A Field-Scale Decision Support System for Assessment and Management of Soil Functions

Agricultural decision support systems (DSS) are mostly focused on increasing the supply of individual soil functions such as e.g. primary productivity or nutrient cycling, while neglecting other important soil functions, such as e.g. water purification and regulation, climate regulation and carbon sequestration, soil biodiversity and habitat provision. Making right management decisions for long-term sustainability is therefore challenging, and farmers and farm advisors would greatly benefit from an evidence-based DSS targeted for assessing and improving the supply of several soil functions simultaneously. To address this, need we designed the Soil Navigator DSS by applying a qualitative approach to multi criteria decision modelling using Decision Expert (DEX) integrative methodology. Multi-criteria decision models for the five main soil functions were developed, calibrated and validated using knowledge of involved domain experts and knowledge extracted from existing datasets by data mining. Subsequently, the five DEX models were integrated into a DSS to assess the soil functions simultaneously, and to provide management advises for improving the performance of prioritized soil functions. To enable communication between the users and the DSS, we developed a user-friendly computer-based graphical user interface, which enables users to provide the required data regarding their field to the DSS and to get textual and graphical results about the performance of each of the five soil functions in a qualitative way. The final output from the DSS is a list of soil mitigation measures that the end-users could easily apply in the field in order to achieve the desired soil function performance. The Soil Navigator DSS has a great potential to complement the Farm Sustainability Tools for Nutrients included in the Common Agricultural Policy 2021-2027 proposal adopted by the European Commission. The Soil Navigator has also a potential to be spatially upgraded to assist decisions on which soil functions to prioritize in a specific region or member state. Furthermore, the Soil Navigator DSS could be used as an educational tool for farmers, farm advisors and students, and its potential should be further exploited for the benefit of farmers and the society as a whole.

[1]  Johan Bouma,et al.  Soil Information in Support of Policy Making and Awareness Raising , 2012 .

[2]  Blaz Zupan,et al.  A function-decomposition method for development of hierarchical multi-attribute decision models , 2004, Decis. Support Syst..

[3]  G. Tóth,et al.  Identifying Gaps between the Legislative Tools of Soil Protection in the EU Member States for a Common European Soil Protection Legislation , 2018, Sustainability.

[4]  Gary Lanigan,et al.  Implications of the proposed Soil Framework Directive on agricultural systems in Atlantic Europe – a review , 2010 .

[5]  Jessica Lindblom,et al.  Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies , 2016, Precision Agriculture.

[6]  B. B. Ghaley,et al.  Making the Most of Our Land: Managing Soil Functions from Local to Continental Scale , 2015, Front. Environ. Sci..

[7]  Alfred E. Hartemink,et al.  Linking soils to ecosystem services — A global review , 2016 .

[8]  Marko Debeljak,et al.  Development of an Agricultural Primary Productivity Decision Support Model: A Case Study in France , 2019, Front. Environ. Sci..

[9]  Alessandro Flammini,et al.  The Contribution of Agriculture, Forestry and other Land Use activities to Global Warming, 1990–2012 , 2015, Global change biology.

[10]  P. Haygarth,et al.  The future of soils and land use in the UK: Soil systems for the provision of land-based ecosystem services , 2009 .

[11]  Sašo Džeroski,et al.  Modeling the risk of water pollution by pesticides from imbalanced data , 2018, Environmental Science and Pollution Research.

[12]  John M. Antle,et al.  Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science , 2017, Agricultural systems.

[13]  Rachel Creamer,et al.  Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation , 2018 .

[14]  Laura Uusitalo,et al.  An overview of methods to evaluate uncertainty of deterministic models in decision support , 2015, Environ. Model. Softw..

[15]  John M. Antle,et al.  Brief history of agricultural systems modeling , 2017, Agricultural systems.

[16]  Rogier P.O. Schulte,et al.  Functional land management: A framework for managing soil-based ecosystem services for the sustainable intensification of agriculture , 2014 .

[17]  Luuk Fleskens,et al.  Soil quality – A critical review , 2018 .

[18]  M. Trnka,et al.  Impacts and adaptation of European crop production systems to climate change , 2011 .

[19]  David C. Rose,et al.  Decision support tools for agriculture: Towards effective design and delivery , 2016 .

[20]  James T. Allison,et al.  Topology optimization for heat conduction using generative design algorithms , 2017 .

[21]  William J Sutherland,et al.  Organising evidence for environmental management decisions: a '4S' hierarchy. , 2014, Trends in ecology & evolution.

[22]  W. Blum.  Functions of Soil for Society and the Environment , 2005 .

[23]  Philippe C. Baveye,et al.  Soil “Ecosystem” Services and Natural Capital: Critical Appraisal of Research on Uncertain Ground , 2016, Front. Environ. Sci..

[24]  Jan Staes,et al.  Harvesting European knowledge on soil functions and land management using multi‐criteria decision analysis , 2019, Soil Use and Management.

[25]  L. Montanarella,et al.  Research needs in support of the European thematic strategy for soil protection , 2004 .

[26]  M. Debeljak,et al.  Modeling of Soil Functions for Assessing Soil Quality: Soil Biodiversity and Habitat Provisioning , 2019, Front. Environ. Sci..

[27]  Sašo Džeroski,et al.  Estimating Drainage Periods for Agricultural Fields from Measured Data: Data‐Mining Methodology and a Case Study (La JailliÈRe, France) , 2015 .

[28]  Jens Bligaard,et al.  Mark Online, a Full Scale GIS-based Danish Farm Management Information System , 2014 .

[29]  K. Vanderlinden,et al.  European long‐term field experiments: knowledge gained about alternative management practices , 2018, Soil Use and Management.

[30]  Peter J. Thorburn,et al.  A Conceptual Framework for Guiding the Participatory Development of Agricultural Decision Support Systems , 2010 .

[31]  Arnold K. Bregt,et al.  The social side of spatial decision support systems: Investigating knowledge integration and learning , 2017 .

[32]  V. Dale,et al.  State of apps targeting management for sustainability of agricultural landscapes. A review , 2019, Agronomy for Sustainable Development.

[33]  A. Leip,et al.  Mitigation potential of soil carbon management overestimated by neglecting N2O emissions , 2018, Nature Climate Change.

[34]  L. Brussaard,et al.  A method to assess ecosystem services developed from soil attributes with stakeholders and data of four arable farms. , 2012, The Science of the total environment.

[35]  Caspar A. Mücher,et al.  A climatic stratification of the environment of Europe , 2005 .

[36]  H. Vereecken,et al.  Stimulation of N2O emission by manure application to agricultural soils may largely offset carbon benefits: a global meta‐analysis , 2017, Global change biology.

[37]  Mildred L. G. Shaw,et al.  Modeling expert knowledge , 1990 .

[38]  Guillermo A. Mendoza,et al.  Multi-criteria decision analysis in natural resource management: A critical review of methods and new modelling paradigms , 2006 .

[39]  G. Tóth,et al.  The elusive role of soil quality in nutrient cycling: a review , 2016 .

[40]  Vladislav Rajkovic,et al.  DEX Methodology: Three Decades of Qualitative Multi-Attribute Modeling , 2013, Informatica.