Articles | Volume 26, issue 20
Hydrol. Earth Syst. Sci., 26, 5373–5390, 2022

Special issue: Experiments in Hydrology and Hydraulics

Hydrol. Earth Syst. Sci., 26, 5373–5390, 2022
Research article
27 Oct 2022
Research article | 27 Oct 2022

FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms

Sara Sadri et al.

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Cited articles

Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315,, 2017. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration – Guidelines for computing crop water requirements, FAO Irrigation and drainage paper 56, FAO, Rome, Italy, (last access: October 2022), 1998. a
Allen, R., Tasumi, M., and Trezza, R.: Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model, J. Irrig. Drain. Eng., 133, 380–394, 2007. a
Andarzian, B., Bannayan, M., Steduto, P., Mazraeh, H., Barati, M., Barati, M., and Rahnama, A.: Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran, Agr. Water Manage., 100, 1–8,, 2011. a
Ash, G. H. B., Shaykewich, C. F., and Raddatz, R. L.: Moisture risk assessment for spring wheat on the eastern Prairies: a water use simulation model, Climatol. Bull., 26, 65–78, 1992. a
Short summary
A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.