote sensing data and C) modelling approaches.
A) Data collection: Four experimental stations located in four main topographical regions of the Plateau are chosen as case studies: 1) Ansai Comprehensive Experimental Station of Soil and Water Conservation; 2) Changwu Agro-ecology Experiment Station; 3) Guyuan Ecological Station; 4) Shenmu Erosion and Environment Station. At each station, treatments of different vegetative covers, slopes, and the practices of soil and water conservation at the plot scale were set up in the 1980s and data collections include: soil water, canopy size, runoff, soil losses and meteorological records. Most of the Chinese members of this project have been involved in prior studies at the stations.
At the slope scale, additional environmental, biological and agronomic data will be monitored in a sub-set of the plots. At watershed scale, four watersheds where the stations are located will be monitored. The spatial distribution of the following variables will be measured: precipitation, soil properties, vegetative types, canopy size, runoff and soil loss.
B) Remote sensing data collection: At the regional scale, remote sensing combined with ground-truthing data will be used to investigate the spatial variability of vegetation type, land cover, productivity, the components of water balance, soil losses, soil type, etc.
C) Modelling approaches: a cascade approach will be used to build an improved model framework applied to different spatial scales. Mechanistic soil-water-plant models will be applied to the slope scale. Their outputs will then be used as inputs for models at watershed level. Spatial empirical/statistical models will be used at the regional level. Observed and collected data from A) and B) will be used to further develop, calibrate and validate our models.
Model simulations at the slope level will be used to reveal the dynamic mechanisms in soil and water in different regions and analyse the effects of vegetation type, soil type, slope degree, climatic factors and management practice. Watershed models will estimate soil and water carrying capacity for different vegetation types, predict the effect of land use/cover changes on soil losses, water cycle and ecosystem services and evaluate management scenarios in the practices of soil and water conservation, vegetative changes, and ecosystem services. The soil and water carrying capacity for different vegetation types and the optimal ecosystem services will be addressed at the regional scale. Outreach workshops and demonstrations will disseminate knowledge to farmers and policy makers.
The proposed research will elucidate the coupled relationships between soil and water processes and agro-ecosystem services at various scales, and evaluate the effects of vegetation cover and changes in land use on water cycle, soil erosion, and ecosystem services across the Loess Plateau.
A) Data collection: Four experimental stations located in four main topographical regions of the Plateau are chosen as case studies: 1) Ansai Comprehensive Experimental Station of Soil and Water Conservation; 2) Changwu Agro-ecology Experiment Station; 3) Guyuan Ecological Station; 4) Shenmu Erosion and Environment Station. At each station, treatments of different vegetative covers, slopes, and the practices of soil and water conservation at the plot scale were set up in the 1980s and data collections include: soil water, canopy size, runoff, soil losses and meteorological records. Most of the Chinese members of this project have been involved in prior studies at the stations.
At the slope scale, additional environmental, biological and agronomic data will be monitored in a sub-set of the plots. At watershed scale, four watersheds where the stations are located will be monitored. The spatial distribution of the following variables will be measured: precipitation, soil properties, vegetative types, canopy size, runoff and soil loss.
B) Remote sensing data collection: At the regional scale, remote sensing combined with ground-truthing data will be used to investigate the spatial variability of vegetation type, land cover, productivity, the components of water balance, soil losses, soil type, etc.
C) Modelling approaches: a cascade approach will be used to build an improved model framework applied to different spatial scales. Mechanistic soil-water-plant models will be applied to the slope scale. Their outputs will then be used as inputs for models at watershed level. Spatial empirical/statistical models will be used at the regional level. Observed and collected data from A) and B) will be used to further develop, calibrate and validate our models.
Model simulations at the slope level will be used to reveal the dynamic mechanisms in soil and water in different regions and analyse the effects of vegetation type, soil type, slope degree, climatic factors and management practice. Watershed models will estimate soil and water carrying capacity for different vegetation types, predict the effect of land use/cover changes on soil losses, water cycle and ecosystem services and evaluate management scenarios in the practices of soil and water conservation, vegetative changes, and ecosystem services. The soil and water carrying capacity for different vegetation types and the optimal ecosystem services will be addressed at the regional scale. Outreach workshops and demonstrations will disseminate knowledge to farmers and policy makers.
The proposed research will elucidate the coupled relationships between soil and water processes and agro-ecosystem services at various scales, and evaluate the effects of vegetation cover and changes in land use on water cycle, soil erosion, and ecosystem services across the Loess Plateau.
Planned Impact
The primary impact will be the coupled benefits of environmental sustainability and the promotion of economic development and social welfare in the Loess region. This will be achieved through better understanding the relationships between the soil and water processes and agro-ecosystem services, including grain production, net primary production, carbon sequestration, water retention and soil erosion control. This research will provide guidance on practices and managements for sustainable ecosystem services in the fragile region, which will secure food production and save water resources in the arid Loess Plateau. Farmers in the area will receive guidance, enabling them to make informed decisions to adopt optimal agricultural and water management practises, thus increasing income by maintaining or increasing agricultural production. Likewise, environmental agencies and other organisations will have the tools to enable more accurate estimation of water balance, enabling stable management of ecosystems services.
