Simulating pest and disease damage in wheat process-based crop models

• Ano de defesa: 2021
• Autor(a) principal: Ferreira, Thiago Berton
• Orientador(a): Pavan, Willingthon
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade de Passo Fundo
• Programa de Pós-Graduação: Programa de Pós-Graduação em Computação Aplicada
• Departamento: Instituto de Ciências Exatas e Geociências – ICEG
• País: Brasil
• Palavras-chave em Português: Informática na agricultura; Engenharia de software; Trigo - Doenças e pragas; Simulação (Computadores)
• Área do conhecimento CNPq: METODOLOGIA E TECNICAS DA COMPUTACAO::ENGENHARIA DE SOFTWARE
• Link de acesso: http://tede.upf.br:8080/jspui/handle/tede/2122

Pests and diseases are known for causing damage to wheat (Triticum aestivumL.) crops and reduce plant development. These biotic stresses are often, or always, associated with crop loss which threatens wheat production and security worldwide. Plant disease models can help estimate the impact of pests and diseases on crop growth however, this is still a challenge since it is poorly recognized as one of the main factors that limit yield. The objective of this project was to develop and test a method to simulate wheat pests and diseases through the CSM-NWheat wheat model and study the reduction in crop development and yield due to biotic stress. This study also compromised to develop a dynamic approach using an MPI (Message Passing Interface) interface to couple the same wheat growth model with multiple disease models to analyze the damaging effects of powdery mildew (Blumeria graminis f. sp. tritici), tan spot (Pyrenophora tritici-repentis) and fusarium head blight (Gibberella zeae) on wheat yield. The newly pest-coupled CSM-NWheat can now estimate damage on leaf area index (LAI), leaf mass, stem mass, root mass, seed mass, necrotic leaf area, assimilates, and the whole/complete plant. Case studies demonstrated the model’s capability of simulating losses due to pest and disease infection similarly to the field observed data. Additionally, the multi-model strategy presented a positive effect on simulating crop losses due to fungal infection as well as a new coupling method for wheat models. This model extension enhanced the accuracy of the wheat model and, compared with the field data, the simulated yield and LAI were improved to a great extent.