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夏玉米不同部位干物质临界氮浓度稀释曲线的构建及对产量的估计

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苏文楠1,2, 解君2, 韩娟1,2, 刘铁宁1,2, 韩清芳,1,2,*1西北农林科技大学农学院 / 农业农村部西北黄土高原作物生理生态与耕作重点实验室, 陕西杨凌 712100
2中国旱区节水农业研究院 / 西北农林科技大学旱区农业水土工程教育部重点实验室, 陕西杨凌 712100

Construction of critical nitrogen dilution curve based on dry matter in diffe rent organs of summer maize and estimation of grain yield

SU Wen-Nan1,2, XIE Jun2, HAN Juan1,2, LIU Tie-Ning1,2, HAN Qing-Fang,1,2,* 1College of Agronomy, Northwest A & F University / Key Laboratory of Crop Physio-ecology and Tillage Science in North-western Loess Plateau, Ministry of Agriculture and Rural Affairs / College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi, China
2Institute of Water Saving Agriculture in Arid Areas of China / Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi, China

通讯作者: *韩清芳, E-mail: hanqf88@nwafu.edu.cn

收稿日期:2020-03-26接受日期:2020-10-14网络出版日期:2021-03-12
基金资助:国家高技术研究发展计划(863计划)项目.2013AA102902
国家公益性行业(农业)科研专项.201303104
国家自然科学基金项目资助.31601256


Received:2020-03-26Accepted:2020-10-14Online:2021-03-12
Fund supported: study was supported by the National High-Tech Research and Development Programs of China “863 Program” for the 12th Five-Year Plants.2013AA102902
Special Fund for Agro-scientific Research in the Public Interest .201303104
National Natural Science Foundation of China.31601256

作者简介 About authors
E-mail: asuwennan@163.com







摘要
准确和动态地诊断营养生长阶段植株氮状况, 对于评估植物氮需求、预测玉米产量以及优化氮素管理至关重要。基于植物的氮诊断工具可优化夏玉米生产中氮素的管理, 本研究旨在开发和验证基于玉米地上部不同部位干物质的临界氮浓度稀释曲线, 并建立玉米相对产量(relative yield, RY)与不同生长阶段氮素营养指数(nitrogen nutrition index, NNI)和累积氮素亏缺(accumulated nitrogen deficit, AND)的关系。本文以2个不同氮效率的品种为试验材料进行连续4年的田间定位试验, 设置4个氮素水平(0、150、225和300 kg hm-2), 分析不同施氮量对2个玉米品种营养生长阶段干物质的影响, 基于叶干物质(leaf dry matter, LDM)、茎干物质(stem dry matter, SDM)和植物干物质(plant dry matter, PDM), 构建不同的临界氮浓度稀释曲线。结果表明, 基于LDM、SDM和PDM建立的临界氮浓度稀释曲线, 均能很好地诊断玉米氮营养状况; 3条临界氮浓度稀释曲线对产量进行预测比较发现, RY与NNI和AND在不同生长阶段之间的相关性均达到显著水平, 相关系数R2值均大于0.65, 其中R2值在V12-VT时期最大, 同时回归模型的验证结果表明, 在V12-VT时期模型显示出可靠性。R2值大于0.92, RMSE值小于10%, 证实了模型在V12和VT两个时期关系的稳定性。总的来说, 一定的条件下, 基于LDM和SDM建立的临界氮浓度稀释曲线可以对基于PDM建立的临界氮浓度稀释曲线进行代替。在V12-VT阶段, RY与NNI和AND的稳定关系很好地说明了在受氮素限制和非氮素限制下RY的变化, 并对夏玉米产量进行准确的估计。本研究为花前玉米的氮肥管理提高粮食产量提供理论依据。
关键词: 临界氮浓度稀释曲线;玉米;氮营养指数;累积氮素亏缺

Abstract
It is essential to accurate and dynamic diagnosis of plant nitrogen status at vegetative growth stage for the assessment of plant nitrogen demand and the prediction of crop yield as well as the optimization of nitrogen management in maize. Plant-based nitrogen diagnostic tool can be used to optimize nitrogen management in summer maize production. The aim of this study was to develop and verify critical nitrogen concentration dilution curves based on dry matter in different tissue of the plant, and to establish the relationship between relative yield (RY) nitrogen nutrition index (NNI), and accumulated nitrogen deficit (AND) at different growth stages in maize. We conducted a 4-year field study using four nitrogen application rates (0, 150, 225, and 300 kg N hm-2) and two maize cultivars (Zhengda 12 and Shaandan 609) to analyze the effects of nitrogen on dry matter at the vegetative growth stage, and based on leaf dry matter (LDM), stem dry matter (SDM), and plant dry matter (PDM), different critical nitrogen concentration dilution curves were developed. The results showed that the critical nitrogen concentration dilution curves based on LDM, SDM and PDM can well diagnose the nitrogen nutrition status of corn. The yield prediction results of three critical nitrogen concentration dilution curves showed that the relationship between RY and NNI, AND at different growth stages was highly significant, and the values of R2 were all greater than 0.65, where R2 was the largest at V12-VT, and the verification of the regression model showed reliable model performance during the V12-VT period, with R2 values greater than 0.92 and RMSE values less than 10%, which confirmed the stability of the relationship between V12 and VT. Generally, under certain conditions, the critical nitrogen concentration dilution curve based on LDM and SDM can be used to replace the critical nitrogen concentration dilution curve based on PDM. The stable relationship between RY and NNI, RY and AND in V12-VT stage can well explain the change of RY under restricted and unrestricted nitrogen and estimate the yield of summer maize. This study provides the basis for nitrogen management of pre-anthesis to improve maize grain yield.
Keywords:critical nitrogen dilution curve;maize;nitrogen nutrition index;accumulated nitrogen deficit


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本文引用格式
苏文楠, 解君, 韩娟, 刘铁宁, 韩清芳. 夏玉米不同部位干物质临界氮浓度稀释曲线的构建及对产量的估计[J]. 作物学报, 2021, 47(3): 530-545. doi:10.3724/SP.J.1006.2021.03021
SU Wen-Nan, XIE Jun, HAN Juan, LIU Tie-Ning, HAN Qing-Fang. Construction of critical nitrogen dilution curve based on dry matter in diffe rent organs of summer maize and estimation of grain yield[J]. Acta Agronomica Sinica, 2021, 47(3): 530-545. doi:10.3724/SP.J.1006.2021.03021


氮肥对粮食增产的贡献率可达30%~50%[1], 就目前中国氮肥的使用现状, 继续增加氮肥投入对提高粮食产量的效果甚微, 并且降低了氮肥的利用效率。同时, 调查结果显示, 约60%~70%的氮肥通过挥发和淋洗等途径有所损失[2,3], 这种现象在陕西关中地区播前一次施入的施肥模式下变得更加严重。因此, 针对作物不同生育阶段精确调控氮肥施用量, 对于增加作物产量, 提高氮肥利用效率和改善环境问题具有重要战略意义[4,5]

进行植株氮营养状况的评估对于作物生长系统的调查、监控、管理以及产量预测至为关键。评估植株氮素营养状况的一些常用方式, 如叶绿素仪[6]和遥感[7], 这些方法存在地域和年际间的不稳定性, 以及成本的问题导致在评估植株氮营养状况时受到限制。临界氮浓度稀释曲线定义为最大作物生长所需的最小氮浓度[8], 由于其在植物氮诊断中的准确性和稳定性, 已引起全世界的广泛关注。随着研究的深入, 国内外****在构建方法和应用区域上对临界氮浓度稀释曲线进行拓展[9,10,11,12]。品种的更新(氮的吸收和利用效率逐渐降低)和环境的变化以及模型软件的发展, 对临界氮浓度稀释曲线参数提出更高的要求。因此, 在不同部位干物质的基础上(而不是仅仅传统上基于植株干物质(plant dry matter, PDM))开发临界氮浓度稀释曲线, 不仅可以更好地理解这一概念, 而且有利于探清基因型和环境带来的作物氮素动态的生理功能的差异, 同时适应模型软件的发展。氮营养指数(nitrogen nutrition index, NNI)作为氮素营养诊断的重要指标, 是用于基于临界氮浓度稀释曲线的氮诊断的工具之一[10,13-15]。NNI这一概念可以成功地区分植株的氮素营养状况, 并且将根据这些曲线得出的诊断工具与作物模拟模型集成在一起, 以协助作物氮素管理[13-14,16-18], 这些关系也已用于估计作物对氮素的需求, 以促进作物生长期间的氮肥施用以及评估籽粒品质[14]。水稻、小麦和玉米营养生长期间NNI与相对产量(relative yield, RY)的关系已用于估算籽粒产量[17,19-20]。作物生长阶段对于确定植物营养状况以预测玉米产量至关重要[13,20], 确定不同作物生长阶段NNI与RY的关系将有助于更好地理解植株与氮的关系, 并有益于对作物氮的动态建模, 因此, 需要进一步调查研究不同生育阶段的NNI和相关氮参数是否可以预测玉米的产量。例如NNI和累积氮亏缺(accumulated nitrogen deficit, AND), 以量化植物中氮营养状态, 对氮素供应的响应以及氮肥管理决策。

