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Agronomy
Special Issues
Precision Water Management
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Precision Water Management

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Water Use and Irrigation".

Deadline for manuscript submissions: closed (25 September 2022) | Viewed by 17825

Special Issue Editors

Dr. Koffi Djaman

E-Mail Website
Guest Editor
Department of Plant and Environmental Science, New Mexico State University, Las Cruces, NM 88003, USA
Interests: reference and crop evapotranspiration; irrigation management; cropping systems; crop physiology; crop response to water and fertilizer
Special Issues, Collections and Topics in MDPI journals
Dr. Vivek Sharma

E-Mail Website
Guest Editor
Agricultural and Biological Engineering Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA
Interests: precision water management; soil and water conservation; irrigation scheduling; evapotranspiration and surface energy balance fluxes; soil water and crop dynamics; crop water productivity; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world’s agricultural and water resource enterprises today are facing formidable challenges in terms of optimizing crop yields by reducing water inputs while minimizing environmental degradation. There is a pressing need for significant changes and novel approaches in agricultural water management to address the issue of water shortages in agriculture. Precision water management strategies involving high-efficiency irrigation management (the right amount of water in the right area at the right time), sensor-based systems (in-field to remote sensing), digital technologies, and consideration of spatiotemporal variability of soil and crop growth at the subfield scale have the potential to reduce water use, maintain yield, maximize water use efficiency, and reduce the environmental impact.

This Special Issue aims to provide a forum of discussion for recent developments and advances in precision water management in diverse agroecosystems and agrometeorological conditions to optimize crop water productivity. Specific topics include but are not limited to:

  • Sensor-based (in-field and remote sensing) precision irrigation scheduling;
  • Crop and soil monitoring for precision water management;
  • Site-specific/variable rate irrigation management;
  • Irrigation system design and performance;
  • Integration of technology, agronomy, and profitability with precision irrigation management;
  • Application of big data, artificial intelligence, machine learning, and crop modeling in precision irrigation management.

Dr. Koffi Djaman
Dr. Vivek Sharma
Guest Editors

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. Agronomy is an international peer-reviewed open access monthly 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

  • crop water productivity/crop water use efficiency
  • irrigation efficiency
  • irrigation scheduling
  • smart irrigation
  • variable rate irrigation
  • deficit irrigation
  • crop water stress
  • crop evapotranspiration
  • crop coefficients
  • soil moisture
  • remote sensing
  • unmanned aerial systems
  • artificial intelligence and machine learning
  • crop modeling

Published Papers (6 papers)

