scholarly journals Cardinal temperatures and modeling of vegetative development in guava

2019 ◽  
Vol 23 (11) ◽  
pp. 819-825
Author(s):  
Mábele de C. Ferreira ◽  
Fabrina B. Martins ◽  
Gabriel W. L. Florêncio ◽  
Liliana A. A. P. Pasin
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Vegetative Growth ◽  
Nonlinear Models ◽  
Mean Square ◽  
Vegetative Development ◽  
Randomized Design ◽  
Sowing Dates ◽  
Completely Randomized Design ◽  
Cardinal Temperatures

ABSTRACT The aim of this study was to estimate cardinal temperatures (base, optimum, and maximum), and simulate the vegetative development of guava crop through phyllochron (linear) and Wang and Engel (nonlinear) models at the seedling phase. The experiment was conducted in Itajubá, MG, Brazil (22° 24’ 46.43” S 45° 26’ 48.94” W), for two years in a completely randomized design with twelve sowing dates and five experimental units per treatment, totaling sixty experimental units. The cardinal temperatures were estimated using twelve methods described in the literature and the values were: base, 10.9 °C, optimum, 17.3 °C and maximum, 51.2 °C. The phyllochron model was superior to Wang and Engel’s model for simulation of vegetative growth of guava, with root mean square error of 1.5 leaves.

scholarly journals Cardinal temperatures for the leaf development of Corymbia citriodora and Eucalyptus urophylla seedlings

2017 ◽  
Vol 52 (5) ◽  
pp. 283-292 ◽  
Author(s):  
Cleverson Henrique de Freitas ◽  
Fabrina Bolzan Martins ◽  
Marcel Carvalho Abreu
Keyword(s):  
Field Experiment ◽  
Leaf Development ◽  
Main Stem ◽  
Eucalyptus Species ◽  
Eucalyptus Urophylla ◽  
Randomized Design ◽  
Sowing Dates ◽  
Completely Randomized Design ◽  
Corymbia Citriodora ◽  
Cardinal Temperatures

Abstract: The objective of this work was to estimate cardinal temperatures and phyllochron for the leaf development of the eucalyptus species Corymbia citriodora and Eucalyptus urophylla, in the seedling phase. A field experiment was carried out in a completely randomized design, in a 2x11 factorial arrangement, with the two eucalyptus species, 11 sowing dates, and five replicates, in the municipality of Itajubá, in the state of Minas Gerais, Brazil. The cardinal temperatures - base, optimum, and maximum - for leaf development were, respectively, 8.7, 17.1, and 41.3°C, for C. citriodora, and 11.5, 17.1, and 40.5°C for E. urophylla. The phyllochron of C. citriodora was 62.95°C day per leaf, and that of E. urophylla was 46.03°C day per leaf, which indicates that E. urophylla requires less energy accumulation than C. citriodora for leaf emission in the main stem.

scholarly journals Comparisons between intake values observed and predicted by nutritional systems for confined nellore cattle

2021 ◽  
Vol 37 ◽  
pp. e37093
Author(s):  
Aline Maria Soares Ferreira ◽  
Simone Pedro da Silva ◽  
Carina Carina Ubirajara De Faria ◽  
Egleu Diomedes Marinho Mendes ◽  
Ester Ferreira Felipe
Keyword(s):  
Root Mean Square Error ◽  
Dry Matter ◽  
Dry Matter Intake ◽  
Mean Square ◽  
Simple Linear Regression ◽  
Initial Weight ◽  
Randomized Design ◽  
Nellore Cattle ◽  
Precision And Accuracy ◽  
Completely Randomized Design

This study compared the dry matter intake (DMI) of Nellore heifers and bulls in the feedlot, predicted by the BR-Corte (2010 and 2016) and NRC (2000) nutritional systems. Hence, two experiments were conducted in a completely randomized design. The first one used 47 Nellore bulls, not castrated, with an average initial weight of 413 kg, and 19 months of age. The second experiment used 24 Nellore heifers with an average initial weight of 300 kg and 23 months of age. The accuracy and approximation of the DMI estimates by the nutritional systems were adjusted with the simple linear regression model and the root mean square error of prediction (RMSEP). The DMI was 8.06 kg day-1 for Nellore heifers and 11.54 kg day-1 for bulls, which are higher than the values ​​ predicted by the nutritional systems. The NRC (2000) and BR-Corte (2010 and 2016) underestimated DMI in 20.84, 20.09, and 19.35% for heifers and 28.07, 16.20, and 11.78% for bulls, respectively. It was concluded that the BR-Corte 2010 and 2016 were the most suitable models to estimate the DMI of Nellore heifers and bulls for higher precision and accuracy.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

scholarly journals Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

scholarly journals Quality of wood in the stands of poplar clones

2008 ◽  
Vol 54 (No. 1) ◽  
pp. 9-16
Author(s):  
R. Petráš ◽  
J. Mecko ◽  
V. Nociar
Keyword(s):  
Root Mean Square Error ◽  
Systematic Error ◽  
Mean Square Error ◽  
Statistical Tests ◽  
Mean Square ◽  
Poplar Clones ◽  
Mean Diameter ◽  
Empirical Material ◽  
Relative Root

The results obtained in research on the quality of raw timber by means of the structure of assortments for the stands of poplar clones Robusta and I-214 are presented in the paper. Models for an estimation of the structure of basic assortments of poplar stands were constructed separately for each clone in dependence on mean diameter, quality of stems, and damage to stems in the stand. The clone Robusta has higher proportions of higher-quality assortments than the clone I-214. The accuracy of models was determined on empirical material. It was confirmed by statistical tests that the models did not have a systematic error. The relative root mean-square error for main assortments of the clone I-214 is 15–27% and Robusta 13–24%.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1967-1971
Author(s):  
Yan Bai ◽  
Xiao Yan Duan ◽  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ethanol Extract ◽  
Mean Square ◽  
Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

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