Soil And Plant Analyzer Development Value Research Articles

Impact of Duckweed (Lemna minor L.) Growing in Paddy Fields on Rice Yield and Its Underlying Causes

Duckweed growing in paddy fields (DGP) has substantially increased because of the effects of climate warming and/or eutrophication in irrigated water. Previous studies have primarily focused on investigating the effects of DGP as a nonchemical agent for enhancing rice productivity on nitrogen utilization in rice paddy fields. However, how DGP impacts rice yield remains poorly understood. Therefore, a field experiment with three representative rice cultivars was conducted to determine the effects of DGP on rice yield, considering ecological factors, photosynthetic capacity, spectral changes, and plant growth. The results showed that DGP significantly reduced the pH value by 0.6 and the daily water temperature by 0.6 °C, accelerated rice heading by 1.6 days and increased the soil and plant analyzer development (SPAD) and photosynthetic rate of leaves by 10.8% and 14.4% on average, respectively. DGP also markedly enhanced the values of various vegetation indices such as RARSc, MTCI, GCI, NDVI705, CI, CIrededge, mND705, SR705, and GM, and the first derivative curve of the rice canopy reflectance spectrum exhibited a ‘red shift’ phenomenon upon DGP treatment. Changes in the aforementioned factors may lead to average increases of 4.7% in plant height, 15.0% in dry matter weight, 10.6% in panicles m−2, 2.3% in 1000-grain weight, and ultimately a 10.2% increase in grain yield. The correlation observed suggested that the DGP-induced enhancement in grain yield can be achieved by reducing the pH and temperature of the paddy water, thus enhancing the SPAD value and photosynthesis of leaves and stimulating rice plant growth. These results could offer valuable theoretical support for the future sustainable development of agriculture and the environment through the biological synergy between rice and duckweed.

Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum

The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.

Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R2 = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R2 = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth.

The prediction model of nitrogen nutrition in cotton canopy leaves based on hyperspectral visible-near infrared band feature fusion.

Hyperspectral remote sensing technology is becoming increasingly popular in various fields due to its ability to provide detailed information about crop growth and nutritional status. The use of hyperspectral technology to predict SPAD (Soil and Plant Analyzer Development) values during cotton growth and adopt precise fertilization management measures is crucial for achieving high yield and fertilizer efficiency. To detect the nitrogen nutrition in cotton canopy leaves quickly, a non-destructive nitrogen nutrition retrieval model was proposed based on the spectral fusion features of the cotton canopy. The hyperspectral vegetation index and multifractal features were fused to predict the SPAD value and identify the amount of fertilizer applied at different levels. The random decision forest algorithm was used as the model predictor and classifier. A method was introduced which was widely used in the fields of finance and stocks (MF-DFA) into the field of agriculture to extract fractal features of cotton spectral reflectance. Comparing the fusion feature with multi-fractal feature and vegetation index, the results showed that the fusion feature parameters had higher accuracy and better stability than using a single feature or feature combination. The R2 was as high as 0.8363, and the RMSE was 1.8767%. Our intelligent model provides a new idea for detecting nitrogen nutrition in cotton canopy leaves rapidly.

Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images

Rice is China’s main crop and its output accounts for 30% of the world’s total annual rice production. Rice growth status is closely related to chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values). The determination of a SPAD value is of great significance to the health status of rice, agricultural irrigation and regulated fertilization. The traditional SPAD value measurement method is not only time-consuming, laborious and expensive but also causes irreparable damage to vegetation. The main aim of the present study is to obtain a SPAD value through the inversion of hyperspectral remote sensing images. In order to achieve this purpose, the hyperspectral image of rice at different growth stages at the canopy scale was first acquired using a hyperspectral imaging instrument equipped with a drone; the spectral characteristics of the rice canopy at different growth stages were analyzed and combined with a ground-level measured SPAD value, the bands with high correlation between the SPAD values and the spectra of the rice canopy at different fertility stages were selected. Subsequently, we combined the spectral characteristics with the continuous projection algorithm to extract the characteristic band and used the PLS method in MATLAB software to analyze and calculate the weight of each type of spectral value and the corresponding canopy SPAD value; we then used the wavelength corresponding to the spectral value with the highest weight as the used band. Secondly, the four methods of univariate regression, partial least squares (PLS) regression, support vector machine (SVM) regression and back propagation (BP) neural network regression are integrated to establish the estimation model of the SPAD value of rice canopy. Finally, the models are used to map the SPAD values of the rice canopy. Research shows that the model with the highest decision coefficient among the four booting stage models is “booting stage-SVR” (R2 = 0.6258), and the model with the highest decision coefficient among the four dairy maturity models is “milk-ripe stage-BP” (R2 = 0.6716), all of which can meet the requirement of accurately retrieving the SPAD value of rice canopy. The above results can provide a technical reference for the accurate, rapid and non-destructive monitoring of chlorophyll content in rice leaves and provide a core band selection basis for large-scale hyperspectral remote sensing monitoring of rice.