基于水稻[21,22,23,24]和小麦[25,26]的植物指数(叶、茎、穗和叶面积指数)的氮浓度临界稀释曲线已经建立并得到了验证。作为“源”器官的叶片, 不同生育阶段由不同叶龄的叶片组成, 不同位置叶片的性质和功能是不同的, 所以, 非破坏性取样且快速实时的单叶监测冠层氮素浓度的氮营养诊断方式的好处被夸大。营养阶段, 叶片作为功能结构, 由于其氮素的重要生理功能, 基于整株植物叶片建立的临界氮浓度稀释曲线, 可以更深入地了解作物氮素状态[10], 同时, 茎作为结构组分, 其干物质对植物总干物质的贡献显著高于叶片干物质, 因此, 它是整株植物临界氮浓度稀释曲线的最主要决定因素。相关研究[21]也指出基于水稻的茎干物质建立临界氮浓度稀释曲线, 可以作为替代整株植物方法来评估植物氮的状况从而用来建立施肥决策。同时, Zhao等[26]指出, 叶片和茎为了满足各自代谢氮和结构氮的需求, 单独建立作物叶片干物质和茎干物质的临界氮浓度稀释曲线非常重要, 这也将有利于作物模拟模型参数的确定[27]。每种诊断方式都存在着利弊, 但对这些方法进行综合研究, 可以对植物氮的状况提供有用的见解。现有的研究大多集中于使用地上部干物质和叶片干物质建立稀释曲线, 尽管基于其他部位的稀释曲线得到了建立, 但数量有限, 在玉米上更是少数。在陕西夏玉米主栽区, 对于临界曲线的研究仅局限于基于整株玉米干物质基础上建立的曲线[28,29], 基于不同部位干物质的临界氮浓度稀释曲线的系统研究还未见报道, 本试验是对临界氮浓度稀释曲线在本生态区的验证和补充。此外, 作物生长期间的不同器官干物质分配和氮素分布, 对于理解实现作物最大产量的氮肥管理及其限制因素具有重要作用。所以, 对于研究基于不同部位干物质的临界氮浓度稀释曲线之间的联系, 以测试作物氮稀释理论在玉米开花前期诊断玉米氮素状况的适用性上还需试验数据来阐明。玉米氮素的诊断能够及时掌握各生育时期的营养状况, 因此, 要准确评估作物的氮素状况, 需要在作物关键生长阶段进行作物的氮素评估, 并以此确定科学合理的施肥决策。综上, 本研究利用不同氮效率夏玉米品种, 开发基于不同部位干物质的临界氮浓度稀释曲线, 以确定它们的差异。在此基础上, 采用作物关键生长阶段NNI和AND对玉米RY进行估算, 在西北气候条件下, 评估玉米的氮素营养状况, 预测产量。这项研究代表了对玉米干物质基础氮浓度稀释曲线的首次最全面的研究, 将为诊断植物氮素状况提供综合方法, 并为玉米关键生长阶段精确氮素管理提供指导, 从而为玉米产量估算提供有用的方法。

1 材料与方法

1.1 试验设计

试验在西北农林科技大学试验基地进行。该试验基地位于陕西省杨凌(34°20′N, 108°24′E, 海拔454.8 m), 属于暖温带季风半湿润气候区。近20年的年平均气温为13.5℃, 每年的总日照时间为2196 h, 该地点的年平均降水量为580.5 mm, 年平均蒸发量为993.2 mm。2014年6月至2017年10月, 连续4个玉米季进行试验。试验地前茬作物为冬小麦, 试验地土壤类型为耧土, 耕层土壤化学性质为: 有机质14.3 g kg-1、全氮1.09 g kg-1、有效磷9.4 mg kg-1和速效钾127 mg kg-1。供试品种为正大12 (氮高效品种, Zhengda 12, ZD)和陕单609 (氮低效品种, Shaandan 609, SD)。试验设计为随机区组设计, 每年每个处理3个重复, 小区面积39 m2, 株距60 cm, 行距25 cm。设置4个施氮水平, 纯氮施用量分别为0 (N0)、150 (N150)、225 (N225)和300 kg hm-2 (N300)。1/2纯氮在玉米播种前施入(播前5 d), 剩余1/2纯氮在玉米大喇叭口期开沟追施。磷和钾肥在播种前一次性施入, 用量均为150 kg hm-2。氮肥为尿素(含N 46.4%), 磷肥为过磷酸钙(含P2O5 12.0%), 钾肥为氯化钾(含K2O 60.0%)。

玉米播种时间分别为2014年6月12日、2015年6月12日、2016年6月17日和2017年6月15日, 在三叶期定苗为67,500株 hm-2。玉米收获时间分别为2014年10月14日、2015年10月15日、2016年10月3日和2017年10月14日。田间管理与当地保持一致, 三至五叶期进行化学除草, 拔节期防治病虫害。试验在大喇叭口期以75 mm的水量进行灌溉。

1.2 测定项目及方法

在玉米的V3、V6、V8、V12、VT和R6时期[分别代表玉米的三叶期、六叶期(拔节期)、八叶期、十二叶期(大喇叭口期)、抽雄期和完熟期], 4年对应的时间在表1中呈现。每个小区选择具有3~5株玉米, VT之前植株分为叶片和茎2个部分, VT之后, 植株分为叶片、茎、穗轴、苞叶和籽粒, 所有部分在105℃杀青30 min, 然后在70℃烘干至恒重, 分别称重, 记录叶片、茎和植株的总质量。粉碎过100目筛后, 称取0.02 g样品, 采用半微量凯氏定氮法测定各部位的氮浓度[30]

Table 1
表1
表12014-2017年干物质取样时期
Table 1Dry matter sampling period from 2014 to 2017
项目ItemV3 (M/D)V6 (M/D)V8 (M/D)V12 (M/D)VT (M/D)R6 (M/D)
20146/277/127/177/308/1310/14
20156/297/157/208/28/1710/15
20167/17/147/217/318/1510/3
20176/297/137/218/48/1310/14
V3、V6、V8、V12、VT和R6分别表示玉米的三叶期、六叶期(拔节期)、八叶期、十二叶期(大喇叭口期)、抽雄期和完熟期。
V3, V6, V8, V12, VT, and R6 represent the third, sixth, eighth, twelfth leaf stages, tasseling and physiological maturity stages, respectively. M/D: month/day.

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玉米收获时, 每个小区随机收获30个玉米进行晾晒和脱粒, 测定籽粒产量, 并按照国家玉米籽粒入库标准(含水量14%)折算产量。

1.3 基于各部位干物质临界氮浓度稀释曲线的建立

根据Justes[8]的方法, 计算临界氮浓度稀释曲线的步骤如下: (1)利用单因素方差分析, 对不同氮水平的地上部植株干物质(叶片干重和茎干重)分为作物生长受氮素限制组和氮充足作物生长不受氮素限制组; (2)对受氮素限制处理的干物质与氮浓度进行线性拟合, 对不受氮素限制处理的干物质取平均值并通过平均值做垂直于横轴的垂线, 2条直线的交点为临界氮浓度; (3)拟合干物质与对应临界氮浓度的散点图, 基于幂回归模型(Freundlich模型)的异速函数用于确定观察到的氮浓度降低与叶片、茎和植株干物质(LDM, SDM和PDM)增加之间的关系。玉米临界氮浓度稀释曲线模型为:

${{N}_{\text{c}}}=a\text{D}{{\text{M}}^{-b}}$
式中, Nc代表作物临界氮含量, g kg-1; DM为地上部干物质, t hm-2; 参数a代表干物质为1 t时的临界氮浓度, 参数b为临界氮浓度稀释曲线斜率的统计学参数。

1.4 临界氮浓度稀释曲线模型的验证

为了校验模型的精度, 本研究通过计算标准化均方根误差(n-RMSE)来评判模型的精度:

$\text{RMSE}=\sqrt{\frac{\sum\nolimits_{i=1}^{n}{{{\left( {{P}_{i}}-{{O}_{i}} \right)}^{2}}}}{n}}$
$n-\text{RMSE}=\frac{\text{RMSE}}{S}\times 100%$
模型的验证采用标准化均方根误差n-RMSE[31]来检测模型的拟合度。

式中: PiOi分别为临界氮测定值和模拟值; n为样本量; S为实测数据的平均值。RMSE值越小, 模拟值与测定值的一致性越好, 偏差越小, 即模型的预测精度越高。Jamieson等[32]认为: n-RMSE<10%, 模型稳定性极好; 10%<n-RMSE<20%, 模型稳定性较好; 20%<n-RMSE<30%, 模型稳定性一般; n-RMSE> 30%, 模型稳定性较差。

