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פותח על ידי קלירמאש פתרונות בע"מ -
Modelling chemical thinning in peach
Year:
1999
Source of publication :
Acta Horticulturae
Authors :
דוד, ישראל
;
.
זילכה, שמואל
;
.
Volume :
499
Co-Authors:
Szafran, E., Department of Life Sciences, Bar-Ilan University, Ramat Gan 52900, Israel, Institute of Horticulture, ARO, Volcani Center, Bet Dagan 50250, Israel
Kizner, Z., Department of Life Sciences, Bar-Ilan University, Ramat Gan 52900, Israel
David, I., Institute of Horticulture, ARO, Volcani Center, Bet Dagan 50250, Israel
Zilkah, S., Institute of Horticulture, ARO, Volcani Center, Bet Dagan 50250, Israel
Facilitators :
From page:
123
To page:
128
(
Total pages:
6
)
Abstract:
A statistical model was developed to make chemical thinning possible in the peach growing industry. The new concept behind this model is that, in addition to the obvious factors - active compound, concentration, volume and timing of the thinning application -an accurate prediction of the tree response should consider the plant's physiological status, and the environmental conditions following the application. The method of model building is presented as a factor analysis on three different sets of parameters - chemical thinning application, environmental conditions and physiological status of the tree -followed by a multivariate linear regression. The number of fruitlets remaining on the trees two weeks after the application was used as the predicted variable, while the predictors were the factors resulting from the factor analysis. Unnecessary variables were taken out of the factor analysis by a repetitive method. The influence of their removal was evaluated by checking the linear regression: prediction coefficient R2, Marlow's total squared error Cp, and regression significance p. The correlations between the predictions of the model and observed results were highly significant. They varied between r=0.66 and r=0.85 when the model equation was applied on same-year observations, and between r=0.42 and r=0.81 when it was applied to those from another year or to data from the whole study.
Note:
Related Files :
factor analysis
Multivariate linear regression
Prunus
עוד תגיות
תוכן קשור
More details
DOI :
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
מאמר מתוך כינוס
;
.
Language:
אנגלית
Editors' remarks:
ID:
23095
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:56
Scientific Publication
Modelling chemical thinning in peach
499
Szafran, E., Department of Life Sciences, Bar-Ilan University, Ramat Gan 52900, Israel, Institute of Horticulture, ARO, Volcani Center, Bet Dagan 50250, Israel
Kizner, Z., Department of Life Sciences, Bar-Ilan University, Ramat Gan 52900, Israel
David, I., Institute of Horticulture, ARO, Volcani Center, Bet Dagan 50250, Israel
Zilkah, S., Institute of Horticulture, ARO, Volcani Center, Bet Dagan 50250, Israel
Modelling chemical thinning in peach
A statistical model was developed to make chemical thinning possible in the peach growing industry. The new concept behind this model is that, in addition to the obvious factors - active compound, concentration, volume and timing of the thinning application -an accurate prediction of the tree response should consider the plant's physiological status, and the environmental conditions following the application. The method of model building is presented as a factor analysis on three different sets of parameters - chemical thinning application, environmental conditions and physiological status of the tree -followed by a multivariate linear regression. The number of fruitlets remaining on the trees two weeks after the application was used as the predicted variable, while the predictors were the factors resulting from the factor analysis. Unnecessary variables were taken out of the factor analysis by a repetitive method. The influence of their removal was evaluated by checking the linear regression: prediction coefficient R2, Marlow's total squared error Cp, and regression significance p. The correlations between the predictions of the model and observed results were highly significant. They varied between r=0.66 and r=0.85 when the model equation was applied on same-year observations, and between r=0.42 and r=0.81 when it was applied to those from another year or to data from the whole study.
Scientific Publication
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