Development of a 10-km resolution global soil profile dataset for crop modeling applications

Eunjin Han,Amor V.M Ines,Jawoo Koo

doi:10.1016/j.envsoft.2019.05.012

Abstract

One major challenge in applying crop simulation models at the regional or global scale is the lack of available global gridded soil profile data. We developed a 10-km resolution global soil profile dataset, at 2 m depth, compatible with DSSAT using SoilGrids1km. Several soil physical and chemical properties required by DSSAT were directly extracted from SoilGrids1km. Pedo-transfer functions were used to derive soil hydraulic properties. Other soil parameters not available from SoilGrids1km were estimated from HarvestChoice HC27 generic soil profiles. The newly developed soil profile dataset was evaluated in different regions of the globe using independent soil databases from other sources. In general, we found that the derived soil properties matched well with data from other soil data sources. An ex-ante assessment for maize intensification in Tanzania is provided to show the potential regional to global uses of the new gridded soil profile dataset.

Highlights

We developed a 10-km resolution gridded global soil profile dataset compatible with DSSAT from SoilGrids datasets
Gijsman et al (2002) noted that it is difficult to recommend a certain well-performing pedo-transfer functions (PTFs) due to big discrepancies between methods in estimating water-retention parameters. They found that the PTFs of Saxton et al (1986) performed better compared with other 7 PTFs they examined in terms of estimating field capacity, wilting point and available water holding capacity for certain soil types in the U.S In addition, Romero et al (2012) adapted the PTF approach by Saxton et al (1986) to estimate soil hydraulic properties for the reanalysis of International Soil Reference and Information Centre (ISRIC)-World Inventory of Soil Emission Potentials (WISE) soil database and for the development of DSSAT
They used pedo-transfer functions developed for tropical soils that need input data e.g., bulk density, cation exchange capacity and pH besides the other soil properties that we used in this study

Summary

Introduction

The needs for assessing impacts of global environmental change on agriculture and food security stimulated the use of process-based crop models at the regional to global scales (Basso et al, 2018; Challinor et al, 2010; Jones et al, 2017; Nelson et al, 2010; Rabbinge and Van Diepen, 2000; Rosenzweig et al, 2014; Rosenzweig et al, 2013; Wu et al, 2010; Xiong et al, 2008). Despite the known issues of scaling up point-scale processed-based crop models onto larger spatial scales (Faivre et al, 2004; Hansen and Jones, 2000), they have been applied at larger scales beyond the spatial footprint, which they were initially developed. This is because crop models allow us to better understand the dynamic interactions between crops and the changing environment, which statistical models are limited from doing. There is a critical need to develop practical solutions to bridge this gap

Methods

Results

Discussion

Conclusion