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

Autor: Parent LE; Département des Sols et de Génie Agroalimentaire, Université Laval, Québec, Québec, Canada.; Departamento de Solos, Universidade Federal de Santa Maria, Camobi - Santa Maria, Rio Grande do Sul, Brazil., Jamaly R; Département des Sols et de Génie Agroalimentaire, Université Laval, Québec, Québec, Canada., Atucha A; Department of Horticulture, College of Agriculture and Life Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America., Jeanne Parent E; Département des Sols et de Génie Agroalimentaire, Université Laval, Québec, Québec, Canada., Workmaster BA; Department of Horticulture, College of Agriculture and Life Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America., Ziadi N; Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Québec, Québec, Canada., Parent SÉ; Département des Sols et de Génie Agroalimentaire, Université Laval, Québec, Québec, Canada.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2021 May 10; Vol. 16 (5), pp. e0250575. Date of Electronic Publication: 2021 May 10 (Print Publication: 2021).
DOI: 10.1371/journal.pone.0250575
Abstrakt: 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.
Competing Interests: LEP received funding from Les Atocas de l’Érable Inc., Les Atocas Blandford Inc., La Cannebergière Inc., and the Natural Sciences and Engineering Research Council of Canada (CRDPJ 469358 – 14 and CG-2254, LEP). This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Databáze: MEDLINE
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