scholarly journals Leaf blade area estimate of Digitaria pentzii under different cutting Heights

2020 ◽  
Vol 21 ◽  
Author(s):  
Raul Caco Alves Bezerra ◽  
Mauricio Luiz de Mello Vieira Leite ◽  
Mirna Clarissa Rodrigues de Almeida ◽  
Leandro Ricardo Rodrigues de Lucena ◽  
Vicente José Laamon Pinto Simões ◽  
...  
Keyword(s):  
Regression Models ◽  
Leaf Blade ◽  
Gravimetric Method ◽  
Power Model ◽  
Average Value ◽  
Area Estimate ◽  
Blade Area ◽  
Cutting Height ◽  
Non Destructive ◽  
Digital Caliper

Abstract Pasture studies require information on leaf area, as it is one of the main parameters for evaluation of plant growth. Thus, the objective of this study was to estimate the leaf blade area of pangolão grass (Digitaria pentzii Stent.) using non-destructive methods by regression model analysis. The experimental design consisted of randomized blocks, with three cutting heights (10, 15, and 20 cm) and four replications. Three hundred leaf blades of pangolão grass were randomly collected, and their respective lengths (L) and widths (W) determined using a digital caliper. The leaf blade area of pangolão grass was estimated by the gravimetric method, using linear and power regression models to explain the leaf blade area as a function of the product of L and maximum W. The real leaf blade area presented an average value of 18.64 cm2, ranging from 4.29 to 45.95 cm2. The leaf blade area of pangolão grass, regardless of cutting height, was estimated with greater accuracy by the power model. The power model, Ŷ=LW1.007, can be used to estimate the leaf blade area of pangolão grass based on leaf blade L and W values.

scholarly journals Regression models for prediction of leaf area in purple ipe [Tabebuia impetiginosa (Mart.)]

2020 ◽  
pp. 654-659
Author(s):  
Jéssica Sayuri Hassuda Santos ◽  
Karina Tiemi Hassuda dos Santos ◽  
Vinicius de Souza Oliveira ◽  
Gleyce Pereira Santos ◽  
Luis Fernando Tavares de Menezes ◽  
...  
Keyword(s):  
Leaf Area ◽  
Regression Models ◽  
Linear Models ◽  
Regression Equations ◽  
Linear Quadratic ◽  
Maximum Width ◽  
Quadratic Potential ◽  
Area Estimate ◽  
Non Destructive

Besides its medicinal and ornamental use, Tabebuia impetiginosa is also very economically important. The achievement of accurate and easy-to-perform tools to determine its leaf area is fundamental for understanding the interaction between the plant and the environment. The objective of this work was to obtain regression equations by using several models that use allometric measurements of the fifth leaflet and to select the most accurate one to determine the leaf area of composite leaves of Tabebuia impetiginosa Mart. in a non-destructive way. By using the dimensions of the fifth leaflet such as - length (LFL in cm), maximum width (WFL in cm) and the product between LFL and WFL (LWFL) of leaf limb, the equations were estimated for linear, quadratic, potential and exponential linear models. The results showed that the determination of leaf area could be performed with excellent precision for leaves of different sizes of this species, using the product of the measurements of length and width of the fifth leaflet. The equation that best expresses the leaf area estimate of the composite leaf of Tabebuia impetiginosa is ELACL = 8.7772 + 2.3840 (LWFL).

scholarly journals Estimating the area and weight of cactus forage cladodes using linear dimensions

2020 ◽  
Vol 43 ◽  
pp. e45460
Author(s):  
Leandro Ricardo Rodrigues de Lucena ◽  
Maurício Luiz de Mello Vieira Leite ◽  
Vicente José Laamon Pinto Simões ◽  
Camila Nóbrega ◽  
Mirna Clarissa Rodrigues Almeida ◽  
...  
Keyword(s):  
Plant Growth ◽  
Regression Model ◽  
Efficient Method ◽  
Gravimetric Method ◽  
Power Model ◽  
Gamma Model ◽  
Linear Dimensions ◽  
Length Width ◽  
Non Destructive ◽  
Nopalea Cochenillifera

