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Research on Farmland Evapotranspiration, Soil Evaporation and Water Effective Utilization

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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 (31 August 2023) | Viewed by 6840

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Special Issue Editor

Dr. Johann Martínez-Lüscher

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Guest Editor

Plant Stress Physiology Group (Associated Unit to CSIC, EEAD, Zaragoza), Universidad de Navarra-BIOMA, Irunlarrea 1, E-31008 Pamplona, Navarra, Spain
Interests: climate change; irrigation; precision agriculture; farm decision support systems

Special Issue Information

Dear Colleagues,

Climate change is progressively increasing temperatures across the globe. In addition, there is an ongoing change in climate patterns, leading to more extreme seasons in temperate regions, as well as an increase in the incidence of extreme events. In many regions, precipitation is becoming scarcer, combined with an increase in crop evapotranspiration due to changes in temperature, air humidity, or insulation. The increasing awareness of desertification and the vulnerability of freshwater ecosystems is also reducing the amount of water available for irrigation. Thus, a more sustainable use of water is required, warranting studies that improve our understanding of crop evapotranspiration, automated soil and plant water status determination, irrigation decision aid systems, soil amendments and irrigation strategies.

In this Special Issue, we aim to improve knowledge related to any aspect of enabling the effective use of water, so that farmers can maintain or increase productivity with reduced precipitation or a smaller budget for irrigation water.

Dr. Johann Martínez-Lüscher
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.

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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

evapotranspiration
irrigation
plant water status
precision agriculture
water use efficiency

Published Papers (5 papers)

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Research

18 pages, 1659 KiB

Open AccessArticle

Predicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillo

by Ricardo Egipto, Arturo Aquino, Joaquim Miguel Costa and José Manuel Andújar

Agronomy 2023, 13(10), 2463; https://doi.org/10.3390/agronomy13102463 - 23 Sep 2023

Cited by 2 | Viewed by 1154

Abstract

This study focuses on assessing the accuracy of supervised machine learning regression algorithms (MLAs) in predicting actual crop evapotranspiration (ETc act) for a deficit irrigated vineyard of Vitis vinifera cv. Tempranillo, influenced by a typical Mediterranean climate. The standard approach of using the Food and Agriculture Organization (FAO) crop evapotranspiration under standard conditions (FAO-56 Kc-ET₀) to estimate ETc act for irrigation purposes faces limitations in row-based, sparse, and drip irrigated crops with large, exposed soil areas, due to data requirements and potential shortcomings. One significant challenge is the accurate estimation of the basal crop coefficient (Kcb), which can be influenced by incorrect estimations of the effective transpiring leaf area and surface resistance. The research results demonstrate that the tested MLAs can accurately estimate ETc act for the vineyard with minimal errors. The Root-Mean-Square Error (RMSE) values were found to be in the range of 0.019 to 0.030 mm·h⁻¹. Additionally, the obtained MLAs reduced data requirements, which suggests their feasibility to be used to optimize sustainable irrigation management in vineyards and other row crops. The positive outcomes of the study highlight the potential advantages of employing MLAs for precise and efficient estimation of crop evapotranspiration, leading to improved water management practices in vineyards. This could promote the adoption of more sustainable and resource-efficient irrigation strategies, particularly in regions with Mediterranean climates. Full article

(This article belongs to the Special Issue Research on Farmland Evapotranspiration, Soil Evaporation and Water Effective Utilization)

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21 pages, 5251 KiB

Open AccessArticle

Effects of Atom Search-Optimized Thornthwaite Potential Evapotranspiration on Root and Shoot Systems in Controlled Carica papaya Cultivation

by Ronnie Concepcion II, Jonah Jahara Baun, Adrian Genevie Janairo and Argel Bandala

Agronomy 2023, 13(10), 2460; https://doi.org/10.3390/agronomy13102460 - 23 Sep 2023

