Leaf area index based nitrogen diagnosis in irrigated lowland rice

Leaf area index(LAI)is used for crop growth monitoring in agronomic research,and is promising to diagnose the nitrogen(N)status of crops.This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice.Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014.Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice.LAI was determined by LI-3000,and estimated indirectly by LAI-2000 during vegetative growth period.Group and individual plant characters(e.g.,tiller number(TN)and plant height(H))were investigated simultaneously.Two N indicators of plant N accumulation(NA)and N nutrition index(NNI)were measured as well.A calibration equation(LAI=1.7787LAI_(2000)–0.8816,R~2=0.870~(**))was developed for LAI-2000.The linear regression analysis showed a significant relationship between NA and actual LAI(R~2=0.863~(**)).For the NNI,the relative LAI(R~2=0.808~(**))was a relatively unbiased variable in the regression than the LAI(R~2=0.33~(**)).The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice(NA=29.778LAI–5.9397;NNI=0.7705RLAI+0.2764).Finally,a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H(LAI=–0.3375(TH×H×0.01)~2+3.665(TH×H×0.01)–1.8249,R~2=0.875~(**)).With these models,the N status of rice can be diagnosed conveniently in the field.     

An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat

The nitrogen nutrition index(NNI) is a reliable indicator for diagnosing crop nitrogen(N) status. However, there is currently no specific vegetation index for the NNI inversion across multiple growth periods. To overcome the limitations of the traditional direct NNI inversion method(NNI_(T1)) of the vegetation index and traditional indirect NNI inversion method(NNI_(T2)) by inverting intermediate variables including the aboveground dry biomass(AGB) and plant N concentration(PNC), this study proposed a new NNI remote sensing index(NNI_(RS)). A remote-sensing-based critical N dilution curve(Nc_(_RS)) was set up directly from two vegetation indices and then used to calculate NNI_(RS). Field data including AGB, PNC, and canopy hyperspectral data were collected over four growing seasons(2012–2013(Exp.1), 2013–2014(Exp. 2), 2014–2015(Exp. 3), 2015–2016(Exp. 4)) in Beijing, China. All experimental datasets were cross-validated to each of the NNI models(NNI_(T1), NNI_(T2) and NNI_(RS)). The results showed that:(1) the NNI_(RS) models were represented by the standardized leaf area index determining index(sLAIDI) and the red-edge chlorophyll index(CI_(red edge)) in the form of NNI_(RS)=CI_(red edge)/(a×sLAIDI~b), where "a" equals 2.06, 2.10, 2.08 and 2.02 and "b" equals 0.66, 0.73, 0.67 and 0.62 when the modeling set data came from Exp.1/2/4, Exp.1/2/3, Exp.1/3/4, and Exp.2/3/4, respectively;(2) the NNI_(RS) models achieved better performance than the other two NNI revised methods, and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14, respectively;(3) when the remaining data were used for verification, the NNI_(RS) models also showed good stability, with RMSE values of 0.09, 0.18, 0.13 and 0.10, respectively. Therefore, it is concluded that the NNI_(RS) method is promising for the remote assessment of crop N status.     

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R²=0.67–0.89, RMSE=13.63–23.71 mg g^–1, MAE=10.75–17.59 mg g^–1) performed better than the direct inversion models (R²=0.61–0.80, RMSE=18.01–25.12 mg g^–1, MAE=12.96–18.88 mg g^–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R²=0.89, RMSE=13.63 mg g^–1, MAE=10.75 mg g^–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.     

