Weather Yield Model for the Semi Tropical Region (Pakistan)

Syed Faizan Haider; K. H. Asif; Amjad Hussain Gilani

doi:10.1007/BF02656947

Impact Factor: 5.8

Volume 9 Issue 3

Jul. 1992

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 1992 > 9(3): 367-372

Weather Yield Model for the Semi Tropical Region (Pakistan)

1.
The Department of Agricultural Meteorology, Uni. of Agriculture, Faisalabad Pakistan,The Department of Agricultural Meteorology, Uni. of Agriculture, Faisalabad Pakistan,Degree College Rawala Kot, Azad Kashmir

doi: 10.1007/BF02656947

Abstract
References
Related papers
PDF

Abstract:
Weather models are essential tools for checking of the effect of the weather elements in terms of their effect on the production of the crop. This research is an attempt to see the effect of only two variables i.e., temperature and rainfall for the division Faisalabad (semitropical region of Pakistan).The model fitted is of the linear form:the values of a,b, c have been found. The expected yield has been calculated by using the aridity indices (X1 and X2 ) and the result in the form of coefficient of determination R2 has been found equal to 0.166. The significance of the regression coefficient has been tested, which shows that the contribution to the yield from aridity index at germination and that at ripening is significant.The wheat yields are the results of a wide variety of variables, most of which show varying degree of relationship with one another, some positive and some negative in terms of output. These variables may be technology, fertilizers, pesticides, epidemics, kinds of seeds used, market price of crop and the area under cultivation etc, which can be the source of variation in the wheat yield. Since rainfall during germination and temperature at the ripening periods are the necessary factors for the yield of wheat, for this purpose these parameters have been studied in order to their contribution.
References

