Agronomy

20 pages, 2569 KiB

Open AccessEditor’s ChoiceArticle

Straw Incorporation and Nitrogen Fertilization Enhance Soil Organic Carbon Sequestration by Promoting Aggregate Stability and Iron Oxide Transformation

by Zhichang Jing, Shirong Zhang, Zeqiang Sun, Zhaohui Liu, Shenglin Liu and Xiaodong Ding

Agronomy 2025, 15(4), 871; https://doi.org/10.3390/agronomy15040871 - 30 Mar 2025

Cited by 1 | Viewed by 591

Abstract

Soil barrenness and a poor stability of organic carbon are important factors restricting sustainable agricultural development. The effects of straw incorporation and nitrogen (N) fertilization on soil aggregates, soil organic carbon (SOC) functional groups, iron (Fe) oxides, and SOC sequestration were evaluated in [...] Read more.

Soil barrenness and a poor stability of organic carbon are important factors restricting sustainable agricultural development. The effects of straw incorporation and nitrogen (N) fertilization on soil aggregates, soil organic carbon (SOC) functional groups, iron (Fe) oxides, and SOC sequestration were evaluated in saline–alkali soil. In this study, we established six treatments involving the combined addition of straw and N in saline–alkali soil in the Yellow River Delta, China, to investigate the changes in SOC. A field experiment was conducted with two N levels (N1, 270 kg N ha⁻¹; N2, 210 kg N ha⁻¹) and three C treatments (S0, 0 kg ha⁻¹; S1, 5000 kg ha⁻¹; S2, 10,000 kg ha⁻¹). Compared with S0 treatments, straw incorporation and N application significantly increased the proportion of small macro-aggregates, and the mean weight diameter (MWD) was increased by 8.3–18.6%. Under the N2 treatment, with an increase in straw incorporation, the contents of organically complexed Fe oxides (Fep), especially small macro-aggregates and micro-aggregates, increased significantly. Meanwhile, polysaccharides-C and aromatic-C were mainly distributed in small macro-aggregates, forming aromatic Fe complexes with Fep and remaining at the aggregate interface. Compared with the N1S0 treatment, SOC storage increased by 3.94% and 5.12% in the N2S1 and N2S2 treatments, respectively. This could primarily be attributed to an improvement in soil structure, the optimization of OC functional group composition, and the formation of organo-Fe complexes. Straw incorporation and N application were optimal management measures and improved C stability and sequestration capacity. A halved straw incorporation and a reduced N application were the best treatment options for saline–alkali soil. Full article

(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)

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17 pages, 1002 KiB

Open AccessEditor’s ChoiceArticle

The Effect of Low Temperature and Low Illumination Intensity on the Photosynthetic Characteristics and Antioxidant Enzyme Activity in the Strawberry

by Xinlong Hu, Chao Xu, Huihui Tao, Siyu Wang, Meng Zhang, Qian Chen, Huanxin Zhang, Guoquan Li and Chengpu Yan

Agronomy 2025, 15(4), 860; https://doi.org/10.3390/agronomy15040860 - 29 Mar 2025

Cited by 1 | Viewed by 425

Abstract

Low temperature (LT) and low illumination (LI) are common meteorological factors posing a great risk to plants. This study aimed to clarify and quantify the effects of LT, LI, and their combined stress (LTLI) on the photosynthetic physiological processes of strawberry plants during [...] Read more.

Low temperature (LT) and low illumination (LI) are common meteorological factors posing a great risk to plants. This study aimed to clarify and quantify the effects of LT, LI, and their combined stress (LTLI) on the photosynthetic physiological processes of strawberry plants during the flowering stage. The results indicated that LI stress increased Chla and b levels in strawberry plants while lowering the chlorophyll a/b ratio. In contrast, LT and LTLI stress reduced chlorophyll content. All stress conditions (LT, LI, and LTLI) decreased net photosynthetic rate, stomatal conductance, transpiration rate, the maximum photochemical efficiency of photosystem II, photosynthetic electron transport rate, and actual photochemical quantum efficiency. These stresses also raised intercellular carbon dioxide concentration, non-photochemical quenching coefficient, and levels of malondialdehyde, proline, hydrogen peroxide, and peroxide ion content. Moreover, LI stress treatment boosted the activity of superoxide dismutase, peroxidase, and catalase, while LT and LTLI stress initially raised the activity of these enzymes before it eventually declined. Importantly, the previously mentioned photosynthetic physiological parameters showed notable changes under the combined stress conditions. Ultimately, the TOPSIS model was used to quantitatively evaluate the impact levels of different stressors and treatment durations on the photosynthetic system of strawberry plants. In conclusion, the synergistic impact of LT and LI results in a reduction in photosynthetic rate and photosystem II activity, a disruption in the equilibrium of the antioxidant system, and an intensification of photoinhibition, ultimately leading to diminished photosynthetic efficiency in plants. Full article

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

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23 pages, 2177 KiB

Open AccessEditor’s ChoiceArticle

Potential of Plant-Based Agents as Next-Generation Plant Growth-Promotors and Green Bactericides Against Pseudomonas savastanoi pv. savastanoi

by Laura Košćak, Janja Lamovšek, Edyta Đermić and Sara Godena

Agronomy 2025, 15(4), 819; https://doi.org/10.3390/agronomy15040819 - 26 Mar 2025

Cited by 1 | Viewed by 598

Abstract

One of the most persistent and damaging diseases in olive trees is olive knot disease. This disease is caused by an infection by the Gram-negative phytopathogenic bacterium Pseudomonas savastanoi pv. savastanoi that is notoriously difficult to control. The increasing demand for eco-friendly and [...] Read more.

One of the most persistent and damaging diseases in olive trees is olive knot disease. This disease is caused by an infection by the Gram-negative phytopathogenic bacterium Pseudomonas savastanoi pv. savastanoi that is notoriously difficult to control. The increasing demand for eco-friendly and sustainable agricultural solutions has driven research into plant-based agents. This study investigated the antibacterial properties of essential oils (EOs) and their constituents, olive mill wastewater (OMWW), the phenolic compound hydroxytyrosol (HTyr), and algae and garlic extracts, as well as copper-based and plant-stimulating commercial products against P. savastanoi pv. savastanoi, a significant olive tree pathogen. Antibacterial activity was determined using the Kirby–Bauer disc diffusion and broth microdilution methods. The EOs derived from Thymus vulgaris (thyme) and Origanum compactum (oregano), and their key components thymol and carvacrol, exhibited the strongest antibacterial efficacy. Conversely, the OMWW, plant-stimulating products, and algae and garlic extracts showed limited to no antibacterial activity in vitro, with their antibacterial properties determined using the disc diffusion method. While the EOs were highly effective in vitro, regardless of the testing method, their efficacy in bacterial growth inhibition was strain- and concentration-dependent, possibly highlighting some metabolic or genetic variability in the target pathogen, even though the MIC values against all tested strains of P. savastanoi pv. savastanoi were equal. Bacterial membrane disruption and the consequent leakage of metabolites were determined as the modes of action of carvacrol and oregano EO. Carvacrol also promoted plant growth in lettuce without significant phytotoxic effects, although minor necrotic lesions were observed in young olive leaves at higher concentrations, presenting these agents as potential next-generation green bactericides. Full article

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

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25 pages, 3670 KiB

Open AccessEditor’s ChoiceArticle

Composting of Olive Mill Wastewater Sludge Using a Combination of Multiple Strategies: Assessment of Improvement in Biodegradability, GHG Emissions, and Characteristics of the End Product

by Miguel Ángel Mira-Urios, José A. Sáez, Luciano Orden, Frutos C. Marhuenda-Egea, Francisco Javier Andreu-Rodríguez, Ana J. Toribio, Enrique Agulló, Maria J. López and Raúl Moral

Agronomy 2025, 15(4), 808; https://doi.org/10.3390/agronomy15040808 - 25 Mar 2025

Cited by 1 | Viewed by 527

Abstract

In this study, several composting strategies such as the use of semipermeable geotextile covers and biochar as an additive were investigated to improve olive mill wastewater (OMW) biodegradability and mitigate greenhouse gas (GHG) emissions during industrial-scale composting. In addition, the final characteristics of [...] Read more.

In this study, several composting strategies such as the use of semipermeable geotextile covers and biochar as an additive were investigated to improve olive mill wastewater (OMW) biodegradability and mitigate greenhouse gas (GHG) emissions during industrial-scale composting. In addition, the final characteristics of the compost obtained and its marketable value were also assessed. For this purpose, four different co-composting mixtures were prepared with OMW as the main ingredient, and two types of manure (cattle and goat manure) and bulking agents (almond pruning and vineyard pruning waste) as N and C sources. The results showed that exothermic behavior and biodegradability were more influenced by the co-composting strategy. The use of biochar as an additive showed a reduction in N losses (−14%) via GHG emissions and a significant improvement in cation exchange capacity (+35%) or the content of humic substances (+10%) in the final product. Lastly, the use of a geotextile cover was shown to be the worst cost-effective strategy, as it did not improve compost quality and showed no effect on GHG emissions. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

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20 pages, 5718 KiB

Open AccessEditor’s ChoiceArticle

Design and Optimization of Divider Head Geometry in Air-Assisted Metering Devices for Enhanced Seed Distribution Accuracy

by Alfarog H. Albasheer, Qingxi Liao, Lei Wang, Elebaid Jabir Ibrahim, Wenli Xiao and Xiaoran Li

Agronomy 2025, 15(4), 769; https://doi.org/10.3390/agronomy15040769 - 21 Mar 2025

Cited by 1 | Viewed by 478

Abstract

Achieving precise seed distribution is essential for optimizing crop yields and agricultural productivity. This study examines the impact of divider head geometry on seed distribution accuracy in pneumatic air seeder systems using rapeseed, wheat, and rice. Three custom-designed divider heads—funnel distributor (A1), closed-funnel [...] Read more.