To achieve the benefits, a series of workshops will be run during the project period to transfer our understanding to local people, mainly targeted at low income farmers. Workshops with main stakeholders including local government and environmental agency in year 3 and 4 will be held to introduce our research findings and recommendations.
To achieve the benefits, a series of workshops will be run during the project period to transfer our understanding to local people, mainly targeted at low income farmers. Workshops with main stakeholders including local government and environmental agency in year 3 and 4 will be held to introduce our research findings and recommendations.
Publications
Cao R
(2018)
Deep soil water storage varies with vegetation type and rainfall amount in the Loess Plateau of China.
in Scientific reports
Carswell AM
(2019)
Impact of transition from permanent pasture to new swards on the nitrogen use efficiency, nitrogen and carbon budgets of beef and sheep production.
in Agriculture, ecosystems & environment
Charlton M
(2018)
Spatial prediction with categorical response variables
Comber A
(2016)
Geographically weighted correspondence matrices for local error reporting and change analyses: mapping the spatial distribution of errors and change
in Remote Sensing Letters
Comber A
(2022)
gwverse: A Template for a New Generic Geographically Weighted R Package
in Geographical Analysis
Comber A
(2018)
Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization
in Journal of Spatial Information Science
Comber A
(2018)
The impact of varying semantics in spatial statistics
Comber A
(2019)
The Forgotten Semantics of Regression Modeling in Geography
in Geographical Analysis
Description | In the Chinese Loess Plateau (CLP), loess samples were collected at five sites through a 50 to 200 m loess profile. The estimated storage of mineral N varied significantly among the five sites, ranging from 0.46 to 2.43 × 104 kg N ha-1. Ammonium exhibited fluctuations and dominated mineral N stocks within the whole profile at the sites, except for the upper 20-30 m at Yangling and Changwu. Measured nitrate content in the entire profile at Fuxian, An'sai and Shenmu is low, but significant accumulations were observed to 30-50 m depth at the other two sites. Analysis of d15N and d18O of nitrate indicates different causes for accumulated nitrate at these two sites. Mineralization and nitrification of manure and organic N respectively contribute nitrate to the 0-12 and 12-30 m profile at Changwu; while nitrification of NH4+ fertilizer, NO3- fertilizer and nitrification of organic N control the nitrate distribution in the 0-3, 3-7 and 7-10 m layer at Yangling, respectively. Furthermore, our analysis illustrates the low denitrification potential in the lower part of the vadose zone. The accumulated nitrate introduced by human activities is thus mainly distributed in the upper vadose zone (above 30 m), indicating, currently, a low nitrate leaching risk to groundwater due to a high storage capacity of the thick vadose zone in the region. We proposed that classification of regional critical zone (CZ) by quantifying the geographical differentiation of CZs in the CLP using a comprehensive clustering approach is fundamental for further advancing CZ observatories and key scientific issues such as CZ interactions at regional scale. We optimally classified the CLP into 8 CZ types. There are large variations on area percentage of different CZs in the CLP with the smallest urbanizing CZ (category V) and the largest Loess hilly- gully agriculture and grassland CZ (category III) accounting for 0.78% and 22.51% of the CLP, respectively. The classified CZs also show complex spatial configurations such as the urbanizing CZs are surrounding by all the other CZs while the mountainous forest CZ (category I) intersect mainly with the flood plain agricultural CZ and the loess hilly-gully agriculture-grassland-woodland transition CZ (category IV). A practical approach establishing CZ observatories in the CLP is to fill the gaps between those existed observatories and the observational requirements revealed by the CZs identified here. |
Exploitation Route | An app could be further developed for fertiliser application guidance. A recommendation could be reported to the local and central government for optimised land uses. |
Sectors | Agriculture Food and Drink Environment |
Description | BBSRC China Partnering Award |
Amount | £30,000 (GBP) |
Funding ID | BB/P025595/1 |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2017 |
End | 03/2020 |
Description | ERDF Cornwall: VizAg - Visualization of agricultural field performance through low-cost modelling |
Amount | £133,000 (GBP) |
Organisation | European Commission |
Department | European Regional Development Fund (ERDF) |
Sector | Public |
Country | Belgium |
Start |
Description | Using the Critical Zone as a framework to understand sustaining the ecosystem services of soil and water (CZO) A UK China Collaboration: adding value and increasing impact |
Amount | £157,333 (GBP) |
Funding ID | NE/S009094/1 |
Organisation | Natural Environment Research Council |
Sector | Public |
Country | United Kingdom |
Start | 01/2019 |
End | 03/2021 |
Title | GWmodel R package |
Description | GWmodel R package is a collection spatial statistical tools for exploring spatial heterogeneity. Continually developed since its release in 2013. |
Type Of Material | Data analysis technique |
