Trends in Global Soil Research and a Microbiome-Based Framework for Soil Health Assessment
Crop Yield Responses to Reduced Solar Radiation in Agrivoltaic Systems: Crop-Specific Patterns and Shading Thresholds
Evaluating Photochemical Efficiency and Recovery Potential in Wheat Varieties with Divergent Drought Tolerance
The Effect of the Freeze–Thaw Process on Plant Available Water and Water-Stable Aggregates as a Function of Soil Tillage and Soil Chemical Quality

Journal Description

Agronomy

Agronomy is an international, peer-reviewed, open access journal on agronomy and agroecology published semimonthly online by MDPI. The Spanish Society of Plant Biology (SEBP) is affiliated with Agronomy and their members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), GEOBASE, PubAg, AGRIS, and other databases.
Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Agronomy and Crop Science)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Agronomy include: Seeds, Agrochemicals, Grasses and Crops.
Journal Cluster of Agricultural Science: Agriculture, Agronomy, Horticulturae, Soil Systems, AgriEngineering, Crops, Seeds, Grasses, Agrochemicals and AI and Precision Agriculture.

Impact Factor: 4.1 (2025); 5-Year Impact Factor: 4.4 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2073-4395

Latest Articles

20 pages, 17915 KB

Open AccessArticle

Linking Soil Health to Soybean (Glycine max L.) Productivity Under Biochar and Organic Fertilizer Application: Evidence from PCA and Correlation Analyses

by Marianus Evarist Ngui, Yong-Hong Lin, Chia-Chung Wang, Ya-Zhen Xu, Chuan-Chi Chien, Rung-Jiun Gau, Yan-Jia Liou and Chun-Shen Cheng

Agronomy 2026, 16(17), 1710; https://doi.org/10.3390/agronomy16171710 (registering DOI) - 3 Sep 2026

Abstract

Increasing fertilizer costs, climate-related stresses, and soil degradation caused by the prolonged use of chemical fertilizers threaten the sustainability of agricultural production. Organic soil amendments offer a promising approach to restoring soil health while reducing dependence on synthetic inputs. This study evaluated the combined effects of biochar and organic fertilizer on soil health and soybean (Glycine max L.) productivity under acidic soil conditions. During the 2024 growing season, a greenhouse pot experiment was conducted using a completely randomized design (CRD) comprising seven treatments. Each treatment was replicated three times, resulting in a total of 21 pots. The treatments consisted of different combinations of biochar (B) and organic fertilizer (F), applied at rates of grams per 10.5 kg of soil: control (B0F0), B35F70, B35F105, B35F140, B70F70, B70F105, and B70F140. Treatment means were compared using the Least Significant Difference (LSD) test at p < 0.05. The results showed that the highest soil pH value (5.41) was recorded under the B35F70 treatment at 45 days after amendment of the acidic soil. Application of the B35F140 treatment resulted in a significant increase (p < 0.05) in electrical conductivity (0.23 mS cm⁻¹) compared with the control. Soil organic matter and available phosphorus reached their highest values under B70F140, at 5.25% and 13.87 mg kg⁻¹, respectively, and were significantly greater than those in the control treatment. Soil available iron (Fe) and manganese (Mn) concentrations also increased significantly (p < 0.05) compared with the control, with the B35F70 and B70F70 treatments resulting in the highest Fe (371.29 mg kg⁻¹) and Mn (37.77 mg kg⁻¹) concentrations, respectively. At 100 days after amendment of the reddish-brown acidic soil, exploratory Pearson correlation analyses were conducted to examine relationships between soil health indicators and soybean performance. Soil available phosphorus and potassium exhibited positive associations with soil pH (r = 0.69 and r = 0.65, respectively; p < 0.01). Soybean growth traits, including plant height and number of leaves, were positively associated with seed yield (r = 0.56 and r = 0.77, respectively; p < 0.01). Furthermore, seed yield was positively correlated with SPAD values (r = 0.80, p < 0.01), soil pH (r = 0.56, p < 0.01), available K (r = 0.68, p < 0.01), and Mg (r = 0.47, p < 0.05). Principal component analysis (PCA) further demonstrated clear treatment clustering and consistent positive relationships among soil properties, plant growth traits, and soybean yield variables. The control treatment was clearly separated from all biochar-organic fertilizer treatments along PC1. Among all treatments, B35F140 (3.33 g biochar kg⁻¹ soil + 13.33 g organic fertilizer kg⁻¹ soil) showed the strongest positive association with soil health and plant growth and produced the highest soybean seed yield (10.77 g plant⁻¹). Overall, the combined use of biochar and organic fertilizer improved soil health, soybean growth, and yield, demonstrating its potential as a sustainable strategy for enhancing soybean productivity under acidic soil conditions. Full article

(This article belongs to the Section Soil and Plant Nutrition)

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26 pages, 5172 KB

Open AccessArticle

Research on Thrips Object Detection and Counting Algorithms in Leaf Backgrounds

by Dongxue Huang, Ming Lei, Zhiliang Zhang, Zhangzhang He, Wei Zhan and Yu Zhang

Agronomy 2026, 16(17), 1709; https://doi.org/10.3390/agronomy16171709 (registering DOI) - 3 Sep 2026

