Abstract
Understanding agricultural ecosystems and their complex interactions with the environment is important for improving agricultural sustainability and environmental protection. Developing the necessary understanding requires approaches that integrate multi-source geospatial data and interdisciplinary relationships at different spatial scales. In order to identify and delineate landscape units representing relatively homogenous biophysical properties and eco-environmental functions at different spatial scales, a hierarchical system of uniform management zones (UMZ) is proposed. The UMZ hierarchy consists of seven levels of units at different spatial scales, namely site-specific, field, local, regional, country, continent, and globe. Relatively few studies have focused on the identification of the two middle levels of units in the hierarchy, namely the local UMZ (LUMZ) and the regional UMZ (RUMZ), which prevents true eco-environmental studies from being carried out across the full range of scales. This study presents a methodology to delineate LUMZ and RUMZ spatial units using land cover, soil, and remote sensing data. A set of objective criteria were defined and applied to evaluate the within-zone homogeneity and between-zone separation of the delineated zones. The approach was applied in a farming and forestry region in southeastern Ontario, Canada, and the methodology was shown to be objective, flexible, and applicable with commonly available spatial data. The hierarchical delineation of UMZs can be used as a tool to organize the spatial structure of agricultural landscapes, to understand spatial relationships between cropping practices and natural resources, and to target areas for application of specific environmental process models and place-based policy interventions.
Similar content being viewed by others
References
Agriculture and Agri-Food Canada, 2013. Canada Land Inventory, National Soil Database. Available at: http://sis.agr.gc.ca/cansis/nsdb/cli/index.html.
Agriculture and Agri-Food Canada, 2014a. Land cover for agricultural regions of Canada, circa 2000. Available at: http://www.agr.gc.ca/eng/?id=1343071073307%23a9.
Agriculture and Agri-Food Canada, 2014b. Detailed Soil Survey Compilations, National Soil Database. Available at: http://sis.agr.gc.ca/cansis/nsdb/dss/v3/index.html.
Benoît, M., Rizzo, D., Marraccini, E., Moonen, A. C., Galli, M., Lardon, S., Rapey, H., Thenail, C., & Bonari, E. (2012). Landscape agronomy: a new field for addressing agricultural landscape dynamics. Landscape Ecology, 27(10), 1385–1394.
Carmona, A., Nahuelhual, L., Echeverría, C., & Báez, A. (2010). Linking farming systems to landscape change: an empirical and spatially explicit study in southern Chile. Agriculture Ecosystems and Environment, 139, 40–50. doi:10.1016/j.agee.2010.06.015.
Cavazza, L. (1996). Agronomia aziendale e agronomia del territorio. Rivista di Agronomia, 30(3), 310–319.
Collins, R. P., JENKINS, A., & Sloan, W. T. (1998). A GIS framework for modelling nitrogen leaching from agricultural areas in the Middle Hills, Nepal. International Journal of Geographical Information Science, 12(5), 479–490.
Dalgaard, T., Hutchings, N. J., & Porter, J. R. (2003). Agroecology, scaling and interdisciplinarity. Agriculture Ecosystems and Environment, 100, 39–51.
Diker, K., Heermann, D. F., & Brodahl, M. K. (2004). Frequency analysis of yield for delineating yield response zones. Precision Agriculture, 5, 435–444.
Easterling, W. E., McKenney, M. S., Rosenberg, N. J., & Lemon, K. M. (1992). Simulations of crop response to climate change: effects with present technology and no adjustments (the ‘dumb farmer’ scenario). Agricultural and Forest Meteorology, 59, 53–73.
Ewert, F., van Ittersum, M. K., Heckelei, T., Therond, O., Bezlepkina, I., & Andersen, E. (2011). Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agriculture Ecosystems and Environment, 142(1–2), 6–17.
Faivre, R., Leenhardt, D., Voltz, M., Benoît, M., Papy, F., Dedieu, G., & Wallach, D. (2004). Spatialising crop models. Agronomie, 24, 205–217.
Godard, C., Roger-Estrade, J., Jayet, P. A., Brisson, N., & Le Bas, C. (2008). Use of available information at a European level to construct crop nitrogen response curves for the regions of the EU. Agricultural Systems, 97, 68–82.
Hazeu, G., Elbersen, B., Andersen, E., Baruth, B., van Diepen, C.A., and Metzger, M.J., 2010. A biophysical typology for a spatially-explicit agri-environmental modelling framework. In: Brouwer, F., Van Ittersum, M.K., ed., Environmental and Agricultural Modelling: Integrated Approaches for Policy Impact Assessment. Springer Academic Publishing, 159–187.
Huffman, T., Coote, D. R., & Green, M. (2012). Twenty-five years of changes in soil cover on Canadian Chernozemic (Mollisol) soils, and the impact on the risk of soil degradation. Canadian Journal of Soil Science, 92, 471–479.
Jagtap, S. S., & Jones, J. W. (2002). Adaptation and evaluation of the CROPGRO-soybean model to predict regional yield and production. Agriculture Ecosystems and Environment, 93, 73–85.
Jellema, A., Stobbelaar, D.-J., Groot, J. C. J., & Rossing, W. A. H. (2009). Landscape character assessment using region growing techniques in geographical information systems. Journal of Environmental Management, 2, S161–S174. doi:10.1016/j.jenvman.2008.11.031.