Achieving precise seed distribution is essential for optimizing crop yields and agricultural productivity. This study examines the impact of divider head geometry on seed distribution accuracy in pneumatic air seeder systems using rapeseed, wheat, and rice. Three custom-designed divider heads—funnel distributor (A1), closed-funnel distributor (A2), and cone-shaped distributor (A3)—were developed for an eight-furrow opener seeding system, each featuring eight outlets per opener. Bench tests at air pressures of 3, 3.5, 4, 4.5, 5, and 5.5 kPa and speeds of 4 and 5 km/h revealed significant variations in seed distribution accuracy among the designs. The A2 distributor demonstrated the lowest coefficient of variation (CV) across all seed types: 4.3%, 2.6%, and 6.95% for A1, A2, and A3 in wheat, respectively; 4.5%, 3.4%, and 6.2% in rice, respectively; and 0.3%, 0.1%, and 1.0% in rapeseed, respectively. Seed types also significantly influenced feed rate uniformity, with average CVs of 2.91% for rapeseed, 3.85% for rice, and 4.90% for wheat. CFD-DEM simulations validated the superior performance of the A2 distributor by analyzing flow fields and velocity distributions, showing reductions in CVs by 19.09–54.55% compared to A1 and A3. Thus, the A2 distributor was identified as the optimal design, significantly improving seeding uniformity across all seed types. In conclusion, this study provides critical insights for redesigning seed drill distribution heads to minimize turbulence in the seed–air mixture transport, enhancing seeding uniformity and increasing crop yields and agricultural productivity. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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26 pages, 10142 KiB

Open AccessEditor’s ChoiceArticle

YOLO-MECD: Citrus Detection Algorithm Based on YOLOv11

by Yue Liao, Lerong Li, Huiqiang Xiao, Feijian Xu, Bochen Shan and Hua Yin

Agronomy 2025, 15(3), 687; https://doi.org/10.3390/agronomy15030687 - 13 Mar 2025

Cited by 8 | Viewed by 3194

Abstract

Accurate quantification of the citrus dropped number plays a vital role in evaluating the disaster resistance capabilities of citrus varieties and selecting superior cultivars. However, research in this critical area remains notably insufficient. To bridge this gap, we conducted in-depth experiments using a [...] Read more.

Accurate quantification of the citrus dropped number plays a vital role in evaluating the disaster resistance capabilities of citrus varieties and selecting superior cultivars. However, research in this critical area remains notably insufficient. To bridge this gap, we conducted in-depth experiments using a custom dataset of 1200 citrus images and proposed a lightweight YOLO-MECD model that is built upon the YOLOv11s architecture. Firstly, the EMA attention mechanism was introduced as a replacement for the traditional C2PSA attention mechanism. This modification not only enhances feature extraction capabilities and detection accuracy for citrus fruits but also achieves a significant reduction in model parameters. Secondly, we implemented a CSPPC module based on partial convolution to replace the original C3K2 module, effectively reducing both parameter count and computational complexity while maintaining mAP values. At last, the MPDIoU loss function was employed, resulting in improved bounding box detection accuracy and accelerated model convergence. Notably, our research reveals that reducing convolution operations in the backbone architecture substantially enhances small object detection capabilities and significantly decreases model parameters, proving more effective than the addition of small object detection heads. The experimental results and comparative analysis with similar network models indicate that the YOLO-MECD model has achieved significant improvements in both detection performance and computational efficiency. This model demonstrates excellent comprehensive performance in citrus object detection tasks, with a precision (P) of 84.4%, a recall rate (R) of 73.3%, and an elevated mean average precision (mAP) of 81.6%. Compared to the baseline, YOLO-MECD has improved by 0.2, 4.1, and 3.9 percentage points in detection precision, recall rate, and mAP value, respectively. Furthermore, the number of model parameters has been substantially reduced from 9,413,574 in YOLOv11s to 2,297,334 (a decrease of 75.6%), and the model size has been compressed from 18.2 MB to 4.66 MB (a reduction of 74.4%). Moreover, YOLO-MECD also demonstrates superior performance against contemporary models, with mAP improvements of 3.8%, 3.2%, and 5.5% compared to YOLOv8s, YOLOv9s, and YOLOv10s, respectively. The model’s versatility is evidenced by its excellent detection performance across various citrus fruits, including pomelos and kumquats. These achievements establish YOLO-MECD as a robust technical foundation for advancing citrus fruit detection systems and the development of smart orchards. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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22 pages, 5343 KiB

Open AccessEditor’s ChoiceArticle

Mechanisms and Management Strategies for Satsuma Mandarin Fruit Cracking

by Yongjie Li, Guoqiang Jin, Mingxia Wen, Xiaoting Zhu and Yongqiang Zheng

Agronomy 2025, 15(3), 698; https://doi.org/10.3390/agronomy15030698 - 13 Mar 2025

Cited by 1 | Viewed by 742

Abstract

The Satsuma mandarin, a prominent fresh citrus variety cultivated in Asia, is susceptible to fruit cracking, a physiological disorder that significantly impacts yield and economic efficiency. This phenomenon occurs during the fruit expansion phase. The present study sought to further elucidate the correlation [...] Read more.

The Satsuma mandarin, a prominent fresh citrus variety cultivated in Asia, is susceptible to fruit cracking, a physiological disorder that significantly impacts yield and economic efficiency. This phenomenon occurs during the fruit expansion phase. The present study sought to further elucidate the correlation between citrus fruit cracking and fruit peel development or mineral elements, as well as to propose efficacious management measures. The present experiment was conducted on Citrus unshiu Marc. cv. ‘Miyagawa Wase’ over two successive seasons—2022 and 2023. The dynamic changes in fruit morphology were recorded using calipers, and the peel strength was assessed via a Plus Texture Analyzer. Paraffin sectioning technology was used to observe the morphological structure of peel cells. At 10 days after full bloom (DAFB), the peel cells exhibited vigorous proliferation, and the fruit and peel thicknesses underwent rapid expansion. At 50–60 d after full bloom, the longitudinal and transverse diameters of the fruit exhibited a marked increase in the growth rate of the former over the latter. At 80 d after full bloom, both the peel thickness change and the fruit growth rate exhibited a marked deceleration, and the albedo layer cells began to show signs of perforation. The following two time points were preliminarily proposed as the key points for the control of citrus fruit cracking: key point one was 50–60 days after full bloom; and key point two was 80–90 days after full bloom. The nitrogen (N), phosphorus (P), and potassium (K) contents in the different orchards were measured via the semi-micro Kjeldahl nitrogen method, the molybdenum–antimony colorimetric method, and flame photometry, respectively. The determination of other mineral elements was conducted by means of inductively coupled plasma spectroscopy. Principal component analysis was employed to analyze the 21-parameter indices of mineral elements in soil and leaf samples from the three orchards with different levels of fruit cracking. The study found that high concentrations of leaf Fe, P, and soil Cu, as well as organic matter content, contributed negatively to the extent of fruit cracking. The impact of diverse control measures on the incidence of fruit cracking was subsequently observed, following the implementation of tree crown spray treatments. The application of 0.5% calcium superphosphate and 0.006% EDTA-Fe, in combination with 10 ppm GA₃ sprayed during two critical periods, significantly reduced fruit cracking and did not adversely affect the internal or external quality of the fruits. The study emphasises the necessity of customising management measures according to the developmental characteristics of citrus fruits, given the observed varietal and regional distinctions in susceptibility to cracking. These findings are pivotal for advancing research in the field of fruit cracking and promoting the healthy development of the industry. Full article

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

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14 pages, 7052 KiB

Open AccessEditor’s ChoiceArticle

Effect of Subsurface Drainpipe Parameters on Soil Water and Salt Distribution in a Localized Arid Zone: A Field-Scale Study

by Hui Wang, Qianqian Zhu, Yuzhuo Pan, Xiaopeng Ma, Feng Ding, Wanli Xu, Yanbo Fu, Qingyong Bian and Mushajiang Kade

Agronomy 2025, 15(3), 678; https://doi.org/10.3390/agronomy15030678 - 11 Mar 2025

Cited by 2 | Viewed by 532

Abstract

The salt distribution characteristics in arid areas are directly related to the sustainable development of agriculture. We study the characteristics of spatial changes of soil water and salt in farmland under the full anniversary of different culvert pipe arrangements and optimize the salt [...] Read more.

The salt distribution characteristics in arid areas are directly related to the sustainable development of agriculture. We study the characteristics of spatial changes of soil water and salt in farmland under the full anniversary of different culvert pipe arrangements and optimize the salt drainage parameters of underground drains suitable for the local area so as to promote the management of saline and alkaline land in Xinjiang. A subsurface drainpipe salinity test was conducted in the Yanqi Basin (Bayingoleng Mongolian Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China) to analyze changes in soil water and salt dynamics before and after irrigation-induced salt flushing, assessing the impact of drainpipe deployment parameters. It was found that at a 1.4 m depth of burial, the maximum desalination rates of soil in different soil layers from the subsurface drainpipes in 20, 30, and 40 m spacing plots were 78.28%, 50.91%, and 54.52%, respectively. At a 1.6 m depth of burial, the maximum desalination rates of soil in different soil layers from the subsurface drainpipes in 20, 30, and 40 m spacing plots were 70.94%, 61.27%, and 44.12%. Reasonable deployment of subsurface drainpipes can effectively reduce soil salinity, increase the desalination rate, and improve soil water salinity condition. This study reveals the influence of the laying parameters of subsurface drainpipes on soil water salinity distribution characteristics in arid zones, which provides theoretical support and practical guidance for the management of soil salinization in arid zones. Full article

(This article belongs to the Section Water Use and Irrigation)

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22 pages, 19774 KiB

Open AccessEditor’s ChoiceArticle

A Fusion XGBoost Approach for Large-Scale Monitoring of Soil Heavy Metal in Farmland Using Hyperspectral Imagery

by Xuqing Li, Huitao Gu, Ruiyin Tang, Bin Zou, Xiangnan Liu, Huiping Ou, Xuying Chen, Yubin Song, Wei Luo and Bin Wen

Agronomy 2025, 15(3), 676; https://doi.org/10.3390/agronomy15030676 - 11 Mar 2025

Cited by 2 | Viewed by 900

Abstract

Heavy metal pollution of farmland is worsened by the excessive introduction of heavy metal elements into soil systems, posing a substantial threat for global food security and human health. The traditional laboratory-based methods for monitoring soil heavy metals are limited for large-scale applications, [...] Read more.