最大氮曲线(Nmax)通过使用不受氮素限制的数据点建立(2014年和2015年的N300处理), 最小氮曲线(Nmin)通过使用受氮素限制处理的数据点建立。2016和2017年的试验数据用于验证NcNmaxNmin曲线[7]

1.5 氮营养指数, 氮亏缺相对产量和氮效率的计算

为了更精确地反映玉米植株氮素是否适宜, 氮素营养指数(NNI)被用来评估玉米生育期内的氮素状况。

$\text{NNI}=\frac{{{N}_{\text{a}}}}{{{N}_{\text{c}}}}$
式中, Na为地上部氮浓度实测值, Nc为地上部氮浓度的临界值。当NNI = 1时氮营养状况为最适的, 当NNI > 1, 氮营养处于过剩的状况, 反之, 当NNI < 1时, 植株处于营养不足的状态。

根据式(4)可以推出玉米氮积累亏缺(AND)[18]:

$\text{AND}={{N}_{\text{cna}}}-{{N}_{\text{na}}}$
式中, AND为氮亏缺量(kg hm-2), Ncna为临界氮浓度下作物氮素积累量(kg hm-2), Nna为作物实际氮素积累量(kg hm-2)。当AND = 0时, 证明作物体内氮素最佳, 当AND > 0时, 说明氮积累不足, 当AND < 0时, 说明氮素积累过量。

相对产量(RY) = 籽粒产量/最高产量

氮肥偏生产力(partial factor productivity nitrogen, PFPN) = 籽粒产量/施氮量

氮肥回收率(recovery efficiency of nitrogen, REN) = (施氮植株含氮量-不施氮植株含氮量)/施氮量

氮素利用效率(nitrogen utilization efficiency, NutE) = 籽粒产量/植株氮素积累量

氮素吸收效率(nitrogen uptake efficiency, NupE) = 植株氮素积累量/(土壤氮+施氮量)

1.6 产量预测模型的建立和验证

对4年的试验数据进行方差分析, 用2014年和2015年的试验数据分别对NNI和AND与RY每个时期进行回归分析, 2016年和2017年获得的试验数据对以上回归模型进行验证。根据回归分析结果, 将4年的数据进行汇总, 以建立最终产量预测模型。预测值和观察值之间的决定系数(R2), 均方根误差(RMSE)和标准化均方根误差n-RMSE用于检验NNI, AND和RY之间的回归模型的拟合度。

1.7 数据分析

每个采样期, 年份和品种的数据, 用SPSS 18.0进行方差分析(SPSS Inc., Chicago, IL, USA)。采用最小显著差异法进行处理间的比较, 显著水平为0.05。Nc曲线回归分析使用Microsoft Excel 2013进行。用SPSS 18.0对RY和NNI与RY和AND进行线性加平台分析。

2 结果与分析

2.1 基于不同部位临界氮浓度稀释曲线构建及临界氮浓度常数的确定

4年的试验结果表明, 2个玉米品种的氮效率存在显著差异(表2), 除2017年氮肥回收效率和2015年N 300处理外, 不同年际, 2个玉米品种氮效率差异均达到显著水平, ZD的氮效率显著高于SD。本试验中, ZD被定义为氮高效品种, SD被定义为氮低效品种。在本研究中, 用2014年和2015年试验数据, 对2个品种分别基于叶片、茎和植株干物质(LDM、SDM和PDM)建立了临界氮浓度稀释曲线。从图1中可以看出, 建立基于叶片、茎和植株干物质的临界氮浓度模型的数据, 适宜叶片, 茎和植株干物质的范围分别是0.76~2.93、1.03~5.14以及1.78~10.59 t hm-2。基于叶片、茎和植株干物质建立的临界氮浓度曲线的参数ab值的范围分别是1.58~2.64和0.204~0.388。其中, 对于参数a来说, 基于叶片干物质的临界氮浓度稀释曲线的a值最大, 最小的是茎干物质临界氮浓度稀释曲线的a值; 而对于参数b来说, 正相反, 基于茎干物质的临界氮浓度稀释曲线的值最大, 而基于叶片干物质的临界氮浓度稀释曲线的值最小。2个品种基于LDM、SDM和PDM临界氮浓度稀释曲线的R2的范围为0.86~0.98。对2个品种分别进行临界氮浓度稀释曲线的拟合, ‘ZD’方程为: Nc =2.64 LDM-0.204Nc =1.58 SDM-0.388Nc =2.33 PDM-0.263; ‘SD’方程为: Nc =2.61 LDM-0.205Nc =1.83 SDM-0.337Nc =2.47 PDM-0.237。为进一步分析基于不同部位建立的临界氮浓度稀释曲线2个品种的差异, 将曲线直线化处理, 采用协方差分析方法[33], 得到结果, 2个品种叶片斜率和截距得到的P值大于0.05, 而茎和植株斜率和截距得到的P值都小于0.05, 说明除叶片外, 基于茎和植株干物质建立的临界氮浓度稀释曲线在品种间存在显著差异。

Table 2
表2
表22014-2017年正大12和陕单609的氮效率
Table 2Nitrogen use efficiencies of ZD and SD from 2014 to 2017
施氮量
Nitrogen rate
品种
Cultivar
氮肥偏生产力
Partial factor productivity nitrogen
(PFPN, kg kg-1)
氮肥回收效率
Recovery efficiency of nitrogen
(REN, %)
氮素利用效率
Nitrogen utilization efficiency
(NutE, kg kg-1)
氮素吸收效率
Nitrogen uptake efficiency
(NupE, kg kg-1)
2014201520162017201420152016201720142015201620172014201520162017
N0ZD79.1 a78.4 a71.0 a72.6 a69.5 a76.6 a79.9 a98.0 a
SD73.0 b69.9 b61.2 b63.3 b62.1 b64.6 b66.8 b76.9 b
N150ZD62.7 a67.9 a64.9 a70.1 a36.2 a44.9 a45.3 a39.5 a60.9 a59.0 a51.4 a57.6 a35.6 a36.9 a37.3 a40.4 a
SD54.3 b55.4 b56.9 b58.3 b29.8 b36.6 b34.4 b39.3 a57.6 b52.7 b49.2 b49.1 b30.8 b30.1 b31.7 b32.0 b
N225ZD46.7 a49.2 a50.8 a50.8 a40.7 a43.5 a38.0 a39.3 a54.9 a54.5 a54.9 a53.9 a30.7 a30.3 a30.5 a24.3 a
SD39.7 b40.8 b43.5 b44.3 b38.1 b36.9 b34.4 b37.7 a48.9 b49.5 b49.0 b48.9 b26.1 b25.2 b25.7 b20.7 b
N300ZD34.0 a35.7 a37.6 a36.7 a35.4 a35.9 a37.0 a32.6 a49.4 a50.2 a48.2 a49.8 a24.4 a23.7 a24.5 a18.4 a
SD30.3 b30.8 b32.4 b32.8 b34.7 a34.2 a32.6 b34.2 a45.3 a45.1 b44.2 b44.3 b21.8 b20.5 b20.8 b15.6 b
方差分析品种Cultivar (C)**************ns****************
ANOVA氮肥Nitrogen rate (N)************nsns****************
品种×氮肥C×N****ns**ns**nsns***ns**ns******
SD:陕单609;ZD:正大12;N0、N150、N225和N300分别代表施氮量0、150、225和300 kg N hm-2
SD: Shaandan 609; ZD: Zhengda 12; N0, N150, N225, and N300 represent four nitrogen application rates at 0, 150, 225, and 300 kg N hm-2, respectively.

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图1

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图1基于叶片、茎和植株干物质建立的2个玉米品种临界氮浓度稀释曲线的差异比较

SD: 陕单609; ZD: 正大12; Nc: 地上部氮浓度的临界值; LDM: 叶片干物质; SDM: 茎干物质; PDM: 植株干物质。“○”表示品种陕单609, “●”表示品种正大12; “──”表示品种陕单609, “- - -”表示品种正大12。** 表示在P < 0.01水平上显著。
Fig. 1Comparison of critical N dilution curves of two maize hybrids on different bases (leaf dry matter basis, stem dry matter basis, and plant dry matter basis) with varied nitrogen rates

SD: Shaandan 609; ZD: Zhengda 12; Nc: critical value of shoot nitrogen concentration; LDM: leaf dry matter basis; SDM: stem dry matter basis; PDM: plant dry matter. Shaandan 609 is marked by “○”, Zhengda 12 is marked by “●”; “──” means the curves of Shaandan 609, “- - -” means the curves of Zhengda 12. ** indicates significant difference at P < 0.01.


