Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
3.0
Journals
Horticulturae
Special Issues
Advances in Irrigation and Fertilization Technologies for Sustainable Plant Production
share announcement

Advances in Irrigation and Fertilization Technologies for Sustainable Plant Production

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Protected Culture".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 305

Special Issue Editors

Prof. Dr. Di Feng

E-Mail Website
Guest Editor
Key Laboratory of Saline-Alkali Soil Improvement and Utilization (Saline-Alkali Land in Arid and Semiarid Regions), Ministry of Agriculture and Rural Affairs, China, Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumchi 830091, China
Interests: optimize saline water utilization; irrigation and fertilizer schedule; plant soil interaction; water-salinity-yield effect; water use efficiency
Dr. Songrui Ning

E-Mail Website
Guest Editor
State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Xi'an University of Technology, Xi'an 710048, China
Interests: modeling; optimize irrigation and fertilizer schedule; crop growth and yield; fertilizer-yield effect; water use efficiency

Special Issue Information

Dear Colleagues,

As global populations continue to grow and climate variability intensifies, the sustainable enhancement of plant production has become an urgent priority. The efficient use of water and nutrients is central to this challenge. Innovations in irrigation and fertilization are not only vital for increasing plant productivity but also for minimizing environmental impacts such as groundwater depletion, soil salinization, and nutrient runoff.

The purpose of this Special Issue “Advances in Irrigation and Fertilization Technologies for Sustainable Plant Production” is to present innovative studies, tools, approaches, and techniques that have been successful in addressing some of these concerns, such as precision irrigation technologies, energy-saving technologies, irrigation and nutrient management, economic and environmental impact assessments, soil fertility and soil enzymes, the coupled simulation of crop growth and soil environment, and soilless and greenhouse and traditional soil cultivation, as well as drone-assisted monitoring, plant and soil nutrition diagnosis, and AI-driven decision support tools that enable precise water–salt–fertilizer management tailored to real-time crop needs and soil conditions. Researchers are invited to contribute original research articles and reviews that encompass a wide range of topics within the realm of irrigation and fertilization technologies in soilless and greenhouse and traditional soil cultivation for plant production. The scope includes, but is not limited to, investigations into plant-specific responses to water and fertigation, environmental implications and risk assessment, and technological innovations driving this field forward.

Prof. Dr. Di Feng
Dr. Songrui Ning
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 250 words) can be sent to the Editorial Office for assessment.

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. Horticulturae 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 2200 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

  • irrigation technology
  • irrigation and fertilizer schedule
  • water and nutrient use efficiency
  • soil water–salt–fertilizer transport
  • intelligent agriculture
  • modeling
  • plant growth and yield
  • fertilizer-yield effect
  • plant phenotypic

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 15798 KB  
Article
Optimizing Priestley–Taylor Model Based on Machine Learning Algorithms to Simulate Tomato Evapotranspiration in Chinese Greenhouse
by Jiankun Ge, Jiaxu Du, Xuewen Gong, Quan Zhou, Guoyong Yang, Yanbin Li, Huanhuan Li, Jiumao Cai, Hanmi Zhou, Mingze Yao, Xinguang Wei and Weiwei Xu
Horticulturae 2026, 12(1), 89; https://doi.org/10.3390/horticulturae12010089 - 14 Jan 2026
Viewed by 152
Abstract
To further improve the prediction accuracy for greenhouse crop evapotranspiration (ET) under different irrigation conditions and enhance irrigation water use efficiency, this study proposes three methods to revise the Priestley–Taylor (PT) model coefficient α for calculating ET at different growth stages: [...] Read more.
To further improve the prediction accuracy for greenhouse crop evapotranspiration (ET) under different irrigation conditions and enhance irrigation water use efficiency, this study proposes three methods to revise the Priestley–Taylor (PT) model coefficient α for calculating ET at different growth stages: (1) considering the leaf senescence coefficient fS, plant temperature constraint parameter ft, and soil water stress index fsw to correct α (MPT model); (2) combining the Penman–Monteith (PM) model to inversely calculate α (PT-M model); (3) using the machine learning XGBoost algorithm to optimize α (PT-M(XGB) model). Accordingly, this study observed the cumulative evaporation (Ep) of a 20 cm standard evaporation pan and set two different irrigation treatments (K0.9: 0.9Ep and K0.5: 0.5Ep). We conducted field measurements of meteorological data inside the greenhouse, tomato physiological and ecological indices, and ET during 2020 and 2021. The above three methods were then used to dynamically simulate greenhouse tomato ET. Results showed the following: (1) In 2020 and 2021, under K0.9 and K0.5 irrigation treatments, the MPT model mean coefficient α for the entire growth stage was 1.27 and 1.26, respectively, while the PT-M model mean coefficient α was 1.31 and 1.30. For both models, α was significantly lower than 1.26 (conventional value) during the seedling stage and the flowering and fruiting stage, rose rapidly during the fruit enlargement stage, and then gradually declined toward 1.26 during the harvest stage. (2) Predicted ET (ETe) using the PT-M model underestimated the observed ET (ETm) by 8.71~16.01% during the seedling stage and the harvest stage, and overestimated by 1.62~6.15% during the flowering and fruiting stage and the fruit enlargement stage; the errors compared to ETm under both irrigation treatments over two years was 0.1~3.3%, with an R2 of 0.92~0.96. (3) The PT-M(XGB) model achieved higher prediction accuracy, with errors compared to ETm under both irrigation treatments over two years of 0.35~0.65%, and R2 above 0.98. The PT-M(XGB) model combined with the XGBoost algorithm significantly improved prediction accuracy, providing a reference for the precise calculation of greenhouse tomato ET. Full article
(This article belongs to the Special Issue Advances in Irrigation and Fertilization Technologies for Sustainable Plant Production)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop