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应用多酚–叶绿素仪监测棉花氮素营养状况研究
引用本文:殷星,侯振安,冶军,闵伟,刘凯,王方斌,廖欢,甘浩天,刘少华,孙嘉璘.应用多酚–叶绿素仪监测棉花氮素营养状况研究[J].植物营养与肥料学报,2021,27(7):1198-1212.
作者姓名:殷星  侯振安  冶军  闵伟  刘凯  王方斌  廖欢  甘浩天  刘少华  孙嘉璘
作者单位:石河子大学农学院农业资源与环境系,新疆石河子 832003
基金项目:国家重点研发计划 (2017YFD0200100,2018YFD0800800);兵团中青年科技创新领军人才计划 (2016BC001)
摘    要:  【目的】  通过田间试验,研究使用多酚–叶绿素仪对棉花进行快速无损氮素营养诊断适宜的指标。  【方法】  田间试验在新疆石河子市进行,设3个施氮处理,分别为施纯N 0、180和240 kg/hm2,分别用N0、N180和N240表示。所有氮肥分5次随滴灌施入,每次施肥后3天,利用多酚–叶绿素仪 (Dualex-4) 和SPAD叶绿素仪分别测定20株棉花叶片的氮平衡指数 (NBI)、Chl值和SPAD值,同步采样测定棉花叶片全氮含量,及0—20 cm、0—40 cm和0—60 cm土层硝态氮含量。  【结果】  随着施氮量的增加棉花叶片全氮含量和土壤硝态氮含量均显著增加。其中,0—40 cm土层硝态氮含量与棉花叶片全氮含量关系最密切。增加氮肥施用量,棉花叶片氮素营养诊断指标NBI、Chl值和SPAD值均显著增加。棉花叶片NBI、Chl和SPAD与叶片全氮含量均呈极显著正相关关系,且相关系数 (r) 均达到0.8以上。相关性模型校验结果表明,棉花叶片全氮含量实测值与预测值的平均相对误差 (RE) 分别为–4.0% (NBI)、–3.1% (Chl) 和–5.7% (SAPD)。其中,氮平衡指数 (NBI) 模型对棉花叶片全氮含量的预测精度最高,与实测值的相关系数达到了0.9143,平均绝对百分比误差 (MAPE) 为6.91%;标准均方根误差 (nRMSE) 为8.21%。棉花叶片NBI、Chl和SPAD与土壤硝态氮的模型决定系数表现为NBI > Chl > SPAD。模型校验分析表明,NBI模型与0—40 cm土层硝态氮实测含量的相关性最高,相关系数为0.9116,预测值与实测值的MAPE和nRMSE分别为14.11%和17.88%。  【结论】  应用多酚–叶绿素仪监测棉花氮素营养,氮平衡指数 (NBI) 与棉花叶片氮含量和0—40 cm土层硝态氮含量的相关性最高,预测值与实测值的误差仅为6.91%和14.11%,可以满足膜下滴灌条件下棉花氮素营养的快速诊断需求。

关 键 词:多酚–叶绿素仪    氮素平衡指数    叶绿素    SPAD    土壤硝态氮含量    棉花叶片全氮含量    模型预测精度
收稿时间:2020-12-23

Application of polyphenol-chlorophyll meter to monitor cotton N nutrition status
YIN Xing,HOU Zhen-an,YE Jun,MIN Wei,LIU Kai,WANG Fang-bin,LIAO Huan,GAN Hao-tian,LIU Shao-hua,SUN Jia-lin.Application of polyphenol-chlorophyll meter to monitor cotton N nutrition status[J].Plant Nutrition and Fertilizer Science,2021,27(7):1198-1212.
Authors:YIN Xing  HOU Zhen-an  YE Jun  MIN Wei  LIU Kai  WANG Fang-bin  LIAO Huan  GAN Hao-tian  LIU Shao-hua  SUN Jia-lin
Institution:Department of Resources and Environmental Science, Colloge of Agronomy, Shihezi University, Shihezi, Xinjiang 832003, China
Abstract:  【Objectives】  We studied the suitable parameter in diagnosing cotton N nutrition using the polyphenol-chlorophyll meter in the field.  【Methods】  A field experiment was set up in Shihezi, Xinjiang, with three N application rates: N 0, 180, and 240 kg/hm2, expressed as N0, N180 and N240. The N fertilizer treatments were topdressed 5 times with drip irrigation. Nitrogen balance index (NBI), Chl, and SPAD values of 20 cotton plants leaves were measured on the third day after topdressing, using both Polyphenol-chlorophyll meter (Dualex-4) and SPAD chlorophyll meter. The total N content of cotton leaves and the nitrate-nitrogen content in 0–20 cm, 0–40 cm and 0–60 cm soil layers were determined simultaneously.  【Results】  With increasing N applicationrate, the total N content of cotton leaves and soil nitrate-nitrogen content increased significantly. The soil NO3–-N content in the 0–40 cm soil layer had the closest relationship with the total N content of cotton leaves. The NBI, Chl, and SPAD values of cotton leaves significantly increased with increasing N application rate, and were positively correlated with the total N content of cotton leaves. The correlation coefficients were all above 0.8. The average relative error (RE) of the measured and predicted values of leaf total N content was –4.0% (NBI), –3.1% (Chl), and –5.7% (SAPD), respectively. The NBI exhibited the highest prediction accuracy for leaf N content, with an r-value of 0.9143, average absolute percentage error (MAPE) of 6.91%, and the standard root mean square error (nRMSE) was 8.21%. The model determination coefficient (R2) of the diagnostic index with soil NO3–-N content was in the descending order NBI > Chl > SPAD. The NBI was the most sensitive to NO3–-N content in 0–40 cm soil layer, with r-value of 0.9116, MAPE of 14.11%, and nRMSE of 17.88%.  【Conclusions】  When using the polyphenol-chlorophyll meter to diagnose N nutrition, we find that the N balance index (NBI) is sensitive and accurate in reflecting cotton leaf N content and the NO3–-N content in 0–40 cm soil layer. The average absolute percentage error (MAPE) between the predicted and measured values is as low as 6.91% and 14.11%, respectively, which could meet the requirement for rapid diagnosis of cotton N nutrition.
Keywords:
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