2.2 临界氮浓度稀释曲线的验证

选取2016年和2017年数据对临界氮浓度曲线进行验证。结果表明, Nc稀释曲线可以区分玉米受氮素限制和不受氮素限制的生长条件。受氮素限制处理的所有数据点基本上都落在Nc稀释曲线的下方, 不受氮素限制处理的数据点落在Nc稀释曲线上或者在其之上(图2)。同时, 校验模型的精度, 其步骤为: 将实测干物质数据点分别带入式(1)中计算临界氮含量模拟值, 将模拟值分别与观测值比较(表3)。氮高效品种ZD基于叶片、茎和植株模型偏差分别为1.8%、16.7%和9.1%, <20%模型稳定性极好或较好, 氮低效品种SD偏差分别为1.4%、3.5%和5.0%, <10%模型稳定性极好。可见, 所构建的2个品种的临界氮浓度稀释曲线模型具有很好的精度, 表明本研究所建立的不同氮效率品种基于各部位的临界氮浓度稀释曲线可进一步用于植株的氮营养诊断。

Table 3
表3
表3临界氮浓度稀释曲线的验证
Table 3Calibration of critical N dilution curve basis on leaf dry matter, stem dry matter and plant dry matter in maize
参数
Parameter
正大12 Zhengda 12陕单609 Shaandan 609
RMSEn-RMSERMSEn-RMSE
叶片Leaf0.0431.7990.0321.354
茎Stem0.19216.7330.0483.549
植株Plant0.1489.1350.0884.996

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图2

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图2用2016-2017年获得的数据验证Nc稀释曲线

符号(○)和(×)分别代表2016年和2017年的不受氮素限制值和受氮素限制值。a、b和c实线分别代表陕单609的叶片、茎和植株的Nc稀释曲线, d、e和f实线分别代表正大12的叶片、茎和植株的Nc稀释曲线。两侧的虚线代表最小和最大的曲线, 用2014-2015年不受氮素限制值(△)和受氮素限制值(◇)获得。NminNmax为氮浓度最小和最大值; LDM: 叶片干物质; SDM: 茎干物质; PDM: 植株干物质。** 表示在P < 0.01水平上显著。
Fig. 2Validation of the N c dilution curve using data from experiments performed from 2016 to 2017

The symbols (○) and (×) represent non-N-limiting and N-limiting values from 2016 to 2017, respectively. The solid curved lines a, b, and c represent the Nc dilution curves of the leaf, stem, and plant of Shandan 609, respectively, and the solid curved lines d, e, and f represent the Nc dilution curves of the leaf, stem, and plant of Zhengda 12, respectively. The dotted lines on either side represent the curves for the minimum limits, which are developed using data from N-limiting (◇) and non-N-limiting (△) treatments from 2014 to 2015. Nmin and Nmax are minimum and maximum of nitrogen concentration; LDM: leaf dry matter basis; SDM: stem dry matter basis, PDM: plant dry matter. ** indicates significant difference at P < 0.01.


2.3 基于不同部位临界氮浓度稀释曲线氮营养指数和相对产量, 氮亏缺和相对产量的关系

NNI与RY, AND与RY之间的关系如图3~图5所示, 随着NNI的增加, RY均呈现线性的增长趋势, 直到RY不随NNI的增加为止, 其变化趋势呈现出线性加平台模式。相反, 随着AND的增加, RY均呈现先不变后线性降低的趋势。基于叶片干物质的临界氮浓度稀释曲线, NNI与RY之间的决定系数范围为0.961~0.997, AND与RY之间的决定系数范围为0.920~0.997, 基于茎干物质的临界氮浓度稀释曲线, NNI与RY之间的决定系数范围为0.956~0.995, AND与RY之间的决定系数范围为0.919~0.991, 基于植株干物质的氮临界曲线, NNI与RY之间的决定系数范围为0.913~0.992, AND与RY之间的决定系数范围为0.969~0.996。因此, 可以从作物营养生长的氮营养状态评价对产量的影响。

图3

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图3基于叶片干物质建立的临界氮浓度曲线得到的RY与NNI和AND的关系

V3、V6、V8、V12、VT和R6分别代表玉米的三叶期、六叶期(拔节期)、八叶期、十二叶期(大喇叭口期)、抽雄期和完熟期。SD: 陕单609; ZD: 正大12; “△”表示品种陕单609, “◇”表示品种正大12; “──”表示品种陕单609, “……”表示品种正大12。** 表示在P < 0.01水平上显著。
Fig. 3Relationships between relative yield (RY) and the nitrogen nutrition index (NNI), relative yield (RY) and the accumulated nitrogen deficit (AND) of two maize hybrids with varied N rates on the base of leaf dry matter N dilution curves

V3, V6, V8, V12, VT and R6 represent the third, sixth, eighth, twelfth leaf stages, tasseling and physiological maturity stages, respectively. SD: Shaandan 609; ZD: Zhengda 12; Shaandan 609 is marked by “△”, Zhengda 12 is marked by “◇”; “──” means the curves of Shandan 609, “……” means the curves of Zhengda 12. ** indicates significant difference at P < 0.01.


图4

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图4基于茎干物质建立的临界氮浓度曲线得到的RY与NNI和AND的关系

缩写和符号同图3。** 表示在P < 0.01水平上显著。
Fig. 4Relationships between relative yield (RY) and the nitrogen nutrition index (NNI), relative yield (RY) and the accumulated nitrogen deficit (AND) of two maize hybrids with varied N rates on the bases of stem dry matter N dilution curves

Abbreviations and symbols are the same as those given in Fig. 3. ** indicates significant difference at P < 0.01.


图5

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图5基于植株干物质建立的临界氮浓度曲线得到的RY与NNI和AND的关系

缩写和符号同图3。** 表示在P < 0.01水平上显著。
Fig. 5Relationships between relative yield (RY) and the nitrogen nutrition index (NNI), relative yield (RY) and the accumulated nitrogen deficit (AND) of two maize hybrids with varied N rates on the bases of plant dry matter N dilution curves

Abbreviations and symbols are the same as those given in Fig. 3. ** indicates significant difference at P < 0.01.


2.4 相对产量模型的验证和最终模型

NNI与RY之间关系的RMSE变化范围为0.03~0.12, n-RMSE的变化范围为2.82~13.7, R2的变化范围为0.76~0.99。AND与RY之间的RMSE的变化范围为0.06~0.13, n-RMSE的变化范围为3.55~14.84, R2的变化范围为0.65~0.98 (表4)。NNI和RY的关系及AND和RY的关系, 在V12和VT两个时期基于叶片、茎和植株氮稀释模型建立的回归关系RMSE和n-RMSE在2个品种中较小, 同时R2较大, 综合来看, 这2个时期模型的稳定性最强, 在NNI与RY之间和AND与RY之间的关系对玉米生产中产量的估计具有实际意义。基于NNI与RY和AND与RY关系的精度和准确度的综合考虑, 对V12和VT两个时期建立相对产量模型(表5), R2>0.941。

Table 4
表4
表4相对籽粒产量(RY)的RMSE, n-RMSE和R2的值(根据2016-2017年不同时期的氮营养指数(NNI)和累积氮亏(AND)预测)
Table 4Values of RMSE, n-RMSE and R2 for prediction of relative yield (RY) from nitrogen nutrition index (NNI), and accumulated nitrogen deficit (AND), respectively, at different growth stages using data obtained in 2016 and 2017
部位
Organ or plant
品种
Cultivar
参数
Parameter
氮亏缺Accumulated nitrogen deficit (AND)氮营养指数Nitrogen nutrition index (NNI)
V3V6V8V12VTV3V6V8V12VT
叶片
Leaf
正大12RMSE0.080.080.090.030.050.080.120.080.050.05
Zhengda 12n-RMSE9.109.0410.413.515.238.8513.658.395.725.31
R20.900.890.860.980.960.900.760.910.950.96
陕单609RMSE0.110.060.080.050.050.080.070.070.030.05
Shaandan 609n-RMSE12.406.868.695.815.408.808.127.902.825.11
R20.830.930.880.950.950.880.900.900.980.96

Stem
正大12RMSE0.110.100.100.060.060.080.100.060.060.06
Zhengda 12n-RMSE11.8310.6610.967.166.678.8111.406.466.796.49
R20.830.850.840.930.940.900.820.940.940.94
陕单609RMSE0.100.100.060.040.040.060.080.050.040.03
Shaandan 609n-RMSE11.0011.126.353.924.186.469.145.574.743.16
R20.870.810.940.970.970.930.870.950.960.98
植株
Plant
正大12RMSE0.150.090.070.060.040.100.090.060.060.04
Zhengda 12n-RMSE17.099.877.816.114.6310.709.977.036.574.38
R20.650.880.920.950.970.860.880.940.940.97
陕单609RMSE0.120.100.080.040.070.080.090.040.030.04
Shaandan 609n-RMSE13.4711.258.324.657.859.249.424.913.034.93
R20.800.860.920.970.930.910.900.970.990.97
V3、V6、V8、V12、VT和R6分别代表玉米的三叶期、六叶期(拔节期)、八叶期、十二叶期(大喇叭口期)、抽雄期和完熟期。
V3, V6, V8, V12, VT, and R6 represent the third, sixth, eighth, twelfth leaf stages, tasseling, and physiological maturity stages, respectively.

