IAPAR Agronomic Institute of Parana

Ponta Grossa, Brazil

IAPAR Agronomic Institute of Parana

Ponta Grossa, Brazil
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Pontes L.S.,IAPAR Agronomic Institute of Parana | Giostri A.F.,Federal University of Paraná | Baldissera T.C.,Epagri | Barro R.S.,Federal University of Rio Grande do Sul | And 4 more authors.
Agronomy Journal | Year: 2016

Plant adaptations to cope with shade may vary according to the degree of shade tolerance and nutrient availability for each species. Studies of different understory species and their responses to combined shade and N effects are important to identify ways to optimize the quantity and quality of forage production. Our objective was to measure the dry matter yield (DMY) and nutritive value of six C4 grasses grown in two systems (full sunlight [FS] vs. a naturally shaded system composed of Eucalyptus dunnii Maiden trees) with two N levels (0 vs. 300 kg N ha–1yr–1) using the same target sward conditions (i.e., 50% depletion of the canopy height set by 95% light interception). Over 3 yr, the decreases in DMY under shade compared with FS ranged from 7% [Urochloa brizantha (Hochst. ex A. Rich) R.D. Webster] to 56% (Cynodon spp.) in fertilized treatments and between 11% (Paspalum notatumFluegge) to 46% (Cynodon spp.) in treatments without N. The N effect was more important to the nutritive value of the forage than the shade effect, particularly for crude protein (CP, +46 g kg–1 with N supply), acid detergent fiber (ADF, –46 g kg–1) and leaf proportion (+11.2%). However, most of the species displayed higher leaf digestibility under shade due to decreases in ADF. Even with intense shading (light approximately 48% of unshaded), the digestible DMY and CP yield under trees were, on average, 70 and 71% of the 6.8 and 1.4 t ha–1 recorded in FS, respectively. © 2016 by the American Society of Agronomy.


Vaz R.Z.,Federal University of Pelotas | Ribeiro E.L.A.,State University Londrina | Restle J.,Federal University of Goais | Vaz F.N.,Federal University of Santa Maria | And 2 more authors.
Bioscience Journal | Year: 2016

Assessment of the productive efficiency of 30 primiparous Aberdeen Angus cows of different body sizes, classified at calving as heavy (375±10.5 kg) or light (283±7.7 kg), and different total milk-yield levels, classified as high (868±24.5 kg) or low (547±18.3 kg). Heavy cows were superior in weight at calving and weaning, but there were no differences in milk yield and weight at birth and weaning of calves. Heavy cows were less efficient than light cows in the production of kilograms of calves per 100 kg of cows at calving and at weaning. High milk producing cows were heavier at calving and had heavier calves at birth and weaning, but did not differ between the milk-yield levels for the variation in daily weight. The variation in daily weight of the calves was greater from high-producing cows. High milk producing dairy beef cows were more efficient at weaning, and their calves required less milk to produce one kilogram of live weight. © 2016, Universidade Federal de Uberlandia. All rights reserved.


Link J.V.,Federal University of Technology of Parana | Lemes A.L.G.,Federal University of Technology of Parana | Marquetti I.,Federal University of Technology of Parana | dos Santos Scholz M.B.,IAPAR Agronomic Institute of Parana | Bona E.,Federal University of Technology of Parana
Chemometrics and Intelligent Laboratory Systems | Year: 2014

The climatic conditions of coffee cultivation give special attributes to the beverage and could increase its value. However, it is essential to prove the geographical and genotypic origin of the cultivar using reliable methods. An example of an artificial neural network (ANN) that has been used for pattern classification is the radial-basis function network (RBF). This study aimed to develop a RBF to classify the geographic and genotypic origin of arabica coffee. For this purpose, spectra obtained in the Fourier transform infrared (FTIR) were analyzed by using RBFs. In the development of networks, other methods were applied for: the choice of network parameters (sequential simplex optimization) and improve the generalization of a neural network (ensemble averaging). The optimized RBFs were able to classify the samples of arabica coffee, both geographically (100% correct classification) and genotypically (94.44%). The performance of the developed RBFs was better than the SIMCA (Soft Independent Modeling of Class Analogies) and multilayer perceptron (MLP) developed for coffee classification. © 2014 Elsevier B.V.


Link J.V.,Federal University of Technology of Parana | Guimaraes Lemes A.L.,Federal University of Technology of Parana | Marquetti I.,Federal University of Technology of Parana | dos Santos Scholz M.B.,IAPAR Agronomic Institute of Parana | Bona E.,Federal University of Technology of Parana
Food Research International | Year: 2014

Several statistical methods have been developed in an attempt to reproduce the human capability of pattern recognition. Self-organizing maps (SOMs) are a type of artificial neural network (ANN) with unsupervised learning designed to examine the structure of multidimensional data. This study aimed to conduct a segmentation of the geographical and genotypic coffee grown in the coffee region of Paraná - Brazil using the SOM for cluster analysis. Fourteen arabica coffee genotypes from two different cities were collected (Paranavaí and Cornélio Procópio). Density, caffeine, chlorogenic acids, tannins, total and reducing sugars, proteins, and lipids of the green coffee beans were analyzed. Using these data, the SOM was able to discriminate the 14 genotypes and also segmentation of the geographical origin was observed. Reducing sugars, caffeine, and chlorogenic acid were the most important variables for separation of the region of cultivation of arabica coffee genotypes. It was concluded that the SOM was able to recognize the coffee genotypes and geographical origin using the chemical profile data. © 2014 Elsevier Ltd.

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