scholarly journals Evapotranspiration and heat unit requirement of cowpea

MAUSAM ◽  
2021 ◽  
Vol 61 (4) ◽  
pp. 565-568
Author(s):  
CHANABASANAGOUDA SANGANAGOUDA PATIL
Keyword(s):  
Multiple Regression ◽  
Crop Coefficient ◽  
Regression Equation ◽  
Actual Evapotranspiration ◽  
Multiple Regression Equation ◽  
Degree Days ◽  
Heat Unit ◽  
Kharif Season

Weighing gravimetric lysimeters are used to measure actual evapotranspiration of cowpea for an experiment conducted for two years during Kharif 2004 and 2005.  A multiple regression equation has been generated for relating crop coefficient of cowpea with week after sowing.  The highest value of crop co-efficient was found to be 1.015 on 6th week after sowing under Bangalore climate, the average values of actual evapotranspiration from cowpea crop and heat unit requirements over a period of 89 days in the Kharif season are found to be 284.8 mm and 1293 degree days respectively.

scholarly journals Influence of actual evapotranspiration, growing degree days and bright sunshine hours on yield of finger millet

MAUSAM ◽  
2021 ◽  
Vol 60 (3) ◽  
pp. 343-348
Author(s):  
C. S. PATIL ◽  
B. PUTTANNA
Keyword(s):  
Multiple Regression ◽  
Finger Millet ◽  
Crop Coefficient ◽  
Regression Equation ◽  
Actual Evapotranspiration ◽  
Multiple Regression Equation ◽  
Growing Degree Days ◽  
Degree Days ◽  
Sunshine Hours ◽  
Rainfed Conditions

Based on the result of an experiment conducted for three years from 1997-1999 during Kharif, under rainfed conditions, a model has been generated for relating crop coefficient of ragi  with time at Bangalore. The peak value of crop coefficient was found to be 1.02 on 66th day after sowing. The multiple regression equation was also generated based on the accumulated values of actual evapotranspiration (AET), growing degree days (GDD) and bright hours of Sunshine (BHS) from sowing to physiological maturity, using the field experimental data for the period 1998-2005. This multiple regression equation can be applied to forecast the yield of finger millet under rainfed conditions. This model was validated for the crop grown during Kharif 2005.

scholarly journals Water and heat unit requirement in different growth stages of Soybean (Glycine max L. Merrill) at Bhopal

MAUSAM ◽  
2021 ◽  
Vol 58 (4) ◽  
pp. 537-542
Author(s):  
I. J. VERMA ◽  
H. P. DAS ◽  
V. N. JADHAV
Keyword(s):  
Crop Coefficient ◽  
Growth Stages ◽  
Maturity Stage ◽  
Degree Days ◽  
Flowering Stage ◽  
Heat Unit ◽  
Soybean Crop ◽  
Pod Development ◽  
Use Efficiency ◽  
Different Growth Stages

In this study, ET data available on Soybean crop for Bhopal during 1991-95 have been utilized.  With regard to water need of the crop, the life span of soybean has been divided into five important growth stages viz., seedling up to 2 weeks after sowing (WAS), vegetative (3-8 WAS), flowering (9-10 WAS), pod development (11-13 WAS), and maturity (14-15 WAS). In this paper, consumptive use of water (ET), Water Use Efficiency (WUE), Heat Units (HU), Heat Use Efficiency (HUE) and crop coefficient (Kc) for different growth stages of the crop have been computed and discussed.                The study revealed that on an average, Soybean crop consumed about 450 mm of water. The average WUE was found to be 3.23 kg /ha/mm. It was also observed that WUE does not depend only on the total amount of water consumed by the crop but also indicates the importance of its distribution during various growth stages. On an average, the crop consumed nearly 7%, 36%, 24%, 25% and 8% of water during seedling, vegetative, flowering, pod development and maturity stage respectively. The crop consumed maximum amount of water during vegetative stage. However, the average weekly ET rate was found to be highest during flowering stage (nearly 52 mm). Average heat unit requirement of soybean was found to be 1694 degree-days. Maximum heat units were required during vegetative stage (638 degree days) followed by pod development stage (358 degree days). The average HUE was found to be 0.86 kg/ha/degree days. Crop coefficient (Kc) values varied in the range 0.30 – 0.45, 0.55 – 0.90, 1.00 – 1.15, 0.85 – 0.70 and 0.55 – 0.40 during seedling, vegetative, flowering, pod development and maturity stage respectively. The crop coefficient values attained the peak during the flowering stage.  

scholarly journals Correlation of 3-Dimensional Shoulder Kinematics to Function in Subjects With Idiopathic Loss of Shoulder Range of Motion

2005 ◽  
Vol 85 (7) ◽  
pp. 636-647 ◽  
Author(s):  
Peter J Rundquist ◽  
Paula M Ludewig
Keyword(s):  
Range Of Motion ◽  
Multiple Regression ◽  
External Rotation ◽  
Regression Equation ◽  
Multiple Regression Equation ◽  
3 Dimensional ◽  
Shoulder Range Of Motion ◽  
Shoulder Kinematics ◽  
And Function ◽  
Shoulder Range

