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dc.contributorStoorvogel, Jetseen_US
dc.contributorClaessens, Lievenen_US
dc.creatorHendriks, Chantalen_US
dc.identifier.citationChantal Hendriks, Jetse Stoorvogel, Lieven Claessens. (26/1/2016). Exploring the challenges with soil data in regional land use analysis. Agricultural Systems, 144, pp. 9-21.en_US
dc.description.abstractOver recent decades, environmentalmodels have gradually replaced traditional, qualitative land evaluation in regional land use analysis (RLUA). This changed the data requirements as the environmental models require quantitative, high resolution and spatially exhaustive data. As resources to collect new data are limited, RLUA often relies on already existing data. These data often do notmeet the data requirements for the environmentalmodels. Hence, a gap developed between the supply and demand of data in RLUA. This study aims to explore and analyse the effect of using different soil datasets in a case study for Machakos and Makueni counties (Kenya). Six soil datasets were available for the study area and showed large differences. For example, average clay percentages varied between 11.7% and 44.4%. The soil datasets were developed under different assumptions on e.g., soil variability. Four assumptionswere verified using a field survey. An ongoing RLUA, the Global Yield Gap Atlas (GYGA) project, was taken as a case study to analyse the effect of using different soil datasets. The GYGA project aims to assess yield gaps defined as the difference between potential or water-limited yields and actual yields. Rain-fed maize is the dominating cropping system in Machakos and Makueni counties. The GYGA project uses soil data for the selection of the most dominant maize growing areas and to simulate water-limited maize yields. The protocols developed by the GYGA project were applied to the six soil datasets. This resulted in the selection of six different maize-growing areas and different water-limited maize yields. Our study clearly demonstrates the large differences between soil datasets. Main challenges with soil data in RLUA are: i) understand the assumptions in soil datasets, ii) create soil datasets that meet the requirements for regional land use analysis, iii) not only rely on legacy soil data but also collect new soil data and iv) validate soil datasets.en_US
dc.publisherElsevier Massonen_US
dc.sourceAgricultural Systems;144,(2016) Pagination 9,21en_US
dc.subjectsystem analysisen_US
dc.subjectlegacy soil dataen_US
dc.subjectcrop growth simulation modelen_US
dc.subjectwater-limited maize yielden_US
dc.titleExploring the challenges with soil data in regional land use analysisen_US
dc.typeJournal Articleen_US
cg.creator.idClaessens, Lieven: 0000-0003-2961-8990en_US
cg.subject.agrovocyield gapen_US
cg.contributor.centerWageningen University & Research Centre - WURen_US
cg.contributor.centerInternational Crops Research Institute for the Semi-Arid Tropics - ICRISATen_US
cg.contributor.crpCRP on Dryland Systems - DSen_US
cg.contributor.funderNot Applicableen_US
cg.coverage.regionEastern Africaen_US
cg.isijournalISI journalen_US
dc.identifier.statusLimited accessen_US
cg.journalAgricultural Systemsen_US

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