Abstract

Thrips are major minute pests that threaten crop growth, and their early and accurate detection is of great significance for pest monitoring and precision control. Existing image-based thrips detection studies have mainly focused on insects captured on sticky traps, whereas direct detection of thrips on plant surfaces under complex leaf-background conditions remains relatively underexplored. Moreover, in leaf-background images, thrips are extremely small, have indistinct contours, and are susceptible to interference from noise such as leaf textures and spots, resulting in insufficient detection and counting accuracy. To address these issues, this study proposes SCIC-DEIM, a small-object thrips detection model based on the end-to-end detector DEIM. First, leaf-background thrips images were collected at Jingchu University of Technology to construct a dataset containing 5618 images and 47,726 thrips instances. Second, focusing on three key aspects—background redundancy suppression, target response enhancement, and detail information recovery—the SCIC-DEIM model was developed. Specifically, the SCConv and IIA modules were introduced into the backbone network to construct SCIStage, and CARAFE was adopted to replace conventional upsampling operations. Experimental results show that, compared with DEIM, SCIC-DEIM improves Precision, mAP@50, and mAP@50:95 by 2.4, 2.0, and 2.9 percentage points, respectively, while reducing GFLOPs by approximately 20.6%. It also achieves improved MAE, RMSE, and R² values compared with DEIM. The proposed algorithm demonstrates strong capability in small-object thrips detection under leaf-background conditions and can provide a methodological reference for intelligent monitoring of tiny pests on leaf surfaces in protected agriculture. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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20 pages, 3578 KB

Open AccessArticle

Effects of Sheep Manure Cake Addition on Surface-Soil Carbon Accumulation and Microbial Carbon Fixation Potential in Reclaimed Soils of the Muli Mining Area

by Bo Wu, Jianli Wu, Jianing Li and Changhui Li

Agronomy 2026, 16(17), 1708; https://doi.org/10.3390/agronomy16171708 (registering DOI) - 3 Sep 2026

Abstract

Improving surface-soil carbon accumulation is important for restoring reclaimed alpine mining soils. We evaluated nine treatments in the Muli mining area, Qinghai, China: a control (CK) and eight sheep manure cake rates (0.025–0.060 m³·m⁻²). Soil total organic carbon (TOC), particulate organic carbon (POC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), 0–10 cm soil organic carbon (SOC) stock, carbon pool management index (CPMI), carbon fixation genes (cbbL and cbbM), and microbial community sequencing profiles were assessed. Sheep manure cake significantly increased the measured carbon fractions and CPMI. The 0–10 cm SOC stock was highest under SM3 (75.82 ± 3.52 Mg C ha⁻¹), followed by SM4 (65.99 ± 3.49 Mg C ha⁻¹), compared with 20.74 ± 1.52 Mg C ha⁻¹ in CK. cbbL and cbbM gene copy numbers peaked under SM2 (0.030 m³·m⁻²), reaching 1.51 × 10⁹ and 1.91 × 10⁷ copies·g⁻¹, respectively. Under the prespecified equal criterion-layer weights, TOPSIS ranked SM2 first; however, sensitivity analysis showed that the leading treatment changed when criterion weights were altered. Thus, SM3–SM4 showed the strongest measured surface-carbon accumulation, whereas SM2 was the highest-ranked treatment only within the adopted TOPSIS weighting scheme. Full article

(This article belongs to the Section Soil and Plant Nutrition)

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18 pages, 648 KB

Open AccessArticle

Using Organic Residues for Fertilization in Maize–Ryegrass Rotations to Enhance Circular Agriculture

by María Campo-Celada, Fernando Vicente, Mario Menéndez-Miranda and Adela Martínez-Fernández

Agronomy 2026, 16(17), 1707; https://doi.org/10.3390/agronomy16171707 (registering DOI) - 3 Sep 2026

Abstract

The increasing demand for sustainable agricultural practices has driven growing interest in organic fertilizers derived from waste streams as alternatives to mineral fertilizers. This study evaluated, in a pilot trial, the effectiveness of using raw digestate, composted digestate and composted sewage sludge in a crop rotation over two consecutive years of maize and Italian ryegrass. Crop yield, forage quality, and soil physicochemical properties were assessed to determine the potential of these recycled organic nitrogen sources. The results differed between the two species of forage. In maize, organic fertilizers maintained yield and forage quality comparable to chemical fertilization, while composted sewage sludge significantly increased average plant height. These findings indicate that recycled organic amendments can partially or totally replace mineral nitrogen fertilizers, contributing to nutrient recycling and waste valorization within a circular economy framework. In contrast, Italian ryegrass showed lower yield under organic fertilization than under chemical fertilization, suggesting that additional management strategies may be required to optimize nutrient availability for this crop. Furthermore, raw digestate application tended to increase soil pH, which may provide benefit subsequent crop. Overall, the results suggest that digestate and sludge-based fertilizers are promising alternatives for maize performance, although their effectiveness in Italian ryegrass requires additional strategies. Full article

(This article belongs to the Special Issue A Path for Circular Economy in Agriculture: From Organic Waste to Sustainable Energy and Soil Fertility)

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22 pages, 1923 KB

Open AccessArticle

Optimized Split Nitrogen Topdressing Under Micro-Sprinkler Fertigation Improves Wheat Yield and Quality via Grain Filling and Nitrogen Translocation

by Shan Li, Jianning He, Ruiqi Li, Xiaofeng Yan, Chao Zhang, Xin Zhao, Hongguang Wang, Qin Fang, Xiaokang Lv and Xu Wang

