کاربرد داده‌های ماهواره‌ای در شبیه‌سازی بهره‌وری آب در مزارع برنج

Aalaee Bazkiaee, P., Kamkar, B., Amiri, E., Kazemi, H. & Rezaei, M. (2021). Effect of irrigation management and planting date on yield and productivity of rice (Oryza sativa L.). Journal of Crop Production, 12(4), 157-170. [In Persian]. doi:10.22069/ejcp.2020.16513.2231

Aalaee Bazkiaei, P., Kamkar, B., Amiri, E., Kazemi, H., & Rezaei, M. (2020). Evaluation of ORYZA2000 model in yield simulation and production productivity of rice under crop managements. Journal of Water and Soil Conservation, 27(1), 49-69. [In Persian]. doi: 10.22069/jwsc.2020.16036.3126

Abdi, A., Asadi Kapourchal, S., Vazifedoust, M., Rezaei, M. and Egdernezhad, A. (2021). Investigation capability of updated SWAP model with satellite images and AquaCrop model in simulating the Hashemi rice yield in Guilan province. Water Management in Agriculture, 8(1), 89-102. [In Persian]. doi: 20.1001.1.24764531.2021.8.1.8.6

Aggarwal, P.K., and Mall, R.K. (2002). Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Climatic Change, 52(3), 331-343. doi: 10.1023/A:1013714506779

Ahmad, S., Ahmad, A., Soler, C.M.T., Ali, H., Zia-Ul-Haq, M., Anothai, J., and Hasanuzzaman, M. (2012). Application of the CSM-CERES-Rice model for evaluation of plant density and nitrogen management of fine transplanted rice for an irrigated semiarid environment. Precision Agriculture, 13, 200-218. doi: 10.1007/s11119-011-9238-1

Ali, A. M., & Aboelghar, M. (2019). Comparative analysis of different methods of leaf area index estimation of Strawberry under Egyptian condition. International Journal of Advanced Remote Sensing and GIS, 8(1), 2963-2970 doi: https://doi.org/10.23953/cloud.ijarsg.405

Amiri, E. & Rezaei, M. (2013). Evaluation of Water Balance Components and Water Productivity of Rice under Interval Irrigation and Nitrogen Fertilizer Conditions. Iranian Journal of lrrigation and Drainage. 4 (6): 306-315. [In Persian].

Amiri, E., Rezaei, M. & Bannayan Awal, M. (2011). Evaluation of the Rice Growth Model ORYZA2000 under Nitrogen and Water Limited Conditions (Calibration and Validation). Water and Soil, 25(4), 757-769. [In Persian]. doi: 10.22067/jsw.v0i0.10206

Amiri, E., Rezaei, M., Bannayan, M. & Soufizadeh, S. (2013). Calibration and Evaluation of CERES Rice Model under Different Nitrogen and Water Management Options in Semi-Mediterranean Climate Condition. Communications in Soil Science and Plant Analysis, 44:1814–1830.

Amiri, E., Rezaei, M., Rezaei, E. E., & Bannayan, M. (2014). Evaluation of Ceres-Rice, Aquacrop and Oryza2000 models in simulation of rice yield response to different irrigation and nitrogen management strategies. Journal of Plant Nutrition, 37(11), 1749-1769. doi: 10.1080/01904167.2014.888750

Arora, V.K. (2006). Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment. Agricultural Water Management, 83: 51–57. doi: 10.1016/j.agwat.2005.09.004.

Badsar, M., Kamkar, B., Soltani, A. & Abdi, O. (2017). Yield gap estimation in wheat-grown fields using GIS and RS approach and SSM model (A case study: Qaresso basin, Gorgan, Iran). Cereal Research, 7(2), 195-215. [In Persian]. doi: 10.22124/c.2017.2547

Bai, L., Long, D., & Yan, L. (2019). Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55(2), 1105-1128. doi: 10.1029/2018WR024162

Bouman, B.A., Humphreys, E., Tuong, T.P., & Barker, R. (2007). Rice and water. Advances in agronomy, 92, 187-237. doi: 10.1016/S0065-2113(04)92004-4

Cai, X. & Rosegrant, M.W. (2003). World water productivity: Current situation and future options. In Water Productivity in Agriculture: Limits and Opportunities for Improvement; Kijne, J.W., Barker, R., Molden, D., Eds.; CABI: Wallingford, UK; International WaterManagement Institute (IWMI): Colombo, Sri Lanka, pp. 163–178.

