Applications of Agro-Hydrological Sensors and Models for Sustainable Irrigation

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 29780

Special Issue Editors


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Guest Editor
Department of Agriculture, Food and Environment (DAFE), University of Pisa, 56124 Pisa, Italy
Interests: agro-hydrological sensing and modeling; precision irrigation; field spectroscopy; evapotranspiration; crop water requirement

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Guest Editor
Department of Chemical and Agricultural Engineering and Technology, Polytechnic School, University of Girona, C/ de Maria Aurèlia Capmany 61, 17003 Girona, Spain
Interests: drip irrigation systems; wastewater reuse in drip irrigation systems; precision irrigation; sustainable irrigation; modelling; sensors for irrigation systems

Special Issue Information

Dear Colleagues,

In the last two decades, research on water resource monitoring and management has mainly been aimed at reducing irrigation water volume and the reduction of energy consumption. At the same time the effects of climate change and agricultural policies, have been major research interests. Therefore, there is an increasing focus on the assessment of irrigation performance for improving water management and to increase the sustainability of irrigated agriculture.

Recent advances in optoelectronics, mechatronics, communications and information technologies allow the implementation of low cost, easy operating and virtual free maintenance of data acquisition systems to be used in soil-crop water status monitoring, as well as in smart irrigation systems. Agro-hydrological models have been recognized as an economic and simple tool to quantify crop water requirements in the decision-making processes, for both farms and basins scales. They can simulate the mass and/or energy exchange processes in the soil-plant-atmosphere continuum under different spatial and temporal scales. These models joint with new technologies such as sensors and remote sensing are promising techniques that have accelerated spatial data collection substantially. Remote sensing and wireless sensor nework can cover scales from a single leaf to complete irrigation systems, and can create data sets for large numbers of soil families and farming conditions.

Hence, properly chosen and calibrated agro-hydrological sensor-model based approach, the likelihood of their use to become widespread increases. Not only the larger commercial farms can afford to pay for remote sensing data, with higher spatial and spectral resolution, but smaller farms could get these new technologies as well.

The SI is centered to attract studies specialized in applications of agro-hydrological sensors and models for water resource management, take into account the sustainability aspect of the adopted monitoring approach and management strategy.


Dr. Giovanni Rallo
Dr. Jaume Puig-Bargués
Guest Editors

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Keywords

  • smart irrigation
  • drought monitoring and mitigation
  • sensor-model-based irrigation
  • nested water–energy use efficiency
  • sustainable irrigation

Published Papers (8 papers)

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Editorial

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4 pages, 217 KiB  
Editorial
Applications of Agro-Hydrological Sensors and Models for Sustainable Irrigation
by Jaume Puig-Bargués and Giovanni Rallo
Water 2022, 14(14), 2274; https://doi.org/10.3390/w14142274 - 21 Jul 2022
Viewed by 1749
Abstract
In the last two decades, research on water resource monitoring and management has mainly been aimed at reducing irrigation water volume and energy consumption [...] Full article

Research

Jump to: Editorial

21 pages, 1259 KiB  
Article
Transpiration and Water Use of an Irrigated Traditional Olive Grove with Sap-Flow Observations and the FAO56 Dual Crop Coefficient Approach
by Àngela Puig-Sirera, Giovanni Rallo, Paula Paredes, Teresa A. Paço, Mario Minacapilli, Giuseppe Provenzano and Luis S. Pereira
Water 2021, 13(18), 2466; https://doi.org/10.3390/w13182466 - 8 Sep 2021
Cited by 14 | Viewed by 3132
Abstract
The SIMDualKc model was applied to evaluate the crop water use and the crop coefficient (Kc) of an irrigated olive grove (Olea europaea L.) located in Sicily, Italy, using experimental data collected from two crop seasons. The model applies the [...] Read more.
The SIMDualKc model was applied to evaluate the crop water use and the crop coefficient (Kc) of an irrigated olive grove (Olea europaea L.) located in Sicily, Italy, using experimental data collected from two crop seasons. The model applies the FAO56 dual Kc approach to compute the actual crop evapotranspiration (ETc act) and its components, i.e., the actual tree transpiration (Tc act), obtained through the basal crop coefficient (Kcb), and soil evaporation according to an evaporation coefficient (Ke). Model calibration was performed by minimizing the difference between the predicted Tc act and the observed daily tree transpiration measured with sap flow instrumentation (TSF field) acquired in 2009. The validation was performed using the independent data set of sap flow measurements from 2011. The calibrated Kcb was equal to 0.30 for the initial and non-growing season stages, 0.42 for the mid-season, and 0.37 for the end season. For both seasons, the goodness-of-fit indicators relative to comparing TSF field with the simulated Tc act resulted in root mean square errors (RMSE) lower than 0.27 mm d−1 and a slope of the linear regression close to 1.0 (0.94 ≤ b0 ≤ 1.00). The olive grove water balance simulated with SIMDualKc produced a ratio between soil evaporation (Es) and ETc act that averaged 39%. The ratio between actual (ETc act) and potential crop evapotranspiration (ETc) varied from 84% to about 99% in the mid-season, indicating that the values of ETc act are close to ETc, i.e., the adopted deficit irrigation led to limited water stress. The results confirm the suitability of the SIMDualKc model to apply the FAO56 dual Kc approach to tree crops, thus assessing the water use of olives and supporting the development of appropriate irrigation management tools that are usable by farmers. A different way to estimate Kcb is based on the approach suggested in 2009 by Allen and Pereira (A&P), which involves the measured fraction of ground covered (shaded) by the crop and the height of the trees. Its application to the studied grove produced the mid-season Kcb values ranging from 0.40–0.45 and end-season Kcb values ranging from 0.35–0.40. The comparison between the A&P-computed Tc act A&P and TSF field shows RMSE values ranging from 0.27 to 0.43 mm d−1, which demonstrates the adequacy of the latter approach for parameterizing water balance models and for irrigation scheduling decision making. Full article
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25 pages, 39229 KiB  
Article
Assessment of Different Pressure Drop-Flow Rate Equations in a Pressurized Porous Media Filter for Irrigation Systems
by Jonathan Graciano-Uribe, Toni Pujol, Jaume Puig-Bargués, Miquel Duran-Ros, Gerard Arbat and Francisco Ramírez de Cartagena
Water 2021, 13(16), 2179; https://doi.org/10.3390/w13162179 - 9 Aug 2021
Cited by 7 | Viewed by 4781
Abstract
The small open area available at the slots of underdrains in pressurized granular bed filters for drip irrigation implies: (1) the existence of a region with non-uniform flow, and (2) local values of modified particle Reynolds number >500. These flow conditions may disagree [...] Read more.
The small open area available at the slots of underdrains in pressurized granular bed filters for drip irrigation implies: (1) the existence of a region with non-uniform flow, and (2) local values of modified particle Reynolds number >500. These flow conditions may disagree with those accepted as valid for common pressure drop-flow rate correlations proposed for packed beds. Here, we carried out detailed computational fluid dynamics (CFD) simulations of a laboratory filter to analyze the results obtained with five different equations of head losses in porous media: (1) Ergun, (2) Darcy-Forchheimer, (3) Darcy, (4) Kozeny-Carman and (5) power function. Simulations were compared with experimental data at different superficial velocities obtained from previous studies. Results for two silica sand media indicated that all equations predicted total filter pressure drop values within the experimental uncertainty range when superficial velocities <38.3 m h−1. At higher flow rates, Ergun equation approximated the best to the observed results for silica sand media, being the expression recommended. A simple analytical model of the pressure drop along flow streamlines that matched CFD simulation results was developed. Full article
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16 pages, 3163 KiB  
Article
Development and Validation of a New Calibration Model for Diviner 2000® Probe Based on Soil Physical Attributes
by Giuseppe Provenzano, Giovanni Rallo, Ceres Duarte Guedes Cabral de Almeida and Brivaldo Gomes de Almeida
Water 2020, 12(12), 3414; https://doi.org/10.3390/w12123414 - 4 Dec 2020
Cited by 4 | Viewed by 2061
Abstract
This study aimed to develop a new model, valid for soil with and without expandable characters, to estimate volumetric soil water content (θ) from readings of scaled frequency (SF) acquired with the Diviner 2000® sensor. The analysis was carried out on six [...] Read more.
