MIDST-CZ: Maximising Impact by Decision Support Tools for sustainable soil and water through UK-China Critical Zone science
Lead Research Organisation:
University of Exeter
Department Name: Geography
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Planned Impact
The following will benefit from this research:
1. Those living in and managing the land for food production, and soil and water quality, and their conservation will benefit from the decision support tools (DST) that will be refined or developed based on our Critical Zone integrated understanding of how the environment functions. These tools will allow these stakeholders to be guided on best management practices for their business and the environment. The DSTs will lead to improvements in their quality of life, ensuring the fundamental needs (generation of food and associated economic development; access to water of appropriate quality) and decisions of how to achieve these, are underpinned by a useful knowledge-base.
2. Commercial organisations that depend on innovation, such as 'app' developers will benefit from our engagement with them to explore in what form the decision support tools should be made available. Moreover, the DSTs will be useful to agronomists and the fertiliser industry. Specific interest from two Chinese companies producing nutrients from sewage and other organic wastes has been demonstrated in letters of support.
3. This joint research will remain of benefit to the NSFC, raising their profile in the UK and amongst other critical zone scientists. The exchanges of skills and information that will occur during this research with Chinese colleagues will build international competitiveness of science in both UK and the UK. The research will ultimately demonstrate to the international scientific community the value sensitive environments, and the benefits of international cooperation in research to tackle grand challenges of food security, land degradation and climate change. It will help consolidate each country's position as a future key research partner, and particularly the NSFC in China as a partner of choice for future co-funded research with the UK.
4. Through publication and conference activity, the Chinese and UK academic parties will demonstrate to the community how their scientific endeavour can be used to create tangible outputs to improve the quality of life and global environment for those on low incomes or managing degraded land. They will benefit through enhanced international standing and resultant funded research collaboration.
5. The wider public, and local communities hosting the research, will benefit during the research activity through research team communication activity that meets their passion for and excites them to understand the natural world more deeply. This also includes those not involved directly in the research who may be asked to help gather data and in turn will receive training in new skills. In turn if this encourages greater interest in how STEM subjects also inform social development, the relevant country science base will benefit.
6. Through progress towards achieving sustainable development goals the global community will benefit.
1. Those living in and managing the land for food production, and soil and water quality, and their conservation will benefit from the decision support tools (DST) that will be refined or developed based on our Critical Zone integrated understanding of how the environment functions. These tools will allow these stakeholders to be guided on best management practices for their business and the environment. The DSTs will lead to improvements in their quality of life, ensuring the fundamental needs (generation of food and associated economic development; access to water of appropriate quality) and decisions of how to achieve these, are underpinned by a useful knowledge-base.
2. Commercial organisations that depend on innovation, such as 'app' developers will benefit from our engagement with them to explore in what form the decision support tools should be made available. Moreover, the DSTs will be useful to agronomists and the fertiliser industry. Specific interest from two Chinese companies producing nutrients from sewage and other organic wastes has been demonstrated in letters of support.
3. This joint research will remain of benefit to the NSFC, raising their profile in the UK and amongst other critical zone scientists. The exchanges of skills and information that will occur during this research with Chinese colleagues will build international competitiveness of science in both UK and the UK. The research will ultimately demonstrate to the international scientific community the value sensitive environments, and the benefits of international cooperation in research to tackle grand challenges of food security, land degradation and climate change. It will help consolidate each country's position as a future key research partner, and particularly the NSFC in China as a partner of choice for future co-funded research with the UK.
4. Through publication and conference activity, the Chinese and UK academic parties will demonstrate to the community how their scientific endeavour can be used to create tangible outputs to improve the quality of life and global environment for those on low incomes or managing degraded land. They will benefit through enhanced international standing and resultant funded research collaboration.
5. The wider public, and local communities hosting the research, will benefit during the research activity through research team communication activity that meets their passion for and excites them to understand the natural world more deeply. This also includes those not involved directly in the research who may be asked to help gather data and in turn will receive training in new skills. In turn if this encourages greater interest in how STEM subjects also inform social development, the relevant country science base will benefit.
