基于光谱指数和偏最小二乘的棉花类胡萝卜素/叶绿素a比值估算

类胡萝卜素(Car)与叶绿素a含量比值(Car/Chla)的变化与植被生长发育变化、环境胁迫及叶片衰老特征等密切相关,可作为植被生理生态及物候的监测指标。不同植被类型和植被品种其色素变化随植被生长发育呈现出不同的变化特征。为了探究适用于干旱区棉花Car/Chla比值估算的光谱指数和估算方法,本研究通过2011年和2012年连续2年的大面积田间试验,获取了棉花不同生育期的叶片及冠层尺度光谱反射率及色素含量信息,对多种光谱指数及偏最小二乘回归(Partial Least Square Regression, PLSR)用于Car/Chla比值和Car估算进行了探讨。对比表明,基于光化学指数(PhotochemicalReflectanceIndex,PRI)的线性和一元二次模型对Car/Chla比值和Car的估算精度最高,由PRI-Car/Chla线性模型得到的叶片和冠层尺度的Car/Chla比值估算值与实测值之间的决定系数R2大于0.6, PRI-Car的R2大于0.36;基于PLSR模型得到的Car/Chla比值估算值与实测值之间的拟合关系略优于基于PRI的估算模型,由其得到的叶片及冠...     

Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression

Many spectral indices have been proposed to derive plant nitrogen (N) nutrient indicators based on different algorithms. However, the relationships between selected spectral indices and the canopy N content of crops are often inconsistent. The goals of this study were to test the performance of spectral indices and partial least square regression (PLSR) and to compare their use for predicting canopy N content of winter wheat. The study was conducted in cool and wet southeastern Germany and the hot and dry North China Plain for three winter wheat growing seasons. The canopy N content of winter wheat varied from 0.54% to 5.55% in German cultivars and from 0.57% to 4.84% in Chinese cultivars across growth stages and years. The best performing spectral indices and their band combinations varied across growth stages, cultivars, sites and years. Compared with the best performing spectral indices, the average value of the R² for the PLSR models increased by 76.8% and 75.5% in the calibration and validation datasets, respectively. The results indicate that PLSR is a potentially useful approach to derive canopy N content of winter wheat across growth stages, cultivars, sites and years under field conditions when a broad set of canopy reflectance data are included in the calibration models. PLSR will be useful for real-time estimation of N status of winter wheat in the fields and for guiding farmers in the accurate application of their N fertilisation strategies.     

Estimation of litchi (Litchi chinensis Sonn.) leaf nitrogen content at different growth stages using canopy reflectance spectra

The estimation of crop nitrogen status in fresh vegetation leaf using field spectroscopy is challenging due to the weak responses on leaf/canopy reflectance and the overlapping with the absorption features of other compounds. Although the spectral indices were proposed in the literature to predict leaf nitrogen content (LNC), the performance of selected spectral indices to estimate the LNC is often inconsistent. Moreover, the models for nitrogen content estimation changed with the growth stage. The goal of this study was to evaluate the performance of published indices, ratio of data difference index (RDDI) and ratio of data index (RDI) developed by band iterative-optimization algorithm in LNC estimation. The correlation analysis, linear regression and cross validation were used to analyze the relationship between spectral data and LNC and construct the best performed estimation model. The study was conducted by the data of five growing seasons of litchi from the orchards in Guangdong Province of China. Results showed that the relationship between chlorophyll (Chl) related spectral indices and LNC varied with the growth stage. Even in flower bud morphological differentiation stage and autumn shoot maturation stage, there were not significant correlations between the proposed spectral indices and LNC. Besides it is difficult to estimate the LNC by the general model across the growth stages due to the integrated effects of cultivar, biochemical, canopy structure, etc. The band iterative-optimization algorithm can improve the sensitivity of spectral data to LNC to some extent. The optimal RDDI performed better than other indices for the synthetic dataset and the dataset in each growth stage. And the sensitive bands selected in the optimal indices at each growth stage are not consistent, which are not only related to the Chl absorption but also other biochemical components, such as starch, lignin, cellulose, protein, etc. In general, the LNC can be estimated by the optimized CR-based RDDI indices in autumn shoot maturation stage, flower spike stage, fruit maturation stage, and flowering stage with the R² > 0.50 and RMSE < 0.14. Although there were the significant relationship between RDIs and RDDIs in flower bud morphological differentiation stage, the highest R²s of the model developed by RDDIs and RDIs were less than 0.50 in cross validation. This study indicated that the applicability of canopy reflectance to estimate litchi LNC was closely related to the growth stage of litchi. Growth stage-specific models will be preferred for estimating litchi LNC estimation.     

Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum

An experiment was conducted under outdoor pot-culture conditions to determine effects of nitrogen (N) deficiency on sorghum growth, physiology, and leaf hyperspectral reflectance properties. Sorghum (cv. DK 44C) was seeded in 360 twelve-litre pots filled with fine sand. All pots were irrigated with half-strength Hoagland's nutrient solution from emergence to 25 days after sowing (DAS). Thereafter, pots were separated into three identical groups and the following treatments were initiated: (1) the control (100% N) continued receiving the half-strength nutrient solution; (2) reduced N to 20% of the control (20% N); and (3) withheld N from the solution (0% N). Photosynthetic rate (Pn), chlorophyll (Chl) and N concentrations, and hyperspectral reflectance of the uppermost, fully expanded leaves were determined at 3- to 4-day-interval from 21 to 58 DAS during the N treatments. Plants were harvested 58 DAS to determine effects of N deficiency on leaf area (LA), biomass accumulation, and partitioning. Nitrogen deficiency significantly reduced LA, leaf Chl content and Pn, resulting in lower biomass production. Decreased leaf Pn due to N deficiency was mainly associated with lower stomatal conductance rather than carboxylation capacity of leaf chemistry. Among plant components of dry weights, leaf dry weight had the greatest and root dry weight had the smallest decrease under N deficiency. Nitrogen-deficit stress mainly increased leaf reflectance at 555 (R₅₅₅) and 715 nm (R₇₁₅) and caused a red-edge shift to shorter wavelength. Leaf N and Chl concentrations were linearly correlated with not only the reflectance ratios of R₄₀₅/R₇₁₅ (r² = 0.68^***) and R₁₀₇₅/R₇₃₅ (r² = 0.64^***), respectively, but also the first derivatives of the reflectance (dR/dλ) in red edge centered 730 or 740 nm (r² = 0.73–0.82^***). These specific reflectance ratios or dR/dλ may be used for rapid and non-destructive estimation of sorghum leaf Chl and plant N status.     

基于高光谱的水稻叶片氮含量估计的深度森林模型研究

高光谱遥感已经成为快速诊断作物水氮状态的一种有效手段。然而,传统的回归方法和机器学习往往难以挖掘高光谱的全部信息,深度神经网络又通常需要大量的训练数据,因此本研究试图探索在少量数据条件下构建深度学习模型并实现叶片氮含量的精准估计。通过在湖北省监利县开展了连续2年不同氮素胁迫水平的水稻试验,测量了作物全生育期内的216组冠层光谱和叶片氮含量。基于一阶导数光谱,本文构建了一种新的深度学习模型(深度森林DF)来进行叶片氮含量的反演,并与2种经典机器学习模型(随机森林RF和支持向量机SVM)和一种深度神经网络模型(多层感知器MLP)进行比较。结果表明,在基于少量高光谱数据的情况下,DF对水稻叶片氮含量的估算精度要高于MLP,其中预测精度最高的模型为全波段光谱反演的DF模型(R²=0.919, RMSE=0.327)。在2种经典机器学习模型中,RF的估计效果优于SVM,但2种模型结果都不够稳定。研究表明,深度森林可以提升高光谱反演叶片氮含量的精度和稳定性,并且可以通过多粒度扫描相对减轻过拟合程度。该研究结果可为少量数据条件下快速监测作物叶片氮含量提供参考。     

Fault diagnosis based on least square support vector machine optimized by genetic algorithm

