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

2017 ◽  
Vol 84 ◽  
pp. 1-15 ◽  
Author(s):  
Francisco M. Padilla ◽  
M. Teresa Peña-Fleitas ◽  
Marisa Gallardo ◽  
Rodney B. Thompson
Keyword(s):  
Vegetation Indices ◽  
Maximum Growth ◽  
Growth And Yield ◽  
Canopy Reflectance

Assessment of the potential to improve N fixation by cowpea(Vigna unguiculata (L.) Walp.) in Ghanaian soils

2001 ◽  
Vol 16 (2) ◽  
pp. 57-65 ◽  
Author(s):  
J.O. Fening ◽  
W. Dogbe ◽  
S.K.A. Danso
Keyword(s):  
Vigna Unguiculata ◽  
Maximum Growth ◽  
N Fertilization ◽  
Growth And Yield ◽  
N Fixation ◽  
Considerable Potential ◽  
Ecological Zones ◽  
Cowpea Cultivars ◽  
Large Populations

AbstractThe potential to improve N fixation by cowpea in Ghanaian soils was examined through: (1) assessment of the natural nodulation of 45 cowpea cultivars in 20 soils sampled from 5 ecological zones; (2) determination of the numbers of cowpea bradyrhizobial isolates in the soils; and (3) determination of the response of cowpea to N fertilization. The ability of 45 cowpea cultivars to nodulate naturally in the various soils showed wide cultural adaptability. Counts of indigenous bradyrhizobia showed that most soils in Ghana contained large populations capable of nodulating cowpea. These ranged from 0.6 × 10 bradyrhizobia cells g soil−1to 3.1 × 104cells g soil−1, with 60% of the soils containing more than 103cells g soil−1. Response of cowpea to N fertilization differed according to soil type. In general all cowpea cultivars showed significant response to increasing N fertilizer applications, indicating that N fixation was not providing the plants with sufficient N for maximum growth and yield. This study suggests that inoculation of cowpea with effective indigenous strains of bradyrhizobial species has considerable potential to improve this situation.

MORPHOLOGICAL AND BIOCHEMICAL RESPOSES OF COW PEA (CV. PUSA BARSATI) GROWN ON FLY ASH AMENDED SOIL IN PRESENCE AND ABSENCE OF MELOIDOGYNE JAVANICA AND RHIZOBIUM LEGUMINOSARUM

1970 ◽  
Vol 17 ◽  
pp. 17-22 ◽  
Author(s):  
Kamal Singh ◽  
A. A. Khan ◽  
Iram Khan ◽  
Rose Rizvi ◽  
M. Saquib
Keyword(s):  
Fly Ash ◽  
Plant Growth ◽  
Rhizobium Leguminosarum ◽  
Growth Yield ◽  
Maximum Growth ◽  
Meloidogyne Javanica ◽  
Growth And Yield ◽  
Negative Effects ◽  
Positive Effects ◽  
Insignificant Difference

Plant growth, yield, pigment and protein content of cow-pea were increased significantly at lower levels (20 and 40%) of fly ash but reverse was true at higher levels (80 and 100%). Soil amended by 60% fly ash could cause suppression in growth and yield in respect to 40% fly ash treated cow-pea plants but former was found at par with control (fly ash untreated plants). Maximum growth occurred in plants grown in soil amended with 40% fly ash. Nitrogen content of cow-pea was suppressed progressively in increasing levels of fly ash. Moreover,  Rhizobium leguminosarum  influenced the growth and yield positively but Meloidogyne javanica caused opposite effects particularly at 20 and 40% fly ash levels. The positive effects of R. leguminosarum were marked by M. javanica at initial levels. However, at 80 and 100% fly ash levels, the positive and negative effects of R. leguminosarum and/or M. javanica did not appear as insignificant difference persist among such treatments.Key words:  Meloidogyne javanica; Rhizobium leguminosarum; Fly ash; Growth; YieldDOI: 10.3126/eco.v17i0.4098Ecoprint An International Journal of Ecology Vol. 17, 2010 Page: 17-22 Uploaded date: 28 December, 2010  

scholarly journals Evaluation of thermotolerant rhizobacteria for multiple plant growth promoting traits from pigeonpea rhizosphere

2017 ◽  
Vol 9 (2) ◽  
pp. 920-923
Author(s):  
Gurmeet Kaur ◽  
Veena Khanna
Keyword(s):  
Salicylic Acid ◽  
High Temperature ◽  
Gibberellic Acid ◽  
Maximum Growth ◽  
Growth Hormones ◽  
Growth And Yield ◽  
Iaa Production ◽  
Plant Growth Promoting ◽  
Growth Promoting ◽  
Plant Growth Promoting Traits