Agronomy 2026, 16(17), 1706; https://doi.org/10.3390/agronomy16171706 (registering DOI) - 3 Sep 2026

Abstract

This study aims to elucidate the effects of split-topdressed nitrogen (N) via micro-sprinkling fertigation on grain filling and N translocation and overcome the bottleneck in the synchronous enhancement of yield-quality under traditional border irrigation with broadcast N application. Using Gaoyou 2018 (strong gluten) and Heng 4399 (medium gluten), four treatments were established: conventional border irrigation with N at jointing (I1), micro-sprinkler irrigation with N at jointing (I2), micro-sprinkler irrigation with N at jointing and anthesis (1:1; I3), and micro-sprinkler irrigation with N at jointing, anthesis, and 12 days after anthesis (DAA) (5:3:2; I4). Results showed that I4 achieved the highest grain yield (10,578.6 kg·ha⁻¹; +9.6% average), primarily attributed to significantly increased thousand-kernel weight (TKW; +1.7–4.2 g). I4 accelerated mid-to-late filling, extended the active filling period, and increased final grain mass by 1.5–4.6 g. I4 promoted post-anthesis N uptake and its remobilization to grains, improving nitrogen uptake efficiency (NUpE). I4 elevated grain protein content (gliadin-to-glutenin ratio +6.0%), improved gluten characteristics, and enhanced bread-baking quality, with these advantages being more pronounced in the strong-gluten cultivar. Overall, I4 synergistically enhances grain yield and quality by optimizing grain filling and N translocation. This study provides a theoretical basis for mitigating N leaching and ammonia volatilization while implementing high-yielding, high-quality cultivation practices. Full article

(This article belongs to the Special Issue Tillage for Resource Use Efficiency: Insights from Crop Physiology)

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11 pages, 260 KB

Open AccessArticle

Planting Geometry Influences Forage Yield, Nutritive Value, and Water Use Efficiency in Summer Cereal-Cowpea Intercropping Systems

by Elora-Danam Ellison, Brock Blaser, Marty Rhoades, Leonard Lauriault and Murali Darapuneni

Agronomy 2026, 16(17), 1705; https://doi.org/10.3390/agronomy16171705 - 3 Sep 2026

Abstract

Despite declining water availability in semi-arid environments, demand for high-quality forages by the livestock and dairy industries continues to grow. Researching alternative crops for high quality forage and water use efficiency remains a priority for these regions to meet this growing demand. Cereal grass-legume intercropping system may provide such a viable alternative to traditional sole cropping to improve nutritive value, while maintaining the current yield levels with limited supply of water. A study was conducted near West Texas A&M University, Canyon, TX, USA. during 2020 and 2021 to evaluate forage sorghum [Sorghum bicolor (L.) Moench]-cowpea [Vigna unguiculata (L.) Walp] and pearl millet [Pennisetum glaucum (L.) Leeke]-cowpea intercrops for forage production, nutritive value (crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro dry matter digestibility (IVTDMD)), and water use efficiency (WUE) under limited irrigation. Treatments were sole pearl millet (PM), sole forage sorghum (FS), sole cowpea (C), or mixtures of either pearl millet-cowpea or forage sorghum-cowpea planted in the same row (PM-C or FS-C), alternating rows (millet-cowpea 1:1 (PM-C 1:1) or forage sorghum-cowpea 1:1 (FS-C 1:1), or two rows alternating (millet-cowpea 2:2 (PM-C 2:2) or sorghum-cowpea 2:2 (FS-C 2:2). Results from both study years indicated that FS-C 1:1 intercrop showed either greater or equal forage dry matter (DM) yield compared to sole FS or sole PM (14.5 Mg ha⁻¹ (FS-C 1:1) vs. 13.8 Mg ha⁻¹ (FS) vs. 11.9 Mg ha⁻¹ (PM)). Similarly, the relative advantage of FS-C 1:1 intercrop in improving WUE was also observed in the study compared to sole FS and sole PM (44 kg ha⁻¹ mm⁻¹ (FS-C 1:1) vs. 41.6 kg ha⁻¹ mm⁻¹ (FS) vs. 35.9 kg ha⁻¹ mm⁻¹ (PM). CP yield per hectare (estimate based on DM yield and percent CP content) was also greater for the forage sorghum-cowpea planted in 1:1 alternate rows. This suggests that producers in the semi-arid USA, especially in the Southern High Plains, can be significantly benefited from the utilization of cereal grass-legume intercropping production systems in lieu of sole cropping as the basis for livestock rations without compromising yield and water productivity. Full article

(This article belongs to the Section Innovative Cropping Systems)

30 pages, 18511 KB

Open AccessArticle

Lightweight TRSNet Integrating Texture–Color Fusion and Scale-Aware Learning for Plum Ripeness Detection in Complex Orchards

by Yang Zhou, Wanqiang Huang, Guoyu Huang, Tianhua Chen, Xinda Liu and Xiuling Yu

Agronomy 2026, 16(17), 1704; https://doi.org/10.3390/agronomy16171704 - 3 Sep 2026