The forage palm is one of the main forages of ruminants in semiarid regions. Measurements of leaf area are required in agronomic studies because they are one of the main parameters used to evaluate plant growth. The objective of this study was to validate and define the best models for estimating the area and weight of Giant Sweet clone (Nopalea cochenillifera) forage cladodes in a non-destructive way based on the linear dimensions of length, width and thickness. There were 432 randomly measured cladodes at 550 days after planting. The length, width and thickness of each cladode were measured using a digital calliper. The cladodes were weighed individually. The cladode area was calculated by the gravimetric method. The power regression model was the most efficient method to explain the cladode area as a function of the product of length by width, while the gamma model was the most efficient method to explain the weight of cladodes as a function of the product of length by width and thickness. The power model,  and gamma model, = 0.536T + 0.028LW, were used to determine the area and weight of Nopalea cochenillifera Giant Sweet clone cladodes, respectively, based on the values of linear dimensions measured independently of the order of the cladode.

scholarly journals A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

FLORESTA ◽  
2019 ◽  
Vol 50 (1) ◽  
pp. 1063
Author(s):  
João Everthon da Silva Ribeiro ◽  
Francisco Romário Andrade Figueiredo ◽  
Ester Dos Santos Coêlho ◽  
Walter Esfrain Pereira ◽  
Manoel Bandeira de Albuquerque
Keyword(s):  
Leaf Area ◽  
Regression Models ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Linear Measurements ◽  
Maximum Width ◽  
Bias Index ◽  
Different Shapes ◽  
Non Destructive

The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

scholarly journals Impact of foreign direct investment on economic growth in the world during 1975–2015

2018 ◽  
Vol 20 (91) ◽  
pp. 28-32
Author(s):  
B. B. Brychka
Keyword(s):  
Economic Growth ◽  
Literature Review ◽  
Regression Models ◽  
Trade Openness ◽  
Positive Influence ◽  
Linear Regression Models ◽  
Average Value ◽  
Gdp Growth Rate ◽  
The World ◽  
The Impact

The study is concentrated on examination the impact of FDI on economic growth in the World during 1975–2015. The study consists of four consecutive parts, including introduction, literature review, model and methodology, data, empirical results and conclusion. Each part of the study is focused on its own goals. According to the results of the literature review, there is positive influence of FDI on economic growth in various countries. Economic growth is one of the most important goals of any country. The country image on the international level is dependent on its economic power. Economic growth provides an opportunity to improve the living standards in the country. Most researchers conclude that there is a positive influence of FDI on the countries’ economic growth. However, the impact of FDI is strong in developing countries. Moreover, this relationship is stronger in countries with higher educational and technological level, trade openness and development of the countries’ stock markets. Economists often build regression models to estimate the relationship between the variables. In order to find the impact of FDI on economic growth, we are going to apply linear regression models. We take two variables as indicators of the countries’ economic growth, including current GDP expressed in U.S dollars, and annual GDP growth rate. Taking into account that the World’s GDP in current U.S dollar is a factor variable with the mentioned resulting variables, the regression equation looks as follows: The R-squared of the built model is 0.99, indicating that roughly 100% of changes in the World’s GDP is caused by the chosen factors. As it is seen from the SAS output, the residuals of dependent variable and factors variables are distributed normally among its average value. Thus, non-normality is not observed in the model. Taking into account the coefficients of the factor variables, the log GDP is most sensitive to the changes in trade as a percent of GDP. The log GDP is not quite sensitive to the changes in FDI, since the coefficient of 0.000128 means that increasing of FDI by one unit increase the logarithmic value of GDP by $ 0.000128.

scholarly journals Bazı Üzüm Çeşitlerinde ve Asma Anaçlarında Yaprak Alanının Belirlenmesi

2020 ◽  
Vol 8 (6) ◽  
pp. 1261-1265
Author(s):  
Adem Yağcı ◽  
Seda Sucu ◽  
Namık Yıldız
Keyword(s):  
Leaf Area ◽  
Leaf Blade ◽  
Optimum Level ◽  
Node Number ◽  
Average Value ◽  
Blade Width ◽  
Actual Field ◽  
Photosynthesis Capacity ◽  
Stem Leaf ◽  
Real Area