6. Through progress towards achieving sustainable development goals the global community will benefit.
Publications
Jiang Z
(2020)
Bedrock geochemistry influences vegetation growth by regulating the regolith water holding capacity.
in Nature communications
Luo Y
(2020)
Modeling soil erosion between 1985 and 2014 in three watersheds on the carbonate-rock dominated Guizhou Plateau, SW China, using WaTEM/SEDEM
in Progress in Physical Geography: Earth and Environment
Liang B
(2020)
Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks
in Progress in Physical Geography: Earth and Environment
Naylor L
(2023)
Achieving Sustainable Earth Futures in the Anthropocene by Including Local Communities in Critical Zone Science
in Earth's Future
| Description | MIDST-CZO used cutting-edge science from the UK/China Critical Zone Observatory projects to develop the foundation of a new class of decision support tools (DSTs) to support agriculture and the environment at the same time. Economic impacts of agricultural management decisions were also considered by adopting the natural capital approach. Another facet of the research was to close resource loops in peri-urban agriculture by making better use of urban waste to produce fertilisers where nutrients are mined from sewage and other urban organic waste streams. Knowledge exchange is pivotal to the success of the project. We actively conducted socio-economic surveys in China to learn about demands for DSTs to support agriculture, based on existing gaps in available tools and improvements that could be made to increase uptake of DST use. This was the first time that humans have been integrated into critical zone science. In collaboration with China, we produced functional DSTs for policy makers that aim to improve farming decisions to decrease environmental threats. The vast dataset generated from the UK/China CZO programme has also been used to develop new models that describe the interaction between agriculture and environmental processes. These will help form the foundation of future DSTs. We have now completed just over 3 years of research on this project. The past two years, 2020-2022, were severely affected by COVID-19 restrictions, which was addressed to some extent by a no-cost 12-month extension and adaptation of our knowledge exchange activities with China. A critical activity to project delivery is Stakeholder Workshop 2 (WP1). In early 2019 we had Stakeholder Consultations with a number of groups through face-to-face meetings covering broad geographical regions of China from Guangzhou to Beijing. We also held Stakeholder Workshop 1 later in 2019 in collaboration with the Jiangsu Academy of Agricultural Sciences. This provided broader interaction with stakeholders at the interface between science and policy. From these activities, we have learnt that targeting agricultural industry conferences will increase the reach of our work for Stakeholder Workshop 2. Webinars and online surveys also formed part of our original KE activities with China. These have been enhanced to maintain continuity during the COVID-19 lockdown, but they do not replace the need for face-to-face interaction with stakeholders. It has been impossible to hold face-to-face activities. Work continued analysing knowledge exchange activities, informed by questionnaires and surveys conducted on previous visits and enhanced by new online surveys. We found that communication from government to farmers differed between regions in China, so local solutions would be needed. In some regions, farmers relied too much on fertiliser providers to make decisions, based primarily on crop yield. However, provincial agricultural research institutes in China are actively addressing knowledge exchange through the provision of online platforms, simple decision support tools and training. There is a strong demand for improved tools to address environmental threats caused by agriculture. Some existing DSTs to address yield and fertiliser use, such as Nutrient Expert, work extremely well, but could be extended to consider environmental impacts. A challenge with survey work is reaching a wide audience, particularly when travel is constrained due to COVID-19 restrictions. Animations with Mandarin and English voiceovers were created to publicise the CZO projects in China. We also established a number of online delivery platforms, such as a WeChat public account that has attracted over 2000 reads to some articles we have posted. Summary sheets are continuously being prepared from our research outputs to be targeted at lay audiences. We also produced a new online survey to understand the needs, habits and preference in the use and development of DST to support evidence-based decision making and promote decision support tools for agricultural management in China. The target audience is civil servants, researchers, and relevant personnel engaged in agricultural work. A desk study on decision support