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Research

17 pages, 4207 KiB  
Article
Dynamics of Crop Evapotranspiration of Four Major Crops on a Large Commercial Farm: Case of the Navajo Agricultural Products Industry, New Mexico, USA
by Koffi Djaman, Komlan Koudahe and Ali T. Mohammed
Agronomy 2022, 12(11), 2629; https://doi.org/10.3390/agronomy12112629 - 26 Oct 2022
Cited by 1 | Viewed by 1975
Abstract
Crop evapotranspiration (ETa) is the main source of water loss in farms and watersheds, and with its effects felt at a regional scale, it calls for irrigation professionals and water resource managers to accurately assess water requirements to meet crop water use. On [...] Read more.
Crop evapotranspiration (ETa) is the main source of water loss in farms and watersheds, and with its effects felt at a regional scale, it calls for irrigation professionals and water resource managers to accurately assess water requirements to meet crop water use. On a multi-crop commercial farm, different factors affect cropland allocation, among which crop evapotranspiration is one of the most important factors regarding the seasonally or annually available water resources for irrigation in combination with the in-season effective precipitation. The objective of the present study was to estimate crop evapotranspiration for four major crops grown on the Navajo Agricultural Products Industry (NAPI) farm for the 2016–2010 period to help crop management in crop plant allocation based on the different objectives of the NAPI. The monthly and seasonal satellite-based ETa of maize, potatoes, dry beans, and alfalfa were retrieved and compared using the analysis of variance and the least significant difference (LSD) at 5% of significance. Our results showed the highly significant effects of year, months, and crops. The year 2020 obtained the highest crop ETa, and July had the most evapotranspiration demand, followed by August, June, September, and May, and the pool of April, March, February, January, December, and November registered the lowest crop ETa. Maize monthly ETa varied from 17.5 to 201.7 mm with an average seasonal ETa of 703.8 mm. The monthly ETa of potatoes varied from 9.8 to 207.5 mm, and their seasonal ETa averaged 600.9 mm. The dry bean monthly ETa varied from 10.4 to 178.4 mm, and the seasonal ETa averaged 506.2 mm. The alfalfa annual ETa was the highest at 1015.4 mm, as it is a perennial crop. The alfalfa monthly ETa varied from 8.2 to 202.1 mm. The highest monthly crop ETa was obtained in July for all four crops. The results of this study are very critical for cropland allocation and irrigation management under limited available water across a large commercial farm with multiple crops and objectives. Full article
(This article belongs to the Special Issue Precision Water Management)
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19 pages, 3831 KiB  
Article
Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status
by Patricia López-García, Diego Intrigliolo, Miguel A. Moreno, Alejandro Martínez-Moreno, José Fernando Ortega, Eva Pilar Pérez-Álvarez and Rocío Ballesteros
Agronomy 2022, 12(9), 2122; https://doi.org/10.3390/agronomy12092122 - 7 Sep 2022
Cited by 7 | Viewed by 2462
Abstract
The development of unmanned aerial vehicles (UAVs) and light sensors has required new approaches for high-resolution remote sensing applications. High spatial and temporal resolution spectral data acquired by multispectral and conventional cameras (or red, green, blue (RGB) sensors) onboard UAVs can be useful [...] Read more.
The development of unmanned aerial vehicles (UAVs) and light sensors has required new approaches for high-resolution remote sensing applications. High spatial and temporal resolution spectral data acquired by multispectral and conventional cameras (or red, green, blue (RGB) sensors) onboard UAVs can be useful for plant water status determination and, as a consequence, for irrigation management. A study in a vineyard located in south-eastern Spain was carried out during the 2018, 2019, and 2020 seasons to assess the potential uses of these techniques. Different water qualities and irrigation application start throughout the growth cycle were imposed. Flights with RGB and multispectral cameras mounted on a UAV were performed throughout the growth cycle, and orthoimages were generated. These orthoimages were segmented to include only vegetation and calculate the green canopy cover (GCC). The stem water potential was measured, and the water stress integral (Sψ) was obtained during each irrigation season. Multiple linear regression techniques and artificial neural networks (ANNs) models with multispectral and RGB bands, as well as GCC, as inputs, were trained and tested to simulate the Sψ. The results showed that the information in the visible domain was highly related to the Sψ in the 2018 season. For all the other years and combinations of years, multispectral ANNs performed slightly better. Differences in the spatial resolution and radiometric quality of the RGB and multispectral geomatic products explain the good model performances with each type of data. Additionally, RGB cameras cost less and are easier to use than multispectral cameras, and RGB images are simpler to process than multispectral images. Therefore, RGB sensors are a good option for use in predicting entire vineyard water status. In any case, field punctual measurements are still required to generate a general model to estimate the water status in any season and vineyard. Full article
