Leaf Chlorophyll Content Index Research Articles

Planting pattern and pre-sowing soil moisture effect on yield of soybean

This study aimed to investigate the effects of planting patterns and pre-sowing soil moisture on the yield of summer soybean [Glycine max (L.) Merr.] in northern China from 2009 to 2010. The experiment comprised three planting patterns, namely, 30 cm uniform row spacing pattern (U), “20 + 40” wide-narrow row planting pattern (WN), and “20 + 40” furrow planting pattern (F). Three pre-sowing soil moisture treatments were performed at 90, 135, and 180 mm and involved irrigation during the winter wheat growth season. The leaf area index and chlorophyll content index of F were higher than those of U and WN under the same pre-sowing soil moisture treatment (P < 0.05). The accumulation of dry matter and the grain yield of F were significantly higher than those of U and WN (P < 0.05) irrespective of water content. The increase in grain yields of summer soybean was significant in 2009 (P < 0.05) but not in 2010 (P > 0.05) for 135 and 180 mm pre-sowing soil moisture under F. In conclusion, F and 180 mm pre-sowing soil moisture treatments were good agronomic measures to increase the yield of summer soybean in northern China.

Physiological changes in eucalyptus hybrids under different irrigation regimes

With the expansion of the cultivation of eucalyptus into areas with limited water resources, recommending genotypes which are tolerant to low water availability is important in order to maximize productivity under such conditions. The aim of this work therefore was to evaluate five hybrids of Eucalyptus grandis x E. urophylla (H1 to H5) subjected to four irrigation regimes in the greenhouse: daily (IR1), every two days (IR2), every four days (IR4) and every six days (IR6). The following characteristics were evaluated: photosynthetic rate (A), transpiration (E), stomatal conductance (gs), leaf water potential (Ψw), leaf relative water content, photochemical efficiency and chlorophyll content index. Evaluations of A, gs and E were carried out on two occasions: 1 - under stress, at the end of the interval between irrigations for each treatment; and 2 - in recovery, 48 h after irrigation for all plants in the experiment. On average, there was reduction of 25 and 40% in A values, 40 and 55% in gs, 15 and 22% in E, and 96 and 103% in Ψw respectively in those plants under regimes IR4 and IR6, compared to under IR1. Stomatal conductance had only partially recovered 48 h after irrigation, and due to being more sensitive, its changes are a good indication of water stress. Hybrid H3 is the most tolerant and H5 the most sensitive to a reduction in water availability in soil.

The Impact of Seed Priming and Aging on Physiological Performance of Chickpea Under Different Irrigation Treatments

Abstract A sub-sample of chickpea (Cicer arietinum L. CV.ILC482) seeds was kept as control and two other subsamples were aged at 40 °C for 3 and 5 days. Consequently, three seed lots with different levels of vigor were provided. These seed lots were soaked in distilled water at 15°C for 12 and 18 hours and then dried back to initial moisture content at a room temperature of 20–22°C. Then seeds were sown in the field as split plot factorial based on RCB design. Hydro-priming improved leaf chlorophyll content index of plants from different seed lots. Hydro-priming also enhanced stomatal conductance of plants from all seed lots under all irrigation levels, but this advantage for plants from low vigor seed lots particularly under limited irrigations was higher than that for other treatments. Plants from high vigor seed lot under different irrigation treatments had higher relative water content, compared with those from low vigor seed lots. Hydro-priming improved relative water content, membrane stability and grain yield of chickpea plants from different seed lots under various irrigation treatments. It was concluded that hydro-priming to some extent can repair aged seeds and improve their performance under different irrigation treatments.

