Methods for downhole soil water sensor calibration—Complications of bulk density and water content variations

Evett, Steven R.; Marek, Gary W.; Colaizzi, Paul D.; Copeland, Karen S.; Ruthardt, Brice B.

doi:10.1002/vzj2.20235

Cited by 6 publications

(8 citation statements)

References 34 publications

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“…Temporally variable soil conditions and choice of methods also influence soil bulk density measurements (Evett et al., 2022; Walter et al., 2016), so even calculating corrections to compensate for different mass‐per‐area sampling artifacts can add to temporal differences in SOC measurements. A systematic bias would be especially likely if soil bulk density measurements are not made on every sample but are a separate measurement and one bulk density value is used as representative of a group of SOC samples.…”

Section: Discussionmentioning

confidence: 99%

Temporal variability is a major source of uncertainty in soil carbon measurements

Wuest,

Durfee

2024

Soil Science Soc of Amer J

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Monitoring changes in soil organic carbon over time is important to many agricultural and environmental goals. Despite decades of measurements, temporal variability in soil carbon measurements has not been studied extensively. In this report, we examine five sets of monthly samples extending up to 3 years each that were collected from field experiments at four locations representative of dryland farming in the Pacific Northwest. The variance from month‐to‐month was 15%–32% of the random error, averaging 20%. This was often greater than the variance between replicate experimental units (2%–42%, averaging 17%). At certain sites, sequential samples were found to be temporally autocorrelated, but no consistent trend patterned on seasonal factors like precipitation was found. This suggests that a single point‐in‐time sample can deviate substantially from the long‐term average soil carbon at the site. We illustrate this problem with the results of repeated soil samples taken from 12 commercial farm fields. We recommend that confidence intervals for soil organic carbon estimates should include variance based on a large population of samples rather than from a single sample set at one timepoint.

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Section: Discussionmentioning

confidence: 99%

Temporal variability is a major source of uncertainty in soil carbon measurements

Wuest,

Durfee

2024

Soil Science Soc of Amer J

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“…Mean bulk density ranges from approximately 1. 4 to 1.7 Mg m -3 ( Evett et al., 2022 ). The source of irrigation water is the Ogallala Aquifer.…”

Section: Methodsmentioning

confidence: 99%

“…The neutron probe (NP) (model 503DR, InstroTek Inc., formerly Campbell Pacific Nuclear, Concord, Calif., USA) readings were taken weekly in the M 100 treatment plots and averaged to determine the amount to irrigate the treatment plots under manual control. The NP was field calibrated in 2021 to an accuracy better than 0.01 m 3 m -3 , resulting in separate calibrations for the three distinct soil horizons (Ap, Bt, and Btk) ( Evett et al., 2022 ). Readings were taken from 10 to 230 cm depths in 20-cm increments using the method described in Evett (2008) .…”

Section: Methodsmentioning

confidence: 99%

Sensor feedback system enables automated deficit irrigation scheduling for cotton

O’Shaughnessy

Colaizzi²,

Bednarz³

2023

Front. Plant Sci.

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Precision irrigation technologies using sensor feedback can provide dynamic decision support to help farmers implement DI strategies. However, few studies have reported on the use of these systems for DI management. This two-year study was conducted in Bushland, Texas to investigate the performance of the geographic information (GIS) based irrigation scheduling supervisory control and data acquisition (ISSCADA) system as a tool to manage deficit irrigation scheduling for cotton (Gossypim hirsutum L). Two different irrigation scheduling methods automated by the ISSCADA system — (1) a plant feedback (designated C) - based on integrated crop water stress index (iCWSI) thresholds, and (2) a hybrid (designated H) method, created to combine soil water depletion and the iCWSI thresholds, were compared with a benchmark manual irrigation scheduling (M) that used weekly neutron probe readings. Each method applied irrigation at levels designed to be equivalent to 25%, 50% and 75% replenishment of soil water depletion to near field capacity (designated I25, I50 and I75) using the pre-established thresholds stored in the ISSCADA system or the designated percent replenishment of soil water depletion to field capacity in the M method. Fully irrigated and extremely deficit irrigated plots were also established. Relative to the fully irrigated plots, deficit irrigated plots at the I75 level for all irrigation scheduling methods-maintained seed cotton yield, while saving water. In 2021, the irrigation savings was a minimum of 20%, while in 2022, the minimum savings was 16%. Comparing the performance of deficit irrigation scheduling between the ISSCADA system and the manual method showed that crop response for all three methods were statistically similar at each irrigation level. Because the M method requires labor intensive and expensive use of the highly regulated neutron probe, the automated decision support provided by the ISSCADA system could simplify deficit irrigation management of cotton in a semi-arid region.