Quantification of Physiological Parameters of Rice Varieties Based on Multi-Spectral Remote Sensing and Machine Learning Models

Estimating plant physiological indicators with remote sensing technology is critical for ensuring precise field management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolution that can be used to clearly identify vegetation. However, the information of UAV image data is relatively complex and difficult to analyze, which is the main problem limiting its large-scale use at present. In order to monitor plant physiological indexes from the multi-spectral data, a new method based on machine learning is studied in this paper. Using UAV for deriving the absorption coefficients of plant canopies and whole leaf area, this paper quantifies the effects of plant physiological indicators such as the soil and plant analyzer development (SPAD) value, whole leaf area, and dry matter accumulation on the relationship between the reflectance spectra. Nine vegetation indexes were then extracted as the sensitive vegetation indexes of the rice physiological indicators. Using the SVM model to predict the SPAD value of the plant, the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) values of the model were 1.90, 1.38, 0.13, 0.86, and 4.13, respectively. The results demonstrate that the rice plants display a considerable biochemical and spectral correlation. Using SVM to predict the SPAD value has a better effect because of a better adaptation and a higher accuracy than other models. This study suggests that the multi-spectral data acquired using UAV can quickly estimate field physiological indicators, which has potential in the pre-visual detection of SPAD value information in the field. At the same time, it can also be extended to the detection and inversion of other key variables of crops.

Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data

Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology can obtain the vertical distribution information of the central and lower leaves of a crop. Furthermore, applications of remote sensing on the vertical distribution of maize canopy components is complicated, and related research is limited. In the current paper, we employed multi-angular spectral data, measured by a self-designed multi-angular observation instrument at view zenith angles (VZAs) of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, to explore the monitoring strategy and monitoring precision of the vertical distribution of chlorophyll content in the maize canopy. This was then used to determine the optimal monitoring method for the chlorophyll content (soil and plant analyzer development (SPAD) value) of each layer. The correlation between SPAD value and chlorophyll sensitivity indices at different growth stages was used as the basis for screening indices and VZAs. The correlation between the selected EPI (eucalyptus pigment index) and REIP (red edge inflection point) indices and chlorophyll content indicated view zenith angles (VZAs) of 0°, 30°, and 40° as optimal for the early growth stage monitoring of chlorophyll content in the 1st, 2nd, and 3rd layers, respectively. These values were associated with RMSEs of 4.14, 1.71, and 1.11 for EPI, respectively; and 4.61, 2.31, and 1.00 for REIP, respectively. In addition, a VZA of 50° was selected to monitor the chlorophyll content of the 1st, 2nd, 3rd, and 4th layers at the late growth stage, with RMSE values of 2.97, 3.50, 2.80, and 4.80 for EPI, respectively; and 3.16, 5.02, 4.55, and 7.85 for REIP, respectively. The results demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the maize canopy chlorophyll content vertical distribution, with the VZAs of different vertical layers varying between the early and late growth stages.

Estimation of SPAD value in waterlogged winter wheat based on characteristic indices of hyperspectral and digital image.

