Host Tree Influences on Longicorn Beetle (Coleoptera: Cerambycidae) Attack in Subtropical Corymbia (Myrtales: Myrtaceae)

Phoracantha longicorn beetles are endemic to Australia, and some species have become significant pests of eucalypts worldwide, yet little is known about their host plant interactions and factors influencing tree susceptibility in Australia. Here, we investigate the host relationships of Phoracantha solida (Blackburn, 1894) on four eucalypt taxa (one pure species and three hybrid families), examining feeding site physical characteristics including phloem thickness, density, and moisture content, and host tree factors such as diameter, height, growth, taper, and survival. We also determine the cardinal and vertical (within-tree) and horizontal (between-tree) spatial distribution of borers. Fewer than 10% of P. solida attacks were recorded from the pure species (Corymbia citriodora subsp. variegate (Hook)), and this taxon also showed the highest survival, phloem thickness, relative growth rate, and bark:wood area. For the two most susceptible taxa, borer severity was negatively correlated with moisture content, and positively related to phloem density. Borers were nonrandomly and nonuniformly distributed within trees, and were statistically aggregated in 32% of plots. More attacks were situated on the northern side of the tree than the other aspects, and most larvae fed within the lower 50 cm of the bole, with attack height positively correlated with severity. Trees with borers had more dead neighbors, and more bored neighbors, than trees without borers, while within plots, borer incidence and severity were positively correlated. Because the more susceptible taxa overlapped with less susceptible taxa for several physical tree factors, the role of primary and secondary chemistries in determining host suitability needs to be investigated. Nevertheless, taxon, moisture content, phloem density, tree size, and mortality of neighboring trees appeared the most important physical characteristics influencing host suitability for P. solida at this site.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.