AbstractBackground and Purpose. People with idiopathic loss of shoulder range of motion (ROM) have difficulty completing activities of daily living. This investigation was performed to determine the association between active glenohumeral ROM and function and to develop a multiple regression equation to explain variation in function in people with idiopathic loss of shoulder motion. Subjects and Methods. This was a comparative study of 21 subjects (18 female, 3 male), using measurements of shoulder kinematics and administration of the Shoulder Rating Questionnaire (SRQ). Electromagnetic tracking sensors monitored the 3-dimensional position of the scapula and humerus throughout active shoulder motions. Correlations were performed between the active ROMs of interest and various demographic factors and the SRQ. A multiple regression equation was generated. Results. A multiple regression equation including scapular-plane abduction, external rotation at the side, external rotation at 90 degrees of abduction, and weight explained 69% of the variation in the SRQ scores. Discussion and Conclusion. The results suggest that active ROM can be used to predict function in people with idiopathic loss of shoulder ROM.

Analysis of Cloud Seeding Efforts in the Tri-County Area,* Oregon, 1950–54

1957 ◽  
Vol 38 (3) ◽  
pp. 134-137 ◽  
Author(s):  
Fred W. Decker ◽  
Russell L. Lincoln ◽  
John A. Day
Keyword(s):  
Multiple Regression ◽  
Regression Equation ◽  
Multiple Regression Equation ◽  
Cloud Seeding ◽  
North Central ◽  
Natural Rainfall ◽  
Cent Level

Using an 11-variable multiple regression equation to compute the “natural” rainfall, it is concluded that commercial cloud seeding during 1950–54 in three north central Oregon counties failed to produce departures in precipitation which are statistically significant at the 5 per cent level of acceptance.

The Use of Econometric Analysis in Forecasting Macroeconomic Processes

2021 ◽  
pp. 28-33
Author(s):  
Aleksandr Kalyanov ◽  
Andrey Shishkin
Keyword(s):  
Multiple Regression ◽  
Econometric Model ◽  
Econometric Analysis ◽  
Regression Equation ◽  
Multiple Regression Equation ◽  
Linear Multiple Regression ◽  
Consumer Prices ◽  
Macroeconomic Indicators ◽  
Primary Impact ◽  
Selection Of

The article discusses the issues related to the influence of various price categories of goods on the level of inflation. The possibility of using econometric analysis for predictive purposes is considered. An econometric model of multiple regression of the influence of consumer prices on the level of inflation is formed. A linear multiple regression equation is constructed. The selection of factors for the construction of an econometric model is made. The main groups of goods are identified, the prices of which form the level of inflation and can have a primary impact. The viability of the model and the possibility of forecasting macroeconomic indicators based on econometric analysis are proved.

Variation in nutrient content of feedingstuffs rich in protein and reassessment of the chemical method for metabolizable energy estimation for poultry

1976 ◽  
Vol 86 (2) ◽  
pp. 293-303 ◽  
Author(s):  
G. N. Lodhi ◽  
Daulat Singh ◽  
J. S. Ichhponani
Keyword(s):  
Multiple Regression ◽  
Crude Protein ◽  
Regression Equation ◽  
Metabolizable Energy ◽  
Multiple Regression Equation ◽  
Regression Equations ◽  
Ether Extract ◽  
Crude Fibre ◽  
Energy Estimation ◽  
Short Period

SummaryA series of five metabolism trials was made to determine apparent nitrogen digestibility and metabolizable energy (ME) contents of protein rich feedingstuffs. The mean nitrogen digestibilities of fish meal, groundnut, mustard, sesame and cottonseed cakes were 66, 69, 68, 57 and 40%, respectively. Corresponding values for metabolizable energy values were 1820, 2460, 2330, 1870 and 1530 kcal/kg, respectively. The metabolizable energy contents of coconut cake, niger cake and blood meal were 1190, 2360 and 2190 kcal/kg, respectively. The quantity of protein, its digestibility and crude fibre content in the cakes are the prime factors for this trend in MB. Simple and multiple regression equations were derived from biologically assayed metabolizable energy and chemically analysed energy-yielding nutrient contents of the feedingstuffs. The simple regression equation is:ME kcal/kg = 32·95 (% crude protein + % ether extract × 2·25+ % available carbohydrate)–29·20.The multiple regression equation is:ME kcal/kg = 370·29 + (24·47 × % crude protein)+ (65·77 × % ether extract)+ (44·07 × % available carbohydrate)- (8·15 × % crude fibre).The correlation coefficients of simple and multiple regression equations were 0·72 and 0·73, respectively, indicating that there is very little advantage for prediction in using the multiple regression equation. The usefulness of the equation for routine checking of poultry feeds for ME is apparent since the nutrients required to predict metabolizable energy can be analysed within a short period of time.

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