A new fault diagnosis model is proposed based on Multi-Class Least Square Support Vector Machine optimized hierarchically by Genetic Algorithm(GA). Original vibration signals are decomposed into several stationary IMFs. Then the instantaneous amplitude energy of the IMFs with fault modulation characteristics is computed and regarded as the input characteristic measure of the Poly-kernel Multi-Class LS-SVM for fault classification. EMD decomposition adaptively isolates the fault modulation signals from original signals. The differences among instantaneous amplitude energy vectors reflect the separability of different fault types. Adopting GA to optimize punish parameter and Poly-kernel parameters hierarchically can not only enhance fault prediction accuracy of Multi-Class LS-SVM with Poly-kernel, but also improve adaptive diagnosis capacity of LS-SVM. The GA-based hierarchical optimization is also applicable to Multi-Class LS-SVM with Lin-kernel, RBF-kernel or Sigmoid-kernel. The deep groove ball bearings fault diagnosis experiment shows the effectivity of this new model.     

Application of partial least squares regression in the forecast of ground subsidence

Taking into account many influence factors of ground subsidence induced by underground exploitation,based on partial least squares multinomial regression,a forecast analysis on the maximum of ground subsidence is carried out.Taking height,depth,obliquity of coal clay and rigidity coefficient as independent variables,and maximum of ground subsidence as dependent variable,the forecast model of maximum of ground subsidence is obtained.It is found that,Press residual value decreases with the increase of number of latent variables,and the number of latent variables is four by Press residual value versus number of latent variables.The normal regression coefficient of height is the largest in the four influence factors,and this indicates that the influence of height is the largest on maximum of ground subsidence.The determination coefficient of forecast model obtained in this paper is 0.915 7,the error of forecast model is ±10.41%.The following conclusion can be drawn that the model based on partial least squares multinomial regression is a better and feasible non linear method.     

Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon

Nitrogen losses from intensive vegetal production systems are commonly associated with contamination of water bodies. Sustainable and optimal economic N management requires correct and timely on-farm assessment of crop N status to detect N deficiency or excess. Optical sensors are promising tools for the assessment of crop N status throughout a crop or at critical times. We evaluated optical sensor measurement of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop N status weekly throughout a muskmelon crop. The Crop Circle ACS 470 was used for reflectance measurement, the SPAD 502 for leaf chlorophyll, and the DUALEX 4 Scientific for leaf chlorophyll and flavonols. Four indices of canopy reflectance (NDVI, GNDVI, RVI, GVI), leaf flavonols and chlorophyll contents and the nitrogen balance index (NBI), the ratio of chlorophyll to flavonols contents, were linearly related to crop N content and to crop Nitrogen Nutrition Index (NNI) throughout most of the crop. NBI most accurately predicted crop N status; in five consecutive weekly measurements, R² values were 0.80–0.95. For NDVI during the same period, R² values were 0.76–0.87 in the first three measurements but R² values in the last two measurements were 0.39–0.45. Similar relationships were found with the three other reflectance indices. Generally, the relationships with NNI were equal to or slightly better than those with crop N content. These optical sensor measurements provided (i) estimation of crop N content in the range 1.5–4.5%, and (ii) an assessment of whether crop N content was sufficient or excessive for optimal crop growth for NNI ranges of 0.8–2.0. Composite equations that integrated the relationships between successive measurements with the optical sensors and crop N content or NNI for periods of ≥2 weeks (often 2–3 weeks) were derived for most indices/parameters. Overall, these results demonstrated the potential for the use of these optical sensor measurements for on-farm monitoring of crop N status in muskmelon.     