Abstract

In complex orchard environments, plum ripeness detection faces large target-scale variations, subtle appearance differences among maturity stages, severe foliage occlusion, and drastic illumination changes. To address these challenges, the Plum Ripeness Detection (PRD) dataset is constructed and a lightweight texture–color residual scale-aware network (TRSNet) based on YOLO11n is introduced. TRSNet integrates a texture–color feature fusion and spatial feature enhancement module, a lightweight detection head, and an improved bounding-box regression strategy, thereby improving detection accuracy while retaining low computational cost. Experiments on PRD yield Precision of 92.5%, Recall of 84.3%, mAP₅₀ of 92.3%, and mAP_50:95 of 81.3%, with 2.18 M parameters and 4.8 G FLOPs, surpassing YOLO11n, RT-DETR, YOLO26n, and other baselines. Cross-orchard and cross-variety evaluations demonstrate robustness to environmental variation and cultivar transfer, and additional tests on a Blueberry dataset confirm generalization potential across fruit types. Deployment on a Jetson Orin Nano embedded platform shows stable real-time inference, supporting efficient yield estimation and growth monitoring in smart orchards. Full article

(This article belongs to the Special Issue Intelligent Detection of Crop Growth Phenotyping Toward Sustainability: Sensors, IoT Technologies and Signal Processing)

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14 pages, 8252 KB

Open AccessArticle

Growth, Reproductive Performance, and Biomass Allocation of Dwarf Winter Wheat ‘Milyang54’ Under Dense Indoor Cultivation Conditions

by Hyeonjin Park, Jin-Kyung Cha, So-Myeong Lee, Youngho Kwon, Jae-Han Son, Ye Rin An, Seung-Kyo Jeong, Woo-Jae Kim and Jong-Hee Lee

Agronomy 2026, 16(17), 1703; https://doi.org/10.3390/agronomy16171703 - 3 Sep 2026

Abstract

Wheat ideotypes for dense indoor cultivation require compact architecture, maintained reproductive development, and sufficient biomass accumulation with high planting density and restricted rooting space. Milyang54 is an extremely short-stature winter wheat line developed as a candidate for space-efficient cultivation, but its response to indoor cultivation conditions differing in planting density and rooting space has not been characterized. In this study, Milyang54 and Jokyoung were grown with eight tray, pot, and spacing treatments in a speed-breeding greenhouse. These treatments were designed to generate contrasting planting densities and rooting-space conditions, ranging from low-density pot cultivation to high-density plug-tray and 1.5 cm spacing treatments. Agronomic and reproductive traits, shoot and root biomass, and root-to-shoot ratio were evaluated using a two-factor GLM with variety, cultivation treatment, and their interaction as fixed effects. Cultivation treatment significantly affected all measured traits, and significant variety × treatment interactions were detected for most traits. Across treatments, Milyang54 had a markedly lower mean plant height than Jokyoung (25.62 vs. 42.81 cm) but showed greater mean grain number per spike (11.03 vs. 7.51), shoot dry weight (25.85 vs. 21.58 g), and root dry weight (1.69 vs. 0.97 g). The grain-number advantage of Milyang54 was particularly evident in high-density and restricted-rooting-space tray treatments, including the 105-, 128-, and 162-cell trays. Root-to-shoot ratios varied among cultivation treatments, but a high ratio was not consistently associated with greater grain number. These results indicate that Milyang54 combines compact plant architecture with maintained reproductive performance and biomass accumulation under dense indoor cultivation conditions, suggesting its potential value as a compact wheat ideotype for controlled-environment agriculture. Full article

(This article belongs to the Section Innovative Cropping Systems)

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21 pages, 5589 KB

Open AccessArticle

Hybrid Protection Against Ergot (Claviceps purpurea (Fr.) Tul.) in Winter Rye (Secale cereale L.): Cultivar Susceptibility Under Artificial Inoculation and In Vitro Screening of Chemical, Biological and Botanical Agents

by Jakub Danielewicz, Joanna Horoszkiewicz, Ewa Jajor, Marek Korbas, Jakub Moskalik, Kamila Roik, Anna Tratwal, Jan Bocianowski, Paweł Dopierała, Kinga Stuper-Szablewska and Maciej Buśko

Agronomy 2026, 16(17), 1702; https://doi.org/10.3390/agronomy16171702 - 3 Sep 2026

Abstract

Ergot, caused by Claviceps purpurea (Fr.) Tul., is a re-emerging disease that is particularly severe under wet weather during cereal flowering. It reduces grain quality and contaminates food and feed with toxic ergot alkaloids. Because no single measure reliably controls it, this study evaluated components of a hybrid protection approach for winter rye, coupling host-based control (cultivar susceptibility) with a panel of chemical, biological and botanical agents assessed in vitro against the pathogen. In a poisoned-medium (agar-dilution) assay, 11 treatments differed highly significantly in the suppression of mycelial growth. Complete inhibition (100%) was obtained with prothioconazole, mandestrobin, a systemic copper formulation, a Bacillus subtilis (QST 713) biocontrol product, mandarin essential oil (EO) (1% and 5%), and lemon and ginger EOs at 5%; azoxystrobin (83.6%) was intermediate, whereas ginger EO at 1% (74.8%) and lemon EO at 1% (30.2%) were significantly less inhibitory, with the EOs showing a clear dose response. In a field trial in which plots were artificially inoculated with a conidial suspension of the pathogen at anthesis, cultivar, location and year all significantly affected ergot content (p < 0.001; model R² = 0.937), which ranged from 0.60% (cultivar DL 19) to 2.69% (cultivar SU Performer). Infestation was governed by a strong location × year interaction, yet a significant cultivar effect was detected within every location and year. Ergot content was very strongly correlated with ergot weight (r = 0.96) but not with yield, indicating that susceptibility was not linearly related to yield in this material and is a cultivar- and environment-driven trait relevant to integrated ergot management. Full article