The amount and area of the leaves should be at an optimum level in order to maintain the product quality and not to adversely affect the vine growth. Because carbohydrates, which are essential for omca and are mostly stored in fruit and wood, are formed by leaves after photosynthesis. Leaf area can be used in many areas. Among these, photosynthesis capacity and plant growth rate may. Various tools and methods (planimetry, leaf area meter, width-product, weight-area calculation, image processing programs, etc.) are used in determining leaf area. In this study, 3 American grape rootstocks (5BB, 110 R, 1103 P) and 5 grapes (Alphonse Lavallée, Italıa, Mıchele Palierı and Narince, Yalova İncisi) were used as material. 20 shoots with 15-25 nodules were taken from the rootstocks and varieties of the omca and the leaves were photocopied according to the order of the node. The actual field values of the leaves were measured with a planimeter. Leaf stem and leaf width and length of the leaves were also measured. Regression analysis was performed between leaf stem, leaf blade width and length, leaf blade × length values and real area. The maximum leaf area on one shoot was 5 BB (2484 cm2) from rootstocks and Narince (2126 cm2) from varieties. All three rootstocks gave the average value of the leaf found in 9th node. In terms of node number, which gives an average value according to the varieties, Alphonse Lavallée, Mıchele Palierı and Yalova İncisi varieties came to the forefront in 11th node. The 13th in Narince cultivar and the 12th in Italia cultivar gave the closest value to the average.

scholarly journals The Influence of Different Light Curing Modes on the Depth of Cure of the Composite Resin

2018 ◽  
Vol 35 (1) ◽  
pp. 58-64
Author(s):  
Nikola Miljković ◽  
Stefan Dačić ◽  
Tamara Karuntanović ◽  
Marko Igić ◽  
Milica Dačić ◽  
...  
Keyword(s):  
Composite Resin ◽  
Composite Layer ◽  
Composite Specimen ◽  
Depth Of Cure ◽  
Average Value ◽  
Clinical Recommendation ◽  
Significant Difference ◽  
Light Curing ◽  
Metal Block ◽  
Digital Caliper

Summary The aim of this study was to investigate the influence of different light curing modes on the depth of cure of the composite resin. The metal block with formed round holes served as a mold for the placement of the composite resin. The composite resin was cured with Penguin DB-685 lamp with four optional working modes: strong, low, gradually strong, and flashing. Unpolymerized part of the composite specimen was removed by scraping with a plastic spatula, and then each specimen was placed into the capsule with 1 ml of ethanol alcohol and mixed for 20 s in amalgamator. The length of the remaining composite specimen was measured with the digital caliper with a precision of 0.01 mm. The measured values were divided by 2 (according to ISO 4049 standard) and then statistically processed. Based on the measured lengths of the polymerized part of the composite specimens, the lowest average value of the depth of cure (2.75 ± 0.08 mm) was determined after polymerization with the low mode and the highest value was obtained (2.98 ± 0.08 mm) with strong polymerization mode. Statistically significant difference (p < 0.05) was determined between low polymerization mode and all other modes (strong, gradually strong and flashing mode). The observed statistically significant differences are not clinically important because all curing modes provided the necessary depth of cure, which is in accordance with the clinical recommendation of 2 mm thick composite layer.

scholarly journals CARS Algorithm-Based Detection of Wheat Moisture Content before Harvest

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 115
Author(s):  
Hong Ji ◽  
Wanzhang Wang ◽  
Dongfeng Chong ◽  
Boyang Zhang
Keyword(s):  
Moisture Content ◽  
Regression Model ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Early Stage ◽  
Least Squares Regression ◽  
Calibration Sample ◽  
Non Destructive ◽  
Plsr Model ◽  
Variable Subset

To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R2 = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R2v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350–2500 nm, bands of 728–907 nm, 1407–1809 nm, and 1940–2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R2c = 0.995, RMSEP = 2.676, R2p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest.

scholarly journals Biomass components of Pennisetum purpureum cv. Roxo managed at different growth ages and seasons

2018 ◽  
Vol 19 (1) ◽  
pp. 11-22 ◽  
Author(s):  
Theyson Duarte MARANHÃO ◽  
Magno José Duarte CÂNDIDO ◽  
Marcos Neves LOPES ◽  
Roberto Cláudio Fernandes Franco POMPEU ◽  
Maria Socorro de Souza CARNEIRO ◽  
...  
Keyword(s):  
Linear Response ◽  
Leaf Blade ◽  
Pennisetum Purpureum ◽  
Maximum Point ◽  
Area Index ◽  
Average Value ◽  
Randomized Design ◽  
Dead Material ◽  
Biomass Components ◽  
Dry Seasons