Temporal disparity in leaf chlorophyll content and leaf area index across a growing season in a temperate deciduous forest

Spatial and temporal variations in canopy structure and leaf biochemistry have considerable influence on fluxes of CO2, water and energy and nutrient cycling in vegetation. Two vegetation indices (VI), NDVI and Macc01, were used to model the spatio-temporal variability of broadleaf chlorophyll content and leaf area index (LAI) across a growing season. Ground data including LAI, hyperspectral leaf reflectance factors (400–2500nm) and leaf chlorophyll content were measured across the growing season and satellite-derived canopy reflectance data was acquired for 33 dates at 1200m spatial resolution. Key phenological information was extracted using the TIMESAT software. Results showed that LAI and chlorophyll start of season (SOS) dates were at day of year (DOY) 130 and 157 respectively, and total season duration varied by 57 days. The spatial variability of chlorophyll and LAI phenology was also analyzed at the landscape scale to investigate phenological patterns over a larger spatial extent. Whilst a degree of spatial variability existed, results showed that chlorophyll SOS lagged approximately 20–35 days behind LAI SOS, and the end of season (EOS) LAI dates were predominantly between 20 and 30 days later than chlorophyll EOS. The large temporal differences between VI-derived chlorophyll content and LAI has important implications for biogeochemical models using NDVI or LAI to represent the fraction of photosynthetically active radiation absorbed by a canopy, in neglecting to account for delays in chlorophyll production and thus photosynthetic capacity.

Toward a Semiautomatic Machine Learning Retrieval of Biophysical Parameters

Biophysical parameters such as leaf chlorophyll content (LCC) and leaf area index (LAI) are standard vegetation products that can be retrieved from Earth observation imagery. This paper introduces a new machine learning regression algorithms (MLRAs) toolbox into the scientific Automated Radiative Transfer Models Operator (ARTMO) software package. ARTMO facilitates retrieval of biophysical parameters from remote observations in a MATLAB graphical user interface (GUI) environment. The MLRA toolbox enables analyzing the predictive power of various MLRAs in a semiautomatic and systematic manner, and applying a selected MLRA to multispectral or hyperspectral imagery for mapping applications. It contains both linear and nonlinear state-of-the-art regression algorithms, in particular linear feature extraction via principal component regression (PCR), partial least squares regression (PLSR), decision trees (DTs), neural networks (NNs), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The performance of multiple implemented regression strategies has been evaluated against the SPARC dataset (Barrax, Spain) and simulated Sentinel-2 (8 bands), CHRIS (62 bands) and HyMap (125 bands) observations. In general, nonlinear regression algorithms (NN, KRR, and GPR) outperformed linear techniques (PCR and PLSR) in terms of accuracy, bias, and robustness. Most robust results along gradients of training/validation partitioning and noise variance were obtained by KRR while GPR delivered most accurate estimations. We applied a GPR model to a hyperspectral HyMap flightline to map LCC and LAI. We exploited the associated uncertainty intervals to gain insight in the per-pixel performance of the model.

A modified linear extrapolation approach towards red edge position detection and stress monitoring of wheat crop using hyperspectral data

The inflection point of spectral reflectance of crop in the red edge region (680–780 nm) is termed as the red edge position (REP), which is sensitive to crop biochemical and biophysical parameters. We propose a technique for automatic detection of four dynamic wavebands, i.e. two in the far-red and two in the near-infrared (NIR) region from hyperspectral data, for REP estimation using the linear extrapolation method. A field experiment was conducted at the SHIATS Farm, Allahabad, India, with four levels of nitrogen and irrigation treatments to assess the sensitivity of REP towards crop stress. A correlation analysis was carried out between REPs and different biophysical parameters, such as leaf area index (LAI) and chlorophyll content index (CCI), recorded in each plot at 50, 70, and 90 days after sowing of wheat crop under the field experiment. The inter-comparison among different REP extraction techniques revealed that the proposed technique, i.e. the modified linear extrapolation (MLE) method, has a better ability to distinguish different crop stress conditions. REPs extracted using the MLE technique showed high correlations with a wide range of LAI, CCI, and LAI × CCI, being comparable with results obtained using the traditional linear extrapolation and polynomial fitting techniques. The behaviour of the new techniques was found to be stable at both narrower and broader bandwidth, i.e. 2 and 10 nm. A new red-edge-based index, i.e. area under REP (AREP), was used to detect the cumulative stress over wheat crop by utilizing the REP and its rate of change information at different crop growth stages. A high coefficient of determination (R2 = 0.89) was found between AREP and dry grain yield (Q ha−1) up to 50 Q ha−1 of wheat crop, whereas, beyond this range the relationship was found to be diminishing.