(This article belongs to the Section Pest and Disease Management)

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25 pages, 1757 KB

Open AccessArticle

Genotype and Environmental Effects on Seed Protein, Fat and Quinolizidine Alkaloids of Andean Lupin (Lupinus mutabilis Sweet)

by Diego Rodríguez-Ortega, Laura Vega, Iván Samaniego, José Luis Zambrano, Santiago Pereira-Lorenzo, Gustavo Guerrero-Marin and Wilson Vásquez-Castillo

Agronomy 2026, 16(17), 1701; https://doi.org/10.3390/agronomy16171701 - 3 Sep 2026

Abstract

Legumes represent a sustainable alternative for the production of nutritious foods, contributing to agroecosystem resilience, biodiversity conservation, and climate change mitigation. Nonetheless, the productivity and nutritional quality of legumes are strongly influenced by environmental factors. These environmental conditions, together with crop management practices, explain a large proportion of the variability observed in chemical composition, highlighting the importance of genotype × environment interactions. Therefore, the aim of this study was to evaluate the effects of contrasting environments on the protein, fat, and quinolizidine alkaloid contents of Andean lupin (Lupinus mutabilis) seeds. Six genotypes of different origins and alkaloid contents were studied. The genotypes were evaluated across five contrasting field and greenhouse environments using a randomized complete block design with three replications. Chemical analyses of the legume grain were carried out in the Nutrition and Quality laboratory at INIAP’s Santa Catalina Experimental Station (INIAP-EESC). The results of the study indicate that the protein content was affected primarily by the environment, followed by the genotype × environment interaction and, to a lesser extent, by genotype. Fat content was similarly influenced by both genotype and environmental conditions, whereas total alkaloid content was predominantly controlled by genotype, which explained approximately 90.7% of the observed variation. Protein content ranged from 49.9 to 56.8 g 100 g⁻¹ DW across environments, while fat content varied between 14.0 and 18.3 g 100 g⁻¹ DW. Among the evaluated genotypes, AndxPro1 exhibited the highest average protein content (54.96 g 100 g⁻¹ DW), whereas LmPro17 combined the highest fat content (19.78 g 100 g⁻¹ DW) with the lowest total alkaloid concentration (0.13 g 100 g⁻¹ DW). Protein content showed high sensitivity to environmental conditions, whereas alkaloid accumulation exhibited strong genetic control and comparatively limited environmental influence. These findings suggest the importance of conducting evaluations of the different varieties of L. mutabilis in different environments to support breeding programs aimed at improving seed quality. Full article

(This article belongs to the Section Plant-Crop Biology and Biochemistry)

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17 pages, 2221 KB

Open AccessArticle

Unveiling the Productivity Potential of Vertical Farming Through Diverse Crop Cultivation: An Empirical Analysis

by Vivek Jadhav, Afsaneh Nematpour, Giuseppina Pennisi, Alessandro Pistillo, Laura Carotti, Alberto Tassinari, Marco Ghio, Silvio Salvi, Michael Martin, Giorgio Gianquinto and Francesco Orsini

Agronomy 2026, 16(17), 1700; https://doi.org/10.3390/agronomy16171700 - 3 Sep 2026

Abstract

While vertical farming may offer high productivity per unit land area and has received increasing attention for urban food production, research has primarily focused on leafy greens and herbs. Although recent studies have also introduced fruiting vegetables and cereal crops, comparative evaluations under standardized environmental conditions remain limited because crop morphology, duration, harvestable organs, and biomass allocation differ substantially among crop types. This study, conducted in AlmaVFarm, the experimental vertical farm of the University of Bologna, evaluated the productivity and resource use efficiency of five representative crops grown under identical environmental conditions. Crops included dwarf corn as a staple crop, dwarf tomato as a fruit crop, basil as an herb, and lettuce and arugula as leafy greens. To enable meaningful cross-crop evaluations, performance was assessed using normalized indicators of land-surface, cultivation volume, light, energy, and cost use efficiencies. Lettuce achieved the highest annual marketable yield (73.2 kg FW m⁻² y⁻¹), producing 46% more than arugula and 35% more than basil. It also exhibited the greatest land-surface use efficiency (65 kg m⁻² y⁻¹), volume use efficiency (24.5 kg m⁻³ y⁻¹), and energy use efficiency (46.7 g FW kWh⁻¹), corresponding to 3.5-fold and 58-fold higher energy efficiency than tomato and corn, respectively, and up to 1.8-fold higher than arugula and basil. The normalized resource use efficiency framework enabled robust comparisons among contrasting crop categories despite their distinct biological characteristics, revealing substantial differences in adaptation to indoor cultivation. These findings provide a quantitative basis for crop selection and diversification strategies in vertical farming while highlighting the need for crop-specific optimization to improve the resource use efficiency and commercial viability of fruit and staple crops in CEA. Full article

(This article belongs to the Section Horticultural and Floricultural Crops)

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12 pages, 411 KB

Open AccessArticle

Long-Term Tillage Effects on Extractable Organic Matter Fractions in Surface Soil on an Agricultural Midwestern Field Site

by Javier M. Gonzalez and Khandakar R. Islam

Agronomy 2026, 16(17), 1699; https://doi.org/10.3390/agronomy16171699 - 3 Sep 2026

