Issues and challenges in landscape models for agriculture: from the representation of agroecosystems to the design of management strategies
Agroecosystems produce food and many other services that are crucial for human well-being. Given the scales at which the processes underlying these services take place, agricultural landscapes appear as appropriate spatial units for their evaluation and management. The design of sustainable agricultural landscapes that value these services has thus become a pressing issue but faces major challenges stemming from the diversity of processes, their interactions and the number of scales at stake. Agricultural landscape modelling can provide a key contribution to this design but must still overcome several difficulties to offer reliable tools for decision makers.
Our study aimed at shedding light on the main scientific and technical difficulties that make the building of landscape models that may efficiently inform decision-makers a complex task, as well as translating them in terms of challenges that can be further investigated and discussed.
We examine current issues and challenges and indicate future research needs to overcome the scientific and technical obstacles in the development of useful agricultural landscape models.
We highlight research perspectives to better couple landscape patterns and process models and account for feedbacks, integrate the decisions of multiple stakeholders, consider the spatial and temporal heterogeneity of data and processes, explore alternative landscape organisations and assess multiobjective performance.
Coping with the issues and challenges discussed in this paper should improve our understanding of agroecosystems and give rise to new hypotheses, thereby informing future research.
KeywordsAgroecosystem Sustainability Management strategies Stakeholder decisions Simulation models Spatial heterogeneity Inference
Support was provided by the PAYOTE scientific network, which is funded by the French National Institute for Agricultural Research (INRA). Authors are grateful to many colleagues for fruitful discussions which contributed to this paper. We thank anonymous reviewers for their helpful comments and suggestions on the manuscript.
- Boussard H, Roche B, Joannon A, Martel G (2017) CAPFarm: a software for crop allocation problem at the farm and landscape levels. INRA, RennesGoogle Scholar
- Coléno FC (2008) A simulation model to evaluate the consequences of Genetic Modification and non-Genetic Modification segregation rules on landscape organisation. J Int Farm Manag 4:33–45Google Scholar
- Forman RTT, Godron M (1986) Landscape Ecology. Cambridge University Press, CambridgeGoogle Scholar
- Martin E, Gascoin S, Grusson Y, Murgue C, Bardeau M, Anctil F, Ferrant S, Lardy R, Le Moigne P, Leenhardt D, Rivalland V, Sánchez Pérez J-M, Sauvage S, Therond O (2016) On the use of hydrological models and satellite data to study the water budget of river basins affected by human activities: examples from the Garonne Basin of France. Surv Geophys 37:223–247CrossRefGoogle Scholar
- Plummer M (2003) JAGS: A Program for Analysis of Bayesian Graphical Models using Gibbs Sampling. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing. Vienna, AustriaGoogle Scholar
- Sciaini M (2018) Package “NMLR”: Simulating Neutral Landscape Models. R Package Version 030Google Scholar
- Todeschini A, Caron F, Fuentes M, Legrand P, Del Moral P (2014) Biips: Software for Bayesian Inference with Interacting Particle Systems. arXiv:1412.3779
- Turner MG, Gardner RH, O’Neill RV (2001) Landscape ecology in theory and practice: pattern and process. Springer, New YorkGoogle Scholar
- Vinatier F, Lagacherie P, Voltz M, Petit S, Lavigne C, Brunet Y, Lescourret F (2016) An unified framework to integrate biotic, abiotic processes and human activities in spatially explicit models of agricultural landscapes. Front Environ Sci. https://doi.org/10.3389/fenvs.2016.00006 CrossRefGoogle Scholar