Impact of cycocel on seed germination and growth in some commercial crops under osmotic stress conditions

Most of plant growth regulators are known to alleviate negative effects of osmotic stress on crop growth. To evaluate the effects of cycocel (CCC), as a growth regulator, on crop seed germination, early growth, and vegetative growth under osmotic stress conditions, a study was conducted under controlled conditions. Six crops, wheat, barley, maize, sunflower, safflower, and rapeseed were grown under five osmotic potentials (0, as non-stress, −0.5, −1.0, −1.5, and −2.0 MPa), which were raised from seeds treated with three CCC concentrations (0, 2.5, and 3.5 g l−1). Reduced germination, seedling and vegetative growth as well as enhanced leaf free proline and chlorophyll content index were observed under osmotic stress. The extent of these changes depended on the type of the used crops. Priming with optimum CCC concentration mitigated to great extent stress-induced adverse effects on these crops. The positive effect of CCC priming on wheat, maize, and rapeseed was observed only at moderate osmotic levels, whereas on barley and safflower the priming effects were found in all levels of osmotic stress. However, sunflower did not respond to CCC priming treatments which might have been due to its thick achene coat. CCC priming was found to be effective in diverting a major proportion of assimilates to root, because root to shoot dry weight ratio increased due to CCC application under all osmotic stress regimes. Since barley and safflower were found to be most responsive to CCC priming, further research for better understanding of mechanisms of CCC priming on their germination and vegetative growth is recommended.

QTLs for Waterlogging Tolerance at Germination and Seedling Stages in Population of Recombinant Inbred Lines Derived from a Cross Between Synthetic and Cultivated Wheat Genotypes

Waterlogging is a widespread limiting factor for wheat production throughout the world. To identify quantitative trait loci (QTLs) associated with waterlogging tolerance at early stages of growth, survival rate (SR), germination rate index (GRI), leaf chlorophyll content index (CCI), root length index (RLI), plant height index (PHI), root dry weight index (RDWI), shoot dry weight index (SDWI), and total dry weight index (DWI) were assessed using the International Triticeae Mapping Initiative (ITMI) population W7984/Opata85. Significant and positive correlations were detected for all traits in this population except RLI. A total of 32 QTLs were associated with waterlogging tolerance on all chromosomes except 3A, 3D, 4B, 5A, 5D, 6A, and 6D. Some of the QTLs explained large proportions of the phenotypic variance. One of these is the QTL for GRI on 7A, which explained 23.92% of the phenotypic variation. Of them, 22 alleles from the synthetic hexaploid wheat W7984 contributed positively. These results suggested that synthetic hexaploid wheat W7984 is an important genetic resource for waterlogging tolerance in wheat. These alleles conferring waterlogging tolerance at early stages of growth in wheat could be utilized in wheat breeding for improving waterlogging tolerance.

Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions

Inversion of radiative transfer models (RTM) using a lookup-table (LUT) approach against satellite reflectance data can lead to concurrent retrievals of biophysical parameters such as leaf chlorophyll content (Chl) and leaf area index (LAI), but optimization strategies are not consolidated yet. ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity of old generation satellite sensors by providing superspectral images of high spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust, accurate, and operational retrieval methods. For three simulated Sentinel settings (S2-10 m: 4 bands, S2-20 m: 8 bands and S3-OLCI: 19 bands) various optimization strategies in LUT-based RTM inversion have been evaluated, being the role of i) added noise, ii) multiple best solutions, iii) combined parameters (Chl ×LAI), and iv) applied cost functions. By inverting the PROSAIL model and using data from the ESA-led field campaign SPARC (Barrax, Spain), it was demonstrated that introducing noise and opting for multiple best solutions in the inversion considerably improved retrievals. However, the widely used RMSE was not the best performing cost function. Three families of alternative cost functions were applied here: information measures, minimum contrast, and M-estimates. We found that so-called “Power divergence measure”, “Trigonometric”, and spectral measure with “Contrast function K(x) = -log(x) + x”, yielded more accurate results, although this also depended on the biophysical parameter. Particularly, when simultaneous retrieval of multiple biophysical parameters is the objective then “Contrast function K(x) = -log(x) + x” provided most consistent optimized estimates of leaf Chl, LAI and canopy Chl across the different Sentinel configurations (relative RMSE: 24-29 %).

Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval

ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r2: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r2: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions.

Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model

Abstract: Lookup-table (LUT)-based radiative transfer model inversion is considered a physically-sound and robust method to retrieve biophysical parameters from Earth observation data but regularization strategies are needed to mitigate the drawback of ill-posedness. We systematically evaluated various regularization options to improve leaf chlorophyll content (LCC) and leaf area index (LAI) retrievals over agricultural lands, including the role of (1) cost functions (CFs); (2) added noise; and (3) multiple solutions in LUT-based inversion. Three families of CFs were compared: information measures, M-estimates and minimum contrast methods. We have only selected CFs without additional parameters to be tuned, and thus they can be immediately implemented in processing chains. The coupled leaf/canopy model PROSAIL was inverted against simulated Sentinel-2 imagery at 20 m spatial resolution (8 bands) and validated against field data from the ESA-led SPARC (Barrax, Spain) campaign. For all 18 considered CFs with noise introduction and opting for the mean of multiple best solutions considerably improved retrievals; relative errors can be twice reduced as opposed to those without these regularization options. M-estimates were found most successful, but also data normalization influences the accuracy of the retrievals. Here, best LCC retrievals were obtained using a normalized “L1 -estimate” function with a relative error of 17.6% (r2 : 0.73), while best LAI retrievals were obtained through non-normalized “least-squares estimator” (LSE) with a relative error of 15.3% (r2 : 0.74).

&amp;lt;i&amp;gt;Bradyrhizobium japonicum&amp;lt;/i&amp;gt; Inoculation and Phosphorus Supplementation on Growth and Chlorophyll Accumulation in Soybean (&amp;lt;i&amp;gt;Glycine max&amp;lt;/i&amp;gt; L.)

A field and glasshouse experiment was conducted to study the effect of B. japonicum inoculation and phosphorus supplementation on growth and leaf chlorophyll content in soybean. The treatments consisted of B. japonicum inoculation (with & without), phosphorus supplementation at the levels of 0, 20, 40 and 80 kgP·ha-1. Both treatments were replicated four times in a split plot design. The following parameters were measured: plant height (cm), number leaves per plant, number of days to 50% flowering, number of days to 50% pod formation, stem girth and leaf area (LA). Results showed that inoculation with B. japonicum significantly increased plant height, number of leaves, leaf chlorophyll content, stem girth, leaf area (LA) and leaf area index (LAI). However, Phosphorus supplementation had significant effects in some parameters measured. The use of effective strain of B. japonicum and phosphorus supplementation was an efficient way of enhancing the growth of soybean.

