Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.3
3.6
Journals
Agriculture
Special Issues
Agronomic Strategies to Improve Adaptability and Stability of Maize Production...
share announcement

Agronomic Strategies to Improve Adaptability and Stability of Maize Production Systems Under Climate Change

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Systems and Management".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 5822

Special Issue Editors

Dr. Csaba Bojtor

E-Mail Website
Guest Editor
Institute of Land Use, Engineering and Precision Farming Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, H-4032 Debrecen, Hungary
Interests: early-stage plant stress detection; fertilization strategies; nutrient uptake dynamics; nutrient use efficiency; plant health; plant nutrition; precision agriculture; smart plant stress diagnostics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adrienn Széles

E-Mail Website
Guest Editor
Institute of Land Use, Engineering and Precision Farming Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, H-4032 Debrecen, Hungary
Interests: agrotechnology; crop production; ecophysiological parameters; fertilization; plant stress physiology; precision farming; precision nutrient management; soil conservation; yield stability; yield quality
Special Issues, Collections and Topics in MDPI journals
Dr. Árpád Illés

E-Mail Website
Guest Editor
Institute of Land Use, Engineering and Precision Farming Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, H-4032 Debrecen, Hungary
Interests: abiotic stress; fertilization; genotype evaluation; irrigation strategies; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With climate change posing significant challenges to global maize production, researchers are developing innovative agronomic strategies in order to enhance the resilience and productivity of crops. Maize, a staple crop that is critical for food security, is highly sensitive to environmental stressors such as extreme temperatures and droughts. The scientific background of climate-related maize research explores how these factors affect growth, yield, and quality, emphasizing the need for developed production systems.

The primary aim of this Special Issue is to improve both the adaptability and stability of maize systems to sustain yields under increasingly variable conditions. This involves a multi-faceted approach, including the optimization of planting dates, the improvement of soil health, the selection of climate-resilient maize varieties, and the enhancement of nutrient management.

Innovative research in this field leverages advanced breeding techniques, precision agriculture technologies, and predictive modeling. These approaches enable researchers to assess climate risks and develop targeted strategies that maximize the efficiency of water use, mitigate soil degradation, and adapt planting practices to shifting weather patterns. Together, these innovations contribute to a more resilient maize production system that is capable of withstanding climate-induced pressures.

This Special Issue also welcomes the submission of research on agronomic field trials and experimental studies, genotype–environment interactions, soil and water management, precision agriculture and digital tools, climate change impact assessments, and reviews and meta-analyses.

Dr. Csaba Bojtor
Prof. Dr. Adrienn Széles
Dr. Árpád Illés
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. 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

  • abiotic stress
  • agronomic strategies
  • climate change
  • climate-smart agriculture
  • maize production
  • precision agriculture
  • genotype resilience
  • water use efficiency

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 (6 papers)

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

Research

16 pages, 2742 KB  
Article
Predicting Weather Station-Scale GPP and ET with Deep Learning for Climate-Resilient Corn Production in the U.S.
by Shiyuan Wang, Haiyang Shi, Ruixiang Gao, Yang Ao and Geping Luo
Agriculture 2026, 16(10), 1068; https://doi.org/10.3390/agriculture16101068 - 13 May 2026
Viewed by 266
Abstract
Over the past two decades, extreme climate and weather events have become increasingly frequent in the United States, and the carbon–water cycle of corn ecosystems has shown high sensitivity to climate change. However, traditional simulation methods that rely on coarse-scale reanalysis data are [...] Read more.
Over the past two decades, extreme climate and weather events have become increasingly frequent in the United States, and the carbon–water cycle of corn ecosystems has shown high sensitivity to climate change. However, traditional simulation methods that rely on coarse-scale reanalysis data are unable to reflect changes in local water and heat conditions accurately. This study combines in situ meteorological observations with remote sensing, using a long short-term memory model to simulate the daily gross primary productivity (GPP) and evapotranspiration (ET) of 684 corn-growing meteorological stations in the United States. In summer, GPP and ET showed a spatial pattern of gradual decrease from the humid eastern region to the arid western region, and the multi-year daily averages at meteorological stations showed a single-peak pattern. The sensitivity of GPP and ET changes is mainly influenced by leaf area index (LAI) and shortwave radiation downward changes, which together explain more than 90% of the main variation in GPP and ET at the meteorological stations. The 2012 drought caused a general decline in GPP and ET, with the peak occurring approximately 15 days earlier than usual. Water use efficiency (GPP/ET) decreased at 85% of the sites (p < 0.05), but photosynthesis per unit leaf area (GPP/LAI) increased at 63% of the sites (p < 0.05). This study demonstrates the importance of meteorological station-scale data for understanding carbon–water flux dynamics in cornfields. Integrating the models developed in this study with medium-to-long-term climate projections will further guide climate-informed agricultural water management and provide reliable accounting and pricing tools for agricultural land carbon markets and carbon trading. Full article
(This article belongs to the Special Issue Agronomic Strategies to Improve Adaptability and Stability of Maize Production Systems Under Climate Change)
►▼ Show Figures

