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Study of reference crop evapotranspiration in I.R. of Iran

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  • Dinpashoh, Yagob

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  • Dinpashoh, Yagob, 2006. "Study of reference crop evapotranspiration in I.R. of Iran," Agricultural Water Management, Elsevier, vol. 84(1-2), pages 123-129, July.
    • Handle: RePEc:eee:agiwat:v:84:y:2006:i:1-2:p:123-129
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    1. Watanabe, Kota & Yamamoto, Takashi & Yamada, Takashi & Sakuratani, Tetsuo & Nawata, Eiji & Noichana, Chairat & Sributta, Akadet & Higuchi, Hirokazu, 2004. "Changes in seasonal evapotranspiration, soil water content, and crop coefficients in sugarcane, cassava, and maize fields in Northeast Thailand," Agricultural Water Management, Elsevier, vol. 67(2), pages 133-143, June.
    2. Pereira, Antonio Roberto & Pruitt, William Oregon, 2004. "Adaptation of the Thornthwaite scheme for estimating daily reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 66(3), pages 251-257, May.
    3. DehghaniSanij, Hossein & Yamamoto, Tahei & Rasiah, Velu, 2004. "Assessment of evapotranspiration estimation models for use in semi-arid environments," Agricultural Water Management, Elsevier, vol. 64(2), pages 91-106, January.
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    1. Anurag Malik & Anil Kumar, 2015. "Pan Evaporation Simulation Based on Daily Meteorological Data Using Soft Computing Techniques and Multiple Linear Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1859-1872, April.
    2. Raziei, Tayeb & Pereira, Luis S., 2013. "Estimation of ETo with Hargreaves–Samani and FAO-PM temperature methods for a wide range of climates in Iran," Agricultural Water Management, Elsevier, vol. 121(C), pages 1-18.
    3. Zhao, Wenzhi & Chang, Xuexiang & Chang, Xueli & Zhang, Dengrong & Liu, Bing & Du, Jun & Lin, Pengfei, 2018. "Estimating water consumption based on meta-analysis and MODIS data for an oasis region in northwestern China," Agricultural Water Management, Elsevier, vol. 208(C), pages 478-489.
    4. Tomas-Burguera, Miquel & Vicente-Serrano, Sergio M. & Grimalt, Miquel & Beguería, Santiago, 2017. "Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula," Agricultural Water Management, Elsevier, vol. 182(C), pages 103-116.
    5. Ayyoub, A. & Er-Raki, S. & Khabba, S. & Merlin, O. & Ezzahar, J. & Rodriguez, J.C. & Bahlaoui, A. & Chehbouni, A., 2017. "A simple and alternative approach based on reference evapotranspiration and leaf area index for estimating tree transpiration in semi-arid regions," Agricultural Water Management, Elsevier, vol. 188(C), pages 61-68.
    6. Kafi, Mohammad & Asadi, Hajar & Ganjeali, Ali, 2010. "Possible utilization of high-salinity waters and application of low amounts of water for production of the halophyte Kochia scoparia as alternative fodder in saline agroecosystems," Agricultural Water Management, Elsevier, vol. 97(1), pages 139-147, January.
    7. Raziei, Tayeb & Pereira, Luis S., 2013. "Spatial variability analysis of reference evapotranspiration in Iran utilizing fine resolution gridded datasets," Agricultural Water Management, Elsevier, vol. 126(C), pages 104-118.
    8. Alireza Sharifi & Yagob Dinpashoh, 2014. "Sensitivity Analysis of the Penman-Monteith reference Crop Evapotranspiration to Climatic Variables in Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5465-5476, December.
    9. Kiyoumars Roushangar & Roghayeh Ghasempour & Vahid Nourani, 2022. "Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 473-488, January.
    10. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).

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