Effects of supplementation on production and age anticipation at first mating of ewe lambs in tropical pastures
Thais Fernanda Farias de Souza ArcoCamila Celeste Brandão Ferreira ÍtavoLuís Carlos Vinhas ÍtavoMaría Inés Lênz SouzaAlexandre Menezes DiasGelson dos Santos DifanteEliane Vianna da Costa e SilvaFernando de Almeida BorgesDyego Gonçalves Lino BorgesKedma Leonora da Silva Monteiro FerelliBruna Junqueira RodriguesAline Aparecida da Silva MiguelC. de GodoyÉvelyn Silva de Melo Soares
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Brachiaria
Texel
Completely randomized design
The morphogenic characteristics of Brachiaria brizantha and Brachiaria decumbens were studied in function of different fertilization. The study was conducted in a green house evaluating two Brachiaria cultivars (B. brizantha cv. Marandu and B. decumbens cv. Basilisk) and five different fertilizations (no fertilizer, P, N, NP and NK). The assay was conducted in a 2 x 5 factorial scheme in a completely randomized design, with four repetitions. The morphogenic evaluations included leaf emergence, leaf elongation rates and filocron. The answer of the cultivars was quite expressive to the studied variables relative to nitrogen supply and its combination with phosphorus and potassium. The Basilisk grass was more productive than Marandu grass. The nitrogen importance as a tool to manipulate plant structure was evidenced.
Brachiaria
Completely randomized design
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Foi conduzido um experimento com o objetivo de avaliar o comportamento de bezerros em pastagens de Brachiaria brizantha e Brachiaria decumbens, no sistema de lotação contínua com taxa de lotação variável. O delineamento experimental foi o inteiramente casualizado, com dois pastos representando os tratamentos, cada um com cinco repetições. O período experimental foi de 30 dias, sendo 20 para adaptação dos animais e 10 para avaliações, que consistiram de três, com duração de 24h cada, em intervalos de cinco dias. Os bezerros pastejaram menos tempo no pasto de Brachiaria brizantha, com tempo de 9,75h, enquanto que o pastejo na Brachiaria decumbens foi de 11,3h. Comportamento diferente foi observado para o tempo de ruminação: 6,8 e 6,4 para os pastos de Brachiaria brizantha e Brachiaria decumbens, respectivamente. O número de bocados por minuto foi menor para a Brachiaria decumbens, 31,15, enquanto que, para a Brachiaria brizantha, os bezerros pastejaram com uma taxa de bocados de 34,91 bocados por minuto. Os bezerros tiveram um ganho de peso médio diário superior no pasto de Brachiaria brizantha (390g dia-1), enquanto que, para a Brachiaria decumbens, o ganho diário foi de 340g dia-1.
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This study aimed to carry out the morphogenic, structural, and bromatological characterization of Brachiaria humidicola cv. BRS Tupi. A completely randomized design was adopted. The treatments consisted of harvesting the forage at 14, 28, 42, and 56 days. Leaf appearance rate, leaf elongation rate, and phyllochron were similar among the treatments (P > 0.05). However, leaf life span varied among treatments (P < 0.05). Dry matter exhibited linear behavior (P < 0.05) whereas mineral matter, crude protein, neutral detergent fiber, acid detergent fiber, and lignin had quadratic behavior. Brachiaria humidicola cv. BRS Tupi exhibits the best forage potential for animal feed between 28 and 42 days of growth.
Brachiaria
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Neutral Detergent Fiber
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Brachiaria (signalgrass) is now the most widely used tropical grass genus in Central and South America. However, Brachiaria spp. can cause hepatogenous photosensitization in livestock. Steroidal saponins, specifically protodioscin, present in Brachiaria spp. may be responsible for liver injury and subsequent photosensitization. The objective of this study was to determine the effect of ensiling Brachiaria decumbens and Brachiaria brizantha or making hay from Brachiaria decumbens on the concentrations of steroidal saponin in these grasses. Brachiaria grass had no detectable levels of the saponin protodioscin after 24 days of ensiling. In addition, in Brachiaria decumbens, the concentration of the protodioscin decreased 48% over the first three days after haymaking and then remained constant. These results suggest that livestock consuming Brachiaria either as silage or hay may have reduced risk of intoxication by protodioscin.
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This study aimed to evaluate the production components of aboveground and total root stock biomass of Brachiaria pastures of different ages after renovation. Three B.brizantha pastures, with one, seven, and nine years after renovation through the Barreirao system, and one B. decumbens pasture twenty years after traditional renovation were evaluated. The areas were located at Goiânia, Brazil (16o35'12;S, 49o21'14;W, 730 m). The one year after renovation pasture showed the highest productivity, 197% higher than the twenty year after renovation pasture. With increasing pasture renovation age, there was a production and regrowth rate decrease due to the degradation process. This aspect may be observed in the root system development, since the total root productivity to 100 cm depth was reduced as pasture age increased, with 9.14 Mg ha-1, 4.87 Mg ha-1, and 2.90 Mg ha-1 for pastures of seven, nine, and twenty years after renovation, respectively. The one year pasture did not show appreciable root contribution. The Brachiaria regrowth pasture was found to be dependent on system N availability; hence, the litter production showed to be a good pasture degradation indicator. The N stocks down to 100 cm depth were of 7.54 Mg ha-1, 8.70 Mg ha-1, and 9.47 Mg ha-1, in the pastures with one, seven, and twenty years, respectively. These values were much lower than those in the nine year after renovation pasture (14.2 Mg ha-1). KEY-WORDS: Brachiaria; pastures; roots; litter.