Cited by 1 | Viewed by 1305

Abstract

Potential evapotranspiration (PET) indicates if a cultivation area is suitable for planting. Currently, site-specific PET models that are based on large geographic regions are vulnerable to inaccurate predictions as a result of climate change and sudden changes in the environmental abiotic stressors that affect plant growth. For the aim of promoting the papaya Sinta F1 cultivar, the study optimized the standard Thornthwaite PET model by integrating three advanced physics-based metaheuristics and evolutionary computing, namely atom search (ASO), differential evolution (DE), and multiverse (MVO) optimizers. The PET value was optimized through minimization as a function of air temperature, light intensity, heat index, and extended heat index. As the PET value approaches 0, it indicates that there is more soil-water content that can be absorbed by plants. Based on the four cultivation treatments (uncontrolled, ASO, DE, and MVO) exposed in three replicates within 90 days, the ASO-optimized Thornthwaite PET-treated (ASO_Th) papaya plants resulted in the highest chlorophyll a and b concentrations, densest stomatal density, concentrated root and stem xylem and phloem vessels, considerable root and stem length, most formed leaf count, and strongest action potentials coming from stem membrane for both light and dark periods. This proves the applicability of the intelligent process in modifying the Thornthwaite model for plant growth promotion. Also, through the developed ASO_Th, the stem length and thickness ratio was improved for mechanical stability to facilitate more branching leaves and potential fruits during the fruiting stage, and the chlorophyll a and b ratio was enhanced, which naturally extended the light energy band for photosynthesis. Overall, the newly developed ASO_Th model may be used to grow papaya seedlings year-round anywhere on Earth if there is a control system to regulate the environmental setting inside the growth chamber. Full article

(This article belongs to the Special Issue Research on Farmland Evapotranspiration, Soil Evaporation and Water Effective Utilization)

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Graphical abstract

15 pages, 3836 KiB

Open AccessArticle

Evaluation and Modelling of Reference Evapotranspiration Using Different Machine Learning Techniques for a Brazilian Tropical Savanna

by Thiago A. Spontoni, Thiago M. Ventura, Rafael S. Palácios, Leone F. A. Curado, Widinei A. Fernandes, Vinicius B. Capistrano, Clóvis L. Fritzen, Hamilton G. Pavão and Thiago R. Rodrigues

Agronomy 2023, 13(8), 2056; https://doi.org/10.3390/agronomy13082056 - 03 Aug 2023

Cited by 2 | Viewed by 1175

Abstract

Meteorological elements can affect the environment and cultures differently and may alter the natural development process contributing significantly to climate change. Meteorological variables of the Brazilian Pantanal were studied and used to determine evapotranspiration with fewer variables. It was found that artificial intelligence can substantially improve environmental modeling when alternative prediction techniques are used, resulting in lower project costs and more reliable results. This work tried to find the best combination by comparing machine learning techniques such as artificial neural networks, random forests, and support vector machines. A new model was created that depends on fewer climatic variables compared to the Penman–Monteith method (the standard method for estimating reference evapotranspiration) and can efficiently describe the reference evapotranspiration. Machine learning techniques are highly efficient for modeling environmental systems since they can process large amounts of data and find the best interactions between the parameters involved. In addition, more than 98% accuracy was obtained using fewer variables compared to the standard method when artificial neural networks are utilized. Full article

(This article belongs to the Special Issue Research on Farmland Evapotranspiration, Soil Evaporation and Water Effective Utilization)

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16 pages, 4465 KiB

Open AccessArticle

Application of Organic Fertilizers Optimizes Water Consumption Characteristics and Improves Seed Yield of Oilseed Flax in Semi-Arid Areas of the Loess Plateau

by Peng Xu, Yuhong Gao, Zhengjun Cui, Bing Wu, Bin Yan, Yifan Wang, Ming Wen, Haidi Wang, Xingkang Ma and Zedong Wen