Relationships between soil respiration and photosynthesis-related spectral vegetation indices in two cropland ecosystems

Empirical evidence for the link between vegetation photosynthesis and soil respiration (Rs) is reported for most ecosystems, but the quantification of photosynthesis parameters seldom use remote sensing data. In an attempt to clarify this issue, we focused on the growing season and examined direct relationships between Rs and photosynthesis-related vegetation indices (VIs) in maize and winter wheat agroecosystems. At seasonal time scale, crop biophysical parameters, such as leaf chlorophyll content (Chlleaf) and green leaf area index (GLAI), explained most Rs variation in the maize and winter wheat fields. Among the selected VIs, enhanced vegetation index (EVI) and red edge chlorophyll index (CIred edge) showed stronger correlations with Chlleaf for maize or GLAI for winter wheat than normalized difference vegetation index (NDVI). Moreover, for both fields, the relationship between daily mean Rs and either EVI or CIred edge was consistently stronger than the relationship between daily mean Rs and NDVI. The reason may be attributed to the saturate of NDVI at high vegetation densities and sensitivity of NDVI to background reflectance. Our results demonstrated that simple VIs (i.e. EVI and CIred edge) based entirely on remote-sensing data can provide better correlations with Rs than a number of variables which are more frequently correlated with Rs in field studies for both maize and winter wheat over the growing season. These results will be helpful for the development of future Rs model over a large spatial scale.

Independent and Combined Effects of High Temperature and Drought Stress During Grain Filling on Plant Yield and Chloroplast EF-Tu Expression in Spring Wheat

High temperature and drought stress are among the two most important environmental factors influencing crop growth, development and yield processes. These two stresses commonly occur in combination. Objectives of this research were to investigate the independent and combined effects of high temperature and drought stress during grain filling on physiological, vegetative and yield traits and expression of a chloroplast protein synthesis elongation factor (EF-Tu) of wheat (Triticum aestivum L.). Two spring wheat cultivars (Pavon-76 and Seri-82) were grown at control temperatures (CT; day/night, 24/14 � C; 16/ 8 h photo/dark period) from sowing to heading. Thereafter, one half of the plants were exposed to high temperature stress (HT; 31/18 � C in Exp. 1 and 34/ 22 � C in Exp. 2), drought stress (withholding water), or a combination of both HT and drought stress. There were significant influences of HT and/or drought stress on physiological, growth and yield traits. There was no cultivar or cultivar by temperature or cultivar by drought interaction effects on most traits. The decreases in leaf photosynthesis were greater at HT compared with drought alone throughout the stress period, and the combination of HT and drought had the lowest leaf photosynthetic rates. Overall, HT or drought had similar effects (about 48‐56 % decrease) on spikelet fertility, grain numbers and grain yield. High temperature decreased grain numbers (by 56 % averaged across both experiments) and individual grain weight (by 25 %), while, respective decreases due to drought were 48 % and 35 %. This suggests that the grain numbers were more sensitive to HT and grain weights to drought for the range of temperatures tested in this research. The interaction between HT and drought stress was significant for total dry weights, harvest index and spikelet fertility, particularly when HT stress was severe (34/22 � C). The combined effects of HT and drought were greater than additive effects of HT or drought alone for leaf chlorophyll content, grain numbers and harvest index. High temperature stress and the combination of HT and drought stress but not drought stress alone resulted in the overexpression of EF-Tu in both spring wheat cultivars.

Drought Effects on Gas Exchange, Chlorophyll, and Plant Growth of 1‐Methylcyclopropene Treated Cotton

