Exploring the design and development of an early-warning forecasting system for enhanced biovigilance to crop diseases in Canada
Citation
Newlands, N.K. 2023. Exploring the design and development of an early-warning forecasting system for enhanced biovigilance to crop diseases in Canada. Proceedings of the Annual Symposium “Wicked Weather, Wicked Pests” of the Professional Pest Management Association of British Columbia (PPMABC), Feb 21, 2023. Kwantlen Polytechnic University, Langley, BC.
Résumé en langage clair
Diseases are widespread and destructive, causing severe yield quantity and quality losses, increasing agricultural production costs, and cumulative human health and environmental risks and impacts. Climate change is anticipated to exacerbate current losses, by introducing complexity and uncertainty on our ability to adequately inform, and effectively protect, food and energy crops to ensure human populations are food, nutrition, water, and energy secure. Crop disease forecasting is advancing rapidly as precision agriculture surveillance and detection technology and Earth observation (EO) satellites are increasingly being used to automate the collection of digital agriculture ‘big’ data. This data is enabling artificial intelligence (AI), predictive algorithms and models to be trained, validated, and improved for forecasting. This presentation showcases and discusses recent AAFC-led modeling research aimed at informing the design and development of an early-warning system, that integrates pathogen monitoring and disease risk forecasting, for enhanced biovigilance to crop diseases in Canada.
Résumé
Diseases are widespread and destructive, causing severe yield quantity and quality losses, increasing agricultural production costs, and cumulative human health and environmental risks and impacts. Climate change is anticipated to exacerbate current losses, by introducing complexity and uncertainty on our ability to adequately inform, and effectively protect, food and energy crops to ensure human populations are food, nutrition, water, and energy secure. Crop disease forecasting is advancing rapidly as precision agriculture surveillance and detection technology and Earth observation (EO) satellites are increasingly being used to automate the collection of digital agriculture ‘big’ data. This data is enabling artificial intelligence (AI), predictive algorithms and models to be trained, validated, and improved for forecasting. This presentation showcases and discusses recent AAFC-led modeling research aimed at informing the design and development of an early-warning system, that integrates pathogen monitoring and disease risk forecasting, for enhanced biovigilance to crop diseases in Canada.