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“…Based on a comparison of 400 paired soil water content estimates from the neutron moisture meter and the soil samples, the neutron moisture meter could estimate soil water content with a root mean squared error of 0.027 cm 3 cm -3 , and the standard deviation of the absolute error was 0.019 cm 3 cm -3 . Although more comprehensive methodologies for neutron moisture meter calibration are now available (Evett et al, 2022), the rapid and crude approach used here provided evidence that reasonable estimates of soil water content could be obtained from the neutron moisture meter, while uncertainties remain.…”

Section: Uncertainty Considerationsmentioning

confidence: 95%

Combining Soil Water Content Data with Computer Simulation Models for Improved Irrigation Scheduling

Thorp

2023

Journal of the ASABE

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Highlights Stand-alone irrigation scheduling models were compared to models combined with soil water content data. Use of soil water content data reduced irrigation recommendations compared to stand-alone models. Cotton fiber yield and water productivity were often not significantly impacted by the reduced irrigation amounts. Weather station aridity and other measurement uncertainties must be addressed to improve the methodology. Abstract. Irrigation scheduling models can be used to guide irrigation management decisions, but their simulations often deviate from reality. Combining in-season field data with models may improve the simulations, leading to better irrigation decisions and improved agronomic outcomes. The objective of this study was to evaluate cotton fiber yield and water productivity outcomes from a field trial that compared three computer simulation models for irrigation scheduling: (1) AquaCrop-OSPy (AQC), (2) the CROPGRO-Cotton module within the DSSAT Cropping System Model (CSM), and (3) the pyfao56 evapotranspiration-based, soil water balance model (FAO). Six irrigation scheduling treatments were established, including the three models used as stand-alone scheduling tools (AQC, CSM, and FAO) and the use of the three models in combination with weekly soil water content (SWC) data from neutron moisture meters (AQCSWC, CSMSWC, and FAOSWC). Two cotton varieties were also evaluated (NexGen 3195 and NexGen 4936). The field trial was conducted during the 2021 and 2022 cotton growing seasons at Maricopa, Arizona. Seasonal irrigation amounts were different among irrigation scheduling treatments (p < 0.05), with 9–21% less water recommended for the AQCSWC, CSMSWC, and FAOSWC treatments as compared to AQC, CSM, and FAO. In 2021, the differences in irrigation amount did not lead to any statistical differences in fiber yield among irrigation treatments, but water productivity for the stand-alone CSM model was significantly reduced compared to the other five irrigation treatments (p < 0.05). In 2022, treatments based on soil water content data reduced yield by 15% as compared to stand-alone model treatments, but the reduction was significant only for FAOSWC. Water productivity differences in 2022 were due to the choice of model rather than the inclusion of soil water content data. In both years, the shorter-season cotton variety (NexGen 3195) yielded greater than the longer-season variety (NexGen 4926), and the former achieved greater water productivity than the latter through the yield improvements (p < 0.05). Taken together, the results suggest that combining soil water content data with irrigation scheduling models was useful for reducing irrigation amounts while often maintaining cotton fiber yield and water productivity; however, issues with weather station aridity and other measurement uncertainties must be addressed to improve the methodology. Keywords: Cotton, Evapotranspiration, Irrigation, Management, Sensor, Simulation, Water.

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Methods for downhole soil water sensor calibration—Complications of bulk density and water content variations

Cited by 6 publications

References 34 publications

Temporal variability is a major source of uncertainty in soil carbon measurements

Temporal variability is a major source of uncertainty in soil carbon measurements

Sensor feedback system enables automated deficit irrigation scheduling for cotton

Combining Soil Water Content Data with Computer Simulation Models for Improved Irrigation Scheduling

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