Abstract

This study evaluated whether long-term no-till and chisel tillage differed in total organic carbon (TOC), total nitrogen (TN), alkaline-extractable fractions, and associated indices. A field experiment in Indiana, USA, began in 1983 with moldboard, ridge, chisel, and no-till systems within four crop-sequence plots. In 1998, moldboard was converted to chisel and ridge to no-till; thus, plots had been under their 2017 tillage system for either 34 or 19 years at sampling. Sixteen composite samples were collected from the 0 to 15 cm layer. Because each crop sequence occupied one main plot, crop sequence was treated as field history rather than as an independent factor. A planned contrast compared current no-till with current chisel while accounting for the historical tillage assignments. Compared with chisel, no-till increased TOC by 11%, total extractable carbon (TEC) by 32%, glucose-equivalent-corrected TEC by 32%, total extractable nitrogen by 50%, and total glucose-equivalent carbon by 31%; TN did not differ. Among the indices, only the humification ratio differed between tillage systems. Tillage effects were more consistent for extractable amounts than for relative or optical properties. Full article

(This article belongs to the Special Issue Soil Organic Matter and Tillage—2nd Edition)

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28 pages, 4469 KB

Open AccessArticle

Dose-Dependent Effects of Nodule-Associated Kosakonia cowanii on Nodulation, Nitrogen Status and Yield Components of Common Bean (Phaseolus vulgaris L.) Under Greenhouse Conditions in Northern Ecuador

by Lennys Berutti-Suárez, José Valdemar Andrade Cadena, Erika Cristina Puerres Caicedo and Orlando Meneses Quelal

Agronomy 2026, 16(17), 1698; https://doi.org/10.3390/agronomy16171698 - 3 Sep 2026

Abstract

This study evaluated the dose-dependent effects of a nodule-associated bacterial isolate subsequently identified by whole-genome sequencing as Kosakonia cowanii on nodulation, nitrogen status, growth, phenology, and yield components of common bean (Phaseolus vulgaris L. cv. Centenario) under greenhouse conditions in northern Ecuador. A randomized complete block design with five treatments and three replicates was used, including three inoculation doses (6 × 10⁹, 6 × 10^4.5 and 6 × 10^2.25 CFU mL⁻¹), a non-inoculated control and a nitrogen-fertilized control. Significant differences were detected for plant height, SPAD index, phenological development, nodulation and 100 seed weight. The lowest inoculation dose (T3) produced the strongest biological response, reaching 227.67 nodules plant⁻¹, approximately 6.5 times more than the non-fertilized control, and the highest SPAD value at 60 days after sowing (41.16), exceeding both the non-fertilized control (26.84) and the nitrogen-fertilized treatment (33.14). T3 also achieved the greatest plant height (80.31 cm) and accelerated flowering and pod formation by up to two days compared with higher inoculation doses. Although grain yield did not differ significantly among treatments because of high experimental variability and limited statistical power, inoculated plants maintained yields comparable to the nitrogen-fertilized control while improving seed filling. Whole genome sequencing confirmed the identity of the dominant genome as K. cowanii (ANI = 96.73%; completeness = 99.03%; contamination = 0.63%), supporting the interpretation that this bacterium acts primarily as a plant growth-promoting and nodule-associated bacterium rather than a classical nitrogen-fixing symbiont. These findings highlight the potential of native K. cowanii as a component of sustainable biofertilization strategies for Andean bean production systems. Full article

(This article belongs to the Section Pest and Disease Management)

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16 pages, 1290 KB

Open AccessArticle

Enhancing Agronomic Soil Functionality via Cover Cropping Management

by Lea Piscitelli, Giovanna Dragonetti, Antonio Trani, Zakaria Islem Ziche and Giuseppe Natale Mezzapesa

Agronomy 2026, 16(17), 1697; https://doi.org/10.3390/agronomy16171697 - 3 Sep 2026

Abstract

This study aims to investigate the early post-termination phase as a critical window for assessing soil responses and improving agricultural management decisions. The experiment was conducted in Southern Italy and arranged as a split–split plot design involving three factors: irrigation system (sprinkler vs. drip); cover crop mixture (fava bean, oat, and rapeseed, vs. vetch, barley, and white mustard); and cover crop termination method (incorporation vs. mulch). Four soil parameters were selected for this experiment: soil organic matter, soil aggregate stability, total nitrogen, and soil infiltration rate. Findings highlight no significant differences in total nitrogen among treatments, whereas the soil infiltration rate increased in drip-irrigated treatments and decreased in sprinkler-irrigated ones. Soil aggregate stability increased across all treatments, as did soil organic matter, which increased by approximately 20% within a few weeks. Overall, the irrigation system acted as the main regulator of early soil physicochemical responses. The interaction between irrigation system and cover crop termination methods produced the largest differences among the investigated parameters. The combination of sprinkler irrigation and biomass incorporation resulted in the highest increase in soil organic matter. Full article

(This article belongs to the Special Issue Effects of Cover Crop, Intercropping and Crop Rotation on Soil Properties)

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19 pages, 4411 KB

Open AccessArticle

Spectral–Textural Feature Fusion Coupled with Machine Learning Improves UAV-Based Estimation of Maize Leaf SPAD Value in Arid Agroecosystems

by Zhenqi Liao, Lu Huang, Hui Zhang, Zhenlin Lai, Hongtai Kou, Yiyao Liu, Xiaoqian Jiang and Junliang Fan

