Impact of climate change on cotton growth and yield (case study: Birjand Plain)

Document Type : Research Paper

Authors

1 Department of Water Engineering, College of Agriculture, University of Zabol, Zabol, Iran.

2 Assistant Professor of Water Engineering Dept. University Of Birjand

3 Department of Water Engineering, College of Agriculture, University of Birjand, Birjand, Iran

4 professor of water engineering Dept. Agriculture faculty. University of Birjand. Iran

Abstract

The aim of this research is to predict the effects of future climate change on cotton yield in Birjand region. In this research, the BCM2 general circulation model under two release scenarios B1 and A1B in three periods (2025 to 2050, 2050 to 2075, and 2075 to 2100) was examined to predict future climate conditions and to generate daily climate parameters of the LARS-WG microscale model. Daily climate data obtained from LARS-WG output were used as inputs for DSSAT model (crop simulation model) to simulate cotton growth under future climate. The selection and preparation of a suitable plot of land for the implementation of the project was done in the beginning of October 2018. The intended experimental design was factorial split plots. The DSSAT model provided acceptable results for cotton yield and phenological stages, and this success was confirmed when the values simulated by the model were compared with the data collected from the field experiments. The maximum NRMSE is related to HW simulation, which is calculated as 9.7%. The value of this index for simulating the phenology stages is much lower and its value is reduced to 1.5%. The results of this research show that the DSSAT model can be a promising tool for predicting yield, leaf area, nitrogen accumulation, phenology and biomass of different cotton cultivars and other crops grown in the region. It seems that this study is useful and appropriate for farmers and their making decisions. The results of the simulations showed that due to future climate change and increase in temperature and carbon dioxide concentration in Birjand city, cotton yield will increase. On average, under all scenarios, the average yield of cotton will increase by 15% in the period of 2025 to 2050, by 15.44% in the period of 2050 to 2075 and by 18.15% in the period of 2075 to 2100. The simulation has shown that climate change increased cotton yield (from 14.73 to 18.53 percent) and reduced the length of the cotton growing season. The main reason for the increase in cotton yield can be attributed to the increase in carbon dioxide concentration.

Keywords

Main Subjects

Impact of climate change on cotton growth and yield (case study: Birjand Plain)

EXTENDED ABSTRACT

Introduction

The global population growth results in a termondous demands for agricultural products accounting for more than 90% of water consumption. Besides, the climate changes, global warming, and water shortage will further generates sustanibility challenges. Agriculture is one of the first sectors that affected by climate change. Hence, evaluating the effect of climate change on the agricultural crops, especially in water-scarce countries like Iran, can play a key role in reducing the unfavour impacts of climate change on development and profitability of this sector. The present srudy was aimed to predict the effects of future climate change on cotton yield in Birjand region.

 

Material and Methodes

In this research, the BCM2 general circulation model under two release scenarios B1 and A1B in three periods (2025 to 2050, 2050 to 2075, and 2075 to 2100) was examined to predict future climate conditions and to generate daily climate parameters of the LARS-WG microscale model. Daily climate data obtained from LARS-WG output were used as inputs for DSSAT model (crop simulation model) to simulate cotton growth under future climate changes. The selection and preparation of a suitable plot of land for the implementation of the project was done in the beginning of October 2018. The intended experimental design was factorial split plots.

 

Results and Disscusion

The DSSAT model provided acceptable results for cotton yield and phenological stages, and this success was confirmed when the values simulated by the model were compared with the data collected from field experiments. The maximum NRMSE is related to HW simulation, which is calculated as 9.7%. The value of this index for simulating the phenology stages is much lower and its value is reduced to 1.5%. Also, the results showed that under the future climate change and decreasing in rainfall in Birjand, the cotton yield will decrease. As a result of applying 10% stress, cotton yield will decrease by 1.8% in B1 scenario and 2.13% in A1B scenario. Morever, our data showed that the DSSAT model can be a promising tool for predicting yield, leaf area, nitrogen accumulation, phenology, and biomass of different cotton cultivars and other crops grown in the region. It seems that this study is useful and appropriate for farmers and their making decisions. The results of the simulations showed that due to future climate change and increase in temperature and carbon dioxide concentration in Birjand city, cotton yield will increase.