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3 讨论

近20年来, 临界氮浓度稀释曲线一直被用于作物的氮精确管理。在不同气候条件下, 在LDM、SDM、穗干物质和PDM基础上建立了临界氮浓度稀释曲线[22,34-36]。本研究在不同氮效率玉米品种上建立临界氮浓度稀释曲线与现有这些曲线进行比较来评估玉米的氮营养状况, 以深入了解这一概念, 然后找到最合适的估算氮营养方法同时建立产量预测模型。

3.1 基于不同部位建立的临界氮浓度稀释曲线之间的对比及与其他模型的比较

与之前玉米、小麦和水稻作物上基于不同部位建立的氮浓度稀释曲线一致[9,19,34], 基于LDM和SDM的临界氮浓度稀释曲线与基于PDM的模型建立基本一致。2个品种各部位的临界氮浓度稀释曲线的决定系数均达到显著水平, 在不同年份间具有很好的稳定性, 因此, 可以用于对关中地区夏玉米氮素的诊断方法。在作物中使用不同的植物指数和氮浓度的关系, 而不是单独使用PDM, 有利于增进对临界氮浓度稀释曲线概念的理解[22]。对本文建立的基于不同部位的临界氮浓度稀释曲线和已经发表的不同指标下的临界氮浓度稀释曲线做综合性的比较。在临界氮浓度稀释曲线中, 参数a代表单位生物量氮浓度; 参数b代表临界氮浓度, 随地上部干物质的增加而递减。与前人研究结果相比(表6), 植株临界氮浓度曲线的结果接近陕西关中地区参数a的范围2.14~2.25, 参数b的范围0.14~0.31, 这2个参数的值低于已有的其他模型[37,38], 进一步说明已有模型在关中地区不适宜, 需要建立属于本生态环境下全面的临界氮浓度稀释曲线。2个品种植株临界氮浓度稀释曲线a值存在显著差异, 说明参数a受到品种的影响。强生才等[28]对不同降雨年型下植株临界氮浓度曲线的建立指出参数b会随降雨年型的改变而改变。本研究结果进一步补充了该地区模型在玉米品种建立上的缺失。同一个试验中基于玉米不同部位建立的临界氮浓度稀释曲线表明, 基于LDM临界曲线参数b值最小, 说明叶片作为主要代谢器官的重要生理作用, 这一结果与在水稻研究中[22]的结果一致。同时, 基于SDM的临界氮浓度稀释曲线参数b大于基于LDM的临界氮浓度稀释曲线的b值, 这表明玉米的茎氮浓度低于叶片氮浓度, 而其稀释速率高于叶片的稀释速率, 这种差异主要是由于茎叶比引起的, 营养生长期间玉米叶片作为主要的光合器官, 大量的氮素从植物的结构组分(茎)转运到代谢组分(叶)以需要维持一定的氮浓度保证光合作用运行, 导致叶片氮浓度的缓慢下降[39]。在玉米不同器官氮积累变化的研究中发现, 叶片氮浓度始终高于茎, 并且随着干物质转运过程的发生而变化, 从而造成了茎氮浓度稀释速率高于叶片[40,41]。基于小麦和水稻LDM的临界氮浓度稀释曲线也得出了几乎相似的b[14,23]。同时氮高效品种与氮低效品种叶片参数ab值相似, 氮高效品种茎和植株的参数a值低于氮低效品种, 而茎和植株的参数b值高于氮低效品种。与不同玉米品种研究结果相似, ab值的变化方向是一致[42], 本试验结果进一步表明, 造成氮效率的差异主要来自于茎的差异而不是叶片。这可能反映了氮高效品种的茎中氮素高的稀释速率, 一方面与其干物质的快速积累有关, 另一方面反映出氮素在不同器官的分配, 氮高效品种在营养生长阶段高的茎氮素稀释速率, 保证转运到叶片更多氮素以维持光合作用, Chen等[43]的结果指出, 与氮低效品种相比, 氮高效品种具有较高的茎的氮转运效率。氮高效品种干物质及氮素在各器官的分配使得其能够在较低的氮素需求下实现高产。玉米基于茎干物质建立的临界氮浓度稀释曲线仍是一个空白, 本试验证明基于SDM的临界氮浓度稀释曲线可以用于玉米氮营养诊断, 参数a比其他作物(小麦和水稻)要小, 这也是造成本地区整株植株水平参数a偏低的原因[37], 参数b与其他作物上相似, 对于不同的氮效率品种来说, b值差异显著, 可以用于品种间的氮营养诊断。

Table 5
表5
表5V12和VT时期氮营养指数(NNI)和累积氮亏缺(AND)的相对产量(RY)的预测模型(根据4年试验数据估算)
Table 5Prediction models for estimation of relative yield (RY) from nitrogen nutrition index (NNI) and accumulated nitrogen deficit (AND) at V12 and VT stage using pooled data of four years
处理
Treatment
生育时期
Growth stage
R2回归方程
Regression equation (RY-AND)
R2回归方程
Regression equation (RY-NNI)
ZD leafV120.94RY = 1.081-0.026AND if AND > 3.5 and RY = 0.99 AND ≤ 3.50.96RY = -0.01+NNI if NNI<0.95 and RY = 0.99 NNI ≥ 0.95
VT0.95RY = 1.138-0.028AND if AND > 5.3 and RY = 0.99 AND ≤ 5.30.96RY = -0.033+1.1NNI if NNI<0.93 and RY = 0.99 NNI ≥ 0.93
SD leafV120.98RY = 1.010-0.019AND if AND > 0.5 and RY = 1 AND ≤ 0.50.98RY = 0.284+0.688NNI if NNI<1.04 and RY = 1 NNI ≥ 1.04
VT0.98RY = 0.997-0.017AND if AND > 0.4 and RY = 0.99 AND ≤ 0.40.98RY = 0.271+0.705NNI if NNI<1.02 and RY = 0.99 NNI ≥ 1.02
ZD stemV120.97RY = 1.079-0.023AND if AND > 3.6 and RY = 0.99 AND ≤ 3.60.97RY = 0.418+0.615NNI if NNI<0.93 and RY = 0.99 NNI ≥ 0.93
VT0.98RY = 1.182-0.024AND if AND > 8 and RY = 0.99 AND ≤ 80.98RY = 0.414+0.678NNI if NNI<0.85 and RY = 0.99 NNI ≥ 0.85
SD stemV120.98RY = 1.014-0.016AND if AND > 1.5 and RY = 0.99 AND ≤ 1.50.98RY = 0.494+0.506NNI if NNI<1 and RY = 1 NNI ≥ 1
VT0.98RY = 1.006-0.01AND if AND > 1.6 and RY = 0.99 AND ≤ 1.60.99RY = 0.565+0.405NNI if NNI < 1.05 and RY = 0.99 NNI ≥ 1.05
ZD plantV120.97RY = 1.148-0.015AND if AND > 10.5 and RY = 0.99 AND ≤ 10.50.97RY = 0.188+0.881NNI if NNI < 0.91 and RY = 0.99 NNI ≥ 0.91
VT0.98RY = 1.072-0.01AND if AND > 8.2 and RY = 0.99 AND ≤ 8.20.98RY = 0.139+0.886NNI if NNI < 0.96 and RY = 0.99 NNI ≥ 0.96
SD plantV120.98RY = 1.048-0.01AND if AND > 4.8 and RY = 1 AND ≤ 4.80.99RY = 0.351+0.669NNI if NNI < 0.97 and RY = 1 NNI ≥ 0.97
VT0.98RY = 1.013-0.005AND if AND > -4.6 and RY = 0.99 AND ≤ -4.60.98RY = 0.412+0.556NNI if NNI < 1.06 and RY = 1 NNI ≥ 1.06
ZD leaf、ZD stem和ZD plant分别代表正大12的叶片、茎和植株;SD leaf、SD stem和SD plant分别代表陕单609的叶片、茎和植株。V12和VT代表十二叶期和抽雄期。
ZD leaf, ZD stem and ZD plant indicate the leaf, stem, and plant of Zhengda 12; SD leaf, SD stem and SD plant indicate the leaf, stem, and plant of Shaandan 609. V12 and VT represent the twelfth leaf stage and tasseling stage.

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Table 6
表6
表6基于不同部位建立的临界氮浓度稀释曲线与其他模型参数的比较
Table 6Comparison of parameters of critical N dilution curves based on different organs and other model parameters
部位
Organ
作物
Crop
地点
Site
ab参考文献
Reference
植株玉米Maize关中平原Guanzhong Plain2.250.27Li Z P et al.[29]
Plant黄淮海平原Huanghuaihai Plain3.340.396Liang X G et al.[38]
华北平原North China Plain2.720.27Yue S C et al.[37]
本试验This study2.470.24
2.330.26
叶片玉米Maize黄淮海平原Huanghuaihai Plain3.450.22Zhao B et al.[10]
Leaf水稻Rice长江中下游平原the Middle-Lower Yangtze Plains3.760.22Wang X L[44]
小麦Wheat长江中下游平原the Middle-Lower Yangtze Plains3.060.15Yao X et al.[23]
关中平原Guanzhong Plain3.960.14Qiang S C et al.[45]
本试验This study2.640.20
2.610.21
小麦Wheat长江中下游平原the Middle-Lower Yangtze Plains2.500.44Wang X L [44]
Stem水稻Rice长江中下游平原the Middle-Lower Yangtze Plains2.260.32Ata-Ul-Karim S T et al.[22]
本试验This study1.580.39
1.830.34
ab为模型参数。a and b are the model parameters.

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3.2 对夏玉米氮素诊断和作物建模的意义

建立基于LDM、SDM和PDM的临界氮浓度稀释曲线的主要目的是用农学的研究方法诊断作物的氮营养状态, 通过临界氮浓度稀释曲线来诊断夏玉米营养生育阶段的氮素盈亏状态, 建立与产量的模型[46]从而对产量进行预测。同时, Zhao等[34]提出开花前玉米的氮素状态与花后穗部显著相关, 所以如果在开花前能很好的诊断玉米氮素状况对于花后施肥和成熟期产量的预测具有一定的预见性。在达到最佳氮肥水平前, 夏玉米的NNI和籽粒产量逐渐增加。本研究中的叶片NNI值的范围为0.66~1.14, 茎NNI值的范围为0.50~1.28, 植株NNI值为0.62~1.20。各部位NNI值达到1左右时, 产量不再增加。Ziadi等[17]确定了当玉米的NNI低于0.93时, NNI与RY之间呈正相关。基于植株NNI与产量的关系显示了使用NNI进行估算作物氮需求、作物氮状况和籽粒产量潜力[6-7,13-14,47]的可行性。V12-VT时期是考虑玉米追施氮肥的关键时期, 并且在这阶段建立的模型具有很强的稳定性。与水稻相比[22], 追肥相对靠后, Zhao等[34]指出玉米更依赖于开花后氮素的状况, 而小麦更依赖于花前氮的状况来满足穗部的生长, 这可能由于, 相比C3作物, C4作物高的光合氮效率使得其可以利用低的氮素来获得高的产量。V12-VT阶段的RY与NNI和RY与AND之间的关系准确地解释了在受氮素限制和不受氮素限制生长条件下RY的变化。因此, 这些阶段可以作为产量预测模型的最佳时期。相反, 早期营养生长过程中RY与NNI和RY与AND之间的关系, 稳定性相对较差, 因此预测玉米产量的准确性较差。造成稳定性较差的原因, 一方面, 在作物早期生长阶段, 茎叶比的变化比后期生长阶段变化更快, 玉米苗期到拔节后期的茎叶比和氮浓度差异较大[10], 另一方面, 大田条件下, 玉米生长不均匀难以评估玉米的生长阶段。这些都会影响基于生长阶段的临界氮浓度稀释曲线对植物氮诊断的准确性。基于不同部位的临界氮浓度稀释曲线的NNI和AND可以对RY进行准确地预测, 顺应精准农业对玉米产量的实时估测提出的要求, 所以上述模型关系可以用于估计玉米籽粒产量和对管理决策的制定。以上提出的诊断方法为作物模型参数的调整优化具有很大的借鉴, 以便在不同的气候条件、基因型和管理实践下应用。这些将有助于提供对作物生长和产量的准确预测, 为生产和管理决策提供支持。这种方法的应用可以帮助根据不同的氮肥施用量设定产量目标。除了破坏性采样方法外, 叶绿素仪和冠层反射率等方法还可以用于NNI的快速和非破坏性估计[7,48-49]。因此, RY和NNI之间的关系的建立对于玉米产量的估算具有实际意义[17]。在本研究中, 关键生育时期, 基于不同部位的临界氮浓度稀释曲线的NNI与RY和AND与RY的关系, 可以看出在V12-VT时期基于LDM、SDM和PDM建立的临界氮浓度稀释曲线的NNI和AND对RY的预测是可靠的。鉴于该模型可准确地探测作物氮素营养状况, 因此, 氮营养指数和氮素亏缺模型可以应用于精准农业变量施肥中。

总而言之, 本研究表明, 基于不同部位的临界氮浓度稀释曲线, 玉米在关键生长阶段RY与NNI和AND的关系可以用于估算籽粒产量。特别是在V12-VT阶段的关系准确地解释了在受氮素限制和不受氮素限制生长条件下RY的偏差, 并且可靠地用于预测籽粒产量。此外, 利用V12-VT阶段的基于不同部位的临界氮浓度稀释曲线预测籽粒产量与玉米生产系统中追施氮肥的时间是同步的。因此, 新建立的模型的实施不仅可以预测玉米产量潜力, 而且有助于玉米生产中精确的氮素管理。为了保证模型更加广泛地应用, 需要对不同气候, 水分和耕作条件下进行模型建立和验证。

4 结论

本研究首次采用不同部位指标建立和比较评价了玉米临界氮浓度稀释曲线。使用独立的试验数据验证了3种临界氮浓度稀释曲线都是可靠的。基于LDM、SDM和PDM建立的临界氮浓度稀释曲线对RY的预测是稳定。因此, 出于成本的考虑, 在一定的条件下, 可以用基于LDM和SDM建立的临界氮浓度稀释曲线可以取代基于PDM建立的临界氮浓度稀释曲线对玉米的氮素进行诊断和产量预测。在受氮素限制条件下, 玉米基于不同部位计算的NNI与产量构成之间显著正相关。这项研究表明, 基于不同部位的临界氮浓度稀释曲线的关键作物生长阶段的RY-NNI和RY-AND关系可以用于为玉米适当的氮肥施用量做出指导。特别是, V12-VT阶段的稳定关系准确地解释了氮素缺乏和最佳生长条件下RY和NNI与RY和AND的变化, 并且可以可靠地对RY进行预测, 从而量化了开花前氮肥的施用量。

参考文献 原文顺序
文献年度倒序
文中引用次数倒序
被引期刊影响因子

Erisman J W, Sutton M A, Galloway J, Klimont Z, Winiwarter W. How a century of ammonia synthesis changed the world
Nat Geosci, 2008,1:636-639.

[本文引用: 1]

王西娜, 王朝辉, 李生秀. 施氮量对夏季玉米产量及土壤水氮动态的影响
生态学报, 2007,27(1):197-204.

[本文引用: 1]

Wang X N, Wang Z H, Li S X. The effect of nitrogen fertilizer rate on summer maize yield and soil water nitrogen dynamics
Acta Ecol Sin, 2007,27(1):197-204 (in Chinese with English abstract).

[本文引用: 1]

Ramanantenasoa M M J, Génermont S, Gilliot J M, Makowski D. Metamodeling methods for estimating ammonia volatilization from nitrogen fertilizer and manure applications
J Environ Manag, 2019,236:195-205.

[本文引用: 1]

Galloway J N, Cowling E B. Reactive nitrogen and the world: 200 years of change
Ambio, 2002,3:64-71.

[本文引用: 1]

宁芳, 张元红, 温鹏飞, 王瑞, 王倩, 董朝阳, 贾广灿, 李军. 不同降水状况下旱地玉米生长与产量对施氮量的响应
作物学报, 2019,45:777-791.

[本文引用: 1]

Ning F, Zhang Y H, Wen P F, Wang R, Wang Q, Dong Z Y, Jia G C, Li J. Responses of maize growth and yield to nitrogen application in dryland under different precipitation conditions
Acta Agron Sin, 2019,45:777-791 (in Chinese with English abstract).

[本文引用: 1]

Zhao B, Ata-Ul-Karim S T, Liu Z D, Zhang J Y, Xiao J F, Liu Z G, Qin A Z, Ning D F, Yang Q X, Zhang Y H, Duan A W. Simple assessment of nitrogen nutrition index in summer maize by using chlorophyll meter readings
Front Plant Sci, 2018,9:11.

