Document Type : Research Article

Authors

Ferdowsi University of Mashhad

Abstract

Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized Precipitation Index (SPI) is useful to characterize the condition of the moisture supply before and during the growing season of crops. Studies have shown that in some areas there is little correlation between spring wheat yield and SPI, while in other areas there is significant relationship between wheat yield and SPI. This difference indicates SPI as an indicator of moisture supply, depend on the study area .The purpose of this study was to determine the most effective period of precipitation during growing season for rainfed barley using variables obtained from moisture supply and precipitation periods in Tabriz. The most effective period of precipitation can be used for the management of rainfed cultivation.
Materials and Methods: Daily temperature and precipitation data of Tabriz station were collected from Iran Meteorological Organization for the years 1955 to 2013. In addition, barley yields data were collected for the years 1977 to 2013. In this study, the occurrence of phenological stages (germination, tillering, anthesis, ripening and harvesting) were estimated using growing degree days (GDD). The SPI value for 28-week time scale of the first week after planting (SPI28) was considered as an indicator of the moisture supply during growing season. SPI28 values less than zero and greater than zero representing different classes of drought and humidity respectively. For correlation analysis, 128 weekly variables were defined at different time scales of daily precipitation data (Table 2). The relationship between the crop yield and precipitation variables were analyzed by linear correlation.
Results and Discussion: The correlation coefficient (r) between precipitation and annual rainfed barley yield were presented in Table 2. The highest correlation between yield and precipitation occurred during the 10-week period between 25 February and 6 May, which was mostly observed at the end of April to mid-May that was coincide with the beginning of anthesis. So it can be concluded that the anthesis stage was the most critical stage to water stress in barley. Based on the SPI28 value greater than zero (wet conditions) or less than zero (dry conditions), the amount of precipitation (between 25 February and 6 May) was divided into two groups. The amount of precipitation between 25 February and 6 May explained 78% of the yield variations when SPI28 was greater than zero (wet conditions). One mm increase in precipitation in this period increased the yield with the rate of 2/76 kg / ha. If early planting conditions is dry (SPI 28

