Scheduling irrigation using an approach based on the van Genuchten model

Liang, Xi; Liakos, V.; Wendroth, Ole; Vellidis, George

doi:10.1016/j.agwat.2016.05.030

Cited by 64 publications

(19 citation statements)

References 31 publications

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“…For this purpose, soil water tension at three depths during the spring of 2016 was monitored at different locations distributed across the different zones at 1‐hour intervals. This variable and the experimental set‐up used here are widely applied for scheduling irrigation (Liang et al, 2016). For site‐specific irrigation management, it is recommended to install soil moisture sensors in each defined zone, but the minimum number varies depending on the characteristics of the field (Barker et al, 2017; Tollner et al, 1991).…”

Section: Resultsmentioning

confidence: 99%

Delineating Site‐Specific Management Zones and Evaluating Soil Water Temporal Dynamics in a Farmer's Field in Kentucky

Reyes

Wendroth

Matocha

et al. 2019

Vadose Zone Journal

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Core Ideas Clay content, soil organic matter, slope, NDVI, and topographic wetness index delineate management zones. Hard and fuzzy clustering analysis resulted in divisions of two and three areas. Wavelet analysis revealed variability in temporal soil water dynamics between zones. Due to spatial variability of soil genesis, topography, and resulting soil properties in farmers' fields, soil and crop processes vary in space and time. Therefore, optimum rates and timing of resource applications, such as nutrients and irrigation water, may vary as well. It remains a challenge to quantify the spatial variability of a field and to identify effective ways to manage fields in a site‐specific manner. The objective of this study was to delineate management zones within a farmer's field based on relatively easily obtainable information that is statistically integrated. Moreover, soil water temporal dynamics should be evaluated regarding their spatial differences in different zones. The set of direct and indirect observations included clay and silt content, apparent electrical conductivity, soil chemical properties (pH; organic matter; and total N, P, K, Ca, Mg, and Zn), satellite‐based normalized difference vegetation index (NDVI), and lidar‐based topographic variables in a western Kentucky field. Several key variables and their capability to describe spatial crop yield variability were identified by using principal component analysis: soil clay content, slope, soil organic matter content, topographic wetness index, and NDVI. Two types of cluster analysis were applied to delineate management zones. The cluster analyses revealed that two to three zones was the optimal number of classes based on different criteria. Delineated zones were evaluated and revealed significant differences in corn (Zea mays L.) yield and temporally different soil moisture dynamics. The results demonstrate the ability of the proposed procedure to delineate a farmer's field into zones based on spatially varying soil and crop properties that should be considered for irrigation management.

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Section: Resultsmentioning

confidence: 99%

Delineating Site‐Specific Management Zones and Evaluating Soil Water Temporal Dynamics in a Farmer's Field in Kentucky

Reyes

Wendroth

Matocha

et al. 2019

Vadose Zone Journal

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“…Soil hydraulic properties may be responsible for soil water content field variability, which in turn affects the spatial distribution of root density [39]. Overall, in the literature it is agreed that irrigation scheduling based on soil properties knowledge allows management of the irrigation in a more efficient way [40]. For this, a great debate is still current on the best choice between accurate-lab or simplified methods for soil hydraulic characterization, and the pros and cons have been clearly summarized in the literature [41].…”

Section: Discussionmentioning

confidence: 99%

Assessment of the Physically-Based Hydrus-1D Model for Simulating the Water Fluxes of a Mediterranean Cropping System

Ventrella

Castellini

Prima

et al. 2019

Water

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In a context characterized by a scarcity of water resources and a need for agriculture to cope the increase of food demand, it is of fundamental importance to increase the water use efficiency of cropping systems. This objective can be meet using several currently available software packages simulating water movements in the "soil-plant-atmosphere" continuum (SPAC). The goal of the paper is to discuss and optimize the strategy for implementing an effective simulation framework in order to describe the main soil water fluxes of a typical horticultural cropping system in Southern Italy based on drip-irrigated watermelon cultivation. The Hydrus-1D model was calibrated by optimizing the hydraulic parameters based on the comparison between simulated and measured soil water content values. Next, a sensitivity analysis of the hydraulic parameters of the Mualem-van Genuchten model was carried out. Hydryus-1D determined simulated soil water contents fairly well, with an average root mean square error below 9%. The main fluxes of the SPAC were confined in a restricted soil volume and were therefore well described by the one-dimensional model Hydrus-1D. Water content at saturation and the fitting parameters α and n were the parameters with the highest impact for describing the soil/plant water balance. vadose zone for a variety of common applications in research and soil/water management (see for example [2][3][4][5][6]).Hydrus software packages are finite element models for simulating the one-and two-or three-dimensional movement of water, heat, and multiple solutes in variably saturated media, and since their implementation they have been extensively applied in various applications. Hydrus-1D, with related manuals and case studies as examples, can be freely downloaded from the Hydrus website (https://www.pc-progress.com/en/Default.aspx?hydrus-1d) [7].In the Mediterranean environment of Southern Italy, one of the first applications of Hydrus-1D concerned the simulation of water movement and solute transport in a variably saturated water flow of fine-textured soil subjected to a fluctuating saline groundwater table [8]. Since then, the use of Hydrus has involved the estimation of the water fluxes under several cropping systems differentiated from a spatial and agronomical point of view.Plastic mulch is an agronomic technology widespread in the world because it allows farmers and engineers: (1) to increase soil temperature, (2) to reduce weed pressure and certain insect pests, (3) to increase the soil moisture, and (4) to improve nutrient use efficiency [9][10][11]. Under cotton cultivation, for example, the effect of plastic mulch on soil water balance under drip irrigation has been examined by Han et al. [9], who revealed a minimal effect on soil water distribution but a real effectiveness on soil water conservation due to a great reduction of soil evaporation.Hydrus was used also to check out the optimal drip lateral depth under a cropping system of eggplant characterized by large inter-row distance and localized irrigati...

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“…Second, to implement this SSM model for irrigation scheduling whereby SSM measurements from sparse soil moisture measurements are available in the field (e.g., [65]), process models are required to forecast soil water storage and movement within the root-zone (e.g., [66,67]). This can be achieved by assimilating VWC measurements at multiple depths from soil moisture probes with mechanistic models using ensemble Kalman filtering (e.g., [68,69]).…”

Section: Limitations Of the Empirical Ssm Retrieval Modelsmentioning

confidence: 99%

Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

2020

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Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.

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Scheduling irrigation using an approach based on the van Genuchten model

Cited by 64 publications

References 31 publications

Delineating Site‐Specific Management Zones and Evaluating Soil Water Temporal Dynamics in a Farmer's Field in Kentucky

Delineating Site‐Specific Management Zones and Evaluating Soil Water Temporal Dynamics in a Farmer's Field in Kentucky

Assessment of the Physically-Based Hydrus-1D Model for Simulating the Water Fluxes of a Mediterranean Cropping System

Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

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