不同光谱植被指数反演冬小麦叶氮含量的敏感性研究

【目的】氮素是作物生长发育过程中最重要的营养元素之一,研究叶氮含量反演的有效光谱指标设置,为应用高光谱植被指数反演作物叶氮含量,以及作物的实时监测与精确诊断提供重要依据。【方法】以冬小麦为例,选取涵盖冬小麦全生育期不同覆盖程度225组冠层光谱与叶氮含量数据,通过遥感方法建立模型,模拟了不同光谱指标,即中心波长、信噪比和波段宽度对定量模型的影响,通过模型精度评价指标决定系数(coefficient of determination,R~2)、根均方差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)、平均相对误差(mean relative error,MRE)和显著性检验水平(P0.01)确定最优模型及最佳指标,分析光谱指标对叶氮含量定量模型反演的敏感性和有效性。【结果】反演冬小麦叶氮含量的最佳植被指数为MTCI_B,与实测叶氮含量的相关性最好(R~2=0.7674,RMSE=0.5511%,MAE=0.4625%,MRE=11.11个百分点,且P0.01),对应的最佳指标为中心波长420 nm、508 nm和405 nm,波段宽度1 nm,信噪比大于70 DB;高覆盖状况反演的最优指数为RVIinf_r(R~2=0.6739,RMSE=0.2964%,MAE=0.2851%,MRE=6.44个百分点,且P0.01),最优中心波长为826 nm和760 nm;低覆盖状况反演的最优指数为MTCI(R~2=0.8252,RMSE=0.4032%,MAE=0.4408%,MRE=12.22个百分点,且P0.01),最优中心波长为750 nm、693 nm和680 nm;应用最适于高低覆盖的植被指数RVIinf_r和MTCI构建的联合反演模型(R~2=0.9286,RMSE=0.3416%,MAE=0.2988%,MRE=7.16个百分点,且P0.01),明显优于最佳单一指数MTCI_B;模拟Hyperion和HJ1A-HSI传感器数据,联合反演模型精度(R~2为0.92—0.93,RMSE在0.37%—0.39%,MAE为0.285%左右,MRE约为7.00个百分点)明显优于单一植被指数反演精度(R~2为0.79—0.81,RMSE为0.63%—0.66%,MAE为0.455%左右,MRE约为10.90个百分点)。【结论】利用高光谱植被指数可有效实现作物叶氮含量反演,作物叶氮含量定量反演对不同光谱指标—中心波长、信噪比和波段宽度,具有较强敏感性。应用多指数联合反演模型,可显著提高反演精度,并且联合反演模型在不同高光谱传感器下有一定普适性。     

基于土壤优化光谱参数估测太湖地区土壤全氮含量

为明确太湖地区土壤全氮的高光谱特征，构建定量分析模型，以江苏省无锡市滨湖区为研究区域，选取地理位置跨度大、土壤质地相似的93个样品，进行土壤风干样品全氮含量测定和光谱数据采集，对光谱反射率进行一阶微分，运用相关系数峰谷值法筛选敏感波长，将敏感波长两两结合进行土壤调节光谱指数（MSASI）运算。将两两结合后敏感波段分别采用多元线性回归分析、人工神经网络分析和偏最小二乘法构建土壤全氮含量的定量高光谱分析模型。结果表明，研究区内土壤全氮含量与光谱反射率呈正相关，敏感波段包括420~444 nm和480~537 nm。基于土壤调节光谱指数的多元线性回归分析对敏感波段诊断的效果最佳（R²=0.98、RMSE=0.04），其精度高、可靠性强，是筛选出的最佳土壤全氮含量估测模型。偏最小二乘法模型（R²=0.70、RMSE=0.13）次之，而人工神经网络模型（R²=0.69、RMSE=0.15）精度最低。该研究结果为太湖地区土壤全氮水平的高光谱快速估测提供了方法借鉴，可为土壤养分精准管理提供技术参考。     