Heavy metal pollution of farmland is worsened by the excessive introduction of heavy metal elements into soil systems, posing a substantial threat for global food security and human health. The traditional laboratory-based methods for monitoring soil heavy metals are limited for large-scale applications, while hyperspectral imagery data-based methods still face accuracy challenges. Therefore, a fusion XGBoost model based on the superposition of ensemble learning and packaging methods is proposed for large-scale monitoring with high accuracy of soil heavy metal using hyperspectral imagery. We took Xiong’an New Area, Hebei Province, as the study area, and acquired heavy metal content using chemical analysis. The XGB-Boruta-PCC algorithm was used for precise feature selection to obtain the final modeled spectral response features. On this basis, the performance indicators of the Optuna-optimized XGBoost model were compared with traditional linear and nonlinear models. The optimal model was extended to the entire region for drawing the spatial distribution map of soil heavy metal content. The results suggested that the XGB-Boruta-PCC method effectively achieved double dimensionality reduction of high-dimensional hyperspectral data, extracting spectral response features with a high contribution, which, combined with the XGBoost model, exhibited greater general estimation accuracies for heavy metal (Pb) in farmland (i.e., Pb: R² = 0.82, RMSE = 11.58, MAE = 9.89). The results of the mapping indicated that there were exceedances for the southwest and parts of the west over the research region. Factories and human activities were the potential causes of heavy metal Pb contamination in farmland. In conclusion, this innovative method can quickly and accurately achieve monitoring large-scale soil heavy metal contamination in farmland, with ZY-1-02E spaceborne hyperspectral imagery proving to be a reliable tool for mapping soil heavy metal in farmland. Full article

(This article belongs to the Special Issue Heavy Metal Pollution and Prevention in Agricultural Soils)

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24 pages, 1814 KiB

Open AccessEditor’s ChoiceArticle

Nutritional and Bioactive Lipid Composition of Amaranthus Seeds Grown in Varied Agro-Climatic Conditions in France

by Ahlem Azri, Sameh Sassi Aydi, Samir Aydi, Mohamed Debouba, Jalloul Bouajila, Muriel Cerny, Romain Valentin, Lucas Tricoulet, Patrice Galaup and Othmane Merah

Agronomy 2025, 15(3), 672; https://doi.org/10.3390/agronomy15030672 - 9 Mar 2025

Cited by 1 | Viewed by 1560

Abstract

Increasing interest has been devoted to the seeds of the amaranth, a plant that has garnered attention for its multifaceted uses in daily life. In this research, we focused on four genotypes of two amaranth species cultivated in two different sites in the [...] Read more.

Increasing interest has been devoted to the seeds of the amaranth, a plant that has garnered attention for its multifaceted uses in daily life. In this research, we focused on four genotypes of two amaranth species cultivated in two different sites in the southwest of France. Oil content, fatty acid composition, and unsaponifiable levels were carried out. The lipid composition was analyzed using Gas Chromatography with Flame Ionization Detection (GC-FID) analysis. The total polyphenol contents (TPC) of different seed extracts were measured by a Folin–Ciocalteu assay. Antioxidants and cytotoxic activities were additionally assessed for the methanol (70%), ethyl acetate, and cyclohexane extracts. Results showed that oil content varied greatly and ranged from 4.3 to 6.4%. Lera cultivated at Riscle had the highest squalene yield, reaching 7.7%. Linoleic acid and oleic acid were the most abundant fatty acids for the four genotypes in two sites, followed by palmitic acid. Triglycerides (TAGs) were the main glycerides in all samples growing in both sites. A total of 44 volatile compounds were identified in Amaranthus seed extracts. The chemical compositions of the amaranth have been discussed as influenced by genetic and environmental factors. These data highlight the bioactive potential of the amaranth seed. Full article

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

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14 pages, 1277 KiB

Open AccessEditor’s ChoiceArticle

Responses of Parasitic Nematodes to Volatile Organic Compounds Emitted by Brassica nigra Roots

by Žiga Laznik, Tímea Tóth, Szabolcs Ádám, Stanislav Trdan, Ivana Majić and Tamás Lakatos

Agronomy 2025, 15(3), 664; https://doi.org/10.3390/agronomy15030664 - 6 Mar 2025

Cited by 2 | Viewed by 997

Abstract

Parasitic nematodes, particularly those in the Rhabditidae family, are vital components of belowground ecosystems, contributing to pest regulation and sustainable agriculture. This study investigated the chemotactic responses of three nematode species—Phasmarhabditis papillosa, Oscheius myriophilus, and O. onirici—to volatile organic [...] Read more.

Parasitic nematodes, particularly those in the Rhabditidae family, are vital components of belowground ecosystems, contributing to pest regulation and sustainable agriculture. This study investigated the chemotactic responses of three nematode species—Phasmarhabditis papillosa, Oscheius myriophilus, and O. onirici—to volatile organic compounds (VOCs) emitted by Brassica nigra roots under herbivory by Delia radicum larvae. Using a chemotaxis assay, the effects of five VOCs (dimethyl sulfide, dimethyl disulfide, allyl isothiocyanate, phenylethyl isothiocyanate, and benzonitrile) were tested at two concentrations (pure and 0.03 ppm) and two temperatures (18 °C and 22 °C). The results revealed that VOCs and temperature significantly influenced nematode responses, while nematode species and VOC concentration showed limited effects. Benzonitrile consistently demonstrated strong chemoattractant properties, particularly for O. myriophilus and O. onirici. Conversely, allyl isothiocyanate exhibited potent nematicidal effects, inhibiting motility and causing mortality. Dimethyl disulfide and dimethyl sulfide elicited moderate to strong attractant responses, with species- and temperature-dependent variations. Significant interactions between VOCs, temperature, and nematode species highlighted the complexity of these ecological interactions. These findings emphasize the ecological roles of VOCs in mediating nematode behavior and their potential applications in sustainable pest management. Benzonitrile emerged as a promising candidate for nematode-based biocontrol strategies, while allyl isothiocyanate showed potential as a direct nematicidal agent. The study underscores the importance of integrating chemical cues into pest management systems to enhance agricultural sustainability and reduce reliance on chemical pesticides. Full article

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

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16 pages, 5596 KiB

Open AccessEditor’s ChoiceArticle

Reducing Uneven Fruit Ripening and Improving the Quality of Durian (Durio zibethinus Murr.) Fruit Using Plastic Mulching Combined with Polyhalite Fertilizer

by Nguyen Kim Quyen, Le Van Dang, Ngo Phuong Ngoc, Le Ngoc Quynh, Nguyen Minh Phuong, Le Minh Ly and Ngo Ngoc Hung

Agronomy 2025, 15(3), 631; https://doi.org/10.3390/agronomy15030631 - 1 Mar 2025

Cited by 2 | Viewed by 2088

Abstract

Uneven fruit ripening (UFR) is currently causing a decrease in the quality and value of “Ri 6” durian fruit. The soil moisture and nutrient (K, Ca, and Mg) levels present during the fruit development stage are the two main factors affecting UFR in [...] Read more.

Uneven fruit ripening (UFR) is currently causing a decrease in the quality and value of “Ri 6” durian fruit. The soil moisture and nutrient (K, Ca, and Mg) levels present during the fruit development stage are the two main factors affecting UFR in durian fruit. However, measurements that can be used to determine the decrease in the UFR rate of durian remain unknown. Therefore, this study sought to evaluate the impact of plastic mulching (PM) and polyhalite fertilizer (PH) on improving the UFR rate and quality of durian fruit. A field study was conducted at three different durian orchards in the Vietnamese Mekong Delta (VMD) throughout two seasons (2022–2023 and 2023–2024). We used PM a month before fruit harvesting, combined with PH applied during the fruit development stage. Four treatments were used: (T1) control; (T2) PM, plastic mulching a month before durian fruit harvesting; (T3) PH, polyhalite fertilizer application (3 kg tree⁻¹ year⁻¹); and (T4) PM + PH, polyhalite fertilizer application (3 kg tree⁻¹ year⁻¹) and plastic mulching a month before durian fruit harvesting. The farmer’s fertilization practice (450 g N–450 g P–450 g K per tree⁻¹ during the fruit development period) was used in all treatments. Parameters such as soil physicochemical properties, fruit quality, and leaf mineral nutrient concentration were investigated at the harvesting stage. The results show that using PM + PH decreased soil moisture (>15%) but increased the concentrations of K, Mg, and Ca in both soil and durian leaves, thereby reducing the UFR rate (>80%) compared with the control. Additionally, applying PM + PH increased the aril proportion (>18%) and total soluble solids (approximately 5%) in durian fruit in comparison with the control. In conclusion, combining PM and PH improved the UFR rate and durian fruit quality. Therefore, we recommend that farmers apply these methods to their durian orchards to decrease physiological disorders and enhance fruit quality, thus contributing to achieving sustainable durian production in the VMD. Full article

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

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23 pages, 1668 KiB

Open AccessEditor’s ChoiceArticle

Double-Cropping Systems Based on Maize, Sorghum, and Alfalfa: Impact on Nutritive Value and Silage Fermentation Quality

by Zhongya Ji, Yu Shi, Liqiu Jiang, Xiaoshan Wang, Guanglong Zhu and Guisheng Zhou

Agronomy 2025, 15(3), 630; https://doi.org/10.3390/agronomy15030630 - 1 Mar 2025

Cited by 1 | Viewed by 664

Abstract

The accelerating development of the forage industry and the land resources finiteness require the high-efficient forage cropping strategies. To investigate the nutritive quality of the mixed forage crop cultivation, a three-round field test of two double-cropping systems (DCSs) based on maize (Dongdan 60 [...] Read more.

