Impacts of Climate Change on Canola Yields and Phenology (Case Study: Chahrmahal Va Bakhtiari, Iran)

Document Type : Full length article

Authors

1 PhD candidate in Agro-Climatology, Faculty of Geographical Sciences and Planning, University of Isfahan, Iran

2 Associate professor of Agro-climatology, Faculty of Geographical Sciences and Planning, University of Isfahan, Iran

3 Assistant professor of Soil & Water Research, Agricultural and Natural Resources Research Center, Chaharmahal va Bakhtiari, Iran

Abstract

Introduction
Climate has a key role in plant growth process and, therefore, it is clear that climate change will directly affect sowing and harvesting of cultivate crops. Many scientists used APSIM to simulate the phenology and yield of canola. To understand the impacts of climate change, it is necessary to project the future climate based on different emission scenarios. These results must be combined with simulation models to predict crop yield and phenological stages. Assessment of the impacts of climate change on phenology and yield of agricultural crops in different regions show different results. The aim of this study is to investigate the impacts of climate change on phenological stages and yield of Canola by APSIM-canola modules.
Materials and methods
Chaharmahl va Bakhtiari in the southwestern Iran is one of the main agricultural production zones in Iran. The soil physical properties were obtained from Agricultural Research center of Farokhshahr. Required meteorological data including precipitation, solar radiation, and daily maximum and minimum temperature were obtained from 6 synoptic weather stations in the study area. The data were used after quality control process. The meteorological data have been converted into compatible text format with APSIM.
Biometric data and phenology
Canola has several varieties. Okapi is one of the winter varieties which were recommended for the study area. Agrometeorological data, including phenology and biometry from 2001 to 2010 were gathered and summarized. The canola phenological stages are including planting, germination, emergence, the first true leaf, rosette, ceasing of the winter growth, budding, stem elongation, flowering, pod, ripening and harvesting. Farming management information such as the amount of fertilizer, irrigation and frost and pest were also recorded. A total of 1700 phenological stages were used over the 10 years of crop evaluation.
Future climate data
Climate change data were downloaded from one of The World Climate Research Programme (WCRP) project so called Coordinated Regional Climate Downscaling Experiment (CORDEX). We used CORDEX MENA data in which the simulations were performed on a rotated grid with the pole at 180°W longitude and 90°N latitude. The domain covers roughly the region from about 27°W to 76°E longitude and 7°S to 45°N latitude. The simulations were carried out using two different resolutions: 0.44° (approx. 50 km) and 0.22° (approx. 25 km). 
Model description
Agricultural Production Systems Simulator (APSIM) is known as a highly advanced simulator agricultural systems in the world. There are 43 selectable varieties of spring and winter canola on APSIM v 7.7, but none of them are now cultivated in the study area. Germination, emergence, end of juvenile phase, flower initiation, flowering, grain filling and maturating are seven simulations of the phenological stages.
Results and discussion
For simulation of each phonological stage, elapsed time from sowing to the first day of reaching at each stage was counted. Water stress, nutrition, photoperiod, and vernalization have influence on the phenological stages (Zhang et al., 2014).
The highest RMSE was in the simulation of the days after sowing to maturity (DTM) stage with 5 days and bias error was -0.7 days. Greatest bias error occurred in simulation of the days after sowing to emergence (DTE). The correlation coefficient of the DTG and DTE was not statistically significant and this indicator in the other stages (P-Value = 0.01) is significant. The strongest correlation was obtained between observed and simulation of the days after sowing to flower initiation (DTFI) and the days after sowing to flowering (DTFL).
Because of the crop management, soil and water conditions, simulation was conducted in three cases of poor, middle and high management. The RMSE in estimation of yield was 329.8 kg/ha which included 7.2% of canola average yield on the study area. The rate of Bias error was 18.2 kg and correlation between actual and simulated data was 0.96. We considered every year of farm management, nutrient and irrigation in the simulation. The results showed that APSIM has reliability skill in simulation. Based on scenario RCP8.5, the DTE, DTFI, DTFL, DTEGF and DTM stages will be reduced from 1 to 13 days and the maximum reduction can be seen in the flowering and grain filling phases.
The results of data from RCP4.5 showed that DTFL and DTEGF stages will decrease from 2 to 3 days and that the greatest rate of decline was observed in the flowering period. DTEJ, DTFI and DTM stages will rise following that. DTFI and DTM stages will be increased up to 3 days. Similar to RCP8.5, DTEJ will be raised up to 9 days. It is expected that with RCP8.5 scenario the average of yield on the optimal nutrition and management will be increased to 18%; whereas in poor management conditions of the yield will be increased 18 and 13.6 percent.  
RCP 4.5 in optimal nutrition and management will be increased 13.4% and in intermediate and poor management it will raise about 14.3 and 13.6 percent. This suggests that without water limit, global warming will have positive impacts on canola yield in this area.  
Conclusion 
The study revealed that APSIM could simulate the yield of canola with RMSE 320 kg/ha. The results showed that with RCP8.5, phenological stages including DTE, DTFL and DTEGF and DTM will be declined. With RCP4.5, phenological stages including DTFL and DTEGF will also be shortened. The higher rate of decline was observed by RCP 8.5 scenario. DTEJ on RCP 8.5 and RCP 4.5 will be longer in 10 days and 9 days, respectively. It is expected that canola yield will be increased in both studied scenarios in optimum nutrition about 18%, more than 13 percent in average and up to 18 percent on low nutrient.  The outlook of Canola-Okapi yield increase in  Iran show a good potential for planting of this variety and this product will be developed in 2030 plan.   