基于数字图像技术的冬油菜氮素营养诊断

【目的】利用田间氮肥梯度试验探讨数字图像技术对冬油菜氮素营养无损评估预测的可行性,明确该技术的最佳数码参数和方程模型,为数字图像技术进行冬油菜氮素无损诊断提供依据。【方法】2013-2014年在湖北省武穴市开展不同施氮处理田间试验,以冬油菜为试验材料,设置不同氮素水平（0、90、180、270和360 kg·hm^-2）,分别于六叶期、十叶期、蕾薹期和开花期,利用数码相机获取冠层数字图像数据,同时采集植株样品分析其生长特征值,研究其相关性并建立氮素营养参数的方程模型。利用2014-2015年独立氮肥水平试验,对上述方程模型拟合精度进行验证并绘制1﹕1线性关系图。【结果】数字图像红光值（R）、红光标准化值（NRI）和绿光与蓝光比值（G/B）与冬油菜氮营养状况常规诊断指标地上部生物量、叶片氮浓度和叶绿素浓度等呈负相关关系,而绿光值（G）、蓝光值（B）、绿光与红光比值（G/R）、蓝光与红光比值（B/R）、绿光标准化值（NGI）和蓝光标准化值（NBI）则与上述指标呈正相关关系,红光标准化值（NRI）与其他数码参数相比能更好地表征冬油菜的氮素营养状况,蕾薹期红光标准化值NRI与氮肥用量、地上部生物量、叶片氮浓度、叶绿素浓度、氮素吸收量和氮营养指数之间的关系可分别用线性方程y(t·hm^-2)=-8.003x+2.706、y(t·hm^-2)=-106.072x+38.200、y(g·kg^-1)=-692.99x+ 261.84、y(mg·g^-1)=-12.750x+5.665、y(kg·hm^-2)=-4087.416x+1414.274和y=-27.198x+9.812来表达,其相关性达到极显著水平。2014-2015年独立试验模型检验结果表明,叶片氮浓度、叶绿素浓度和氮营养指数实测值与预测值的决定系数R²分别为0.917^**、0.746^**和0.953^**;均方根误差RMSE分别为0.821、0.330和0.228;相对误差RE %分别为26.32%、28.57%和28.39%,模型预测精度较好。【结论】数字图像技术可以用于冬油菜氮素营养的评估预测,评估时期为蕾薹期（包括）之前均可,最佳预测参数为红光标准化值NRI,参数的最佳方程模型为直线方程函数。     

基于双波段光谱仪CGMD-302的小麦叶面积指数和叶干重监测

为探究双波段光谱仪CGMD-302在监测小麦长势上的可靠性与精准性,同时使用高光谱仪UniSpec SC与双波段光谱仪CGMD-302测试各生育时期小麦冠层信息,并定量分析了植被指数NDVI、RVI、DVI与叶面积指数和叶片干重之间的线性关系。结果表明,基于相同波段反射率计算出的高光谱仪植被指数和双波段光谱仪植被指数均能较好监测小麦群体长势。在CGMD-302监测的叶面积指数模型中,拟合方程的决定系数(R~2)均高于0.89,用以检验模型的均方根误差(RMSE)和相对误差(RE)分别小于0.792和0.225;叶片干重模型中,决定系数(R2)均高于0.85,用以检验模型的均方根误差(RMSE)和相对误差(RE)分别小于440kg/hm~2和0.239。通过分析发现,施氮270kg/hm~2既能保证产量又能兼顾品质,可作为适宜施氮量。适宜施氮量下,拔节期和孕穗期小麦适宜叶面积指数分别为:3.65±0.09和5.95±0.32;适宜叶干重分别为:(1 554±168)和(2 231±130)kg/hm~2。结合CGMD-302监测模型可推算出拔节期和孕穗期适宜冠层群体的植被指数区间并应用于冠层群体诊断。     

Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content(LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to determine LWC. Thus, based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage. Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature. Combined with methods such as vegetation index construction, correlation analysis, regression analysis, BP neural network(BPNN), etc., we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC. LWC decreased faster under severe stress than under slight stress, but the effect of long-term slight stress was greater than that of short-term severe stress. The sensitive spectral bands of LWC were located in the visible(VIS, 400–780 nm) and short-wave infrared(SWIR, 1 400–2 500 nm) regions. The BPNN Model with the original spectrum at 648 nm, the first derivative spectrum at 500 nm, the red edge position(λr), the new vegetation index RVI(437, 466), NDVI(437, 466) and NDVI′(747, 1 956) as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set: R~2=0.889, RMSE=0.138; validation set: R~2=0.891, RMSE=0.518). These results have important theoretical significance and practical application value for the precise control of waterlogging stress.     

