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The Rothamsted Landscape Model is a suite of interacting process-based modules that simulate soil processes, (including soil organic matter, soil nutrient and water dynamics), livestock production and crop growth and yield, including interactions with arable weeds. The model is spatially explicit with adjacent pieces of land (fields or watercourses) linked to simulate spatial movement of nutrients, water (and in the future pests).

The model components are based on well-established models such as RothC and LINTUL (as described in Coleman et al., 2017) but also include many new routines, for example (i) the production and emissions from dairy, based on work done by Misselbrook et al.) on the Defra funded project ‘Improvements to the UK Greenhouse Gas Inventory’ (ii) erosion, and (iii) a novel trait-based weed model (Metcalfe et al., 2019).


Notable capabilities include the ability to run UK based scenarios to simulate:

(i) Arable crop yields, emissions, and nutrient run-offs

(ii) Milk production from UK dairy sector and associated environmental impacts

(iii) The response of various weed species to crop management

(iv) Assess trade-offs between multiple objectives (for example production, soil-erosion and emissions)

The model has been coupled with a management optimisation system, that can be used to explore means to improve environmental outcomes from farming while maintaining or increasing production. The model is currently being used in a number of projects by Rothamsted and our collaborators.


  • Delivering Sustainable Systems BB/J/00426x/1) funded by Biotechnology and Biological Sciences Research Council (BBSRC),
  • Achieving Sustainable Agricultural Systems (NEC05829 LTS-M ASSIST) funded by BBSRC and NERC,
  • Soils 2 Nutrition (S2N) grant number BB/P012671/1
  • LTLS, funded by NERC grant NE/J011568/
  • IWMPraise Horizon 2020 grant agreement no 727321
  • Targets for Sustainable And Resilient Agriculture (TSARA) funded by a DEFRA and EU collaborative project
  • TGRAINS: Transforming and Growing Relationships within regional food systems for Improved Nutrition and Sustainability, funded by the BBSRC under the GFS Resilience call.


Coleman, K., Whitmore, A. P., Hassall, K. L., Shield, I., Semenov, M. A., Dobermann, A., Bourhis, Y., Eskandary, A., and Milne, A. E. 2021. The potential for soybean to diversify the production of plant-based protein in the UK. Sci. Total Environ. 767:144903.

Metcalfe, H., Milne., A.E., Delledale, F., Storkey, J. 2020. Using Functional Traits to Model Annual Plant Community Dynamics, Ecology, 101(11), e03167.

Milne, A.E., Coleman, K., Todman, L.C., Whitmore, A.P. 2020. Model-based optimisation of agricultural profitability and nutrient management: a practical approach for dealing with issues of scale. Environmental Monitoring and Assessment, 192:730. https://doi.org10.1007/s10661-020-08699-z

Todman LC, Coleman K, Milne AE, Gil JDB, Reidsma P, Schwoob M-H, et al. Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes. Science of The Total Environment 2019; 687: 535-545,

Muhammed SE, Coleman K, Wu LH, Bell VA, Davies JAC, Quinton JN, et al. Impact of two centuries of intensive agriculture on soil carbon, nitrogen and phosphorus cycling in the UK. Science of the Total Environment 2018; 634: 1486-1504,

Coleman, K., Muhammed, S. E., Milne, A. E., Todman, L. C., Dailey, A. G., Glendining, M. J., and Whitmore, A. P. 2017. The landscape model: A model for exploring trade-offs between agricultural production and the environment. Sci. Total Environ. 609:1483-1499