A preliminary investigation into honey bee (Apis mellifera) pollination of canola (Brassica napus cv. Karoo) in Western Australia

2000 ◽  
Vol 40 (3) ◽  
pp. 439 ◽  
Author(s):  
R. Manning ◽  
R. Manning ◽  
J. Boland ◽  
J. Boland
Keyword(s):  
Apis Mellifera ◽  
Brassica Napus ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Honey Bee ◽  
Preliminary Investigation ◽  
Conservative Estimate ◽  
Branch Number ◽  
Explanatory Variables ◽  
Pod Yield

The aim of this preliminary experiment was to evaluate the effect of distance from the apiary on pod yield in canola. Beehives were used at a density of 1.28 hives/ha. The results showed that the number of pods/plant decreased as distance from the apiary increased, when plant height and branch number were used as explanatory variables. Multiple linear regression indicated a predicted pod loss of 15.3 pods/plant over a distance of 1000 m from an apiary. This was equivalent to a 16% loss based on an average of 59 plants/m2 and average pod production of 5666 pods/m2 from this experiment. For a 2 t/ha crop this would be equivalent to about 320 kg/ha. The results are only indicative because of the variation in the crop studied and lack of replication, but may, in fact, be a conservative estimate.

scholarly journals Statistical Modelling of the Capital Asset Pricing Model (CAPM)

2018 ◽  
Vol 7 (2) ◽  
pp. 146
Author(s):  
Silvi Qemo ◽  
Eahab Elsaid
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Statistical Modelling ◽  
Expected Returns ◽  
Explanatory Variables ◽  
Bid Ask Spread ◽  
Model Average

The purpose of this study is to derive a multiple linear regression model of the CAPM. More specifically, to test for other potential explanatory variables that can be added to the basic linear regression model for the expected returns on Apple Inc. The following explanatory variables were examined: share volume, outstanding shares, closing bid/ask spread, high/low spread and average spread. Using daily returns of Apple Inc. stock from 2007 till 2014 we were able to create a multiple linear regression model of CAPM that increase the R2 value from the basic linear regression model and enhances the amount of variability in the returns on an asset. This is an important modification that can help better forecast returns on assets.Keywords: CAPM; multiple linear regression model; average spread; variability in the returns

scholarly journals Multiple Linear Regression: Bayesian Inference for Distributed and Big Data in the Medical Informatics Platform of the Human Brain Project

2018 ◽  
Author(s):  
Lester Melie-Garcia ◽  
Bogdan Draganski ◽  
John Ashburner ◽  
Ferath Kherif
Keyword(s):  
Big Data ◽  
Linear Regression ◽  
Human Brain ◽  
Multiple Linear Regression ◽  
Medical Informatics ◽  
Data Privacy ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Explanatory Variables ◽  
Human Brain Project

ABSTRACTWe propose a Multiple Linear Regression (MLR) methodology for the analysis of distributed and Big Data in the framework of the Medical Informatics Platform (MIP) of the Human Brain Project (HBP). MLR is a very versatile model, and is considered one of the workhorses for estimating dependences between clinical, neuropsychological and neurophysiological variables in the field of neuroimaging. One of the main concepts behind MIP is to federate data, which is stored locally in geographically distributed sites (hospitals, customized databases, etc.) around the world. We restrain from using a unique federation node for two main reasons: first the maintenance of data privacy, and second the efficiency in management of big volumes of data in terms of latency and storage resources needed in the federation node. Considering these conditions and the distributed nature of data, MLR cannot be estimated in the classical way, which raises the necessity of modifications of the standard algorithms. We use the Bayesian formalism that provides the armamentarium necessary to implement the MLR methodology for distributed Big Data. It allows us to account for the heterogeneity of the possible mechanisms that explain data sets across sites expressed through different models of explanatory variables. This approach enables the integration of highly heterogeneous data coming from different subjects and hospitals across the globe. Additionally, it offers general and sophisticated ways, which are extendable to other statistical models, to suit high-dimensional and distributed multimodal data. This work forms part of a series of papers related to the methodological developments embedded in the MIP.

scholarly journals POLLEN GIVING PLANTS VISITED BY THE HONEY BEE (APIS MELLIFERA L.)

2019 ◽  
pp. 59-66
Author(s):  
Zheko Radev
Keyword(s):  
Apis Mellifera ◽  
Brassica Napus ◽  
Key Words ◽  
Honey Bee ◽  
Honey Bees ◽  
Centaurea Cyanus ◽  
Key Words Honey Bee ◽  
Pollen Traps ◽  
Bee Colonies ◽  
Floral Specialization

The analysis of the honey plants in the area of apiculture is very important about the development, reproduction and productivity of bee colonies. The knowledge of the floral specialization of Apis mellifera L. is main point for good beekeeping practices. The bees have visited 46 species of honey plants from 41 genera and 22 families. The honey bees prefer to collect pollen from 2 to 5-6 plant species during every single month. Bees mainly collect pollen from two or three plants every month. The agricultural species Brassica napus as well as the meadow flora – Сentaurea solstitialis and Centaurea cyanus are the most visited honey plants during their flowering. Bees prefer to collect pollen from 16 plants out of 46 visited taxons. Not all plants in the area serve as a source of pollen for the bees. The greatest amount of collected pollen comes from Brassica napus – 3798.69 g. The visited cultivated honey taxons are around 22 % but about 56.5 % of the total amount collected pollen. Around 78 % of the visited plants are common natural as well as about 43.5 % of the total amount collected pollen. Key words: honey bee, honey plants, pollen, pollen traps, melissopalynologia, specialization

scholarly journals Study on Chemical and Bioactive Components of Different Floral Sources’ Honey in Nepal

