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1.
为进一步深化作物长势遥感监测机理与方法,给大田管理及时提供信息与技术,结合2011-2013年定点观测试验,以HJ-1A/1B数据为遥感影像源,研究了返青期冬小麦主要生长指标、籽粒品质参数和产量间及其与遥感变量间的定量关系,分别构建及评价基于HJ-1A/1B影像遥感变量的返青期叶面积指数、生物量、SPAD值和叶片含氮量监测模型。结果表明,返青期,归一化植被指数（NDVI）、比值植被指数（RVI）、蓝光波段反射率（B1）和RVI可分别作为监测冬小麦叶面积指数、生物量、SPAD和叶片含氮量的敏感遥感变量,所构建的遥感监测模型可靠且精度较高,模型的决定系数（R2）分别为0.62、0.56、0.46和0.58,均方根误差（RMSE）分别为0.42、452.3 kg·hm-2、4.39和0.54%。同时,对冬小麦不同等级主要生长指标进行遥感监测并制图,量化表达了主要生长指标区域空间分布。  相似文献   

2.
为了快速监测小麦叶片水分含量,以敏感波段组和植被指数组2种变量分别作为输入变量,以地面同步观测的冬小麦叶片含水量作为输出变量,分别采用偏最小二乘(partial least squares,PLS)、极限学习机(extreme learning machine,ELM)和粒子群算法(particle swarm optimization,PSO)优化极限学习机,建立冬小麦叶片含水量预测模型,并对其反演效果进行比较。结果表明,光谱反射率和植被指数与叶片含水量之间存在较为密切的相关性,依此确定的敏感光谱波段为红光、蓝光和近红外波段,敏感植被指数为绿度指数、过红指数、归一化绿红差值指数、三角形植被指数和过绿指数。从2种变量的建模效果看,基于植被指数组构建的模型的精度和稳定性均优于敏感波段组,其中基于植被指数组的PSO-ELM模型在6个叶片水分含量反演模型中表现最佳,其R2和RMSE分别为0.98和0.26%。利用最优模型反演得到研究区冬小麦叶片含水量的分布范围为45%~75%,平均为64.57%,反演结果与地面实测较相符,说明基于无人机光谱数据通过建立以植被指数为变量的PSO-ELM模型可实现对冬小麦叶片水分含量的精准预测。  相似文献   

3.
为解决大田冬小麦叶片叶绿素含量估测模型精度低、通用性弱的问题,在获取冬小麦拔节期和抽穗期冠层红光波段反射率(BRred)和近红外波段反射率(BRnir)的基础上,计算归一化差值植被指数(NDVI)、差值植被指数(DVI)、比值植被指数(RVI)、土壤调节植被指数(SAVI)、改进型比值植被指数(MSR)、重归一化植被指数(RDVI)、II型增强植被指数(EVI2)和非线性植被指数(NLI)等8个植被指数。经统计分析,选择与叶片叶绿素含量(SPAD值)相关性较好的5个遥感光谱指标(NDVI、MSR、NLI、BRred和RVI)作为输入变量,建立了冬小麦叶片叶绿素含量的BP神经网络估测模型(WWLCCBP),并对估测模型进行精度验证。结果表明,WWLCCBP估测模型在拔节期估测的决定系数(r2)为0.84,均方根误差(RMSE)为5.39,平均相对误差(ARE)为9.87%。抽穗期的估测效果与拔节期较为一致。将WWLCCBP和高分六号影像...  相似文献   

4.
Vegetation indices are widely used as model inputs and for non‐destructive estimation of biomass and photosynthesis, but there have been few validation studies of the underlying relationships. To test their applicability on temperate fens and the impact of management intensity, we investigated the relationships between normalized difference vegetation index (NDVI), leaf area index (LAI), brown and green above‐ground biomass and photosynthesis potential (PP). Only the linear relationship between NDVI and PP was management independent (R2 = 0·53). LAI to PP was described by a site‐specific and negative logarithmic function (R2 = 0·07–0·68). The hyperbolic relationship of LAI versus NDVI showed a high residual standard error (s.e.) of 1·71–1·84 and differed between extensive and intensive meadows. Biomass and LAI correlated poorly (R2 = 0·30), with high species‐specific variability. Intensive meadows had a higher ratio of LAI to biomass than extensive grasslands. The fraction of green to total biomass versus NDVI showed considerable noise (s.e. = 0·13). These relationships were relatively weak compared with results from other ecosystems. A likely explanation could be the high amount of standing litter, which was unevenly distributed within the vegetation canopy depending on the season and on the timing of cutting events. Our results show there is high uncertainty in the application of the relationships on temperate fen meadows. For reliable estimations, management intensity needs to be taken into account and several direct measurements throughout the year are required for site‐specific correction of the relationships, especially under extensive management. Using NDVI instead of LAI could reduce uncertainty in photosynthesis models.  相似文献   