To explore the optimal monitoring method for soil and plant analyzer development (SPAD) of winter wheat under waterlogging stress based on hyperspectral and digital image techno-logy, the correlations between SPAD of the waterlogged winter wheat and fifteen indices of hyperspectral characteristic and fourteen indices of digital image feature were analyzed under a micro-plot which could be irrigated and drainage separately. Then, the BP neural network models for SPAD estimation were constructed based on the optimal monitoring feature indices. Compared with the normal winter wheat, SPAD and the value of hyperspectral reflectance did not change under short-term waterlogging (less than 7 d), whereas the SPAD was significantly decreased after more than 12 d waterlogging treatment with the value being close to zero at the late stage of growth. The estimation accuracy based on the digital image characteristics of green minus red, excess red index, norma-lized redness index and excess green index showed similar results compared to that using the BP network model based on the characteristics of the corresponding hyperspectral band. The highest R2 between the measured value and the predicted value was 0.86, while the root mean square error (RMSE) was 3.98. Compared with the BP network models built with the digital image feathers, the accuracy of the models based on the four hyperspectral characteristic indices (carotenoid reflex index, yellow edge amplitude, normalized difference vegetation index and structure insensitive pigment index) for SPAD was significantly improved, with the highest R2 of 0.97 and the lowest RMSE of 1.95. Our results suggest that both hyperspectral and digital image technology could be used to estimate SPAD value of waterlogged winter wheat and that the BP network model based on hyperspectral characteristic indices performed better in the estimation accuracy.

Effects of The Use of Colored Cover Materials and Led Lighting in Greenhouses on Plant Nutrient Concentration: Case of Tomato Plant (Solanum lycopersicum L.)

In this study, the effects of four different color plastic greenhouse treatments on nutrient concentrations of leaf and fruit, leaf SPAD (Soil and Plant Analyzer Development) values and some yield parameters of tomato were tried to be determined. For this purpose, plastic greenhouses with Red (RedG), Blue (BlueG), Colorless (ConG) and white Led (LedG) lighting have been used as materials in the research. The used plastic greenhouses are 3 m in width, 6 m in length, 2 m in sidewall height and 2.8 m in roof ridge height. Led lighting is; Three hours of extra lighting is planned for one hour before sunrise, 2 hours after sunset. The research was carried out in ISUBU (Isparta), Faculty of Agriculture, field area in the summer season in 2019. The nutrient elements of K, Ca, Mg, P, Cu, Zn, Mn and Fe were determined in the leaf and of the tomato plant. It has been determined that four different greenhouse treatments have a positive effect on K, Ca, Cu, Zn, Fe nutrients in leaf, and K, Ca, P, Mg nutrient in fruit. In terms of SPAD values, all treatments were determined to be higher than the control treatment. It was determined that the highest SPAD value was in BlueG treatment. When the efficiency-related values are examined; It was determined that the highest fruit number, fruit weight and yield were in BlueG treatment. BlueG treatment was followed by RedG, ConG, and LedG treatment respectively in terms of efficiency. As a result, considering the effects of four different greenhouse treatments on macro and micronutrient nutritions SPAD values, and yield values, it was concluded that the most suitable treatments for tomato cultivation in greenhouses is BlueG treatment and RedG treatment should not be ignored.

Effects of tillage depth on soil hydrological characteristics and potato yield on northwest Loess Plateau

<p indent=0mm>The subsoiling can break the plough pan and optimize soil hydrological characteristics. However, the study on the effects of tillage depths on soil water characteristics, potato water consumption and yield, is insufficient. The field experiment was conducted with three tillage depths of <sc>15 cm</sc> (TT), <sc>40 cm</sc> (VRT), and <sc>60 cm</sc> (VRT6), to investigate the effect of tillage depths on the yield and water utilization of potato in semiarid rain-fed area in 2016 and 2017 on northwest Loess Plateau. The parameters of soil bulk density, soil hydrological properties, potato SPAD (soil and plant analyzer development) value, leaf area index (LAI), and tuber yield were measured. The soil bulk density, and increased saturation moisture, capillary moisture and field water capacity of VRT6 treatment compared with VRT and TT were decreased in <sc>40–60 cm</sc> and <sc>0–60 cm</sc> profile, respectively. Water consumption of VRT6 treatment was significantly increased in pre-squaring stage, resulting in the decrement of soil water storage in <sc>0–100 cm</sc> profile by <sc>22.3 mm</sc> and <sc>49.0 mm</sc> in 2016, and <sc>43.9 mm</sc> and <sc>56.6 mm</sc> in 2017. Water consumption in pre-squaring stage of VRT6 was significantly increased by <sc>42.2 mm</sc> and <sc>38.3 mm</sc> compared with VRT and TT treatment in 2017, respectively. Based on the excessive water consumption in pre-flowering period, the LAI of VRT6 was significantly higher than TT in whole growth period, than VRT in post-squaring period, and the SPAD value was higher than VRT in expanding stage in 2017, which indicated that the vigorous growth was subsistent in VRT6 treatment. The tuber yield of VRT6 were significantly higher than TT, but it significantly decreased in 2017 compared to VRT, and the water use efficiency (WUE) of VRT6 were significantly decreased by 61.2%–67.5% and 41.0%–53.5%, as compared with VRT and TT. Consequently, the suitable tillage depth for potato cultivation was <sc>40 cm</sc> in semiarid area, which could optimize the soil hydrological properties and potato water consuming process, and relieved the drought stress efficiently, resulting in significantly higher tuber yield and WUE.