 

Conclusion

 On average, under all scenarios, the average yield of cotton will increase by 15% in the period of 2025 to 2050, by 15.44% in the period of 2050 to 2075 and by 18.15% in the period of 2075 to 2100. The simulation has shown that climate change increased the yield (from 14.73 to 18.53 percent) and reduced the length of cotton growing season. The main reason for the increase in cotton yield can be attributed to the increase in carbon dioxide concentration.

References

Bavani, A. M., &Morid, S. (2006). Impact of climate change on the water resources of ZayandehRud Basin. JWSS-Isfahan University of Technology, 9(4), 17-28.‏ https://doi.org/20.1001.1.24763594.1384.9.4.4.8.
Delghandi M., Andarzian B., Brumandnasab S., Mesbah E., &Jawaheri A. (2013). Evaluation of the CERES-Wheat model DSSAT4.5 version in growth simulation. Performance and phonological stages of wheat under different management conditions of water allocation in the field, Ahwaz, Water and Soil Journal, No1 . (PP 82-91), (In persian).
Eini NargeseH.,Dayhemfard R., Sufizadeh S., Haghighat M., & Nuri A. (2015). Prediction of Climate Change Effects on Wheat yield of Fars province using APSIM model, Crop production publication. NO 4 (PP 203-224), (In persian).
Fathi, S. A., &Navabi, F. (2008). Effect of drought stress on yield and its components in four cotton genotypes in Darab region.
Hundal, S. S. (1997). Application of the CERES–Wheat model to yield predictions in the irrigated plains of the Indian Punjab. The Journal of Agricultural Science, 129(1), 13-18.‏ https://doi.org/10.1017/S0021859697004462.
IPCC. 1992. IPCC first report on climate change: The 1990 and 1992 IPCC assessment. WMO, Rome, Italy.
IPCC., 2007., Summary for policy makers Climate change: The physical science basis. Contribution of working group I to the forth assessment report. Cambridge University Press, 881 PP.
Jafarzadeh, A., 2016. Preparation of groundwater exploitation model for determining the optimal cultivation pattern in climate change conditions, Plain of Birjand, Master's thesis, university of Birjand(In persian).
Jamieson, P. D., Porter, J. R., Goudriaan, J., Ritchie, J. V., Van Keulen, H., &Stol, W. (1998). A comparison of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought. Field Crops Research, 55(1-2), 23-44.‏ https://doi.org/10.1016/S0378-4290(97)00060-9.
Koocheki, A., Nassiri, M., Soltani, A., Sharifi, H., &Ghorbani, R. (2006). Effects of climate change on growth criteria and yield of sunflower and chickpea crops in Iran. Climate Research, 30(3), 247-253. https://doi.org/10.3354/cr030247.
Ludwig, F., &Asseng, S. (2006). Climate change impacts on wheat production in a Mediterranean environment in Western Australia. Agricultural Systems, 90(1-3), 159-179.‏ https://doi.org/10.1016/j.agsy.2005.12.002.
Mahrukashani A., Soltani A., Galeshi S., &kalatearabi M. (2010). Estimation of Genetic Factors and Evaluation of DSSAT Model for Golestan Province Golestan, Electronic Journal of Crop Production, NO.2  https://doi.org/20.1001.1.2008739.1389.3.2.15.2
Meza, F. J., Silva, D., & Vigil, H. (2008). Climate change impacts on irrigated maize in Mediterranean climates: evaluation of double cropping as an emerging adaptation alternative. Agricultural systems, 98(1), 21-30.‏ https://doi.org/10.1016/j.agsy.2008.03.005.
NajafiMud M. (1997) The Effect of Two Methods of Irrigation and Rainfall Irrigation on the Performance and Quality of Cotton, Master's thesis, university of Mashhad(In persian).
Nicholls, N. (1997). Increased Australian wheat yield due to recent climate trends. Nature, 387(6632), 484-485. https://doi.org/10.1038/387484a0.