基于高光谱遥感的叶片总初级生产力GPP反演

为探究利用高光谱植被指数反演叶片总初级生产力(GPP)的模型,以湖北省武汉大学试验田油菜和小麦叶片高光谱反射率和光照强度(PARin)为数据源,利用7种植被指数与PARin的乘积分别反演2种植被叶片GPP,构建线性及非线性回归模型,并对模型进行验证。结果表明:1)从油菜生理特点出发,需要分生育期建模。在选择的7种植被指数中,花期SR构建的一次模型效果最优,建模和验模R2分别为0.80和0.82,RMSE不超过2.85g/(m~2·d);荚果期选择CIred edge和MTCI为优选模型,建模和验模R2为0.84和0.72,RMSE3.91g/(m~2·d);全时期基于红边波段的CIred edge、MTCI为优选模型,建模集R2达到0.80,RMSE3.67g/(m~2·d),验模R2达到0.65,RMSE3.92g/(m~2·d);2)小麦中NDVI模型效果最优,建模集R2=0.59,RMSE=2.80g/(m~2·d),验模R2=0.67,RMSE=3.39g/(m~2·d)。将油菜与小麦做对比,基于红边波段的植被指数CIred edge和MTCI对2种植被差异不敏感,R2为0.72~0.73,表明CIred edge和MTCI模型可以用于小麦和油菜叶片GPP的统一反演。     

谷子苗期氮高效品种筛选及相关特性分析

【目的】评价不同基因型谷子苗期氮素吸收利用差异性,筛选谷子氮高效利用基因型材料,为谷子氮高效利用品种选育和机理研究提供理论依据。【方法】采用沙培盆栽试验,以具有代表性生态类型的79个谷子品种为材料,分析其在低氮(0.2 mmol·L~(-1))和高氮(6 mmol·L~(-1))处理下茎叶干物重、含氮量、氮素吸收量、氮素吸收与利用效率的差异及相关性,并划分不同生态类型品种的氮效率类型。【结果】供试谷子品种在2个氮素水平条件下的茎叶干物重(CV_(N0.2) 35.39%和CV_(N6) 50.83%)、氮素含量(CV_(N0.2) 11.52%和CV_(N6) 11.22%)、氮素吸收量(CV_(N0.2) 32.82%和CV_(N6) 48.46%)、氮素吸收效率(CV_(N0.2) 32.82%和CV_(N6) 48.45%)、氮素利用效率(CV_(N0.2) 11.53%和CV_(N6) 11.27%)和氮效率(CV_(N0.2) 35.35%和CV_(N6) 50.61%)均存在较大差异。不同生态类型谷子品种的氮素吸收和利用效率差异显著,西北春谷类型氮素吸收效率的变化(CV_(N0.2) 39.99%和CV_(N6) 54.38%)显著高于华北夏谷类型(CV_(N0.2)29.31%和CV_(N6) 45.68%)和东北春谷类型(CV_(N0.2) 29.49%和CV_(N6) 40.30%),而氮素利用效率以华北夏谷类型品种间差异最大(CV_(N0.212.03%和CV_(N6) 12.70%)。茎叶干物重与氮素吸收和氮素利用效率呈极显著正相关(P0.01),相关系数分别为R~2_(N0.2)=0.1827**和R~2_(N6)=0.1027**及R~2_(N0.2)=0.8985**和R~2_(N6)=0.9442**;氮效率与氮素吸收量极显著正相关,与氮含量极显著负相关,相关系数分别为R~2_(N0.2)=0.8985**和R~2_(N6)=0.9442**及R~2_(N0.2)=0.1962**和R~2_(N6)=0.0998**;氮素利用效率与氮含量极显著负相关,相关系数分别为R~2_(N0.2)=0.9924**和R~2_(N6)=0.9910**。氮素吸收效率与氮素含量和氮素利用效率间无显著相关性。以两氮素水平条件下茎叶干物重和氮效率的平均值为标准,将3种生态类型的谷子品种划分为4种氮效率类型,双高效型、双低效型、高氮高效型和低氮高效型。其中,东北春谷双高效型和高氮高效型品种所占比重最高(P_(东北)52.9%P_(西北)36.0%P_(华北)29.7%和P_(东北)23.5%P_(华北)18.9%P_(西北)4.0%),双低效型比重最低(P_(东北)17.6%P_(华北)32.4%P_(西北)36.0%),而低氮高效型在西北春谷类型中所占比重最高(P_(西北)24.0%P_(华北)18.9%P_(东北)5.9%)。【结论】不同谷子品种苗期氮效率差异显著,且西北春谷类型品种间氮素吸收效率差异最大,华北夏谷类型品种间氮素利用效率差异最大;氮素吸收效率和利用效率之间无显著相关性,应作为2个独立的氮效率指标进行评价和改良。     

