Since there are no currently active contests, we have switched Climate CoLab to read-only mode.
Learn more at
Skip navigation
Share via:


Agromet Based Early Warning System for Cotton-Wheat in Punjab-Pakistan



Agriculture is highly prone to weather and climate of an area, and therefore changes in global climate could have major effects on crop yields, and ultimately on food supply. Food security, is appear likely to be most important factor to meet the challenges of feeding a global population by increasing the grain yield of field crops; that is arising in the agriculture sector from few decades.The Intergovernmental Panel on Climate Change (IPCC, 2014) reported that average global temperatures have increased by about 20C since the industrial revolution. Over past hundred years, the global temperature has increased by 1.50C and is expected to reach 2.5-7.80C by the end of next century. On the other hand, CO2 concentration in the atmosphere has increased drastically from 340 ppm to 520 ppm and is likely to be doubled in 2100. This change is attributed mainly to the over exploitation and misuse of natural resources for various anthropogenic developmental activities, deforestation and industrialization resulting in aberrant weather events like changes in rainfall patterns, droughts and floods, heat and cold waves, outbreaks of insect-pests and diseases etc. This will serve/impact a lot on socio-economic benefits of farmers, policy makers, scientists etc. working with agricultural business directly or indirectly.

Models are being used to assess how changes in environmental conditions, particularly CO2 concentration, temperature and precipitation, may affect crop productivity. Whatever the implications, confidence in the performance of models is improved by comparing them for the same input conditions.Crop simulation models are efficient tools for agronomic management strategy development and evaluation.

The main thrust of this study is to quantify the possible impacts of climate change on the productivity of wheat and cotton under the agro-ecological conditions of Pakistan by the integration of different crop and climate models (DSSAT, APSIM, and SimCLIM models).

What actions do you propose?

The main thrust of this study is to quantify the possible impacts of climate change on the productivity of wheat and cotton under the agro-ecological conditions of Pakistan by the integration of different models Decision Support System and Technology Transfer (DSSAT),Agricultural Production Systems sIMulator (APSIM) and Simulator of Climate Change Risks and Adaptation Initiatives(SimClim). Since each climate model has its own uncertainty, more than one climate model will be better for dealing with the accurate projection problem. This will provide necessary information to both the farmers and the decision makers to develop appropriate plans to reduce the effects of climatic changes. The PI has extensive experience with these crops and has ample experimental data sets that have been collected during the past 10 years.


·         Calibration and evaluation of DSSAT, APSIM and SimCLIM models for cotton and wheat.

·         Development of site-specific production technology package for enhancing cotton and wheat productivity under predicted climatic and economic scenarios.

·         The project will strengthen the teaching and research facilities in the department and will improve the skill of the academic staff which in turn will benefit the community on large scale in the field of Weather forecasting crop simulation/modeling, climate modeling & other latest techniques.

·         Capacity building of stakeholders through outreach programs and working with multiple crop and economic models by undertaking climate analysis and scenario generation for validation and aggregation techniques.

·         Develop data base for the quantification of different models for future use in the assessment of severity climate change

·         Publication of at least 2-3 research papers in international peer reviewed journal having impact factor.

Selection of Crops

Increased agricultural production and high crops yield is essential for food security which make the farming systems less vulnerable to climate change. Important crops, such as wheat, rice, sugarcane maize and cotton account for 25.6 percent of the value added in overall agriculture and 5.3 percent of GDP. In this context wheat (2.1% in GDP and 10.0% to the value added in agriculture) and cotton (1.5% in GDP and 7.1 % in agriculture value addition) are major crops of Pakistan not only in respect of local consumption but Pakistan is also exporter of these crops. These crops were grown on approximately 9.18 and 2.96million hectares with a total production of 27.75million tons of wheat and 13.98 million bales of cotton (GOP, 2015). They are grown in different agro-ecological zones of Pakistan with each zone representing diverse edaphic, social, hydrological and climatic conditions.


So there is need to provide information for the assessment of decision and policy makers to develop appropriate strategies to reduce the exposed climate changes or to adapt tothem. Traditional methods are only site specific to produce results from experiment. Thus an articulate framework for analyzing the process of yield formation is lacking. The old approaches ofyield component analysis and growth analysis have failed to provide this framework. Therefore, the results from such studies provide few insights into the causes of crop responses to agronomic treatments.Global circulation model (GCM) predictions have ability to warn about frequency and severity of these changes in future and their profound biological, societal, and environmental impacts.


Detailed methodology

Three models (two crop models eg. DSSAT, APSIM, and one climate model eg.  SimCLIM) will be calibrated with the data (that includes soil, cropmanagement, climate, phenology, biomass, LAI, and yield components) collected for wheat and cotton from different locations in different cropping zones. Cultivar coefficients will be determined that describe cultivar characteristics like days to anthesis and maturity, grain number per panicle, grain filling rate, grain weight, tillering coefficient and temperature tolerance coefficient etc. (Hunt and Boote, 1998). To select the mostsuitable set of coefficients an iterative approach proposed by Hunt et al. (1993) will beused. Moreover, deficiencies in the models will be identified and corrections will besuggested. Furthermore, the models will be evaluated with independent data set i.e. not used during calibration.