5.
An active crop canopy reflectance sensor could be used to increase N-use efficiency in maize (Zea mays L.), if temporal and spatial variability in soil N availability and plant demand are adequately accounted for with an in-season N application. Our objective was to evaluate the success of using an active canopy sensor for developing maize N recommendations. This study was conducted in 21 farmers’ fields from 2007 to 2009, representing the maize production regions of east central and southeastern Pennsylvania, USA. Four blocks at each site included seven sidedress N rates (0–280 kg N ha−1) and one at-planting N rate of 280 kg N ha−1. Canopy reflectance in the 590 nm and 880 nm wavelengths, soil samples, chlorophyll meter (SPAD) measurements and above-ground biomass were collected at the 6th–7th-leaf growth stage (V6–V7). Relative amber normalized difference vegetative index (ANDVIrelative) and relative SPAD (SPADrelative) were determined based on the relative measurements from the zero sidedress treatment to the 280 kg N ha−1 at-planting treatment. Observations from the current study were compared to relationships between economic optimum N rate (EONR) and ANDVIrelative, presidedress NO3 test (PSNT), or SPADrelative that were developed from a previous study. These comparisons were based on an absolute mean difference (AMD) between observed EONR and the previously determined predicted relationships. The AMD for the relationship between EONR and ANDVIrelative in the current study was 46 kg N ha−1. Neither the PSNT (AMD = 66 kg N ha−1) nor the SPADrelative (AMD = 72 kg N ha−1) provided as good an indicator of EONR. When using all the observations from the two studies for the relationships between EONR and the various measurements, ANDVIrelative (R2 = 0.65) provided a better estimate of EONR than PSNT (R2 = 0.49) or SPADrelative (not significant). Crop reflectance captured similar information as the PSNT and SPADrelative, as reflected in strong relationships (R2 > 0.60) among these variables. Crop canopy reflectance using an active sensor (i.e. ANDVIrelative) provided as good or better an indicator of EONR than PSNT or SPADrelative, and provides an opportunity to easily adjust in-season N applications spatially.  相似文献   

6.
Non-destructive and quick assessment of leaf nitrogen (N) status is important for dynamic management of nitrogen nutrition and productivity forecast in crop production. This research was undertaken to make a systematic analysis on the quantitative relationship of leaf nitrogen concentrations (LNCs) to different hyperspectral vegetation indices with multiple field experiments under varied nitrogen rates and varied types in rice (Oryza sativa L.). The results showed that some published indices had good relations with LNC such as two-band indices, R750/R710 (ZM), Gitelson and Merzlyak index two (GM-2), R735/R720 (RI-1dB), R738/R720 (RI-2dB) and the normalized difference red edge index (NDRE), three-band indices, adjusted normalized index 705 (mND705), physiological reflectance index c (PRIc), terrestrial chlorophyll index (MTCI), and red edge position derived with four point linear interpolation (REP_LI). Three-band indices performed better than two-band indices, with MTCI exhibiting the best logarithmic relation to LNC in rice. Then, hyper-spectral vegetation indices computed with random two bands (λ1 and λ2) from 400 to 2500 nm range were related to LNC of rice. The results indicated that two-band indices combined with bands of 550–600 nm and 500–550 nm in green region had good relationships with LNC, and simple ratio index SR(533,565) performed the best in all two-band indices, similar to the published three-band indices (mND705, PRIc and MTCI). New three-band indices R434/(R496 + R401) and R705/(R717 + R491) were proposed for prediction of LNC with improved ability over the SR(533,565) and published spectral indices. Moreover, R705/(R717 + R491) performed well in all the data from ground spectra, modeled AVIRIS and Hyperion spectra, and acquired Hyperion image hyperspectra. The R434/(R496 + R401) also exhibited well in both ground and modeled AVIRIS and Hyperion image spectra, but could not be tested with the acquired Hyperion image because of the absence in radiometric calibration of the bands less than 416 nm. Overall, the newly developed three-band spectral index R705/(R717 + R491) should be a good indicator of LNC at ground and space scales in rice. Yet, these new indices still need to be tested with more remote sensors based on ground, airborne and spaceborne, and verified widely in other ecological locations involving different cultivars and production systems.  相似文献   