ITRAQ-based quantitative proteomic and physiological analysis of the response to N deficiency and the compensation effect in rice

BackgroundThe crop growth compensation effect is a naturally biological phenomenon, and nitrogen (N) is essential for crop growth and development, especially for yield formation. Little is known about the molecular mechanism of N deficiency and N compensation in rice. Thus, the N-sensitive stage of rice was selected to study N deficiency at the tillering stage and N compensation at the young panicle differentiation stage. In this study, a proteome analysis was performed to analyze leaf differentially expressed proteins (DEPs), and to investigate the leaf physiological characteristics and yield under N deficiency and after N compensation.ResultsThe yield per plant presented an equivalent compensatory effect. The net photosynthetic rate, optimal/maximal quantum yield of photosystem II (Fv/Fm), soil and plant analyzer development (SPAD) value, and glutamic pyruvic transaminase (GPT) activity of T1 (N deficiency at the tillering stage, and N compensation at the young panicle differentiation stage) were lower than those of CK (N at different stages of growth by constant distribution) under N deficiency. However, after N compensation, the net photosynthetic rate, Fv/Fm, SPAD value and GPT activity were increased. Using an iTRAQ-based quantitative approach, a total of 1665 credible proteins were identified in the three 4-plex iTRAQ experiments. Bioinformatics analysis indicated that DEPs were enriched in photosynthesis, photosynthesis-antenna proteins, carbon metabolism and carbon fixation in the photosynthetic organism pathways. Moreover, the photosynthesis-responsive proteins of chlorophyll a-b binding protein, ribulose bisphosphate carboxylase small chain and phosphoglycerate kinase were significantly downregulated under N deficiency. After N compensation, chlorophyll a-b binding protein, NADH dehydrogenase subunit 5, NADH dehydrogenase subunit 7, and peroxidase proteins were significantly upregulated in rice leaves.ConclusionThrough physiological and quantitative proteomic analysis, we concluded that a variety of metabolic pathway changes was induced by N deficiency and N compensation. GO and KEGG enrichment analysis revealed that DEPs were significantly associated with photosynthesis pathway-, energy metabolism pathway- and stress resistance-related proteins. The DEPs play an important role in the regulation of N deficiency and the compensation effect in rice.

Mechanisms and uncertainties of Zn supply on regulating rice Cd uptake

Application of Zinc (Zn) is considered an effective measure to reduce Cadmium (Cd) uptake and toxicity in Cd-contaminated soils for many plant species. However, interaction between Zn and Cd in rice plant is complex and uncertain. In this study, four indica rice cultivars were selected to evaluate the effect of Zn exposure in an EGTA-buffered nutrient solution under varying Zn activities and a field level of Cd activity to characterize the interaction between Zn and Cd in rice. Severe depression in shoots’ biomass, tiller number, and SPAD (Soil and Plant Analyzer Development) value were found at both Zn deficiency and Zn phytotoxicity levels among four tested rice cultivars. There existed a strong antagonism interaction between Zn and Cd in both shoot and root from Zn deficiency to Zn phytotoxicity. The reduction of Cd accumulation in roots and shoots could be explained by the competition between Zn and Cd as well as the dilution effect of increasing biomass. The conflicting effect of Zn supply on Cd uptake may be attributed to the increasing transfer ratio of Cd from root to shoot with the increasing Zn2+ activities and the strong depression of Fe and Mn in shoots with the increasing Zn2+ activities as well as the variation of genotypes. Balance between Zn and Cd should be considered in field application.