Development of critical nitrogen dilution curve in rice based on leaf dry matter

The critical nitrogen (N_c), defined as the minimum N concentration required for maximum growth, is proposed for diagnosis of the in-season N status in crop plants. It has been established for several crops including rice on whole-plant dry matter (DM) basis but has not been determined for canopy leaf basis. This research was undertaken to develop a new N_c dilution curve based on leaf dry matter (L_DM) and to assess its applicability to estimate the level of N nutrition for Japonica rice in east China. Three field experiments were conducted with varied N rates (0–360 kg N ha⁻¹) and three Japonica rice (Oryza sativa L.) hybrids, Lingxiangyou-18 (LXY-18), Wuxiangjing-14 (WXJ-14) and Wuyunjing (WYJ) in Jiangsu province of east China. Five hills from each plot were sampled from active tillering to heading for growth analysis and leaf N determination. The N_c dilution curve on leaf N concentration was described by the equation N_c = 3.76W^−0.218, when L_DM ranged from 0.67 to 4.25 t ha⁻¹. However, for L_DM < 0.67 t ha⁻¹, the constant critical value N_c = 4.09%L_DM was applied. This N_c dilution curve on L_DM basis was slightly higher than the curves on plant DM basis in Japonica rice, yet both lower than the reference curve of high yielding Indica rice in tropics. The N nutrition index (NNI) and accumulated N deficit (N_and) of leaves ranged from 0.65 to 1.06 and 79.62 to −6.39 kg ha⁻¹, respectively, during main growth stages under varied N rates in 2010 and 2011. The results indicate that the present N_c dilution curve and derived NNI and N_and adequately identified the situations of N-limiting and non-N-limiting nutrition in two rice varieties and could be used as reliable indicators of N status during growth of Japonica rice in east China.     

增强叶片氮素输出对水稻分蘖和碳代谢的影响

增施氮肥是保证水稻高产的重要栽培措施,但高氮肥投入所增加的植株氮素积累大部分滞留在营养器官中,对产量的促进作用有限。叶片是氮素储存的主要器官及籽粒氮素的主要供给源。为了明确植株中氮素分配对水稻生长的影响,本研究将拟南芥铵转运蛋白基因AtAMT1.2在水稻韧皮部特异表达,促进叶片氮素输出,检测转基因水稻植株在不同氮肥浓度下的生长情况。试验结果显示,高氮下pOsSUT1::AtAMT1.2转基因水稻分蘖数、氮素利用效率显著增加,叶片中糖输出量增加,分蘖芽中独角金内酯途径相关基因OsTB1、OsD14表达水平下调。研究说明增加叶片氮素输出能够增大叶片中糖向分蘖芽的转运量,促进分蘖生长,从而提高了有效分蘖数并带来了更高的氮素利用效率。     

Data fusion of spectral,thermal and canopy height parameters for improved yield prediction of drought stressed spring barley

Yield modelling based on visible and near infrared spectral information is extensively used in proximal and remote sensing for yield prediction of crops. Distance and thermal information contain independent information on canopy growth, plant structure and the physiological status. In a four-years′ study hyperspectral, distance and thermal high-throughput measurements were obtained from different sets of drought stressed spring barley cultivars. All possible binary, normalized spectral indices as well as thirteen spectral indices found by others to be related to biomass, tissue chlorophyll content, water status or chlorophyll fluorescence were calculated from hyperspectral data and tested for their correlation with grain yield. Data were analysed by multiple linear regression and partial least square regression models, that were calibrated and cross-validated for yield prediction. Overall partial least square models improved yield prediction (R² = 0.57; RMSEC = 0.63) compared to multiple linear regression models (R² = 0.46; RMSEC = 0.74) in the model calibration. In cross-validation, both methods yielded similar results (PLSR: R² = 0.41, RMSEV = 0.74; MLR: R² = 0.40, RMSEV = 0.78). The spectral indices R₇₈₀/R₅₅₀, R₇₆₀/R₇₃₀, R₇₈₀/R₇₀₀, the spectral water index R₉₀₀/R₉₇₀ and laser and ultrasonic distance parameters contributed favourably to grain yield prediction, whereas the thermal based crop water stress index and the red edge inflection point contributed little to the improvement of yield models. Using only more uniform modern cultivars decreased the model performance compared to calibrations done with a set of more diverse cultivars. The partial least square models based on data fusion improved yield prediction (R² = 0.62; RMSEC = 0.59) compared to the partial least square models based only on hyperspectral data (R² = 0.48; RMSEC = 0.69) in the model calibration. This improvement was confirmed by cross-validation (data fusion: R² = 0.39, RMSEV = 0.76; hyperspectral data only: R² = 0.32, RMSEV = 0.79). Thus, a combination of spectral multiband and distance sensing improved the performance in yield prediction compared to using only hyperspectral sensing.     