Drought impacts cotton (Gossypium hirsutum L.), affecting its physiological, morphological, and agronomic parameters. Ethylene, a growth hormone, can induce fruit abscission. Ethylene inhibitors, such as 1‐methylcyclopropene (1‐MCP), may minimize the loss of reproductive structures. The objective of this study was to examine the effects of 1‐MCP on gas exchange, plant growth/development, and yield components of cotton under drought during the reproductive phase. A 2‐yr greenhouse study was conducted as a 2 × 2 factorial design in a split‐block arrangement with five replications. Treatments were two 1‐MCP rates (0 and 2.4 g a.i. L−1) during a 14‐h overnight incubation that were then subjected to two water regimes (control and drought). Drought started to impact gas exchange at a moderate water stress, 5 DAT (days after 1‐MCP treatment) (–1.4 MPa). The 1‐MCP increased water use efficiency (WUE) in well‐watered plants at 1 DAT. Many of the yield components, plant mapping, and biomass parameters investigated were adversely affected by drought. However, drought increased specific leaf weight (SLW), chlorophyll (Chl) content, and harvest index. The 1‐MCP improved reproductive node numbers mainly during drought, but did not lead to a better harvest index, since 1‐MCP caused high fruit abscission. In conclusion, 1‐MCP did not ameliorate the detrimental effects of drought on gas exchange when water stress started to impact cotton plants. Additionally, 1‐MCP had little or no positive effect on plant mapping, dry matter partitioning, and Chl content.

Seed Vigor and Field Performance of Winter Oilseed Rape (Brassica napus L.) Cultivars

The effect of seed vigor on field performance of two oilseed rape cultivars (Licord and Okapi) was investigated in 2008 at the Research Farm of the University Tabriz, Iran. The experiment was arranged as factorial based on RCB design with three replications. A sub-sample of seeds of each cultivar was kept as control. The other two sub-samples of each cultivar with about 15% moisture content were artificially deteriorated at 40C for 9 and 12 days (Licord) and 7 and 10 days (Okapi). Consequently, three seed lots for each cultivar with different levels of vigor were provided. The results showed that the mean emergence time increased with decreasing seed vigor. However, the highest percentage of seedling emergence was obtained for the high quality seed lot and decreased with decreasing seed lot vigor. Decreasing seed vigor led to significant reduction in ground cover of both cultivars; mainly due to poor stand establishment. Ground cover of Licord was higher than that of Okapi. Plants from high vigor seed lots of both cultivars had higher leaf chlorophyll content index at early stages of growth, compared with those from low quality seed lots. Grains per plant, 1000 grain weight and grain yield per plant for plants from deteriorated seed lots were higher than those from high-vigor seed lot. However, these advantages in individual plant performance were not sufficient to compensate for low stand establishment. Consequently, grain yield per unit area significantly improved with increasing seed vigor of oilseed rape cultivars, even within the range of acceptable germination.

Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status indirectly by estimating leaf and canopy chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content of spring wheat (Triticum aestivum L.) during the growth stages between pre-tillering (Zadoks Growth Stage (ZGS 15)) to booting (ZGS50). Spring wheat was grown under different N fertility rates (0–200 kg N ha−1) in 2002. Canopy reflectance, leaf chlorophyll content, N content and leaf area index (LAI) values were measured. There was a weakly significant trend for the PROSAIL model to over-estimate LAI and under-estimate leaf chlorophyll content. To compensate for this interdependency by the model, a canopy chlorophyll content parameter (the product of leaf chlorophyll content and LAI) was calculated. The estimation accuracy for canopy chlorophyll content was generally low earlier in the growing season. This failure of the PROSAIL model to estimate leaf and canopy variables could be attributed to model sensitivity to canopy architecture. Earlier in the growing season, full canopy closure was not yet achieved, resulting in a non-homogenous canopy and strong soil background interference. The canopy chlorophyll content parameter was predicted more accurately than leaf chlorophyll content alone at booting (ZGS 45). A strong relationship between canopy chlorophyll content and canopy N content at ZGS 45 indicates that the PROSAIL model may be used as a tool to predict wheat N status from canopy reflectance measurements at booting or later.