Nitrogen Fertilization in Soil Affects Physiological Characteristics and Quality of Green Tea Leaves

To study the effects of soil nitrogen (N) fertilization on tea growth, quality and yield, a controlled experiment with green tea [Camellia sinensis (L.) O. Ktze] was conducted. Five N fertilization treatments in soil were designed: 0, 0.97, 1.94, 3.88, and 5.82 g/kg/pot, which were subsequently recorded as N0, N1, N2, N3, and N4. The changes to young shoot biomass, total N and carbon (C), Soil and Plant Analyzer Development (SPAD) value, photosynthetic parameters, senescent characteristics, endogenous hormones, and the quality of green tea leaves were investigated. The results showed that with the increase in N fertilization level, the young shoot biomass, total N and C, SPAD value, net photosynthetic rate (PN), transpiration rate (Tr), stomatal conductance (gS), superoxide dismutase activity, indoleacetic acid, gibberellin, zeatin (ZT), caffeine, and amino acids increased at first and then decreased, the maximums appeared at 3.88 g/kg/pot; whereas the intercellular CO2 concentration (Ci), malondialdehvde contents, abscisic acid (ABA), polyphenol contents, and the ratio of polyphenols (PP) to free amino acid decreased at first and then increased, the minimums appeared at 3.88 g/kg/pot. The immediately significant change in all parameters appeared after 1 month of N treatments. The experiment showed that 3.88 g/kg/pot N fertilization level was the best for growth, quality, and yield of tea, which could provide a theoretical basis for short-term N fertilization management in tea tree.

Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors

The accuracy of detecting the chlorophyll content in the canopy and leaves of citrus plants based on sensors with different scales and prediction models was investigated for the establishment of an easy and highly-efficient real-time nutrition diagnosis technology in citrus orchards. The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor, canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and then analyzed. Additionally, the associations of the leaf SPAD (soil and plant analyzer development) value with the ratio vegetation index (RVI) and normalized differential vegetation index (NDVI) were analyzed. The leaf SPAD value predictive model was established by means of univariate and multiple linear regressions and the partial least squares method. Variable distribution maps of the relative canopy chlorophyll content based on spectral reflectance in the orchard were automatically created. The results showed that the correlations of the SPAD values obtained from the Multiplex 3.6 sensor, FieldSpec4 radiometer and low-altitude multispectral remote sensing were highly significant. The measures of goodness of fit of the predictive models were R2=0.7063, RMSECV=3.7892, RE=5.96%, and RMSEP=3.7760 based on RVI(570/800) and R2=0.7343, RMSECV=3.6535, RE=5.49%, and RMSEP=3.3578 based on NDVI[(570,800)(570,950)(700,840)]. The technique to create spatial distribution maps of the relative canopy chlorophyll content in the orchard was established based on sensor information that directly reflected the chlorophyll content of the plants in different parts of the orchard, which in turn provides evidence for implementation of orchard productivity evaluation and precision in fertilization management. Keywords: citrus, remote sensing, bio-sensor, chlorophyll detection, spectrum, ratio vegetation index (RVI), normalized differential vegetation index (NDVI), spatial distribution map DOI: 10.25165/j.ijabe.20181102.3189 Citation: Wang K J, Li W T, Deng L, Lyu Q, Zheng Y Q, Yi S L, et al. Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors. Int J Agric & Biol Eng, 2018; 11(2): 164–169.

A method for screening copper-tolerant rice (Oryza sativa L.) cultivars based on hydroponic experiments and cluster analysis

ABSTRACTA method for screening copper (Cu)-tolerant rice cultivars was studied by combining hydroponic experiments and cluster analysis, and the classification of cultivars in Cu stress tolerance was done. In the first hydroponic experiment, seedlings of Jiahe991 and Xiushui114 were planted in nutrient solution with different Cu2+ concentrations from 10 to 1800 μg/L. Results indicated that the toxic threshold of Cu concentration in solution ranged from 900 to 1200 μg/L, since SPAD (Soil and Plant Analyzer Development, SPAD-502, a portable chlorophyll meter, Minolta Camera Co. Ltd., Japan) values of leaves and seedlings biomass of the treatments with ≥900 and/or 1200 µg/L were significantly lower than the control. The second experiment was conducted with 16 local rice cultivars under three Cu treatments (10, 1000, and 1500 μg/L). The 16 cultivars were well classified into tolerant, normal, and sensitive groups as a result of cluster analysis based on the relative SPAD (Soil and Plant Analyzer Development, SPAD-502, a portable chlorophyll meter, Minolta Camera Co. Ltd., Japan) value, shoot and root dry weights, root length and root dehydrogenase activity, and oxidizing capacity and shoot Cu concentration. Xiushui123, Xiushui134, Jiahe991, and Xianghu301 belonged to the tolerant group; Xiushui137 belonged to the sensitive group. The cluster analysis based on hydroponic experiments is an effective method for identifying rice cultivars that are tolerant to Cu stress. In addition, four cultivars (Xiushui123, Xiushui134, Jiahe991, and Xianghu301) are recommended in local practice.

