江西双季稻氮素监测诊断模型的建立与应用

doi:10.13287/j.1001-9332.202002.029

应用生态学报 ›› 2020, Vol. 31 ›› Issue (2): 433-440.doi: 10.13287/j.1001-9332.202002.029

江西双季稻氮素监测诊断模型的建立与应用

李艳大^1*, 曹中盛¹, 孙滨峰¹, 叶春¹, 舒时富¹, 黄俊宝¹, 王康军¹, 田永超²

¹江西省农业科学院农业工程研究所/江西省智能农机装备工程研究中心/江西省农业信息化工程技术研究中心, 南昌 330200;
²南京农业大学/国家信息农业工程技术中心, 南京 210095

收稿日期:2019-04-26 出版日期:2020-02-15 发布日期:2020-02-15
通讯作者: * E-mail: liyanda2008@126.com
作者简介:李艳大, 男, 1980年生, 研究员。主要从事信息农学与农机化技术研究。E-mail: liyanda2008@126.com
基金资助:
本文由国家重点研发计划项目(2016YFD0300608)、国家青年拔尖人才支持计划项目、江西省科技计划项目(20182BCB22015,20161BBI90012,20192BBF60052)、江西省“双千计划”项目和江西省“远航工程”项目资助

Model construction and application for nitrogen nutrition monitoring and diagnosis in double-cropping rice of Jiangxi Province, China

LI Yan-da^1*, CAO Zhong-sheng¹, SUN Bin-feng¹, YE Chun¹, SHU Shi-fu¹, HUANG Jun-bao¹, WANG Kang-jun¹, TIAN Yong-chao²

¹Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, China;
²Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture, Nanjing 210095, China

Received:2019-04-26 Online:2020-02-15 Published:2020-02-15
Contact: * E-mail: liyanda2008@126.com
Supported by:
This work was supported by the National Key R&D Program of China (2016YFD0300608), the National Program for Support of Top-notch Young Professionals, the Jiangxi Science and Technology Program (20182BCB22015, 20161BBI90012, 20192BBF60052), the Jiangxi “Double-Thousand Plan”, and the Jiangxi “Voyage Project”.

摘要/Abstract

摘要： 建立基于光谱仪的江西双季稻氮素监测诊断模型,可指导氮肥精确施用,达到双季稻丰产、提质、增效的目的。本研究开展了不同早、晚稻品种与氮素水平的小区试验,采用GreenSeeker光谱仪和作物生长监测诊断仪(CGMD)于分蘖期和拔节期测定了早、晚稻冠层光谱植被指数和植株氮积累量,建立了双季稻植株氮积累量光谱监测模型,并采用独立的田间试验数据对模型进行检验。利用双季稻丰产栽培经验及建立的氮素光谱诊断模型,对双季稻分蘖肥和穗肥施氮量进行定量推荐。结果表明: 双季稻氮肥施用关键期(分蘖期和拔节期)基于两种光谱仪的光谱植被指数与植株氮积累量均呈显著正相关,分蘖期和拔节期的模型预测效果比生长前期模型好。基于GreenSeeker光谱仪的归一化差值植被指数(NDVI_(780,660))的指数方程可较好地预测植株氮积累量,模型决定系数(R²)为0.92～0.94,模型检验的均方根误差(RMSE)、相对均方根误差(RRMSE)和相关系数(r)分别为3.09～5.96 kg·hm^-2、5.8%～18.5%和0.92～0.98;基于CGMD光谱仪的差值植被指数(DVI_(810,720₎)的线性方程可较好地预测植株氮积累量,R²为0.90～0.93,模型检验的RMSE、RRMSE和r分别为3.71～6.33 kg·hm^-2、11.7%～14.3%和0.93～0.96。基于CGMD光谱仪的模型推荐的施氮量高于基于GreenSeeker光谱仪的模型推荐的施氮量;模型生成的精确施氮方案较传统农户方案减少施氮量5.5 kg·hm^-2,氮肥农学利用率提高0.8%,纯收益提高128元·hm^-2。用双季稻氮素光谱诊断方法指导施肥能在增产的同时,降低成本,增加纯收益,对科学指导双季稻生产具有重要意义。

Abstract: The spectrometer-based nitrogen (N) nutrition monitoring and diagnosis models for double-cropping rice in Jiangxi is important for recommending precise N topdressing rate, achieving high yield, improving grain quality and increasing economic efficiency. Field experiments were conducted in Jiangxi in 2016 and 2017, involving different early rice and late rice cultivars and N application rates. Plant N accumulation (PNA) and canopy spectral vegetation indices (VIs) were measured at tillering and jointing stages with two spectrometers, i.e., GreenSeeker (an active multispectral sensor containing 780 and 660 nm wavelengths) and crop growth monitoring and diagnosis apparatus (CGMD, a passive multispectral sensor containing 810 and 720 nm wavelengths). The VI-based models of PNA were established from a experimental dataset and then validated using an independent dataset. The N topdressing rates for tillering and jointing stages were calculated using the newly developed N spectral diagnosis model and higher yield cultivation experience of double-cropping rice. The results showed that the VIs from two spectrometers were strongly positively correlated with PNA at both growth stages, with the model performance for tillering or jointing stages was better than that for the early growth stages. The exponential equation of normalized difference vegetation index (NDVI_(780,660)) from GreenSeeker could be used to estimate PNA with a determination coefficient (R²) in the range of 0.92-0.94, the root mean square error (RMSE), relative root mean square error (RRMSE) and correlation coefficient (r) of model validation in the range of 3.09-5.96 kg·hm^-2, 5.8%-18.5% and 0.92-0.98, respectively. The linear equation of difference vegetation index (DVI_(810,720)) from CGMD could be used to estimate PNA with a R² in the range of 0.90-0.93, the RMSE, RRMSE and r of model validation in the range of 3.71-6.33 kg·hm^-2, 11.7%-14.3% and 0.93-0.96, respectively. The recommended N topdressing rate with CGMD was higher than that with GreenSeeker. Compared with conventional farmer’s plan, the precision N application plan reduced N fertilizer application rate by 5.5 kg·hm^-2, while N agronomic efficiency and net income was improved by 0.8% and 128 yuan·hm^-2, respectively. Application of the spectral monitoring and diagnosis method to guiding fertilization could reduce cost and increase grain yield and net income, and thus had great potential for guiding double-cropping rice production.

李艳大, 曹中盛, 孙滨峰, 叶春, 舒时富, 黄俊宝, 王康军, 田永超. 江西双季稻氮素监测诊断模型的建立与应用[J]. 应用生态学报, 2020, 31(2): 433-440.

LI Yan-da, CAO Zhong-sheng, SUN Bin-feng, YE Chun, SHU Shi-fu, HUANG Jun-bao, WANG Kang-jun, TIAN Yong-chao. Model construction and application for nitrogen nutrition monitoring and diagnosis in double-cropping rice of Jiangxi Province, China[J]. Chinese Journal of Applied Ecology, 2020, 31(2): 433-440.

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江西双季稻氮素监测诊断模型的建立与应用

Model construction and application for nitrogen nutrition monitoring and diagnosis in double-cropping rice of Jiangxi Province, China

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