7.
为探索渍害胁迫下冬小麦灾损程度的可视化监测方法,通过田间试验,分析了麦田16个常用图像特征指数在不同受渍时间下的变化特征及其与冬小麦SPAD值、产量和千粒重的相关关系,并建立了基于图像特征指数衰减量的冬小麦渍害估算模型。结果表明,随渍水时间的增加,红光(R)、红光标准化值(NRI)、超红指数(EXR)、植被颜色指数(CIVE)极显著上升,而绿光标准化值(NGI)、归一化绿红差值指数(NGRDI)、绿-红差值指数(GMR)、超绿指数(EXG)、绿红比值指数(GRVI)则极显著下降;且这9个图像特征指数均与冬小麦SPAD值、产量和千粒重呈极显著相关,相关系数的最大绝对值分别为0.92、0.85和0.91;基于图像指数衰减量所建的SPAD值、产量和千粒重减少量的估算模型均以二次多项式最优,且以CIVE指数衰减量构建的SPAD值、产量和千粒重减少量估算模型的预测精度最高,验证集决定系数分别达到0.98、0.95、0.96。因此,数字图像技术可用于冬小麦渍害监测,且以基于CIVE指数的监测效果最佳。  相似文献   

8.
为探讨利用高光谱技术快速无损地监测小麦白粉病灾情的方法,通过人工田间诱发白粉病,在灌浆期对不同发病等级(病情指数)的冬小麦进行冠层高光谱测定,对原始光谱数据进行一阶微分处理,筛选最佳光谱特征参量和植被指数,构建冬小麦白粉病病情指数反演模型。结果表明,在冠层尺度,小麦白粉病"红边"位置均在730nm左右(±1nm);经验证,5种模型中三角植被指数(TVI)模型估算精度最好,r2和RMSE分别达到了0.700和0.112,与精度最低的优化土壤调节植被指数(OSAVI)模型相比,r2提高了0.071,RMSE降低了0.013。小麦白粉病"红边"蓝移现象并不明显;五种模型r2都达到了0.6以上,说明高光谱技术都能够有效地对冬小麦白粉病病情指数进行无损、快速、精确的反演,其中TVI的反演精度最佳。  相似文献   

9.
为构建冬小麦冠层临界植被指数时序模型,探究实时、无损诊断冬小麦全生育期氮素营养状况的可能性,基于冬小麦不同生育时期氮营养指数(nitrogen nutrition index,NNI)与相对产量的关系确定NNI临界值,并利用归一化红边植被指数(normalized difference red edge,NDRE)与NNI的定量关系确定临界NDRE值,进而以累积生长度日为时间驱动因子,利用双Logistic函数构建临界NDRE时序模型并用于诊断,且对诊断结果进行了验证。结果表明,NNI与冬小麦相对产量在不同生育时期均呈现明显的线性加平台关系(R2在0.76以上),在开花—灌浆期表现最好;NNI与NDRE呈显著的幂函数关系(R2在0.76以上),在孕穗—开花期表现最好;临界NDRE时序诊断模型在拔节后期、孕穗期、开花期的诊断精度较高;适期播种时冬小麦在180 kg·hm-2施氮水平下整个生育期均处于轻微氮亏缺或氮适宜状态,为较优施氮量。适期播种时冬小麦氮素营养状况主要受施氮水平的制约;过晚播时受播期的影响,不同施氮水平下冬小麦全生育期均处于氮亏缺状态。综上,依据氮营养指数与相对产量所构建的临界NDRE时序模型能够较准确地实时诊断冬小麦不同生育时期的氮素营养状况,并为作物氮肥精确管理提供技术方法。  相似文献   

