Universität zu Köln

Boroughani, Mahdi, Mohammadi, Maziar, Mirchooli, Fahimeh and Fiedler, Stephanie (2022). Assessment of the impact of dust aerosols on crop and water loss in the Great Salt Desert in Iran. Comput. Electron. Agric., 192. OXFORD: ELSEVIER SCI LTD. ISSN 1872-7107

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Abstract

Knowledge of the spatial distribution of dust aerosols and their effects on crops is important for policy formulation and food security. This study aims to investigate the impact of dust source susceptibility areas (DSSA) on the loss of agricultural crop and corresponding water consumption in terms of Water Footprint in the Great Salt Desert, Iran. To this goal, MODIS satellite images during the 2005-2020 period were used to identify dust sources and 135 dust source zones were identified. Machine learning algorithm viz. Random Forest (RF), generalized linear model (GLM), and Artificial neural network (ANN) were tested to reproduce DSSA. The best method was RF and applied to calculate and classify DSSA in five risk levels from very low to very high. The amount of wheat production under high risk of DSSA was estimated using the average crop yield from recent years using agriculture statistics. We calculated the loss of crops and corresponding water consumption for three scenarios, assuming a typical loss of 20, 40, and 60% of the wheat production for better crop loss estimation. Finally, the spatial relationships between wheat farmland and high-risk DSSA were assessed using ordinary least squares regression (OLS) and geographically weighted regression (GWR) at sub-watershed scale. The area of wheat cultivation in high and very high risk of DSSA is 10188.04 km(2), which is 36% of all agricultural land for wheat in the region. Loss of wheat crop to DSSA meant that 1270.58 to 3811 million m(3) water used for the production of wheat were lost, corresponding to 2%, to 7% of lost water compared to the total water consumption for wheat production in the study area.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Boroughani, Mahdi UNSPECIFIED UNSPECIFIED UNSPECIFIED Mohammadi, Maziar UNSPECIFIED UNSPECIFIED UNSPECIFIED Mirchooli, Fahimeh UNSPECIFIED UNSPECIFIED UNSPECIFIED Fiedler, Stephanie UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-577437
DOI:	10.1016/j.compag.2021.106605
Journal or Publication Title:	Comput. Electron. Agric.
Volume:	192
Date:	2022
Publisher:	ELSEVIER SCI LTD
Place of Publication:	OXFORD
ISSN:	1872-7107
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language GEOGRAPHICALLY WEIGHTED REGRESSION; BASIN Multiple languages Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/57743