The accelerating development of the forage industry and the land resources finiteness require the high-efficient forage cropping strategies. To investigate the nutritive quality of the mixed forage crop cultivation, a three-round field test of two double-cropping systems (DCSs) based on maize (Dongdan 60 and Dongdan 1331) and sorghum (1230 and cfsh30) as the summer forage crop and alfalfa as the preceding winter forage crop were compared. This study investigated the impact on nutritive value and silage fermentation quality. The M-A system (alfalfa following a preceding crop of maize) outperformed the S-A system (alfalfa following a preceding crop of sorghum) in silage fermentation quality (by 2.81 of M and 2.22 of A), crude ash (by 0.94% of M and 3.5% of A), phosphorus content (by 0.1% of M and 0.17% of A), and potassium content (by 0.47% of M and 0.41% of A). Within the M-A, the maize Dongdan 60 (M1)–alfalfa WL525 (A) combination under the suitable sowing condition (D1) achieved the best nutritive quality exhibiting, not only the highest Flieg score (88.17 of M1 and 92.5 of A) but also the highest crude ash content (6.71% of M1 and 11.82% of A), phosphorus content (0.38% of M1 and 0.48% of A), and potassium content (1.68% of M1 and 1.55% of A). Delayed sowing reduced nutrient accumulation and altered fermentation profiles, highlighting the importance of timely sowing. The study revealed that the double-cropping rotation of maize and alfalfa is a promising strategy to optimize nutritive quality. Full article

(This article belongs to the Section Innovative Cropping Systems)

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19 pages, 4538 KiB

Open AccessEditor’s ChoiceArticle

Design and Test of the Outside-Filling Chinese Chive Adjustable-Capacity Precision Seed-Metering Device

by Guoqiang Dun, Yuhan Wei, Xinxin Ji, Shang Gao, Yingyi Pei, Yang He and Chao Ma

Agronomy 2025, 15(3), 622; https://doi.org/10.3390/agronomy15030622 - 28 Feb 2025

Cited by 1 | Viewed by 510

Abstract

In order to innovate the planting mode and improve the quality of Chinese chive, we designed an outside-filling Chinese chive adjustable-capacity precision seed-metering device with an adjustable number of sown seeds. The diameter, number of shaped holes, and seed slot parameters of the [...] Read more.

In order to innovate the planting mode and improve the quality of Chinese chive, we designed an outside-filling Chinese chive adjustable-capacity precision seed-metering device with an adjustable number of sown seeds. The diameter, number of shaped holes, and seed slot parameters of the seeding plate were designed based on the physical characteristics and agronomic planting requirements of the Haoji Chinese chive. A simulation of the seed-metering device’s seeding process was carried out using EDEM software. To carry out the quadratic general rotary combination design experiment, use seed slot diameter and seed slot depth as test factors, longitudinal concentration and transverse concentration as evaluation indexes, and carry out the bench validation test and comparison test under the optimal parameter combination. In the simulation test, the factors affecting the longitudinal concentration in order of priority were seed slot depth and seed slot diameter, and the factors affecting the transverse concentration in order of priority were seed slot diameter and seed slot depth. The optimal parameters were seed slot diameter of 3.075 mm, seed slot depth of 3.323 mm, longitudinal concentration of 0.563, and transverse concentration of 0.634. In the bench test, the relative error of longitudinal concentration was 3.20%, the relative error of transverse concentration was 2.47%, and the number of seeds sown per hole was linearly correlated with the length of the seed slot. The results of the bench test and simulation test are close to each other, which proves that the outside-filling Chinese chive adjustable-capacity precision seed-metering device has a better sowing effect, and the number of sowing grains can be adjusted. Full article

(This article belongs to the Section Farming Sustainability)

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16 pages, 1644 KiB

Open AccessEditor’s ChoiceArticle

Phytoremediation of Total Petroleum Hydrocarbons-Contaminated Soils: Case Study of Jerusalem Artichokes with Cost Analysis and Biomass Conversion

by Mantas Rubežius, Žygimantas Kidikas, Christopher Kick and Alfreda Kasiulienė

Agronomy 2025, 15(3), 601; https://doi.org/10.3390/agronomy15030601 - 28 Feb 2025

Cited by 1 | Viewed by 763

Abstract

The application of environmentally friendly technologies, such as phytoremediation, for contaminated soil remediation and biofuel generation should be one of the goals of sustainable development. Phytoremediation is based on the use of plants and their associated microorganisms to clean contaminated soils, resulting in [...] Read more.

The application of environmentally friendly technologies, such as phytoremediation, for contaminated soil remediation and biofuel generation should be one of the goals of sustainable development. Phytoremediation is based on the use of plants and their associated microorganisms to clean contaminated soils, resulting in a positive impact on the environment and the production of biomass that can be utilized for biofuel production. Combining phytoremediation with advanced thermochemical conversion technologies like thermo-catalytic reforming process (TCR) allows for the production of high-quality biochar, bio-oil comparable to fossil crude oil, and hydrogen-rich syngas. This study presents a full-scale phytoremediation experiment conducted at a former oil storage site using energy crops like Jerusalem artichokes (Helianthus tuberosus), where the biomass was later converted into biofuel and other by-products using lab-scale technology. Significant and promising results were obtained: (i) within two years, the initial total petroleum hydrocarbons (TPH) contamination level (698 mg/kg) was reduced to a permissible level (146 mg/kg); (ii) the yield of the harvested Jerusalem artichoke biomass reached 18.3 t/ha dry weight; (iii) the thermochemical conversion produced high-quality products, such as a thermally stable oil a higher heating value (HHV) of 33.85 MJ/kg; (iv) the two-year phytoremediation costs for the rejuvenated soil amounted to3.75 EUR/t. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

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20 pages, 5150 KiB

Open AccessEditor’s ChoiceArticle

Effects of Nitrogen Application at Different Panicle Development Stages on the Panicle Structure and Grain Yield in Hybrid Indica Rice Cultivars

by Qiguang Zhang, Jie Sun, Longping Wang, Jun Chen, Jian Ke and Liquan Wu

Agronomy 2025, 15(3), 595; https://doi.org/10.3390/agronomy15030595 - 27 Feb 2025

Cited by 1 | Viewed by 497

Abstract

To increase the seed setting rate and yield of large-panicle rice varieties, one agronomic and breeding strategy is to increase the proportion of spikelets in the middle portion of the panicle as many of the lower spikelets fail to produce grains. Current nitrogen [...] Read more.

To increase the seed setting rate and yield of large-panicle rice varieties, one agronomic and breeding strategy is to increase the proportion of spikelets in the middle portion of the panicle as many of the lower spikelets fail to produce grains. Current nitrogen management during panicle development mainly focuses on fertilization at the emergence of the top fourth leaf, which increases the number of secondary branch spikelets on the lower part of the panicle. Two-year field experiments were conducted in 2021 and 2022 with two typical large-panicle hybrid indica rice cultivars, IIYM86 and JLY8612. Nitrogen was applied at the emergence of the top fifth (TL5), fourth (TL4), third (TL3), and second (TL2) leaves, with no nitrogen application as a control. This study aimed to investigate the effects of nitrogen application on the panicle structure, seed setting rate, and grain yield at different stages of panicle development. Nitrogen application at TL3 achieved the highest grain yield, followed by application at TL4, for both cultivars over the two years. TL3 did not significantly alter the number of spikelets per panicle but increased the proportion of spikelets located in the middle part of the panicle and reduced the proportions in the upper and lower parts compared to TL4. These effects were attributed to a significant increase in secondary branch spikelet differentiation in the middle part and a decrease in secondary branch spikelet differentiation in the upper and lower parts. Compared to TL4, TL3 significantly increased the seed setting rate by 9.46 and 9.48% and the grain yield by 6.86 and 8.92% in IIYM86 and JLY8612, respectively. In TL3, the significant increase in secondary branch spikelet differentiation in the middle part was primarily due to significantly reduced indole acetic acid (IAA) and an increased cytokinin/IAA ratio, which inhibited apical dominance. The significant decrease in secondary branch spikelet differentiation in the lower part of TL3 was mainly related to a significant increase in IAA and a reduction in the cytokinin/IAA ratio. Transcriptome analysis of young panicles confirmed these results, and differentially expressed genes between TL3 and TL4 were primarily enriched in plant hormone signal transduction related to IAA biosynthesis and degradation. These findings indicate that postponing nitrogen application until TL3 can improve the PTI and the seed setting rate by regulating hormonal balance, thereby optimizing nitrogen management during panicle development in large-panicle hybrid indica rice cultivars. Full article

(This article belongs to the Special Issue Molecular Mechanism of Quality Formation in Rice)

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19 pages, 3839 KiB

Open AccessEditor’s ChoiceArticle

YOLO-YSTs: An Improved YOLOv10n-Based Method for Real-Time Field Pest Detection

by Yiqi Huang, Zhenhao Liu, Hehua Zhao, Chao Tang, Bo Liu, Zaiyuan Li, Fanghao Wan, Wanqiang Qian and Xi Qiao

Agronomy 2025, 15(3), 575; https://doi.org/10.3390/agronomy15030575 - 26 Feb 2025

Cited by 9 | Viewed by 2184

Abstract

The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly [...] Read more.

