Abstract

Junglerice (Echinochloa colona) is one of the most important annual weeds affecting crops in Argentina. A predictive seedling emergence model based on thermal time was developed and validated. Monitoring of seedling emergence was performed weekly during the growing season in a soybean field over four years. Cumulative thermal time, expressed in growing degree days (GDD), was used as the independent variable for predicting cumulative emergence. The variations in mean air temperature between late August and early September have determined a period with a conserved pattern over the years. That period had a close linear relationship (r² = 0.99) with the beginning of seedling emergence. A double-logistic model fitted junglerice seedling emergence better than Gompertz, Logistic or Weibull functions. Model validation showed a good performance in predicting the seedling emergence (r² = 0.99). Based on findings of this study it is possible to predict junglerice emergence by air temperature and, thus, to contribute reliably to the rational management of this weed.

摘要

Junglerice ( Echinochloa colona ) 是影响阿根廷作物的最重要的一年生杂草之一。开发并验证了基于热时间的预测幼苗出苗模型。在四年的大豆田中，在生长季节每周进行一次出苗监测。以生长期天数 (GDD) 表示的累积热时间被用作预测累积出苗的自变量。8 月下旬至 9 月上旬之间平均气温的变化确定了一个多年来具有保守模式的时期。那个时期有密切的线性关系（r ²= 0.99) 随着幼苗出苗的开始。双逻辑模型比 Gompertz、Logistic 或 Weibull 函数更适合丛林水稻幼苗的出苗。模型验证在预测幼苗出苗方面表现良好（r ² = 0.99）。根据这项研究的结果，可以通过气温预测丛林草的出现，从而可靠地促进这种杂草的合理管理。