Keywords

1- Alijani F., Karbasi A., Mozafari M. 2012. Survey of the Effects of Climate Change on Yield of Irrigated Wheat in Iran. Agricultural Economic and Development, 76: 143-167. (in Persian)
2- Arshad S., Morid S., Mobasheri M.R., Alikhani M.A. 2009. Development of Agricultural Drought Risk Assessment Model for Kermanshah Province (Iran), using satellite data and intelligent methods. Options Mediterraneennes, 80: 303–310.
3- Asadi H., Neishaboori M.R., Siadat H. 2003. Evaluating the Wheat Response Factor to Water (Ky) in Different Growth Stages in Karaj.Iranian Journal of Agriculture Science, 34: 579-576. (in Persian with English abstract)
4- Edwards D.C., and McKee T.B. 1997. Characteristics of 20th century drought in the United States at multiple time scales. Climatology Rep. 97–2, Department of Atmospheric Science, Colorado State University, Fort Collins, CO, May, 155 pp.
5- Ghorbani Kh., Khalili A., Iran Nejad P. 2008. Regional Estimation of Rainfed Wheat Yield Based on Precipitation Data. Agricultural Biotechnology, 8: 89-101. (in Persian with English abstract)
6- He Y., Wei Y., Depauw R., Qian B., Lemke R., Singh A., Cuthbert R., Mcconkey B., Wang H. 2013. Spring Wheat Yield in the Semiarid Canadian Prairies : Effects of Precipitation Timing and Soil Texture over Recent 30 Years. Field Crops Research, 149: 329–337.
7- Hlavinka P., Trnka M., Semera´ dova D., Dubrovsky´ M., Zˇ alud Z., Mozˇny M. 2009. Effect of drought on yield variability of key crops in Czech Republic. Agricultural and Forest Meteorology, 149: 431–442.
8- Kutcher H.R., Warland J.S., Brandt S.A. 2010. Temperature and precipitation effects on canola yields in Saskatchewan , Canada. Agricultural and Forest Meteorology, 150: 161–165.
9- Landau S., Mitchell R. A.C., Barnett V., Colls J.J., Craigon J., Payne R.W. 2000. A parsimonious, multiple-regression model of wheat yield response to environment. Agricultural and Forest Meteorology, 101: 151–166.
10- Licker R., Kucharik C.J., Thierry Dore, Lindeman M.J, Makowski D. 2013. Climatic impacts on winter wheat yields in Picardy , France and Rostov , Russia : 1973 – 2010. Agricultural and Forest Meteorology, 176: 25–37.
11- Mavromatis T. 2007. Drought index evaluation for assessing future wheat production in Greece. International Journal of Climatology, 27: 911–924.
12- McKee T.B., Doesken T.B., Kleist N.J. 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of 8th Conference on applied Climatology,17-22 Jan,. American Meteorological Society, Boston, 179–184.
13- Mkhabela M., Bullock P., Gervais M., Finlay G., Sapirstein H. 2010. Assessing indicators of agricultural drought impacts on spring wheat yield and quality on the Canadian prairies. Agricultural and Forest Meteorology, 150: 399–410.
14- Mosaedi A., Kahe M. 2008. The Assessing Precipitation Effects on Yield Productions of Wheat and Barley in Golestan Province. Journal of Agricultural Sciences and Natural Resources, 15: 206-218. (in Persian with English abstract)
15- Naresh Kumar M., Murthy C.S., Sesha M.V.R., Roy P.S. 2009. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Meteorological Applications, 16: 381–389.
16- Nielsen D.C., Halvorson A.D., Vigil M.F. 2010. Critical precipitation period for dryland maize production. Field Crops Research, 118: 259–263.
17- Qian B., De Jong R., Warren R., Chipanshi A., Hill H. 2009. Statistical spring wheat yield forecasting for the Canadian prairie provinces. Agricultural and Forest Meteorology, 149:1022–1031.
18- Quiring S.M., Papakryiakou T.N. 2003. An evaluation of agricultural drought indices for the Canadian prairies. Agricultural and Forest Meteorology, 118:49–62.
19- Sabziparvar A.A., Torkaman M., Maryanaji Z. 2013. Investigating the Effect of Agroclimatic Indices and Variables on Optimum Wheat Performance (Case study: Hamedan Province). Journal of Water and Soil, 26:1554-1567. (in Persian with English abstract)
20- Shookohi1 M., Bazrafshan J., khalili A,. Ghahreman N. 2011. Regional Assessment of Agricultural Drought Risk For Rainfed Barley. p. 303-311. 1st National Conference on Drought and Climate change. Research Institute for Water Scarcity and Drought in Agriculture and Natural Resources, May 18, 2011-Karaj, Iran. (in Persian)
21- Snyder R.L. 1985. Hand Calculating Degree Days. Agricultural and Forest Meterology, 35:353—358.
22- Sohrabie Mollayousef S., Fakheri Fard A., Bozorg Haddad O. 2012. Assessment the Effect of Intermittent Rainfall of Autumn and Winter on Annual Dry Farming Yield by Using the Time-Rain Indicator (RTI). Journal of Water and Soil, 26: 75-84. (in Persian with English abstract)
23- Talliee A.A, Bahramy N. 2003.The Effects of Rainfall and Temperature on the Yield of Dryland Wheat In Kermanshah Province. Journal of Water Research in Agriculture (Journal of Soil and Water Sciences), 17: 106-113. (in Persian with English abstract)
24- Tavakoli A.R. 2012. Single Irrigation and Sowing Date for Rainfed Barley in Maragheh Region and Estimation of Production Functions. Journal of Agricultural Engineering Research, 13:39-56. (in Persian with English abstract)
25- Vicente‐Serrano S.M., Cuadrat‐Prats J.M., Romo A. 2006. Early prediction of crop production using drought indices at different time‐scales and remote sensing data: application in the Ebro Valley (north‐east Spain). International Journal of Remote Sensing, 27: 511–518.
26- Wu H., Hubbard K.G., Wilhite D.A. 2004. An Agricultural Drought Risk-Assessment Model For Corn And Soybeans. International Journal of Climatology, 24:723–741
27- Zareabyaneh H., Bayat Varkeshi M.. Ildoromi A. 2012.Assessment of the effect of some climatic parameters, and ENSO phenomenon on wheat and barley yield (Case Study: Region of Hamedan). Iranian Water Research Journal, 9: 181-192. (in Persian)
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