PGPR strains exhibiting optimum functional traits at high temperature and are compatible with Rhizobium can be used in pigeonpea as biofertilizer. A total of 45 rhizobacterial isolates were isolated from 13 different locations of pigeonpearhizospheric soil of Punjab. Out of the 45 isolates, 5 isolates selected on the basis of maximum growth at 30°C and 40°C were morphologically and biochemically characterized, belonging to genera Pseudomonas (P-6, P-9) and Bacillus (P-30, P-31, P-32). Selected isolates were further evaluated for the production of IAA, GA, SA and flavonoids. IAA production was estimated in the range from 0.45-25.13 μg/ml and 4.62-34.34 μg/ml in the presence of tryptophan at 30 and 40°C respectively. Maximum gibberellic acid production was recorded with P-30 (108.99 μg/ml and 112.12 μg/ml) at 30 and 40°C respectively. Similarly maximum salicylic acid was also estimated with P-30 (157.2 μg/ml) followed by P-31 (141.0 μg/ml) at 40°C. All the isolates were also found to produce flavonoids ranged from 2.98 - 4.40 μg/ml at 40 °C. Isolates P-30, P-31 showed superior production of growth hormones and flavonoid-like compounds can further be tested under the field conditions to enhance growth and yield of pigeonpea.

scholarly journals BIOEFFICACY OF NANO NUTRIENTS (N, Zn & Cu) ON YIELD OF CAPSICUM

2021 ◽  
Vol 21 (2) ◽  
Author(s):  
J. Sam Ruban ◽  
B. Gayathri ◽  
C. Jeyaraj
Keyword(s):  
Conventional Method ◽  
Block Design ◽  
Maximum Growth ◽  
Growth And Yield ◽  
Yield Parameters ◽  
Randomized Block Design ◽  
Prime Source

Vegetables are the prime source of vitamins and minerals. As the population increases there is also increase in demand for nutritional vegetables, but in the conventional method of horticulture the production and productivity is considerably less. Thus to increase the productivity and to feed the over burgeoning population there is a need for novel fertilizers such as Nano fertilizers. An experiment was hence conducted in Chinapettai village, Panruti to investigate the Bio-efficacy of Nano nutrients (Nano Nitrogen, Zinc and Copper) on growth and yield of Capsicum. The experiment was carried out in RBD design (Randomized block design) with three replications and ten treatments. Results showed that the treatment with 100% RD-N+100% RD-P + 100 % RD-K + Nano N + Nano Cu + Nano Zn followed by 100% RD-N +100% RD-P + 100 % RD-K + Nano N and 75% RD-N+100% RD-P + 100 % RD-K + Nano N + Nano Cu + Nano Zn recorded maximum growth and yield parameters. In contrast 50% RD-N + 100% RDP + 100 % RD-K + Nano N showed increase in yield than the control (100% RDF (-N: -P: -K)(250:150:150kg/ha)) to conclude that Nano nitrogen could have compensated the 50% urea recommendation in conventional fertilizer and also had enhanced effect than control.

scholarly journals Comparison of Empirical and Physical Modelling for Estimation of Biochemical and Biophysical Vegetation Properties: Field Scale Analysis across an Arctic Bioclimatic Gradient

2020 ◽  
Vol 12 (18) ◽  
pp. 3073
Author(s):  
Blair E. Kennedy ◽  
Douglas J. King ◽  
Jason Duffe
Keyword(s):  
Numerical Optimization ◽  
Vegetation Indices ◽  
Physical Modelling ◽  
Predictive Performance ◽  
High Arctic ◽  
Canopy Reflectance ◽  
Area Index ◽  
Empirical Modelling ◽  
Plant Area Index ◽  
Reflectance Data

To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.

scholarly journals Effects of Nitrogen Phosphorus Potassium and Sulphur on Growth Yield and Nutrient Content of Strawberry (Fragaria ananassa)

2016 ◽  
Vol 9 (1) ◽  
pp. 99-108 ◽  
Author(s):  
CA Afroz ◽  
MAH Shimul ◽  
M Ikrum ◽  
MA Siddiky ◽  
MA Razzaque
Keyword(s):  
Positive Impact ◽  
Block Design ◽  
Growth Yield ◽  
Nutrient Content ◽  
Maximum Growth ◽  
Fruit Weight ◽  
Growth And Yield ◽  
Control Treatment ◽  
Research Centre ◽  
Nutrient Contents

