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Advances in Irrigation Technology and Adaptation to Climate Change

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

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 12855

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Special Issue Editor

Prof. Dr. Andre Daccache

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Guest Editor

Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA
Interests: precision irrigation; remote sensing; crop and soil spatial variability; water resources management; irrigation engineering; crop and climate change modeling

Special Issue Information

Dear Colleagues,

Competition for water resources is expected to increase, with a particular impact on agriculture. To sustain the global food production system, agriculture uses 70% of all freshwater withdrawals, on average. Dwindling water supply, population growth, urbanization, and climate change mean that growers need to produce more food with less water. Agriculture today is experiencing a new technological revolution that is changing and shaping the way we produce food. Farmers are increasingly relying on science and technology to collect and analyze data, monitor growth and demand, control input application, and make smarter planning decisions. This provides a great opportunity for agriculture to cope with the future challenges in food security, water scarcity, and climate change.

In this Special Issue, we are soliciting papers that apply/discuss technological solutions that enhance crop water use efficiency, irrigation and drainage performance, or improve the management of agricultural water resources. We are also calling for studies that discuss options for climate change adaptation and mitigation in agricultural water that range from plant to global level. We encourage your contributions and look forward to your submissions.

Prof. Dr. Andre Daccache
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. 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

precision irrigation
remote sensing
evapotranspiration
irrigation engineering
variable rate irrigation
climate change
food security
systematic review
water management
sensors
water stress
machine learning

Published Papers (3 papers)

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Research

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12 pages, 1908 KiB

Open AccessArticle

A Simple Application for Computing Reference Evapotranspiration with Various Levels of Data Availability—ETo Tool

by Gonçalo C. Rodrigues and Ricardo P. Braga

Agronomy 2021, 11(11), 2203; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11112203 - 30 Oct 2021

Cited by 4 | Viewed by 2240

Abstract

Reference evapotranspiration (ETo) estimations may be used to improve the efficiency of irrigated agriculture. However, its computation can be complex and could require numerous weather data that are not always available for many locations. Different methods are available to estimate ETo when limited data are available, and the assessment of the most accurate one can be difficult and time consuming. There are some standalone softwares available for computing ETo but none of them allow for the comparison of different methods for the same or different datasets simultaneously. This paper aims to present an application for estimating ETo using several methods that require different levels of data availability, namely FAO-56 Penman–Monteith (PM), the Original and the three modified Hargreaves–Samani (HS and MHS1, MHS2 and MHS3), Trajkovic (TR) and the single temperature procedure (MaxTET). Also, it facilitates the comparison of the accuracy estimation of two selected methods. From an example case, for where the application was used to compute ETo for three different locations, results show that the application can easily and successfully estimate ETo using the proposed methods, allowing for statistical comparison of those estimations. HS proves to be the most accurate method for the studied locations; however, the accuracy of all methods tends to be lower for costal locations than for more continental sites. With this application, users can select the best ETo estimation methods for a specific location and use it for irrigation purposes. Full article

(This article belongs to the Special Issue Advances in Irrigation Technology and Adaptation to Climate Change)

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14 pages, 1701 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate

by Gonçalo C. Rodrigues and Ricardo P. Braga

Agronomy 2021, 11(10), 2077; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11102077 - 18 Oct 2021

Cited by 11 | Viewed by 2659

Abstract

This study aims at assessing the accuracy of estimating daily reference evapotranspiration (ETo) computed with NASA POWER reanalysis products. Daily ETo estimated from local observations of weather variables in 14 weather stations distributed across Alentejo Region, Southern Portugal were compared with ETo derived from NASA POWER weather data, using raw and bias-corrected datasets. Three different methods were used to compute ETo: (a) FAO Penman-Monteith (PM); (b) Hargreaves-Samani (HS); and (c) MaxTET. Results show that, when using raw NASA POWER datasets, a good accuracy between the observed ETo and reanalysis ETo was observed in most locations (R² > 0.70). PM shows a tendency to over-estimating ETo with an RMSE as high as 1.41 mm d⁻¹, while using a temperature-based ET estimation method, an RMSE lower than 0.92 mm d⁻¹ is obtained. If a local bias correction is adopted, the temperature-based methods show a small over or underestimation of ETo (–0.40 mm d⁻¹ ≤ MBE < 0.40 mm d⁻¹). As for PM, ETo is still underestimated for 13 locations (MBE < 0 mm d⁻¹) but with an RMSE never higher than 0.77 mm d⁻¹. When NASA POWER raw data is used to estimate ETo, HS_Rs proved the most accurate method, providing the lowest RMSE for half the locations. However, if a data regional bias correction is used, PM leads to the most accurate ETo estimation for half the locations; also, when a local bias correction is performed, PM proved the be the most accurate ETo estimation method for most locations. Nonetheless, MaxTET proved to be an accurate method; its simplicity may prove to be successful not only when only maximum temperature data is available but also due to the low data required for ETo estimation. Full article

(This article belongs to the Special Issue Advances in Irrigation Technology and Adaptation to Climate Change)

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Other

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16 pages, 4038 KiB

Open AccessEditor’s ChoiceSystematic Review

Soil Properties Prediction for Precision Agriculture Using Visible and Near-Infrared Spectroscopy: A Systematic Review and Meta-Analysis

by Arman Ahmadi, Mohammad Emami, Andre Daccache and Liuyue He

Agronomy 2021, 11(3), 433; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11030433 - 26 Feb 2021

Cited by 40 | Viewed by 6821

Abstract

Reflectance spectroscopy for soil property prediction is a non-invasive, fast, and cost-effective alternative to the standard laboratory analytical procedures. Soil spectroscopy has been under study for decades now with limited application outside research. The recent advancement in precision agriculture and the need for the spatial assessment of soil properties have raised interest in this technique. The performance of soil spectroscopy differs from one site to another depending on the soil’s physical composition and chemical properties but it also depends on the instrumentation, mode of use (in-situ/laboratory), spectral range, and data analysis methods used to correlate reflectance data to soil properties. This paper uses the systematic review procedure developed by the Centre for Evidence-Based Conservation (CEBC) for an evidence-based search of soil property prediction using Visible (V) and Near-InfraRed (NIR) reflectance spectroscopy. Constrained by inclusion criteria and defined methods for literature search and data extraction, a meta-analysis is conducted on 115 articles collated from 30 countries. In addition to the soil properties, findings are also categorized and reported by different aspects like date of publication, journals, countries, employed regression methods, laboratory or in-field conditions, spectra preprocessing methods, samples drying methods, spectroscopy devices, wavelengths, number of sites and samples, and data division into calibration and validation sets. The arithmetic means of the coefficient of determination (R²) over all the reports for different properties ranged from 0.68 to 0.87, with better predictions for carbon and nitrogen content and lower performance for silt and clay. After over 30 years of research on using V-NIR spectroscopy to predict soil properties, this systematic review reveals solid evidence from a literature search that this technology can be relied on as a low-cost and fast alternative for standard methods of soil properties prediction with acceptable accuracy. Full article

(This article belongs to the Special Issue Advances in Irrigation Technology and Adaptation to Climate Change)

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