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Main Subjects

References

پرتووش، م. (1394). تخمین شست‏وشوی نیترات از ناحیة ریشة نیشکر با استفاده از مدل APSIM-SWIM، پایان‏نامة کارشناسی ارشد، دانشکدة علوم کشاورزی، دانشگاه بوعلی سینا.
پژوهشکدة هواشناسی (1383). پهنه‏بندی اقلیمی استان چهارمحال و بختیاری.
عباسپور، م.، عباسپور، ا.، (1391). بررسی کارایی مدل APSIM برای مدیریت کود نیتروژن و آبیاری در محصول ذرت، اولین همایش ملی توسعه پایدار کشاورزی و محیط زیست سالم، همدان، دانشگاه آزاد اسلامی واحد همدان، شرکت هم‌اندیشان محیط زیست فردا.
فرجی، ا. (1384). ارزیابی اثرات تاریخ کاشت بر میزان روغن و عملکرد کلزا، مجلة علومزراعیایران، 7(3): 189ـ201.
قربانی، خ. و سلطانی، ا. (1393). اثر تغییر اقلیم بر عملکرد سویا در منطقة گرگان، نشریة پژوهش‏هایتولیدگیاهی، 21(2): 67ـ85.
کوچکی، ع.؛ نصیری محلاتی، م.؛ علیزاده، ا. و گنجعلی، ع. (1388). مدل‏سازی تأثیر تغییر اقلیم بر رفتار گُل‏دهی زعفران، مجلة پژوهش‏های زراعی ایران، 7(2): 583ـ594.
محنت‏کش، ع. (1391). مدل‏سازی خاک-‏ زمین‏نما و پیش‏بینی تولید گندم دیم به کمک مدل‏های مختلف در مناطقی از زاگرس مرکزی، پایان‏نامة کارشناسی ارشد، دانشکدة کشاورزی، دانشگاه صنعتی اصفهان.
Abaspoor, M.; Abaspoor A.; (2012). Evaluation of APSIM Model Efficiency for Nitrogen Fertilizer and Irrigation Management in Corn Crop, First National Conference on Sustainable Agriculture Development and Healthy Environment, Hamedan, Islamic Azad University, Hamedan Branch, Fars Foundation for Environmentalists.
Ahmed, M.; Akram, M.N.; Asim, M.; Aslam, M.; Hassan, F.; Higgins, S.; Stockle, C.O. and Hoogenboom, G. )2016(. Calibration and validation of APSIM-Wheat and CERES-Wheat for spring wheat under rainfed conditions: Models evaluation and application, Computers and Electronics in Agriculture, 123: 384-401.
Antel, J.M. (2010). Adaptation of agriculture and food system to climate change: policy issues, Resources for the future, issue brief 10-30, PP. 12.
Anwar, M.R.; Liu, D.L.; Farquharson, R.; Macadam, I.; Abadi, A.; Finlayson, J.; Wang, B. and Ramilan, T. (2015). Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia, Agricultural Systems, 132: 133-144.
APSIM (2016). http://www.apsim.info
Archontoulis, S.V.; Miguez, F.E.; & Moore, K.J. (2014). A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM model: Application to soybean, Environmental Modelling & Software, 62(0): 465-477.
Asseng, S.; Jamieson P.D.; Kimball, B.; Pinter, P.; Sayre, K.; Bowden, J.W. and Howden S.M. (2004) Simulated wheat growth affected by rising temperature, increased water deficit and elevated atmospheric CO2. Field Crops Res,  85: 85-102.
Bao, Y.; Hoogenboom, G.; McClendon, R. and Urich, P. (2015). Soybean production in 2025 and 2050 in the southeastern USA based on the SimCLIM and the CSM-CROPGRO-Soybean models, Clim Res, 66: 73-89.