Leenhardt, D., Wallach, D., Le Moigne, P., Guérif, M., Bruand, A., & Casterad, M. A. (2006). Using crop models for multiple fields. In D. Wallach, D. Makowski, & J. W. Jones (Eds.), Working with dynamic crop models (Evaluation, analysis, parameterization, and applications, pp. 209–248). Amsterdam: Elsevier.
Leenhardt, D., Angevin, F., Biarnès, A., Colbach, N., & Mignolet, C. (2010). Describing and locating cropping systems on a regional scale. A review. Agronomy for Sustainable Development, 30, 131–138.
Maxwell, S. K., Nuckols, J. R., & Ward, M. H. (2006). A method for mapping corn using the US Geological Survey 1992 National Land Cover Dataset. Computers and Electronics in Agriculture, 51(1–2), 54–65.
McGarigal, K., & Marks, B. (1994). FRAGSTATS—spatial pattern analysis program for quantifying landscape structure (Vol. 2.0). USA.: Oregon State University, Corvallis, Oregon.
Mignolet, C., Schott, C., & Benoit, M. (2007). Spatial dynamics of farming practices in the Seine basin: methods for agronomic approaches on a regional scale. Science of the Total Environment, 375, 13–32.
MODIS, 1999. MODIS vegetation index (MOD 13): algorithm theoretical basis document [online]. Tucson, University of Arizona. Available from: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf. Accessed 13 Mar 2014.
Moral, F. J., Terrón, J. M., & Rebollo, F. J. (2011). Site-specific management zones based on the Rasch model and geostatistical techniques. Computers and Electronics in Agriculture, 75, 223–230.
O’Neill, R. V., & King, A. W. (1998). Homage to St. Michael: or why are there so many books on scale? In D. L. Peterson & V. T. Parker (Eds.), Ecological scale: theory and applications (pp. 3–15). New York: Columbia University Press.
Paruelo, J. M., Jobbágy, E. G., & Sala, O. E. (2001). Current distribution of ecosystem functional types in temperate South America. Ecosystems, 4, 683–698.
Rempel, R. S., Kaukinen, D., & Carr, A. P. (2012). Patch Analyst and Patch Grid. Ontario Ministry of Natural Resources. Centre for Northern Forest Ecosystem Research. Thunder Bay, Ontario.
Seppelt, R., Muller, F., Schroder, B., & Volk, M. (2009). Challenges of simulating complex environmental systems at the landscape scale: a controversial dialogue between two cups of espresso. Ecological Modelling, 220, 3481–3489.
Soil Landscapes of Canada Working Group, 2010. Soil Landscapes of Canada version 3.2. Agriculture and Agri-Food Canada.
Statistics Canada, 2014. 2011 Census of Agriculture. Available at: http://www.statcan.gc.ca/ca-ra2011/index-eng.htm.
Therond, O., Hengsdijk, H., Casellas, E., Wallach, D., Adam, M., Belhouchette, H., Oomen, R., Russell, G., Ewert, F., Bergez, J., Janssen, S., Wery, J., & Van Ittersum, M. K. (2011). Using a cropping system model at regional scale: low-data approaches for crop management information and model calibration. Agriculture Ecosystems and Environment, 142(1–2), 85–94.
Uhlig, P. C., & Jordan, J. (1996). A spatial hierarchical framework for the co-management of ecosystems in Canada and the United States for the upper Great Lakes region. Environmental Monitoring and Assessment, 39, 59–73.
van Ittersum, M. K., & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Research, 52, 197–208.
van Ittersum, M. K., & Donatelli, M. (2003). Modelling cropping systems—highlights of the symposium and preface to the special issues. European Journal of Agronomy, 18, 187–394.
Veldkamp, A., Kok, K., De Koning, G. H. J., Schoorl, J. M., Sonneveld, M. P. W., & Verburg, P. H. (2001). Multi-scale system approaches in agronomic research at the landscape level. Soil and Tillage Research, 58, 129–140.
Wardlow, B. D., Egbert, S. L., & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment, 108(3), 290–310.
White, J. W., Corbett, J. D., & Dobermann, A. (2002). Insufficient geographic characterization and analysis in the planning, execution and dissemination of agronomic research? Field Crops Research, 76, 45–54.
Wolf, J., & Van Oijen, M. (2002). Modelling the dependence of European potato yields on changes in climate and CO2. Agricultural and Forest Meteorology, 112, 217–231.
Wu, J. G., & David, J. L. (2002). A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications. Ecological Modelling, 153(1–2), 7–26.
Zhou, Y. C., Narumalani, S., Waltman, W. J., Waltman, S. W., & Palecki, M. A. (2003). A GIS-based spatial pattern analysis model for eco-region mapping and characterization. International Journal of Geographical Information Science, 17(5), 445–462.
Acknowledgments
The research was funded by the Agriculture and Agri-Food Canada Sustainable Agricultural Environment Systems (SAGES) project “Determining interactions between land use and climate to evaluate impacts and adaptations to climate variability and change” and supported by the Program for Key Youth Teachers in Heilongjiang Provincial University “Remote sensing of agricultural hazards within the Corn Zone, Heilongjiang province” (1251G010).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, H., Huffman, T., Liu, J. et al. Integration of multi-disciplinary geospatial data for delineating agroecosystem uniform management zones. Environ Monit Assess 187, 4102 (2015). https://doi.org/10.1007/s10661-014-4102-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-014-4102-1