The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A Land System representation for global assessments and land‐use modeling
Open accessAbstract
Current global scale land‐change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land‐use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human‐environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi‐)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land‐use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land‐change models that include the human drivers of land change are best parameterized at sub‐global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Global Consequences of Land Use
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., et al. (2005) · Science · doi:10.1126/science.1111772 · Cited by 13,272
Proximate Causes and Underlying Driving Forces of Tropical Deforestation
GEIST, H. J., & LAMBIN, E. F. (2002) · BioScience · doi:10.1641/0006-3568(2002)052[0143:pcaudf]2.0.co;2 · Cited by 3,123
The emergence of land change science for global environmental change and sustainability
Turner, B. L., Lambin, E. F., & Reenberg, A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0704119104 · Cited by 2,216
Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000
Ramankutty, N., Evan, A. T., Monfreda, C., & Foley, J. A. (2008) · Global Biogeochemical Cycles · doi:10.1029/2007gb002952 · Cited by 1,942
Forest transitions: towards a global understanding of land use change
Rudel, T. K., Coomes, O. T., Moran, E., Achard, F., Angelsen, A., Xu, J., & Lambin, E. (2005) · Global Environmental Change · doi:10.1016/j.gloenvcha.2004.11.001 · Cited by 1,387
‘Roads, land use, and deforestation: a spatial model applied to Belize’
Chomitz, K. M., & Gray, D. A. (1996) · The World Bank Economic Review · doi:10.1093/wber/10.3.487 · Cited by 729
Agricultural intensification and changes in cultivated areas, 1970–2005
Rudel, T. K., Schneider, L., Uriarte, M., Turner, B. L., DeFries, R., Lawrence, D., Geoghegan, J., Hecht, S., Ickowitz, A., Lambin, E. F., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812540106 · Cited by 611
The yield gap of global grain production: A spatial analysis
Neumann, K., Verburg, P. H., Stehfest, E., & Müller, C. (2010) · Agricultural Systems · doi:10.1016/j.agsy.2010.02.004 · Cited by 587 · cited 3×
“In agricultural systems, drivers of intensification are manifold (Lambin et al., 2001; Keys & McConnell, 2005; Neumann et al., 2010).”
BackgroundMethodSelf-citationLotze‐Campen, H., Müller, C., Bondeau, A., Rost, S., Popp, A., & Lucht, W. (2008) · Agricultural Economics · doi:10.1111/j.1574-0862.2008.00336.x · Cited by 325
Cited by
Samberg, L. H., Gerber, J. S., Ramankutty, N., Herrero, M., & West, P. C. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/12/124010 · Cited by 451
van Asselen, S., & Verburg, P. H. (2013) · Global Change Biology · doi:10.1111/gcb.12331 · Cited by 411
Self-citationBiodiversity at risk under future cropland expansion and intensification
Kehoe, L., Romero-Muñoz, A., Polaina, E., Estes, L., Kreft, H., & Kuemmerle, T. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0234-3 · Cited by 395
Challenges and opportunities in mapping land use intensity globally
Kuemmerle, T., Erb, K., Meyfroidt, P., Müller, D., Verburg, P. H., Estel, S., Haberl, H., Hostert, P., Jepsen, M. R., Kastner, T., et al. (2013) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2013.06.002 · Cited by 381
Self-citationThe impacts of warfare and armed conflict on land systems
Baumann, M., & Kuemmerle, T. (2016) · Journal of Land Use Science · doi:10.1080/1747423x.2016.1241317 · Cited by 167
Metadata
- Authors
- van Asselen, S., & Verburg, P. H.
- Venue
- Global Change Biology
- Publisher
- Wiley
- Published
- 2012-07-10
- Volume
- 18
- Issue
- 10
- Pages
- 3125-3148
- ISSN
- 1354-1013 (ISSN-L)
- 1365-2486
- OpenAlex
- W2158704757
- Cited by
- 197
- References
- 9
- FWCI
- 8.60
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Land System representation for global assessments and land‐use modeling. https://app.cochat.ai/papers/10.1111/j.1365-2486.2012.02759.x
- The paper
- van Asselen, S., & Verburg, P. H. (2012). A Land System representation for global assessments and land‐use modeling. Global Change Biology. https://doi.org/10.1111/j.1365-2486.2012.02759.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