10.
Several sensor systems are available for ground-based remote sensing in crop management. Vegetation indices of multiple active and passive sensors have seldom been compared in determining plant health. This work describes a study comparing active and passive sensing systems in terms of their ability to recognize agronomic parameters. One bi-directional passive radiometer (BDR) and three active sensors, including the Crop Circle, GreenSeeker, and an active flash sensor (AFS), were tested for their ability to assess six destructively determined crop parameters. Over 2 years, seven wheat (Triticum aestivum L.) cultivars were grown with nitrogen supplies varying from 0 to 220 kg ha−1. At three developmental stages, the crop reflectance was recorded and sensor-specific indices were calculated and related to N levels and the crop parameters, fresh weight, dry weight, dry matter content, as percent of dry weight to fresh weight, N content, aboveground N uptake, and the nitrogen nutrition index. The majority of the tested indices, based on different combinations of wavelengths in the visible and near infrared spectral ranges, showed high r2-values when correlated with the crop parameters. However, the accuracy of discriminating the influence of varying N levels on various crop parameters differed between sensors and showed an interaction with growing seasons and developmental stage. Visible- and red light-based indices, such as the NDVI, simple ratio (R780/R670), and related indices tended to saturate with increasing crop stand density due to a decreased sensitivity of the spectral signal. Among the destructively assessed biomass parameters, the best relationships were found for N-related parameters, with r2-values of up to 0.96. The near infrared-based index R760/R730 was the most powerful and temporarily stable index indicating the N status of wheat. This index was delivered by the BDR, Crop Circle, and AFS. Active spectral remote sensing is more flexible in terms of timeliness and illumination conditions, but to date, it is bound to a limited number of indices. At present, the broad spectral information from bi-directional passive sensors offers enhanced options for the future development of crop- or cultivar-specific algorithms.  相似文献   

11.
《Plant Production Science》2013,16(4):400-411
Abstract

Non-destructive monitoring and diagnosis of plant nitrogen (N) concentration are of significant importance for precise N management and productivity forecasting in field crops. The present study was conducted to identify the common spectra wavebands and canopy reflectance spectral parameters for indicating leaf nitrogen concentration (LNC, mg N g-1 DW) and to determine quantitative relationships of LNC to canopy reflectance spectra in both rice (Oryza sativa L.) and wheat (Triticum aestivum L.). Ground-based canopy spectral reflectance and LNC were measured with seven field experiments consisting of seven different wheat cultivars and five different rice cultivars and varied N fertilization levels across three growing seasons for wheat and four growing seasons for rice. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalized difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC of wheat and rice increased with increasing N fertilization rates. Canopy reflectance, however, was a more complicated relationship under different N application rates. In the near infrared portion of the spectrum (760?1220 nm), canopy spectral reflectance increased with increasing N supply, whereas in the visible region (460?710 nm), canopy reflectance decreased with increasing N supply. For both rice and wheat, LNC was best estimated at 610, 660 and 680 nm. Among all possible RVI, DVI and NDVI of key bands from the MSR16 radiometer, NDVI(1220, 610) and RVI(1220, 610) were most highly correlated to LNC in both wheat and rice. In addition, the correlations of NDVI(1220, 610) and RVI(1220, 610) to LNC were found to be higher than those of individual wavebands at 610, 660 and 680 nm in both wheat and rice. Thus LNC in both wheat and rice could be indicated with common wavebands and vegetation indices, but separate regression equations are necessary for precisely describing the dynamic change patterns of LNC in wheat and rice. When independent data were fit to the derived equations, the root mean square error (RMSE) values for the predicted LNC with NDVI(1220, 610) and RVI(1220, 610) relative to the observed values were 10.50% and 10.52% in wheat, and 13.04% and 12.61% in rice, respectively, indicating a good fit. These results should improve the knowledge on non-destructive monitoring of leaf N status in cereal crops.  相似文献   

