Preprints
https://doi.org/10.5194/hess-2022-96
https://doi.org/10.5194/hess-2022-96
 
31 Mar 2022
31 Mar 2022
Status: this preprint is currently under review for the journal HESS.

FarmCan: A Physical, Statistical, and Machine Learning Model to Forecast Crop Water Deficit at Farm Scales

Sara Sadri1, James S. Famiglietti1, Ming Pan2, Hylke E. Beck3, Aaron Berg4, and Eric F. Wood Sara Sadri et al.
  • 1University of Saskatchewan, Global Institute for Water Security, SK S7N 3H5, Canada
  • 2Scripps Institution of Oceanography, UCSD, La Jolla, CA 92093, U.S.A.
  • 3Joint Research Centre of the European Commission, Ispra 21027, Italy
  • 4University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
  • deceased, Nov 2021

Abstract. In the coming decades, a changing climate, growing global population, and rising food prices will have significant yet uncertain impacts on both water and food security. The loss of high-quality land, the slowing in annual yield of major cereals, and increasing fertilizer use, all indicate that strategies are needed for monitoring and predicting ongoing and future water deficits on farms for better agricultural water management decisions. Most such activities are based on in-situ measurements which are costly, hard to scale, and ignore the wealth of spatial and temporal information from remotely-sensed data. In this study, we designed FarmCan, a novel and robust climate-informed machine learning (ML) framework to predict crop water demand at the farm scale with up to 14 days lead time. We use a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with inputs from farmers, a Random Forest (RF) algorithm, and precipitation (P) prediction from MSWEP to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM). Our study shows that SM and RZSM are the variables that are more correlated with P, while PET and ET do not show a strong correlation with P, SM, and RZSM. Our case study of 4 farms in the Canadian Prairies Ecozone (CPE) using R2, RMSE, and KGE indicators, shows that our algorithm was able to forecast crop water requirements 14 days in advance reasonably well. We also found that during 2020, RF forecasted ET and PET and needed irrigation (NI) with more accuracy than SM and RZSM, although this might vary based on the soil type, location, year of study, and crop type. Due to the forecasting capability and transferability of the mechanism developed, FarmCan is a promising tool for use in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government' policy concerns.

Sara Sadri et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-96', Anonymous Referee #1, 29 Apr 2022
  • RC2: 'Comment on hess-2022-96', Geoff Pegram, 06 May 2022
  • RC3: 'Comment on hess-2022-96', Anonymous Referee #3, 10 May 2022
  • RC4: 'Comment on hess-2022-96', Anonymous Referee #4, 12 May 2022
    • AC5: 'Reply on RC4', Sara Sadri, 23 Sep 2022
  • RC5: 'Comment on hess-2022-96', Anonymous Referee #5, 12 May 2022
    • AC6: 'Reply on RC5', Sara Sadri, 23 Sep 2022
  • RC6: 'Comment on hess-2022-96', Anonymous Referee #6, 13 May 2022
    • AC7: 'Reply on RC6', Sara Sadri, 23 Sep 2022
  • RC7: 'Comment on hess-2022-96', Anonymous Referee #7, 13 May 2022
    • AC8: 'Reply on RC7', Sara Sadri, 23 Sep 2022
  • RC8: 'Comment on hess-2022-96', Anonymous Referee #8, 14 May 2022
    • AC9: 'Reply on RC8', Sara Sadri, 23 Sep 2022
  • RC9: 'Comment on hess-2022-96', Anonymous Referee #9, 15 May 2022
    • AC10: 'Reply on RC9', Sara Sadri, 23 Sep 2022
  • RC10: 'Comment on hess-2022-96', Anonymous Referee #10, 16 May 2022
    • AC11: 'Reply on RC10', Sara Sadri, 23 Sep 2022
  • RC11: 'Comment on hess-2022-96', Anonymous Referee #11, 17 May 2022
    • AC12: 'Reply on RC11', Sara Sadri, 23 Sep 2022
  • RC12: 'Comment on hess-2022-96', Anonymous Referee #12, 18 May 2022
    • AC13: 'Reply on RC12', Sara Sadri, 23 Sep 2022
  • CC1: 'Comment on hess-2022-96', Nils-Otto Kitterød, 18 May 2022
    • AC14: 'Reply on CC1', Sara Sadri, 24 Sep 2022
  • CC2: 'Comment on hess-2022-96', Panayiotis Dimitriadis, 22 May 2022
    • AC15: 'Reply on CC2', Sara Sadri, 24 Sep 2022
    • AC16: 'Reply on CC2', Sara Sadri, 24 Sep 2022
  • EC1: 'Editor comment', Daniel Green, 08 Jun 2022
    • AC1: 'Reply on EC1', Sara Sadri, 21 Aug 2022

Sara Sadri et al.

Sara Sadri et al.

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Short summary
In this research, we developed FarmCan; a farm-scale hydro-climatic machine learning framework to advise farmers. FarmCan uses remote sensing data, farmers’ input, and the Random Forrest algorithm to forecast crop water deficit up to 14 days at farm-scale. FarmCan also uses a crop-specific schedule to use surface or root-zone soil moisture from SMAP L4 soil moisture products. We also did various testing on comparing the different inputs and remote sensing data.