Sitansu Pan and N. K. Mishra

Epidemiological studies on some diseases of guava (Psidium guajava L.)
... predictive purpose from regression equations. The simple correlation coefficient matrix showed significantly positive correlation of canker severity with maximum relative humidity at 1% level whereas, anthracnose correlated well with minimum relative humidity, temperature and number of rainy days at 5% level. The negative correlation was obtained for stem canker and dry rot with all parameters at 1% significant level. In case of Phytophthora fruit rot, positi...
Hongqun Li1, Xiaoli Liu2,*, Zhenmin Lian3,RenheWang4, Yongbin Wang4, Yongyao Fu1 andDingyi Wang5
...dent variables. Finally, regression equation with the lower Akaike’s Information Criterion for small sample sizes (AICc) value was regarded as the optimal model. The model indicated that nest-site success of brown-eared pheasants was negatively related to cover of shrubs, and first-order interaction between cover of trees and cover of shrub at a height of 1.0 m, suggesting bigger cover of shrubs, cover of trees and cover of shrub at height of 1.0 m were ...
Mujahid Niaz Akhtar* and Amjad Farooq
...imum temperature. Linear regression equation is used to predict bollworm population to minimize economic losses. 

Syed Muhammad Hassan Raza1*, Syed Amer Mahmood1, Syeeda Areeba Gillani1, Syed Shehzad Hassan1, Muneeb Aamir1, Muhammad Saifullah1, Mubashar Basheer1, Atif Ahmad1, Saif-ul-Rehman2 and Tariq Ali1 

...ession and generated the regression equation. The NDVI of rice crop in 2018 was 0.72 and the predicted yield was estimated as 2.05 ton/ha. Satellite derived rice area in 2018 was 689580 ha, which was substituted in the regression equation to predict the CRS based area that was 654966 ha. The net rice production for the year 2019, was predicted as 1.42 m tons. The remote sensing tools, datasets and the methodology is easy to ...
Jun Yan Bai*, Xiao Hong Wu, Shuai Yang, You Zhi Pang, Heng Cao, Hong Deng Fan, Xue Yan Fu, Kun Peng Shi and Xiao Ning Lu
...le quail (P<0.05).The regression equation of body weight to body size of Savimai quail was Y=-64.849+16.153X1+27.5X2+11.6 X3, with a fitting degree of 0.865. The regression equation of body weight to body size of French giant quail was Y= -148.103+11.851X1+20.927X2+59.278X4, with a fitting degree of 0.883. X1, X2, X3 and X

Pakistan Journal of Zoology


Vol. 52, Iss. 4, Pages 1225-1630


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