Zhang Zheng, Dai Weimin, Song Xiaoling, Qiang Sheng
Weed Research Laboratory, Nanjing Agricultural University, Nanjing, China.
Pest Manag Sci. 2014 May;70(5):716-24. doi: 10.1002/ps.3649. Epub 2013 Oct 13.
A heavy infestation of weedy rice leading to no harvested rice has never been predicted in China due to a lack of knowledge about the weedy rice seed bank. We studied the seed-bank dynamics of weedy rice for three consecutive years and analyzed the relationship between seed-bank density and population density in order to predict future weedy rice infestations of direct-seeded rice at six sites along the Yangtze River in Jiangsu Province, China.
The seed-bank density of weedy rice in all six sites displayed an increasing trend with seasonal fluctuations. Weedy rice seeds found in the 0-10 cm soil layer contributed most to seedling emergence. An exponential curve expressed the relationship between cultivated rice yield loss and adult weedy rice density. Based on data collected during the weedy rice life-cycle, a semi-empirical mathematic model was developed that fits well with the experimental data in a way that could be used to predict seed-bank dynamics.
By integrating the semi-empirical model and the exponential curve, weedy rice infestation levels and crop losses can be predicted based on the seed-bank dynamics so that a practical control can be adopted before rice planting.
由于对杂草稻种子库缺乏了解，中国从未预测过杂草稻的严重侵染会导致无收成的情况。我们连续三年研究了杂草稻的种子库动态，并分析了种子库密度与种群密度之间的关系，以便预测中国江苏省长江沿岸六个地点直播稻田未来杂草稻的侵染情况。
所有六个地点的杂草稻种子库密度均呈现出随季节波动的上升趋势。在0-10厘米土层中发现的杂草稻种子对幼苗出土的贡献最大。栽培水稻产量损失与成年杂草稻密度之间的关系用指数曲线表示。基于杂草稻生命周期内收集的数据，建立了一个半经验数学模型，该模型与实验数据拟合良好，可用于预测种子库动态。
通过整合半经验模型和指数曲线，可以根据种子库动态预测杂草稻的侵染水平和作物损失，从而在水稻种植前采取切实可行的防治措施。