Cheyglinted, S., Ranamukhaarachchi, S. L., & Singh, G. (2001). Assessment of the CERES-Rice model for rice production in the Central Plain of Thailand. The Journal of Agricultural Science, 137(3), 289-298. doi: 10.1017/S0021859601001319

Cui, Z., Zhang, H., Chen, X., Zhang, C., Ma, W., Huang, C., & Dou, Z. (2018). Pursuing sustainable productivity with millions of smallholder farmers. Nature, 555(7696), 363-366. doi: 10.1038/nature25785

Davatgar, N. (2010). Predicting rice plant performance under water limitation conditions using plant growth and development simulation models at a regional scale. PhD thesis in soil science, Faculty of Agriculture, University of Tabriz. 250 pages. [In Persian].

Dente, L., Satalino, G., Mattia, F., & Rinaldi, M. (2008). Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote sensing of Environment, 112(4), 1395-1407. doi: 10.1016/j.rse.2007.05.023

Fatimah, K. (2018). Evaluation of Agricultural Subsidies and the Welfare of Farmers; Malaysia Agricultural Subsidies Report 2018; IDEAS Policy Research Berhad: Kuala Lumpur, Malaysia: pp. 1–64.

Huai, H., Zhang, Q., Li, Z., Liang, L., & Tang, X., (2024). Analysis of Crop Irrigation Water Requirements and Water Scarcity Footprint in the Beijing–Tianjin–Hebei Region Based on the GeoSim–AquaCrop Model. Agronomy, 14 (192): 1-12.  doi: 10.3390/agronomy14010192

IRRI. (2008). Background Paper: The Rice Crisis: What Needs to Be Done? IRRI, Los Baños, Philippines, www.irri.org/12pp

Ishfaq, M., Farooq, M., Zulfiqar, U., Hussain, S., Akbar, N., Nawaz, A., & Anjum, S. A. (2020). Alternate wetting and drying: A water-saving and ecofriendly rice production system. Agricultural Water Management, 241, 106363. doi: org/10.1016/j.agwat.2020.106363

Jin, N., Tao, B., Ren, W., He, L., Zhang, D., Wang, D., & Yu, Q. (2022). Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data. Agricultural water management, 266, 107583.

Kawakita, S., Yamasaki, M., Teratani, R., Yabe, S., Kajiya-Kanegae, H., Yoshida, H., Fushimi, E. & Nakagawa, H. (2023). Dual ensemble approach to predict rice heading date by integrating multiple rice phenology models and machine learning-based genetic parameter regression models. Agricultural and Forest Meteorology. 334, 109821. doi: 10.1016/j.agrformet.2023.109821

Li, T., Humphreys, E., Gill, G., & Kukal, S. S. (2011). Evaluation and application of ORYZA2000 for irrigation scheduling of puddled transplanted rice in North West India. Field Crops Research, 122(2), 104-117. https://doi.org/10.1016/j.fcr.2011.03.004

Liu, H. L., Yang, J. Y., Drury, C. A., Reynolds, W. D., Tan, C. S., Bai, Y. L., & Hoogenboom, G. (2011). Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production. Nutrient cycling in agroecosystems, 89, 313-328. doi: 10.1007/s10705-010-9396-y

Mojid, M. A., & Mainuddin, M. (2021). Water-saving agricultural technologies: Regional hydrology outcomes and knowledge gaps in the eastern Gangetic Plains-A review. Water, 13(5), 636. doi: 10.3390/w13050636

Nain, A. S., & Kersebaum, K. C. (2007). Calibration and validation of CERES model for simulating water and nutrients in Germany Ajeet Singh Nain¹ and Kurt Christian Kersebaum. In Modelling water and nutrient dynamics in soil-crop systems: Applications of different models to common data sets-Proceedings of a workshop held 2004 in Müncheberg, Germany (p. 161). Springer Science & Business Media.

Nisar, S., & Arora, V. K. (2018). Analysing dry-seeded rice responses to planting time and irrigation regimes in a subtropical environment using ORYZA2000 model. Agricultural Research, 7, 424-431. doi: 10.1007/s40003-018-0331-9

Peng, J., Liu, T., Chen, J., Li, Z., Ling, Y., De Wulf, A., & De Maeyer, P. (2023). The conflicts of agricultural water supply and demand under climate change in a typical arid land watershed of Central Asia. Journal of Hydrology: Regional Studies, 47, 101384. doi: 10.1016/j.ejrh.2023.101384

Piao, C., Peng, L., Zhou, L., Ma, D., Friedlingstein, L., Tan, Y., & Zhang, F. (2010). The impacts of climate change on water resources and agriculture in China. Nature, 467(7311), 43-51. doi:10.1038/nature09364