基于上部叶片SPAD值估算小麦氮营养指数

快速、准确的监测诊断小麦氮营养状态对于评价小麦长势、指导氮肥运筹并预测籽粒产量均具有重要的意义。基于2009-2011年的大田试验,系统分析了小麦上部4张单叶不同叶位的SPAD值和归一化SPAD指数(NDSPAD_<em>ij</em>)与氮营养指数的定量关系,通过简单分组线性回归筛选出在不同年际和不同品种间表现稳定的氮营养指数(NNI)定量方程。结果表明,小麦上部不同叶位SPAD值和NNI随施氮量提高而提高,而NDSPAD_<em>ij</em>随施氮量的提高而降低。小麦单叶SPAD值与NNI的关系呈显著正相关,但这种关系在品种或年份之间不稳定,对小麦氮素诊断存在风险;除NDSPAD₁₂外,NDSPAD_<em>ij</em>与NNI之间呈显著负相关,经简单分组线性分析发现NDSPAD₁₄与NNI之间在年份和品种之间表现最稳定,能够较好的定量估算氮营养指数,从而快速诊断小麦氮素是否亏缺。;Rapid, accurate, and dynamic diagnosis of nitrogen nutrition status is essential for evaluating vigor, for predicting production rates, and for agricultural management of cultivated wheat. To determine the critical nitrogen concentration dilution curve for wheat, two field experiments with different levels of N application (0, 75, 150, 225, 300 and 375 kg/hm²) were conducted in Yizheng, China. According to the procedure in Justes's curve, the dry matter and nitrogen concentrations under different nitrogen treatments could be compared by analysis of variance at the 5% probability level. Based on this curve, we developed a nitrogen nutrition index (NNI). In this index, when NNI=1, nitrogen nutrition was considered to be optimum, NNI>1 indicated excess nitrogen, and NNI<1 indicated nitrogen deficiency. We further investigated relationships between NNI and soil and plant analyzer development (SPAD) chlorophyll meter values and normalized differential SPAD_<em>ij</em> (NDSPAD_<em>ij</em>) values obtained at the top four leaf positions in wheat. We conducted a linear parallel curve analysis with grouped data to determine if the linear quantitative equation relating NNI to SPAD value or NDSPAD_ijshowed differences between varieties and/or years. The results showed that the curve had specific biological significance. The relationship between the critical nitrogen concentration and wheat dry matter could be described by the following negative power equations Y16: N_cnc=4.65DM^-0.44 and N13: N_cnc=4.33DM^-0.45. The critical nitrogen curves of the two cultivars of wheat had the same coefficient b but different coefficients a. Y16 had a higher nitrogen accumulation capacity than N13. The NNI values ranged from 0.37 to 1.28, and increased with higher nitrogen application rates under the different nitrogen levels. The optimum nitrogen level was 150 kg/hm² in 2009–2010, but was 225 kg/hm² in 2010-2011. The SPAD values increased with increasing N application rates, and ranged from 34.3 to 52.8. In contrast, the NDSPAD_<em>ij</em> value decreased with increasing nitrogen application rates, which showed that increasing amounts of nitrogen could decrease the differences in SPAD value among the top four single leaves of wheat. The NDSPAD_ijvalues ranged from 0.01 to 0.143. The SPAD value at the top four single leaf positions were significantly positively related to NNI. The correlation coefficients ranged from 0.666 to 0.823. The strongest correlation was between the SPAD value at the top leaf (T1) and NNI, while the weakest correlation was between the SPAD value at the second leaf (T2) and NNI. A linear parallel curve analysis with grouped data approximated the relationship between SPAD and NNI and its differences between varieties and years. This will be useful for diagnoses of wheat nitrogen nutrition. In contrast, NDSPAD_<em>ij</em> was significantly negatively related to NNI, except for NDSPAD₁₂. The correlation coefficients ranged from 0.01 to -0.849. The strongest correlation was between NDSPAD₁₄ and NNI, while the weakest correlation was between NDSPAD₁₂ and NNI. A linear parallel curve analysis with grouped data demonstrated the relationship between NDSPAD₁₄and NNI (NNI=-2.019NDSPAD₁₄+1.18, <em>R</em>=-0.838^{* *}) stably across variety and year differences. This analysis could be used to assess NNI quantitatively and to diagnose wheat nitrogen deficiency quickly.

