Skip to main content Accessibility help
×
Home
Hostname: page-component-cf9d5c678-mpvvr Total loading time: 0.315 Render date: 2021-07-29T22:27:10.726Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

OPTIMIZED SHIFTS IN SOWING TIMES OF FIELD CROPS TO THE PROJECTED CLIMATE CHANGES IN AN AGRO-CLIMATIC ZONE OF PAKISTAN

Published online by Cambridge University Press:  16 March 2016

MUHAMMAD TOUSIF BHATTI
Affiliation:
International Water Management Institute, 12 km Multan Road, Chowk Thokar Niaz Baig, Lahore, Pakistan Centre of Excellence in Water Resources Engineering, University of Engineering and Technology, Lahore, Pakistan
KHALED S. BALKHAIR
Affiliation:
Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, P.O. Box 80208, Jeddah 21589, Saudi Arabia Center of Excellence in Desalination Technology, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia
AMJAD MASOOD
Affiliation:
Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, P.O. Box 80208, Jeddah 21589, Saudi Arabia
SALEEM SARWAR
Affiliation:
SMEC- Engineering General Consultants, 49 D-1, Gulburg III, Lahore, Pakistan
Corresponding
E-mail address:

Summary

This paper evaluates 30-year (2013–2042) projections of the selected climatic parameters in cotton/wheat agro-climatic zone of Pakistan. A statistical bias correction procedure was adopted to eliminate the systematic errors in output of three selected general circulation models (GCM) under A2 emission scenario. A transfer function was developed between the GCM outputs and the observed time series of the climatic parameters (base period: 1980–2004) and applied to GCM future projections. The predictions detected seasonal shifts in rainfall and increasing temperature trend which in combination can affect the crop water requirements (CWR) at different phonological stages of the two major crops (i.e. wheat and cotton). CROPWAT model is used to optimize the shifts in sowing dates as a climate change adaptation option. The results depict that with reference to the existing sowing patterns, early sowing of wheat and late sowing of cotton will favour decreased CWR of these crops.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Asian Development Bank (ADB). (2005). Agricultural growth and rural poverty: A review of the evidence. Pakistan, Islamabad: Resident Mission of Asian Development Bank.Google Scholar
Bastidas, A. M., Setiyono, T. D., Dobermann, A., Cassman, K. G., Elmore, R. W., Graef, G. L. and Specht, J. E. (2008). Soybean sowing date, the vegetative, reproductive, and agronomic impacts. Crop Science 48:727740.CrossRefGoogle Scholar
Chaudhari, Q. Z. (1994). Pakistan's summer monsoon rainfall associated with global and regional circulation features and its seasonal prediction. Proceedings of the International Conference on Monsoon Variability and Prediction, Trieste, Italy, 9–13 May 1994.Google Scholar
Diaz-Nieto, J. and Wilby, R. L. (2005). A comparison of statistical downscaling and climate change factor methods: Impacts on low flows in the river Thames, United Kingdom. Climatic Change 69(2):245268.CrossRefGoogle Scholar
Doria, R. O. (2010). Impact of climate change on crop water requirements in eastern Canada. Quebec, Canada: McGill University Montreal.Google Scholar
D'Orgeval, T., Boulanger, J.-P., Capalbo, M. J., Guevara, E., Penalba, O. and Meira, S. (2010). Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites. Climatic Change 98:565580.CrossRefGoogle Scholar
Evans, A. (2010). An overview: Water quality, environment and climate change. In National conference on water, food security and climate change in Sri Lanka, June 2009, Vol 2, vii–xi. (Eds Evans, A. and Jinapala, K.). Colombo: International Water Management Institute.Google Scholar
FAO. (2009). Climate change and bioenergy challenges for food and agriculture. The challenge Key issues Rome. Italy.Google Scholar
Fereres, E. and González-Dugo, V. (2009). Improving productivity to face water scarcity in irrigated agriculture. In Crop physiology: Applications for genetic improvement and agronomy, 123143 (Eds Sadras, V. O. and Valderini, D. F.). Amsterdam: Elsevier.Google Scholar
Food and Agriculture Organization (FAO). (1992). CROPWAT- a computer program for irrigation planning and management. FAO Irrigation and Drainage Paper No. 46. Rome.Google Scholar
Food and Agriculture Organization (FAO). (1998). Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage paper No. 56. Rome.Google Scholar
Food and Agriculture Organization (FAO). (2009). Climate change and bioenergy challenges for food and agriculture, Rome, Italy (available at http://www.fao.org).Google Scholar
Hanssen-Bauer, I., Forland, E. and Haugen, J. (2003). Temperature and precipitation scenarios for norway: Comparison of results from dynamical and empirical down-scaling. Climate Research 25(1):1527.CrossRefGoogle Scholar
Huda, S., Sadras, V., Wani, S. and Mei, X. (2011). Food security and climate change in the Asia-Pacific region: Evaluating mismatch between crop development and water availability. International Journal of Biodiversity Science and Management 2(2):137144.Google Scholar
Hussain, I., Hussain, Z. and Sial, M. H. (2011). Water balance, supply and demand and irrigation efficiency of Indus River Basin. Pakistan Economic and Social Review 49(1):1338.Google Scholar
Hussain, S. S. and Mudasser, M. (2007). Prospects for wheat production under changing climate in mountain areas of Pakistan – an econometric analysis. Agricultural Systems 94(2):494501.CrossRefGoogle Scholar
