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Understanding Soil Water Content for Irrigation Management

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Soil and Water".

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 8033

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Dr. Paraskevi Londra

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Guest Editor

Assistant Professor, Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Street, 11855 Athens, Greece
Interests: soil physics; plant soil–water interaction; flow and transport in soils; horticultural substrates; vadose zone hydrology; water resource management
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Prof. Dr. George Kargas

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Guest Editor

Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, Greece
Interests: soil physics; flow and transport in soils; dielectric sensors; salinity; irrigation and drainage
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Special Issue Information

Dear Colleagues,

As agriculture is the largest consumer of water, with approximately 70% of global withdrawals used for irrigation, the proper management of irrigation water is critical for ensuring global water and food security.

The knowledge of soil moisture and the understanding of soil moisture concepts and thresholds, which affect plant growth, chemical transport, soil temperature and groundwater recharge, are necessary to achieve effective irrigation management.

Monitoring soil moisture by sensors and correctly interpreting the sensors measurements is one of the most promising methods in proper irrigation management. Additionally, the techniques of soil moisture remote sensing gain more and more ground in the mapping of soil moisture at various spatiotemporal scales.

This Special Issue aims in providing advances in the fields of soil moisture-based irrigation management, irrigation scheduling based on soil moisture monitoring sensors, irrigation scheduling based on soil moisture remote sensing, water use efficiency, soil water balance, soil water movement and drainage in irrigated agriculture, irrigation systems and one-, two- and three-dimensional soil water movement, soil water-plant relationships and the effect of irrigation method and scheduling on plant growth, management of irrigation water to address the soil salinity problem.

Dr. Paraskevi Londra
Prof. Dr. George Kargas
Guest Editors

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Keywords

soil water content
soil water tension
dielectric sensors
soil hydraulic properties
water use efficiency
water stress
deficit irrigation
irrigation system
precision agriculture
remote sensing

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Published Papers (4 papers)

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Research

22 pages, 5382 KiB

Open AccessArticle

Development and Application of the Snow, Soil Water and Water Balance Model (SNOSWAB), an Online Model for Daily Estimation of Snowpack Processes, Soil Water Content and Soil Water Balance

by Serban Danielescu

Water 2024, 16(11), 1503; https://doi.org/10.3390/w16111503 - 24 May 2024

Viewed by 1755

Abstract

SNOSWAB (Snow, Soil Water and Water Balance) is a unique online deterministic model built using tipping-bucket approaches that allows for the daily estimation of (i) snowpack processes; (ii) soil water content; and (iii) soil water budget. SNOSWAB is most suitable for modeling field-scale processes for vertically and horizontally homogeneous soils, and its applicability is not limited to specific climate zones or geographical areas. The model is freely available, and its streamlined online interface integrates powerful calibration, visualization and data export routines. In this study, SNOSWAB development and a conceptual model, as well as an example of its application using data collected during a 12-year (2008–2019) field study conducted at the Agriculture and Agri-Food Canada Harrington Experimental Farm (HEF) on Prince Edward Island (PEI), Canada, are presented. Input data consisting of daily air temperature, total precipitation, rainfall and evapotranspiration were used in conjunction with soil properties and daily soil water content, snowpack thickness, surface runoff and groundwater recharge to calibrate (2010–2014) and validate (2015–2019) the model. For both the calibration and validation simulations, the statistical indicators used for evaluating model performance indicated, in most cases, high model fitness (i.e., R² > 0.5, NRMSE < 50% and −25% < PBIAS < 25%) for the various time intervals and parameters analyzed. SNOSWAB fills an existing gap in the online environment and, due to its ease of use, robustness and flexibility, shows promise to be adopted as an alternative for more complex, standalone models that might require extensive resources and expertise. Full article

(This article belongs to the Special Issue Understanding Soil Water Content for Irrigation Management)

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26 pages, 2608 KiB

Open AccessArticle

Comparison of Soil Hydraulic Properties Estimated by Steady- and Unsteady-Flow Methods in the Laboratory