不同水氮耦合条件下棉花冠层NDVI分析与产量估算

为研究冠层归一化差值植被指数(Normalized difference vegetation index,NDVI)在棉花重要生育时期估算棉花产量的可行性,使用GreenSeeker分别对不同灌水施肥条件下棉花光谱反射率NDVI值进行测定优化,建立NDVI值与产量关系数学模型,并对模型精度进行验证。结果显示:不同水氮组合随着生育期的推移棉花冠层NDVI值变化趋势基本一致,都呈"低-高-低"的变化规律;选取在棉花出苗后80、105和140d冠层NDVI值分别与产量进行相关性分析,得出冠层NDVI值与产量具有明显的正相关关系,相关系数分别为R2=0.376 0,0.093 4,0.363 9。利用独立的试验数据对相关性最高的水氮组合棉花出苗后80d的产量模型进行模型验证,其相关系数R2=0.712 6,均方根误差(Root mean square error,RMSE)561.04kg/hm2。因此,棉花出苗后80d的冠层NDVI值可以估测棉花产量。     

应用RZWQM模型模拟华北玉米土壤剖面水氮迁移及淋溶特征

以河北石家庄大河实验站玉米-小麦轮作系统为研究对象,应用RZWQM模型对玉米季土壤剖面水分和硝态氮含量、硝态氮淋溶和氨挥发特征以及作物产量进行模拟,提出控制硝态氮淋溶的玉米施肥方案。设置冬小麦-夏玉米轮作周期施氮量分别为575、400、215、0 kg·hm-24个处理,应用轮作周期施氮量为575 kg·hm-2处理玉米季土壤剖面含水量、硝态氮和产量数据进行模型参数率定,应用其他3个施肥处理进行模型参数的验证。结果表明:率定与验证过程中土壤剖面含水量均方根误差方差分别为0.003 6、0.010 6 cm3·cm-3,4个处理土壤剖面硝态氮RMSE分别为6.56、7.30、3.64、1.53mg·kg-1。在模型参数率定和验证的基础上,开展了RZWQM模型对轮作制度下玉米季土壤硝态氮淋溶和氮挥发的预测,虽然预测结果与实际值存在偏差,但在总体上RZWQM模型可以较好地模拟华北石家庄地区土壤剖面水氮的迁移转化,并且可为日后进一步准确预测和估算更大地区的土壤硝态氮淋溶提供一种便捷可靠的方法。     

