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.

Hanging drop: An in vitro air toxic exposure model using human lung cells in 2D and 3D structures

Using benzene as a candidate air toxicant and A549 cells as an in vitro cell model, we have developed and validated a hanging drop (HD) air exposure system that mimics an air liquid interface exposure to the lung for periods of 1 h to over 20 days. Dose response curves were highly reproducible for 2D cultures but more variable for 3D cultures. By comparing the HD exposure method with other classically used air exposure systems, we found that the HD exposure method is more sensitive, more reliable and cheaper to run than medium diffusion methods and the CULTEX (R) system. The concentration causing 50% of reduction of cell viability (EC50) for benzene, toluene, p-xylene, m-xylene and o-xylene to A549 cells for 1 h exposure in the HD system were similar to previous in vitro static air exposure. Not only cell viability could be assessed but also sub lethal biological endpoints such as DNA damage and interleukin expressions. An advantage of the HD exposure system is that bioavailability and cell concentrations can be derived from published physicochemical properties using a four compartment mass balance model. The modelled cellular effect concentrations EC50(cell) for 1 h exposure were very similar for benzene, toluene and three xylenes and ranged from 5 to 15 mmol/kg(dry weight) which corresponds to the intracellular concentration of narcotic chemicals in many aquatic species, confirming the high sensitivity of this exposure method. (C) 2013 Elsevier B.V. All rights reserved.

Modelling predicts positive and negative interactions between three Australian tropical tree species in monoculture and binary mixture

Computer modelling promises to be an important tool for analysing and predicting interactions between trees within mixed species forest plantations. This study explored the use of an individual-based mechanistic model as a predictive tool for designing mixed species plantations of Australian tropical trees. The `spatially explicit individually based-forest simulator' (SeXI-FS) modelling system was used to describe the spatial interaction of individual tree crowns within a binary mixed-species experiment. The three-dimensional model was developed and verified with field data from three forest tree species grown in tropical Australia. The model predicted the interactions within monocultures and binary mixtures of Flindersia brayleyana, Eucalyptus pellita and Elaeocarpus grandis, accounting for an average of 42% of the growth variation exhibited by species in different treatments. The model requires only structural dimensions and shade tolerance as species parameters. By modelling interactions in existing tree mixtures, the model predicted both increases and reductions in the growth of mixtures (up to +/-50% of stem volume at 7 years) compared to monocultures. This modelling approach may be useful for designing mixed tree plantations.

Detection and use of QTL for complex traits in multiple environments.

QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls.

Economic aspects of post-border weed risk management

Aim: Decision-making in weed management involves consideration of limited budgets, long time horizons, conflicting priorities, and as a result, trade-offs. Economics provides tools that allow these issues to be addressed and is thus integral to management of the risks posed by weeds. One of the critical issues in weed risk management during the early stages of an invasion concerns feasibility of eradication. We briefly review how economics may be used in weed risk management, concentrating on this management strategy. Location: Australia. Methods: A range of innovative studies that investigate aspects of weed risk management are reviewed. We show how these could be applied to newly invading weeds, focussing on methods for investigating eradication feasibility. In particular, eradication feasibility is analysed in terms of cost and duration of an eradication programme, using a simulation model based on field-derived parameter values for chromolaena, Chromolaena odorata. Results: The duration of an eradication programme can be reduced by investing in progressively higher amounts of search effort per hectare, but increasing search area will become relatively more expensive as search effort increases. When variation in survey and control success is taken into account, increasing search effort also reduces uncertainty around the required duration of the eradication programme. Main conclusions: Economics is integral to the management of the risks posed by weeds. Decision analysis, based on economic principles, is now commonly used to tackle key issues that confront weed managers. For eradication feasibility, duration and cost of a weed eradication programme are critical components; the dimensions of both factors can usefully be estimated through simulation. © 2013 John Wiley & Sons Ltd.