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Agriculture
Special Issues
Model-Based Evaluation of Crop Agronomic Traits
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Model-Based Evaluation of Crop Agronomic Traits

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 725

Special Issue Editors

Dr. Ning Yao

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Guest Editor
College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling 712100, China
Interests: drought evolution; climate change; drought prediction; drought characteristics
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Liu

E-Mail Website
Guest Editor
College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China
Interests: crop modeling; climate change; climate extremes; water stress; phenology
Special Issues, Collections and Topics in MDPI journals
Dr. Linchao Li

E-Mail Website
Guest Editor
College of Agronomy, Inner Mongolia Agricultural University, Hohhot 010019, China
Interests: climate change; sustainable agriculture; climatology; hydrology
Dr. Na Li

E-Mail Website
Guest Editor
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Interests: agricultural ecological environment; crop model; climate change; management decision-making; disaster risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increase in population, limited arable land resources cannot be expanded indefinitely. In this context, agronomic trait evaluation has become a key technical tool to break through the resource constraints and guarantee food security. In the past, crop improvement mainly relied on field trials and phenotypic observations, which were both time-consuming and resource-consuming; this greatly limited the efficiency and effectiveness of crop improvement. However, with the advent of crop modeling, coupled with the challenges of climate change, the model-based assessment of crop agronomic traits has become an important tool for modern agriculture, integrating advances in computational modeling, remote sensing, and data analysis. It has revolutionized the way researchers assess traits such as yield potential, drought tolerance, and nutrient use efficiency. The purpose of this Special Issue is to compile cutting-edge research on the development, validation, and application of models in crop science. We hope that the papers solicited will deepen our understanding of genotype–environment interactions, optimize agricultural management strategies, and improve prediction accuracy under different climatic conditions. The scope of research includes but is not limited to mechanistic and empirical models, machine learning models, and high-throughput phenotyping models.

Dr. Ning Yao
Dr. Jian Liu
Dr. Linchao Li
Dr. Na Li
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. Agriculture 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

  • crop modeling
  • agronomic traits
  • climate resilience
  • precision agriculture
  • digital agriculture

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Published Papers (1 paper)

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Research

26 pages, 10157 KiB  
Article
Improving Soil Moisture Estimation by Integrating Remote Sensing Data into HYDRUS-1D Using an Ensemble Kalman Filter Approach
by Yule Sun, Quanming Liu, Chunjuan Wang, Qi Liu and Zhongyi Qu
Agriculture 2025, 15(12), 1320; https://doi.org/10.3390/agriculture15121320 - 19 Jun 2025
Viewed by 285
Abstract
Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework [...] Read more.
Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R2 value of 0.7964 and a root mean square error (RMSE) of 0.021 cm3·cm−3. HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm3·cm−3. The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm3·cm−3, with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions. Full article
(This article belongs to the Special Issue Model-Based Evaluation of Crop Agronomic Traits)
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