This study aimed to develop a new model, valid for soil with and without expandable characters, to estimate volumetric soil water content (θ) from readings of scaled frequency (SF) acquired with the Diviner 2000® sensor. The analysis was carried out on six soils collected in western Sicily, sieved at 5 mm, and repacked to obtain the maximum and minimum bulk density (ρb). During an air-drying process SF values, the corresponding gravimetric soil water content (U) and ρb were monitored. In shrinking/swelling clay soils, due to the contraction process, the variation of dielectric permittivity was affected by the combination of the mutual proportions between the water volumes and the air present in the soil. Thus, to account for the changes of ρb with U, the proposed model assumed θ as the dependent variable being SF and ρb the independent variables; then the model’s parameters were estimated based on the sand and clay fractions. The model validation was finally carried out based on data acquired in undisturbed monoliths sampled in the same areas. The estimated θ, θestim, was generally close to the corresponding measured, θmeas, with Root Mean Square Errors (RMSE) generally lower than 0.049 cm3 cm−3, quite low Mean Bias Errors (MBE), ranging between −0.028 and 0.045 cm3 cm−3, and always positive Nash-Sutcliffe Efficiency index (NSE), confirming the good performance of the model. Full article
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22 pages, 6443 KiB  
Article
Optimization-Based Water-Salt Dynamic Threshold Analysis of Cotton Root Zone in Arid Areas
by Hui Wu, Shaozhong Kang, Xiaojuan Li, Ping Guo and Shunjun Hu
Water 2020, 12(9), 2449; https://doi.org/10.3390/w12092449 - 31 Aug 2020
Cited by 8 | Viewed by 2747
Abstract
Threshold levels of soil moisture and salinity in the plant root zone can guide crop planting and farming practices by providing a baseline for adjusting irrigation and modifying soil salinity. This study describes a method of soil water and salinity control based on [...] Read more.
Threshold levels of soil moisture and salinity in the plant root zone can guide crop planting and farming practices by providing a baseline for adjusting irrigation and modifying soil salinity. This study describes a method of soil water and salinity control based on an optimized model for growing cotton in an arid area. Experiments were conducted in Akesu Irrigation District, southern Xinjiang, northwest China, to provide data for cotton yield and soil water content and salinity in the root zone at different growth stages. The sensitivity of cotton to soil water content and salinity was predicted for different growth periods using a modified Jensen model. An optimization model with 480 boundary conditions was created, with the objective of maximizing yield, to obtain the dynamically varying water and salt threshold levels in the root zone for scenarios that included three initial soil moisture content values (W0), eight irrigation quantities (M), five initial soil salt content values (S0), and four irrigation water salinity levels (K). Results showed that the flowering–boll stage is the crucial period for cotton yield, and the threshold levels of soil water content and salinity in the cotton root zone varied with the boundary conditions. The scenario chosen for the research area in this study was W0 = 0.85θfc (θfc is field capacity), S0 = 4 g kg−1, M = 400 mm, K = 0 g L−1. The predicted threshold levels of soil water for different growth stages (seedling, bud, flowering–boll, and boll-opening) were respectively 0.75–0.85θfc, 0.65–0.75θfc, 0.56–0.65θfc, and 0.45–0.56θfc. Corresponding threshold levels of salt were 4–4.16, 4.16–4.39, 4.39–4.64, and 4.64–4.97 g kg−1 when no action was taken to remove salt from the root zone. This study provides an innovation method for the determination of dynamically varying soil water content and salt thresholds. Full article
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14 pages, 2293 KiB  
Article
Hydraulic Performance and Modelling of Pressurized Drip Irrigation System
by Eddy Herman Sharu and Mohd Shahrizal Ab Razak
Water 2020, 12(8), 2295; https://doi.org/10.3390/w12082295 - 15 Aug 2020
Cited by 11 | Viewed by 4676
Abstract
This study was conducted at Laman Sayur, Malaysia Agro Exposition Park Serdang (MAEPS), to investigate the hydraulic performance of a small-scale drip irrigation system. The modelling was carried out using EPANET software to understand how the drip irrigation system is operated. Model results [...] Read more.
This study was conducted at Laman Sayur, Malaysia Agro Exposition Park Serdang (MAEPS), to investigate the hydraulic performance of a small-scale drip irrigation system. The modelling was carried out using EPANET software to understand how the drip irrigation system is operated. Model results show that the errors are small, i.e., 2.2% and 3.0% for pressures, and 1.7% for discharge in lateral pipe 1 and lateral pipe 2. The root mean square error (RMSE) and the mean bias error (MBE) for discharge were recorded at 0.04 L/h and 0.03 L/h for lateral pipe 1 and 0.04 L/h and 0.02 L/h for lateral pipe 2. RMSE and MBE for pressure were recorded at 0.61 m and 0.68 m for lateral pipe 1, and 0.79 m and 0.68 m for lateral pipe 2, respectively. These results show that the model yields good performance. For hydraulic performance, the field measurement was conducted with four operating pressures: P1 (15.3), P2 (20.4), P3 (25.5), and P4 (28.6) meters. The hydraulic parameters evaluated were the coefficient of uniformity (CU), the emission uniformity (EU), the coefficient of variation (CV), and the emitter flow variation (EFV). The operating pressure during the measurement is constant