Effects of soil data accuracy on outputs of irrigation scheduling tools

Authors: MEHATA, MUKESH - Oklahoma State University; DATTA, SUMON - Oklahoma State University; TAGHVAEIAN, SALEH - Oklahoma State University; MIRCHI, ALI - Oklahoma State University; Moriasi, Daniel

Submitted to: Journal of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/25/2022
Publication Date: 3/22/2023
Citation: Mehata, M., Datta, S., Taghvaeian, S., Mirchi, A., Moriasi, D.N. 2023. Effects of soil data accuracy on outputs of irrigation scheduling tools. Journal of the ASABE. 66(3):677-687. https://doi.org/10.13031/ja.15323.
DOI: https://doi.org/10.13031/ja.15323

Interpretive Summary: Modeling tools such as soil water balance model (SWBM) have been used for irrigation scheduling to save energy, minimize possible negative effects on the environment, increase crop yield, and improve the financial viability of agricultural production using scarce water resources under a changing climate. The SWBM requires several key inputs, including soil data, which can be obtained from publicly available databases such as the United States Department of Agriculture's Web Soil Survey (WSS) or field measurements, also known as in-situ sampling (ISS). Whereas the publicly available WSS data are inexpensive, inaccuracies in these datasets could hamper efficient agricultural water management based on SWBM irrigation scheduling. This study investigated the effects of soil data accuracy (WSS versus ISS) on irrigation scheduling in three regions of western Oklahoma. The results show that relying on publicly available WSS soil data for SWBM-based irrigation scheduling may have limited impacts on irrigation recommendations at regional scale, but more pronounced effects at field scale depending on the magnitude of errors in the WSS data. For example, differences in irrigation demand estimates when WSS data were used instead of ISS reached 20% at one site but were within ±9% among the regions. These findings can help producers, irrigation planners, and policy makers evaluate the tradeoffs of more accurate but more expensive field measurements versus less accurate but publicly available soils data.

Technical Abstract: A widely used irrigation scheduling method is based on modeling soil water balance, which requires several key inputs, including soil data. Many scheduling tools rely on publicly available soil data, such as the United States Department of Agriculture's Web Soil Survey (WSS). While soil survey data are a valuable source of information for general farm and natural resource planning and management at large scales, inaccuracies in soil conditions at field and subfield scales can hamper efficient agricultural water management through science-based irrigation scheduling. To illuminate the implications of the localized inaccuracies, this study estimates the errors in WSS soil textural data in three regions of western Oklahoma through comparison with in-situ sampling (ISS) data. The effects of errors on estimated water fluxes of a soil water balance model were also investigated for dominant crops of each region over a 15-year (2006-2020) period. The findings demonstrate that WSS soil textures were finer than ISS at most sites and soil layers, resulting in generally larger root zone total available water estimates. Differences in irrigation demand estimates when WSS data were used instead of ISS reached 20% at one site but were within ±9% among the regions. Half of the estimated irrigation differences for all sites, years, and crops were within ±25 mm. Soil evaporation, deep percolation, and runoff fluxes were also impacted by soil data source, albeit to a smaller degree than irrigation, at levels and directions (over or underestimation) that were dependent on the sign and magnitude of WSS errors, as well as precipitation amounts and timing.