Use of a crop model and soil moisture sensors for estimating soil moisture and irrigation applications in a production soybean field

Hodges, Blade; Tagert, Mary Love M.; Paz, Joel O.

doi:10.1007/s00271-022-00802-1

Cited by 4 publications

(2 citation statements)

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“…shown to help conserve water and energy by avoiding overirrigation, increase water productivity, and reduce risk of ground water pollution in various investigations around the world [2][3][4][5]. Water and energy conservation and protecting ground water have been highlighted in several sustainable development goals indicators (SDG), for which soil sensors can be very helpful [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…A general calibration method has been proposed by Roberti et al [29], but only for large-scale sensor networks which is not suitable for irrigation scheduling at farm level. Besides correction and calibration efforts, improving the use of soil sensors for irrigation have been explored using complementary crop simulation models, machine learning, and meteorological data [3,9,30]. However, the complexity of these protocols can be a limiting factor for more widespread adoption amongst farmers.…”

Section: Introductionmentioning

confidence: 99%

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Versatile simplistic correction of T-higrow sensors for improved soil moisture measurement accuracy

Abdelal,

Al-Kilani

2024

Meas. Sci. Technol.

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The use of soil moisture sensors for irrigation can help reduce water and energy consumption and risks of groundwater contamination, which are essential aspects for pursuing sustainable development goals. However, increased adoption of this technology is limited by calibration requirements, technical complexities, and sensor costs. In this work, a simplified method for reducing the measurement error of a recently released low-cost soil sensor (T-Higrow) is presented. The method only requires measurements of a dry sample from the target soil, which are inputted into a simple correction formula to reduce the measurement error at higher moisture levels. The requirements of the proposed method are simple enough for most labs or extension services. This method was compared to the commonly used linear, polynomial, and logarithmic regression models based on repeated bench-scale experiments within 0-35% moisture range in silt and sandy loam soils and in silica sand. Uncorrected sensor readings correlated well with soil moisture (r: 0.94-0.98), but with significant overestimation (25-60% error). The simplified correction method showed comparable error reduction to regression models across all soil types. All methods reduced error down to 2-10% (0.02-0.1 cm3/cm3) and maintained high correlations (r >0.94), except for logarithmic regression which reduced correlation by around 3%. Variability amongst sensor measurements was generally low (Standard Deviation: 0.01-0.03) particularly at moisture ranges below 20%, this was also the case for sensor-to-sensor variability (Standard Deviation: 0.01-0.03). Sensor evaluation and calibration works are needed to increase the accessibility to this technology for improved water and energy conservation.

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