Assessment of plant nitrogen status using chlorophyll fluorescence parameters of the upper leaves in winter wheat

Non-destructive, rapid diagnosis of plant nitrogen status is important for the evaluation of wheat growth and the dynamic management of nitrogen nutrition. Two wheat cultivars, Zhengmai 366 (high protein content) and Aikang 58 (medium protein content) were grown in field trials at five different nitrogen levels (0, 90, 180, 270 and 360 kg ha⁻¹) in two consecutive growing seasons at Zhengzhou, China. Leaf chlorophyll fluorescence (ChlF) parameters, leaf and stem biomass, and nitrogen content were measured simultaneously at different growth stages, establishing an evaluation model of plant nitrogen nutrition in wheat using ChlF parameters. The results showed that the differences in ChlF parameters between the three top leaves (1–3LFT) was small from the reviving to the flowering stages. With increasing nitrogen levels, the difference in ChlF parameters between the fourth leaf (4LFT) and the first three leaves (1–3LFT) decreased gradually, indicating that 4LFT is sensitive to N fertilizer application and has a disadvantage in competition for redistributed N. The correlation coefficients between ChlF parameters for the upper, fully expanded leaves and N concentration of the corresponding leaves were 0.628 for F_v, 0.607 for F_m, 0.579 for F_v/F_o, and 0.600 for F_v/F_m at P < 0.01, but only 0.248 for F_o at P < 0.05. At the reviving and jointing stages, the relationships between the normalized differences between 1–2LFT and 4LFT (NDF_12/4) for F_v/F_o and F_v/F_m to plant nitrogen concentration (PNC) were the most significant (r < − 0.79, P < 0.001), the determination coefficient (R²) for F_v/F_m was much higher than for F_v/F_o, and the two regression equations were grouped at reviving and jointing with similar R² values between the stages. At booting and flowering, the normalized differences between 1–2LFT and 4LFT for F_o, F_m, and F_v better reflected the changes in PNC; the R² values were 0.654–0.797 (P < 0.001) at booting and 0.515–0.584 (P < 0.001) at anthesis, and the two regression equations were grouped at booting and anthesis with greater differences in R² between the stages. The unified regression equation could be used to express the relationship between plant nitrogen sufficiency index (NSI) and ChlF parameters with R² values of 0.623 (P < 0.001) for NDF_12/4 for F_v/F_m, and 0.567 (P < 0.001) for NDF_12/4 for F_v/F_o during the reviving and jointing stages, while R² = 0.666 (P < 0.001) for NDF_12/4 for F_m and 0.615 (P < 0.001) for NDF_12/4 for F_v during booting and anthesis. These results show that the relationship between NDF and NSI was stable and reliable over the different years, varieties, and N supply levels. We conclude that the spatial differences in ChlF parameters between 1–2LFT and 4LFT should be ideal indicators of plant nitrogen status in wheat, and will provide a decision-making method for N diagnosis and regulation in field production.     

Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen

Dynamic simulations models may enable for farmers the evaluation of crop and soil management strategies, or may trigger crop and soil management strategies if they are used as warning systems, e.g. for drought risks and for nutrients shortage. Predictions by simulation models may differ from field observations for a variety of reasons, and such deviations can be revealed instantly by traditional or by new field monitoring techniques. The objective of this study was to improve simulation results by integrating remote sensing observations during the growing season in the simulation (i.e. run-time calibration). The Rotask 1.0 simulation model was used as it simulates daily interactions between climate (radiation temperature, vapour pressure, wind speed, precipitation), soils (water holding capacities, soil organic matter dynamics, evaporation) and crops (light interception, dry matter production, nitrogen uptake, transpiration). Various run-time calibration scenarios for replacing simulated values by remotely observed values were tested. For a number of times in the growing season, simulated values of leaf area index (LAI) and canopy nitrogen contents were replaced with values estimated from remote sensing. Field experiments were carried out in the Netherlands in 1997 (validation) and 1998 (calibration) with potato variety Bintje. Destructive field samplings were performed to follow LAI and canopy nitrogen development in the growing season. Remote sensing observations at canopy level were taken by CropScan™ equipment, covering the electromagnetic spectrum between 460–810 nm in eight spectral bands. LAI and canopy nitrogen were monitored at various moments throughout the growing season by relating them with vegetation indices (VI) that were calculated from the combination of specific remote sensing bands. The results of this study show that run-time calibration of mechanistic simulation models may enhance simulation accuracy, depending on the method how additional information is integrated. It is advised to synchronize dry matter balances and internal nitrogen balances in accordance with adjustments to observed calibration variables (in this case LAI and canopy nitrogen content). It is shown an integrated approach follows the actual crop–soil system more closely, which is helpful for specific crop management and precision agriculture in general. Run-time calibration with variables that can be estimated from remote sensing observations gives more accurate simulation results of variables that can not be observed directly, e.g. the evolution of soil inorganic nitrogen contents. High frequencies of remote sensing observations and interpolation in between them, allow reconstructing the evolution of LAI and canopy nitrogen contents to be integrated in the simulation, thereby increasing simulation accuracy of other model variables.     

Hyper-spectral estimation of wheat biomass after alleviating of soil effects on spectra by non-negative matrix factorization

Hyper-spectral technology has been proven to be an effective method for the fast and non-destructive monitoring of crop biomass. However, the biomass estimation accuracy of this method is limited due to the effects of background factors, such as soils and water. In this study, a spectral separation method, non-negative matrix factorization (NMF), was proposed to alleviate the effects of soil on spectra. With the application of the NMF method, pure vegetation spectra were extracted from the field-observed spectra of wheat canopy, which were collected in four growing seasons from the tillering to the booting stages of wheat. Then, prediction models of wheat biomass (WB) were established and validated using the extracted spectra with the partial least squares regression (PLSR) method. The results showed that the NMF method could effectively separate the vegetation spectra from the mixed canopy spectra. Based on the extracted vegetation spectra, the WB prediction accuracy could be greatly improved with an increase of 31.7% for the R²_p and an increase of 46.6% for the ratio of performance to deviation (RPD) as compared to the original spectra, indicating that the NMF method could significantly improve the performance of the WB prediction model. This method has potential application in the estimation of biomass using remote sensing technology.     

Variation in partial resistance to barley leaf rust (Puccinia hordei) and agronomic characters of Ethiopian landrace lines

An inventory of 481 lines derived from 12 Ethiopian barley (Hordeum vulgare L.) landraces and the checks was made for partial resistance to Puccinia hordei under greenhouse and field conditions at Adet, Ambo and Sinana Agricultural Research Centers in 2003 and 2004 cropping seasons in Ethiopia. The experiments were laid out in a triple lattice design. Each plot consisted of two rows of 1–m long with spacing of 0.20 m between rows. The overall mean leaf rust epidemics varied from area under disease progress curve (AUDPC) of 86 to 1,835. The disease was as high as AUDPC 1,378 on the susceptible check L94. Highly significant variations were recorded between and within the landraces/lines in leaf rust incidence, severity, days to heading, plant height, thousand seed weight and yield. Similarly, the variations between and within barley groups from three altitude areas and three ear-types were significant. Landraces 1686, 3255, 3262 and 3783 had the least and landraces 219900, 3975 and 3980 had the highest leaf rust severity. Of the 481 lines tested, 413 (86%) had significantly lower disease than the susceptible check, but not than the partial resistant check Vada. In contrast, the yields were more for lines with less disease than for those with high. The frequency of resistant landraces/lines was more in altitude 2,301–2,500 m, and irregular and two rows ear-types than in lower altitude areas and six rows ear-type. Nevertheless, the correlation and regression analysis revealed the adverse effect of the disease in the yields of barley. The 413 lines with high infection types at seedling stage and lower AUDPC under field conditions possess partial resistance to leaf rust.     