12.
为及时、准确地掌握小麦产量动态信息,基于无人机遥感平台,分别分析了小麦4项生理指标［地面实测叶面积指数、叶片含氮量、叶片含水量及叶片叶绿素相对含量（SPAD值）］及10项植被指数与产量的相关性,以筛选出与产量最为敏感的生理指标与植被指数,并比较了3种建模方法（一元回归UR、多元逐步回归SMLR和主成分回归PCAR）在小麦各生育时期估产的适用性,进而得到小麦最优估产模型。结果表明：（1）不同生育时期两类变量与产量的相关性变化特征一致,均表现为抽穗期>灌浆期>成熟期;不同生理指标、植被指数与产量的相关性在各生育时期均存在差异,生理指标表现为叶片含氮量>LAI>SPAD>叶片含水量;而植被指数在各时期表现不同;（2）以生理指标与植被指数为自变量,采用SMLR模型构建的抽穗期估产模型拟合精度最高,R、RMSE和nRMSE分别为0.828、362.53 kg·hm-2和12.35%;（3）小麦估产模型在各生育时期的预测精度表现为抽穗期>灌浆期>成熟期。  相似文献   

13.
Three large deformation rheological tests, the Kieffer dough extensibility system, the D/R dough inflation system and the 2 g mixograph test, were carried out on doughs made from a large number of winter wheat lines and cultivars grown in Poland. These lines and cultivars represented a broad spread in baking performance in order to assess their suitability as predictors of baking volume. The parameters most closely associated with baking volume were strain hardening index, bubble failure strain, and mixograph bandwidth at 10 min. Simple correlations with baking volume indicate that bubble failure strain and strain hardening index give the highest correlations, whilst the use of best subsets regression, which selects the best combination of parameters, gave increased correlations with R2=0.865 for dough inflation parameters, R2=0.842 for Kieffer parameters and R2=0.760 for mixograph parameters.  相似文献   

14.
New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice   总被引：17，自引：0，他引：17  
Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)>0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.  相似文献   

15.
The objective of this study was to develop a whole-process model for explaining genotypic and environmental variations in the growth and yield of irrigated rice by incorporating a newly developed sub-model for plant nitrogen (N) uptake into a previously reported model for simulating growth and yield based on measured plant N. The N-uptake process model was developed based on two hypotheses: (1) the rate of root system development in the horizontal direction is proportional to the rate of leaf area index (LAI) development, and (2) root N-absorption activity depends on the amount of carbohydrate allocated to roots. The model employed two empirical soil parameters characterizing indigenous N supply and N loss. Calibration of the N-uptake process sub-model and validation of the whole-process model were made using plant N accumulation, and growth and yield data obtained from a cross-locational experiment on nine rice genotypes at seven locations in Asia, respectively. Calibration of the N-uptake process sub-model indicated that a large genotypic difference exists in the proportionality constant between rate of root system development and that of LAI development during early growth stages. The whole-process model simultaneously explained the observed genotypic and environmental variation in the dynamics of plant N accumulation (R2 = 0.91 for the entire dataset), above-ground biomass growth (R2 = 0.94), LAI development (R2 = 0.78) and leaf N content (R2 = 0.79), and spikelet number per unit area (R2 = 0.78) and rough grain yield (R2 = 0.81). The estimated value of the site (field)-specific soil parameter representing the rate of N loss was negatively correlated with cation exchange capacity of the soil and was approximated by a logarithmic function of cation exchange capacity for seven sites (R2 = 0.95). Large yearly and locational variations were estimated in the soil parameter for representing the rate of indigenous N supply at 25 °C. With the use of these two soil parameters, the whole-system model explained the observed genotypic and environmental variations in plant N accumulation, growth and yield of rice in Asia.  相似文献   

