Amplitude térmica para germinação de conídios de Drechslera tritici-repentis

Tonin, Rosane Baldiga; Ranzi, Camila; Câmera, Juliane Nicolodi; Forcelini, Carlos Alberto; Reis, Erlei Melo

doi:10.1590/0100-5405/1881

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…These affect the rate of organic matter decomposition in contact with the soil and affect the efficiency of nitrogen use by the plant (Acosta et al, 2014). Air temperature is also a determining factor in the yield development, since it acts as a catalyst for biological processes, which is why plants require a minimum and maximum temperature for normal physiological activities (Guarienti et al, 2004;Tonin et al, 2014a;Tonim et al, 2014b). Arenhardt et al (2015), highlight that longs rainfall season reduce the efficiency of harnessing sunlight and nutrients to photosynthesis, interfering in the development, productivity and quality of grains during harvest (Castro, 2012;Mamann et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence by Artificial Neural Networks to Simulate Oat (Avena sativa L.) Grain Yield Through the Growing Cycle

Scremin

Silva

Carvalho

et al. 2020

JAS

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Artificial neural networks simulating oat grain yield throughout the crop cycle, can represent an innovative proposal regarding management and decision making, reducing costs and maximizing profits. The objective of the study is to develop biomathematical models via artificial neural networks, capable of predicting the productivity of oat grains by meteorological variables, nitrogen management and biomass obtained throughout the development cycle, making it possible to plan more efficient and sustainable managements. In each cultivation system (soybeans/oats; maize/oats), two experiments were carried out in 2017 and 2018, one for analyzing grain yield and the other for cutting every 30 days to obtain biomass. The experiments were conducted in a randomized block design with four replications for four levels of N-fertilizer (0, 30, 60 and 120 kg ha-1), applied in the stage of the 4th expanded leaf. The use of the artificial neural network makes it possible to predict grain yield by harvesting the biomass obtained at any stage of oat development, together with the handling of the nitrogen dose and meteorological information during cultivation. Therefore, a new tool to aid the simulation of oat productivity throughout the cycle, facilitating faster decision making for more efficient and sustainable management with the crop.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence by Artificial Neural Networks to Simulate Oat (Avena sativa L.) Grain Yield Through the Growing Cycle

Scremin

Silva

Carvalho

et al. 2020

JAS

View full text Add to dashboard Cite

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“…In addition to precipitation, the air temperature also acts with a strong action on the nitrogen utilization dynamics and expression of productivity. Temperature acts as a catalyst for biological processes, which is why plants require a minimum and maximum temperature for normal physiological activities (Tonin et al, 2014;Marolli et al, 2017). In cereals, such as oats and wheat, the milder temperatures and quality of solar radiation throughout the day, favor the tillering and filling of grains, with direct effects on productivity (Castro et al, 2012;Arenhardt et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Time of nitrogen supply in yield, industrial and chemical quality of oat grains

Kraisig

Silva

Carvalho

et al. 2020

Rev. bras. eng. agríc. ambient.

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The timing of nitrogen application in oats can improve the efficiency of use of the nutrient and the expression of productivity and quality of grains. The objective of the study was to indicate the best time of application of nitrogen in oats, considering different conditions of nitrogen dose in the sowing/topdressing in the expression of productivity and the effects on the indicators of industrial and chemical quality of grains, considering the main succession systems of cereal cultivation in Brazil. The experiment was conducted in 2018, in Augusto Pestana, RS, Brazil. The design was randomized blocks with four repetitions, in a 3 × 4 factorial scheme, with three doses of nitrogen at sowing (0, 30 and 60 kg ha-1), changing the topdressing dose by the total supply of 70 and 100 kg ha-1 in succession system soybean/oats and corn/oats, respectively, with the timings of supply in topdressing at 0, 10, 30 and 60 days after emergence. The total doses were defined looking for grain yield expectations of 4000 kg ha-1. In the management of nitrogen in oats, the absence of the nutrient at sowing and application of the total dose in topdressing around 30 days after emergence provides advantageous results, improving productivity and indicators of industrial and chemical quality of grains.

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“…The temperature, sunlight and solar radiation also influence the productivity (Souza et al, 2013). The temperature Romitti et al 907 acts as a catalyst of the biological process, reason why the plants require the minimum and maximum temperature for normality of the physiological activity (Tonin et al, 2014). In oat, the favorable weather is described as that with milder temperatures and quality of solar radiation in favoring the tillering and grain filling.…”

Section: Resultsmentioning

confidence: 99%

The sowing density on oat productivity indicators

Romitti¹,

Dornelles²,

Silva³

et al. 2017

Afr. J. Agric. Res.

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The sowing density adjustment in oatcan maximize the productivity expression.The aim of this study is to define the behavior of productivity expression of biomass, grains, straw andharvest indexthrough increasing sowing density in the main biotype cultivated in Southern Brazil. It proposesthe possibility of indicating higher sowing density to the productivity maximization of biomass and grains.With the densityadjusted to the grain productivity to simulate the reflexes on the biological and straw productivity and harvest index compared to recommendeddensity, considering high and reduced tillering cultivars in different succession systems.The study was carried out in 2013, 2014 and 2015 in randomized blockdesign with four replications in a 4 × 2 factorial scheme, for sowing density (100, 200, 300, 600 and 900 m -2 ) and oat cultivars (Brisasul and URS Taura), respectively, in the corn/oat and soybean/oat succession system.With the increase in sowing density, the biological and straw productivity evidence alinear behavior and the grain productivity and harvest index ofquadratic behavior, regardless of the cultivar,agricultural year and succession system. It is possible to indicate a higher sowing density to the biomass and grain productivity maximization with sowing density close to 500 seeds m -2 in the main succession systems.In high and reduced tillering cultivars, the adjusted density compared tothe recommended increasedthe biological and straw productivity, regardless of agricultural year and succession systems.

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Amplitude térmica para germinação de conídios de Drechslera tritici-repentis

Cited by 6 publications

References 6 publications

Artificial Intelligence by Artificial Neural Networks to Simulate Oat (Avena sativa L.) Grain Yield Through the Growing Cycle

Artificial Intelligence by Artificial Neural Networks to Simulate Oat (Avena sativa L.) Grain Yield Through the Growing Cycle

Time of nitrogen supply in yield, industrial and chemical quality of oat grains

The sowing density on oat productivity indicators

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