基于数码图像的甜菜氮素近地遥感监测模型

为定量研究利用数码图像进行甜菜冠层叶片氮含量(Leaf nitrogen content,LNC)时空变化监测的适宜性及准确性,以2014年内蒙古赤峰市松山区太平地镇田间试验为基础,在甜菜各生长阶段采集甜菜冠层数码图像,利用数字图像处理技术对图像进行分割并提取红光值(R)、绿光值(G)和蓝光值(B)。分析R/B、G/B等9个颜色参数与不同生育期冠层LNC的相关性,并研究冠层LNC随施氮量的变化规律,探寻适宜于甜菜氮素营养监测的关键生育时期及最佳颜色参数。分别利用支持向量机(Support vector machine,SVM)和BP人工神经网络(BackPropagation artificial neural network,BP-ANN)建立甜菜冠层LNC预测模型。研究结果表明,BP-ANN预测模型具有较高且较稳定的预测精度,其验证集的决定系数R~2和均方根误差RMSE分别为0.74和2.35,与SVM模型相比,BP-ANN模型的决定系数R~2提高了12.12%,均方根误差RMSE降低了8.09%。     

生姜叶片氮含量的高光谱遥感估算模型研究

作物叶片氮含量的快速估算对于及时了解作物长势、病虫害监测以及产量评估具有重要意义。该文以经济作物生姜为研究对象,获取了2015年4月-9月不同品种、不同生育期和不同氮肥梯度下生姜叶片的高光谱和氮含量数据,对比分析了比值植被指数、归一化植被指数、植被指数组合形式对生姜叶片氮含量的估算效果。在此基础上,基于波段组合算法,筛选出了生姜叶片氮含量的敏感波段,并构建了两个新型光谱指数NDSI_((754,713))和RSI_((754,713))。结果表明,所选择的植被指数中,MCARI(705,750)/OSAVI(705,750)对生姜叶片氮含量估算效果最好,模型精度R~2、RMSE和RE分别为0.73、0.27、11.64%;利用波段组合算法构建的归一化光谱指数NDSI(754,713)对生姜叶片氮含量估算效果要优于MCARI(705,750)/OSAVI(705,750),模型估算精度R~2达0.83,使用的敏感波段713 nm与754 nm均位于植被的"红边"区域。对所建模型进行验证,叶片氮含量的预测值和实测值具有较好的一致性,验证样本R~2为0.78,RMSE为0.20,RE为9.81%。上述分析结果可为农业管理部门及时掌握生姜长势信息、制定施肥策略提供技术支持。     

控释氮肥对洋葱-棉花套作体系产量及土壤氮含量的影响

2012—2013年在济宁市鱼台县,通过大田试验研究了速效氮肥和控释氮肥在0、100、200、300 kg·hm-2 4个氮素水平下对洋葱-棉花套作体系产量及土壤氮素含量变化的影响。结果表明:氮素用量200、300 kg·hm-2时,速效氮肥和控释氮肥处理棉花产量显著高于氮素用量100 kg·hm-2处理;氮素用量100、200 kg·hm-2时,控释氮肥棉花产量较速效氮肥处理分别显著增加17.3%和7.7%;施氮200 kg·hm-2的控释氮肥处理较氮素用量100 kg·hm-2的控释氮肥处理的籽棉显著增产14.5%,但与施氮300 kg·hm-2的控释氮肥处理相比差异不显著。控释氮肥较速效氮肥更能提高0~20 cm土层NO-3-N的含量,但对土壤中NH+4-N含量无显著影响。施用控释氮肥能够提高洋葱和棉花产量,施氮量为200 kg·hm-2的控释氮肥处理为本试验条件下的最优施肥处理。     

Nitrogen Nutrition Index and Its Relationship with N Use Efficiency,Tuber Yield,Radiation Use Efficiency,and Leaf Parameters in Potatoes