tool (DSTs) use in China's agriculture found over 400 working examples. Weaknesses included a focus on yield, without consideration of environmental and economic impacts. The data are analysed and a paper will be submitted in 2021 by the University of Aberdeen team. China has a strong research programme developing DSTs for agriculture. One of the leading tools is Nutrient Expert, which is very effective at improving nutrient use efficiency for a range of crops across China. Rothamsted Research and their partners in China worked with a DST to predict the interaction between soil carbon storage and crop yield for the Loess Plateau. This couples agricultural output with environmental impacts. Further work needs face-to-face interaction, but a paper is expected this year. Dr Boyi Liang from Peking University completed a visiting fellowship at the University of Exeter in 2020 that was aligned to MIDST. He continues to conduct CZO research complementary to the project and produced a paper using neural networks to predict crop yield. Di Sheng, a postdoctoral fellow at the University of Exeter commenced in 2021. She has brought together agricultural, environmental and economic modelling to drive a new class of DSTs. In the 9 months that she has been in post, she now has a regional model on crop choice optimisation based on economic and environmental drivers for Guizhou. Research continued at the Peri-Urban CZOs. Nanjing University and Leeds University furthered their partnership with joint PhDs as part of a Key Belt Project. Chemical (Queens University Belfast) and microbial (Sheffield University) analysis continues with some delays due to restricted lab and field access. A CSC funded PhD student working at the University of Sheffield for 1 year is exploring fertiliser impact, including the use of composted manure on environmental impacts. |
| Exploitation Route | This project focused on developing decision support tools that use the critical zone approach. They are to guide better agricultural practices that have lower environmental impact. As we are encompassing a wide range of processes that occur in the critical zone, from the top of vegetation to groundwater, we are attempting to tackle previously neglected threats such as the deep leaching of nitrogen fertiliser to groundwater. We provided a scientific foundation that can be accessed by the IT industry and others to develop simple tools that are accessible to a range of users from farmers to policy-makers. Our research also demonstrated the importance of incorporating human behaviour and drivers into understanding how critical zone processes are affected by land management. |
| Sectors | Agriculture, Food and Drink,Environment,Government, Democracy and Justice |
| URL | http://www.czo.ac.cn |
| Description | Drawing on the results obtained in the first phase of the UK/China CZO projects, we developed tools to help make decisions that guide more sustainable farming practices in China. These included regional models and simpler Decision Support Tools that were underpinned by critical zone science. This brings in processes involved in landscape evolution, such as geochemistry, deep processes like nitrogen leaching and the impacts of different management practices. A major advance of our approach was bringing in economic impacts into the critical zone science approach. This used a natural capital approach to assess the economic implications of different management approaches, drawing on earlier research that used the underlying geology and its weathering to predict crop yields. We produced functioning decision support tools for the regions where our critical zone observatories are located. Moreover, we assessed over 400 existing decision support tools, but found that most were inappropriate because they were either too limited or geographically inappropriate for China. Only about a dozen existing DSTs were thought to be suitable for use in China. There was a gap, however, in decision support tools that incorporated both agricultural and economic impacts. Our research also explored the use of urban waste streams for fertiliser development. Nutrients are being mined from sewage and manufactured into a product that is not too dissimilar in appearance to conventional chemical fertiliser. There are concerns about pathogens and heavy metals that are being assessed further using advanced molecular biology techniques and plant biochemistry to assess pollutant uptake. Impact was hampered significantly by COVID-19 travel and working restrictions. Travel to China remained restricted from December 2019 (a few weeks after our last stakeholder event in China) until the end of the project so we were not able to conduct much of the impact work in China. We had hoped to travel to China before March 2022 when the project ended, but was not possible. Via the British Embassy Beijing and FASE, we were able to disseminate our research findings to over 2300 people in a webinar hosted in September 2022. Teams in China have pursued DST modelling with the critical zone data and adopted the approach developed in the project of incorporating a human element to get a broader, interdisciplinary understanding. |