Genetic analysis of iron chlorosis tolerance in Prunus rootstocks

The high economic losses caused by the occurrence of iron chlorosis in Prunus orchards in the Mediterranean area justifies the implementation of breeding programs to generate high-performance rootstocks for different edaphoclimatic area conditions. For that reason, the genetic control of iron chlorosis tolerance was studied in an F1 population derived from a three-way interspecific cross between a Myrobalan plum (P 2175) and an almond × peach hybrid (Felinem). Several phenotypic measurements were assessed to guarantee an accurate data set for genetic analysis. SPAD (Soil and Plant Analyzer Development) values, chlorophyll concentration, and visual diagnostic symptoms were highly correlated with leaf chlorosis in trees. SPAD value was the most reliable measure, since it was an objective, unbiased, and non-destructive method. Two significant quantitative trait loci (QTLs) involved in SPAD and chlorophyll concentration were identified for Felinem in linkage groups 4 and 6. Both QTLs were detected in four of the six consecutive years of the experiment. For P 2175, two of the three putative QTLs identified, pspad4.1 and chl4.1, were placed in linkage group 4. These QTLs were related to the SPAD values and chlorophyll concentration, respectively, and co-localized with QTLs detected in the Felinem map affecting the same traits. Candidate gene PFIT, related to iron metabolism, was localized within the confidence interval of the QTL in linkage group 4. This research suggests an association of this chromosome region with tolerance to iron chlorosis in Prunus, and it provides a first approach to localize candidate genes involved in tolerance to this abiotic stress.

Detection of Nitrogen Content Changes of Rape Leaf Using Hyperspectral Imaging

Hyperspectral imaging (400-1000nm) and artificial neural network (ANN) techniques were investigated for the detection of nitrogen content changes of rape leaf. Measuring SPAD value of rape leaf by using SPAD (Soil and Plant Analyzer Development).A hyperspectral imaging system was established to acquire hyperspectral data. Principal component analysis(PCA) was used to obtain principal component images, as well as to select the optimal wavelength(s). ANN was applied to establish the model between the spectral reflection values and SPAD values. The prediction results were obtained for the nitrogen content of rape leaf with the correlation of prediction of R=0.9237. The results show that the hyperspectral imaging has good classification on different nitrogen content of rape leaf.

A Quantitative Trait Locus for Chlorophyll Content and its Association with Leaf Photosynthesis in Rice

Abstract Leaf photosynthesis, an important determinant of yield potential in rice, can be estimated from measurements of chlorophyll content. We searched for quantitative trait loci (QTLs) for Soil and Plant Analyzer Development (SPAD) value, an index of leaf chlorophyll content, and assessed their association with leaf photosynthesis. QTL analysis derived from a cross between japonica cultivar Sasanishiki and high-yielding indica cultivar Habataki detected a QTL for SPAD value on chromosome 4. This QTL explained 31% of the total phenotypic variance, and the Habataki allele increased the SPAD value. Chromosomal segment substitution line (CSSL) with the corresponding segment from Habataki had a higher leaf photosynthetic rate and SPAD value than Sasanishiki, suggesting an association between SPAD value and leaf photosynthesis. The CSSL also had a lower specific leaf area (SLA) than Sasanishiki, reflecting its thicker leaves. Substitution mapping under Sasanishiki genetic background demonstrated that QTLs for SPAD value and SLA were co-localized in the 1,798-kb interval. The results suggest that the phenotypes for SPAD value and SLA are controlled by a single locus or two tightly linked loci, and may play an important role in increasing leaf photosynthesis by increasing chlorophyll content or leaf thickness, or both.