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Precision Agricultural Water Management and Water Use Efficiency Assessment
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Precision Agricultural Water Management and Water Use Efficiency Assessment

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

Deadline for manuscript submissions: closed (1 November 2022) | Viewed by 3092

Special Issue Editors

Prof. Dr. Jinglei Wang

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Guest Editor
Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang 453003, China
Interests: intelligent irrigation; agricultural big data; remote sensing technology; GIS; crop high-efficiency water use; cloud computing; water resources and environment; crop-water model; soil hydraulics; spatial-temporal variation and scale conversion technology of crop water requirement
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Baozhong Zhang

E-Mail Website
Guest Editor
State Key Laboratory of Simulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Interests: evapotranspiration; scale up; time series; water management
Prof. Dr. Yufeng Luo

E-Mail Website
Guest Editor
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
Interests: water-saving irrigation; water-food-environment nexus; crop water requirements; smart irrigation technologies; paddy rice irrigation; climate change; irrigation-induced cooling effects; non-point source pollution control

Special Issue Information

Dear Colleagues,

Agricultural systems are vulnerable to climatic variability. In the coming decades, it is projected that the spatiotemporal variation of precipitation will have a devastating impact on the spatiotemporal distribution of water resources, leading to severe floods or droughts. Precision agriculture water management based on a regional agricultural water supply and the improvement of agricultural water efficiency are important measures to achieve a high yield and stable production. With the fast development of sensors and computer and communication technologies, the nondestructive and timely assessment of crop water requirements has become a new research direction. Many scholars have conducted many studies on crop water information perception, water use estimation and regional water management based on Big Data and deep learning, obtaining new findings and developing new technologies in the process. This Special Issue focuses on the research advances in precision agricultural water management and the theoretical and technological assessment of water efficiency. This Special Issue aims to collect original, high-quality research and review articles.

Prof. Dr. Jinglei Wang
Prof. Dr. Baozhong Zhang
Prof. Dr. Yufeng Luo
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. 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

  • rapid and effective acquisition of crop growth
  • multi-source information fusion technology for irrigation decisions
  • evaluation systems and indicators for agriculture water management
  • modeling and models for the optimal choice of agriculture water resources
  • big data and deep learning

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Published Papers (1 paper)

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Research

16 pages, 3955 KiB  
Article
Coupling Process-Based Models and Machine Learning Algorithms for Predicting Yield and Evapotranspiration of Maize in Arid Environments
by Ahmed Attia, Ajit Govind, Asad Sarwar Qureshi, Til Feike, Mosa Sayed Rizk, Mahmoud M. A. Shabana and Ahmed M.S. Kheir
Water 2022, 14(22), 3647; https://doi.org/10.3390/w14223647 - 12 Nov 2022
Cited by 20 | Viewed by 3546
Abstract
Crop yield prediction is critical for investigating the yield gap and potential adaptations to environmental and management factors in arid regions. Crop models (CMs) are powerful tools for predicting yield and water use, but they still have some limitations and uncertainties; therefore, combining [...] Read more.
Crop yield prediction is critical for investigating the yield gap and potential adaptations to environmental and management factors in arid regions. Crop models (CMs) are powerful tools for predicting yield and water use, but they still have some limitations and uncertainties; therefore, combining them with machine learning algorithms (MLs) could improve predictions and reduce uncertainty. To that end, the DSSAT-CERES-maize model was calibrated in one location and validated in others across Egypt with varying agro-climatic zones. Following that, the dynamic model (CERES-Maize) was used for long-term simulation (1990–2020) of maize grain yield (GY) and evapotranspiration (ET) under a wide range of management and environmental factors. Detailed outputs from three growing seasons of field experiments in Egypt, as well as CERES-maize outputs, were used to train and test six machine learning algorithms (linear regression, ridge regression, lasso regression, K-nearest neighbors, random forest, and XGBoost), resulting in more than 1.5 million simulated yield and evapotranspiration scenarios. Seven warming years (i.e., 1991, 1998, 2002, 2005, 2010, 2013, and 2020) were chosen from a 31-year dataset to test MLs, while the remaining 23 years were used to train the models. The Ensemble model (super learner) and XGBoost outperform other models in predicting GY and ET for maize, as evidenced by R2 values greater than 0.82 and RRMSE less than 9%. The broad range of management practices, when averaged across all locations and 31 years of simulation, not only reduced the hazard impact of environmental factors but also increased GY and reduced ET. Moving beyond prediction and interpreting the outputs from Lasso and XGBoost, and using global and local SHAP values, we found that the most important features for predicting GY and ET are maximum temperatures, minimum temperature, available water content, soil organic carbon, irrigation, cultivars, soil texture, solar radiation, and planting date. Determining the most important features is critical for assisting farmers and agronomists in prioritizing such features over other factors in order to increase yield and resource efficiency values. The combination of CMs and ML algorithms is a powerful tool for predicting yield and water use in arid regions, which are particularly vulnerable to climate change and water scarcity. Full article
(This article belongs to the Special Issue Precision Agricultural Water Management and Water Use Efficiency Assessment)
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