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Agronomy
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Crop Production in the Era of Climate Change
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Crop Production in the Era of Climate Change

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1612

Special Issue Editors

Dr. Anastasios Zotos

E-Mail Website
Guest Editor
Department of Sustainable Agriculture, University of Patras, Seferi 2, 30100 Agrinio, Greece
Interests: ecology; vegetation; sustainable agriculture; plant ecophysiology; abiotic stress; precision agriculture; irrigation management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Angelos A. Patakas

E-Mail Website
Guest Editor
Department of Food Science & Technology, University of Patras, Seferi 2, 30100 Agrinio, Greece
Interests: plant–water relationships; plant ecophysiology; plant stress physiology; abiotic stress; precision agriculture; smart agriculture; irrigation management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change poses a significant challenge to crop production, requiring innovative strategies to ensure global food security. Plants have developed mechanisms to resist drought, such as deep root systems, osmotic adjustment, and stomatal regulation, enhancing water-use efficiency (WUE). Predictive models and climate simulations could be used by scientists to forecast the adaptability of plant species under extreme climatic conditions and therefore the development of resilient crop varieties. In this framework, precision agriculture, and in particular the implementation of remote sensing and IoT devices, can be useful tools for optimizing the use of natural resources, mitigating the impacts of climate change. Innovative technologies, such as biotechnology, are being employed to produce crops with traits such as enhanced heat tolerance and faster growth cycles, improving their performance in extreme environmental conditions. Furthermore, cultivating crops with a high capacity for carbon dioxide sequestration, such as industrial plants or other plant species, can be used for the sequestration of greenhouse gases, mitigating the impact of climate change.

Dr. Anastasios Zotos
Prof. Dr. Angelos A. Patakas
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. 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

  • abiotic stress
  • climate change
  • smart agriculture
  • precision agriculture
  • remote sensing
  • Internet of Things (IoT)

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Published Papers (3 papers)