according to the specified pressure. The results show that CU, CV, and EU are in the excellent classification, and values of CU and EU have more than 95% efficiency. The value for CV is below 0.03, which is excellent. The EFV is 10% when operating at 25.5 m and 15.3 m and is considered desirable. On the other hand, for the 28.6 m and 15.3 m operating pressures, the EFV parameters were recorded at 13.6% and 10.29%, respectively, and are classified acceptable. This study concluded that the operating pressures, P2 (20.4 m) and P3 (25.5 m), were performed under excellent classification for all hydraulic parameters evaluated. Based on the outputs from the model, it is inferred that the existing drip irrigation system at Laman Sayur MAEPS is operated in an over-powered state. With the current pump power consumption, the irrigation system could be operated at a minimum of four times the capacity of the existing irrigation system. To reduce the power consumption, it is suggested that the system is operated at a lower pumping power. This would minimize the operating costs especially for the development of a new drip irrigation system that has the same capacity as the drip irrigation system that is currently being operated at Laman Sayur, MAEPS Serdang. Full article
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26 pages, 3549 KiB  
Article
Parameterization of Soil Hydraulic Parameters for HYDRUS-3D Simulation of Soil Water Dynamics in a Drip-Irrigated Orchard
by Jesús María Domínguez-Niño, Gerard Arbat, Iael Raij-Hoffman, Isaya Kisekka, Joan Girona and Jaume Casadesús
Water 2020, 12(7), 1858; https://doi.org/10.3390/w12071858 - 28 Jun 2020
Cited by 19 | Viewed by 4828
Abstract
Although surface drip irrigation allows an efficient use of water in agriculture, the heterogeneous distribution of soil water complicates its optimal usage. Mathematical models can be used to simulate the dynamics of water in the soil below a dripper and promote: a better [...] Read more.
Although surface drip irrigation allows an efficient use of water in agriculture, the heterogeneous distribution of soil water complicates its optimal usage. Mathematical models can be used to simulate the dynamics of water in the soil below a dripper and promote: a better understanding, and optimization, of the design of drip irrigation systems, their improved management and their monitoring with soil moisture sensors. The aim of this paper was to find the most appropriate configuration of HYDRUS-3D for simulating the soil water dynamics in a drip-irrigated orchard. Special emphasis was placed on the source of the soil hydraulic parameters. Simulations parameterized using the Rosetta approach were therefore compared with others parameterized using that of HYPROP + WP4C. The simulations were validated on a seasonal scale, against measurements made using a neutron probe, and on the time course of several days, against tensiometers. The results showed that the best agreement with soil moisture measurements was achieved with simulations parameterized from HYPROP + WP4C. It further improved when the shape parameter n was empirically calibrated from a subset of neutron probe measurements. The fit of the simulations with measurements was best at positions near the dripper and worsened at positions outside its wetting pattern and at depths of 80 cm or more. Full article
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15 pages, 1995 KiB  
Article
Modeling Approaches for Determining Dripline Depth and Irrigation Frequency of Subsurface Drip Irrigated Rice on Different Soil Textures
by Gerard Arbat, Sílvia Cufí, Miquel Duran-Ros, Jaume Pinsach, Jaume Puig-Bargués, Joan Pujol and Francisco Ramírez de Cartagena
Water 2020, 12(6), 1724; https://doi.org/10.3390/w12061724 - 17 Jun 2020
Cited by 28 | Viewed by 3625
Abstract
Water saving techniques such as drip irrigation are important for rice (Oriza sativa L.) production in some areas. Subsurface drip irrigation (SDI) is a promising alternative for intensive cropping since surface drip irrigation (DI) requires a higher degree of labor to allow [...] Read more.
Water saving techniques such as drip irrigation are important for rice (Oriza sativa L.) production in some areas. Subsurface drip irrigation (SDI) is a promising alternative for intensive cropping since surface drip irrigation (DI) requires a higher degree of labor to allow the use of machinery. However, the semi-aquatic nature of rice plants and their shallow root system could pose some limitations. A major design issue when using SDI is to select the dripline depth to create appropriate root wetting patterns as well as to reduce water losses by deep drainage and evaporation. Soil texture can greatly affect soil water dynamics and, consequently, optimal dripline depth and irrigation frequency needs. Since water balance components as deep percolation are difficult to estimate under field conditions, soil water models as HYDRUS-2D can be used for this purpose. In the present study, we performed a field experiment using SDI for rice production with Onice variety. Simulations using HYDRUS-2D software successfully validated soil water distribution and, therefore, were used to predict soil water contents, deep drainage, and plant water extraction for two different dripline depths, three soil textures, and three irrigation frequencies. Results of the simulations show that dripline depth of 0.15 m combined with one or two daily irrigation events maximized water extraction and reduced percolation. Moreover, simulations with HYDRUS-2D could be useful to determine the most appropriate location of soil water probes to efficiently manage the SDI in rice. Full article
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