Figure 1

35 pages, 3541 KB  
Article
Projected Climate-Driven Shifts in Maize Production in Bosnia and Herzegovina: Regional Analysis Using Agroclimatic Indicators and Modelling Tools
by Daniela Soares, Sabrija Čadro, Marko Ivanišević, Dženan Vukotić, João Rolim, Teresa A. Paço and Paula Paredes
Agriculture 2026, 16(9), 934; https://doi.org/10.3390/agriculture16090934 - 23 Apr 2026
Viewed by 550
Abstract
This study assesses the impacts of climate change (CC) on maize production in Bosnia and Herzegovina, comparing ten maize-producing municipalities and using Gradiška as a case study. Agroclimatic indicators and ISAREG-based soil water balance simulations were used to evaluate regional suitability for future [...] Read more.
This study assesses the impacts of climate change (CC) on maize production in Bosnia and Herzegovina, comparing ten maize-producing municipalities and using Gradiška as a case study. Agroclimatic indicators and ISAREG-based soil water balance simulations were used to evaluate regional suitability for future maize production. Projections indicate substantial increases in average temperatures of 2 to 6 Celsius by the end of the century, depending on the RCP scenario, together with important reductions in accumulated mean precipitation, particularly during summer. Rising temperatures accelerate maize phenology, shortening growth cycles and enabling double-cropping opportunities for short-season cycles. Medium-season cycles may become feasible in most regions, while long-season cycles remain constrained in high-altitude areas due to thermal requirements. Rainfed maize in Gradiška is expected to face increased relative evapotranspiration deficits under future ‘hot & dry’ conditions, with potential relative yield losses due to water deficit of up to 12%. Irrigated maize shows a variation in irrigation requirements from −26% to +8% relative to the baseline, which reflects the combined effect of a shortened crop growth cycle under higher temperatures and increased evapotranspiration demand under drier conditions. Regions with high soil water-holding capacity are the most resilient, while areas with shallow soils or Mediterranean climates are more vulnerable under future conditions. The findings underscore the need for agronomic adaptation measures to the projected CC impacts, including supplemental irrigation, drought-tolerant cultivars, and potential adjustment of sowing. Full article
(This article belongs to the Special Issue Agronomic Strategies to Improve Adaptability and Stability of Maize Production Systems Under Climate Change)
►▼ Show Figures