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Research

16 pages, 2270 KiB  
Article
Lodging Resistance of Japonica Hybrid Rice Plants Studied in Relation to Mechanical and Physicochemical Characteristics
by Liying Zhang, Zuobin Ma, Na He, Zhiqiang Tang, Changhua Wang, Wenjing Zheng, Hui Wang, Guomin Sui, Hong Gao and Lili Wang
Agronomy 2025, 15(3), 699; https://doi.org/10.3390/agronomy15030699 - 13 Mar 2025
Viewed by 317
Abstract
The research on rice lodging resistance holds immeasurable value for achieving high yield, stable production, and superior quality of rice. To investigate the effects of mechanical properties and physicochemical characteristics of Japonica hybrid rice on its lodging resistance ability under natural field cultivation [...] Read more.
The research on rice lodging resistance holds immeasurable value for achieving high yield, stable production, and superior quality of rice. To investigate the effects of mechanical properties and physicochemical characteristics of Japonica hybrid rice on its lodging resistance ability under natural field cultivation conditions, LY1052, LY9906, and GY1, which were mainly popularized in northern China, were selected as the experimental subjects, and NL313, Japonica hybrid rice prone to lodging, was taken as the control (NL313).The max bending force, breaking moment, bending section coefficient, single stem weight mass moment, bending strength, Young’s elastic modulus, inertia moment, and other mechanical indexes were measured by the bending test and tensile test, and the correlations between mechanical indexes, physicochemical indexes, and lodging index were studied. There was an extremely significant difference in the lodging index of experimental subjects and control (NL313) (p < 0.05). Therefore, it was concluded that the lower plant height and lighter panicle were not the stronger lodging resistance under appropriate cultivation conditions. Optimization of rice plant-type structure can achieve the unity of high culm and high yield. The lodging resistance of rice could be improved by shortening the internode length, increasing the tissue thickness and vascular bundle area, and increasing the content of cellulose and potassium in the stem. It was also found that the lodging resistance of rice plants was positively correlated with the maximum stem bending force, breaking moment, bending section coefficient, bending strength, and Young’s elastic modulus (p < 0.01) and negatively correlated with single stem weight mass moment and inertia moment (p < 0.01). It is feasible to select them as reference indexes of the lodging resistance of rice. The experimental results not only help to enrich the theoretical system of rice lodging resistance research but also provide an essential reference and basis for formulating scientific cultivation and management measures and breeding lodging-resistant rice varieties in practical production, which is of great significance for ensuring global food security and promoting sustainable agricultural development. Full article
(This article belongs to the Special Issue Crop Production in the Era of Climate Change)
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30 pages, 16605 KiB  
Article
Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model
by Qianchuan Mi, Zhiguo Huo, Meixuan Li, Lei Zhang, Rui Kong, Fengyin Zhang, Yi Wang and Yuxin Huo
Agronomy 2025, 15(3), 696; https://doi.org/10.3390/agronomy15030696 - 13 Mar 2025
Viewed by 576
Abstract
Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges such as data accessibility and soil heterogeneous necessitate the use [...] Read more.
Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges such as data accessibility and soil heterogeneous necessitate the use of numerical simulations for their effective regional-scale applications. The existing simulation methods like physical process models and machine learning (ML) algorithms have limitations: physical models struggle with parameter acquisition at regional scales, while ML algorithms face difficulties in agricultural settings due to the presence of crops. As a more advanced and complex branch of ML, deep learning algorithms face even greater limitations related to crop growth and agricultural management. To address these challenges, this study proposed a novel hybrid monitoring system that merged ML algorithms with a physical process model. Initially, we employed the Random Forest (RF) regression model that integrated multi-source environmental factors to estimate soil moisture prior to the sowing of winter wheat, achieving an average coefficient of determination (R2) of 0.8618, root mean square error (RMSE) of 0.0182 m3 m−3, and mean absolute error (MAE) of 0.0148 m3 m−3 across eight soil depths. The RF regression models provided vital parameters for the operation of the Water Balance model for Winter Wheat (WBWW) at a regional scale, enabling effective drought monitoring and assessments combined with the Soil Moisture Anomaly Percentage Index (SMAPI). Subsequent comparative analyses between the monitoring system-generated results and the actual disaster records during two regional-scale drought events highlighted its efficacy. Finally, we utilized this monitoring system to examine the spatiotemporal variations in drought patterns in the HHH region over the past two decades. The findings revealed an overall intensification of drought conditions in winter wheat, with a decline in average SMAPI at a rate of −0.021% per year. Concurrently, there has been a significant shift in drought patterns, characterized by an increase in both the frequency and extremity of drought events, while the duration and intensity of individual drought events have decreased in a majority of the HHH region. Additionally, we identified the northeastern, western, and southern areas of HHH as areas requiring concentrated attention and targeted intervention strategies. These efforts signify a notable application of multi-source data fusion techniques and the integration of physical process models within a big data context, thereby facilitating effective drought prevention, agricultural management, and mitigation strategies. Full article
(This article belongs to the Special Issue Crop Production in the Era of Climate Change)
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28 pages, 4133 KiB  
Article
A Dynamic Monitoring Framework for Spring Low-Temperature Disasters Affecting Winter Wheat: Exploring Environmental Coercion and Mitigation Mechanisms
by Meixuan Li, Zhiguo Huo, Qianchuan Mi, Lei Zhang, Jianying Yang, Fengyin Zhang, Rui Kong, Yi Wang and Yuxin Huo
Agronomy 2025, 15(2), 337; https://doi.org/10.3390/agronomy15020337 - 28 Jan 2025
Cited by 1 | Viewed by 516
Abstract
The implementation of real-time dynamic monitoring of disaster formation and severity is essential for the timely adoption of disaster prevention and mitigation measures, which in turn minimizes disaster-related losses and safeguards agricultural production safety. This study establishes a low-temperature disaster (LTD) monitoring system [...] Read more.
The implementation of real-time dynamic monitoring of disaster formation and severity is essential for the timely adoption of disaster prevention and mitigation measures, which in turn minimizes disaster-related losses and safeguards agricultural production safety. This study establishes a low-temperature disaster (LTD) monitoring system based on machine learning algorithms, which primarily consists of a module for identifying types of disasters and a module for simulating the evolution of LTDs. This study firstly employed the KNN model combined with a piecewise function to determine the daily dynamic minimum critical temperature for low-temperature stress (LTS) experienced by winter wheat in the Huang-Huai-Hai (HHH) region after regreening, with the fitting model’s R2, RMSE, MAE, NRMSE, and MBE values being 0.95, 0.79, 0.53, 0.13, and 1.716 × 10−11, respectively. This model serves as the foundation for determining the process by which winter wheat is subjected to LTS. Subsequently, using the XGBoost algorithm to analyze the differences between spring frost and cold damage patterns, a model for identifying types of spring LTDs was developed. The validation accuracy of the model reached 86.67%. In the development of the module simulating the evolution of LTDs, the XGBoost algorithm was initially employed to construct the Low-Temperature Disaster Index (LTDI), facilitating the daily identification of LTD occurrences. Subsequently, the Low-Temperature Disaster Process Accumulation Index (LDPI) is utilized to quantify the severity of the disaster. Validation results indicate that 79.81% of the test set samples exhibit a severity level consistent with historical records. An analysis of the environmental stress-mitigation mechanisms of LTDs reveals that cooling induced by cold air passage and ground radiation are the primary stress mechanisms in the formation of LTDs. In contrast, the release of latent heat from water vapor upon cooling and the transfer of sensible heat from soil moisture serve as the principal mitigation mechanisms. In summary, the developed monitoring framework for LTDs, based on environmental patterns of LTD formation, demonstrates strong generalization capabilities in the HHH region, enabling daily dynamic assessments of the evolution and severity of LTDs. Full article
(This article belongs to the Special Issue Crop Production in the Era of Climate Change)
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