Multi-source spatial data-based invasion risk modeling of Striga (Striga asiatica) in Zimbabwe |
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| Authors: | Bester Tawona Mudereri Elfatih Mohamed Abdel-Rahman Timothy Dube Tobias Landmann Zeyaur Khan Emily Kimathi |
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| Institution: | 1. International Centre of Insect Physiology and Ecology (icipe), Nairobi, Kenya;2. Department of Earth Sciences, University of Western Cape, Bellville, South Africabmudereri@icipe.org https://orcid.org/0000-0001-9407-7890;4. Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum, North Sudanhttps://orcid.org/0000-0002-5694-0291;5. Department of Earth Sciences, University of Western Cape, Bellville, South Africahttps://orcid.org/0000-0003-3456-8991;6. RSS-Remote Sensing Solutions Gmbh, Munich, Germany;7. International Centre of Insect Physiology and Ecology (icipe), Nairobi, Kenyahttps://orcid.org/0000-0002-3548-7563 |
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| Abstract: | ABSTRACTMonitoring of destructive invasive weeds such as those from the genus Striga requires accurate, near real-time predictions and integrated assessment techniques to enable better surveillance and consistent assessment initiatives. Thus, in this study, we predicted the potential ecological niche of Striga (Striga asiatica) weed in Zimbabwe, to identify and understand its propagation and map potentially vulnerable cropping areas. Vegetation phenology from remote sensing, bioclimatic and other environmental variables (i.e. cropping system, edaphic, land surface temperature, and terrain) were used as predictors. Six machine learning modeling techniques and the ensemble model were evaluated on their suitability to predict current and future Striga weed distributional patterns. The mentioned predictors (n = 40) were integrated into six models with “presence-only” training and evaluation data, collected in Zimbabwe over the period between the 12th and 28th of March 2018. The area under the curve (AUC) and true skill statistic (TSS) were used to measure the performance of the Striga modeling framework. The results showed that the ensemble model had the strongest Striga occurrence predictive power (AUC = 0.98; TSS = 0.93) when compared to the other modeling algorithms. Temperature seasonality (Bio4), the maximum temperature of the warmest month (Bio5) and precipitation seasonality (Bio15) were determined to be the most dominant bioclimatic variables influencing Striga occurrence. “Start of the season” and “season minimum value” of the “Enhanced Vegetation Index base value” were the most relevant remote sensing-based variables. Based on projected climate change scenarios, the study showed that up to 2050, the suitable area for Striga propagation will increase by ~ 0.73% in Zimbabwe. The present work demonstrated the importance of integrating multi-source data in predicting possible crop production restraints due to weed propagation. The results can enhance national preparedness and management strategies, specifically, if the current and future risk areas can be identified for early intervention and containment |
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| Keywords: | Climate variability food security machine learning niche modeling remote sensing sub-Saharan Africa Striga weeds |
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![ORCID Icon]({"type":"image","src":"/templates/jsp/images/orcid.png"} "ORCID Icon")
https://orcid.org/0000-0001-9407-7890;4. Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum, North Sudan