Agronomy 2026, 16(17), 1696; https://doi.org/10.3390/agronomy16171696 - 3 Sep 2026

Abstract

Rapid and accurate monitoring of leaf nitrogen status is a key step in achieving precise fertilizer management for maize. However, UAV-based multispectral approaches relying solely on vegetation indices (VIs) may suffer from spectral saturation under dense canopies and provide limited information on canopy structural variation. This study investigated whether integrating VIs and texture features (TFs) extracted from UAV multispectral imagery could improve maize SPAD estimation under different water-nitrogen management conditions and drip irrigation configurations. Time-series UAV images were collected throughout two spring maize growing seasons in a typical arid agricultural region, from which 48 VIs and 40 TFs were derived. Pearson correlation analysis combined with best subset regression was applied to identify optimal feature combinations, and five machine learning models (PLSR, ELM, RBFNN, SVM, and BPNN) were evaluated using different input feature sets. The results showed that spectral–textural feature fusion generally improved SPAD estimation performance, with validation accuracy increasing for four of the five evaluated algorithms. RBFNN achieved the highest accuracy using VIs alone (R² = 0.807), whereas BPNN performed best when VIs and TFs were combined (R² = 0.833). Overall, UAV-based spectral–textural fusion coupled with machine learning provides an effective approach for monitoring maize chlorophyll status and supporting precision nitrogen management in arid agricultural regions. Full article

(This article belongs to the Special Issue Smart Sensing and Sustainable Crop Management Strategies for Agriculture in Arid and Semiarid Regions)

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27 pages, 6603 KB

Open AccessArticle

A National High-Resolution Digital Soil Information System for Spain Based on More Than 50,000 Harmonized Soil Observations and Machine Learning

by Arturo Jiménez-Beneite, Alberto Gutiérrez, Vanessa Paredes-Gómez and David A. Nafría

Agronomy 2026, 16(17), 1695; https://doi.org/10.3390/agronomy16171695 - 2 Sep 2026

Abstract

Accurate and harmonized soil information is fundamental for sustainable agricultural management, environmental assessment, and land-use planning. This study developed a national Digital Soil Mapping (DSM) framework for mainland Spain and the Balearic Islands using a harmonized database of more than 50,000 georeferenced legacy soil observations. Seven key soil properties (clay, silt, sand, pH, organic matter, available phosphorus, and exchangeable potassium) were modelled using environmental covariates derived from Sentinel-2 imagery, terrain attributes, climate, and land-use data. Three machine-learning algorithms were evaluated, with Cubist providing the best predictive performance and therefore selected for mapping all soil properties. Model validation showed the highest accuracy for soil pH (R² = 0.60), sand (R² = 0.51), and organic matter (R² = 0.50), whereas moderate performance was obtained for silt and clay. Available phosphorus and exchangeable potassium showed lower predictive accuracy, reflecting their strong dependence on management practices. The resulting maps were generated at 20 m spatial resolution and generalized to 200 m for dissemination, providing a harmonized national soil property dataset for Spain. This operational and reproducible framework supports agricultural and environmental decision-making and can be adapted for soil mapping in other regions using open-access data and open-source tools. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Agronomic Application for Mapping and Modeling Soil Properties)

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30 pages, 5588 KB

Open AccessArticle

Estimating Nitrogen, Phosphorus, and Potassium Content in Pear Trees Using UAV-Based Multispectral Imagery

by Feipeng Jia, Zhicheng Jia, Bolin Wang, Jincheng Liu, Jingcheng Wu, Wenjun Pi and Yuxin Cao

Agronomy 2026, 16(17), 1694; https://doi.org/10.3390/agronomy16171694 - 2 Sep 2026

Abstract

Machine learning combined with multispectral remote sensing provides an efficient and non-destructive approach for monitoring fruit tree nutrient status. However, conventional machine learning methods have limited ability to capture complex spectral-feature interactions, while deep neural networks often suffer from overfitting under small-sample field conditions. This study developed a UAV-based multispectral framework for estimating leaf nitrogen (N), phosphorus (P), and potassium (K) contents in a commercial pear orchard. Five-band spectral reflectance data were used to generate 21 vegetation indices (VIs), and Pearson correlation analysis and recursive feature elimination (RFE) were applied for feature optimization. A Deep Forest (DF)-based framework and a gradient boosting enhanced variant (GB-DF) were developed and compared with convolutional neural networks (CNN), support vector regression (SVR), random forest (RF), and gradient boosting decision tree (GBDT). Results showed that GB-DF achieved the best performance for N, P, and K estimation, with R² values of 0.6959, 0.7535, and 0.7216, and RMSE values of 0.7877, 0.1280, and 0.6059 g/kg, respectively. Moreover, GB-DF required only 6.35 s for training, being approximately twice as fast as GBDT and 5.7 times faster than CNN. The proposed GB-DF framework shows potential to improve nutrient estimation accuracy under limited-sample conditions and provides an effective solution for UAV-based nutrient mapping and variable-rate fertilization in precision orchards. Full article

(This article belongs to the Section Soil and Plant Nutrition)

