مدل‌سازی عملکرد گندم و جو دیم بر اساس شاخص‌های خشکسالی و متغیرهای هواشناسی

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه فردوسی مشهد

2 دانشگاه بوعلی سینا، همدان

چکیده

نوسانات عوامل آب‌ و هوایی و تنش‌های حاصل از آن‌ها نقش مهمی در مقدار تولید محصولات کشاورزی به‌ویژه در شرایط دیم دارند. در این تحقیق ارتباط بین عملکرد محصولات گندم و جو دیم با متغیرهای آب و هوائی شامل: دمای حداقل، دمای میانگین، دمای حداکثر، بارندگی، تبخیروتعرق و شاخص‌های خشکسالی شامل: شاخص بارش استاندارد شده (SPI) و شاخص شناسائی خشکسالی (RDI) در ایستگاه‌های بجنورد، مشهد و بیرجند بررسی و مدل‌سازی گردید. با استفاده از روش تجزیه به مؤلفه‌های اصلی (PCA) دوره‌های موثر بر تنش‌‌های آب‌ و هوایی و خشکی از میان 34 دوره‌ شامل 1، 2، 3، 4، 6 و 9 ماهه و دورة مرطوب انتخاب شده برای هر یک از متغیر‌ها تعیین گردیدند. نتایج نشان داد که در ایستگاه بجنورد برای برآورد عملکرد محصولات گندم و جو مدل‌های ساخته ‌شده بر اساس متغیرهای شاخص SPI، در ایستگاه مشهد مدل‌های ترکیبی و در ایستگاه بیرجند برای گندم مدل ترکیبی و برای جو مدل ساخته شده بر اساس شاخص RDI دارای بیش‌‌‌ترین دقت و صحت می‌باشند. بر اساس معادلات استخراج شده، در بجنورد تنش‌های ناشی از خشکسالی در دوره 4 ماهه منتهی به فروردین، مشهد 2 ماهه مهر و آبان و بیرجند 2 ماهه منتهی به اسفند و ماه خرداد بیش‌ترین تاثیر را بر عملکرد دارند. تنش‌های ناشی از حداقل و حداکثر دما در بجنورد در دوره‌های 9 ماهه منتهی به خرداد، مشهد 6 ماهه منتهی به خرداد و بیرجند 6 ماهه منتهی به اسفند بیش‌ترین تاثیر را بر عملکرد دارند.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling Rain-fed Wheat and Barley based on Meteorological Features and Drought Indices

نویسندگان [English]

  • A. Mosaedi 1
  • S. Mohammadi Moghaddam 1
  • M. Ghabaei Sough 2
1 Ferdowsi University of Mashhad
2 Bu-Ali Sina University, Hamedan
چکیده [English]

Introduction: Weather features and their variations have an important role in the yield of agricultural products, especially in rain-fed conditions. The main metrological variables that affected yields consist of precipitation, temperature, soil moisture and solar radiation. Also, drought is one of the major constraints to production, especially the mid-season drought which occurs during the podand seed formation stages and the terminal drought which occurs during the pod filling stage. The results of investigating the relation between drought indices such as Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Crop Moisture index (CMI) and Z index with crop yields indicated the capability of these indices to estimate variations in crop yields. The objective of this study in the first step is investigation of relations among wheat and barley crop yields with climatic variables and SPI and RDI drought indices based on Principle Component Analysis (PCA) method at Bojnourd, Mashhad and Birjand stations. In addition, by selecting the prominent variables via PCA method, the best models of estimating each crop’s yield based on multivariate regression methods at selected stations were determined.
Materials and Methods: In this study, the relationship between yields of rain-fed wheat and barley with weather variables consisting of minimum, mean and maximum temperature, precipitation, evapotranspiration and drought indices including SPI and RDI were investigated and modeled at Bojnourd, Mashhad and Birjand stations. For this purpose, the values of each variable were calculated for 34 time scales of 1, 2, 3, 4, 6, and 9 months and wet periods (nine 1-month periods, eight 2- month periods, seven 3- month periods, six 4- month periods, two 6- month periods, one wet period (5 or 7-month) and one 9-month period). After that, the main influencing variables were chosen among investigated time periods for each variable by using the method of principal component analysis (PCA). In continuation, the selected variables via PCA technique were used in the multivariate regression methods to create the best model of predicting wheat and barley yields based on each mentioned variable and combination of them. The performance of the established model was evaluated based on Ideal Point Error (IPE) criteria and the best predicting model of wheat and barley was selected for each region.
Results and Discussion: The results showed that applying PCA technique as a powerful statistical tool leads to decrease of the error and inflation of constructed models. This is done by reducing the volume of data and selecting influencing variables. Based on the PCA results by choosing only four components the 90 percent and greater than variation of crop yields are estimated and the first component includes time periods of spring and winter months. Investigation of the results of the best model at the given stations based on IPE criteria show that the constructed models based on variables of SPI index have more accuracy for predicting yields of wheat and barley at station of Bojnourd, at Mashhad station the created models based on a combination of variables and at Birjand station a model based on a combination of variables and a created model according to RDI variables was used that has more accuracy for predicting yields of wheat and barley, respectively. Comparing the estimated and actual values of wheat and barley yields indicate that the correlation coefficients of the models when applied to estimate the yield of wheat and barley at Bojnourd station resulted in 68 and 69 percent, at Mashhad station 89 and 86 percent and at Birjand station 66 and 74 percent, respectively.The performance evaluation graph shown in Fig. 1 can be used to illustrate model performance and to diagnose model bias.
Conclusion: According to the results, a relation between crop yields and combination of metrological variables and drought indices is more positive and stronger than only metrological variables combination. The results showed that the variables of temperature, precipitation and evapotranspiration are to be considered. Also, the evaluation model indicated that the RDI index is more suitable for predicting rain-fed wheat and barley yields.

