&lt;b&gt;Harvest growth stages in soybean cultivars intended for silage

Oliveira, Raquel Dias; Silva, Carla Michelle da; Mielezrski, Fábio; Lima, Josenildo Silva de; Edvan, Ricardo Loiola

doi:10.4025/actascianimsci.v38i4.31837

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…This higher productive potential of TCPY of the hybrid P30F53 is related to its higher values of biomass yield (Table 2). For the P1630 hybrid, maize+soybean intercropping system results in higher crude protein yield in the silage and per area, collaborating with other studies (Oliveira et al, 2016;Sánchez et al, 2010;Stella et al, 2016).…”

Section: Bromatological Traits Of Maize+soybean Silagesupporting

confidence: 67%

Forage Yield and Silage Quality of Intercropped Maize+Soybean With Different Relative Maturity Cycle

Batista¹,

Adami²,

Sartor³

et al. 2018

JAS

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The success of maize+soybean intercrop depends on the correct synchronism between species phenological stages at the silage point. Due to it, the experiment was carried out to evaluate maize+soybean intercrop forage yield and silage quality using crops with different maturity cycle combination. The experiment used a randomized complete block design with a 2 × 3 factorial scheme. Treatments consisted of two maize hybrids (1: P1630YHR-early cycle and 2: middle cycle P30F53VYHR) and two soybean cultivars (P95R51-maturity cycle of 5.1; TMG7062-maturity cycle of 6.2) and one control represented by maize monocrop. Silage harvesting was performed when maize had reached 2/3 milk line stage. Intercropping soybean into maize did not affect its biomass yield. Both soybean cultivars present compatible cycles for ensiling together with maize hybrids, since they were in phenological stages from R5.3 to R7 by the time maize was at its optimum stage for ensiling. There was interaction between species for the soybean biomass yield. Maize hybrid P30F53 produced higher biomass yield than P1630 what also resulted in higher amount of total crude protein yield. Intercrop P1630-P95R51 produced 458 Kg ha-1 of crude protein more than maize monocrop. Maize+soybean intercropping system results in higher silage crude protein percentage and yield per area (Kg of CP ha-1).

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Section: Bromatological Traits Of Maize+soybean Silagesupporting

confidence: 67%

Forage Yield and Silage Quality of Intercropped Maize+Soybean With Different Relative Maturity Cycle

Batista¹,

Adami²,

Sartor³

et al. 2018

JAS

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“…ensiling is recommended at the R6 grain stage, which is characterized as the total grain-filling phase (de Córdova Gobetti et al, 2011;Dias et al, 2010;Oliveira et al, 2016).…”

mentioning

confidence: 99%

Intercropped maize‐soybean silage: Effects on forage yield, fermentation pattern and nutritional composition

et al. 2021

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In Brazil, maize (Zea mays) is the main crop cultivated for silage production due to its excellent dry matter (DM) yield, nutritional composition (high starch content) and adaptability of Brazilian agricultural systems (Daniel et al., 2019). Maize is also an easy forage to ensile due to its low buffering capacity, high sugar content as well as high indigenous lactic acid bacteria counts (Jatkauskas & Vrotniakiene, 2013;Parvin et al., 2010). Ruminant diets formulated with maize silage often needs protein supplements to meet animal requirements. Legume crops, on the other hand, present higher protein content than maize silage (Dewhurst, 2013); in this way, mixing

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“…At these later stages, the grains have completely filled the cavity near the pod and are similar to the pods collected at harvest (Oliveira, Silva, Mielezrski, Lima, & Edvan, 2016).…”

Section: Introductionmentioning

confidence: 58%

<b>Estimating soybean yields with artificial neural networks

et al. 2018

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ABSTRACT. The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI. Estimativa da produtividade de soja com redes neurais artificiais RESUMO. Para estimar a produtividade de muitas culturas, incluído a soja, são utilizados modelos estatísticos complexos, que torna restrito o acesso a essa prática. Uma alternativa a estes modelos é a utilização de sistemas computacionais empregando Redes Neurais Artificiais (RNA). Este trabalho teve por objetivo estimar a produtividade da soja baseada nos hábitos de crescimento, densidade de semeadura e características agronômicas usando RNA do tipo Multilayer Perceptron (MLP). Para isto foram utilizados dados agronômicos da cultura da soja obtidos em experimento conduzido na safra 2013/2014 em Anápolis, Estado de Goiás, Brasil, cujos dados foram normalizados em intervalo compatível para trabalho com RNA e em seguida feito o treinamento de várias RNAs para a escolha da RNA com melhor performance. Após o treinamento das redes, foi realizada a análise de performance para seleção da RNA com a performance mais adequada ao problema. A RNA selecionada apresentou um índice de acerto de 98% com os dados do treinamento e um acerto de 72% com dados de validação. A aplicação das RNAs do tipo MLP nos dados do experimento conduzido demonstram que é possível estimar a produtividade da soja baseando-se nas características agronômicas, hábito de crescimento e densidade populacional por meio da IA. Palavras-chave:Glycine max (L.) Merrill; características agronômicas; modelagem; MLP; perceptron.

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<b>Harvest growth stages in soybean cultivars intended for silage

Cited by 5 publications

References 18 publications

Forage Yield and Silage Quality of Intercropped Maize+Soybean With Different Relative Maturity Cycle

Forage Yield and Silage Quality of Intercropped Maize+Soybean With Different Relative Maturity Cycle

Intercropped maize‐soybean silage: Effects on forage yield, fermentation pattern and nutritional composition

<b>Estimating soybean yields with artificial neural networks

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