Basak, J.K.; Ali, M.A.; Islam, M.N. and Rashid, M.A. (2010). Assessment of the effect of climate change on Boro rice production in Bangladesh using DSSAT model, Journal of Civil Engineering, 38: 95-108.
Bidinger, F.; Musgrave, R.B. and Fischer, R.A. (1977). Contribution of stored pre-anthesisassimilate to grain yield in wheat and barley, Nature, 270: 431-433.
Chai, T. and Draxler, R.R. (2014). Root mean square error (RMSE) or mean absolute error (MAE). Arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7: 1247-1250.
CORDEX (2016). http://www.cordex.org/index.php.
Easterling, W.; Aggarwal, P.; Batima, P.; Brander, K.; Erda, L.; Howden, M.; Kirilenko, A.; Morton, J.; Soussana, J.F. and Schmidhuber, J. (2007). Food, fibre, and forest products, In ML Parry, OF Canziani, JP Palutikof, PJvd Linden, CE Hanson, eds, Climate Change, 2007, Impacts, Adaptation and Vulnerability, Cambridge University Press, Cambridge, UK, PP. 273-313.
FAO (2003). world agriculture towards 2015/2030. An FAO perspective, http://www.fao.org .
FAO (2013). http://www.fao.org/ag/ca/1a.html.
Faraji, A. (2005). Evaluaion of the effect of sowing date on grain and oil yield and yield components of four canola genotypes in Gonbad, Iranian Journal of Crop Sciences, 7(3): 189-201.
Gaydon, D.;  Balwinder-Singh, S.; Wang, E.; Poulton, P.L.; Ahmad, B.; Ahmed, F.; Akhter, S.; Ali, I.; Amarasingha, R.; Chaki, A.K.; Chen, C.; Choudhury, B.U.; Darai, R.; Das, A.; Hochman, Z.; Horan, H.; Hosang, E.Y.; Vijaya Kumar, P.; Khan, A.S.M.M.R.; Laing, A.M.; Liu, L.; Malaviachichi, M.A.P.W.K.; Mohapatra, K.P.; Power, M.A.B.; Radanielson, A.M.; Rai, G.S.; Rashid, M.H.; Rathanayake, W.M.U.K.; Sarker, M.M.R.; Sena, D.R.; Shamim, M.; Subash, N.; Suriadi, A.; Suriyagoda, L.D.B.; Wang, G.; Wang, J.; Yadav, R.K. and Roth, C.H. (2017). Evaluation of the APSIM model in cropping systems of Asia, Field Crops Research, 204: 52-75.
Ghorbani, K. and Soltani, A. (2014). The effect of climate change on soybean yield in Gorgan, Journal of Plant Production Research, 21: 67-85.
Gebbing, T.; Schnyder, H. and Kuhbauch, W. (1999). The utilization of pre-anthesis reserves in grain filling of wheat: assessment by steady-state 13CO2/12CO2 labelling, Plant Cell Environ, 22: 851-858.
Huntingford, C.; Lambert, F.H.; Gash, J.H.C.; Taylor, C.M. and Challinor, A.J. (2005). Aspects of climate change prediction relevant to crop productivity. Philos. Trans, R. Soc. B, 360, 1999-2009.
IPCC (2007). In: Parry, M.L.; Canziani, O.F.; Palutikof, J.P.; Vander, L.P.J. and Hanson, C.E. (Eds.), Impacts, Adaptation and Vulnerability: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
IPCC (2014), Climate Change 2014 Synthesis Report. Summary for Policymakers. Contribution of Working Group I, II and III to Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). In: R. Pachauri and L. Meyer (eds). Geneva, Switzerland. 151p.