Graphical abstract

18 pages, 1596 KB  
Article
Potassium Silicate Supplementation Accelerates Recovery from Combined Salinity–Waterlogging Stress in Maize
by Chang-Wook Park, Sang-Mo Kang, Byeong-Hun Kim, Moon-Sub Lee, Da-Sol Lee, In-Jung Lee and Bong-Gyu Mun
Agriculture 2026, 16(5), 622; https://doi.org/10.3390/agriculture16050622 - 8 Mar 2026
Viewed by 508
Abstract
In reclaimed and poorly drained soils, combined salinity–waterlogging stress markedly inhibits the early vegetative growth of maize. In this study, maize seedlings at 12 days after sowing (DAS) were subjected to combined stress by immersing the entire root system in 200 mM NaCl [...] Read more.
In reclaimed and poorly drained soils, combined salinity–waterlogging stress markedly inhibits the early vegetative growth of maize. In this study, maize seedlings at 12 days after sowing (DAS) were subjected to combined stress by immersing the entire root system in 200 mM NaCl for 7 d (stress; ST), then transferred to recovery conditions and supplied potassium at equivalent activity (5 mM K+; soil drench) as KH2PO4 (ST + K + P), K2SO4 (ST + K + S), and potassium silicate (ST + K + Si) at 0 and 5 days after treatment (DAT). Morphological traits, chlorophyll fluorescence, and gas-exchange parameters were measured at PreTR (immediately after stress termination), 5 DAT, and 10 DAT. Phytohormone, mineral nutrient profiles, oxidative stress markers and redox status, osmotic and metabolic parameters, and the expression patterns of key ion transport and stress-responsive genes were quantified at 0 and 10 DAT. The effects of K supplementation were evident across the growth- and photosynthesis-related indicators. Treatment groups (ST + K + Si, ST + K + S, and ST + K + P) exhibited significantly higher carbon fixation capacity than ST at 10 DAT. The Na/K ratio was also notably reduced in all K-supplemented groups, indicating that ionic homeostasis was restored with K supplementation through improvements in various stress response indicators such as phytohormones, osmotic adjustment, and antioxidant responses. The potassium- and silicon-treated group showed the greatest recovery effect, which may reflect the physiological characteristics of cereal species. Overall, these findings provide foundational data for the development of cultivation technology to expand the cultivation area of maize. Full article
(This article belongs to the Special Issue Agronomic Strategies to Improve Adaptability and Stability of Maize Production Systems Under Climate Change)
►▼ Show Figures

Figure 1

29 pages, 3592 KB  
Article
Opportunities, Limitations, and Soil Microbial Predictors of Yield Response to Bacillus atrophaeus and Mycorrhiza in Silage Maize
by Matthias Thielicke, Lena Geist, Bettina Eichler-Löbermann, Renate Wolfer, Richard Thiem, Martin Wendt and Frank Eulenstein
Agriculture 2026, 16(5), 523; https://doi.org/10.3390/agriculture16050523 - 27 Feb 2026
Viewed by 478
Abstract
Nutrient surpluses in regions with intensive livestock farming challenge sustainable crop production and have driven interest in alternative fertilization strategies and microbial biostimulants. Although microbial inoculation (MO) has been extensively studied in plant production, its agronomic relevance under field conditions remains controversial due [...] Read more.
Nutrient surpluses in regions with intensive livestock farming challenge sustainable crop production and have driven interest in alternative fertilization strategies and microbial biostimulants. Although microbial inoculation (MO) has been extensively studied in plant production, its agronomic relevance under field conditions remains controversial due to inconsistent outcomes. To address these inconsistencies, we conducted three-year field trials on two well-fertilized sandy sites in northern Germany. A microbial consortium consisting of Rhizoglomus irregulare, Funneliformis mosseae, Funneliformis caledonium, and Bacillus atrophaeus Abi05 was applied to silage maize (cultivar Amaroc S230) under contrasting fertilization regimes. In two of three years, microbial inoculation increased dry mass yield in the absence of starter fertilization, whereas both a high nutrient input variant (100 kg ha−1 diammonium phosphate, DAP) and a lower nutrient input organo-mineral microgranular fertilizer (25 kg ha−1) suppressed inoculant effects. Notably, yields from plots amended solely with the microbial inoculant reached at least the same level as those obtained with starter fertilization. In the third year, under drought conditions, defined as soil water contents below 10% in the 0–30 cm depth, no positive yield responses to microbial inoculation were observed. Quantitative PCR-based analyses of pre-sowing soils revealed that the abundances of Firmicutes, β-Proteobacteria, and total fungi were associated with yield responses, with Firmicutes and β-Proteobacteria showing negative and fungi showing positive correlations; together, these microbial predictors explained 38% of the variance in inoculant-induced yield response. Our findings demonstrate that soil microbiome characteristics can predict inoculant performance and that microbial inoculation is most effective without starter fertilization and under adequate soil moisture. Full article
(This article belongs to the Special Issue Agronomic Strategies to Improve Adaptability and Stability of Maize Production Systems Under Climate Change)
►▼ Show Figures

