Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement

Jonathan J Ojeda; Ehsan Eyshi Rezaei; Tomas A Remenyi; Mathew A Webb; Heidi A Webber; Bahareh Kamali; Rebecca M B Harris; Jaclyn N Brown; Darren B Kidd; Caroline L Mohammed; Stefan Siebert; Frank Ewert; Holger Meinke

doi:10.1016/j.scitotenv.2019.135589

Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement

Sci Total Environ. 2020 Mar 25:710:135589. doi: 10.1016/j.scitotenv.2019.135589. Epub 2019 Nov 19.

Authors

Affiliations

¹ Tasmanian Institute of Agriculture, University of Tasmania, Sandy Bay Campus, Hobart, Tasmania 7005, Australia. Electronic address: jonathan.ojeda@utas.edu.au.
² Department of Crop Sciences, University of Göttingen, Von-Siebold-Strasse 8, 37075 Göttingen, Germany.
³ School of Geography and Spatial Sciences, University of Tasmania, Sandy Bay Campus, Hobart, Tasmania 7005, Australia.
⁴ Natural Assets Spatial Intelligence, Department of Primary Industries Parks Water and Environment Tasmania, 171 Westbury Road, Prospect, TAS 7250, Australia; Sydney Institute of Agriculture, The University of Sydney, Biomedical Building C81, 1 Central Avenue, Australian Technology Park, Eveleigh, NSW 2015, Australia.
⁵ Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany.
⁶ CSIRO Agriculture and Food, 15 College Rd., Sandy Bay, Tasmania 7005, Australia.
⁷ Natural Assets Spatial Intelligence, Department of Primary Industries Parks Water and Environment Tasmania, 171 Westbury Road, Prospect, TAS 7250, Australia.
⁸ Tasmanian Institute of Agriculture, University of Tasmania, Sandy Bay Campus, Hobart, Tasmania 7005, Australia.
⁹ Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany; Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany.

PMID: 31787284
DOI: 10.1016/j.scitotenv.2019.135589

Abstract

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAE_c) and soil (DAE_s) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAE_c and DAE_s of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998-2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference (rAD¯) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a rAD¯ of 0.7-12.1%, with high values especially for areas with pronounced differences in elevation. The rAD¯ of soil data was higher (5.6-26.3%) than rAD¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed (rAD¯ = 14.5%) than irrigated conditions (rAD¯ = 3.0%). The rAD¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

Keywords: Data aggregation; Data resolution; Model uncertainty; Regional modelling; Scale; Spatial heterogeneity.

MeSH terms

Agricultural Irrigation
Australia
Climate Change
Data Aggregation
Soil*
Solanum tuberosum*
Tasmania
Water

Substances

Soil
Water