کلیدواژه‌ها [English]

  • Multivariate Regression
  • Principal Component Analysis (PCA)
  • Reconnaissance Drought Index (RDI)
  • Termal stress
1- Allen R.G., Pereira L.S., Raes D., and Smith M. 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56, Rome, Italy.
2- Azizi Gh., and Yarahmadi D. 2003. The relationship between wheat yield and climatic parameters using a regression model (Case study: Plain Silakhor). Geographical Research Quarterly, 44: 23–29. (in Persian)
3- Bannayan M., Sanjani S., Alizadeh A., Sadeghi Lotfabadi S., and Mohamadian A. 2010. Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research, 118: 105–114.
4- Bazgeer S., and Kamali Gh.A. 2008. Wheat yield prediction using agro meteorological indices for some regions of the Western of the country. Journal of Agricultural Science and Naturural Resoures, 15(2): 113–121. (in Persian with English abstract)
5- Elshorbagy, A., Corzo G., Srinivasulu S., and Solomatine D. 2009. Experimental investigation of the predictive capabilities of soft computing techniques in hydrology. Centre for Advanced Numerical Simulation (CANSIM). Department of Civil & Geological Engineering, University of Saskatchewan, Saskatoon, SK, CANADA. 49 p.
6- Farajzadeh Asl M., Kashki A., and Shayan S. 2009. Analysis of rain-fed wheat yield product variability using climate change approach (Case study area: Khorasan Razavi province). Journal of modares, 13(3): 227–256. (in Persian)
7- Feizi Asl V., Jafarzadeh J., Abdolrahmani B., Mosavi S.B., and Karimi E. 2010. Studies on the effects of climatic factors on dryland wheat grain yield in maragheh region. Journal of Iranian Field Crop Research, 8(1): 1–11. (in Persian with English abstract)
8- Ghorbani Kh., Khalili A., and Iran Nejad P. 2008. Regional estimation of rainfed wheat yield based on precipitation data. J of agriculture research, 8(1), 89–101. (in Persian with English abstract)
9- Hlavinka P., Trnka M., Semer adova D., Dubrovsky M., Zalud Z., and Mozny M. 2009. Effect of drought on yield variability of key crops in Czech Republic. Agricultural and Forest Meteorology, 149: 431–442.
10- Hosseini M.T., Siosemarde A, Fathi P., and Siosemarde M. 2007. Application of artificial neural network (ANN) and multiplue regression for estimating the performance of dry farming wheat yield in Ghorveh region, Kurdistant province. Agriculture Research, 7(1): 54–41.
11- IPCC, 2007. Synthesis Report 2007: AR4, Cambridge University Press, Cambridge, United Kingdomand New York, USA.
12- Jayanthi H., Gregory J., Husak Funk C., Magadzire T., Chavula, A., and Verdin J.P. 2013. Modelin g rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall-Southern Malawi case study. International Journal of Disaster Risk Reduction, 4: 71–81.
13- Jongrungklanga N., Toomsana B., Vorasoota N., Jogloya S., Booteb K.J., Hoogenboomc G., and Patanothaia A. 2013. Drought tolerance mechanisms for yield responses to pre-flowering drought stress of peanut genotypes with different drought tolerant levels, Field Crops Research, 144: 34–42.
14- Lamasson T. 1947. The development in range management: the influence of rainfall on the prosperity of eastern Montana. Mimegraphed Rep,7, Regioni, U.S, forest service:1878–1946.
15- Landau S., Mitchell R.A.C., Barnett V., Colls J.J., Craigon J., Moore K.L., and Payne R.W. 1998. Testing winter wheat simulation models predictions against observed UK grain yields. Agricultural and Forest Meteorology, 89: 85–99.
16- Landau S., Mitchell R.A.C., Barnett V., Colls J.J., Craigon J., Moore K.L and Payne R.W. 2000. A parsimonious, multiple-regression model of wheat yield response to environment. Agricultural and Forest Meteorology, 101: 151–166.
17- Lloyd-Hughes B., and Saunders M.A. 2002. A drought climatology for Europe. International Journal of Climatology, 22: 1571–1592.