Figure 1

19 pages, 3033 KB  
Article
Optimizing Nitrogen Fertilization in Maize Production to Improve Yield and Grain Composition Based on NDVI Vegetation Assessment
by Árpád Illés, Csaba Bojtor, Endre Harsányi, János Nagy, Lehel Lengyel and Adrienn Széles
Agriculture 2025, 15(21), 2279; https://doi.org/10.3390/agriculture15212279 - 31 Oct 2025
Cited by 2 | Viewed by 1497
Abstract
Nitrogen fertilization is essential for balancing maize yield, grain composition, and environmental sustainability. This study aimed to evaluate the relationship between nitrogen (N) supply, grain quality traits, and yield potential using UAV-based Normalized Difference Vegetation Index (NDVI) monitoring in a long-term fertilization field [...] Read more.
Nitrogen fertilization is essential for balancing maize yield, grain composition, and environmental sustainability. This study aimed to evaluate the relationship between nitrogen (N) supply, grain quality traits, and yield potential using UAV-based Normalized Difference Vegetation Index (NDVI) monitoring in a long-term fertilization field experiment in Eastern Hungary. Six N levels (0–300 kg ha−1) were tested during two consecutive growing seasons (2023–2024) under varying climatic conditions. The obtained results showed that moderate N doses (120–180 kg ha−1) provided the optimal nutrition level for maize, significantly increasing yield compared to the control (+5.086 t ha−1 in 2024), while excessive fertilization above 180 kg ha−1 did not result in any substantial yield gains; however, it significantly modified grain composition. Higher N supply enhanced protein content (+0.95% between 0 and 300 kg ha−1) and reduced starch percentage, confirming the protein–starch trade-off, whereas oil content was less affected by nitrogen fertilization, similarly to previous results. The strongest correlation between NDVI values and yield was measured at the post-silking stage (112 DAS; R = 0.638 in 2023, R = 0.634 in 2024), indicating the suitability of NDVI monitoring for in-season yield prediction. Overall, NDVI-based monitoring proved effective not just for optimizing N management but also for supporting site specific fertilization strategies to enhance maize productivity and nutrient use efficiency. Full article
(This article belongs to the Special Issue Agronomic Strategies to Improve Adaptability and Stability of Maize Production Systems Under Climate Change)
►▼ Show Figures

Figure 1

24 pages, 2199 KB  
Article
Predictive Modelling of Maize Yield Under Different Crop Density Using a Machine Learning Approach
by Dragana Stevanović, Vesna Perić, Svetlana Roljević Nikolić, Violeta Mickovski Stefanović, Violeta Oro, Marijenka Tabaković and Ljubiša Kolarić
Agriculture 2025, 15(20), 2138; https://doi.org/10.3390/agriculture15202138 - 14 Oct 2025
Viewed by 1596
Abstract
In the face of increasing climate variability, understanding the dynamics of plant-to-plant interactions within crops is becoming increasingly important. This study aimed to examine plant responses to varying intensities of inter-plant competition, induced bz different planting densities, to enhance the accuracy of future [...] Read more.
In the face of increasing climate variability, understanding the dynamics of plant-to-plant interactions within crops is becoming increasingly important. This study aimed to examine plant responses to varying intensities of inter-plant competition, induced bz different planting densities, to enhance the accuracy of future yield prediction models. Six hybrids were grown at three planting densities (S1, S4, S7). Grain yield and yield components were estimated at four developmental points during grain filling (V1 to V4). These regression models and machine learning (ML) were applied to predict maize production under variable weather conditions. The factor year was the main source of variability, with less favourable conditions in the second year (G2) reducing yield by approximately 1–2%. Lower planting density (S1) improved individual plant development and yield components, while maximum density (S7) resulted in higher grain yield despite reduced individual performance. Hybrid H5 showed strong tolerance to high density, producing the highest yield under S7 conditions. Machine learning models accurately predicted key seed quality traits—moisture, oil, and protein—with performance metrics exceeding 80% accuracy. Specifically, R2 values reached 0.82 for moisture content and 0.77 for oil concentration, indicating strong predictive capability. These findings support careful selection of hybrids and optimal planting density strategies in future cropping systems to increase yield and maintain seed quality in different environments. Full article
(This article belongs to the Special Issue Agronomic Strategies to Improve Adaptability and Stability of Maize Production Systems Under Climate Change)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop