...
首页> 外文期刊>Field Crops Research >Use of a new sigmoid growth equation to estimate organ area indices from canopy area index in winter oilseed rape (Brassica napus L.)
【24h】

Use of a new sigmoid growth equation to estimate organ area indices from canopy area index in winter oilseed rape (Brassica napus L.)

机译:使用新的S型增长方程从冬油菜(Brassica napus L.)的冠层面积指数估算器官面积指数

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

In oilseed rape, the formation of organ area indices (AI), in particular of leaves and pods, is a key process determining seed yield. However, analyses of AI dynamics in rapeseed are rare in literature. This is because destructive measurements of organ area are usually labour-intensive and often impaired by large sampling errors. Here, we present a new approach for estimating AI of different organs from total canopy area index using a model that is based on combining growth equations. The proposed model provides a compact and robust tool for describing the time courses of area indices of aerial organs from the beginning of shooting until ripening. Key characteristics of AI development are defined by the model in unique way. The model was successfully tested against sets of data either taken from literature or from own experiments, including different years, locations, cultivars, and levels of nitrogen fertilisation. Data from non-destructive measurements of total canopy area index were used for adjusting the model to different scenarios. The performance of the model can be further improved specifying parameter values for different growth conditions and cultivars. Nevertheless, the model in its present form together with given parameterisation complies well with key characteristics of AI formation and may be used to reduce laborious destructive measurements.
机译:在油菜中,器官面积指数(AI)的形成,特别是叶子和荚果的形成,是决定种子产量的关键过程。但是,在油菜中对AI动力学进行分析的文献很少。这是因为器官面积的破坏性测量通常是劳动密集型的,并且通常会因较大的采样误差而受损。在这里,我们提出了一种新方法,可使用基于组合生长方程的模型从总冠层面积指数估算不同器官的AI。所提出的模型提供了一种紧凑而强大的工具,用于描述从射击开始到成熟的空中器官面积指数的时间过程。该模型以独特的方式定义了AI开发的关键特征。该模型已针对来自文献或来自自身实验的数据集成功进行了测试,包括不同的年份,位置,品种和氮肥水平。来自总冠层面积指数的非破坏性测量的数据用于将模型调整为适用于不同场景。指定不同生长条件和品种的参数值可以进一步提高模型的性能。尽管如此,该模型的当前形式以及给定的参数化都很好地符合了AI形成的关键特征,可用于减少费力的破坏性测量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号