The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly alternative to pesticides. However, the small size and dense distribution of pests on yellow sticky traps lead to lower detection accuracy when using lightweight models. On the other hand, large models suffer from longer training times and deployment difficulties, posing challenges for pest detection in the field using edge computing platforms. To address these issues, this paper proposes a lightweight detection method, YOLO-YSTs, based on an improved YOLOv10n model. The method aims to balance pest detection accuracy and model size and has been validated on edge computing platforms. This model incorporates SPD-Conv convolutional modules, the iRMB inverted residual block attention mechanism, and the Inner-SIoU loss function to improve the YOLOv10n network architecture, ultimately addressing the issues of missed and false detections for small and overlapping targets while balancing model speed and accuracy. Experimental results show that the YOLO-YSTs model achieved precision, recall, mAP₅₀, and mAP_50–95 values of 83.2%, 83.2%, 86.8%, and 41.3%, respectively, on the yellow sticky trap dataset. The detection speed reached 139 FPS, with GFLOPs at only 8.8. Compared with the YOLOv10n model, the mAP₅₀ improved by 1.7%. Compared with other mainstream object detection models, YOLO-YSTs also achieved the best overall performance. Through improvements to the YOLOv10n model, the accuracy of pest detection on yellow sticky traps was effectively enhanced, and the model demonstrated good detection performance when deployed on edge mobile platforms. In conclusion, the proposed YOLO-YSTs model offers more balanced performance in the detection of pest images on yellow sticky traps. It performs well when deployed on edge mobile platforms, making it of significant importance for field pest monitoring and integrated pest management. Full article

(This article belongs to the Special Issue In-Field Detection and Monitoring Technology in Precision Agriculture—2nd Edition)

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21 pages, 3470 KiB

Open AccessEditor’s ChoiceArticle

Systematic Identification of Phosphate Transporter Family 1 (PHT1) Genes and Their Expression Profiling in Response to Low Phosphorus and Related Hormones in Fagopyrum tataricum (L.) Gaertn.

by Yanyu Zhou, Jianjiang Fan, Qingtao Wu, Haihua Wang, Xiaoyan Huang, Limei Liao, Huan Xie and Xixu Peng

Agronomy 2025, 15(3), 576; https://doi.org/10.3390/agronomy15030576 - 26 Feb 2025

Cited by 1 | Viewed by 605

Abstract

Accumulating evidence suggests that the plasma membrane-localized phosphate transporter 1 (PHT1) family plays a fundamental role in the absorption, translocation, and re-mobilization of phosphorus in plants. Buckwheat (Fagopyrum spp.) exhibits high efficiency in phosphate uptake and wide adaptability to grow in under-fertilized [...] Read more.

Accumulating evidence suggests that the plasma membrane-localized phosphate transporter 1 (PHT1) family plays a fundamental role in the absorption, translocation, and re-mobilization of phosphorus in plants. Buckwheat (Fagopyrum spp.) exhibits high efficiency in phosphate uptake and wide adaptability to grow in under-fertilized soils. Despite their physiological importance, a systematic analysis of PHT1 genes in buckwheat has not been conducted yet. In this study, we performed a genome-wide identification and expression profile of the PHT1 gene family in Tartary buckwheat (Fagopyrum tataricum Gaertn). A total of eleven putative PHT1 genes (FtPHT1;1 to 1;11) were identified with an uneven distribution on all the F. tataricum chromosomes except for chromosomes 2, 3, and 5. All the FtPHT1s share the conserved domain GGDYPLSATIxSE, a typical signature of PHT1 transporters. A phylogenetic analysis indicated that FtPHT1 proteins could be clustered into four distinct subgroups, well supported by the exon–intron structure, consensus motifs, and the domain architecture. A gene duplication analysis suggested that tandem duplication may largely contribute to the expansion of the FtPHT1 gene family members. In silico predictions of cis-acting elements revealed that low-phosphate-responsive elements, such as W-box, P1BS, and MBS, were enriched in the promoter regions of FtPHT1 genes. Quantitative real-time PCR assays showed differential but partially overlapping expression patterns of some FtPHT1 genes in various organs under limited Pi supply and hormone stimuli, implying that these FtPHT1 transporters may be essential for Pi uptake, translocation, and re-mobilization, possibly through signaling cross-talk between the low phosphate and hormones. These observations provide molecular insights into the FtPHT1 gene family, which paves the way to a functional analysis of FtPHT1 members in the future. Full article

(This article belongs to the Special Issue Crop Genomics and Omics for Future Food Security)

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24 pages, 954 KiB

Open AccessEditor’s ChoiceReview

Towards Climate-Smart Agriculture: Strategies for Sustainable Agricultural Production, Food Security, and Greenhouse Gas Reduction

by Wogene Kabato, Girma Tilahun Getnet, Tamrat Sinore, Attila Nemeth and Zoltán Molnár

Agronomy 2025, 15(3), 565; https://doi.org/10.3390/agronomy15030565 - 25 Feb 2025

Cited by 11 | Viewed by 6453

Abstract

Without transformative adaptation strategies, the impact of climate change is projected to reduce global crop yields and increase food insecurity, while rising greenhouse gas (GHG) emissions further exacerbate the crisis. While agriculture is a major contributor to climate change through unsustainable practices, it [...] Read more.

Without transformative adaptation strategies, the impact of climate change is projected to reduce global crop yields and increase food insecurity, while rising greenhouse gas (GHG) emissions further exacerbate the crisis. While agriculture is a major contributor to climate change through unsustainable practices, it also offers significant opportunities to mitigate these emissions through the adoption of sustainable practices. This review examines climate-smart agriculture (CSA) as a key strategy for enhancing crop productivity, building climate resilience, and reducing GHG emissions, while emphasizing the need for strategic interventions to accelerate its large-scale implementation for improved food security. The analysis revealed that while nitrogen use efficiency (NUE) has improved in developed countries, the global NUE remains at 55.47%, emphasizing the need for precision nutrient management and integrated soil fertility strategies to enhance productivity and minimize environmental impacts. With 40% of the world’s agricultural land already degraded, sustainability alone is insufficient, necessitating a shift toward regenerative agricultural practices to restore degraded soil and water by improving soil health, enhancing biodiversity, and increasing carbon sequestration, thus ensuring long-term agricultural resilience. CSA practices, including precision agriculture, regenerative agriculture, biochar application, and agroforestry, improve soil health, enhance food security, and mitigate greenhouse gas emissions. However, result variability highlights the need for site-specific strategies to optimize benefits. Integrating multiple CSA practices enhances soil health and productivity more effectively than implementing a single practice alone. Widespread adoption faces socio-economic and technological barriers, requiring supportive policies, financial incentives, and capacity-building initiatives. By adopting climate-smart technologies, agriculture can transition toward sustainability, securing global food systems while addressing climate challenges. Full article

(This article belongs to the Special Issue Net-Zero Emissions for Sustainable Food Production and Land Management)

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18 pages, 2821 KiB

Open AccessEditor’s ChoiceArticle

Anaerobic Soil Disinfestation as a Tool for Nematode and Weed Management in Organic Sweetpotato

by Simardeep Singh, Matthew Cutulle, William Rutter, Phillip A. Wadl, Brian Ward and Churamani Khanal

Agronomy 2025, 15(3), 548; https://doi.org/10.3390/agronomy15030548 - 24 Feb 2025

Cited by 1 | Viewed by 933

Abstract

Anaerobic soil disinfestation (ASD) is a promising alternative to synthetic chemical-driven pest management methods facilitated by incorporating carbon sources into the soil, tarping the soil with plastic mulch, and irrigating to soil saturation. To evaluate the impact of ASD on southern root-knot nematode [...] Read more.

Anaerobic soil disinfestation (ASD) is a promising alternative to synthetic chemical-driven pest management methods facilitated by incorporating carbon sources into the soil, tarping the soil with plastic mulch, and irrigating to soil saturation. To evaluate the impact of ASD on southern root-knot nematode [Meloidogyne incognita (Kofoid & White), SRKN] and yellow nutsedge (Cyperus esculentus L.) management in organically grown sweetpotato, greenhouse studies were conducted. The treatments were structured as a factorial of two carbon amendments [chicken manure + molasses (CM + M), and no additional carbon (control)] by 20 sweetpotato genotypes with 4 replications using a randomized complete block design. The results suggest that the microcosms receiving the carbon amendment spent the most time under anaerobic conditions (<200 mvh). Planting of sweetpotato genotypes in CM + M-treated microcosms resulted in 60–90% and 56–92% suppression of soil population and egg reproduction of SRKN as compared to no additional carbon. The application of CM + M reduced overall weed cover by 79% relative to the control. Sweetpotatoes in CM + M-treated microcosms had significantly higher dry above-ground biomass (6.8 g) as compared to the control (3.6 g). The results of this study demonstrated that ASD has the potential to manage nematodes and weeds in organic sweetpotato production systems. Full article

(This article belongs to the Special Issue Integrated Water, Nutrient, and Pesticide Management of Fruit Crop)

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16 pages, 5045 KiB

Open AccessEditor’s ChoiceArticle

Slope Position Modulates Soil Chemical Properties and Microbial Dynamics in Tea Plantation Ecosystems

by Limei Li, Lijiao Chen, Hongxu Li, Yuxin Xia, Houqiao Wang, Qiaomei Wang, Wenxia Yuan, Miao Zhou, Juan Tian and Baijuan Wang

Agronomy 2025, 15(3), 538; https://doi.org/10.3390/agronomy15030538 - 23 Feb 2025

Cited by 1 | Viewed by 732

Abstract

As a perennial plant, the nutrient supply for tea bushes is predominantly dependent on the soil. Yunnan tea plantations exhibit significant topographic slope variations, yet the combined impact of slope positions on soil chemistry and microbial communities remains unexplored. This study investigated soil [...] Read more.

As a perennial plant, the nutrient supply for tea bushes is predominantly dependent on the soil. Yunnan tea plantations exhibit significant topographic slope variations, yet the combined impact of slope positions on soil chemistry and microbial communities remains unexplored. This study investigated soil chemical properties and microbial community structures across three distinct slope areas within a single tea plantation. The results showed that the contents of organic matter (OM), total nitrogen (TN), and available nutrients (AN) at the top of the slope (TS) were significantly higher than those at the foot of the slope (FS) (p < 0.001), while the cation exchange capacity (CEC) and total potassium (TK) reached peak levels in the middle of the slope (MS), with FS having the lowest nutrient levels. Redundancy analysis (RDA) indicated that bacterial communities were primarily influenced by TK, magnesium (Mg), CEC, total phosphorus (TP), and pH, whereas fungal communities were mainly regulated by TK, Mg, and CEC, highlighting the role of soil chemical properties in shaping microbial diversity and distribution. Bacterial composition showed no significant slope-related differences, but fungal communities varied notably at the family/genus levels. MS exhibited the highest microbial network complexity, suggesting stronger species interactions. Bacterial metabolic functions and fungal trophic modes were conserved across regions, indicating functional stability independent of structural changes. This study reveals slope-driven soil-microbial dynamics in Yunnan tea plantations, offering insights into microbial assembly and adaptation under topographic gradients. These findings support precision fertilization, ecological conservation, and the sustainable management of slope tea plantations. Full article

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

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23 pages, 5260 KiB

Open AccessEditor’s ChoiceArticle

The Effects of Nitrogen Reduction and Sheep Manure Incorporation on the Soil Characteristics and Microbial Community of Korla Fragrant Pear Orchards

by Wenge Xie, Xing Shen, Wei Li, Linsen Yan, Jie Li, Bangxin Ding and Zhongping Chai

Agronomy 2025, 15(3), 545; https://doi.org/10.3390/agronomy15030545 - 23 Feb 2025

Cited by 1 | Viewed by 629

Abstract

Excessive use of nitrogen fertilizer affects the sustainable development of the Korla fragrant pear orchard. Semi-decomposed sheep manure is favored because of its advantages of being pollution-free, containing more microorganisms, and being friendly to soil. However, the effects of nitrogen fertilizer combined with [...] Read more.