2019 ◽  
Vol 11 ◽  
pp. 51-59
Author(s):  
Samiksha Bhattarai ◽  
Ujjwol Subedi ◽  
Uttam Kumar Bhattarai ◽  
Roman Karki ◽  
Pravin Ojha
Keyword(s):  
Antioxidant Activity ◽  
Apis Mellifera ◽  
Brassica Napus ◽  
Chemical Composition ◽  
Honey Bee ◽  
Reducing Sugars ◽  
Fagopyrum Esculentum ◽  
Total Acidity ◽  
Bioactive Components ◽  
Flavonoid Content

Honey samples of commercial honey bee (Apis mellifera) were collected from different bee keepers in Nepal. Total 16 different samples from Dang, Chitwan, Nawalparasi, Sarlahi, Makwanpur and Rautahat districts of Nepal were obtained, representing honey of 4 different floral sources ‘Chiuri’ (Diploknema butyracea), ‘Rudhilo’ (Pogostemon plectranthoides), Mustard (Brassica napus), and Buckwheat (Fagopyrum esculentum). Chemical composition and bioactive components of the honey samples were studied.Moisture content, pH, total acidity of the examined honey samples was found to be in the range of 19.30 ± 0.87 to 20.15 ± 1.39 %, 3.35 ± 0.63 to 4.80 ± 0.15, 109.25 ± 2.06 to 191.25 ± 14.73 meq/kg, respectively. Antioxidant activity, polyphenol, and flavonoid content were found to be in the range of 51.51 ± 4.95 to 97.84 ± 3.75 %, 17.82 ± 1.61 to 59.34 ± 2.77 mg GAE/100g, 1.22 ± 0.65 to 3.86 ± 0.80 mg GAE/100g, respectively. TSS, reducing sugars and HMF content ranged from 77.5 ± 0.46 to 78.0 ± 0.91 oBx, 64.06 ± 1.99 to 70.76 ± 1.26%, and 49.5 ± 4.50 to 214 ± 39.20 mg/kg respectively.

scholarly journals Multiple Linear Regression Models for Predicting Nonpoint-Source Pollutant Discharge from a Highland Agricultural Region

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1156 ◽  
Author(s):  
Jae Cho ◽  
Jong Lee
Keyword(s):  
Linear Regression ◽  
Root Mean Square Error ◽  
Multiple Linear Regression ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sediment Runoff ◽  
Mean Square ◽  
Pollutant Load ◽  
Explanatory Variables ◽  
Pollutant Discharge

Sediment runoff from dense highland field areas greatly affects the quality of downstream lakes and drinking water sources. In this study, multiple linear regression (MLR) models were built to predict diffuse pollutant discharge using the environmental parameters of a basin. Explanatory variables that influence the sediment and pollutant discharge can be identified with the model, and such research could play an important role in limiting sediment erosion in the dense highland field area. Pollutant load per event, event mean concentration (EMC), and pollutant load per area were estimated from stormwater survey data from the Lake Soyang basin. During the wet season, heavy rains cause large amounts of suspended sediment and the occurrence of such rains is increasing due to climate change. The explanatory variables used in the MLR models are the percentage of fields, subbasin area, and mean slope of subbasin as topographic parameters, and the number of preceding dry days, rainfall intensity, rainfall depth, and rainfall duration as rainfall parameters. In the MLR modeling process, four types of regression equations with and without log transformation of the explanatory and response variables were examined to identify the best performing regression model. The performance of the MLR models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), coefficient of variation of the root mean square error (CV(RMSE)), the ratio of the RMSE to the standard deviation of the observed data (RSR) and the Nash–Sutcliffe model efficiency (NSE). The performance of the MLR models of pollutant load except total nitrogen (TN) was good under the condition of RSR, and satisfactory for the NSE and R2. In the EMC and load/area models, the performance for suspended solids (SS) and total phosphorus (TP) was good for the RSR, and satisfactory for the NSE and R2. The standardized coefficients for the models were analyzed to identify the influential explanatory variables in the models. In the final performance evaluation, the results of jackknife validation indicate that the MLR models are robust.

scholarly journals ANALISIS SPASIAL INDUSTRI KECIL DAN MENENGAH DI PROPINSI NUSA TENGGARA TIMUR

2011 ◽  
Vol 9 (2) ◽  
pp. 156
Author(s):  
Zainal Arifin
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Raw Materials ◽  
Small And Medium Enterprises ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Explanatory Variables ◽  
Business Units ◽  
Medium Enterprises

This study aims to identify patterns of spatial concentration of Small and Medium Enterprises in East Nusa Tenggara judging by the amount of labor and production as well as the factors that affect the employment period 2005-2009. Analysis tools used include: Spatial Analysis, Geographic Information Systems, and multiple linear regression. This study found that the distribution of Small and Medium Enterprises in East Nusa Tenggara is not evenly distributed geographically, when viewed from the employment and production quantities. In some  counties and cities experienced employment and production quantities are high, while some others were experiencing employment and production quantities are low. It also reinforced the results of multiple linear regression analysis with panel data with the result that all explanatory variables X1 (business units), X2 (investment), X3 (production) and X4 (raw materials) are able to explain to the employment of Small and Medium Industries in East Nusa Tenggara.

Sign in / Sign up

Export Citation Format

Share Document