Variable-rate in corn sowing for maximizing grain yield

Abstract Sowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that aff...

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Autores principales:	Eder Eujácio da Silva, Fábio Henrique Rojo Baio, Daniel Fernando Kolling, Renato Schneider Júnior, Alex Rogers Aguiar Zanin, Danilo Carvalho Neves, João Vítor Pereira Ferreira Fontoura, Paulo Eduardo Teodoro
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7511166092cc4080b912d4e8a768b2ec
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spelling	oai:doaj.org-article:7511166092cc4080b912d4e8a768b2ec2021-12-02T17:40:06ZVariable-rate in corn sowing for maximizing grain yield10.1038/s41598-021-92238-42045-2322https://doaj.org/article/7511166092cc4080b912d4e8a768b2ec2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92238-4https://doaj.org/toc/2045-2322Abstract Sowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that affect grain yield, in order to generate a model to define the optimum sowing rate as a function of the attributes identified, and determine which vegetative growth indices can be used to predict yield most accurately. The experiment was conducted in Chapadão do Céu-GO in 2018 and 2019 at two different locations. Corn was sown as the second crop after the soybean harvest. The hybrids used were AG 8700 PRO3 and FS 401 PW, which have similar characteristics and an average 135-day cropping cycle. Tested sowing rates were 50, 55, 60, and 65 thousand seeds ha−1. Soil attributes evaluated included pH, calcium, magnesium, phosphorus, potassium, organic matter, clay content, cation exchange capacity, and base saturation. Additionally, we measured the correlation between the different vegetative growth indices and yield. Linear correlations were obtained through Pearson’s correlation network, followed by path analysis for the selection of cause and effect variables, which formed the decision trees to estimate yield and seeding density. Magnesium and apparent electrical conductivity (ECa) were the most important soil attributes for determining sowing density. Thus, the plant population should be 56,000 plants ha−1 to attain maximum yield at ECa values > 7.44 mS m−1. In addition, the plant population should be 64,800 plants ha−1 at values < 7.44 mS m−1 when magnesium levels are greater than 0.13 g kg−1, and 57,210 plants ha−1 when magnesium content is lower. Trial validation showed that the decision tree effectively predicted optimum plant population under the local experimental conditions, where yield did not significantly differ among populations.Eder Eujácio da SilvaFábio Henrique Rojo BaioDaniel Fernando KollingRenato Schneider JúniorAlex Rogers Aguiar ZaninDanilo Carvalho NevesJoão Vítor Pereira Ferreira FontouraPaulo Eduardo TeodoroNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Eder Eujácio da Silva Fábio Henrique Rojo Baio Daniel Fernando Kolling Renato Schneider Júnior Alex Rogers Aguiar Zanin Danilo Carvalho Neves João Vítor Pereira Ferreira Fontoura Paulo Eduardo Teodoro Variable-rate in corn sowing for maximizing grain yield
description	Abstract Sowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that affect grain yield, in order to generate a model to define the optimum sowing rate as a function of the attributes identified, and determine which vegetative growth indices can be used to predict yield most accurately. The experiment was conducted in Chapadão do Céu-GO in 2018 and 2019 at two different locations. Corn was sown as the second crop after the soybean harvest. The hybrids used were AG 8700 PRO3 and FS 401 PW, which have similar characteristics and an average 135-day cropping cycle. Tested sowing rates were 50, 55, 60, and 65 thousand seeds ha−1. Soil attributes evaluated included pH, calcium, magnesium, phosphorus, potassium, organic matter, clay content, cation exchange capacity, and base saturation. Additionally, we measured the correlation between the different vegetative growth indices and yield. Linear correlations were obtained through Pearson’s correlation network, followed by path analysis for the selection of cause and effect variables, which formed the decision trees to estimate yield and seeding density. Magnesium and apparent electrical conductivity (ECa) were the most important soil attributes for determining sowing density. Thus, the plant population should be 56,000 plants ha−1 to attain maximum yield at ECa values > 7.44 mS m−1. In addition, the plant population should be 64,800 plants ha−1 at values < 7.44 mS m−1 when magnesium levels are greater than 0.13 g kg−1, and 57,210 plants ha−1 when magnesium content is lower. Trial validation showed that the decision tree effectively predicted optimum plant population under the local experimental conditions, where yield did not significantly differ among populations.
format	article
author	Eder Eujácio da Silva Fábio Henrique Rojo Baio Daniel Fernando Kolling Renato Schneider Júnior Alex Rogers Aguiar Zanin Danilo Carvalho Neves João Vítor Pereira Ferreira Fontoura Paulo Eduardo Teodoro
author_facet	Eder Eujácio da Silva Fábio Henrique Rojo Baio Daniel Fernando Kolling Renato Schneider Júnior Alex Rogers Aguiar Zanin Danilo Carvalho Neves João Vítor Pereira Ferreira Fontoura Paulo Eduardo Teodoro
author_sort	Eder Eujácio da Silva
title	Variable-rate in corn sowing for maximizing grain yield
title_short	Variable-rate in corn sowing for maximizing grain yield
title_full	Variable-rate in corn sowing for maximizing grain yield
title_fullStr	Variable-rate in corn sowing for maximizing grain yield
title_full_unstemmed	Variable-rate in corn sowing for maximizing grain yield
title_sort	variable-rate in corn sowing for maximizing grain yield
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7511166092cc4080b912d4e8a768b2ec
work_keys_str_mv	AT edereujaciodasilva variablerateincornsowingformaximizinggrainyield AT fabiohenriquerojobaio variablerateincornsowingformaximizinggrainyield AT danielfernandokolling variablerateincornsowingformaximizinggrainyield AT renatoschneiderjunior variablerateincornsowingformaximizinggrainyield AT alexrogersaguiarzanin variablerateincornsowingformaximizinggrainyield AT danilocarvalhoneves variablerateincornsowingformaximizinggrainyield AT joaovitorpereiraferreirafontoura variablerateincornsowingformaximizinggrainyield AT pauloeduardoteodoro variablerateincornsowingformaximizinggrainyield
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Variable-rate in corn sowing for maximizing grain yield

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