Excessive use of nitrogen fertilizer affects the sustainable development of the Korla fragrant pear orchard. Semi-decomposed sheep manure is favored because of its advantages of being pollution-free, containing more microorganisms, and being friendly to soil. However, the effects of nitrogen fertilizer combined with sheep manure on soil nutrient cycling and microbial community in pear orchards are still unclear. This study involved a two-year field experiment to investigate fertilization’s effects on the 0–20 cm soil layer of 10–12-year-old Korla fragrant pear trees at maturity. The purpose of this study was to explore the effect of reducing nitrogen fertilizer combined with sheep manure on soil fertility and microbial community in Korla fragrant pear orchard. The treatments of no nitrogen fertilizer (N0), conventional fertilization (N), 20% reduction in nitrogen based on conventional fertilization (N2), a combination of 20% nitrogen reduction with sheep manure F1 (22,500 kg·hm⁻²), and 20% nitrogen reduction with sheep manure F2 (33,750 kg·hm⁻²) formed the experimental treatment of nitrogen reduction with sheep manure, denoted as N2F1 and N2F2. The results showed that nitrogen application increased soil physicochemical indicators but decreased soil pH and bacterial community richness and diversity. After nitrogen reduction, soil total nitrogen (TN), alkaline hydrolysis nitrogen (AN), available phosphorus (AP), microbial biomass nitrogen (SMBN), bacterial community richness, fungal community evenness, and diversity were inhibited, but bacterial community diversity was increased. Nitrogen reduction combined with sheep manure treatment increased the content of nitrate nitrogen (NO₃⁻–N), ammonium nitrogen (NH₄⁺–N), soil organic matter (SOM), pH, microbial biomass carbon (SMBC), and SMBN and increased the evenness and diversity of the bacterial community but inhibited the richness of the bacterial community. Among them, N2F2 treatment had the best effect on SMBC and SMBN. Soil pH, NO₃⁻–N, and SOM were the primary environmental variables influencing bacterial and fungal community levels. The application of nitrogen significantly influenced pear orchard yields, but the yield of pears treated showed no significant variation with nitrogen reduction and nitrogen reduction combined with sheep manure based on complete nitrogen application. In summary, 20% nitrogen reduction (300 kg·hm⁻²) combined with 22,500–33,750 kg·hm⁻² sheep manure better promotes the stability and health of soil microbial communities, and the use of organic fertilizer represents the most efficient approach to quickly enhancing soil fertility and the variation of microbial communities. These findings are highly relevant when improving land productivity, ensuring food security, and promoting environmental sustainability in fruit tree farming. Full article

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

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34 pages, 4325 KiB

Open AccessEditor’s ChoiceReview

Boosting Aeroponic System Development with Plasma and High-Efficiency Tools: AI and IoT—A Review

by Waqar Ahmed Qureshi, Jianmin Gao, Osama Elsherbiny, Abdallah Harold Mosha, Mazhar Hussain Tunio and Junaid Ahmed Qureshi

Agronomy 2025, 15(3), 546; https://doi.org/10.3390/agronomy15030546 - 23 Feb 2025

Cited by 2 | Viewed by 2930

Abstract

Sustainable agriculture faces major issues with resource efficiency, nutrient distribution, and plant health. Traditional soil-based and soilless farming systems encounter issues including excessive water use, insufficient nutrient uptake, nitrogen deficiency, and restricted plant development. According to the previous literature, aeroponic systems accelerate plant [...] Read more.

Sustainable agriculture faces major issues with resource efficiency, nutrient distribution, and plant health. Traditional soil-based and soilless farming systems encounter issues including excessive water use, insufficient nutrient uptake, nitrogen deficiency, and restricted plant development. According to the previous literature, aeroponic systems accelerate plant growth rates, improve root oxygenation, and significantly enhance water use efficiency, particularly when paired with both low- and high-pressure misting systems. However, despite these advantages, they also present certain challenges. A major drawback is the inefficiency of nitrogen fixation, resulting in insufficient nutrient availability and heightened plant stress from uncontrolled misting, which ultimately reduces yield. Many studies have investigated plasma uses in both soil-based and soilless plant cultures; nevertheless, however, its function in aeroponics remains unexplored. Therefore, the present work aims to thoroughly investigate and review the integration of plasma-activated water (PAW) and plasma-activated mist (PAM) in aeroponics systems to solve important problems. A review of the current literature discloses that PAW and PAM expand nitrogen fixation, promote nutrient efficiency, and modulate microbial populations, resulting in elevated crop yields and enhanced plant health, akin to soil-based and other soilless systems. Reactive oxygen and nitrogen species (RONS) produced by plasma treatments improve nutrient bioavailability, root development, and microbial equilibrium, alleviating critical challenges in aeroponics, especially within fine-mist settings. This review further examines artificial intelligence (AI) and the Internet of Things (IoT) in aeroponics. Models driven by AI enable the accurate regulation of fertilizer concentrations, misting cycles, temperature, and humidity, as well as real-time monitoring and predictive analytics. IoT-enabled smart farming systems employ sensors for continuous nutrient monitoring and gas detection (e.g., NO₂, O₃, NH₃), providing automated modifications to enhance aeroponic efficiency. Based on a brief review of the current literature, this study concludes that the future integration of plasma technology with AI and IoT may address the limitations of aeroponics. The integration of plasma technology with intelligent misting and data-driven control systems can enhance aeroponic systems for sustainable and efficient agricultural production. This research supports the existing body of research that advocates for plasma-based innovations and intelligent agricultural solutions in precision farming. Full article

(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)

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16 pages, 6981 KiB

Open AccessEditor’s ChoiceArticle

Three-Dimensional Spatial Perception of Blueberry Fruits Based on Improved YOLOv11 Network

by Kun Zhao, Yuhuan Li and Zunmin Liu

Agronomy 2025, 15(3), 535; https://doi.org/10.3390/agronomy15030535 - 22 Feb 2025

Cited by 1 | Viewed by 799

Abstract

The automated harvesting of blueberries using a picking robot places a greater demand on the 3D spatial perception performance, as the robot’s grasping mechanism needs to pick blueberry fruits accurately at specific positions and in particular poses. To achieve this goal, this paper [...] Read more.

The automated harvesting of blueberries using a picking robot places a greater demand on the 3D spatial perception performance, as the robot’s grasping mechanism needs to pick blueberry fruits accurately at specific positions and in particular poses. To achieve this goal, this paper presents a method for blueberry detection, 3D spatial localization, and pose estimation using visual perception, which can be deployed on an OAK depth camera. Firstly, a blueberry and calyx scar detection dataset is constructed to train the detection network and evaluate its performance. Secondly, the blueberry and calyx scar detection model based on a lightweight YOLOv11 (the eleventh version of You Only Look Once) network with an improved depth-wise separable convolution (DSC) module is designed, and a 3D coordinate system relative to the camera is established to calculate the 3D pose of the blueberry fruits. Finally, the above detection model is deployed using the OAK depth camera, leveraging its depth estimation module and three-axis gyroscope to obtain the 3D coordinates of the blueberry fruits. The experimental results demonstrate that the method proposed in this paper can accurately identify blueberry fruits at various maturity levels, achieving a detection accuracy of 95.8% mAP50-95, a maximum positioning error of 7.2 mm within 0.5 m, and an average 3D pose error of 19.2 degrees (around 10 degrees at the ideal picking angle) while maintaining a detection frame rate of 13.4 FPS (frames per second) on the OAK depth camera, providing effective picking guidance for the mechanical arm of picking robots. Full article

(This article belongs to the Special Issue Imaging Technology for Detecting Crops and Agricultural Products—3rd Edition)

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14 pages, 2126 KiB

Open AccessEditor’s ChoiceArticle

Predicting and Mapping of Soil Organic Matter with Machine Learning in the Black Soil Region of the Southern Northeast Plain of China

by Yiyang Li, Gang Yao, Shuangyi Li and Xiuru Dong

Agronomy 2025, 15(3), 533; https://doi.org/10.3390/agronomy15030533 - 22 Feb 2025

Cited by 1 | Viewed by 858

Abstract

The estimation of soil organic matter (SOM) content is essential for understanding the chemical, physical, and biological functions of soil. It is also an important attribute reflecting the quality of black soil. In this study, machine learning algorithms of support vector machine (SVM), [...] Read more.

