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Predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments

These two-year experiments determined the best predictors of bean growth and fly, root rots, and weed development in different cultivars, planting dates and weed treatments across 256 plots. Root rot diseases were naturally caused by Fusarium solani and Rhizoctonia solani. Weed management treatments...

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Detalles Bibliográficos
Autores principales: Nazer Kakhki, Seyed Hossein, Taghaddosi, Mohamad Vali, Moini, Mohamad Rahim, Naseri, Bita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640973/
https://www.ncbi.nlm.nih.gov/pubmed/36387563
http://dx.doi.org/10.1016/j.heliyon.2022.e11322
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author Nazer Kakhki, Seyed Hossein
Taghaddosi, Mohamad Vali
Moini, Mohamad Rahim
Naseri, Bita
author_facet Nazer Kakhki, Seyed Hossein
Taghaddosi, Mohamad Vali
Moini, Mohamad Rahim
Naseri, Bita
author_sort Nazer Kakhki, Seyed Hossein
collection PubMed
description These two-year experiments determined the best predictors of bean growth and fly, root rots, and weed development in different cultivars, planting dates and weed treatments across 256 plots. Root rot diseases were naturally caused by Fusarium solani and Rhizoctonia solani. Weed management treatments involved: herbicide (Imazethapyr or Trifluralin) application, hand-weeding and control. Parameters estimated by exponential, Gaussian and linear-by-linear models fitted to bean, disease and weed datasets were considered as progress curve elements. Factor analysis detected the most predictive variables to characterize bean growth and production, disease, fly and weed development over season. There were significant correlations between considered plant, disease, pest and weed descriptors. Based on principal component analysis, considered bean-disease-fly-productivity-weed predictors justified 70% of total variance in datasets. Finally, multivariate regression model involving eight selected predictors explained a noticeable part (63%) of yield variations. Such information may improve accuracy of future efforts to monitor bean, disease, fly and weed development, predict bean yield and develop integrative field management programs.
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spelling pubmed-96409732022-11-15 Predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments Nazer Kakhki, Seyed Hossein Taghaddosi, Mohamad Vali Moini, Mohamad Rahim Naseri, Bita Heliyon Research Article These two-year experiments determined the best predictors of bean growth and fly, root rots, and weed development in different cultivars, planting dates and weed treatments across 256 plots. Root rot diseases were naturally caused by Fusarium solani and Rhizoctonia solani. Weed management treatments involved: herbicide (Imazethapyr or Trifluralin) application, hand-weeding and control. Parameters estimated by exponential, Gaussian and linear-by-linear models fitted to bean, disease and weed datasets were considered as progress curve elements. Factor analysis detected the most predictive variables to characterize bean growth and production, disease, fly and weed development over season. There were significant correlations between considered plant, disease, pest and weed descriptors. Based on principal component analysis, considered bean-disease-fly-productivity-weed predictors justified 70% of total variance in datasets. Finally, multivariate regression model involving eight selected predictors explained a noticeable part (63%) of yield variations. Such information may improve accuracy of future efforts to monitor bean, disease, fly and weed development, predict bean yield and develop integrative field management programs. Elsevier 2022-10-31 /pmc/articles/PMC9640973/ /pubmed/36387563 http://dx.doi.org/10.1016/j.heliyon.2022.e11322 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Nazer Kakhki, Seyed Hossein
Taghaddosi, Mohamad Vali
Moini, Mohamad Rahim
Naseri, Bita
Predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments
title Predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments
title_full Predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments
title_fullStr Predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments
title_full_unstemmed Predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments
title_short Predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments
title_sort predict bean production according to bean growth, root rots, fly and weed development under different planting dates and weed control treatments
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640973/
https://www.ncbi.nlm.nih.gov/pubmed/36387563
http://dx.doi.org/10.1016/j.heliyon.2022.e11322
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