18 pages, 4520 KB

Open AccessArticle

Effects of Drip Fertilization on Soil Nitrogen Accumulation and Bacterial Communities in Citrus Orchards of Different Ages in the Red Soil Region of Southern China

by Liting Sheng, Feiyang Sun, Xiaoan Chen, Shunan Dong, Yunxi Zhu, Jie Zhang, Jihong Xia and Ying Zou

Agronomy 2026, 16(17), 1693; https://doi.org/10.3390/agronomy16171693 - 2 Sep 2026

Abstract

Due to the multiple benefits of drip fertilization, such as water conservation, fertilizer reduction and environmental protection, it has been widely adopted in orchard cultivation in China. However, the effects of drip fertilization on soil nitrogen in citrus orchards of different ages and their mechanisms are still unclear. Therefore, this study carried out soil surveys in three citrus (Citrus reticulata Blanco) orchards of different ages under two irrigation and fertilizer treatments (NF: normal fertilization; DF: drip fertilization) in Nanfeng City, Jiangxi Province, renowned as the “Chinese Citrus Township”. Soil nitrogen and properties, bacterial communities, N-cycling functional genes and their correlations were analyzed. In the results, soil TN content reduced under DF in the orchard that was 1–2 years old but increased in the orchard that was >10 years old. Soil SN under DF was generally higher and more responsive to tree age, mainly driven by SON. Soil bacterial diversity and composition were influenced by DF, but related to tree age, soil N-cycling showed an “active retention” mode under DF. Soil nitrogen was more related to soil physicochemical properties in the irrigation stage but to soil bacterial factors in the non-irrigation stage. This study can provide a theoretical basis to promote the differentiated application of water–fertilizer integrated technology in citrus orchards of different ages. Full article

(This article belongs to the Section Horticultural and Floricultural Crops)

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23 pages, 2910 KB

Open AccessArticle

Long-Term Organic Amendment Regimes Reshape Soil Micro-Food Web Structure and Multitrophic Co-Occurrence Networks in a Wheat–Maize Rotation

by Rui Yang and Bo Zhu

Agronomy 2026, 16(17), 1692; https://doi.org/10.3390/agronomy16171692 - 2 Sep 2026

Abstract

Organic amendments can improve soil C storage, nutrient availability, and crop production, particularly in purple soils with low organic matter and weak nutrient retention. They may also alter soil micro-food web processes. However, how different amendments affect bacteria–fungi–nematode associations remains unclear. We hypothesized that, relative to mineral fertilization alone, organic amendments would improve soil nutrient conditions, reshape bacterial and fungal communities, and increase microbivorous nematode abundance, whereas combined organic–mineral inputs would increase multitrophic co-occurrence network complexity. In a long-term wheat–maize rotation, soils were sampled in both seasons in 2023 under seven treatments: no fertilization (NF), mineral fertilization (NPK), manure (OM), straw return (RSD), mineral fertilization combined with manure (OMNPK), mineral fertilization combined with straw return (RSDNPK), and mineral fertilization combined with biochar (BCNPK). Among the organic amendment treatments, only RSD reduced wheat and maize yields relative to NPK. Fertilization mainly affected microbial community composition and nematode trophic structure, while microbial α diversity changed little. Network organization differed among fertilization regimes and between crop seasons. This study provides new insights into how long-term organic amendment regimes differentially regulate crop productivity, soil resource conditions, and soil micro-food webs, and advances our understanding of belowground multitrophic responses across wheat and maize seasons. Full article

(This article belongs to the Section Soil and Plant Nutrition)

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27 pages, 14766 KB

Open AccessArticle

ReSynDet: A Generate–Assess–Train Framework for Reliable Use of Synthetic Images in Data-Scarce Brassica chinensis Object Detection

by Fangjie Zhang and Fanhuai Shi

Agronomy 2026, 16(17), 1691; https://doi.org/10.3390/agronomy16171691 - 2 Sep 2026

Abstract

In precision agriculture, object detection is often limited by the scarcity of image data, as agricultural data collection is inherently constrained by season, geographical location and crop growth cycles. Generative AI offers a practical way to expand limited datasets and enrich data diversity. However, generation artifacts may introduce unreliable supervision. To address the challenge of scarce image data, we propose ReSynDet, a Generate–Assess–Train framework for reliable use of synthetic images in data-scarce Brassica chinensis object detection. In the Generate stage, we introduce a Flux.1-fill-based out-painting pipeline to produce diverse synthetic images from individual real images without additional model training. In the Assess stage, we propose Degradation-Chain Ranking-based Generated Image Quality Assessment (DCR-GIQA), which learns image-level fidelity scores from ordered degradation chains and replaces manual quality annotation with self-supervised ranking. In the Train stage, we propose GIQA-guided Semi-Supervised Object Detection (SSOD), where synthetic images serve as unlabeled data in teacher–student SSOD without requiring additional manual annotation, and GIQA-derived scores are incorporated as image-level weights into the unsupervised loss to emphasize high-fidelity synthetic images while softly suppressing unreliable ones. Experiments with only 6 labeled real images containing 75 plant instances and 120 unlabeled synthetic images show that ReSynDet improves

{A P}_{50 : 95}

from 61.30 to 66.44 and F1-score from 71.86 to 76.99. These results support the effectiveness of ReSynDet in improving object detection under the evaluated data-scarce Brassica chinensis setting. Full article

(This article belongs to the Special Issue Enhancing Generalization in Agricultural AI: Bridging Data Gaps and Boosting Model Robustness)

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