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Crop Monitoring Strategies for Precise Irrigation Management
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Crop Monitoring Strategies for Precise Irrigation Management

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

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 22294

Special Issue Editor

Dr. Gregorio Egea

E-Mail Website
Guest Editor
School of Agricultural Engineering, University of Sevilla, Ctra. Utrera Km 1, 41013 Seville, Spain
Interests: irrigation management; deficit irrigation; precision agriculture; crop monitoring; crop modelling; plant phenotyping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The relevance of irrigated agriculture is well known, with 40% of the world's agricultural output produced on only 20% of the cultivated area. There is no doubt that irrigated agriculture is essential in achieving the objective of feeding a world population in a state of constant growth. However, the important role that irrigated agriculture must play is not without major difficulties, such as the scarcity of fresh water in many producing regions and the large volumes of water already devoted to irrigation (70% of the world’s water demand).

Irrigated agriculture faces, therefore, the challenge of producing more with a similar amount of, or even fewer, water resources. The scientific community and the stakeholders involved in water management are challenged to develop and implement irrigation strategies to increase irrigation water productivity. New technologies (e.g., sensors, wireless sensor networks, unmanned aerial vehicles (UAVs), ICTs, cloud computing) can contribute enormously to this end. The goal of this Special Issue is to provide a collection of manuscripts that present innovative studies, tools, approaches, and techniques that have been successful in optimizing irrigation water at farm level. Submissions on (but not limited to) the following topics are invited: (1) plant-based sensing for water stress monitoring, (2) soil moisture-based irrigation management, (3) UAV-based precision irrigation, (4) automated irrigation scheduling, (5) wireless sensor networks for irrigation management, (6) methods to estimate actual crop evapotranspiration, (7) variable rate irrigation, and (8) decision support systems for sustainable irrigation.

Dr. Gregorio Egea
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Water stress
  • Crop water status
  • Soil water content
  • Sensors
  • Proximal sensing
  • Remote sensing
  • Variable rate irrigation
  • Actual crop evapotranspiration
  • Unmmaned aerial vehicles
  • Thermal sensing

Published Papers (4 papers)

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Research

18 pages, 4769 KiB  
Article
Long-Term Assessment of Reference Baselines for the Determination of the Crop Water Stress Index in Maize under Mediterranean Conditions
by Alejandro Prior, Orly Enrique Apolo-Apolo, Pedro Castro-Valdecantos, Manuel Pérez-Ruiz and Gregorio Egea
Water 2021, 13(21), 3119; https://doi.org/10.3390/w13213119 - 05 Nov 2021
Cited by 3 | Viewed by 2130
Abstract
Canopy temperature has been proposed as a relevant variable for crop water stress monitoring. Since crop temperature is highly influenced by the prevailing climatic conditions, it is usually normalized with indices such as the crop water stress index (CWSI). The index requires the [...] Read more.
Canopy temperature has been proposed as a relevant variable for crop water stress monitoring. Since crop temperature is highly influenced by the prevailing climatic conditions, it is usually normalized with indices such as the crop water stress index (CWSI). The index requires the use of two baselines that relate canopy temperature under maximum stress and non-water stress conditions with vapor pressure deficit (VPD). These reference baselines are specific to each crop and climatic region. In maize, they have been extensively studied for certain climatic regions but very little is known on their suitability to be used under Mediterranean-type conditions nor their temporal stability, both diurnally and between seasons. Thus, the objective of this work was to determine the reference baselines for maize grown under Mediterranean conditions, as well as its diurnal and long-term stability. An experiment was conducted for 3 years in a maize breeding field, under well-watered and water-stressed irrigation treatments. The determined reference baselines for computing CWSI in maize have shown to be stable in the long term but markedly influenced by the meteorological variations between 10–17 h UTC (Coordinated Universal Time). These results indicate that several reference baselines should be used for CWSI computing throughout the abovementioned time interval. The CWSI values calculated for well-watered and water-stressed maize breeding plots using the reference baselines derived in this study were successfully correlated with other physiological indicators of plant water stress. Full article
(This article belongs to the Special Issue Crop Monitoring Strategies for Precise Irrigation Management)
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15 pages, 2193 KiB  
Article
Assessing the Water-Stress Baselines by Thermal Imaging for Irrigation Management in Almond Plantations under Water Scarcity Conditions
by Saray Gutiérrez-Gordillo, Iván Francisco García-Tejero, Víctor Hugo Durán Zuazo, Amelia García Escalera, Fernando Ferrera Gil, José Juan Amores-Agüera, Belén Cárceles Rodríguez and Virginia Hernández-Santana
Water 2020, 12(5), 1298; https://doi.org/10.3390/w12051298 - 04 May 2020
Cited by 8 | Viewed by 2880
Abstract
This work examines the use of thermal imaging to determine the crop water status in young almond trees under sustained deficit irrigation strategies (SDIs). The research was carried out during two seasons (2018–2019) in three cultivars (Prunus dulcis Mill., cvs. Guara, Lauranne, [...] Read more.
This work examines the use of thermal imaging to determine the crop water status in young almond trees under sustained deficit irrigation strategies (SDIs). The research was carried out during two seasons (2018–2019) in three cultivars (Prunus dulcis Mill., cvs. Guara, Lauranne, and Marta) subjected to three irrigation treatments: a full irrigation treatment (FI) at 100% of irrigation requirements (IR), and two SDIs that received 75% and 65% of the IR, respectively. Crop water monitoring was done by measurements of canopy temperature, leaf water potential (Ψleaf), and stomatal conductance. Thermal readings were used to define the non-water-stress baselines (NWSB) and water-stress baselines (WSB) for each treatment and cultivar. According to our findings, Ψleaf was the most responsive parameter to reflect differences in almond water status. In addition, NWSB and WSB allowed the determination of the crop water-stress index (CWSI) and the increment of canopy temperature (ITC) for each SDI treatment, obtaining threshold values of CWSI (0.12–0.15) and ITC (~1 °C) that would ensure maximum water savings by minimizing the effects on yield. The findings highlight the importance of determining the different NWSB and WSB for different almond cultivars and its potential use for proper irrigation scheduling. Full article
(This article belongs to the Special Issue Crop Monitoring Strategies for Precise Irrigation Management)
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17 pages, 2343 KiB  
Article
A Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques
by Roque Torres-Sanchez, Honorio Navarro-Hellin, Antonio Guillamon-Frutos, Rubén San-Segundo, Maria Carmen Ruiz-Abellón and Rafael Domingo-Miguel
Water 2020, 12(2), 548; https://doi.org/10.3390/w12020548 - 15 Feb 2020
Cited by 46 | Viewed by 7446
Abstract
Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of [...] Read more.
Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of factors that the technician considers when managing irrigation in an optimal way. Automatic learning systems propose an alternative to traditional irrigation management by means of the automatic elaboration of predictions based on the learning of an agronomist (DSS). The aim of this paper is the study of several learning techniques in order to determine the goodness and error relative to expert decision. Nine orchards were tested during 2018 using linear regression (LR), random forest regression (RFR), and support vector regression (SVR) methods as engines of the irrigation decision support system (IDSS) proposed. The results obtained by the learning methods in three of these orchards have been compared with the decisions made by the agronomist over an entire year. The prediction model errors determined the best fitting regression model. The results obtained lead to the conclusion that these methods are valid engines to develop automatic irrigation scheduling systems. Full article
(This article belongs to the Special Issue Crop Monitoring Strategies for Precise Irrigation Management)
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20 pages, 2786 KiB  
Article
Performance of Soil Moisture Sensors in Florida Sandy Soils
by Rhuanito Soranz Ferrarezi, Thiago Assis Rodrigues Nogueira and Sara Gabriela Cornejo Zepeda
Water 2020, 12(2), 358; https://doi.org/10.3390/w12020358 - 28 Jan 2020
Cited by 20 | Viewed by 8585
Abstract
Soil moisture sensors can improve water management efficiency by measuring soil volumetric water content (θv) in real time. Soil-specific calibration equations used to calculate θv can increase sensor accuracy. A laboratory study was conducted to evaluate the performance of several commercial [...] Read more.
Soil moisture sensors can improve water management efficiency by measuring soil volumetric water content (θv) in real time. Soil-specific calibration equations used to calculate θv can increase sensor accuracy. A laboratory study was conducted to evaluate the performance of several commercial sensors and to establish soil-specific calibration equations for different soil types. We tested five Florida sandy soils used for citrus production (Pineda, Riviera, Astatula, Candler, and Immokalee) divided into two depths (0.0–0.3 and 0.3–0.6 m). Readings were taken using twelve commercial sensors (CS650, CS616, CS655 (Campbell Scientific), GS3, 10HS, 5TE, GS1 (Meter), TDT-ACC-SEN-SDI, TDR315, TDR315S, TDR135L (Acclima), and Hydra Probe (Stevens)) connected to a datalogger (CR1000X; Campbell Scientific). Known amounts of water were added incrementally to obtain a broad range of θv. Small 450 cm3 samples were taken to determine the gravimetric water content and calculate the θv used to obtain the soil-specific calibration equations. Results indicated that factory-supplied calibration equations performed well for some sensors in sandy soils, especially 5TE, TDR315L, and GS1 (R2 = 0.92) but not for others (10HS, GS3, and Hydra Probe). Soil-specific calibrations from this study resulted in accuracy expressed as root mean square error (RMSE) ranging from 0.018 to 0.030 m3 m−3 for 5TE, CS616, CS650, CS655, GS1, Hydra Probe, TDR310S, TDR315, TDR315L, and TDT-ACC-SEN-SDI, while lower accuracies were found for 10HS (0.129 m3 m−3) and GS3 (0.054 m3 m−3). This study provided soil-specific calibration equations to increase the accuracy of commercial soil moisture sensors to facilitate irrigation scheduling and water management in Florida sandy soils used for citrus production. Full article
(This article belongs to the Special Issue Crop Monitoring Strategies for Precise Irrigation Management)
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