SUMMARY This study was carried out aiming to evaluate the biomass components of elephant grass cv. Roxo at seven growth ages, during rainy, transition and dry seasons. A completely randomized design was adopted with a split plot arrangement over time. The treatments consisted of seven growth ages (9, 18, 27, 36, 45, 54 and 63) and three seasons (rainy, transition and dry). The variables green forage biomass, dead forage biomass, green stem biomass and green leaf blade biomass showed positive linear responses to age and had their magnitude influenced by the evaluated seasons. The live/dead material ratio showed a decreasing linear response as a function of age. The leaf blade/stem ratio showed a negative linear adjustment in the rainy season, reaching a critical value of 1.0 at 59 days, and it showed a quadratic adjustment in the transition season, with the maximum point at 27.53 days; however, this ratio was not influenced by age in the dry season, revealing an average value of 2.22 ± 0.27. The canopy height and leaf area index showed a positive linear response to age in the three seasons. Tiller population density showed quadratic behaviour for age, with maximum estimated values of 134 and 110 til. m-2 at 31.24 and 37.40 days in the rainy and dry seasons, respectively. Rainfall seasonality influences the magnitude of the daily increase of the distinct biomass components of Pennisetum purpureum cv. Roxo.

scholarly journals Estimation of total leaf area and D leaf area of pineapple from biometric characteristics

2018 ◽  
Vol 40 (6) ◽  
Author(s):  
Marlúcia Pereira dos Santos ◽  
Victor Martins Maia ◽  
Fernanda Soares Oliveira ◽  
Rodinei Facco Pegoraro ◽  
Silvânio Rodrigues dos Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Regression Models ◽  
Carbon Fixation ◽  
Pearson Correlation ◽  
Leaf Length ◽  
Total Leaf Area ◽  
Total Leaf ◽  
Pearson Correlation Analysis ◽  
Biometric Measurements ◽  
Non Destructive

Abstract The estimation of pineapple total leaf area by simple, fast and non-destructive methods allow inferences related to carbon fixation estimative, biotic and abiotic damages and correlating positively with yield. The objective was to estimate D leaf area and total leaf area and of ‘Pérola’ pineapple plants from biometric measurements. For this purpose, 125 slips were selected and standardized by weight for planting in pots. Nine months after planting in a greenhouse, the plants were harvested to evaluate the total leaf area of the plant, D leaf area and D leaf length and width using a portable leaf area meter. Pearson correlation analysis was made and it was observed significative positive and strong correlation among the studied variables. Then, regression models were adjusted. It was observed that the D leaf area of ‘Pérola’ pineapple can be estimated from the length and width of this same leaf and the total leaf area can be estimated from the D leaf area.

scholarly journals Methods of the Detection and Identification of Structural Defects in Saturated Metallic Composite Castings

2017 ◽  
Vol 17 (3) ◽  
pp. 37-44 ◽  
Author(s):  
K. Gawdzińska
Keyword(s):  
Electron Microscopy ◽  
Complex Structure ◽  
Gravimetric Method ◽  
Structural Defects ◽  
X Ray ◽  
Metallic Composite ◽  
Detection And Identification ◽  
Metallic Composites ◽  
Non Destructive ◽  
Scanning Electron

Abstract Diagnostics of composite castings, due to their complex structure, requires that their characteristics are tested by an appropriate description method. Any deviation from the specific characteristic will be regarded as a material defect. The detection of defects in composite castings sometimes is not sufficient and the defects have to be identified. This study classifies defects found in the structures of saturated metallic composite castings and indicates those stages of the process where such defects are likely to be formed. Not only does the author determine the causes of structural defects, describe methods of their detection and identification, but also proposes a schematic procedure to be followed during detection and identification of structural defects of castings made from saturated reinforcement metallic composites. Alloys examination was conducted after technological process, while using destructive (macroscopic tests, light and scanning electron microscopy) and non-destructive (ultrasonic and X-ray defectoscopy, tomography, gravimetric method) methods. Research presented in this article are part of author’s work on castings quality.

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