<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Almeida, Alice</style></author><author><style face="normal" font="default" size="100%">Tomé, Margarida</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sistema para a Predição do Crescimento da Cortiça</style></title><secondary-title><style face="normal" font="default" size="100%">Silva Lusitana</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">accumulated cork thickness in complete rings</style></keyword><keyword><style  face="normal" font="default" size="100%">cork growth</style></keyword><keyword><style  face="normal" font="default" size="100%">growth models</style></keyword><keyword><style  face="normal" font="default" size="100%">prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">total cork thickness prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008///</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">83 - 95</style></pages><isbn><style face="normal" font="default" size="100%">0870-6352 UL - http://www.scielo.gpeari.mctes.pt/scielo.php?script=sci_arttext&amp;pid=S0870-63522008000100005&amp;nrm=iso</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">. Cork growth modelling was based on two models: cork growth model for complete rings and a model for total cork thickness prediction. The cork growth model was developed using data from 189 cork samples taken at breast height. Four biological growth functions were considered using their formulation as difference equations. The evaluation of the fitting and predictive ability of the models was based on model efficiency and on several statistics computed with the press residuals, complemented with graphical analysis to assess the regression assumptions. The model selected - Lundqvist-A - can be used to predict cork thickness in complete rings at any year of the cork rotation. The model for the prediction of total cork thickness is used to predict total cork thickness from the thickness in complete rings</style></abstract><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;publisher: scielopt</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Almeida, Alice</style></author><author><style face="normal" font="default" size="100%">Tomé, Margarida</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sistema para a Predição do Crescimento da Cortiça</style></title><secondary-title><style face="normal" font="default" size="100%">Silva Lusitana</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">accumulated cork thickness in complete rings</style></keyword><keyword><style  face="normal" font="default" size="100%">cork growth</style></keyword><keyword><style  face="normal" font="default" size="100%">growth models</style></keyword><keyword><style  face="normal" font="default" size="100%">prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">total cork thickness prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">scielopt</style></publisher><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">83-95</style></pages><isbn><style face="normal" font="default" size="100%">0870-6352 UL - http://www.scielo.gpeari.mctes.pt/scielo.php?script=sci_arttext&amp;pid=S0870-63522008000100005&amp;nrm=iso</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">. Cork growth modelling was based on two models: cork growth model for complete rings and a model for total cork thickness prediction. The cork growth model was developed using data from 189 cork samples taken at breast height. Four biological growth functions were considered using their formulation as difference equations. The evaluation of the fitting and predictive ability of the models was based on model efficiency and on several statistics computed with the press residuals, complemented with graphical analysis to assess the regression assumptions. The model selected - Lundqvist-A - can be used to predict cork thickness in complete rings at any year of the cork rotation. The model for the prediction of total cork thickness is used to predict total cork thickness from the thickness in complete rings</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">GARCIACRIADO, B</style></author><author><style face="normal" font="default" size="100%">GARCIACIUDAD, A</style></author><author><style face="normal" font="default" size="100%">PEREZCORONA, M E</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PREDICTION OF BOTANICAL COMPOSITION IN GRASSLAND HERBAGE SAMPLES BY NEAR-INFRARED REFLECTANCE SPECTROSCOPY</style></title><secondary-title><style face="normal" font="default" size="100%">JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">BOTANICAL COMPOSITION</style></keyword><keyword><style  face="normal" font="default" size="100%">grassland</style></keyword><keyword><style  face="normal" font="default" size="100%">NEAR</style></keyword><keyword><style  face="normal" font="default" size="100%">prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><publisher><style face="normal" font="default" size="100%">JOHN WILEY &amp; SONS LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">BAFFINS LANE CHICHESTER, W SUSSEX, ENGLAND PO19 1UD</style></pub-location><volume><style face="normal" font="default" size="100%">57</style></volume><pages><style face="normal" font="default" size="100%">507-515</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Near infrared reflectance spectroscopy (NIRS) was evaluated as a method&lt;br/&gt;to predict the botanical composition of seminatural grassland in&lt;br/&gt;`dehesa' systems. Samples of herbaceous biomass were harvested over&lt;br/&gt;four consecutive years, determining in each-by manual separation-the&lt;br/&gt;proportion by weight of the following taxonomic groups: grasses,&lt;br/&gt;legumes and the rest of the families in a single block ('others'). &lt;br/&gt;After reconstructing the natural samples they were analysed by NIRS. &lt;br/&gt;One set of samples (calibration set) was selected for the development of&lt;br/&gt;the equations, assaying different mathematical treatments (log 1/R,&lt;br/&gt;first derivative and second derivative). The ranges of coefficients of&lt;br/&gt;multiple determination and standard errors of calibration, respectively,&lt;br/&gt;for the various components were: grasses, 0.86 to 0.92 and 6.66 to&lt;br/&gt;9.14; legumes, 0.77 to 0.81 and 6.82 to 7.43; and `others', 0.85 to 0.88&lt;br/&gt;and 8.17 to 9.54. The remaining samples not included in the development&lt;br/&gt;of the NIRS equations (prediction set) were used for the purposes of&lt;br/&gt;validating the best equations. Standard errors of performance were: &lt;br/&gt;grasses, 6.12; legumes, 7.56 and `others', 7.70.</style></abstract></record></records></xml>