The experiment was conducted at Horticulture Research Centre, Gazipur, Bangladesh, to study the effects of N, P, K, and S on growth, yield and nutrient content of strawberry following Randomized Complete Block Design (RCBD) method. There were 4 levels of different nutrients and there was a positive impact of each fertilizer combinations on yield, yield parameters and nutrient contents of BARI Strawberry except control treatment. The highest values of plant height (25.60 cm); number of leaves (21.66), flowers (125.33), fruits (12.35),destroyed fruits (11), fruit weight (215.10 g) plant-1 and fruit length (4.16 cm), fruit diameter (3.41cm), individual fruit weight (17.85 g) and fruit yield (11.50 t ha-1) were found in treatment of 115,40,110 and 25 kg ha-1NPKS, respectively. Among the fertilizers, the single effect of N (115 kg ha-1), P (40 kg ha-1), K (110 kg ha-1) and S (25 kg ha-1) gave maximum growth and yield of strawberry. The highest concentration of N, P, K and S were found in shoot and fruit of strawberry when N, P, K and S fertilizers were used 140,60,135 and 35 kg ha-1, respectively.J. Environ. Sci. & Natural Resources, 9(1): 99-108 2016

Multi-angular observation of forest canopy reflectance based on a hyperspectral UAV imaging platform

2020 ◽  
Author(s):  
Feng Qiu ◽  
Qian Zhang
Keyword(s):  
Forest Canopy ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Distribution Patterns ◽  
Background Information ◽  
Canopy Reflectance ◽  
Bidirectional Reflectance ◽  
Forest Canopies ◽  
Ground Distance ◽  
Observation Method

<p>Forest canopy reflectance varies with solar and observation geometries and shows distinct anisotropic characteristics. The bidirectional reflectance distribution function (BRDF) of forest canopies is influenced by canopy structure, leaf biochemistry and background reflectance. Multi-angular remote sensing observations of forest canopies provide much more information about canopy structure and background information compared with the nadir observations. The development of unmanned aerial vehicle (UAV) provides great opportunities for multi-angular observations in forests. We developed a solid method to obtained bidirectional reflectance of forest canopies based on a hyperspectral UAV imaging platform in this study. With this multi-angular observation method, we obtained canopy reflectance images with the view zenith angle (VZA) varying from 60° (forward) to 60° (backward) at fixed interval (10°), as well as the hotspot and darkspot images in the principle plane in conifer forests. Since the single pixel with very high spatial resolution (around 10 cm) in the UAV images are not representative for the study of the whole forest canopy, several pixels in the central of each images were selected and averaged to determine the canopy reflectance. Variations of the averaged reflectance with ground distance represented by the selected pixels were analyzed and the optimum ground distance for study the multi-angular forest canopy reflectance was determined. The observed canopy reflectance peaks at the hotspot and clear images of the hotspot are observed. The sensitivities of canopy reflectance to VZAs vary with spectral bands. The reflectance at red bands near 680 nm are most sensitive to VZA. Some common used vegetation indices, such as NDVI, EVI, MTCI, PRI, also vary greatly with VZAs and demonstrate different spatial distribution patterns. The observations fit well with the 4-Scale geometric-optical model simulations. The multi-angular observation methods based on UAV platform have the advantages of efficient and effective in multi-angular observation with higher flexibility in VZA adjustment and lower cost, compared with the airborne or spaceborne sensors. This multi-angular observation method is very useful for study the BRDF and canopy structural and biochemical characteristics of forests and has great potential in forestry and ecological studies.</p>

Spectroscopic retrieval of above-ground crop nitrogen content with a hybrid machine learning regression method

2020 ◽  
Author(s):  
Katja Berger ◽  
Gustau Camps-Valls ◽  
Jochem Verrelst ◽  
Jean-Baptiste Féret ◽  
Matthias Wocher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nitrogen Content ◽  
Confidence Intervals ◽  
Vegetation Indices ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Specific Protein ◽  
Canopy Reflectance ◽  
Absorption Bands ◽  
Model Inversion