Determination of sufficiency values of canopy reflectance vegetation indices for maximum growth and yield of cucumber

Large nitrogen (N) fertilizer applications are a feature of intensive vegetable production systems, and optimal N management is required to maximize N use efficiency and minimize N losses. Vegetation indices (VIs) of canopy reflectance, measured with proximal sensors, are generally strongly related to crop N status. For practical application, sufficiency values that distinguish between N deficiency and sufficiency are required. In this work, sufficiency values of VIs for maximum crop growth and for yield were determined for two cucumber crops grown in contrasting seasons (Autumn and Spring). Canopy reflectance was measured with a Crop Circle ACS-470 sensor. Sufficiency values for maximum growth were based on the relationships of VIs with the Nitrogen Nutrition Index (NNI), i.e. the ratio between actual and critical crop N contents. Sufficiency values for maximum yield were based on linear-plateau relationships of yield with VIs. Strong relationships were obtained between all VIs and both NNI and yield for most of the weekly measurements during both crops. For NNI, best-fit relationships were linear, quadratic, power or exponential, and had coefficients of determination (R²) of 0.61–0.98. For yield, most linear-plateau relationships between yield and VIs had R² values of 0.47–0.89. VIs based on reflectance in green and red edge had slightly better relationships with NNI and yield than VIs in the red, with the Green Normalized Difference Vegetation Index (GNDVI) and the Green Ratio Vegetation Index (GRVI) being the most sensitive and consistent indices for estimating both crop NNI and yield. Relationships between VIs and NNI and yield for all weekly measurements of each crop, and for the two crops combined, were also analyzed to provide unique sufficiency values for maximum growth and yield that applied to the entire crop cycle of each crop and of both crops considered together. Overall, there were slight differences between sufficiency values for maximum growth and for maximum yield and the unique sufficiency values were generally intermediate to the weekly sufficiency values. This study demonstrated the potential for using VIs for monitoring crop N nutrition and yield in cucumber. The calculated sufficiency values of VIs may facilitate the use of proximal optical sensors in farming practice for optimal N management through periodic monitoring for deviation from sufficiency values.     

不同叶龄蘖、穗氮肥组合对粳稻产量及氮素利用的影响

以主茎叶片数不同的粳稻品种吉粳88 (14片)、沈农265 (15片)和沈农1401 (16片)为试材,采用大田筒栽方式,在总施氮量225kghm–2及轻简施肥(基肥、蘖肥、穗肥)模式基础上,设置基蘖肥∶穗肥6∶4和8∶2两种施肥比例,并分设不同源、库叶龄期施氮组合即不同叶龄蘖、穗肥精确施氮组合。分析了不同源库期氮肥运筹模式对水稻农艺性状、产量及氮素利用特性的影响。结果表明:(1)在有效穗数、分化颖花数、产量和氮素利用率方面,吉粳88、沈农265、沈农1401不同氮肥运筹下最佳蘖、穗肥叶龄组合均为6∶4显著高于8∶2。(2)不同氮肥运筹下,吉粳88在8叶(叶龄指数57.1%)、沈农265在9叶(叶龄指数60.0%)、沈农1401在10叶(叶龄指数62.5%)时,即叶龄指数在60%左右时,施用蘖肥效果最佳,最终穗数最多,对保蘖起主要作用;吉粳88在11叶(叶龄指数78.6%)、沈农265在12叶(叶龄指数80.0%)、沈农1401在13叶(叶龄指数81.3%)时,即叶龄指数在80%左右时,施用穗肥效果最佳,最终穗粒数最多,对促花起主要作用。(3)吉粳88-6∶4 (8, 11),沈农26...     