Year Produced | 2017 |
Provided To Others? | Yes |
Impact | "GWmodel" produced 66,200 Google hits. |
URL | https://www.jstatsoft.org/article/view/v063i17 |
Title | Simulated winter wheat and maize above-ground dry matter, soil organic matter and soil water, Changwu, China 1983-2015 and future climate scenarios to 2049 |
Description | The model-generated dataset includes simulated daily dry matter accumulation of above-ground organs (leaves, stems and grains) of winter wheat and maize, soil water content in different soil layers and organic matter stocks in the topsoil and subsoil layers, and final crop dry matter from 1983 to 2004 (wheat) or 2015 (maize). A prediction of the variables under various future climatic scenarios is also included. The SPACSYS model was applied to a historic experimental site on the Loess Plateau in China. Observed crop yields of winter wheat from 1993 to 2004 and maize from 1983 to 2015 were used to validate the model. The validated model was run again under different climate scenarios from 2015 to 2049 to predict daily dry matter accumulation of above-ground organs including leaves, stems and grains, daily soil water content in different layers and soil organic carbon stocks in the topsoil and subsoil layers. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
URL | https://catalogue.ceh.ac.uk/id/03e74f94-88a5-4f09-b9ea-1447dd3e2b85 |
Description | CAAS |
Organisation | Chinese Academy of Agricultural Sciences |
Country | China |
Sector | Academic/University |
PI Contribution | Providing research facilities and resources, including the North Wyke Farm Platform and co-supervising PhD students. Input to joint publications. |
Collaborator Contribution | Supporting travel and subsistence costs of visiting researchers. Providing expertise and scientific staff time in soil quality and environment. Input to joint publications. |
Impact | joint publications: Liang, S., Sun, N., Zhang, X., Li, Y., Xu, M., and Wu, L. (2019). Modeling crop yield and nitrogen use efficiency in wheat and maize production systems under future climate change. Nutrient Cycling in Agroecosystems 115, 117-136. Ren, F., Sun, N., Xu, M., Zhang, X., Wu, L., and Xu, M. (2019). Changes in soil microbial biomass with manure application in cropping systems: A meta-analysis. Soil and Tillage Research 194, 104291. degree awarded |
Start Year | 2016 |
Description | CAU |
Organisation | China Agricultural University (CAU) |
Country | China |
Sector | Academic/University |
PI Contribution | Providing research facilities and resources, including the North Wyke Farm Platform. Input to joint publications. |
Collaborator Contribution | Providing expertise and scientific staff time in modelling. Input to joint publications. |
Impact | joint publication Wu, L., Feng, L., Li, Y., Wang, J., and Wu, L. (2019). A Yield-related agricultural drought index reveals spatio-temporal characteristics of droughts in southwestern China. Sustainability 11,, 714. |
Start Year | 2018 |
Description | NWA&FU |
Organisation | North West Agriculture and Forestry University |
Country | China |
Sector | Academic/University |
PI Contribution | Providing research facilities and resources, including the North Wyke Farm Platform. Input to joint publications. |
Collaborator Contribution | Supporting travel and subsistence costs of visiting students. Providing expertise and scientific staff time in nutrient losses to water and modelling. Input to joint publications. |
Impact | joint publications: Ge, J., Wang, S., Fan, J., Gongadze, K., and Wu, L. (2020). Soil nutrients of different land-use types and topographic positions in the water-wind erosion crisscross region of China's Loess Plateau. Catena 184, 104243. Zhao, C., Jia, X., Gongadze, K., Shao, M. a., Wu, L., and Zhu, Y. (2019). Permanent dry soil layers phenomenon on China's Loess Plateau. Scientific Reports 9, 3296. |
Start Year | 2017 |
Title | GWmodel R package |
Description | A suite of spatial statistical modelling tools See https://www.jstatsoft.org/article/view/v063i17 |
Type Of Technology | Software |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | 66200 Google hits |
URL | https://CRAN.R-project.org/package=GWmodel |
Description | GW models, CAS, Research Centre for Eco-Environmental Sciences, Beijing, China (two-day workshop with A Comber) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | GW models, CAS, Research Centre for Eco-Environmental Sciences, Beijing, China (two-day workshop with A Comber) |
Year(s) Of Engagement Activity | 2017 |
Description | Geographically Weighted PCA: Introductions and Uses. Spatial Accuracy Conference, Beijing, China (one-day workshop with A Comber) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Geographically Weighted PCA: Introductions and Uses. Spatial Accuracy Conference, Beijing, China (one-day workshop with A Comber) |
Year(s) Of Engagement Activity | 2018 |
Description | training course |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Postgraduate students |
Results and Impact | two-day training course on the SPACSYS model held at North West A&F University. After the training, the participants are expected to understand mechanisms and functions implemented in the model, know how to connect the SQL server to the model, be able to run a simulation by operating various inputs and parameter and eventually create new simulations with own study. |
Year(s) Of Engagement Activity | 2019 |