Nyabako, Tinashe, Mvumi, Brighton M., Stathers, Tanya ORCID: https://orcid.org/0000-0002-7767-6186, Mlambo, Shaw and Mubayiwa, Macdonald (2020) Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: a machine learning approach. Journal of Stored Products Research, 87:101592. ISSN 0022-474X (doi:10.1016/j.jspr.2020.101592)

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Abstract

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and collated with weather data for each of the sites. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern Africa climatic conditions and can be improved with more input data for greater precision models to build decision-support tools for maize-based production systems.

Item Type:	Article
Uncontrolled Keywords:	prediction model, insect grain damage prediction, decision tree, decision-support tools
Subjects:	S Agriculture > S Agriculture (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Natural Resources Institute Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department Faculty of Engineering & Science > Natural Resources Institute > Postharvest Science and Technology Research Group
Last Modified:	08 Apr 2021 01:38
URI:	http://gala.gre.ac.uk/id/eprint/27332

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