The estimation of soil organic matter (SOM) content is essential for understanding the chemical, physical, and biological functions of soil. It is also an important attribute reflecting the quality of black soil. In this study, machine learning algorithms of support vector machine (SVM), neural network (NN), decision tree (DT), random forest (RF), extreme gradient boosting machine (GBM), and generalized linear model (GLM) were used to study the accurate prediction model of SOM in Tieling County, Tieling City, Liaoning Province, China. The models were trained by using 1554 surface soil samples and 19 auxiliary variables. Recursive feature elimination was used as a feature selection method to identify effective variables. The results showed that Normalized Difference Vegetation Index (NDVI) and elevation were key auxiliary variables. Based on 10-fold cross-validation, the RF model had the highest prediction accuracy. In terms of accuracy, the coefficient of determination of RF was 0.77, and the root mean square error was 2.85. The average soil organic matter content was 20.15 g/kg. The spatial distribution of SOM shows that higher content is concentrated in the east and west, while lower content is found in the middle. The SOM content of cultivated land was lower than that of forest land. Full article

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

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21 pages, 976 KiB

Open AccessEditor’s ChoiceReview

Engineering Synthetic Microbial Communities: Diversity and Applications in Soil for Plant Resilience

by Arneeb Tariq, Shengzhi Guo, Fozia Farhat and Xihui Shen

Agronomy 2025, 15(3), 513; https://doi.org/10.3390/agronomy15030513 - 20 Feb 2025

Cited by 2 | Viewed by 2514

Abstract

Plants host a complex but taxonomically assembled set of microbes in their natural environment which confer several benefits to the host plant including stress resilience, nutrient acquisition and increased productivity. To understand and simplify the intricate interactions among these microbes, an innovative approach—Synthetic [...] Read more.

Plants host a complex but taxonomically assembled set of microbes in their natural environment which confer several benefits to the host plant including stress resilience, nutrient acquisition and increased productivity. To understand and simplify the intricate interactions among these microbes, an innovative approach—Synthetic Microbial Community (SynCom)—is practiced, involving the intentional co-culturing of multiple microbial taxa under well-defined conditions mimicking natural microbiomes. SynComs hold promising solutions to the issues confronted by modern agriculture stemming from climate change, limited resources and land degradation. This review explores the potential of SynComs to enhance plant growth, development and disease resistance in agricultural settings. Despite the promising potential, the effectiveness of beneficial microbes in field applications has been inconsistent. Computational simulations, high-throughput sequencing and the utilization of omics databases can bridge the information gap, providing insights into the complex ecological and metabolic networks that govern plant–microbe interactions. Artificial intelligence-driven models can predict complex microbial interactions, while machine learning algorithms can analyze vast datasets to identify key microbial taxa and their functions. We also discuss the barriers to the implementation of these technologies in SynCom engineering. Future research should focus on these innovative applications to refine SynCom strategies, ultimately contributing to the advancement of green technologies in agriculture. Full article

(This article belongs to the Special Issue Functional Diversity of Soil Microbial Communities in Environments Shaped by Anthropogenic Activities)

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16 pages, 2580 KiB

Open AccessEditor’s ChoiceArticle

Optimized Phosphorus Application Enhances Canopy Photothermal Responses, Phosphorus Accumulation, and Yield in Summer Maize

by Qirui Yang, Huiyu Zhang, Xiao Zhang, Sainan Geng, Yinjie Zhang, Yuhong Miao, Lantao Li and Yilun Wang

Agronomy 2025, 15(3), 514; https://doi.org/10.3390/agronomy15030514 - 20 Feb 2025

Cited by 3 | Viewed by 603

Abstract

The improper application of phosphorus (P) fertilizers not only leads to resource wastage and environmental concerns but also disrupts the normal growth and yield formation of maize. This study aims to explore the effects of varying P application rates on the growth, yield, [...] Read more.

The improper application of phosphorus (P) fertilizers not only leads to resource wastage and environmental concerns but also disrupts the normal growth and yield formation of maize. This study aims to explore the effects of varying P application rates on the growth, yield, photothermal response characteristics, P accumulation dynamics, and P recovery efficiency (PRE) in summer maize, which provides a theoretical foundation for the efficient and scientific application of P fertilizers. Field experiments were conducted over two growing seasons (2021−2022) in Wen County, Henan Province, with P application rates set at 0, 30, 60, 90, and 120 kg·P₂O₅·ha⁻¹. At maturity, maize yield and its components were quantified. During key growth stages—jointing, tasseling, silking, and grain filling—plant height, leaf area, Soil and Plant Analyzer Development (SPAD) value, the fraction of photosynthetically active radiation (FPAR), canopy temperature, acid phosphatase activity (ACP), and P accumulation were measured. The results indicated that maize grain yield initially increased with P application, peaking at an average increase of 7.92–15.88%, before decreasing. The optimal P application rates were determined to be 113 kg·P₂O₅·ha⁻¹ and 68 kg·P₂O₅·ha⁻¹, respectively. P application significantly lowered canopy temperature and leaf ACP activity while significantly increasing the SPAD value and FPAR at 90 kg·P₂O₅·ha⁻¹. Logistic regression analysis of P accumulation revealed that increasing P rates enhanced the maximum (V_max) and mean (V_mean) accumulation rates, as well as the total P accumulation. Moderate P application also improved P absorption in various plant tissues and promoted the transfer of P to the grains. However, PRE, partial factor productivity from P fertilizer (PPFP), and P agronomic efficiency (PAE) declined at higher P rates. In conclusion, P fertilization enhanced maize yield, promoted growth, improved P utilization, and optimized photothermal response characteristics across different growth stages. Based on these findings, the recommended P application rate for summer maize is between 70 and 110 kg·P₂O₅·ha⁻¹. Full article

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

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16 pages, 3782 KiB

Open AccessEditor’s ChoiceArticle

Intercropping Different Legumes in Tea Plantation Improves Soil Properties and Tea Quality Components by Regulating Rhizosphere Soil Microorganisms

by Mengjuan Chen, Pinqian Zhou, Qiang Bao, Hua Wang, Yuanjiang Wang and Haiping Fu

Agronomy 2025, 15(3), 511; https://doi.org/10.3390/agronomy15030511 - 20 Feb 2025

Cited by 2 | Viewed by 645

Abstract

Intercropping legumes is an effective and sustainable planting pattern that has the benefit of decreasing chemical fertilizer input and improving the soil environment. However, the effects of chemical fertilizer reduction and intercropping different legumes on soil nutrients, microorganisms, and tea quality remain elusive. [...] Read more.

Intercropping legumes is an effective and sustainable planting pattern that has the benefit of decreasing chemical fertilizer input and improving the soil environment. However, the effects of chemical fertilizer reduction and intercropping different legumes on soil nutrients, microorganisms, and tea quality remain elusive. Hereby, compared with 100% chemical fertilizer (CK), Sesbania cannabina (SC) and Crotalaria pallida Blanco (CP) were selected as the intercropped plant with 70% chemical fertilizer to investigate its effect on soil nutrients, microorganisms, and tea quality. The results showed that compared with monocropping, intercropping legumes had greater concentrations of the soil labile organic matter, nitrate nitrogen, ammonia nitrogen, inorganic nitrogen, and alkali-hydrolyzable nitrogen. Intercropping systems significantly enhanced the content of non-ester-type catechins (catechin and gallocatechin) and ester-type catechins (epicatechin gallate). In SC, the content of gallocatechin, catechin, and epicatechin gallate increased by 146.67%, 107.69%, and 21.05%, respectively, while in CP, the content of these three compounds increased by 166.67%, 84.62%, and 19.08%, respectively. Significant differences in microbial composition were also observed under different systems. Actinobacteria, Rhodoplanes, and Thaumarchaeota were obviously enhanced in SC, while Rhodanobacter, Pseudolabrys, and Pedosphaera were manifestly improved in CP compared to those in the monoculture. Moreover, intercropping legumes significantly increased the abundances of CNP cycling functional genes, such as gpmB, mch, accD6, pgi-pmi, mcr, glmS, ACOX1 and fadB (carbohydrate metabolism), nirD and narI (nitrification), pmoB-amoB and hao (dissimilatory N reduction), and phoN (organic phosphoester hydrolysis). The relationship between intercropping systems and tea qualities was mainly established by soil nutrition and the abundance of C and N cycling functional microorganisms. This study provides more information on the relationship between soil nutrients, functional genes of microorganisms, and tea quality under tea/legume intercropping systems in tea plantations and offers a basis for the higher-performance intercropping pattern. Full article

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

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24 pages, 9588 KiB

Open AccessEditor’s ChoiceArticle

Evapotranspiration Partitioning for Croplands Based on Eddy Covariance Measurements and Machine Learning Models

by Jie Zhang, Shanshan Yang, Jingwen Wang, Ruiyun Zeng, Sha Zhang, Yun Bai and Jiahua Zhang

Agronomy 2025, 15(3), 512; https://doi.org/10.3390/agronomy15030512 - 20 Feb 2025

Cited by 2 | Viewed by 858

Abstract

Accurately partitioning evapotranspiration (ET) of cropland into productive plant transpiration (T) and non-productive soil evaporation (E) is important for improving crop water use efficiency. Many methods, including machine learning methods, have been developed for ET partitioning. However, the applicability of machine learning models [...] Read more.

Accurately partitioning evapotranspiration (ET) of cropland into productive plant transpiration (T) and non-productive soil evaporation (E) is important for improving crop water use efficiency. Many methods, including machine learning methods, have been developed for ET partitioning. However, the applicability of machine learning models in cropland ET partitioning with diverse crop rotations is not clear. In this study, machine learning models are used to predict E, and T is obtained by calculating the difference between ET and E, leading to the derivation of the ratio of transpiration to evapotranspiration (T/ET). We evaluated six machine learning models (i.e., artificial neural networks (ANN), extremely randomized trees (ExtraTrees), gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost)) on partitioning ET at 16 cropland flux sites during the period from 2000 to 2020. The evaluation results showed that the XGBoost model had the best performance (R = 0.88, RMSE = 6.87 W/m², NSE = 0.77, and MAE = 3.41 W/m²) when considering the meteorological data, ecosystem sensible heat flux, ecosystem respiration, soil water content, and remote sensing vegetation indices as input variables. Due to the unavailability of observed E or T data at the 16 cropland sites, we used three other widely used ET partitioning methods to indirectly validate the accuracy of our ET partitioning results based on XGBoost. The results showed that our T estimation results were highly consistent with their T estimation results (R = 0.83–0.91). Moreover, based on the XGBoost model and the three other ET partitioning methods, we estimated the ratio of transpiration to evapotranspiration (T/ET) for different crops. On average, maize had the highest T/ET of 0.619 ± 0.119, followed by soybean (0.618 ± 0.085), winter wheat (0.614 ± 0.08), and sugar beet (0.611 ± 0.065). Lower T/ET was found for paddy rice (0.505 ± 0.055), winter barley (0.590 ± 0.058), potato (0.540 ± 0.088), and rapeseed (0.522 ± 0.107). These results suggest the machine learning models are easy and applicable for cropland T/ET estimation with different crop rotations and reveal obvious differences in water use among different crops, which is crucial for the sustainability of water resources and improvements in cropland water use efficiency. Full article

(This article belongs to the Special Issue Advanced Machine Learning in Agriculture)

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31 pages, 8541 KiB

Open AccessEditor’s ChoiceArticle

Assessing Soil Water Dynamics in a Drip-Irrigated Grapefruit Orchard Using the HYDRUS 2D/3D Model: A Comparison of Unimodal and Bimodal Hydraulic Functions

by Giasemi Morianou, George P. Karatzas, George Arampatzis, Vassilios Pisinaras and Nektarios N. Kourgialas

Agronomy 2025, 15(2), 504; https://doi.org/10.3390/agronomy15020504 - 19 Feb 2025

Cited by 1 | Viewed by 909

Abstract

This study examines the impact of soil hydraulic parameterization on simulating soil water content in a drip-irrigated grapefruit orchard (Citrus paradisi Mac.) using precise laboratory measurements and the HYDRUS 2D/3D model. Undisturbed soil samples were analyzed for water retention and saturated hydraulic [...] Read more.