| First Year Of Impact | 2021 |
| Sector | Agriculture, Food and Drink,Government, Democracy and Justice |
| Impact Types | Policy & public services |
| Title | Karst CZ Crop Yield Models |
| Description | We address the current limitations of crop modelling for Karst landscapes by assembling spatial data, for karst dry-lands of Guizhou, that represent the principal controls on plant growth and crop production and explore the influence these have on crop yield per unit planting area (hereinafter is called crop yield) for seven crop species. It is the first time that multi-factorial analysis has been undertaken to explain spatial patterns of crop yields in this environment. The approach is made possible by the application of powerful and novel machine learning technology to precisely simulate the spatial variation of crop yields. Two types of artificial neural network (ANN) were used to: (1) detect the spatial pattern of the crop yield in Guizhou Province, and (2) to quantify the effects of fertilization, meteorological, edaphic and lithological factors on the spatial pattern of crop yield. This research is the first step in developing powerful decision support tools based on the CZ approach to guide land management and farming decisions in karst regions. The approach has wider potential in karst and complex landscapes in other geographic regions of the world. Two artificial neural networks we used are Self-organization Feature Map and Back Propagation Artificial Neural Network. A self-organization feature map (SOFM) neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. It is trained by unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method for performing dimensionality reduction. The advantage of SOFM is that it can preserve the topological properties of the input space by using a neighbourhood function. It has been widely used in classification and clustering analysis. Back propagation (BP) is an algorithm widely used in the training of feedforward neural networks for supervised learning. It is one of the most widely applied neural network models across different research disciplines. BP networks can be used to learn and store a great deal of mapping relations of an input-output model, with no need to disclose in advance the mathematical equation that describes these mapping relations. The algorithm uses the mean square error and gradient descent algorithm to achieve the correction of the network connection weights, and its goal is to minimize the difference between the mean square error of the actual output and the regulations output. Datasets used in this study can be classified into meteorological data, crop yield, fertilization, soil properties and lithological data. We selected seven main crop types that are produced in dry-lands, including four kinds of food crop (wheat, maize, soybean and potato) and three commercial crops (rapeseed, peanut and flue-cured tobacco). The relative crop yield data were compiled from the year book of 2015 of each prefecture, as well as meteorological data and fertilization information (consumption of chemical fertilizers including N/P/K and compound fertilizer). Soil properties and lithological data was imported from Harmonized World Soil Database (HWSD, version 1.2) and geochemical atlas collected from the Bureau of Geology and Mineral Exploration of Guizhou Province. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2020 |
| Provided To Others? | No |
| Impact | Impact expected after publication. |
| Description | Peking University & Chinese Academy of Sciences Ecosystem Services Modelling |
| Organisation | Chinese Academy of Sciences |
| Department | Institute of Geographic Sciences and Natural Resource Research |
| Country | China |
| Sector | Charity/Non Profit |
| PI Contribution | Close collaborative working with Prof.s Hongyan Liu and Jian Peng of the College of Urban and Environmental Sciences and Prof Xinyu Zhang of the Institute of Geographical Sciences and Natural Resources Research. Contribution to ecosystem service monitoring and modelling and development of decision support tools including discussion of experimental design and analytical approach. Contribution to publications through feedback on draft papers. Collaboration and coordination with Dr Boyi Liang during his secondment to University of Exeter. |