Knowledge about crop growth processes in relation to N limitation is necessary to optimize N management in farming system. Plant-based diagnostic method, for instance nitrogen nutrition index (NNI) were used to determine the crop nitrogen status. This study determines the relationship of NNI with agronomic nitrogen use efficiency (AE_N), tuber yield, radiation use efficiency (RUE) and leaf parameters including leaf area index (LAI), areal leaf N content (N_AL) and leaf N concentration (N_L). Potatoes were grown in field at three N levels: no N (N1), 150 kg N ha⁻¹ (N2), 300 kg N ha⁻¹ (N3). N deficiency was quantified by NNI and RUE was generally calculated by estimating of the light absorbance on leaf area. NNI was used to evaluate the N effect on tuber yield, RUE, LAI, N_AL, and N_L. The results showed that NNI was negatively correlated with AE_N, N deficiencies (NNI<1) which occurred for N1 and N2 significantly reduced LAI, N_L and tuber yield; whereas the N deficiencies had a relative small effect on N_AL and RUE. To remove any effect other than N on these parameters, the actual ratio to maximum values were calculated for each developmental stage of potatoes. When the NNI ranged from 0.4 to 1, positive linear relationships were obtained between NNI and tuber yield, LAI, N_L, while a nonlinear regression fitted the response of RUE to NNI.     

基于遥感技术的不同施氮水平下小麦条锈病病情反演研究

为构建不同施氮条件下,小麦条锈病病情光谱反演模型,设置了在不同氮素水平条件下接种小麦条锈病,将菌情指数与植被指数、一阶微分参数进行回归分析,构建抽穗期、开花期、灌浆期、乳熟期共5个模型。为了评估施氮量对病情反演模型的影响,在模型中加入氮素因子,模型病情反演预测效果表明,抽穗期模型加入氮素因子后预测效果有所提高,抽穗期的模型1-1(R2=0.392 8,P=0.005 4)、1-2(R2=0.449 8,P=0.011 3)、2-2(R2=0.573 3,P=0.001 7)预测效果较好且较稳定,开花期、灌浆期、乳熟期模型预测效果不理想。本研究结果表明,可以利用植被指数、一阶微分参数较好反演抽穗期小麦条锈病病情,加入氮素因子后预测效果有所提高,说明氮素因子对病情反演有影响。     

覆膜条件下对AquaCrop模型冬小麦生长动态和土壤水分模拟效果的评价分析

【目的】通过评价AquaCrop模型对覆膜条件下冬小麦的生长发育、土壤水分、产量以及水分利用效率的模拟效果,为AquaCrop模型在覆膜条件下的校准和应用提供科学的方法和理论依据。【方法】试验设臵不覆盖(CK)和白色地膜覆盖(PM)两个处理,于2013年10月至2016年6月年在陕西杨凌进行田间试验,利用2014—2015年度试验数据对AquaCrop模型进行参数校准,利用2013—2014年度和2015—2016年度的冬小麦观测数据对AquaCrop模型进行验证。【结果】AquaCrop模型较好地模拟了冬小麦冠层覆盖度,冠层覆盖度模拟值和实测值之间的决定系数(R2)为0.86—0.99,均方根误差(RMSE)为2.1%—8.1%。AquaCrop模型也较好地模拟了冬小麦生物量和土壤贮水量,其中地上部生物量的模拟值和实测值之间的R2均大于0.95,RMSE为0.814—1.933 t·hm-2;CK土壤贮水量模拟值和实测值间的相关系数均大于0.85,PM土壤贮水量模拟值和实测值间的相关系数均大于0.75,CK和PM土壤贮水量模拟值和实测值的均方根误差表现为9.2 mmRMSE17.6 mm,标准均方根误差(NRMSE)小于5.5%。冬小麦产量实测值和模拟值相对误差(RE)为-4.4%—9.0%,PM产量实测值和模拟值的平均值较CK分别提高40.5%和40.3%,表现出较好的一致性,处理间成显著性差异。水分利用效率实测值和模拟值RE为-10.4%—-1.5%,PM水分利用效率实测值和模拟值的平均值较CK分别提高54.1%和47.5%,同样表现出较好的一致性,处理间成显著性差异。在冠层覆盖度、地上部生物量、产量和水分利用效率方面,模型模拟值和实测值的变化趋势基本一致,且PM模拟值和实测值间均较CK表现出显著性差异。【结论】AquaCrop模型能够较好地模拟覆膜条件下冬小麦生长发育过程,可以用于覆膜条件下作物生产力的模拟和预测,为AquaCrop模型的推广应用提供了可靠的数据支持。     

