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

This collaborative UK-China project proposes will establish a suite of Decision Support Tools (DSTs) that incorporate knowledge advances from the ongoing Phase 1 UK-China Critical Zone science research programme. With these advances, DSTs currently used in the UK and China will be adapted, expanded, tested and applied. The DSTs, data sets and decision outcomes will guide and evaluate site-specific innovation in soil and water management, and create roadmaps to scale up impact outcomes and plans to regional and national scale in China. The project will integrate the teams and research results of the 5 projects funded in Phase 1. Phase 2 will deliver immediate innovation in decision support methods and their application, and pathways to long-term impact and ODA outcomes: to restore ecosystems, improve soil fertility and water quality, improve farming livelihoods and improve food and water security. Impact delivery will focus on 5 critical zone observatories (CZOs) in China, established in Phase 1, which are located in regions with large-scale environmental and economic challenges related to degraded soil and water resources.

1. Hydro Karst CZO in SW China - land use and water quality linked to nutrient contamination of aquifers and surface waters in transmissive carbonate terrain
2. Peri-Urban CZO in the Yangtze delta - soil contamination from urban atmospheric deposition and intensification of agricultural chemical use
3. SPECTRA Karst CZO in SW China - Ecosystem degradation and karstic desertification linked with soil erosion and loss of soil fertility
4. Red Soils CZO - loss of soil fertility from intensification of agricultural production
5. Loess CZO - ecosystem degradation under intensification of rural land use

Participatory research with stakeholders in China will identify the land, water and food demand conflicts that need be addressed in the regions of the CZOs. This KE is designed to avoid a recognised mismatch between the way DSTs are conceptualised by scientists and how DSTs may be most effectively used by stakeholders. Recent reviews of DSTs for land, water and food sustainability provide a platform for assessing the suitability of different DSTs to the challenges of China. The UK and China teams and stakeholders will identify the most promising DST approaches and test these using the data sets of the 5 CZOs. The outputs will be tested with a wider set of DST users and potential users. The outcomes will allow mapping DSTs and their suitability to address soil and water management challenges at the 5 CZO sites. This work will assess effectiveness of the DSTs across the scales of interest for stakeholder groups at the sites. This will include identifying sustainable practices, the scale at which the CZO and related measurements strengthen the evidence base and inform practices for management decisions, and how adaptations in a DST methodology would improve site-specific application. The project will apply national data sets on the geographic variability of soil and water resource demand and use patterns, and natural conditions of geology, soil, vegetation cover, climate and weather. Through engagement with regional planners, the project will design pathways to scale up the DST outcomes for application in regional-scale resource planning. The final stage will be synthesis of the adapted, applied and upscaled DST methods into practical guidance in how to deploy the DSTs in regionally specific contexts, the capabilities of different DSTs and applicability of DST outputs. The institutional partners in China will publish the guidance and organisations will be identified and trained as superusers to disseminate training. Superusers will conduct a series of regional workshops in China, led by Chinese partners, to create a network of users who are at the forefront of innovation in soil and water management planning and implementation.

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.

Publications

10 25 50
 
Description We are addressing the current limitations of crop modelling for Karst landscapes of Guizhou by assembling spatial data that represent the principal controls on plant growth and crop production and exploring the influence these have on crop yield per unit planting area 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, soil and geological factors on the spatial pattern of crop yield. This research is the first step in developing powerful decision support tools based on the holistic Critical Zone 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.
Exploitation Route Once developed, the crop models will be of value to policy-makers considering the trade-offs between ecosystem services in the complex Karst landscpaes of Guizhou, where the per capita GDP is well below the average for China and rural poverty is a threat to sustainable development.
Sectors Agriculture, Food and Drink,Environment,Government, Democracy and Justice

 
Description Prof Hallett (University of Aberdeen) has reported ongoing use of project findings in his report as PI of the overall project (this includes engagement with business and policy makers in the 2019 symposium listed under engagement activity.
First Year Of Impact 2019
Sector Agriculture, Food and Drink
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.
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.
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.
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.
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.

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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