Sensitivity of Five Container-grown Nursery Species to Chlorine in Overhead Irrigation Water

Phytotoxic responses of five container-grown nursery species (Spiraea japonica ‘Goldmound’, Hydrangea paniculata ‘Grandiflora’, Weigela florida ‘Alexandra’, Physocarpus opulifolius ‘Summer Wine’, and Salix integra ‘Hakura Nishiki’) to chlorinated irrigation water and critical free chlorine thresholds were evaluated. Plants were overhead-irrigated with water containing 0, 2.5, 5, 10, and 20 mg·L−1 of free chlorine for 6 weeks. The following measurements were used to assess the treatments: visual injury, growth, leaf chlorophyll content index, leaf chlorophyll fluorescence, leaf net CO2 exchange rate, and stomatal conductance. All species exhibited one or more signs of chlorine injury, including foliar necrotic mottling, foliar necrosis and chlorosis, decreased plant height, and increased premature abscission of foliage with species varying in sensitivity to free chlorine concentrations of irrigation water. The results indicated that the critical free chlorine threshold of S. japonica, H. paniculata, W. florida, and S. integra was 2.5 mg·L−1 and 5 mg·L−1 for P. opulifolius. Our results suggested that irrigation water containing free chlorine less than 2.5 mg·L−1 should not adversely affect the growth or appearance of ornamental woody shrubs.

Growth and flower quality of four Rosa hybrida L. cultivars in response to propagation by stenting or cutting in soilless culture

Performance of four Rosa hybrida L. cultivars (‘African Dawn’, ‘Ilios’, ‘Maroussia’ and ‘Soprano’) was evaluated. They were grown either on their own roots or grafted (stenting) onto Rosa canina L. ‘Inermis’ rootstock in a polyethylene greenhouse with hydroponics system. Parameters of plant growth and flower quality were investigated for two successive harvesting years (2005 and 2006). Results indicated that, all the cultivars were superior for most of the parameters studied when grafted onto rootstock compared to being on their own roots. Flowering stem fresh weight and diameters, flower fresh and dry weight, flower diameter and length, petal number, leaf chlorophyll content and quality index were higher in grafted plants compared to those propagated by cuttings. However, highest flowering stem length and number were observed in plants propagated by cutting, although not significant as compared with stenting method.

Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data

Reflectance data in the green, red and near-infrared wavelength region were acquired by the SPOT high resolution visible and geometric imaging instruments for an agricultural area in Denmark (56°N, 9°E) for the purpose of estimating leaf chlorophyll content ( C ab ) and green leaf area index (LAI). SPOT reflectance observations were atmospherically corrected using aerosol data from MODIS and profiles of air temperature, humidity and ozone from the Atmospheric Infrared Sounder (AIRS), and used as input for the inversion of a canopy reflectance model. Computationally efficient inversion schemes were developed for the retrieval of soil and land cover-specific parameters which were used to build multiple species and site dependent formulations relating the two biophysical properties of interest to vegetation indices or single spectral band reflectances. Subsequently, the family of model generated relationships, each a function of soil background and canopy characteristics , was employed for a fast pixel-wise mapping of C ab and LAI. The biophysical parameter retrieval scheme is completely automated and image-based and solves for the soil background reflectance signal, leaf mesophyll structure, specific dry matter content, Markov clumping characteristics, C ab and LAI without utilizing calibration measurements. Despite the high vulnerability of near-infrared reflectances ( ρ nir) to variations in background properties, an efficient correction for background influences and a strong sensitivity of ρ nir to LAI, caused LAI– ρ nir relationships to be very useful and preferable over LAI–NDVI relationships for LAI prediction when LAI > 2. Reflectances in the green waveband ( ρ green) were chosen for producing maps of C ab . The application of LAI–NDVI, LAI– ρ nir and C ab – ρ green relationships provided reliable quantitative estimates of C ab and LAI for agricultural crops characterized by contrasting architectures and leaf biochemical constituents with overall root mean square deviations between estimates and in-situ measurements of 0.74 for LAI and 5.0 μg cm − 2 for C ab . The results of this study illustrate the non-uniqueness of spectral reflectance relationships and the potential of physically-based inverse and forward canopy reflectance modeling techniques for a reasonably fast and accurate retrieval of key biophysical parameters at regional scales.