<p>Proteins are the major nitrogen-containing biochemical constituents of plants. Since nitrogen (N) cannot be measured directly using remote sensing data, leaf protein content constitutes a valid proxy for this main limiting plant nutrient. In the past, mainly linear parametric algorithms, such as vegetation indices, have been employed to retrieve this non-state variable from optical reflectance data. Moreover, most studies solely relied on the relationship of chlorophyll content with nitrogen. In contrast, our study presents a hybrid model inversion scheme of a physically-based approach via protein retrieval combined with advanced machine learning regression. The leaf optical properties PROSPECT-PRO model, including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. A generic synthetic database of model input parameters with corresponding reflectance was simulated and used for training two different machine learning regression methods: a standard homoscedastic Gaussian Process (GP) and a variational heteroscedastic GP regression that accounts for signal-to-noise correlations. Both GP methods have the interesting feature of providing confidence intervals for the estimates. As part of multiple field campaigns, carried out in the scientific preparation framework of the Environmental Mapping and Analysis Program (EnMAP), spectra of maize and winter wheat were acquired to simulate EnMAP data and plant-organ-specific nitrogen measurements were destructively collected for validation. Both GP models yielded excellent performance in learning the nonlinear relationship between specific protein absorption bands and area-based above-ground N. They also performed similar or even outperformed other nonlinear nonparametric approaches. Physical validation of the estimates against in situ nitrogen measurements from leaves plus stalks yielded a root mean square error (RMSE) of 2.5 g/m². The variational heteroscedastic GP provided a more differentiated pattern of uncertainty with tighter confidence intervals within low-value regimes compared to the standard GP. The inclusion of fruit nitrogen content for validation deteriorated the results of all models, which can be explained by the inability of radiation in the optical domain to penetrate the thick tissues of maize cobs and wheat ears. Following some further validation exercises, we aim to implement GP-based algorithms for global agricultural monitoring of above-ground N derived from future satellite imaging spectroscopy data.</p>

scholarly journals Spatio-Temporal Determination of Irrigated Paddy Rice Pixels Using Evapotranspiration and Vegetation Indices: A Case Study for Ca River Basin in Vietnam

2020 ◽  
Vol 08 (12) ◽  
pp. 94-107
Author(s):  
Chung N. Luong ◽  
Lan T. Ha ◽  
Thanh C. Pham ◽  
Hung X. Dinh ◽  
Thanh T. Hoang ◽  
...  
Keyword(s):  
River Basin ◽  
Vegetation Indices ◽  
Paddy Rice ◽  
Spatio Temporal ◽  
Ca River

scholarly journals Replacement of Chemical Fertilizer by Beverage Sludge to Reduce Environmental Pollution

2021 ◽  
Author(s):  
Md. Abul Hasanath ◽  
Ganesh Chandra Saha ◽  
Md. Siddique Alam ◽  
Md. Nashir Uddin
Keyword(s):  
Environmental Pollution ◽  
Maximum Yield ◽  
Growth Yield ◽  
Maximum Growth ◽  
Organic Content ◽  
Chemical Fertilizer ◽  
Growth And Yield ◽  
Control Treatment ◽  
Low Grade ◽  
Wastewater Generation

Abstract Wastewater generation from beverage industries is on the rise as the demand and consumption surge worldwide. The typical ingredients of beverages are carbonated water, saccharides, sweetener, fruit pulp, flavoring agent, color, preservatives, and salts. Only 20% concentration of the mixture goes to the bottle and the remaining becomes wastewater. However, nutrients and organics remain in wastewater and are left in sludge after going through ETP. The presence of these nutrients makes the beverage sludge useful for the cultivation that can not only decrease the application of chemical fertilizers but also combat the environmental pollution. Indian spinach and Okra have been cultivated in six different mixtures containing beverage sludge and soil to study their effects on growth, yield, food value and nutrient. Soil nutrients, organic content, EC, and pH have been analyzed to assess the suitability of sludge for cultivation. The control treatment was designed by 100% soil and gradually 20, 40, 60, 80 and 100% soil were replaced by beverage sludge in other treatments. The maximum growth of Indian Spinach and Okra was observed 120% and 125% higher at 38 days after sowing on the treatment of 80% sludge and 20% soil compared to the control treatment. Similarly, the maximum yield of Indian spinach and Okra was computed to be nine and two times higher than the control on the same treatment. Food values (ascorbic acid, β- carotene, and protein) and nutrients (Fe, Ca, Mg, K, P and Zn) were found to increase with the increasing amount of beverage sludge while those satisfy the standards of USDA. Without using any kind of fertilizer in low grade soil, the beverage sludge has shown the potentiality in both growth and yield. It turns out that beverage sludge can be used as a substitute for chemical fertilizer with an optimum amount of 80%.

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