Quantifying nitrogen status of corn (Zea mays L.) in the field by reflectance measurements

Nutrient deficiencies can seriously reduce yield and economic returns to farmers. Tools that can rapidly quantify the nutritional status of plants are needed for efficient fertilizer management. Reflectance measurements have shown to be a useful tool to identify the nutritional status of different plant species. A set of calibration curves relating reflectance ratios to the nitrogen (N), phosphorus (P), magnesium (Mg), and iron (Fe) concentrations in corn leaves was established in greenhouse trials in a previous study. In this paper these calibrations were examined for their ability to identify nutrient deficiencies under field conditions. A 2-year field experiment was conducted to check and define the regions of the spectra that are influenced by leaf N concentration and to set up possible equations for quantifying the leaf N status in the field. The experiment was carried out on a loess derived soil in south-western Germany. Reflectance of corn leaves, from plants grown with six different N fertilization treatments ranging from 0 to 160 N kg ha⁻¹, was determined once a week from the beginning of June until the end of July. Reflectance measurements were performed at the 4th leaf of corn plants with a digital LEICA S1 Pro camera under controlled light conditions. Reflectance was measured in different wavelength ranges in the visible and infrared spectra. Leaf scans were evaluated within the L*a*b*-color system. Total N concentration of corn leaves was determined chemically and correlated with reflectance patterns. Significant correlations between corn N status and leaf reflectance changes were obtained at a nitrogen level of N<3.0%. Reflectance patterns at 510₇₈₀, 516₁₃₀₀, 540₁₃₀₀ nm were found most suitable to the corn N status in the field regardless of the year or sampling date. The results indicate that the spectral patterns and the defined calibration curves of N deficiency from greenhouse studies could be used in field studies. Thus, reflectance measurements may serve as a rapid, non-destructive approach to discriminate nitrogen deficiency in the field.     

The effect of nitrate nutrition on iron chlorosis and leaf growth in sunflower (Helianthus annuus L.)

The objective of this work was to investigate the effects of nutrient solution pH, nitrogen form (NO₃, NH₄NO₃), bicarbonate and different Fe concentrations in the nutrient solution on the Fe concentration in roots and on the development of Fe deficiency symptoms in sunflower plants (Helianthus annuus L.). High pH in the nutrient solution induced by nitrate supply or by a pH-stat device led to increased Fe concentrations in roots and low leaf Fe concentrations associated with a significant decrease in leaf chlorophyll concentration manifested by yellow leaves. Plants of the nitrate fed treatments with 1 μM Fe in the nutrient solution were also characterized by reduced leaf growth and by the suppression of new leaf formation. The reduced leaf growth and the suppression of new leaves only occurred with nitrate and not with NH₄NO₃ in all treatments with 1 μM Fe in the nutrient solution. All symptoms were removed by a high Fe concentration in the nutrient solution (100 μM) at low external pH proving that suppression of leaf formation, reduced leaf growth and low chlorophyll concentration were caused by Fe deficiency. In the nitrate treatment with a low Fe supply (1 μM Fe) and pH 4 in the nutrient solution leaf chlorophyll concentrations similar to the controls were found. In comparison to control plants (NH₄NO₃, 1 μM Fe), leaf growth was still significantly reduced, and new leaf formation was suppressed. The chlorophyll concentration and CO₂ assimilation rate did not differ from those of the control plants. These results show that Fe deficiency is also characterized by small green leaves and the suppression of leaf formation. At the onset of leaf development, leaf growth and new leaf formation may respond more sensitively to poor Fe efficiency than chlorophyll concentration. In experiments with NO₃ plus HCO₃, simulating soil solution conditions prevailing in calcareous soils, the Fe efficiency of the youngest leaves was poor, showing retarded leaf growth and low chlorophyll concentration.     

Effect on leaf photosynthetic rate by leaf blast for rice cultivars with different types and levels of resistance

Summary The effect of an inoculation with Pyricularia oryzae (isolate P06-6) on net leaf photosynthetic rate of rice (Oryza sativa) was studied with four cultivars. Measurements were taken on the sixth leaf of the main culm of plants in the early tillering stage. On cultivars CO39, IR50 and IR64 a susceptible infection type developed, but a clear difference in relative infection efficiency of the cultivars was observed. The highest number of lesions developed on leaves of CO39, whereas the lowest number was found on leaves of IR64. For all three cultivars the effect of a single lesion on the reduction in net leaf photosynthetic rate was found to be equal to a reduction in leaf area of three times the area occupied by the visible lesion. On IR68, a cultivar with complete resistance, brown specks of pinpoint size appeared without any effect on net leaf photosynthetic rate.