Monitoring soil water content for decision supporting in agricultural water management based on critical threshold values adopted for Andosol in the temperate monsoon climate

Kassaye, Kassu Tadesse; Boulangé, Julien; Lam, Van Thinh; Saito, Hirotaka; Watanabe, Hirozumi

doi:10.1016/j.agwat.2019.105930

Cited by 23 publications

(15 citation statements)

References 37 publications

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“…The longer the time allowed for water infiltration, the deeper the water infiltrates into the soil. There was a lag time between the initiation of water infiltration and soil water content rise at a deeper soil depth [27]. A similar observation was also observed for silty and sandy soils.…”

Section: Resultssupporting

confidence: 82%

“…The natural way to achieve the PAW's soil water content range is by rainfall [25]. Alternatively, a soil moisture sensor installed at a soil depth could measure soil water content and indicating the need for water irrigation when it falls below the PWP's soil water content level [27]. This method is appropriate given that the existing water irrigation system and the availability of water resources in the immediate surrounding area can supply the water demand.…”

Section: Introductionmentioning

confidence: 99%

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Water Infiltration into Sand, Silt, and Clay at Field Capacity

Giap

Rudiyanto

Sulaiman

2021

ARFMTS

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Field capacity (FC), permanent wilting (PWP), and plant available water (PAW) are essential parameters to estimate for soils because they are essential for water irrigation management. However, these parameters were reported in water volume per unit soil volume. Knowing the soil required water volume does not imply immediate water availability, that is, the speed at which the water could be supplied to the soil. This is because there is a lag time between water irrigation initiation and the water increment in the soil depth. This study uses the field capacity’s soil water content to simulate the water infiltration using Richards’ equation. The studied soil medium was silt, sand, and clay. The study allows an estimate of water infiltration time and infiltrated water to relate to the soil depth of interest. The clayey has the highest FC, and the silty soil has the highest PAW. The results revealed silty soil could contain more readily water for plant growth than sand and clay. This study also revealed silty soil to be a better soil medium than sand and clay. It has the best tradeoff between water infiltration time and the infiltrated amount of water for plant absorption. This study’s coupling technique will be a useful tool for farmers and field practitioners to assess any site based on the soil texture at an early stage of water irrigation investigation.

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Section: Resultssupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Water Infiltration into Sand, Silt, and Clay at Field Capacity

Giap

Rudiyanto

Sulaiman

2021

ARFMTS

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“…Continuous monitoring of the soil's hydraulic and chemical properties is well-developed in soil studies, mainly in the agricultural sector (Dabach et al, 2015;Farsad et al, 2019;Kassaye et al, 2020). A large selection of water content sensors, tensiometers, and suction lysimeters are available.…”

Section: Common Methods For Vadose Zone Characterizationmentioning

confidence: 99%

Vadose Zone Monitoring as a Key to Groundwater Protection

Dahan

2020

Front. Water

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Currently, monitoring programs designed for groundwater protection are mainly based on information from observation wells. This, however, creates a paradox, since identification of pollution in well water is clear evidence that the groundwater is already polluted. The poor state of contaminated aquifers all over the world, and the inability, in practice, to fully remediate contaminated aquifers suggest that groundwater monitoring alone has failed to provide the vital information required to prevent groundwater pollution. That said, groundwater pollution initiates on the land surface, and the contaminants have to traverse the unsaturated zone, long before reaching the water table. Therefore, monitoring programs that can provide real-time information on the hydraulic and chemical state of the unsaturated zone are essential for achieving early warnings of pollution potential and providing imperative protection from pollution hazards. Currently, most of the commercially available monitoring technologies are rather limited in their capability to provide early alerts of pollution processes taking place deep in the unsaturated zone, above the water table. Accordingly, monitoring technologies for the unsaturated zone have to be engineered as “off-the-shelf” commercial products, made available for application by practitioners in all fields of hydrology. From scientific and technological points of view, such ambitions are not out of reach. Yet they require an urgent call for a revolutionary shift in monitoring focus, from the groundwater itself to the unsaturated zone above it.

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“…The higher this value is, the more difficult it is for the plant to extract water and therefore the risk of hydric stress occurs. Cultivators can directly used this information to manage their campaign [7] or can be transformed into Soil Water Content (SWC) using Van Genuchten's equation [8], if the soil characterisation is well known. This value is acquired via a tensiometer implant at the depth where you wish to observe.…”

Section: Introductionmentioning

confidence: 99%

Short term soil moisture forecasts for potato crop farming: A machine learning approach

Dubois

Teytaud

Vérel

2021

Computers and Electronics in Agriculture

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Agricultural decision-making is crucial for future yields. In the context of smart farming, grower combine information from sensors located close to their crops with agronomic models to help them to better understand their crops. Irrigation management is therefore based on extrapolation of data and/or agronomic model responses. This problem can be seen as a learning task for which machine learning techniques have proven their relevance in many and diverse applications. In this paper we place ourselves in the context of potato farming, a crop for which irrigation plays a crucial role. We model the problem of soil water potential prediction as a learning problem solved by supervised leaning algorithms. The problem appears to be difficult since there are several potential inputs, and several outputs to predict. Experiments are conducted on several scenarios with data acquired during 3 years. We demonstrate the possibility of applying feature selection method to automatically design models with features relevant to the problem at hand while having good performances. We have also demonstrated the relevance of the machine learning for this kind of problem, since the methods are able to correctly predict the next water potential values.

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Monitoring soil water content for decision supporting in agricultural water management based on critical threshold values adopted for Andosol in the temperate monsoon climate

Cited by 23 publications

References 37 publications

Water Infiltration into Sand, Silt, and Clay at Field Capacity

Water Infiltration into Sand, Silt, and Clay at Field Capacity

Vadose Zone Monitoring as a Key to Groundwater Protection

Short term soil moisture forecasts for potato crop farming: A machine learning approach

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