References
Abedinpour, M., Sarangi, A., Rajput, T.B.S., Singh, M., Pathak, H., & Ahmad, T. (2012). Performance evaluation of AquaCrop model for maize crop in a semi-arid environment. Agricultural Water Management, 110, 55–66. doi:10.1016/j.agwat.2012.04.001
Alinejadian Bidabadi, A., Jorooni, E., Barzegar, A.R., & Maleki, A. (2016). The effect of different irrigation levels on water use efficiency on the basis of maize grain and soil moisture variations. Journal of Water and Irrigation Management, 6(1), 47–59. doi:10.22059/JWIM.2016.60919. [In Persian]
Allen, R.G. (1998). Crop Evapotranspiration-guideline for computing crop water requirements. Irrigation and Drainage, 56, 300. doi:10.3178/jjshwr.16.589
Andarzian, B., Bannayan, M., Steduto, P., Mazraeh, H., Barati, M.E., Barati, M.A., & Rahnama, A. (2011). Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management, 100(1), 1–8. doi:10.1016/j.agwat.2011.08.023
Chen, S., Jiang, T., Ma, H., He, C., Xu, F., Malone, R.W., Feng, H., Yu, Q., Siddique, K.H.M., & He, J. (2020). Dynamic within-season irrigation scheduling for maize production in Northwest China: A method based on weather data fusion and yield prediction by DSSAT. Agricultural and Forest Meteorology, 285, 107928. doi:10.1016/j.agrformet.2020.107928
Ebrahimpour, M., Ghahreman, N., Liaghat, A. (2013). Using the SIMETAW model to simulate climate variables and investigate the effect of climate change on potential evapotranspiration (case study: Mashhad). Iranian Journal of Soil and Water Research, 43(4), 353-360. doi:10.22059/IJSWR.2013.35354. [In Persian]
FAO, (2023) Crops and livestock products. Food and Agriculture Organization, Rome, Italy.
Feng, D., Li, G., Wang, D., Wulazibieke, M., Cai, M., Kang, J., Yuan, Z., & Xu, H. (2022). Evaluation of AquaCrop model performance under mulched drip irrigation for maize in Northeast China. Agricultural Water Management, 261, 107372. doi:10.1016/
j.agwat.2021.107372
Foster, T., Brozović, N., Butler, A.P., Neale, C.M. U., Raes, D., Steduto, P., Fereres, E., & Hsiao, T.C. (2017). AquaCrop-OS: An open source version of FAO’s crop water productivity model. Agricultural Water Management, 181, 18–22. doi:10.1016/j.agwat.2016.11.015
Geerts, S., Raes, D., & Garcia, M. (2010). Using AquaCrop to derive deficit irrigation schedules. Agricultural Water Management, 98(1), 213–216). doi:10.1016/j.agwat.2010.07.003
Gu, Z., Qi, Z., Burghate, R., Yuan, S., Jiao, X., & Xu, J. (2020). Irrigation scheduling approaches and applications: A review. Journal of Irrigation and Drainage Engineering, 146(6), 4020007.
doi:10.1061/(ASCE)IR.1943-4774.0001464
Heng, L.K., Hsiao, T., Evett, S., Howell, T., & Steduto, P. (2009). Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy Journal, 101(3), 488–498. doi:10.2134/agronj2008.0029xs
Hoshmand, A., Forotan, M., & Boromandnasab, S. (2014). Evaluation of deficit irrigation and sown pattern on yield and water use efficiency of maize (KSC-704).
Journal of Irrigation Sciences and Engineering,
37(3), 43–52. dor:
20.1001.1.25885952.1393.37.3.5.7. [In Persian]
Hsiao, T.C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., & Fereres, E. (2009). Aquacrop-The FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal, 101(3), 448–459. doi:10.2134/agronj2008.0218s
Moghbel, F., Mosaedi, A., Aguilar, J., Ghahraman, B., Ansari, H., & Gonçalves, M.C. (2022). Bayesian calibration and uncertainty assessment of HYDRUS-1D model using GLUE algorithm for simulating corn root zone salinity under linear move sprinkle irrigation system. Water, 14(24), 4003. doi:10.3390/
w14244003
Raes, D., Steduto, P., Hsiao, T.C., & Fereres, E. (2009). Aquacrop-The FAO crop model to simulate yield response to water: II. main algorithms and software description. Agronomy Journal, 101(3), 438–447. doi:10.2134/agronj2008.0140s
Ramos, T.B., Simionesei, L., Jauch, E., Almeida, C., & Neves, R. (2017). Modelling soil water and maize growth dynamics influenced by shallow groundwater conditions in the Sorraia Valley region, Portugal. Agricultural Water Management, 185, 27–42. doi:10.1016/j.agwat.2017.02.007
Saeidi, R. (2021). Investigation of the intra-seasonal sensitivity of Maize evapotranspiration to water stress, at different irrigation levels. Soil and Water, 35(3), 348-335. doi: 10.22067/JSW.2021.68147.1011. [In Persian]
Saeidinia, M., Nasrolahi, A.H., & Sharifipour, M. (2019). Investigating the ability of crop water stress index for irrigation scheduling and estimating corn forage yield. Iranian Journal of Soil and Water Research, 50(3), 555–565. doi:10.22059/IJSWR.2018.268113.668038. [In Persian]
Sakaki, T., Limsuwat, A., & Illangasekare, T.H. (2011). A simple method for calibrating dielectric soil moisture sensors: Laboratory validation in sands. Vadose Zone Journal, 10(2), 526–531. doi:10.2136/vzj2010.0036
Schober, P., Boer, C., & Schwarte, L.A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. doi:10.1213/ANE.0000
000000002864
Simionesei, L., Ramos, T B., Palma, J., Oliveira, A. R., & Neves, R. (2020). IrrigaSys: A web-based irrigation decision support system based on open source data and technology. Computers and Electronics in Agriculture, 178, 105822. doi:10.1016/j.compag.2020.
105822
Song, L., Jin, J., & He, J. (2019). Effects of severe water stress on maize growth processes in the field. Sustainability, 11(18), 5086. doi:10.3390/su11185086
Steduto, P., Hsiao, T.C., Raes, D., & Fereres, E. (2009). Aquacrop-the FAO crop model to simulate yield response to water: I. concepts and underlying principles. Agronomy Journal, 101(3), 426–437. doi:10.2134/agronj2008.0139s
Tehrani, A., Ziaei, A.N., & Naghedifar, S.M. (2023). Irrigation scheduling of walnut seedlings using HYDRUS-1D and taguchi optimization approach. Journal of Irrigation and Drainage Engineering, 149(1), 4022045. doi:10.1061/(ASCE)IR.1943-4774.00017
Trout, T.J., & DeJonge, K.C. (2017). Water productivity of maize in the US high plains. Irrigation Science, 35(3), 251–266. doi:10.1007/s00271-017-0540-1
)