DOI:10.3389/fpls.2018.00011URLPMID:29403521 [本文引用: 2]
Rapid and non-destructive diagnostic tools to accurately assess crop nitrogen nutrition index (NNI) are imperative for improving crop nitrogen (N) diagnosis and sustaining crop production. This study was aimed to develop the relationships among NNI, leaf N gradient, chlorophyll meter (CM) readings gradient, and positional differences chlorophyll meter index [PDCMI, the ratio of CM readings between different leaf layers (LLs) of crop canopy] and to validate the accuracy and stability of these relationships across the different LLs, years, sites, and cultivars. Six multi-N rates (0-320 kg ha(-1)) field experiments were conducted with four summer maize cultivars (Zhengdan958, Denghai605, Xundan20, and Denghai661) at two different sites located in China. Six summer maize plants per plot were harvested at each sampling stage to assess NNI, leaf N concentration and CM readings of different LLs during the vegetative growth period. The results showed that the leaf N gradient, CM readings gradient and PDCMI of different LLs decreased, while the NNI values increased with increasing N supply. The leaf N gradient and CM readings gradient increased gradually from top to bottom of the canopy and CM readings of the bottom LL were more sensitive to changes in plant N concentration. The significantly positive relationship between NNI and CM readings of different LLs (LL1 to LL3) was observed, yet these relationships varied across the years. In contrast, the relationships between NNI and PDCMI of different LLs (LL1 to LL3) were significantly negative. The strongest relationship between PDCMI and NNI which was stable across the cultivars and years was observed for PDCMI1-3 (NNI = -5.74 x PDCMI1-3+1.5, R(2) = 0.76(**)). Additionally, the models developed in this study were validated with the data acquired from two independent experiments to assess their accuracy of prediction. The root mean square error value of 0.1 indicated that the most accurate and robust relationship was observed between PDCMI1-3 and NNI. The projected results would help to develop a simple, non-destructive and reliable approach to accurately assess the crop N status for precisely managing N application during the growth period of summer maize crop.

Zhao B, Liu Z D, Ata-Ul-Karim S T, Xiao J F, Liu Z G, Qi A Z, Ning D F, Nan J Q, Duan A W. Rapid and nondestructive estimation of the nitrogen nutrition index in winter barley using chlorophyll measurements
Field Crops Res, 2016,185:59-68.

[本文引用: 4]

Justes E, Mary B, Meynard J M, Machet J M, Thelier-Huche L. Determination of A critical nitrogen dilution curve for winter-wheat Crops
Ann Bot, 1994,74:397-407.

[本文引用: 2]

Ata-Ul-Karim S T, Yao X, Liu X J, Cao W X, Zhu Y. Development of critical nitrogen dilution curve ofjaponica rice in Yangtze River Reaches
Field Crops Res, 2013,149:149-158.

[本文引用: 2]

Zhao B, Ata-Ul-Karim S T, Liu Z, Ning D F, Xiao J F, Liu Z G, Qin A Z, Nan J Q, Duan A W. Development of a critical nitrogen dilution curve based on leaf dry matter for summer maize
Field Crops Res, 2017,208:60-68.

[本文引用: 5]

Wang X L, Ye T Y, Ata-Ul-Karim S T, Zhu Y, Liu L L, Cao W X, Tang L. Development of a critical nitrogen dilution curve based on leaf area duration in wheat
Front Plant Sci, 2017,8:1517.

URLPMID:28928757 [本文引用: 1]

付江鹏, 贺正, 贾彪, 刘慧芳, 李振洲, 刘志. 滴灌玉米临界氮稀释曲线与氮素营养诊断研究
作物学报, 2020,46:290-299.

[本文引用: 1]

Fu J P, He Z, Jia B, Liu H F, Li Z Z, Liu Z. Critical nitrogen dilution curve and nitrogen nutrition diagnosis of maize with drip irrigation
Acta Agron Sin, 2020,46:290-299 (in Chinese with English abstract).

[本文引用: 1]

Ata-Ul-Karim S T, Liu X J, Lu Z Z, Yuan Z F, Zhu Y, Cao W X. In-season estimation of rice grain yield using critical nitrogen dilution curve
Field Crops Res, 2016,195:1-8.

[本文引用: 4]

Ata-Ul-Karim S T, Liu X J, Lu Z Z, Zheng H B, Cao W X, Zhu Y. Estimation of nitrogen fertilizer requirement for rice crop using critical nitrogen dilution curve
Field Crops Res, 2017,201:32-40.

[本文引用: 4]

岳松华, 刘春雨, 黄玉芳, 叶优良. 豫中地区冬小麦临界氮稀释曲线与氮营养指数模型的建立
作物学报, 2016,42:909-916.

[本文引用: 1]

Yue S H, Liu C Y, Huang Y F, Ye Y L. Simulating critical nitrogen dilution curve and modeling nitrogen nutrition index in winter wheat in central Henan area
Acta Agron Sin, 2016,42:909-916 (in Chinese with English abstract).

[本文引用: 1]

Yao Y K, Miao Y X, Cao Q, Wang H Y, Gnyp M L, Bareth G, Khosla R, Yang W, Liu F Y, Liu C. In-season estimation of rice nitrogen status with an active crop canopy sensor
IEEE J-Stars, 2014,7:4403-4413.

[本文引用: 1]

Ziadi N, Bélanger G, Claessens A, Lefebvre L, Cambouris A N, Tremblay N, Nolin M C, Parent L E. Determination of a critical nitrogen dilution curve for spring wheat
Agron J, 2010,102:241-250.

[本文引用: 3]

Lemaire G, Jeuffroy M H, Gastal F. Diagnosis tool for plant and crop N status in vegetative stage: Theory and practices for crop N management
Eur J Agron, 2008,28:614-624.

[本文引用: 2]

Zhang K, Yuan Z F, Yang T C, Lu Z Z, Cao Q, Tian Y C, Zhu Y, Cao W X, Liu X J. Chlorophyll meter-based nitrogen fertilizer optimization algorithm and nitrogen nutrition index for in-season fertilization of paddy rice
Agron J, 2020,112:1-13.

[本文引用: 2]

Ziadi N, Brassard M, Bélanger G, Claessens A, Tremblay N, Cambouris A, Nolin M, Parent L E. Chlorophyll measurements and nitrogen nutrition index for the evaluation of corn nitrogen status
Agron J, 2008,100:271-273.

[本文引用: 2]

Ata-Ul-Karim S T, Yao X, Liu X J, Cao W X, Zhu Y. Determination of critical nitrogen dilution curve based on stem dry matter in rice
PLoS One, 2014,9:e104540.

DOI:10.1371/journal.pone.0104540URLPMID:25127042 [本文引用: 2]

Ata-Ul-Karim S T, Zhu Y, Liu X J, Cao Q, Tian Y C, Cao W X. Comparison of different critical nitrogen dilution curves for nitrogen diagnosis in rice
Sci Rep, 2017,7:42679.

DOI:10.1038/srep42679URLPMID:28262685 [本文引用: 6]
The critical nitrogen (N) dilution curve is a suitable analytical tool for in-season estimation of N status to implement precision N management. This study was undertaken for a comprehensive comparison of N dilution curves in Japonica and Indica rice to investigate, whether a single curve can be used for both rice ecotypes and to determine the most robust plant index for assessing N status in rice ecotypes. The different N dilution curves were developed based on plant dry matter (PDM), leaf area index (LAI), leaf dry matter (LDM) and stem dry matter (SDM) for N diagnosis in Japonica and Indica rice. The comparison of N dilution curves of two rice ecotypes showed non-significant differences, therefore a single/unified curve can be used to assess plant N status for precision N management in both rice ecotypes. The relationships between PDM based, with LAI, LDM, and SDM based N nutrition index, accumulated N deficit and N requirement, indicated that leaf based approaches could be used as substitutes for PDM approach. The lower coefficient b values of LDM based curve (due to efficient physiological N use in leaves) implied that LDM was the most appropriate approach for developing N curve as compared to other approaches.

Yao X, Zhao B, Tian Y C, Liu X J, Ni J, Cao W X, Zhu Y. Using leaf dry matter to quantify the critical nitrogen dilution curve for winter wheat cultivated in eastern China
Field Crops Res, 2014,159:33-42.

[本文引用: 3]

Huang S Y, Miao Y X, Cao Q, Yao Y K, Zhao G M, Yu W F, Shen J N, Yu K, Bareth G. A new critical nitrogen dilution curve for rice nitrogen status diagnosis in Northeast China
Pedosphere, 2018,28:814-822.

[本文引用: 1]

Zhao B, Yao X, Tian Y C, Liu X J, Ata-Ul-Karim S T, Ni J, Cao W X, Zhu Y. New critical nitrogen curve based on leaf area index for winter wheat
Agron J, 2014,106:379-383.

[本文引用: 1]

Zhao B, Ata-Ui-Karim S T, Yao X, Tian Y C, Cao W X, Zhu Y Liu X J. A new curve of critical nitrogen concentration based on spike dry matter for winter wheat in Eastern China
PLoS One, 2016,11:e164545.

[本文引用: 2]

Zhao Z G, Wang E L, Wang Z M, Zang H Z, Liu Y P, Angus J F. A reappraisal of the critical nitrogen concentration of wheat and its implications on crop modeling
Field Crops Res, 2014,164:65-73.

