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The response of weed and crop species to shading: Which parameters explain weed impacts on crop production?

机译:杂草和作物种类对阴影的响应:哪些参数解释了对作物生产的影响?

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Crops compete with weeds for light, and choosing competitive crop species contributes to managing weeds. The objective was to identify which crop and weed parameters related to competition for light drive weed harmfulness for crop production. In a previous experiment, we measured parameters to characterize species potential plant morphology in unshaded conditions and species response to shading for a range of 60 crop and annual weed species. Here, we integrated the measured parameter values into an existing simulation model that uses an individual-based 3D representation of crop-weed canopies to predict weed dynamics and crop production from pedoclimate and cropping system information. The model, i.e. FlorSys, was used to run virtual experiments in seven French and Spanish regions, with 272 cropping systems varying in terms of crop rotations, herbicide use and tillage intensity etc. A series of statistical methods (RLQ, fourth corner analysis, Principal Component Analysis, Pearson correlation coefficients, analysis of variance) were used to identify the key weed and crop parameters that drive crop yield loss and other weed harmfulness indicators. The weed species that caused the highest yield loss had a large leaf area at emergence. When young, they presented a large specific leaf area and a uniform leaf area distribution along plant height. They were also taller per unit plant biomass and their populations were more homogeneous in terms of plant width. At later stages, harmful weed species presented a smaller interception area to herbicides, with thicker leaves located lower on the plant. When shaded, harmful weed species shifted their leaves upwards and decreased their plant width per unit biomass. Weed-suppressive crop species had a large specific leaf area, wider plants per unit biomass, and a uniform leaf area distribution along plant height. When shaded, they increased their plant height and width per unit biomass. There was a trade-off between parameters driving potential crop production and those minimizing weed-inflicted yield losses.
机译:作物与杂草的光线竞争,选择竞争性作物物种有助于管理杂草。目的是确定与轻型驱动杂草杂草的竞争相关的作物和杂草参数。在先前的实验中,我们测量的参数在未体现的条件下表征物种潜在植物形态,并对遮蔽的物种反应为60种作物和年度杂草种类。这里,我们将测量的参数值集成到现有的仿真模型中,该模拟模型使用基于各个基于各个裁剪檐篷的3D表示来预测从小型和裁剪系统信息预测杂草动态和作物生产。该模型,即Florsys,用于在七个法国和西班牙地区运行虚拟实验,在作物旋转,除草剂使用和耕作强度等方面有272种种植系统。一系列统计方法(RLQ,第四个角分析,校长。组分分析,Pearson相关系数,方差分析)用于识别推动作物产量损失和其他杂草危害指标的关键杂草和作物参数。引起最高产量损失的杂草物种在出现时具有大叶面积。当年轻时,它们呈现出大型的特定叶面积和沿植物高度的均匀叶面积分布。它们的每单位植物生物量也高,并且它们在植物宽度方面的群体更加均匀。在以后的阶段,有害杂草物种向除草剂呈现较小的拦截区域,厚度较厚的叶片位于植物上。当阴影时,有害的杂草物种向向上移动并减少其每单位生物质的植物宽度。杂草抑制作物物种具有大的特定叶面积,每单位生物质较宽的植物,以及沿植物高度的均匀叶面积分布。当着色时,它们会增加其植物高度和每单位生物质的宽度。参数之间的权衡驾驶潜在作物生产以及最小化杂草造成的产量损失的参数之间。

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