(This article belongs to the Special Issue Precision Water Management)
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18 pages, 33537 KiB  
Article
Soil Water Dynamics, Effective Rooting Zone, and Evapotranspiration of Sprinkler Irrigated Potato in a Sandy Loam Soil
by Koffi Djaman, Komlan Koudahe, Aminou Saibou, Murali Darapuneni, Charles Higgins and Suat Irmak
Agronomy 2022, 12(4), 864; https://doi.org/10.3390/agronomy12040864 - 31 Mar 2022
Cited by 11 | Viewed by 4278
Abstract
Potato (Solanum tuberosum L.) is a very sensitive crop to water stress and timely irrigation water management improves tuber yield and quality. The objectives of this study were to (1) investigate soil water dynamics under potato crops across their root zone and [...] Read more.
Potato (Solanum tuberosum L.) is a very sensitive crop to water stress and timely irrigation water management improves tuber yield and quality. The objectives of this study were to (1) investigate soil water dynamics under potato crops across their root zone and (2) estimate potato crop evapotranspiration (ETa) under sprinkler irrigation on the sandy loam soil. The field experiment was conducted during the 2018 and 2019 growing seasons at the Navajo Farms within the Navajo Agricultural Products Industry, Farmington, NM. Two irrigation scheduling methods were evaluated as FAO-56 approach evapotranspiration-based scheduling and soil moisture sensing irrigation scheduling. Sentek capacitance soil moisture probe was used across four commercial potato fields in each year after calibration to the soil texture just after installation. Crop Evapotranspiration values estimated by the water balance method and the two-step approach were compared to the satellite-based models used in OpenET. The results showed that the potato’s effective rooting zone is the upper 40 cm soil layer. Potato plants extracted more than 50% of total water from the upper 15 cm of the soil profile and about 85% from the upper 40 cm of the soil profile. Little water amount was extracted from the 40–60 cm soil water. Potato crop seasonal evapotranspiration averaged 580 to 645 mm in 2018 and 2019, respectively. The Two-step approach ETa values of 795.5 and 832.7 mm in 2018 and 2019, respectively, were higher than the soil water balance estimated ETa. The satellite modeled ETa varied with field and years and ranged from 437 to 759 mm and averaged 570.4 mm for the 2016–2020 period. Soil moisture probe-based irrigation scheduling improved irrigation water management and the irrigation water use of potatoes in the semiarid climate. Full article
(This article belongs to the Special Issue Precision Water Management)
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16 pages, 2977 KiB  
Article
Real Time Soil Moisture (RTSM) Based Irrigation Scheduling to Improve Yield and Water-Use Efficiency of Green Pea (Pisum sativum L.) Grown in North India
by Arunadevi K., Singh M., Denny Franco, Prajapati V. K., Ramachandran J. and Maruthi Sankar G. R.
Agronomy 2022, 12(2), 278; https://doi.org/10.3390/agronomy12020278 - 21 Jan 2022
Cited by 12 | Viewed by 3079
Abstract
A field experiment on green pea (Pisum Sativum L.) was conducted under drip irrigation to determine the irrigation schedule based on real-time soil moisture measurements with irrigation treatments (main plots) and fertilizer treatments (sub-plots) in a split-plot design with three replications. Main [...] Read more.
A field experiment on green pea (Pisum Sativum L.) was conducted under drip irrigation to determine the irrigation schedule based on real-time soil moisture measurements with irrigation treatments (main plots) and fertilizer treatments (sub-plots) in a split-plot design with three replications. Main plots consisted of fourirrigation levels at different matric potential ranges (I1: −20 kPa; I2: −30 kPa; I3: −35 kPa; and I4: −40 kPa), while the sub-plots consisted of three fertigation levels (F1: 120%, F2: 100% and F3: 80%) of recommended dose of fertilizers (40:60:50 kg/ha of NPK). The tensiometer with digital pressure transducer transferred the soil matric potential data to the irrigation controller, which activated the solenoid valves for irrigation. Observations were collected on plant growth parameters, pod yield, and quality parameters. Descriptive statistics of different plant growth parameters were made. The higher SMP threshold (−20 kPa) and lower SMP threshold (−40 kPa) greatly reduced the yield and water-use efficiency. Considering the results, real-time soil moisture-based irrigation at the soil matric potential threshold level of −30 kPa with 120% of recommended dose of fertilizers through fertigation was recommended for attaining maximum green pea pod yield and water-use efficiency under semi-arid Inceptisols. Full article