Agronomy 2023, 13(7), 1755; https://doi.org/10.3390/agronomy13071755 - 28 Jun 2023

Cited by 1 | Viewed by 1515

Abstract

Organic fertilizers are an important source of nutrients for improving farmland fertility. To explore high-yield, efficient and green production technology for oilseed flax in dryland agricultural areas, a field split plot experiment was conducted in the semi-arid area of the Loess Plateau in northwest China from April to August in 2020 and 2021. The study compared and analyzed the effects of different nutrient sources and their application rates on water consumption characteristics, grain yield and water use efficiency of oilseed flax. The main plots were fertilizer types (sheep manure, chicken manure and chemical fertilizer), while the subplots were fertilizer application rates (sheep manure: S1-12,500 kg·hm⁻² and S2-25,000 kg·hm⁻²; chicken manure: C1-5800 kg·hm⁻² and C2-11,600 kg·hm⁻²; chemical fertilizer: F1-N 112.5 kg·hm⁻², P 75 kg·hm⁻², K 67.5 kg·hm⁻² and F2-N 225 kg·hm⁻², P₂O₅ 150 kg·hm⁻², K₂O 135 kg·hm⁻²). The results showed that compared with chemical fertilizers, organic fertilizers significantly increased the soil water storage capacity of the 0–160 cm soil layer during the whole growth period of oilseed flax and significantly reduced water consumption. During two growing seasons, the application of 25,000 kg·hm⁻² sheep manure significantly reduced water consumption during the seedling-bud period and green fruit period-maturity period of oilseed flax by 16.13% and 23.19% compared with CK, respectively. Thousand-grain weight, yield and water use efficiency were significantly increased by 14.70%, 48.32% and 61.29%, respectively. These results indicate that the application of 25,000 kg·hm⁻² sheep manure can significantly increase soil water storage capacity of the 0–160 cm soil layer, reduce water consumption during the whole growth period of oilseed flax and thus improve grain yield and water use efficiency of oilseed flax. It is a suitable fertilization technology for the high-yield, efficient and green production of oilseed flax in the semi-arid areas of northwest Loess Plateau. Full article

(This article belongs to the Special Issue Research on Farmland Evapotranspiration, Soil Evaporation and Water Effective Utilization)

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15 pages, 1350 KiB

Open AccessArticle

Estimation of Reference Crop Evapotranspiration with Three Different Machine Learning Models and Limited Meteorological Variables

by Stephen Luo Sheng Yong, Jing Lin Ng, Yuk Feng Huang and Chun Kit Ang

Agronomy 2023, 13(4), 1048; https://doi.org/10.3390/agronomy13041048 - 03 Apr 2023

Cited by 6 | Viewed by 1423

Abstract

Precise reference crop evapotranspiration (ET₀) estimation plays a key role in agricultural fields as it aids in the proper operation and management of irrigation scheduling. However, reliable ET₀ estimation poses a challenge when there is insufficient or incomplete long-term meteorological data at the East Coast Economic Region (ECER), Malaysia, where the economy is highly dependent on agricultural crop production. This study evaluated the performances of different standalone machine learning (ML) models, namely, the light gradient boosting machine (LGBM), decision forest regression (DFR), and artificial neural network (ANN) models using four different combinations of meteorological variables. The incorporation of solar radiation enhanced the accuracy of the standalone ML models, demonstrating the role of energetic factors in the evapotranspiration mechanism. Additionally, both the ANN and LGBM models showed overall satisfactory performances, and were thus recommended them as alternate models for ET₀ estimation. This was owing to their good capability in capturing the non-linearity and interaction process among the meteorological variables. The outcomes of this study will be advantageous to farmers and policymakers in determining the actual crop water demands to maximize crop productivity in data-scarce tropical regions. Full article

(This article belongs to the Special Issue Research on Farmland Evapotranspiration, Soil Evaporation and Water Effective Utilization)

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