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Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean (Glycine max [L.] Merrill) Cultivar Augusta

cris.virtual.author-orcid0000-0003-3721-6473
cris.virtual.author-orcid0000-0002-2214-406X
cris.virtual.author-orcid0000-0002-2222-6866
cris.virtual.author-orcid0000-0001-9442-3124
cris.virtual.author-orcid0000-0002-6824-156X
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid3fe42726-36c4-478a-818f-a10f72d4a6ef
cris.virtualsource.author-orcidf05e8789-119d-453f-9d8c-5ae717b7917e
cris.virtualsource.author-orcid09c81af2-c2b2-424c-b2ec-4cda600883bf
cris.virtualsource.author-orcide8127d2e-9d17-4f80-89ba-4750ab4c308d
cris.virtualsource.author-orcid6722cf4e-e1bd-4281-81fb-a6c1618817c8
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enGenotype and weather conditions play crucial roles in determining the volume and stability of a soybean yield. The aim of this study was to identify the key meteorological factors affecting the harvest date (model M_HARV) and yield of the soybean variety Augusta (model M_YIELD) using a neural network sensitivity analysis. The dates of the start of flowering and maturity, the yield data, the average daily temperatures and precipitation were collected, and the Selyaninov hydrothermal coefficients were calculated during a fifteen-year study (2005–2020 growing seasons). During the experiment, highly variable weather conditions occurred, strongly modifying the course of phenological phases in soybean and the achieved seed yield of Augusta cultivar. The harvesting of mature soybean seeds took place between 131 and 156 days after sowing, while the harvested yield ranged from 0.6 t·ha−1 to 2.6 t·ha−1. The sensitivity analysis of the MLP neural network made it possible to identify the factors which had the greatest impact on the tested dependent variables among all the analyzed factors. It was revealed that the variables assigned ranks 1 and 2 in the sensitivity analysis of the neural network forming the M_HARV model were total rainfall in the first decade of June and the first decade of August. The variables with the highest impact on the Augusta soybean seed yield (model M_YIELD) were the mean daily air temperature in the second decade of May and the Seljaninov coefficient values calculated for the sowing–flowering date period.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Bioinżynierii
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.affiliation.instituteKatedra Genetyki i Hodowli Roślin
dc.contributor.authorNiedbała, Gniewko
dc.contributor.authorKurasiak-Popowska, Danuta
dc.contributor.authorPiekutowska, Magdalena
dc.contributor.authorWojciechowski, Tomasz
dc.contributor.authorKwiatek, Michał Tomasz
dc.contributor.authorNawracała, Jerzy
dc.date.access2025-12-09
dc.date.accessioned2025-12-10T09:51:27Z
dc.date.available2025-12-10T09:51:27Z
dc.date.copyright2022-05-25
dc.date.issued2022
dc.description.abstract<jats:p>Genotype and weather conditions play crucial roles in determining the volume and stability of a soybean yield. The aim of this study was to identify the key meteorological factors affecting the harvest date (model M_HARV) and yield of the soybean variety Augusta (model M_YIELD) using a neural network sensitivity analysis. The dates of the start of flowering and maturity, the yield data, the average daily temperatures and precipitation were collected, and the Selyaninov hydrothermal coefficients were calculated during a fifteen-year study (2005–2020 growing seasons). During the experiment, highly variable weather conditions occurred, strongly modifying the course of phenological phases in soybean and the achieved seed yield of Augusta cultivar. The harvesting of mature soybean seeds took place between 131 and 156 days after sowing, while the harvested yield ranged from 0.6 t·ha−1 to 2.6 t·ha−1. The sensitivity analysis of the MLP neural network made it possible to identify the factors which had the greatest impact on the tested dependent variables among all the analyzed factors. It was revealed that the variables assigned ranks 1 and 2 in the sensitivity analysis of the neural network forming the M_HARV model were total rainfall in the first decade of June and the first decade of August. The variables with the highest impact on the Augusta soybean seed yield (model M_YIELD) were the mean daily air temperature in the second decade of May and the Seljaninov coefficient values calculated for the sowing–flowering date period.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,7
dc.description.number6
dc.description.points100
dc.description.versionfinal_published
dc.description.volume12
dc.identifier.doi10.3390/agriculture12060754
dc.identifier.eissn2077-0472
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/6314
dc.identifier.weblinkhttps://www.mdpi.com/2077-0472/12/6/754
dc.languageen
dc.relation.ispartofAgriculture (Switzerland)
dc.relation.pagesart. 754
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.ensoybean
dc.subject.enyield
dc.subject.ensensitivity analysis
dc.subject.envegetation period
dc.subject.enweather conditions
dc.subject.enartificial neural network
dc.titleApplication of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean (Glycine max [L.] Merrill) Cultivar Augusta
dc.title.volumeSpecial Issue Digital Innovations in Agriculture
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.volume12