[1]	WANG Hesong, JIA Gensuo, 2012: Satellite-Based Monitoring of Decadal Soil Salinization and Climate Effects in a Semi-arid Region of China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1089-1099. doi: 10.1007/s00376-012-1150-8
[2]	Xiaoli LIU, Kerui MIN, Jianren SANG, Simin MA, 2023: Classification of Hailstone Trajectories in a Hail Cloud over a Semi-Arid Region in China, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1877-1894. doi: 10.1007/s00376-023-2156-0
[3]	ZHANG Renjian, FU Congbin, HAN Zhiwei, ZHU Chongshu, 2008: Characteristics of Elemental Composition of PM2.5 in the Spring Period at Tongyu in the Semi-arid Region of Northeast China, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 922-931. doi: 10.1007/s00376-008-0922-7
[4]	Fiona WILLIAMSON, Rob ALLAN, Guoyu REN, Tsz-cheung LEE, Wing-hong LUI, Hisayuki KUBOTA, Jun MATSUMOTO, Jürg LUTERBACHER, Clive WILKINSON, Kevin WOOD, 2018: Collating Historic Weather Observations for the East Asian Region: Challenges, Solutions, and Reanalyses, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 899-904. doi: 10.1007/s00376-017-7259-z
[5]	He Jianzhong, He Jinhai, 1993: Nondispersive Periodic Solution of a Barotropic Semi-Geostrophic Model, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 465-474. doi: 10.1007/BF02656971
[6]	Xianhong MENG, Shihua LYU, Zhaoguo LI, Yinhuan AO, Lijuan WEN, Lunyu SHANG, Shaoying WANG, Mingshan DENG, Shaobo ZHANG, Lin ZHAO, Hao CHEN, Di MA, Suosuo LI, Lele SHU, Yingying AN, Hanlin NIU, 2023: Dataset of Comparative Observations for Land Surface Processes over the Semi-Arid Alpine Grassland against Alpine Lakes in the Source Region of the Yellow River, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1142-1157. doi: 10.1007/s00376-022-2118-y
[7]	SONG Yaoming, GUO Weidong, ZHANG Yaocun, 2009: Numerical Study of Impacts of Soil Moisture on the Diurnal and Seasonal Cycles of Sensible/Latent Heat Fluxes over Semi-arid Region, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 319-326. doi: 10.1007/s00376-009-0319-2
[8]	Jiang Shangcheng, Ye Qian, Yang Xifeng, An Gang, Xiangqiang Wu, 2000: Climatological Features of the Global Tropical Subsidence Region Based on Satellite Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 391-402. doi: 10.1007/s00376-000-0031-8
[9]	WANG Xiaocong, LIU Yimin, WU Guoxiong, Shian-Jiann LIN, BAO Qing, 2013: The Application of Flux-Form Semi-Lagrangian Transport Scheme in a Spectral Atmosphere Model, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 89-100. doi: 10.1007/s00376-012-2039-2
[10]	Jie TANG, Chungang CHEN, Xueshun SHEN, Feng XIAO, Xingliang LI, 2021: A Positivity-preserving Conservative Semi-Lagrangian Multi-moment Global Transport Model on the Cubed Sphere, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1460-1473. doi: 10.1007/s00376-021-0393-7
[11]	HUANG Bo, CHEN Dehui, LI Xingliang, LI Chao, , 2014: Improvement of the Semi-Lagrangian Advection Scheme in the GRAPES Model: Theoretical Analysis and Idealized Tests, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 693-704. doi: 10.1007/s00376-013-3086-z
[12]	JIANG Zhihong, DING Yuguo, ZHENG Chunyu, CHEN Weilin, 2011: An Improved, Downscaled, Fine Model for Simulation of Daily Weather States, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1357-1366. doi: 10.1007/s00376-011-0086-8
[13]	K. Hussain, Ruby Riffat, A. Shaukat, M. Ashraf Siddiqui, 1990: A Study of Suspended Participate Matter in Lahore (Pakistan), ADVANCES IN ATMOSPHERIC SCIENCES, 7, 178-185. doi: 10.1007/BF02919155
[14]	Xiaojuan LIU, Guangjin TIAN, Jinming FENG, Bingran MA, Jun WANG, Lingqiang KONG, 2018: Modeling the Warming Impact of Urban Land Expansion on Hot Weather Using the Weather Research and Forecasting Model: A Case Study of Beijing, China, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 723-736. doi: 10.1007/s00376-017-7137-8
[15]	JIN Ling, Fanyou KONG, LEI Hengchi, and HU Zhaoxia, 2014: A Methodological Study on Using Weather Research and Forecasting (WRF) Model Outputs to Drive a One-Dimensional Cloud Model, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 230-240. doi: 10.1007/s00376-013-2257-2*
[16]	Xiaobin LIN, Zhiping WEN, Wen ZHOU, Renguang WU, Ruidan CHEN, 2017: Effects of Tropical Cyclone Activity on the Boundary Moisture Budget over the Eastern China Monsoon Region, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 700-712. doi: 10.1007/s00376-017- 6191-6
[17]	ZHOU Mengzi, WANG Huijun, 2015: Potential Impact of Future Climate Change on Crop Yield in Northeastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 889-897. doi: 10.1007/s00376-014-4161-9
[18]	Dezhen YIN, Fang LI, Yaqiong LU, Xiaodong ZENG, Zhongda LIN, Yanqing ZHOU, 2024: Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 420-434. doi: 10.1007/s00376-023-2234-3
[19]	DONG Wenjie, CHOU Jieming, FENG Guolin, 2007: A New Economic Assessment Index for the Impact of Climate Change on Grain Yield, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 336-342. doi: 10.1007/s00376-007-0336-y
[20]	CHEN Wen, ZHU Deqin, LIU Huizhi, SUN Shufen, 2009: Land-Air Interaction over Arid/Semi-arid Areas in China and Its Impact on the East Asian Summer Monsoon. Part I: Calibration of the Land Surface Model (BATS) Using Multicriteria Methods, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1088-1098. doi: 10.1007/s00376-009-8187-3

PDF

Get Citation+

Export:

Article Metrics

Article Views: 1314 Times

PDF downloads: 1203 Times

Cited by: Times

Proportional views

Manuscript History

Manuscript received: 10 July 1992

Manuscript revised: 10 July 1992

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Weather Yield Model for the Semi Tropical Region (Pakistan)

1. The Department of Agricultural Meteorology, Uni. of Agriculture, Faisalabad Pakistan,The Department of Agricultural Meteorology, Uni. of Agriculture, Faisalabad Pakistan,Degree College Rawala Kot, Azad Kashmir

Manuscript received: 1992-07-10
Manuscript revised: 1992-07-10

Abstract: Weather models are essential tools for checking of the effect of the weather elements in terms of their effect on the production of the crop. This research is an attempt to see the effect of only two variables i.e., temperature and rainfall for the division Faisalabad (semitropical region of Pakistan).The model fitted is of the linear form:the values of a,b, c have been found. The expected yield has been calculated by using the aridity indices (X1 and X2 ) and the result in the form of coefficient of determination R2 has been found equal to 0.166. The significance of the regression coefficient has been tested, which shows that the contribution to the yield from aridity index at germination and that at ripening is significant.The wheat yields are the results of a wide variety of variables, most of which show varying degree of relationship with one another, some positive and some negative in terms of output. These variables may be technology, fertilizers, pesticides, epidemics, kinds of seeds used, market price of crop and the area under cultivation etc, which can be the source of variation in the wheat yield. Since rainfall during germination and temperature at the ripening periods are the necessary factors for the yield of wheat, for this purpose these parameters have been studied in order to their contribution.