Current and next-year cranberry yields predicted from local features and carryover effects.

Wisconsin and Quebec are the world leading cranberry-producing regions. Cranberries are grown in acidic, naturally low-fertility sandy beds. Cranberry fertilization is guided by general soil and tissue nutrient tests in addition to yield target and vegetative biomass. However, other factors such as...

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Autores principales:	Léon Etienne Parent, Reza Jamaly, Amaya Atucha, Elizabeth Jeanne Parent, Beth Ann Workmaster, Noura Ziadi, Serge-Étienne Parent
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d6d30c4eecf044dd82098951a514e44a
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spelling	oai:doaj.org-article:d6d30c4eecf044dd82098951a514e44a2021-12-02T20:05:41ZCurrent and next-year cranberry yields predicted from local features and carryover effects.1932-620310.1371/journal.pone.0250575https://doaj.org/article/d6d30c4eecf044dd82098951a514e44a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250575https://doaj.org/toc/1932-6203Wisconsin and Quebec are the world leading cranberry-producing regions. Cranberries are grown in acidic, naturally low-fertility sandy beds. Cranberry fertilization is guided by general soil and tissue nutrient tests in addition to yield target and vegetative biomass. However, other factors such as cultivar, location, and carbon and nutrient storage impact cranberry nutrition and yield. The objective of this study was to customize nutrient diagnosis and fertilizer recommendation at local scale and for next-year cranberry production after accounting for local factors and carbon and nutrient carryover effects. We collected 1768 observations from on-farm surveys and fertilizer trials in Quebec and Wisconsin to elaborate a machine learning model using minimum datasets. We tested carryover effects in a 5-year Quebec fertilizer experiment established on permanent plots. Micronutrients contributed more than macronutrients to variation in tissue compositions. Random Forest model related accurately current-year berry yield to location, cultivars, climatic indices, fertilization, and tissue and soil tests as features (classification accuracy of 0.83). Comparing compositions of defective and successful tissue compositions in the Euclidean space of tissue compositions, the general across-factor diagnosis differed from the local factor-specific diagnosis. Nutrient standards elaborated in one region could hardly be transposed to another and, within the same region, from one bed to another due to site-specific characteristics. Next-year yield and nutrient adjustment could be predicted accurately from current-year yield and tissue composition and other features, with R2 value of 0.73 in regression mode and classification accuracy of 0.85. Compositional and machine learning methods proved to be effective to customize nutrient diagnosis and predict site-specific measures for nutrient management of cranberry stands. This study emphasized the need to acquire large experimental and observational datasets to capture the numerous factor combinations impacting current and next-year cranberry yields at local scale.Léon Etienne ParentReza JamalyAmaya AtuchaElizabeth Jeanne ParentBeth Ann WorkmasterNoura ZiadiSerge-Étienne ParentPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0250575 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Léon Etienne Parent Reza Jamaly Amaya Atucha Elizabeth Jeanne Parent Beth Ann Workmaster Noura Ziadi Serge-Étienne Parent Current and next-year cranberry yields predicted from local features and carryover effects.
description	Wisconsin and Quebec are the world leading cranberry-producing regions. Cranberries are grown in acidic, naturally low-fertility sandy beds. Cranberry fertilization is guided by general soil and tissue nutrient tests in addition to yield target and vegetative biomass. However, other factors such as cultivar, location, and carbon and nutrient storage impact cranberry nutrition and yield. The objective of this study was to customize nutrient diagnosis and fertilizer recommendation at local scale and for next-year cranberry production after accounting for local factors and carbon and nutrient carryover effects. We collected 1768 observations from on-farm surveys and fertilizer trials in Quebec and Wisconsin to elaborate a machine learning model using minimum datasets. We tested carryover effects in a 5-year Quebec fertilizer experiment established on permanent plots. Micronutrients contributed more than macronutrients to variation in tissue compositions. Random Forest model related accurately current-year berry yield to location, cultivars, climatic indices, fertilization, and tissue and soil tests as features (classification accuracy of 0.83). Comparing compositions of defective and successful tissue compositions in the Euclidean space of tissue compositions, the general across-factor diagnosis differed from the local factor-specific diagnosis. Nutrient standards elaborated in one region could hardly be transposed to another and, within the same region, from one bed to another due to site-specific characteristics. Next-year yield and nutrient adjustment could be predicted accurately from current-year yield and tissue composition and other features, with R2 value of 0.73 in regression mode and classification accuracy of 0.85. Compositional and machine learning methods proved to be effective to customize nutrient diagnosis and predict site-specific measures for nutrient management of cranberry stands. This study emphasized the need to acquire large experimental and observational datasets to capture the numerous factor combinations impacting current and next-year cranberry yields at local scale.
format	article
author	Léon Etienne Parent Reza Jamaly Amaya Atucha Elizabeth Jeanne Parent Beth Ann Workmaster Noura Ziadi Serge-Étienne Parent
author_facet	Léon Etienne Parent Reza Jamaly Amaya Atucha Elizabeth Jeanne Parent Beth Ann Workmaster Noura Ziadi Serge-Étienne Parent
author_sort	Léon Etienne Parent
title	Current and next-year cranberry yields predicted from local features and carryover effects.
title_short	Current and next-year cranberry yields predicted from local features and carryover effects.
title_full	Current and next-year cranberry yields predicted from local features and carryover effects.
title_fullStr	Current and next-year cranberry yields predicted from local features and carryover effects.
title_full_unstemmed	Current and next-year cranberry yields predicted from local features and carryover effects.
title_sort	current and next-year cranberry yields predicted from local features and carryover effects.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d6d30c4eecf044dd82098951a514e44a
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Current and next-year cranberry yields predicted from local features and carryover effects.

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