18- Meyer S.J., Hubbard K.G., and Wilhite D.A. 1991. The relationship of climatic indices and variables to corn (maize) yields: a principal components analysis. Agriculture and Forest Meteorology, 55: 59–84.
19- Mishra A., and Singh V. 2010. A review of drought concepts. Journal of Hydrology, 391: 202–216.
20- Quiring S.R., Papakryiakou J. 2003. An evaluation of agricultural drought indices for the Canadian prairies. Agriculture and Forest Meteorology, 118: 49– 62.
21- Mohammadi Moghaddam S., Mosaedi A., Jankju M., and Mesdaghi M. 2013. Modeling plants yield based on climatic factors and drought indices in selected sites of the provinces of Central and Qom in Iran. Journal water & soil, 27(6): 1190 –1206. (in Persian with English abstract)
22- Mosaedi A., and Kahe M., 2008. The Assessing Precipitation Effects on Yield Productions of Wheat and Barley in Golestan Province. Journal Agricultural Scince Natural Resoures, 15(4): 206–218. (in Persian with English abstract)
23- Rahmani A., Khalili A., and liyaghat A. 2009. Quantitative survey of drought effects on barley yield in East Azerbaijan multiple regression method. Journal of Science and Technology of Agriculture and Natural Resources, 12(44): 25–36. (in Persian with English abstract)
24- Sabziparvar A.A., Torkaman M., and Maryanaji Z. 2012. Investigating the Effect of Agroclimatic Indices and Variables on Optimum Wheat Performance (Case study: Hamedan Province). Journal of Water and Soil, 26(6): 1554–1567.
25- Scian B.V., and Bouza M.E. 2005. Environmental variables related to wheat yields in the semi-arid pampa region of Argentina. Journal of arid Environments, 61: 669–679.
26- Sharatt B.S., Knight C.W., and Wooding, F. 2003. Climatic impact on small grain production in the Subarctic Region of the United States. Arctic, 56(3): 219–226.
27- Smart A., Dunn B., Johnson P., Xu L., and Gates R. 2007. Using Weather Data to Explain Herbage Yeild on Three Greate Plain Plant Communities. Rangland Ecology and Management, 60 (2): 146–153.
28- Stern N. 2006. Review on the economics of climate change. HM Treasury, London.
29- Talliee A., and Bahramy N. 2003. The effects of rainfall and temperature on the yeild of dryland wheat in Kermanshah province. Water and Soil Sciences, 17(1): 106–113.
30- Tatari M. 2008. Dryland wheat yield prediction in Khorasan using climte and edaphic data by applying neural networks. Ph.D Dissertation, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran. (in Persian with English abstract)
31- Torell L., McDaniel K., and Koren V. 2011. Estimating grass yield on blue grama range from seasonal rainfall and soil moisture measurements. Rangeland Ecology and Management 64(1): 56–66.
32- Tsakiris G., Pangalou D., and Vangelis H. 2007. Regional drought as selectssment base lectd on the Reconnaissance Drought Index (RDI). Water Resource Management, 21: 821–833.
33- Vangelis H., Tigkas D., and Tsakiris G. 2013. The effect of PET method on Reconnaissance Drought Index (RDI) calculation. Journal of Arid Environments, 88: 130–140.
34- Wu D., Yu Q., Lu C., and Hengsdijik H. 2006. Quantifying production potentials of winter wheat in the North Chian Plain. Europ. Journal of Agronomy, 24: 266–235.
35- Xiao G., Zhang Q., Li Y., Wang R., Yao Y., Zhao H., and Bai H. 2010. Impact of temperature increase on the yield of winter wheat low and high altitudes in semiarid northwestern china. Agricultural Water Management, 97: 1360–1364.
36- Yamoah C.F., Varvel G.E., Francis A., and Waltman W.J. 1998. Weather and management impact on crop yield variability in rotation. Journal of Production Agriculture, 11(2): 161–225.
37- Yazdan Panah H. 2010. Determining the effect of climatic elements on the yield of dry farmed wheat in east Azarbaijan province by using intelligent neural network. Geography and development. 8(20): 133–144. (in Persian)