【作者】 曹强；

【导师】 江荣风； 苗宇新；

【作者基本信息】 中国农业大学 ， 植物营养， 2014， 博士

【摘要】 氮肥过量施用及施用时期的不合理是我国冬小麦和水稻生产中的主要问题,导致了较低的氮肥利用率及较高的环境污染风险。然而,在实际生产中又缺乏有效的氮素营养诊断方法和氮肥推荐算法。本研究通过多年多点的冬小麦以及寒地水稻的田间试验,系统地评价了Crop Circle ACS-470主动冠层传感器在当季作物关键生育期估测其氮素营养状况指标的潜力,并分别发展了基于该传感器的氮肥推荐算法。此外,通过4年的定位试验评价了不同氮素管理策略应用于冬小麦/夏玉米轮作体系的效果。综合全文研究结果,本文获得的主要结论如下：(1)通过3年的冬小麦田间试验,评价Crop Circle ACS-470与GreenSeeker传感器在估测冬小麦氮素营养状况时的表现。其结果表明,与GreenSeeker传感器相比,当地上部生物量大于6000kg ha-1时,获取于Crop Circle ACS-470传感器的基于红边的指数CIRE与MSR_RE具有更好的估测能力；当植株吸氮量大于130kg ha-1时,基于绿光的指数CIG和MSR_G的估测能力更好。对于植株氮浓度与氮营养指数而言,Crop Circle ACS-470传感器的表现要优于GreenSeeker传感器,其中,GRDVI在估测氮营养指数表现最好(R2=0.78)。(2)通过4年的冬小麦田间试验,获取于Crop Circle ACS-470传感器的指数CIG以及由指数GRDVI计算的INSEY可以在冬小麦Feekes4-7时期准确估测植株前期吸氮量和产量潜力,其R2分别为0.87和0.61。基于此结果发展了基于该传感器的冬小麦氮肥推荐算法。9个农户的验证试验表明,与农户传统施肥习惯相比,基于该传感器的精准氮素管理策略可以在获得相同产量的前提下显著地提高氮肥偏生产力59-74%。(3)通过2年的寒地水稻田间试验及农户验证试验系统地评价了Crop Circle ACS-470主动冠层传感器在水稻关键生育时期估测其氮素营养状况的潜力。其结果表明,MCARI1在全生育时期可以准确估测水稻地上部生物量(R2=0.79)和植株吸氮量(R2=0.83),并可以克服GreenSeeker NDVI的饱和问题。4个基于红边波段的指数(RESAVI、MRESAVI、REDVI和RERDVI)均与氮营养指数线性相关(R2=0.76)。对于植株氮浓度而言,最高的R2仅为0.33,且在使用农户田块的数据进行验证时没有一个指数的表现令人满意。(4)通过2年的寒地水稻田间试验,其结果表明,在不同的生育时期,无论是估测产量潜力还是估测其对于追施氮肥的反应,Crop Circle ACS-470传感器的表现均优于GreenSeeker传感器。基于此结果,同时根据氮肥最优推荐施肥算法发展了基于该传感器的寒地水稻精准氮素管理策略。(5)通过4年的定位试验,系统地评价了不同氮素管理策略在冬小麦/夏玉米轮作体系上的应用效果。其研究结果表明,基于土壤无机氮测试技术、GreenSeeker传感器、绿色窗口以及区域优化的氮肥管理策略的施氮量较农户传统施氮管理分别降低了45%、62%、46%以及40%,从而提高了氮肥利用效,减少了氮素盈余及表观氮素损失,且各管理策略间的产量无显著差异。比较而言,在本试验条件下,基于GreenSeeker传感器的精准氮素管理策略是应用于华北平原冬小麦厦玉米轮作体系上较优的氮肥管理策略。更多还原

【Abstract】 Over-application of nitrogen (N) fertilizers as well as improper timing are among the major problems for winter wheat and rice management in China, and have resulted in very low N use efficiencies (NUE) and high risk of environmental contamination. However, effective N nutritional status diagnosis methods and N fertilizer recommendation algorithms are lacking in practical production. In this study, we systematically evaluated the potential of Crop Circle ACS-470sensor with the configuration of NIR, red edge and green bands for estimating winter wheat and rice N status at critical growth stages, and developed this sensor-based precision N management strategy by conducting several site-years of field experiments. In addition, four years of fixed field experiments were conducted four years to evaluate different N management strategies for intensive winter wheat-summer maize double cropping system in North China Plain (NCP). The main results were concluded as follows:(1) Three years of winter wheat field experiments were conducted to evaluate the performance of Crop Circle ACS-470and GreenSeeker for estimating N status. The results indicated that Crop Circle CIRE and MSR_RE indices performed better for estimating biomass when biomass was above6000kg ha-1and Crop Circle CIG and MSR_G performed better for estimating plant N uptake when it was above130kg ha-1, as compared with GreenSeeker sensor. The Crop Circle ACS-470sensor had better performance for estimating plant N concentration and NNI than GreenSeeker. Across site-years, growth stages and varieties, the GRDVI performed consistently well for estimating NNI, with R2being0.78.(2) The Crop Circle CIG and the INSEY calculated with GRDVI could be used to estimate early-season plant N uptake and grain yield potential at Feekes growth stages4-7, with R2being0.87and0.61, respectively. The Crop Circle sensor-based N fertilizer recommendation algorithm was developed for winter wheat in NCP. Nine on-farm evaluation experiments indicated that this Crop Circle sensor-based precision N management strategy achieved similar grain yield as farmer’s practice, but significantly increased partial factor productivity of N fertilizer by59-74%.(3) The results of calibration experiments and on-farm validation experiments indicated that MCARI1had consistent correlations with rice aboveground biomass (R2=0.79) and plant N uptake (R2=0.83) across growth stages. It could overcome the saturation effect of GreenSeeker NDVI. Four red edge-based indices, RESAVI, MRESAVI, REDVI and RERDVI, performed equally well for estimating NNI across growth stages (R2=0.76). For rice plant N concentration, the highest R2was0.33, and none of the indices performed satisfactorily with validation using farmers’ field data.(4) The Crop Circle ACS-470sensor had better performance than GreenSeeker sensor for estimating rice yield potential and responsiveness to additional topdressing N application at stem elongation, booting, or heading stage. Based on these results, the Crop Circle sensor-based precision N management strategy was developed for rice according to the N fertilization optimization algorithm in Northeast China. (5) Four years of fixed field experiments were conducted to evaluate different N management strategies for the intensive winter wheat-summer maize double cropping system in NCR The N fertilizer rate determined with soil Nmin-based in-season root zoon N management (IRNM-soil Nmin), GreenSeeker-based precision N management (PNM-GS), green window-based in-season N management strategy (INM-GW) and regional optimum N management (RONM) were significantly reduced45%,62%,46%and40%compared to the farmer’s N practice (FNP), respectively, without significant change in grain yield. As a result, the IRNM-soil Nmin, PNM-GS, INM-GW and RONM increased N use efficiency, reduced N surplus, apparent N losses and GHG emission. In conclusion, the PNM-GS is the better strategy for winter wheat-summer maize double cropping system in NCP for improved NUE and reduced environmental contamination compared with other N management strategy in this study.更多还原