Intrapopulation variation in an Australian population of the North American thrips, Bagnalliella yuccae (Thysanoptera: Phlaeothripidae), a new record from Australia.

The genus Bagnalliella Karny is an endemic North American genus of Phlaeothripidae with 7 species associated with the New World plant genus Yucca; 2 Old World species currently placed in the genus are probably not congeneric. The number of sensoria on antennal segments III and IV has been used to distinguish the Bagnalliella species on Yucca, but an invasive population of Bagnalliella yuccae (Hinds) is reported here from Australia, at Brisbane, Queensland, in which the number of sensoria varied between individuals and even between left and right antennae of single individuals. These observations cast considerable doubt on the validity of some of the North American species of Bagnalliella. The Australian population was damaging young leaves of Yucca elephantipes, and indicates the ease with which thrips can be distributed by the horticultural trade.

Genetic and Malt Quality Diversity of East Asian Two-Rowed Barley Accessions in Relationship to North American Malting Barley.

Because of epidemics of Fusarium head blight (FHB; caused by Fusarium graminearum Schwabe [teleomorph Gibberella zeae (Schwein.) Petch]) in the northern Great Plains of the United States and Canada in the past two decades, malting barley breeders have been forced to use nonadapted barley (Hordeum vulgare L.) accessions as sources of FHB resistance. Many of the resistant accessions are from East Asia, and limited information is available on their genetic diversity and malt quality. The objectives of this study were to determine the genetic diversity among 30 East Asian accessions and two North American cultivars. Genetic diversity was based on 49 simple-sequence repeat markers. All accessions were tested for barley grain brightness; protein content; 1,000-kernel weight; malting loss; fine-grind malt extract; content of plump kernels, free amino nitrogen, soluble protein, and wort beta-glucan; the Kolbach index (i.e., the ratio of malt soluble protein to malt total protein); a-amylase activity; diastatic power; won color; and wort viscosity. A few accessions had equal quality compared with Harrington and Conlon barley for individual traits but not for all. Qing 2, Mokkei 93-78, and Nitakia 48 could be excellent sources for increased malt extract; Nitakia 48 is a possible source for low wort viscosity; and Mokkei 93-78 and Nitakia 48 are putative sources of low beta-glucan content. The cluster analyses also implied that the malt quality of an accession cannot be predicted based on the country where it was developed.

Stochastic model for simulating maize yield

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment. © 2012 American Society of Agricultural and Biological Engineers.