The occurrence of wild cotton <Emphasis Type="Italic">Gossypium mustelinum</Emphasis> Miers ex Watt populations in the coast of Pernambuco,Northeastern Brazil     

Edson Ferreira da Silva  Luiz Sergio Costa Duarte Filho  Ana Kelly dos Santos Maia  Allison Vieira da Silva  Ana Carolina Borges Lins e Silva 《Genetic Resources and Crop Evolution》2018,65(6):1551-1557

In this study, we describe three populations of the wild cotton Gossypium mustelinum Miers ex Watt in the coastal plain to the north of the State of Pernambuco, Northeast Brazil. These populations occur in urban areas in current expansion and under imminent risks of extinction, due to land use changes for house constructions. Distances between these new occurrences and the populations already mapped in the Northeast semiarid Caatinga (Rio Grande do Norte, Ceará, and Bahia) range from approximately 270–950 km. Besides distant, the populations on the coastal plain of Pernambuco also occur in very distinct climatic and edaphic conditions. Aiming at allowing the ex situ conservation of these genetic resources, branches of 66 plants were collected, propagated by the asexual breeding method cutting to regenerate an accession of each plant. These now constitute a germplasm collection in the Department of Agronomy of the Federal Rural University of Pernambuco (UFRPE), in Recife, Pernambuco, Brazil.  相似文献   

Artificial neural networks to estimate the productivity of soybeans and corn by chlorophyll readings     

Gabriela K. Michelon  Paulo L. de Menezes  Claudio L. Bazzi  Ermínio P. Jasse  Paulo S. G. Magalhães  Lígia F. Borges 《Journal of plant nutrition》2018,41(10):1285-1292

Crop productivity prediction techniques assist with adjusting for potential agronomic problems during the growing season. Several authors have reported that there is a correlation between leaf chlorophyll (Chl) content and yield. This study developed independent artificial neural network (ANN) models for soybean and corn in order to predict the crops' productive potentials using their respective yields and leaf Chl content data, measured at three stages of plant development. The ANN was deemed ready for testing through verification of the mean squared error and the number of epochs while training the neural network. While the model obtained when Chl was measured in the V6 stage of development explained more than 50% of the productivity data in corn, the models obtained for soybean did not explain more than 10% of the observed data. Attempts to improve the model through changes of the architecture of the neural network did not show any improvement in model.  相似文献