Li Yan-da, Cao Zhong-Sheng, Sun Bin-Feng, Ye Chun, Shu Shi-Fu, Huang Jun-Bao, Wang Kang-Jun, Tian Yong-Chao
Institute 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.
Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture, Nanjing 210095, China.
Ying Yong Sheng Tai Xue Bao. 2020 Feb;31(2):433-440. doi: 10.13287/j.1001-9332.202002.029.
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) from GreenSeeker could be used to estimate PNA with a determination coefficient (R) 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, 5.8%-18.5% and 0.92-0.98, respectively. The linear equation of difference vegetation index (DVI) from CGMD could be used to estimate PNA with a R 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, 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, while N agronomic efficiency and net income was improved by 0.8% and 128 yuan·hm, 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.
基于光谱仪的江西双季稻氮素营养监测与诊断模型，对于推荐精确的氮肥追肥量、实现高产、改善稻米品质及提高经济效益具有重要意义。2016年和2017年在江西开展了田间试验，涉及不同的早稻和晚稻品种以及氮肥施用量。在分蘖期和拔节期，使用两台光谱仪，即GreenSeeker（一种有源多光谱传感器，包含780和660纳米波长）和作物生长监测与诊断仪（CGMD，一种无源多光谱传感器，包含810和720纳米波长），测定了植株氮素积累量（PNA）和冠层光谱植被指数（VIs）。基于实验数据集建立了基于植被指数的PNA模型，然后使用独立数据集进行验证。利用新开发的氮素光谱诊断模型和双季稻高产栽培经验，计算了分蘖期和拔节期的氮肥追肥量。结果表明，两台光谱仪的植被指数在两个生长阶段均与PNA呈极显著正相关，分蘖期或拔节期的模型性能优于生育前期。GreenSeeker的归一化差值植被指数（NDVI）指数方程可用于估算PNA，决定系数（R）在0.92 - 0.94之间，模型验证的均方根误差（RMSE）、相对均方根误差（RRMSE）和相关系数（r）分别在3.09 - 5.96 kg·hm、5.8% - 18.5%和0.92 - 0.98之间。CGMD的差值植被指数（DVI）线性方程可用于估算PNA，R在0.90 - 0.93之间，模型验证的RMSE、RRMSE和r分别在3.71 - 6.33 kg·hm、11.7% - 14.3%和0.93 - 0.96之间。CGMD推荐的氮肥追肥量高于GreenSeeker。与传统农民施肥方案相比，精准施氮方案使氮肥施用量减少了5.5 kg·hm，而氮肥农学效率和纯收入分别提高了0.8%和128元·hm。应用光谱监测与诊断方法指导施肥可降低成本、提高粮食产量和纯收入，因此在指导双季稻生产方面具有巨大潜力。