by Dimitrios Koka, George Kargas and Paraskevi A. Londra

Water 2023, 15(20), 3554; https://doi.org/10.3390/w15203554 - 12 Oct 2023

Cited by 1 | Viewed by 1326

Abstract

In this study, soil hydraulic conductivity (K) and soil sorptivity (S) values estimated by applying various steady- and unsteady-flow methods using cumulative infiltration data of three disturbed soils (sandy loam, loam, clay) obtained from a disc infiltrometer in the laboratory at various negative pressure heads were compared. The steady-flow methods used were those of Ankeny et al. and Reynolds and Elrick as well as Logsdon and Jaynes, while the unsteady-flow methods were those of Haverkamp et al. (two-term (2T) and three-term (3T) infiltration equations) and Zhang. The method of White et al., which is a steady-flow method but also uses unsteady-flow infiltration data, was also examined. The results showed that the three steady-flow methods, as well as the Zhang equation, for values of the van Genuchten coefficient n > 1.35, tend to give similar values of K. The 2T infiltration equation with β = 0.6 provided hydraulic conductivity values greater than those estimated by the steady-state methods but gave negative K values in some cases. The values of the coefficients C₁ and C₂ of the 2T equation were affected by the infiltration time. The coefficient C₁ increased while C₂ decreased with increasing time when the cumulative linearization method (CL) was applied, but the change in C₁ tended to be smaller than that in C₂. The inverse solution of the 3T equation using the Excel Solver application for β = 0.75 and β = 1.6, when positive values of K were obtained, approached better the K values estimated by the steady-flow methods compared with those estimated using β = 0.6. Regarding the estimation of S from the unsteady-flow equations (2T, 3T, Zhang), comparable S values were obtained by all equations. The differences between the S values of the various methods are smaller compared to those of K, and S is less affected than K in terms of time. The problem of negative estimates of K might be attributed to the fact that the soils used in this study are classified as soils situated in the domain of lateral capillarity or are not completely homogeneous or soil compaction is observed at some depth. In the case where the soils are not completely homogeneous, the Sequential Infiltration Analysis (SIA) method with β = 0.75 corresponding to the soil types studied was proved to be effective in estimating K values. Full article

(This article belongs to the Special Issue Understanding Soil Water Content for Irrigation Management)

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27 pages, 5324 KiB

Open AccessArticle

Effect of Subirrigation and Silicon Antitranspirant Application on Biomass Yield and Carbon Dioxide Balance of a Three-Cut Meadow

by Joanna Kocięcka, Marcin Stróżecki, Radosław Juszczak and Daniel Liberacki

Water 2023, 15(17), 3057; https://doi.org/10.3390/w15173057 - 26 Aug 2023

Viewed by 1722

Abstract

Meadows are valuable areas that play an important role in the carbon cycle. Depending on several factors, these areas can be carbon sinks or net emitters of carbon dioxide (CO₂) into the atmosphere. In the present study, the use of an antitranspirant (AT) with silicon and the groundwater level in a subirrigation system in a three-cut meadow were evaluated on the carbon dioxide exchange balance and the yield of aboveground biomass. The study was carried out in four experimental plots: with high groundwater level (HWL), with a high water level with AT application (HWL_Si), with a lower groundwater level (LWL), and with a lower groundwater level and AT application (LWL_Si). Flux measurements were made using the closed dynamic chamber method. In the drier and colder 2021, the meadow was a net CO₂ emitter (mean annual net ecosystem exchange (NEE) of all plots: +247.4 gCO₂-C·m⁻²y⁻¹), whereas in the more wet and warmer 2022, assimilation outweighed emissions (mean annual NEE of all plots: −187.4 gCO₂-C·m⁻²y⁻¹). A positive effect of the silicon antitranspirant application was observed on the reduction of carbon dioxide emissions and the increase of gross primary production (GPP) from the plots with higher groundwater levels. For the area with lower water levels, the positive impact of AT occurred only in the second year of the experiment. The yield of aboveground biomass was higher by 5.4% (in 2021) up to 11.7% (in 2022) at the plot with the higher groundwater level. However, the application of AT with silicon contributed to yield reduction in each cut, regardless of the groundwater level. On an annual basis, AT application with silicon reduced the yield by 11.1–17.8%. Full article

(This article belongs to the Special Issue Understanding Soil Water Content for Irrigation Management)

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12 pages, 2245 KiB

Open AccessArticle

Evaluation and Development of Pedotransfer Functions and Artificial Neural Networks to Saturation Moisture Content Estimation

by Josué Trejo-Alonso, Sebastián Fuentes, Nami Morales-Durán and Carlos Chávez

Water 2023, 15(2), 220; https://doi.org/10.3390/w15020220 - 4 Jan 2023

Cited by 2 | Viewed by 2142

Abstract

Modeling of irrigation and agricultural drainage requires knowledge of the soil hydraulic properties. However, uncertainty in the direct measurement of the saturation moisture content (

θ_{s}

) has been generated in several methodologies for its estimation, such as Pedotransfer Functions (PTFs) and [...] Read more.

Modeling of irrigation and agricultural drainage requires knowledge of the soil hydraulic properties. However, uncertainty in the direct measurement of the saturation moisture content (

θ_{s}

) has been generated in several methodologies for its estimation, such as Pedotransfer Functions (PTFs) and Artificial Neuronal Networks (ANNs). In this work, eight different PTFs were developed for the (

θ_{s}

) estimation, which relate to the proportion of sand and clay, bulk density (BD) as well as the saturated hydraulic conductivity (

K_{s}

). In addition, ANNs were developed with different combinations of input and hidden layers for the estimation of

θ_{s}

. The results showed R

^{2}

values from

0.9046 \leq R^{2} \leq 0.9877

for the eight different PTFs, while with the ANNs, values of R

^{2} > 0.9891

were obtained. Finally, the root-mean-square error (RMSE) was obtained for each ANN configuration, with results ranging from

0.0245 \leq RMSE \leq 0.0262

. It was found that with particular soil characteristic parameters (% Clay, % Silt, % Sand, BD and

K_{s}

), accurate estimate of

θ_{s}

is obtained. With the development of these models (PTFs and ANNs), high R

^{2}

values were obtained for 10 of the 12 textural classes. Full article

(This article belongs to the Special Issue Understanding Soil Water Content for Irrigation Management)

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