Rabiei M., Ghaysari M., &Mirlatifi M. (2011). Determination of DSSATv4.5 model in order to simulate nitrate leaching in corn field at different levels of water and nitrogen fertilizer, Journal of Agricultural Science and Technology and Natural Resources NO 63. https://doi.org/20.1001.1.24763594.1392.17.63.7.5.
Rawlins, S. L. (1991). Global environmental change and agriculture. Journal of Production Agriculture, 4(3), 291-293.
Rodríguez Díaz, J. A., Weatherhead, E. K., Knox, J. W., & Camacho, E. (2007). Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain. Regional Environmental Change, 7, 149-159.‏ https://doi.org/10.1007/s10113-007-0035-3.
Rosenzweig, C. (1989). Global climate change: Predictions and observations. American Journal of Agricultural Economics, 71(5), 1265-1271.‏ https://doi.org/10.2307/1243119.
Rosenzweig, C., &Tubiello, F. N. (2007). Adaptation and mitigation strategies in agriculture: an analysis of potential synergies. Mitigation and adaptation strategies for global change, 12, 855-873.‏ https://doi.org/10.1007/s11027-007-9103-8.
Saadati, Z., Delbari, M., Amiri, A., Panahi, M., Rahimian, M. h. And Ghodsi, M., (2016). Evaluation of CERES-Wheat model in simulating the yield of wheat cultivars under different irrigation treatments. Journal of Soil and Water Protection.5 (3):73-84
Sepaskhah, A., & Tavakoli, A. (2006). Principles and Applications of Irrigation, Iran National Irrigation and Drainage Committee.‏
Shiferaw, B. A., Okello, J., & Reddy, R. V. (2009). Adoption and adaptation of natural resource management innovations in smallholder agriculture: reflections on key lessons and best practices. Environment, development and sustainability, 11, 601-619.‏ https://doi.org/10.1007/s10668-007-9132-1.
Smit, B., & Skinner, M. W. (2002). Adaptation options in agriculture to climate change: a typology. Mitigation and adaptation strategies for global change, 7(1), 85-114.‏ https://doi.org/10.1023/A:1015862228270.
Soltani, A., Robertson, M. J., Mohammad-Nejad, Y., & Rahemi-Karizaki, A. (2006). Modeling chickpea growth and development: Leaf production and senescence. Field crops research, 99(1), 14-23.‏ https://doi.org/10.1016/j.fcr.2006.02.005.
Timsina, J., & Humphreys, E. J. A. S. (2006). Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review. Agricultural systems, 90(1-3), 5-31.‏   https://doi.org/10.1016/j.agsy.2005.11.007.
Trnka, M., Dubrovský, M., &Žalud, Z. (2004). Climate change impacts and adaptation strategies in spring barley production in the Czech Republic. Climatic Change, 64(1-2), 227-255. https://doi.org/0.1023/B:CLIM.0000024675.39030.96‏.
Tubiello, F. N., Rosenzweig, C., Goldberg, R. A., Jagtap, S., & Jones, J. W. (2002). Effects of climate change on US crop production: simulation results using two different GCM scenarios. Part I: wheat, potato, maize, and citrus. Climate research, 20(3), 259-270.‏ https://doi.org/10.3354/cr020259‏.
Verge, X. P. C., De Kimpe, C., & Desjardins, R. L. (2007). Agricultural production, greenhouse gas emissions and mitigation potential. Agricultural and forest meteorology, 142(2-4), 255-269. https://doi.org/10.1016/j.agrformet.2006.06.011.
Zabihi H R,. (2016), Preparation of groundwater exploitation model for determining the optimal cultivation pattern in climate change conditions, Plain of Birjand, Master's thesis, university of Birjand (In persian).
Zhao, H., Gao, G., Yan, X., Zhang, Q., Hou, M., Zhu, Y., & Tian, Z. (2011). Risk assessment of agricultural drought using the CERES-Wheat model: a case study of Henan Plain, China. Climate Research, 50(2-3), 247-256.‏ https://doi.org/10.3354/cr01060.
Iranian Journal of Soil and Water Research
Volume 54, Issue 8
November 2023
Pages 1131-1145

Files

Share

How to cite

Statistics