(This article belongs to the Special Issue Precision Water Management)
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14 pages, 1442 KiB  
Article
Development of an Automatic Irrigation Method Using an Image-Based Irrigation System for High-Quality Tomato Production
by Fei Zhao, Hideo Yoshida, Eiji Goto and Shoko Hikosaka
Agronomy 2022, 12(1), 106; https://doi.org/10.3390/agronomy12010106 - 1 Jan 2022
Cited by 6 | Viewed by 2692
Abstract
In this study, we developed an automatic irrigation method using an image-based irrigation system for high-quality tomato production in a greenhouse by investigating effects of a diurnal periodic cycle of irrigation on the photosynthesis, growth, yield, and fruit quality of tomatoes. The diurnal [...] Read more.
In this study, we developed an automatic irrigation method using an image-based irrigation system for high-quality tomato production in a greenhouse by investigating effects of a diurnal periodic cycle of irrigation on the photosynthesis, growth, yield, and fruit quality of tomatoes. The diurnal periodic cycle in a moderate wilting–full recovery treatment (MR) with a medium threshold value was more frequent than that in a severe wilting–full recovery treatment (SR) with a high threshold value. Mean daily maximum wilting ratios for MR and SR were 7.2% and 11.3%, respectively, when wilting ratios were set to threshold values of 7% and 14%, respectively. Total irrigation amounts in MR and SR were similar and lower than that in the untreated control. Net photosynthetic rate decreased under water stress, with values in MR being higher than that in SR, and recovered rapidly to more than 90% of its maximum value following irrigation. Plant growth and fruit yield per plant in MR and SR were lower than that in the control. Water stress treatment could improve fruit quality when it commenced at the anthesis stage or early fruit development stage. Total irrigation amount was a more important parameter than the threshold value for controlling the growth, yield, and fruit quality of tomatoes. Full article
(This article belongs to the Special Issue Precision Water Management)
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17 pages, 12690 KiB  
Article
Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry
by Travis L. Roberson, Mike J. Badzmierowski, Ryan D. Stewart, Erik H. Ervin, Shawn D. Askew and David S. McCall
Agronomy 2021, 11(10), 1960; https://doi.org/10.3390/agronomy11101960 - 29 Sep 2021
Cited by 4 | Viewed by 1951
Abstract
The need for water conservation continues to increase as global freshwater resources dwindle. Turfgrass mangers are adapting to these concerns by implementing new tools to reduce water consumption. Time-domain reflectometer (TDR) soil moisture sensors can decrease water usage when scheduling irrigation, but nonuniformity [...] Read more.
The need for water conservation continues to increase as global freshwater resources dwindle. Turfgrass mangers are adapting to these concerns by implementing new tools to reduce water consumption. Time-domain reflectometer (TDR) soil moisture sensors can decrease water usage when scheduling irrigation, but nonuniformity across unsampled locations creates irrigation inefficiencies. Remote sensing data have been used to estimate soil moisture stress in turfgrass systems through the normalized difference vegetation index (NDVI). However, numerous stressors other than moisture constraints impact NDVI values. The water band index (WBI) is an alternative index that uses narrowband, near-infrared light reflectance to estimate moisture limitations within the plant canopy. The green-to-red ratio index (GRI) is a vegetation index that has been proposed as a cheaper alternative to WBI as it can be measured using digital values of visible light instead of relying on more costly hyperspectral reflectance measurements. A replicated 2 × 3 factorial experimental design was used to repeatedly measure turf canopy reflectance and soil moisture over time as soils dried. Pots of ‘007’ creeping bentgrass (CBG) and ‘Latitude 36’ hybrid bermudagrass (HBG) were grown on three soil textures: United States Golf Association (USGA) 90:10 sand, loam, and clay. Reflectance data were collected hourly between 07:00 and 19:00 using a hyperspectral radiometer and volumetric water content (VWC) data were collected continuously using an embedded soil moisture sensor from soil saturation until complete turf necrosis by drought stress. The WBI had the strongest relationship to VWC (r = 0.62) compared to GRI (r = 0.56) and NDVI (r = 0.47). The WBI and GRI identified significant moisture stress approximately 28 h earlier than NDVI (p = 0.0010). Those metrics also predicted moisture stress prior to fifty percent visual estimation of wilt (p = 0.0317), with lead times of 12 h (WBI) and 9 h (GRI). By contrast, NDVI provided 2 h of prediction time. Nonlinear regression analysis showed that WBI and GRI can be useful for predicting moisture stress of CBG and HBG grown on three different soil textures in a controlled environment. Full article
(This article belongs to the Special Issue Precision Water Management)
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