16.
新型植被指数及其在水稻叶面积指数估算上的应用   总被引：8，自引：0，他引：8  
叶面积指数LAI不仅是陆表植被系统的一个重要属性,而且是全球水平衡、碳循环等模型中的重要输入参数。首先通过使用水稻小区试验冠层光谱数据模拟Landsat 5卫星蓝、绿、红光波段;其次分析了各个波段对LAI的敏感性;然后分析了由这个3个波段的所有组合分别代替常规NDVI中的红光波段构成的VNDVI对LAI变化的反应和对LAI的估算能力;最后使用不同条件下的水稻数据进行验证。结果表明,在不同的LAI范围,红绿蓝光3个波段对LAI有不同的敏感性。当LAI<3时,红蓝光波段敏感性较高。虽然这时绿光波段的敏感性不如红蓝光波段,然而绿光波段在更大的范围对LAI都有相当的敏感性。当采用红绿蓝光波段的各种组合构成植被指数时,如果要使这些植被指数不出现饱和现象,并使对LAI的敏感性有意义,其前提是要求这个波段或是波段组合的值要大于0.024,即VNDI(visible NDVI)公式中的VIS>0024,否则将可能产生饱和现象,而使LAI估算准确度降低。综合比较所有由红绿蓝光波段各种组合构成的植被指数对LAI的估算能力,认为GNDVI和GBNDVI与LAI有比较好的关系。使用其他条件下的水稻数据对各种NDVI的LAI估算能力进行了验证,仍然得到了同样的结论。可见,GNDVI和GBNDVI在估算LAI时确实比传统NDVI具有更好的效果。  相似文献   

17.
为筛选可用于干旱半干旱区春小麦冠层叶绿素含量估算的高光谱植被指数,2017年通过测定春小麦关键生育时期冠层的田间高光谱与叶绿素含量,利用光谱指数波段优化算法分别计算400～1 300 nm光谱波段中不同波段两两组合的比值光谱指数(ration spectral index,RSI)、归一化光谱指数(normalized difference spectral index,NDSI)、叶绿素指数(chlorophyll index,CI)、简化光谱指数(CI/NDSI,NPDI),并将这些参数及其他17个不同高光谱植被指数分别与实测冠层叶绿素含量进行Pearson相关分析,通过变量重要性准则筛选最优光谱参数,使用偏最小二乘回归法建立冠层叶绿素含量的预测模型。结果表明:(1)RSIs、NDSIs、CIs和NPDIs与冠层叶绿素含量的相关性都优于前人研究中定义的17种高光谱植被指数,并且冠层叶绿素含量与NDSI(R_(849),R_(850))、RSI(R_(849),R_(850)),CI(R_(849),R_(850))和NPDI(R_(849),R_(850))表现出强相关性。(2)用此4个优化光谱指数分别建模时,以CI(R_(849),R_(850))、 CI(R_(539),R_(553))、 CI(R_(540),R_(553))、 CI(R_(536),R_(553))为自变量的X-3模型预测精度最高(r~2=0.74,RMSE=0.272 mg·g~(-1))。(3)结合4个优化光谱指数构建的组合模型预测精度,其r~2=0.83,RMSE=0.187 mg·g~(-1)。  相似文献   

18.
Cropping systems in farmland areas of Iran are characterized by continuous cultivation of crops with consumption of chemical fertilizers leading to serious soil erosion and fertility decline. Information regarding the simultaneous evaluation of crop rotation and fertilization on the canola is lacking. Hence, field experiments were conducted during 2007-2010 using split-split plot design. Three crop rotations: chickpea, sunflower, wheat, and canola (R1); green manure, chickpea, green manure, wheat, green manure and canola (R2); canola, wheat, and canola (R3) were used as main plots. Sub plots were consisted of six methods of fertilization including (N1): farmyard manure (FYM); (N2): compost; (N3): chemical fertilizers; (N4): FYM + compost and (N5): FYM + compost + chemical fertilizers; and control (N6). Four levels of biofertilizers consisted of (B1): phosphate solubilizing bacteria (PSB); (B2): Trichoderma harzianum; (B3): PSB + T. harzianum; and (B4): without biofertilizers were arranged in the sub-sub plots. Results showed that green manure application in canola rotation (R2) increased grain yield and nutrient uptake. Combined application of FYM, compost and chemical fertilizers (N5) elevated the nitrogen uptake rate and grain oil yield. Simultaneous use of PSB and T. harzianum (B3) resulted in the increase of nitrogen and sulfur contents of grain. R2 rotation with regard to its biological and environmental efficiencies accompanied with FYM + compost and B3 (PSB + T. harzianum) is suggested as a low input system to obtain a more sustainable and productive farming in canola.  相似文献   