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Element-based textures are a kind of texture formed by nameable elements, the texels [1], distributed according to specific statistical distributions; it is of primary importance many sectors, namely textile, fashion and interior design industry. State-of-theart texture descriptors fail to properly characterize element-based texture, so we present Texel-Att to fill this gap. Texel-Att is the first fine-grained, attribute-based representation and classification framework for element-based textures. It first individuates texels, characterizing them with individual attributes; subsequently, texels are grouped and characterized through layout attributes, which give the Texel-Att representation. Texels are detected by a Mask-RCNN, trained on a brand-new element-based texture dataset, ElBa, containing 30K texture images with 3M fully-annotated texels. Examples of individual and layout attributes are exhibited to give a glimpse on the level of achievable graininess. In the experiments, we present detection results to show that texels can be precisely individuated, even on textures in the wild; to this sake, we individuate the element-based classes of the Describable Texture Dataset (DTD), where almost 900K texels have been manually annotated, leading to the Element-based DTD (E-DTD). Subsequently, classification and ranking results demonstrate the expressivity of Texel-Att on ElBa and E-DTD, overcoming the alternative features and relative attributes, doubling the best performance some cases; finally, we report interactive search results on ElBa and E-DTD: with Texel-Att on the E-DTD dataset we are able to individuate within 10 iterations the desired texture the 90% of cases, against the 71% obtained with a combination of the finest existing attributes so far. Dataset and code is available at this https URL
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Element-based textures are a kind of texture formed by nameable elements, the texels [1], distributed according to specific statistical distributions; it is of primary importance in many sectors, namely textile, fashion and interior design industry. State-of-theart texture descriptors fail to properly characterize element-based texture, so we present Texel-Att to fill this gap. Texel-Att is the first fine-grained, attribute-based representation and classification framework for element-based textures. It first individuates texels, characterizing them with individual attributes; subsequently, texels are grouped and characterized through layout attributes, which give the Texel-Att representation. Texels are detected by a Mask-RCNN, trained on a brand-new element-based texture dataset, ElBa, containing 30K texture images with 3M fully-annotated texels. Examples of individual and layout attributes are exhibited to give a glimpse on the level of achievable graininess. In the experiments, we present detection results to show that texels can be precisely individuated, even on textures "in the wild"; to this sake, we individuate the element-based classes of the Describable Texture Dataset (DTD), where almost 900K texels have been manually annotated, leading to the Element-based DTD (E-DTD). Subsequently, classification and ranking results demonstrate the expressivity of Texel-Att on ElBa and E-DTD, overcoming the alternative features and relative attributes, doubling the best performance in some cases; finally, we report interactive search results on ElBa and E-DTD: with Texel-Att on the E-DTD dataset we are able to individuate within 10 iterations the desired texture in the 90% of cases, against the 71% obtained with a combination of the finest existing attributes so far. Dataset and code is available at https://github.com/godimarcovr/Texel-Att
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Element-based textures are a kind of texture formed by nameable elements, the texels [1], distributed according to specific statistical distributions; it is of primary importance many sectors, namely textile, fashion and interior design industry. State-of-theart texture descriptors fail to properly characterize element-based texture, so we present Texel-Att to fill this gap. Texel-Att is the first fine-grained, attribute-based representation and classification framework for element-based textures. It first individuates texels, characterizing them with individual attributes; subsequently, texels are grouped and characterized through layout attributes, which give the Texel-Att representation. Texels are detected by a Mask-RCNN, trained on a brand-new element-based texture dataset, ElBa, containing 30K texture images with 3M fully-annotated texels. Examples of individual and layout attributes are exhibited to give a glimpse on the level of achievable graininess. In the experiments, we present detection results to show that texels can be precisely individuated, even on textures in the wild; to this sake, we individuate the element-based classes of the Describable Texture Dataset (DTD), where almost 900K texels have been manually annotated, leading to the Element-based DTD (E-DTD). Subsequently, classification and ranking results demonstrate the expressivity of Texel-Att on ElBa and E-DTD, overcoming the alternative features and relative attributes, doubling the best performance some cases; finally, we report interactive search results on ElBa and E-DTD: with Texel-Att on the E-DTD dataset we are able to individuate within 10 iterations the desired texture the 90% of cases, against the 71% obtained with a combination of the finest existing attributes so far. Dataset and code is available at this https URL
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Texture (cosmology)
Representation
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