Karim, Z.; Hussain; S.G.; Ahmed, M.; (1996).  Assessing impacts of climatic variations on foodgrain prpduction in Bangladesh  Water Air Soil Pollut, 92: 53.
Kassie, B.T.; Asseng, S.; Reimund, P.R.; Huib, H.; Alex, C.R. and Martin, K.V.I. (2015). Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models, Climate change, 129: 145-158.
Keating, B.A.; Carberry, P.S.; Hammer, G.L.; Probert, M.E.; Robertson, M.J.; Holzworth, D.; Huth, N.I.; Hargreaves, J.N.G.; Meinke, H.; Hochman, Z.; Mclean, G.; Verburg, K.; Snow, V.; Dimes, J.P.; Silburn, M.; Wang, E.; Brown, S.; Bristow, K.L.; Asseng, S.; Chapman, S.; Mccown, R.L.; Freebairn, D.M. and Smith, C.J. (2003). An overviewof APSIM, a model designed for farming systems simulation, Eur. J. Agron., 18: 267-288.
Kimbr, D. and Gregory, D.Y.M. (1999). Rape, physiology, Bio technology, translations Azizi, M., Soltani, A., Khorasani S.
Koochaki, A.R.; Nasiri Mahallati, M. and Khorramdel, S. (2014). Iranian agriculture in transition to climate change and global warming, ferdosi unversity of mashahd press, Publication Press, 628: 56.
Mall, R.K. and Aggarwal, P.K. (2002). Climate Change and Rice Yields in Diverse Agro-Environments of India. I. Evaluation of Impact Assessment Models, Climatic Change, 52: 315.
Mall, R.K.; Lal, M.; Bhatia, V.S.; Rathore, L.S. and Singh, R. (2004). Mitigating climate change impact on soybean productivity in India: a simulation study, Agricultural and Forest Meteorology, 121: 113-125.
McCown, R.L.; Hammer, G.L.; Hargreaves, J.N.G.; Holzworth, D.P. and Freebairn, D.M. (1996). APSIM: A novel software system for model development, model testing and simulation in agricultural systems research, Agric. Syst., 50: 255-271.
Mohanty, M.; Probert, M.E.; SammiReddy, K.; Dalal, R.C.; Mishra, A.K.; Rao, A.S.; Singh, M. and Menzies, N.W. (2012). Simulating soybeanewheat cropping systems: APSIM model parameterization and validation, Agric. Ecosyst. Environ, 152: 68-78.
Nissanka, S.P.; Karunaratne, A.S.; Weerakoon, R.P.W.M.W.; Thorburn, P.J. and Daniel, W. (2015). Calibration of the phenology sub-model of APSIM-Oryza: Going beyond goodness of fit, Environmental Modelling and Software, 70: 128-137.
Partovash, M. (2015). Estimation of nitrate washout from cane roots using APSIM-SWIM model, Master's Degree, Faculty of Agricultural Sciences, Bu-Ali Sina University.
Robertson, M.J.; Holland, J.; Cawley, S.; Bambach, R.; Cocks, B. and Watkinson, A.R. (2001). Phenology of canola cultivars in the northern region and implications for frost risk, 10th Australian Agronomy Conference, Hobart, Tasmania.
Singels, A.; Jones, M.M.F.; Ruane, A.C. and Thorburn, P. (2014). Predicting climate change impacts on sugarcane production at sites in Australia, Brazil and South Africa using the Canegro model, Sugar Tech, 16(4): 347-355. (also published in Int. Sugar J.,  115: 874-881.