19.
Simple plant-based diagnostic tools can be used to determine crop P status. Our objectives were to establish the relationships between P and N concentrations of the uppermost collared leaf (PL and NL) of spring wheat (Triticum aestivum L.) and maize (Zea mays L.) during the growing season and, in particular, to determine the critical leaf P concentrations required to diagnose P deficiencies. Various N applications were evaluated over six site-years for wheat and eight site-years for maize (2004-2006) with adequate soil P for growth. Phosphorus and N concentrations of the uppermost collared leaf were determined weekly and the relationships between leaf N and P concentrations were established using only the sampling dates from the stem elongation stage for wheat and from the V8 stage of development for maize. Leaf P concentration generally decreased with decreasing N fertilization. Relationships between PL and NL concentrations (mg g−1 DM) using all site-years and sampling dates were described by significant linear-plateau functions in both maize (PL = 0.82 + 0.089 NL if NL ≤ 32.1 and PL = 3.7 if NL > 32.1; R2 = 0.41; P < 0.001) and wheat (PL = 0.02 + 0.106 NL if NL ≤ 33.2 and PL = 3.5 if NL > 33.2; R2 = 0.42; P < 0.001). Variation among sampling dates in the relationships were noted. By restricting the sampling dates [413-496 growing degree days (5 °C basis) in wheat (i.e., stem elongation) and 1494-1579 crop heat units in maize (i.e., silking), relationships for wheat (PL = 0.29 + 0.073 NL, R2 = 0.66; P < 0.001) and maize (PL = 1.04 + 0.084 NL, R2 = 0.66; P < 0.001) were improved. In maize, expressing P and N concentrations on a leaf area basis (PLA and NLA) at silking further improved the relationship (PLA = 0.002 + 0.101 NLA, R2 = 0.80; P < 0.001). Predictive models of critical P concentration as a function of N concentration in the uppermost collared leaf of wheat and maize were established which could be used for diagnostic purposes.  相似文献   

20.
Nitrogen (N) use efficiency (NUE), defined as grain produced per unit of fertilizer N applied, is difficult to predict for specific maize (Zea mays L.) genotypes and environments because of possible significant interactions between different management practices (e.g., plant density and N fertilization rate or timing). The main research objective of this study was to utilize a quantitative framework to better understand the physiological mechanisms that govern N dynamics in maize plants at varying plant densities and N rates. Paired near-isogenic hybrids [i.e., with/without transgenic corn rootworm (Diabrotica sp.) resistance] were grown at two locations to investigate the individual and interacting effects of plant density (low—54,000; medium—79,000; and high—104,000 pl ha−1) and sidedress N fertilization rate (low—0; medium—165; and high—330 kg N ha−1) on maize NUE and associated physiological responses. Total aboveground biomass (per unit area basis) was fractionated and both dry matter and N uptake were measured at four developmental stages (V14, R1, R3 and R6). Both plant density and N rate affected growth parameters and grain yield in this study, but hybrid effects were negligible. As expected, total aboveground biomass and N content were highly correlated at the V14 stage. However, biomass gain was not the only factor driving vegetative N uptake, for although N-fertilized maize exhibited higher shoot N concentrations than N-unfertilized maize, the former and latter had similar total aboveground biomass at V14. At the R1 stage, both plant density and N rate strongly impacted the ratio of total aboveground N content to green leaf area index (LAI), with the ratio declining with increases in plant density and decreases in N rate. Higher plant densities substantially increased pre-silking N uptake, but had relatively minor impact on post-silking N uptake for hybrids at both locations. Treatment differences for grain yield were more strongly associated with differences in R6 total biomass than in harvest index (HI) (for which values never exceeded 0.54). Total aboveground biomass accumulated between R1 and R6 rose with increasing plant density and N rate, a phenomenon that was positively associated with greater crop growth rate (CGR) and nitrogen uptake rate (NUR) during the critical period bracketing silking. Average NUE was similar at both locations. Higher plant densities increased NUE for both medium and high N rates, but only when plant density positively influenced both the N recovery efficiency (NRE) and N internal efficiency (NIE) of maize plants. Thus plant density-driven increases in N uptake by shoot and/or ear components were not enough, by themselves, to increase NUE.  相似文献   

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