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水稻单因素非结构肥料响应模型及其田间试验验证。

Unary Non-Structural Fertilizer Response Model for Rice Crops and Its Field Experimental Verification.

机构信息

Soil and Fertilizer Institute, Fujian Academy of Agricultural Science, Fuzhou, 350013, China.

Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China.

出版信息

Sci Rep. 2018 Feb 12;8(1):2792. doi: 10.1038/s41598-018-21163-w.

Abstract

The quadratic polynomial fertilizer response model (QPFM) is the primary method for implementing quantitative fertilization in crop production, but the success rate of this model's recommended fertilization rates in China is low because the model contains a high setting bias. This paper discusses a new modelling method for expanding the applicability of QPFM. The results of field experiments with 8 levels of N, P, or K fertilization showed that the dynamic trend between rice yield increases and fertilizer application rate exhibited a typical exponential relationship. Therefore, we propose a unary non-structural fertilizer response model (NSFM). The responses of 18 rice field experiments to N, P, or K fertilization indicated that the new models could significantly predict rice yields, while two experimental fitting results using the unary QPFM did not pass statistical significance tests. The residual standard deviations of 13 new models were significantly lower than that of the unary QPFM. The linear correlation coefficient of the recommended application rates between the new model and the unary QPFM reached a significant level. Theoretical analysis showed that the unary QPFM was a simplified version of the new model, and it had a higher fitting precision and better applicability.

摘要

二次多项式肥料响应模型(QPFM)是作物生产中实施定量施肥的主要方法,但由于该模型存在较高的设定偏差,其推荐施肥率在中国的成功率较低。本文讨论了一种扩展 QPFM 适用性的新建模方法。8 个氮、磷或钾施肥水平的田间试验结果表明,水稻产量增加与施肥率之间的动态趋势呈典型的指数关系。因此,我们提出了一种一元非结构性肥料响应模型(NSFM)。18 个水稻田间试验对氮、磷或钾施肥的响应表明,新模型可以显著预测水稻产量,而使用一元 QPFM 的两个实验拟合结果未通过统计显著性检验。13 个新模型的残差标准偏差显著低于一元 QPFM。新模型和一元 QPFM 之间推荐施用量的线性相关系数达到显著水平。理论分析表明,一元 QPFM 是新模型的简化版本,具有更高的拟合精度和更好的适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac7/5809603/8905c5d82025/41598_2018_21163_Fig1_HTML.jpg

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