Prathumchai, K., Nagai, M., Tripathi, N. K., & Sasaki, N. (2018). Forecasting transplanted rice yield at the farm scale using moderate-resolution satellite imagery and the AquaCrop model: a case study of a rice seed production community in Thailand. ISPRS International. Journal of Geo-Information, 7(2), 73. doi: 10.3390/ijgi7020073

Rao, N.R., Garg, P.K., & Ghosh, S.K. (2006). Estimation and comparison of leaf area index of agricultural crops using IRS LISS-III and EO-1 Hyperion images. Journal of the Indian Society of Remote Sensing, 34(1): 69-78.

Rezaei, M. (2008). The final report of the research project on the effect of intermittent irrigation and different amounts of nitrogen fertilizer on the yield of Hashemi local variety rice. Publications of Rice Research Institute of Iran. [In Persian].

Rezaei, M. & Noori, M. (2002). Effect of irrigation interval on water use and rice yield in Guilan. 11th Seminar of Iranian National Committee on Irrigation and Drainage. Iran, Tehran. [In Persian].

Rezaei, M., Shahnazari, A., Raeini Sarjaz, M., & Vazifedoust, M. (2016). Improving agricultural management in a large-scale paddy field by using remotely sensing data in the CERES-Rice model. Irrigation and drainage, 65(2): 224-228. doi: 10.1002/ird.1961

Richards, M. & Sander, B. (2014). Alternate wetting and drying in irrigated rice. IRRI CCAFS and Info Notes. http://www.agritech.tnau.ac.in/agriculture/pdf/csa_pdf/Alternate_wetting_and_drying_in_irrigated_rice_InfoNote.pdf

Saadati, Z., Pirmoradian, N. & Rezaei, M. (2011). Calibration and validation of AquaCrop model in rice growth simulation under different irrigation managements. ICID 21st International Congress on Irrigation and Drainage, Tehran, Iran.

Sandhu, R., & Irmak, S. (2019). Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation. Agricultural Water Management. 97 (11), 1838–1846. doi: 10.1016/j.agwat.2019.105687

Soltani, A., & Hoogenboom, G. (2007). Assessing crop management options with crop simulation models based on generated weather data. Field Crops Research, 103(3), 198-207. doi: 10.1016/j.fcr.2007.06.003

Timsina, J., & Humphreys. E. (2006). Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review. Agricultural Systems, 90: 5–31. doi: 10.1016/j.agsy.2005.11.007

Wang, H., Zhu, Y., Li, W., Cao, W., & Tian, Y. (2014). Integrating remotely sensed leaf area index and leaf nitrogen accumulation with Rice Grow model based on particle swarm optimization algorithm for rice grain yield assessment. Journal of Applied Remote Sensing, 8(1), 083674-083674. doi: 10.1117/1.JRS.8.083674

Wikarmpapraharn, C. & Kositsakulchai E. (2010). Evaluation of ORYZA2000 and CERES-Rice Models under Potential Growth Condition in the Central Plain of Thailand. Thai Journal of Agricultural Science. 43: 17-29

Xue, C., Yang, X., Bouman, B.A.M., Deng, W., Zhang, Q., Yan, W., Zhang, T., Rouzi, A. & Wang, H. (2008). Optimizing yield, water requirements, and water productivity of aerobic rice for the North China Plain. Irrigation Science, 26, 459–474. doi: 10.1007/s00271-008-0107-2

Yadav, S., Li, T., Humphreys, E., Li, T., Gill, G. and Kukal S.S. (2012). Evaluation of tradeoffs in land and water productivity of dry seeded rice as affected by irrigation schedule. Field Crops Research. 128. 180-190. doi: 10.1016/j.fcr.2012.01.005

Yu, Q., & Cui, Y. (2022). Improvement and testing of ORYZA model water balance modules for alternate wetting and drying irrigation. Agricultural Water Management, 271, 107802. doi: 10.1016/j.agwat.2022.107802

Yuan, S., Peng, S., & Li, T. (2017). Evaluation and application of the ORYZA rice model under different crop managements with high-yielding rice cultivars in central China. Field Crops Research, 212, 115-125. doi: 10.1016/j.fcr.2017.07.010

Zhao, Y., Chen, S. & Shen, S. (2013). Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation. Ecological modelling. 270, 30–42.

Zhuo, W., Fang, S., Gao, X., Wang, L., Wu, D., Fu, S., & Huang, J. (2022). Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation, 106, 102668.