Record Details

Exploring the challenges with soil data in regional land use analysis

MELSpace

View Archive Info
 
 
Field Value
 
Title Exploring the challenges with soil data in regional land use analysis
 
Creator Hendriks, Chantal
 
Contributor Stoorvogel, Jetse
Claessens, Lieven
 
Subject system analysis
legacy soil data
crop growth simulation model
water-limited maize yield
 
Description Over 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.
 
Date 2016-01-26
2017-02-08T23:04:00Z
2017-02-08T23:04:00Z
 
Type Journal Article
 
Identifier http://oar.icrisat.org/id/eprint/9476
https://mel.cgiar.org/reporting/download/hash/FrpXm23y
Chantal 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.
https://hdl.handle.net/20.500.11766/5594
Limited access
 
Language en
 
Rights CC-BY-NC-4.0
 
Format PDF
 
Publisher Elsevier Masson
 
Source Agricultural Systems;144,(2016) Pagination 9,21