During the calibration and evaluation process potential deficiencies in the simulation outcomes will also be determined, especially for the very hot conditions of Pakistan and recommendations for model improvement will be provided.

Assessment of climate change impact

Future Climate change predictions will be developed by Pakistan Meteorological Department (PMD) for Pakistan using the output ofGlobal Circulation Models (GCMs). These predictions will be used to estimate their impacts on crop yield. Long time series of climate data of selected crops and zoneswill be analyzed to assess the variability and change in the past by SPSS or other suitable software. After calibration and evaluation, the DSSAT, APSIM and SimCLIM models will be run to determine the impacts of climate change on growth and productivity of selected crops (wheat and cotton).


At the end of the project the recommendations will be handed over to the policy makers and farmers with climate change scenarios for adopting best management practices and possible solution of their problem which they are facing directly into the field.


Seminars/workshops/conferences will be held to share knowledge, capacity building and technology transfer to stakeholders including students, researchers, academia, policy makers and farmers. Farming community in the vicinity will be benefited by the expert opinion by the application of this new field of early warning system and to make possible to save their crops (wheat & cotton) before the time and make better decisions.Therefore, we anticipate that the findings of the proposed research shall be useful to the government agencies that deal with agricultural enhancement, poverty eradication and meteorological services because all will have a better understanding of how and when to relay relevant information to reduce people’s vulnerability to weather extremes.

Who will take these actions?

Research institute and universities to train students, Farming Community and other stakeholders of this project will help for the execution of the project.

Where will these actions be taken?

All over the province of Punjab in Pakistan. We are open to any additional collaboration with other partners in the different parts within the country and also seeking for international collaboration. 

What are other key benefits?

·         Building up of climate change scenarios (2025-2050).

·         Prediction will be possible by the comparison of observed & simulation data and model

·         Capacity building of researchers, students, scientists, farmers and stakeholders representing different organizations, universities, institutes of the Punjab and other provinces of Pakistan.

·         Proposing of new production technologies for different crops grown under different agro-ecological zones of Pakistan to avoid upcoming climate hazards.

·         Quantification and early warning from economical losses caused by climate change.

·         Quantification of climate change impacts on crop productivity at different locations Punjab and other provinces of Pakistan.

What are the proposal’s costs?

(The proposed budget is for 03 Years).

1. Operating Cost: 0.6 M (US$).

2. Capital Cost:       0.36 M (US$).

3. Miscellaneous etc.  0.1 M (US$.).

Time line

Related proposals




1.      Cuculeanu V, Tuinea P, Balteanu D (2002) Climate change impacts in Romania: vulnerability and adaptation options. Geol J;57:203–9.

2.      GOP. 2015. Economic Survey of Pakistan, Finance Division, Economic Advisor’s Wing, Islamabad, Pakistan.

3.      Hautier Y, Tilman D, Isbell F, Seabloom EW, Borer ET, Reich PB (2015) Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348:336–340

4.      Hertel TW (2016) Food security under climate change. Nature Clim Change 6:10–13

5.      Hunt, L. A. and K. J. Boote (1998) Data for model operation, calibration and evaluation. In: Tsuji, G. Y., G., Hoogenboom, and P. K. Thornton, (Eds.), Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, The Netherlands, PP. 9–39.

6.      Hunt, L. A., S. Parajasingham, J. W. Jones, G. Hoogenboom, D. T. Imamura, and R. M. Ogoshi (1993) Gencalc—Software to facilitate the use of crop models for analyzing field experiments. Agron. J. 85, 1090–1094.

7.      IPCC (2014) Fourth Assessment Report, contribution of WG-II mitigation, adaptation, vulnerability and projections of climate change.

8.      IPCC (2014) Asia in climate change, impacts, adaptation and vulnerability contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

9.      Nasim W, Belhouchette H, Tariq M, Fahad S, Hammad HM, Mubeen M, Munis MFH, Chaudhary HJ, Khan I, Mahmood F, Abbas T, Rasul F, Nadeem M, Bajwa AA, Ullah N, Alghabari F, Saud S (2016a) Correlation studies on nitrogen for sunflower crop across the agroclimatic variability. Environ SciPollut Res 23(4):3658–3670

10.  Nasim W, Belhouchette H, Ahmad A, Rahman MH, Jabran K, Ullah K, Fahad S, Shakeel M, Hoogenboom G (2016b) Modelling climate change impacts and adaptation strategies for sunflower in Punjab-Pakistan. Outlook on Agriculture 45(1):39–45

11.  EasterlingDR,Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074

12.  Patz JA, Campbell-Lendrum D, Holloway T, Foley JA (2005) Impact of regional climate change on human health. Nature 438:310–317

13.  Rosenzweig C, Karoly D, Vicarelli M, Neofotis P, Wu Q, Casassa G, Menzel A, Root TL, Estrella N, Seguin B, Tryjanowski P, Liu C, Rawlins S, Imeson A (2008) Attributing physical and biological impacts to anthropogenic climate change. Nature 453:353–357

14.  Wilby RL, Dawson CW, Barrow EM (2002) SDSM a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17:145–157