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A Single-Time Survey Method to Predict the Daily Weed Density for Weed Control Decision-Making

机译:一次性调查方法预测杂草控制决策的每日杂草密度

摘要

Decision-making processes must indicate if, how, and when weed control should be practiced. So far, Decision Support Systems (DSSs) for weed control to prevent crop yield losses can guide decisions on ‘‘if ’’ and ‘‘how.’’ Experience shows that farmers need a DSS that can also guide when to treat, but this can only be done if the actual weed density observed in the field is known during the crop cycle. Emergence models allow the prediction of daily density, but precision depends on the survey date. This study focuses on the estimation of the date of the survey for the best prediction of the daily density throughout the crop cycle. The predicted daily density of each species can be used by DSSs without any further survey, saving time and money and improving the use of the DSSs. Results showed that the best date is when the actual density of each weed reaches or exceeds 50% emergence, and this is earlier than the critical point date, supporting the validity of the date estimation method. The possibility to provide specific advice for farmers considering a proper mortality rate of weed seedlings is then discussed. The ability to optimize the date of sampling can improve the reliability of decision-making tools for integrated weed management, in agreement with the European Union goal of sustainable use of pesticides and more environmentally sustainable cropping systems through the use of integrated pest management.
机译:决策过程必须表明是否,如何以及何时进行除草。到目前为止,用于控制杂草以防止作物减产的决策支持系统(DSS)可以指导有关“如果”和“如何”的决策。经验表明,农民需要一种还可以指导何时进行处理的DSS,但这仅当知道在作物周期内田间观察到的实际杂草密度时,才能执行此操作。出现模型可以预测日密度,但精度取决于调查日期。这项研究着重于调查日期的估计,以便对整个作物周期的日密度进行最佳预测。 DSS可以使用每种物种的预计日密度,而无需进行任何进一步调查,从而节省了时间和金钱,并改善了DSS的使用。结果表明,最佳日期是每株杂草的实际密度达到或超过50%出现的时间,并且早于临界点日期,这支持了日期估计方法的有效性。然后讨论了考虑适当的杂草幼苗死亡率为农民提供具体建议的可能性。优化采样日期的能力可以提高杂草综合管理决策工具的可靠性,这与欧盟通过使用虫害综合管理实现农药可持续利用和更环境可持续的种植系统的目标相一致。

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