| Collaborator Contribution | Several members of the teams of Prof Liu, Prof Peng and Prof Zhang are working on ecosystem service delivery in Karst environments of SW China. They bring expertise in ecology and ecosystem modelling and access to vital Province-Wide Databases. Dr Boyi Liang spent January-August 2020 at the University of Exeter as a visiting postdoctoral fellow working directly on the project, developing decision support tools. He is funded primarily from Peking University who pay his salary. His work is of vital importance to project delivery and it will hopefully allow us to exceed the project objectives. We are providing direct supervision, with a range of CoIs contributing to his research. He is able to conduct unique work that bridges geochemistry, soil science, hydrology and economics. |
| Impact | Collaboration with the same groups within the SPECTRA programme led to several publications and the new collaborative work under MIDST-CZ is currently in preparation for publication and in review. Publications include: Liang B, Liu H, Quine T, Chen X, Hallett P, Cressey E, ... Hartley I. (2020). Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks. Progress in Physical Geography: Earth and Environment, doi: 10.1177/0309133320956631 |
| Start Year | 2019 |
| Description | Peking University & Chinese Academy of Sciences Ecosystem Services Modelling |
| Organisation | Peking University |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Close collaborative working with Prof.s Hongyan Liu and Jian Peng of the College of Urban and Environmental Sciences and Prof Xinyu Zhang of the Institute of Geographical Sciences and Natural Resources Research. Contribution to ecosystem service monitoring and modelling and development of decision support tools including discussion of experimental design and analytical approach. Contribution to publications through feedback on draft papers. Collaboration and coordination with Dr Boyi Liang during his secondment to University of Exeter. |
| Collaborator Contribution | Several members of the teams of Prof Liu, Prof Peng and Prof Zhang are working on ecosystem service delivery in Karst environments of SW China. They bring expertise in ecology and ecosystem modelling and access to vital Province-Wide Databases. Dr Boyi Liang spent January-August 2020 at the University of Exeter as a visiting postdoctoral fellow working directly on the project, developing decision support tools. He is funded primarily from Peking University who pay his salary. His work is of vital importance to project delivery and it will hopefully allow us to exceed the project objectives. We are providing direct supervision, with a range of CoIs contributing to his research. He is able to conduct unique work that bridges geochemistry, soil science, hydrology and economics. |
| Impact | Collaboration with the same groups within the SPECTRA programme led to several publications and the new collaborative work under MIDST-CZ is currently in preparation for publication and in review. Publications include: Liang B, Liu H, Quine T, Chen X, Hallett P, Cressey E, ... Hartley I. (2020). Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks. Progress in Physical Geography: Earth and Environment, doi: 10.1177/0309133320956631 |
| Start Year | 2019 |
| Description | British Embassy Beijing Presentation (Quine) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Policymakers/politicians |
| Results and Impact | Prof Quine gave a talk to Embassy Staff engaged on several different portfolios within the British Embassy in Beijing and held face to face discussions with Neal Carlin, Head of Climate Change and Environment, and Dai Qing, Climate and Environmental Policy Advisor. The aims and principal findings of the Newton/NERC-NSFC Critical Zone Observatory Programme were presented, with elaboration of the SPECTRA project, and the aims and plans with regard to MIDST-CZ were explained. There was a good level of discussion and engagement from Embassy staff and visitors. . |
| Year(s) Of Engagement Activity | 2019 |
| Description | Jiangsu Academy of Agricultural Sciences Symposium |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | On 21st - 23rd November 2019, Jiangsu Academy of Agricultural Sciences (JAAS), Department of Agriculture and Rural Affairs of Jiangsu Province, Soil society of Jiangsu Province jointly organised 'Symposium on the efficient use of soil, fertiliser and water resources in the Yangtze River Economic Belt' and 'The 5th Jiangsu Academic Forum on Excellent Young Scientists in Soil and Agriculture' in Nanjing, Jiangsu Province in China. The meeting was attended by 300+ people comprising mainly applied agricultural scientists, with representation from government ministries (>5 people) and industry (>10 people). MIDST team Co-hosted the "Symposium on the experience and problems of the efficient use of soil, fertiliser and water in the Yangtze River Economic Belt" along with the Station of Farmland Quality and Agricultural Environment Protection of Jiangsu Province. We were communicated with applied agricultural scientists in China who work more closely at the interface between science and farming than the Chinese collaborators on MIDST, and introduced our work and project to the attendances |
| Year(s) Of Engagement Activity | 2019 |