基于关联分析的新陆早棉花品种农艺和纤维品质性状优异等位基因挖掘

【目的】寻找与新陆早棉花品种农艺和纤维品质性状相关联的分子标记,鉴别与这些性状相关的优异等位变异及携带优异等位变异基因的典型载体材料,为新陆早棉花品种分子设计育种奠定基础。【方法】利用筛选出分布于26条染色体且多态性高的75对SSR标记对51份新陆早棉花品种进行多态性扫描;采用R语言编程软件对多环境的表型性状绘制boxplot图;在对供试材料进行群体结构和连锁不平衡分析的基础上,利用TASSEL软件中MLM（mixed linear model）方法进行分子标记与15个性状的关联分析;依据计算的表型效应值,鉴别和统计优异等位变异的位点及典型材料。【结果】通过群体遗传结构分析将51份新陆早棉花品种划分为4个亚群结构。针对15个表现型性状的BLUP（best linear unbiased prediction）结果进行统计和分析,鉴别出极显著和显著相关的性状。分析结果显示,在4种环境条件下,棉花5个性状（果枝始节高、果枝始节数、衣分、纤维上半部长度和短纤维率）变化趋势稳定,10个性状（株高、果枝数、叶枝数、有效铃数、单铃籽棉重、单铃皮棉重、马克隆值、比强度、纤维整齐度和纤维伸长率）较稳定。通过关联分析,获得与农艺性状相关的等位变异位点117个（P＜0.05）,其中对9个农艺性状贡献率（R²）最大的等位变异位点分别为：BNL3650b（株高,R²=11.78;果枝始节高,R²=20.80;果枝始节数,R²=11.54）、NAU3995c（果枝数,R²=14.86）、BNL119b（叶枝数,R²=9.7）、NAU3995d（有效铃数,R²=14.98）、BNL3255a （单铃籽棉重,R²=11.11）、NAU1071a（单铃皮棉重,R²=10.15）和BNL663a（衣分,R²=12.42）。与纤维品质相关的等位变异位点55个（P＜0.05）,其中分别对6个纤维品质性状贡献率最大的等位变异位点为：NAU1103b（纤维上半部长度,R²=6.4）、NAU1071a（纤维比强度,R²=7.57）、BNL3140b（马克隆值,R²=12.06）、BNL3650b(纤维整齐度,R²=13.47)、BNL1421a（短纤维率,R²=13.04）和BNL2960b（纤维伸长率,R²=11.67）。共检测到39个与农艺（29个）和纤维品质（10个）性状相关的位点（P＜0.01）,对表型变异解释率范围为6.45%-20.8%,平均值为11.14%,同时检测到与2个以上性状相关联的位点47个。携带优异等位变异基因的典型材料共计17份。通过与已经报道的棉花农艺和纤维品质性状相关的QTL（quantitative trait loci）比较,检测的27个QTL在前人研究中已经报道,其中BNL3650（纤维整齐度）、BNL3033（马克隆值）、NAU3254（纤维伸长率）、GH132（衣分）、TMB1618（比强度）、BNL1421（比强度和纤维整齐度）和BNL119（纤维伸长率）7个QTL具有相同的关联性状。【结论】51份原种新陆早棉花品种的群体遗传结构简单,连锁不平衡水平低,表型性状在2种环境条件下变化趋势较稳定。基于SSR的关联分析,发掘了一些与农艺和纤维品质相关的优异等位变异基因及典型材料。