Sommer, R.; Glazirina, M.; Yuldashev, T.; Otarov, A. and Ibraeva, M. (2013). Impact of climate change on wheat productivity in Central Asia. Agriculture, Ecosystems and Environment, 178: 78-99.
Sun, H.; Zhang, X.; Wang, E.; Chen, S.; Shao, L. and Qin, W.A. (2016). Assessing the contribution of weather and management to the annual yield variation of summer maize using APSIM in the North China Plain, Field Crops Research, 194: 94-102.
Tubiello, F.N.; Soussana, J.F.; Howden, S.M. and Easterling, W. (2007). Crop and pasture response to climate change, Proc Natl Acad Sci USA,  104: 19686-19690.
Turner, N.C.; Molyneux, N.; Yang, S.; Xiong, Y.C. and Siddique, K.H.M. (2011). Climate change in south-west Australia and north-west China: challenges and opportunities for crop production, Crop Pasture Sci., 62: 445-456.
Vernon, L. and Van, G.D. (2006). Potential impacts of climate change on agricultural land use suitability: canola, Department of Agriculture and Food, Western Australia. Report 303.
Van Oort, P.A.J.; Zhang, T.; de Vries, M.E.; Heinemann, A.B. and Meinke, H. (2011). Correlation between temperature and phenology prediction error in rice (Oryza sativa L.), Agric. For. Meteorol, 151: 1545-1555.
USDA (2016). http://www.ers.usda.gov/topics/crops/soybeans-oil-crops/canola.aspx.
Wang, E.; Robertson, M.J.; Hammer, G.L.; Carberry, P.S.; Holzworth, D.; Meinke, H.; Chapman, S.C.; Hargreaves, J.N.G.; Huth, N.I. and McLean, G. (2002). Development ofa generic crop model template in the cropping system model APSIM, EuropeanJournal of Agronomy, 18: 121-140.
Yang, X.; Ch., C.; Luo, Q.; Li, L. and   Yu, Q. (2011). Climate change effects on wheat yield and water use in oasis Cropland, International Journal of Plant Production, 5: 1.
Zeleke, K.T.; Luckett, D.J. and Cowley, R.B. (2014). The influence of soil water conditions on canola yields and production in Southern Australia Original Research Article Agricultural Water Management, 144: 20-32.
Zeng, W.; Wu, J.; Hoffmann, M.P.; Xu, C.; Ma, T. and Huang, J. (2016). Testing the APSIM sunflower model on saline soils of Inner Mongolia, China, Field Crops Research, 192: 42-54.
Zhang, Y.; Feng, L.; Wang, J.; Wang, E. and Xu, Y. (2012). Using APSIM to explore wheat yield response to climate change in the North China Plain: the predicted adaptationof wheat cultivar types to vernalization, Journal of Agricultural Science, 1: 1-13.
Zhao, G.; Bryan, B.A. and Song, X. (2014). Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters, Ecological Modelling,  279: 1-11.
Physical Geography Research
Volume 50, Issue 2
July 2018
Pages 373-389

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History

  • Receive Date: 07 August 2017
  • Revise Date: 07 December 2017
  • Accept Date: 25 January 2018

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