Integrating socio-economic and biophysical assessments using a land use allocation model

This work is devoted to bridging the gap between large‐area, economically driven macromodels such as the Canadian Regional Agriculture Model (CRAM) and small‐area biophysically based process models used in environmental assessments through the development of a Land Use Allocation Model (LUAM). LUAM...

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Main Authors: Du, Y., Huffman, T., Toure, S., Feng, F., Gameda, Samuel, Green, M., Liu, T., Shi, X.
Format: Journal Article
Language:Inglés
Published: Wiley 2013
Subjects:
Online Access:https://hdl.handle.net/10568/152754
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author Du, Y.
Huffman, T.
Toure, S.
Feng, F.
Gameda, Samuel
Green, M.
Liu, T.
Shi, X.
author_browse Du, Y.
Feng, F.
Gameda, Samuel
Green, M.
Huffman, T.
Liu, T.
Shi, X.
Toure, S.
author_facet Du, Y.
Huffman, T.
Toure, S.
Feng, F.
Gameda, Samuel
Green, M.
Liu, T.
Shi, X.
author_sort Du, Y.
collection Repository of Agricultural Research Outputs (CGSpace)
description This work is devoted to bridging the gap between large‐area, economically driven macromodels such as the Canadian Regional Agriculture Model (CRAM) and small‐area biophysically based process models used in environmental assessments through the development of a Land Use Allocation Model (LUAM). LUAM is designed to enable environmental assessments of economic scenarios to be conducted by allocating crop area changes predicted for large areas by CRAM to much smaller Soil Landscapes of Canada (SLC) polygons through an optimization method based on land capability, relative crop productivity and current land use. To develop the procedures, we used linear programming to optimize crop production for large areas under current commodity prices and land productivity ratings and then allocated the results to much smaller soil‐landscape polygons based on land capability. To assess the validity of our prototype LUAM, we compared the predicted crop areas with actual crop data from the Census of Agriculture using the method of cumulative residuals (MCR). We concluded that this version of the LUAM model can predict the location of land use to some extent, but requires further refinement. The potential for further development of LUAM using the Land Suitability Rating System (LSRS) is discussed.
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spelling CGSpace1527542024-11-15T08:52:40Z Integrating socio-economic and biophysical assessments using a land use allocation model Du, Y. Huffman, T. Toure, S. Feng, F. Gameda, Samuel Green, M. Liu, T. Shi, X. land use agricultural policies soil models land allocation optimization methods This work is devoted to bridging the gap between large‐area, economically driven macromodels such as the Canadian Regional Agriculture Model (CRAM) and small‐area biophysically based process models used in environmental assessments through the development of a Land Use Allocation Model (LUAM). LUAM is designed to enable environmental assessments of economic scenarios to be conducted by allocating crop area changes predicted for large areas by CRAM to much smaller Soil Landscapes of Canada (SLC) polygons through an optimization method based on land capability, relative crop productivity and current land use. To develop the procedures, we used linear programming to optimize crop production for large areas under current commodity prices and land productivity ratings and then allocated the results to much smaller soil‐landscape polygons based on land capability. To assess the validity of our prototype LUAM, we compared the predicted crop areas with actual crop data from the Census of Agriculture using the method of cumulative residuals (MCR). We concluded that this version of the LUAM model can predict the location of land use to some extent, but requires further refinement. The potential for further development of LUAM using the Land Suitability Rating System (LSRS) is discussed. 2013-03 2024-10-01T13:55:09Z 2024-10-01T13:55:09Z Journal Article https://hdl.handle.net/10568/152754 en Limited Access Wiley Du, Y.; Huffman, T.; Toure, S.; Feng, F.; Gameda, Samuel; Green, M.; Liu, T.; Shi, X. 2013. Integrating socio-economic and biophysical assessments using a land use allocation model. Soil Use and Management 29(1): 140-149. https://doi.org/10.1111/sum.12018
spellingShingle land use
agricultural policies
soil
models
land allocation
optimization methods
Du, Y.
Huffman, T.
Toure, S.
Feng, F.
Gameda, Samuel
Green, M.
Liu, T.
Shi, X.
Integrating socio-economic and biophysical assessments using a land use allocation model
title Integrating socio-economic and biophysical assessments using a land use allocation model
title_full Integrating socio-economic and biophysical assessments using a land use allocation model
title_fullStr Integrating socio-economic and biophysical assessments using a land use allocation model
title_full_unstemmed Integrating socio-economic and biophysical assessments using a land use allocation model
title_short Integrating socio-economic and biophysical assessments using a land use allocation model
title_sort integrating socio economic and biophysical assessments using a land use allocation model
topic land use
agricultural policies
soil
models
land allocation
optimization methods
url https://hdl.handle.net/10568/152754
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