[本文引用: 1]

强生才, 张富仓, 向友珍, 张燕, 闫世程, 邢英英. 关中平原不同降雨年型夏玉米临界氮稀释曲线模拟及验证
农业工程学报, 2015,31(17):168-175.

[本文引用: 2]

Qiang S C, Zhang F C, Xiang Y Z, Zhang Y, Yan S C, Xing Y Y. Simulation and validation of critical nitrogen dilution curve for summer maize in Guanzhong Plain during different rainfall years
Trans CSAE, 2015,31(17):168-175 (in Chinese with English abstract).

[本文引用: 2]

李正鹏, 冯浩, 宋明丹. 关中平原冬小麦临界氮浓度稀释曲线和氮营养指数研究
农业机械学报, 2015,46(10):177-183.

[本文引用: 2]

Li Z P, Feng H, Song M D. Development and validation of critical nitrogen content curve for maize in Guanzhong area
Trans CSAM, 2015,46(10):135-141 (in Chinese with English abstract).

[本文引用: 2]

Bremner J M, Mulvancy C S. Nitrogen-total. In: Page A L, Miller R H, Keeney D R, eds. Methods of Soil Analysis. Madison: American Society of Agronomy, 1982. pp 595-624.
[本文引用: 1]

Yang J, Greenwood D J, Rowell D L, Wadsworth G A, Burns I G. Statistical methods for evaluating a crop nitrogen simulation model, N_ABLE
Agric Syst, 2000,64:37-53.

[本文引用: 1]

Jamieson P D, Porter J R, Wilson D R. A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand
Field Crops Res, 1991,27:337-350.

[本文引用: 1]

He Z Y, Qiu X L, Ata-Ul-Karim S T, Li Y D, Liu X J, Cao Q, Zhu Y, Cao W X, Tang L. Development of a critical nitrogen dilution curve of double cropping rice in south China
Front Plant Sci, 2017,8:638.

DOI:10.3389/fpls.2017.00638URLPMID:28503181 [本文引用: 1]
The concept of critical nitrogen (Nc) concentration can be implemented to diagnose in-season plant nitrogen (N) status for optimizing N fertilizer management. The Nc dilution curves have been established for rice (Oryza sativa L.) grown in different climatic regions, yet no attempt has been made to develop the Nc dilution curve for double cropping rice regions. This study was undertaken to develop the Nc dilution curves for double cropping rice in south China for assessment of in-season N status and to establish the relationships N nutrition index (NNI) and relative yield (RY) for in-season prediction of rice grain yield. Three different N application rate field experiments using six Indica rice varieties, including two early rice hybrids and four late rice hybrids were carried out in east China. The Nc dilution curves based on whole plant N concentration were determined and described as, Nc = 3.37 W(-0.44) for early rice and Nc = 3.69 W(-0.34) for late rice. The constant N concentration at early growth stage was 3.31 and 3.15% DM for early and late rice, respectively. Late rice showed a higher capacity of N accumulation and a lower rate of N decline per unit shoot biomass as compared to early rice. The curves for present study were different from the existing reference curves for Indica and Japonica rice grown in different rice growing regions. Integrated N nutrition index (NNIint) based on Nc was used to estimate RY at different growth periods using linear regression functions. The results showed that the critical curves and relationship between NNIint and RY could be used as a reliable indicator of N status diagnosis, grain yield prediction as well as to provide technical support in N management for double cropping rice in south China.

Zhao B, Niu X L, Ata-Ul-Karim S T, Wang L G, Duan A W, Liu Z D, Lemaire G. Determination of the post-anthesis nitrogen status using ear critical nitrogen dilution curve and its implications for nitrogen management in maize and wheat
Eur J Agron, 2020,113:125967.

[本文引用: 4]

Song L G, Wang S, Ye W J. Establishment and application of critical nitrogen dilution curve for rice based on leaf dry matter
Agron J, 2020,10:367.



梁效贵, 张经廷, 周丽丽, 李旭辉, 周顺利. 华北地区夏玉米临界氮稀释曲线和氮营养指数研究
作物学报, 2013,39:292-299.

[本文引用: 1]

Liang X G, Zhang J T, Zhou L L, Li X H, Zhou S L. Critical nitrogen dilution curve and nitrogen nutrition index for summer maize in north China plain
Acta Agron Sin, 2013,39:292-299 (in Chinese with English abstract).

[本文引用: 1]

Yue S C, Sun F L, Meng Q F, Zhao R F, Li F, Chen X P, Zhang F S, Cui Z L. Validation of a critical nitrogen curve for summer maize in the North China Plain
Pedosphere, 2014,24:76-83.

[本文引用: 3]

Liang X G, Zhang Z L, Zhou L L, Shen S, Gao Z, Zhang L, Lin S, Pan Y Q, Zhou S L. Localization of maize critical N curve and estimation of NNI by Chlorophyll
Int J Plant Prod, 2018,12:85-94.

[本文引用: 2]

Novoa R, Loomis R. Nitrogen and plant production
Plant Soil, 1981,58:177-204.

[本文引用: 1]

侯有良, Brien L O, 钟改荣. 小麦不同器官氮素累积分布动态规律的研究
作物学报, 2001,27:493-499.

[本文引用: 1]

Hou Y L, Brien L O, Zhong G R. Study on the dynamic change of the distribution and accumulation of nitrogen in different plant parts of wheat
Acta Agron Sin, 2001,27:493-499 (in Chinese with English abstract).

[本文引用: 1]

Sinclair T R, Horie T. Leaf nitrogen, photosynthesis and crop radiation use efficiency: a review
Crop Sci, 1989,29:90-98.

[本文引用: 1]

安志超, 黄玉芳, 汪洋, 赵亚南, 岳松华, 师海斌, 叶优良. 植物营养与肥料学报, 2019,25:123-133.
[本文引用: 1]

An Z C, Huang Y F, Wang Y, Zhao Y N, Yue S H, Shi H B, Ye Y L. Critical nitrogen concentration dilution model and nitrogen nutrition diagnosis in summer maize with different nitrogen efficiencies
J Plant Nutr Fert, 2019,25:123-133 (in Chinese with English abstract).

[本文引用: 1]

Chen Y L, Xiao C X, Chen X C, Li Q, Zhang J, Chen F J, Yuan L X, Mi G H. Characterization of the plant traits contributed to high grain yield and high grain nitrogen concentration in maize
Field Crops Res, 2014,159:1-9.

[本文引用: 1]

王晓玲. 长江中下游稻麦两熟区冬小麦植株器官临界氮浓度模型构建及氮素诊断调控研究. 南京农业大学博士学位论文,
江苏南京, 2017.

[本文引用: 2]

Wang X L. Study on Construction Critical Nitrogen Concentration Dilution Models Based on Plant Organs and Diagnosis and Regulation of Wheat in the Middle and Lower Reaches of the Yangtze River. PhD Dissertation of Graduate School of Nanjing Agricultural University
Nanjing, Jiangsu, China, 2017 (in Chinese with English abstract).

[本文引用: 2]

强生才, 张富仓, 田建柯, 吴悠, 闫世程, 范军亮. 基于叶片干物质的冬小麦临界氮浓度稀释曲线模拟研究
农业机械学报, 2015,46(11):121-128.

[本文引用: 1]

Qiang S C, Zhang F C, Tian J K, Wu Y, Yan S C, Fan J L. Deve lopment of critical nitrogen dilution curve in winter wheat based on leaf dry matter
Trans CSAM, 2015,46(11):121-128 (in Chinese with English abstract).

[本文引用: 1]

Stockle C O, Debaeke P. Modeling crop nitrogen requirements: a critical analysis
Eur J Agron, 1997,7:161-169.

[本文引用: 1]

刘朋召, 师祖姣, 宁芳, 王瑞, 王小利, 李军. 不同降雨状况下渭北旱地春玉米临界氮稀释曲线与氮素营养诊断
作物学报, 2020,46:1225-1237.

[本文引用: 1]

Liu P Z, Shi Z J, Ning F, Wang R, Wang X L, Li J. Critical nitrogen dilution curves and nitrogen nutrition diagnosis of spring maize under different precipitation patterns in Weibei dryland
Acta Agron Sin, 2020,46:1225-1237 (in Chinese with English abstract).

[本文引用: 1]

Yuan Z F, Ata-Ul-Karim S T, Cao Q, Lu Z Z, Cao W X, Zhu Y, Liu X J. Indicators for diagnosing nitrogen status of rice based on chlorophyll meter readings
Field Crops Res, 2016,185:12-20.

[本文引用: 1]

Zhao B, Duan A W, Ata-Ul-Karim S T, Liu Z D, Chen Z F, Gong Z H, Zhang J Y, Xiao J F, Liu Z G, Qin A Z, Ning D F. Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize
Eur J Agron, 2018,93:113-125.

[本文引用: 1]

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