Ines, A. V. M. and Hansen, J. W. (2006). Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology 138(1-4):4453.CrossRefGoogle Scholar
IPCC, 2007: Climate Change. (2007). Impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change, 976 (Eds Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J. and Hanson, C. E.). Cambridge, UK: Cambridge University Press.Google Scholar
Jalota, S. K., Kaur, H., Ray, S. S., Vashisht, B. B. and Bal, S. K. (2012). Mitigating future climate change effects by shifting planting dates of crops in rice – wheat cropping system. Regional Environmental Change 12:913922.CrossRefGoogle Scholar
Jamieson, P. D. and Cloughley, C. G. (2001). Chapter 5: Impact of climate change on wheat production. In The effects of climate change and variation in New Zealand an assessment using the CLIMPACTS system, 5763. (Eds Warrick, R. A., Kenny, G. J. and Harman, J. J.). Hamilton: Waikato Print.Google Scholar
Law, A. M. and Kelton, W. D. (1982). Simulation Modeling and Analysis. USA: McGraw-Hill Book Co.Google Scholar
Mahbub ul Haq Human Development Centre (MHHDC). (2005). Human development in South Asia: Human security in South Asia. Karachi: Oxford University Press.Google Scholar
Mahbub ul Haq Human Development Centre (MHHDC). (2010). Human development in South Asia: Food security in South Asia. Karachi: Oxford University Press.Google Scholar
Malik, S. M., Awan, H. and Khan, N. (2012). Mapping vulnerability to climate change and its repercussions on human health in Pakistan. Globalization and Health 8:31.CrossRefGoogle ScholarPubMed
Nelson, G. C., Rosegrant, M. W., Palazzo, A., Gray, I., Ingersoll, C., Robertson, R., Tokgoz, S., Zhu, T., Sulser, T. B., Ringler, C., Msangi, S. and You, L. (2010). Food security, farming and climate change to 2050: Scenarios, results, policy, options. Washington: International Food Policy Research Institute (IFPRI).Google Scholar
Nkomozepi, T. and Chung, S. O. (2012). Assessing the trends and uncertainty of maize net irrigation water requirement estimated from climate change projections for Zimbabwe. Agricultural Water Management 111:6067.CrossRefGoogle Scholar
O'Brien, K. L. and Leichenko, R. M. (2000). Double exposure: assessing the impacts of climate change within the context of economic globalization. Global Environmental Change 10:221232.CrossRefGoogle Scholar
Panofsky, H. A. and Brier, G. W. (1968). Some application of statistics to meteorology. 224. University Park: Penn. State University, College of Earth and Mineral Sciences.Google Scholar
Piani, C., Haerter, J. and Coppola, E. (2010). Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1):187192.CrossRefGoogle Scholar
Pimentel, D. (1993). Climate changes and food supply. Forum for Applied Research and Public Policy 8(4):5460.Google Scholar
Sacks, W. J., Deryng, D. and Foley, J. A. (2010). Crop planting dates: An analysis of global patterns. Global Ecologyand Biography 19:607620.Google Scholar
Santer, B. D., Wigley, T. M. L., Schlesinger, M.E. and Mitchell, J. F. B. (1990). Developing climate scenarios from equilibrium GCM results. Max-Planck-Institut für Meteorologie Report No. 47, Hamburg, Germany, 29.Google Scholar
Shah, A., Akmal, M., Asim, M., Farhatullah, A., Raziuddin, A. and Rafi, A. (2012). Maize growth and yield in Peshawar under changing climate. Pakistan Journal of Botany 44 (6):19331938.Google Scholar
Sultana, H., Ali, N., Iqbal, M. M. and Khan, A. M. (2009). Vulnerability and adaptability of wheat production in different climatic zones of Pakistan under climate change scenarios. Climatic Change 94:123142.CrossRefGoogle Scholar
Tingem, M., Rivington, M., Bellocchi, G. and Jeremy, S. A. (2008). Comparative assessment of crop cultivar and sowing dates as adaptation choice for crop production in response to climate change in Cameroon. The African Journal of Plant Science and Biotechnology 2(1):1017.Google Scholar
Ullah, M. K., Habib, Z. and Muhammad, S. (2001). Spatial distribution of reference and potential evapotranspiration across the indus basin irrigation systems. IWMI Working Paper 24, International Water Management Institute, Lahore, Pakistan.Google Scholar
Vom Brocke, K., Trouche, G., Weltzien, E., Kondombo-Barro, C. P., Sidibe, A., Zougmoré, R. and Gozé, E. (2014). Helping farmers adapt to climate and cropping system change through increased access to sorghum genetic resources adapted to prevalent sorghum cropping systems in Burkina Faso. Experimental Agriculture 50(2):284305. doi:10.1017/S0014479713000616.CrossRefGoogle Scholar
Vyas, S., Nigam, R. and Patel, N. K. (2013). Extracting regional pattern of wheat sowing dates using multispectral and high temporal observations from indian geostationary satellite. Journal of Indian Society of Remote Sensing. DOI 10.1007/s12524-013-0266-3.CrossRefGoogle Scholar
Widmann, M., Bretherton, C. S. and Salathé, E. P. Jr. (2003). Statistical precipitation downscaling over the northwestern united states using numerically simulated precipitation as a predictor. Journal of Climate 16:799816.2.0.CO;2>CrossRefGoogle Scholar
4
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

OPTIMIZED SHIFTS IN SOWING TIMES OF FIELD CROPS TO THE PROJECTED CLIMATE CHANGES IN AN AGRO-CLIMATIC ZONE OF PAKISTAN
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

OPTIMIZED SHIFTS IN SOWING TIMES OF FIELD CROPS TO THE PROJECTED CLIMATE CHANGES IN AN AGRO-CLIMATIC ZONE OF PAKISTAN
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

OPTIMIZED SHIFTS IN SOWING TIMES OF FIELD CROPS TO THE PROJECTED CLIMATE CHANGES IN AN AGRO-CLIMATIC ZONE OF PAKISTAN
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *