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Refinement of an Aflatoxin Prediction Model Using Field and Greenhouse Data to Elucidate Physiological Mechanisms of Aflatoxin Contamination in Peanut

机译:利用田间和温室数据细化黄曲霉毒素预测模型,阐明花生中黄曲霉毒素污染的生理机制

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Weather, irrigation and aflatoxin concentration data collected over a twelve year period from a peanut irrigation experiment conducted at the USDA-ARS Multi-crop Irrigation Research Farm in Shellman, GA was used to evaluate the performance of the CROPGRO-Peanut-Aflatoxin module of the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. The model’s performance of yield and aflatoxin prediction was evaluated by using the Root Mean Square Error (RMSE), index of agreement (d-statistic) and the R 2 of plotted simulated versus observed values. DSSAT’s soil temperature module was also examined and compared to the Erosion/Productivity Impact Calculator (EPIC) soil temperature module and to the daily measured soil temperature at 5 centimeters from the field. For yield, DSSAT-CROPGRO-Peanut had an R 2 value of 0.75, a RMSE of 778 kg/ha and d-statistic of 0.911. The aflatoxin model had an R 2 of 0.29 and RMSE of 11 ppb. The model predicted increases in aflatoxin concentrations only during periods of drought stress when the soil temperature was in a certain range. However, aflatoxin concentration was over predicted for small values or values of zero. In comparison to DSSAT, EPIC had inferior predictions of both soil temperature and aflatoxin concentration, indicating that the DSSAT module is the preferred option for further model development.The aflatoxin model will be further refined using the results of an ongoing fine-scale greenhouse experiment. The effect of environmental conditions on aflatoxin contamination will be examined by using direct inoculation with Aspergillus parasiticus within the pod zone while tagging pod cohorts weekly and simultaneously monitoring soil moisture, soil temperature and air temperature. The effect of seed age and maturity on aflatoxin contamination will thus be examined. The current findings of this experiment will be discussed.
机译:在美国乔治亚州谢尔曼市的USDA-ARS多作灌溉研究农场进行的花生灌溉实验中,收集了十二年期间的天气,灌溉和黄曲霉毒素浓度数据,以评估该作物的CROPGRO-Peanut-Aflatoxin模块的性能农业技术转让决策支持系统(DSSAT)作物模型。通过使用均方根误差(RMSE),一致性指数(d统计)和绘制的模拟值与观察值的R 2来评估模型的产量和黄曲霉毒素的预测性能。还检查了DSSAT的土壤温度模块,并将其与“侵蚀/生产率影响计算器”(EPIC)的土壤温度模块进行了比较,并与距田地5厘米处的每日测得的土壤温度进行了比较。就产量而言,DSSAT-CROPGRO-花生的R 2值为0.75,RMSE为778 kg / ha,d统计量为0.911。黄曲霉毒素模型的R 2为0.29,RMSE为11 ppb。该模型预测仅在土壤温度处于一定范围内的干旱胁迫期间,黄曲霉毒素的浓度才会增加。但是,对于较小的值或零值,黄曲霉毒素的浓度被高估了。与DSSAT相比,EPIC对土壤温度和黄曲霉毒素浓度的预测均较差,这表明DSSAT模块是进一步开发模型的首选选项。黄曲霉毒素模型将通过正在进行的小规模温室试验的结果进一步完善。将通过在豆荚区域内直接接种寄生虫曲霉,同时每周对豆荚队列进行标记并同时监测土壤湿度,土壤温度和气温,来检查环境条件对黄曲霉毒素污染的影响。因此,将检查种子年龄和成熟度对黄曲霉毒素污染的影响。将讨论该实验的当前发现。

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