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.

Evaluation of the efficacy and economics of irrigation management, plant resistance and Brassica(spot)(TM) models for management of white blister on Brassica crops

Options for the integrated management of white blister (caused by Albugo candida) of Brassica crops include the use of well timed overhead irrigation, resistant cultivars, programs of weekly fungicide sprays or strategic fungicide applications based on the disease risk prediction model, Brassica(spot)(TM). Initial systematic surveys of radish producers near Melbourne, Victoria, indicated that crops irrigated overhead in the morning (0800-1200 h) had a lower incidence of white blister than those irrigated overhead in the evening (2000-2400 h). A field trial was conducted from July to November 2008 on a broccoli crop located west of Melbourne to determine the efficacy and economics of different practices used for white blister control, modifying irrigation timing, growing a resistant cultivar and timing spray applications based on Brassica(spot)(TM). Growing the resistant cultivar, 'Tyson', instead of the susceptible cultivar, 'Ironman', reduced disease incidence on broccoli heads by 99 %. Overhead irrigation at 0400 h instead of 2000 h reduced disease incidence by 58 %. A weekly spray program or a spray regime based on either of two versions of the Brassica(spot)(TM) model provided similar disease control and reduced disease incidence by 72 to 83 %. However, use of the Brassica(spot)(TM) models greatly reduced the number of sprays required for control from 14 to one or two. An economic analysis showed that growing the more resistant cultivar increased farm profit per ha by 12 %, choosing morning irrigation by 3 % and using the disease risk predictive models compared with weekly sprays by 15 %. The disease risk predictive models were 4 % more profitable than the unsprayed control.

Spatiotemporal aspects of Hendra Virus infection in Pteropid Bats (Flying-Foxes) in Eastern Australia

Hendra virus (HeV) causes highly lethal disease in horses and humans in the eastern Australian states of Queensland (QLD) and New South Wales (NSW), with multiple equine cases now reported on an annual basis. Infection and excretion dynamics in pteropid bats (flying-foxes), the recognised natural reservoir, are incompletely understood. We sought to identify key spatial and temporal factors associated with excretion in flying-foxes over a 2300 km latitudinal gradient from northern QLD to southern NSW which encompassed all known equine case locations. The aim was to strengthen knowledge of Hendra virus ecology in flying-foxes to improve spillover risk prediction and exposure risk mitigation strategies, and thus better protect horses and humans. Monthly pooled urine samples were collected from under roosting flying-foxes over a three-year period and screened for HeV RNA by quantitative RT-PCR. A generalised linear model was employed to investigate spatiotemporal associations with HeV detection in 13,968 samples from 27 roosts. There was a non-linear relationship between mean HeV excretion prevalence and five latitudinal regions, with excretion moderate in northern and central QLD, highest in southern QLD/northern NSW, moderate in central NSW, and negligible in southern NSW. Highest HeV positivity occurred where black or spectacled flying-foxes were present; nil or very low positivity rates occurred in exclusive grey-headed flying-fox roosts. Similarly, little red flying-foxes are evidently not a significant source of virus, as their periodic extreme increase in numbers at some roosts was not associated with any concurrent increase in HeV detection. There was a consistent, strong winter seasonality to excretion in the southern QLD/northern NSW and central NSW regions. This new information allows risk management strategies to be refined and targeted, mindful of the potential for spatial risk profiles to shift over time with changes in flying-fox species distribution.

NIR model development and robustness in prediction of melon fruit total soluble solids

Near infrared spectroscopy (NIRS) can be used for the on-line, non-invasive assessment of fruit for eating quality attributes such as total soluble solids (TSS). The robustness of multivariate calibration models, based on NIRS in a partial transmittance optical geometry, for the assessment of TSS of intact rockmelons (Cucumis melo) was assessed. The mesocarp TSS was highest around the fruit equator and increased towards the seed cavity. Inner mesocarp TSS levels decreased towards both the proximal and distal ends of the fruit, but more so towards the proximal end. The equatorial region of the fruit was chosen as representative of the fruit for near infrared assessment of TSS. The spectral window for model development was optimised at 695-1045 nm, and the data pre-treatment procedure was optimised to second-derivative absorbance without scatter correction. The 'global' modified partial least squares (MPLS) regression modelling procedure of WINISI (ver. 1.04) was found to be superior with respect to root mean squared error of prediction (RMSEP) and bias for model predictions of TSS across seasons, compared with the 'local' MPLS regression procedure. Updating of the model with samples selected randomly from the independent validation population demonstrated improvement in both RMSEP and bias with addition of approximately 15 samples.

Application of a model to assess aflatoxin risk in peanuts.

When exposed to hot (22-35 degrees C) and dry climatic conditions in the field during the final 4-6 weeks of pod filling, peanuts (Arachis hypogaea L.) can accumulate highly carcinogenic and immuno-suppressing aflatoxins. Forecasting of the risk posed by these conditions can assist in minimizing pre-harvest contamination. A model was therefore developed as part of the Agricultural Production Systems Simulator (APSIM) peanut module, which calculated an aflatoxin risk index (ARI) using four temperature response functions when fractional available soil water was <0.20 and the crop was in the last 0.40 of the pod-filling phase. ARI explained 0.95 (P <= 0.05) of the variation in aflatoxin contamination, which varied from 0 to c. 800 mu g/kg in 17 large-scale sowings in tropical and four sowings in sub-tropical environments carried out in Australia between 13 November and 16 December 2007. ARI also explained 0.96 (P <= 0.01) of the variation in the proportion of aflatoxin-contaminated loads (>15 mu g/kg) of peanuts in the Kingaroy region of Australia during the period between the 1998/99 and 2007/08 seasons. Simulation of ARI using historical climatic data from 1890 to 2007 indicated a three-fold increase in its value since 1980 compared to the entire previous period. The increase was associated with increases in ambient temperature and decreases in rainfall. To facilitate routine monitoring of aflatoxin risk by growers in near real time, a web interface of the model was also developed. The ARI predicted using this interface for eight growers correlated significantly with the level of contamination in crops (r=095, P <= 0.01). These results suggest that ARI simulated by the model is a reliable indicator of aflatoxin contamination that can be used in aflatoxin research as well as a decision-support tool to monitor pre-harvest aflatoxin risk in peanuts.

Potential Distribution and Risk Assessment of an Invasive Plant Species: A Case Study of Hymenachne amplexicaulis in Australia

Given the limited resources available for weed management, a strategic approach is required to give the best bang for your buck. The current study incorporates: (1) a model ensemble approach to identify areas of uncertainty and commonality regarding a species invasive potential, (2) current distribution of the invaded species, and (3) connectivity of systems to identify target regions and focus efforts for more effective management. Uncertainty in the prediction of suitable habitat for H. amplexicaulis (study species) in Australia was addressed in an ensemble-forecasting approach to compare distributional scenarios from four models (CLIMATCH; CLIMEX; boosted regression trees [BRT]; maximum entropy [Maxent]). Models were built using subsets of occurrence and environmental data. Catchment risk was determined through incorporating habitat suitability, the current abundance and distribution of H. amplexicaulis, and catchment connectivity. Our results indicate geographic differences between predictions of different approaches. Despite these differences a number of catchments in northern, central, and southern Australia were identified as high risk of invasion or further spread by all models suggesting they should be given priority for the management of H. amplexicaulis. The study also highlighted the utility of ensemble approaches in indentifying areas of uncertainty and commonality regarding the species invasive potential.

Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0.69 to 0.88 with correlations among harvest seasons within trials greater than 0.96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G x E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.