From data to harvest: Leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change

Author:

Mahdizadeh Gharakhanlou Navid,Perez Liliana

Funder

Fonds de recherche du Québec – Nature et technologies

Alliance de recherche numérique du Canada

Australian Research Council

Mitacs

Institut de Valorisation des Données

Publisher

Elsevier BV

Reference99 articles.

1. InfoCrop: a dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description;Aggarwal;Agric. Syst.,2006

2. Agriculture and Agri-Food Canada (AAFC), 2014. Native Pollinators and Agriculture in Canada. Retrieved on 13 April 2024, from https://publications.gc.ca/collections/collection_2014/aac-aafc/A59-12-2014-eng.pdf.

3. Agriculture and Agri-Food Canada (AAFC), 2024a. Canadian Crop Yields - Historic Yields of Major Crops (Pre-packaged CSV files). Retrieved on 22 April 2024, from https://open.canada.ca/data/en/dataset/9253a01b-f1d9-4b67-ba98-857667827c7b/resource/b04e8717-50da-4f24-b4a1-d6c4bf283a9e.

4. Yield forecasting of spring maize using remote sensing and crop modeling in Faisalabad-Punjab Pakistan;Ahmad;Journal of the Indian Society of Remote Sensing.,2018

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