Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multi-component metrics

Choudhury, Malini Roy and Mellor, Vincent and Das, Sumanta and Christopher, Jack and Apan, Armando ORCID: and Menzies, Neal W. and Chapman, Scott and Dang, Yash P. (2021) Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multi-component metrics. Agricultural Water Management, 255:107007. ISSN 0378-3774


Crop water use can be a useful indicator of in-season crop conditions including health and greenness on sodic soils. However, in-field manual measurements of crop water use can be tedious, time and labour-intensive and may also exhibit large spatial variability, and inefficient for surveying large areas. Here we propose a novel approach to estimate in-field crop water use of wheat using spatial interpolation techniques at critical crop development stages (tillering and close to flowering) on a moderately sodic and a highly sodic site in southern Queensland, Australia. The six spatial interpolation techniques; ordinary kriging, empirical Bayes kriging, inverse distance weighting, spline, local polynomial interpolation, and radial basis function were employed on ground-measured crop water use data and compared to accurately estimate the spatial distribution of crop water use. Initially, the comparison was made using root mean square error, mean absolute error, and coefficient of determination values using cross-validation. For a more clear comparison and to account for complexities of site-specific crop water use distribution, the standardized, multi-component model performance efficiency metrics, i.e. Kling-Gupta efficiency and spatial efficiency were adopted and tested over cross-validation techniques. Results showed that Kling-Gupta efficiency was slightly better at selecting the optimum interpolation models for crop water use estimation at a field scale than the spatial efficiency metric. Spline and local polynomial interpolation were relatively better estimators of crop water use for both the sites at tillering, while the radial basis function and spline were superior close to flowering. The estimated crop water use was positively and closely associated with seasonal GreenSeeker® normalized difference vegetation index data (R2 = 0.49 and 0.39 at tillering; and R2 = 0.71 and 0.62 at close to flowering, for the moderately sodic and highly sodic sites, respectively). The research improves our understanding of selecting appropriate spatial interpolation methods for in-season crop water use estimation that could help detect changes in crop health due to in-field and seasonal variations of crop water use in a sodic soil environment.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Date Deposited: 13 Jul 2021 00:06
Last Modified: 27 Oct 2021 01:58
Uncontrolled Keywords: cross-validation; Kling-Gupta efficiency; spatial efficiency; normalized difference vegetation index; crop development stage; seasonal variations
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling
09 Engineering > 0909 Geomatic Engineering > 090999 Geomatic Engineering not elsewhere classified
Fields of Research (2020): 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490507 Spatial statistics
40 ENGINEERING > 4013 Geomatic engineering > 401399 Geomatic engineering not elsewhere classified
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300206 Agricultural spatial analysis and modelling
Socio-Economic Objectives (2008): B Economic Development > 82 Plant Production and Plant Primary Products > 8205 Winter Grains and Oilseeds > 820507 Wheat
Socio-Economic Objectives (2020): 26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2603 Grains and seeds > 260312 Wheat
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