This study examines the impact of soil hydraulic parameterization on simulating soil water content in a drip-irrigated grapefruit orchard (Citrus paradisi Mac.) using precise laboratory measurements and the HYDRUS 2D/3D model. Undisturbed soil samples were analyzed for water retention and saturated hydraulic conductivity using high-precision instruments, and parameters were estimated with unimodal and bimodal Van Genuchten functions. Soil water dynamics under deficit (80% of crop evapotranspiration, ET_C) and full irrigation (100% ET_C) were simulated, accounting for circular drip emitters. Calibration relied on soil water content data collected at varying depths and distances from the emitters. Results from the fitting process with laboratory-measured data for water retention and hydraulic conductivity indicate that the bimodal function provided more accurate parameter estimates, yielding lower RMSE for soil water content (0.0026 cm³ cm⁻³) and hydraulic conductivity (0.1143 cm day⁻¹), compared to the unimodal (0.0047 cm³ cm⁻³ and 0.1586 cm day⁻¹). HYDRUS simulations also demonstrated superior calibration metrics for the bimodal function with RMSE, MAE, and NSE values of 0.024 cm³ cm⁻³, 0.016 cm³ cm⁻³, and 0.892 respectively, compared to 0.025 cm³ cm⁻³, 0.017 cm³ cm⁻³, and 0.883 for the unimodal function. Although differences between the functions were small, the bimodal model’s slight performance gain comes with added complexity and uncertainty in parameter estimation. These findings highlight the critical role of precise parameterization in refining irrigation strategies and ensuring sustainable water use in citrus orchards. Full article

(This article belongs to the Special Issue Optimal Water Management and Sustainability in Irrigated Agriculture—2nd Edition)

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16 pages, 4396 KiB

Open AccessEditor’s ChoiceArticle

Microbial Communities in Continuous Panax notoginseng Cropping Soil

by Li Liu, Jingheng Wu, Minpeng Liu, Mulan Wang, Yuewen Huo, Fugang Wei and Min Wu

Agronomy 2025, 15(2), 486; https://doi.org/10.3390/agronomy15020486 - 18 Feb 2025

Cited by 1 | Viewed by 832

Abstract

Panax notoginseng is a prominent traditional Chinese medicinal herb, yet its yield and quality are significantly constrained by continuous cropping obstacles, primarily stemming from soil-related issues. This study analyzed soils subjected to various degrees of continuous P. notoginseng cultivation, soils without P. notoginseng [...] Read more.

Panax notoginseng is a prominent traditional Chinese medicinal herb, yet its yield and quality are significantly constrained by continuous cropping obstacles, primarily stemming from soil-related issues. This study analyzed soils subjected to various degrees of continuous P. notoginseng cultivation, soils without P. notoginseng planting, and natural forest floor soil without P. notoginseng planting. The objective was to investigate variations in soil microbial communities, physicochemical properties, and enzyme activities across different cropping conditions. Macro-genome sequencing was employed to reveal microbial shifts and key factors influencing rhizosphere microbial communities. Notably, the natural forest floor soil exhibited the highest levels of soil organic matter, soil organic carbon, total nitrogen, and available potassium. Furthermore, continuous cropping soils showed the highest levels of pH, available phosphorus, electrical conductivity, and total potassium. The activities of catalase, urease, acid phosphatase, sucrase, and soil FDA hydrolase decreased significantly after continuous cropping, but increased again after five years of fallowing. Microbial analysis revealed that Bacteroidetes, Firmicutes, and Chloroflexi dominated the soils without P. notoginseng planting, whereas Proteobacteria, Actinobacteria, and Acidobacteria were the predominant phyla in continuous cropping and natural forest floor soils. Continuous cropping led to an increase in Acidobacteria, Gemmatimonadetes, and Chloroflexi, while fallowing reduced Actinobacteria. Gemmatimonades was almost exclusively present in the continuous cropping soils. Overall, continuous P. notoginseng planting altered the soil nutrients and microbial composition. Key factors influencing microbial communities included pH, nitrate nitrogen, available phosphorus, available potassium, and electrical conductivity. The study suggests that attention should be paid to scientific and rational fertilization practices to mitigate the effects of continuous cropping. Additionally, a fallow period of more than five years is recommended. The proper application of probiotic fertilizers is also advised. Finally, cultivating P. notoginseng under forest conditions is recommended as a viable method. Full article

(This article belongs to the Section Farming Sustainability)

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22 pages, 9277 KiB

Open AccessEditor’s ChoiceArticle

LRNTRM-YOLO: Research on Real-Time Recognition of Non-Tobacco-Related Materials

by Chunjie Zhang, Lijun Yun, Chenggui Yang, Zaiqing Chen and Feiyan Cheng

Agronomy 2025, 15(2), 489; https://doi.org/10.3390/agronomy15020489 - 18 Feb 2025

Cited by 2 | Viewed by 877

Abstract

The presence of non-tobacco-related materials can significantly compromise the quality of tobacco. To accurately detect non-tobacco-related materials, this study introduces a lightweight and real-time detection model derived from the YOLOv11 framework, named LRNTRM-YOLO. Initially, due to the sub-optimal accuracy in detecting diminutive non-tobacco-related [...] Read more.

The presence of non-tobacco-related materials can significantly compromise the quality of tobacco. To accurately detect non-tobacco-related materials, this study introduces a lightweight and real-time detection model derived from the YOLOv11 framework, named LRNTRM-YOLO. Initially, due to the sub-optimal accuracy in detecting diminutive non-tobacco-related materials, the model was augmented by incorporating an additional layer dedicated to enhancing the detection of small targets, thereby improving the overall accuracy. Furthermore, an attention mechanism was incorporated into the backbone network to focus on the features of the detection targets, thereby improving the detection efficacy of the model. Simultaneously, for the introduction of the SIoU loss function, the angular vector between the bounding box regressions was utilized to define the loss function, thus improving the training efficiency of the model. Following these enhancements, a channel pruning technique was employed to streamline the network, which not only reduced the parameter count but also expedited the inference process, yielding a more compact model for non-tobacco-related material detection. The experimental results on the NTRM dataset indicate that the LRNTRM-YOLO model achieved a mean average precision (mAP) of 92.9%, surpassing the baseline model by a margin of 4.8%. Additionally, there was a 68.3% reduction in the parameters and a 15.9% decrease in floating-point operations compared to the baseline model. Comparative analysis with prominent models confirmed the superiority of the proposed model in terms of its lightweight architecture, high accuracy, and real-time capabilities, thereby offering an innovative and practical solution for detecting non-tobacco-related materials in the future. Full article

(This article belongs to the Special Issue Robotics and Automation in Farming)

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16 pages, 3086 KiB

Open AccessEditor’s ChoiceArticle

Dual-Channel Co-Spectroscopy–Based Non-Destructive Detection Method for Fruit Quality and Its Application to Fuji Apples

by Xin Liang, Tian Jiang, Wanli Dai and Sai Xu

Agronomy 2025, 15(2), 484; https://doi.org/10.3390/agronomy15020484 - 17 Feb 2025

Cited by 1 | Viewed by 627

Abstract

Visible/near-infrared spectroscopy is widely used for non-destructive fruit quality detection, but the high cost of spectrometers (400–1100 nm range) in sorting equipment limits its accessibility. This study proposes a dual-channel co-spectroscopy method to address this issue. Using apples’ soluble solids content as the [...] Read more.

Visible/near-infrared spectroscopy is widely used for non-destructive fruit quality detection, but the high cost of spectrometers (400–1100 nm range) in sorting equipment limits its accessibility. This study proposes a dual-channel co-spectroscopy method to address this issue. Using apples’ soluble solids content as the research target, a dual-channel platform was constructed to optimize parameters for full-transmission spectral signal acquisition. Spectral data were collected using dual channels (400–700 nm and 700–1100 nm bands, separated by filters) and a single channel (400–1100 nm range). Preprocessing methods (MSC, SNV, FD, SD, SG) and feature extraction algorithms (CARS, SPA, UVE) were applied, followed by PLSR modeling. The dual-channel method with Raw spectrum + FD + CARS + PLSR achieved optimal results, with R²_v = 0.88, RMSEP = 0.39 for the 400–700 nm band, and R²_v = 0.94, RMSEP = 0.33 for the 700–1100 nm band. The single-channel method with Raw spectrum + MSC + CARS + PLSR achieved R²_v = 0.90, RMSEP = 0.36. These findings validate dual-channel co-spectroscopy as a cost-effective, accurate solution for non-destructive fruit quality detection, providing a practical approach to reduce spectrometer costs and enhance sorting system efficiency. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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14 pages, 2241 KiB

Open AccessEditor’s ChoiceArticle

Comparative Effects of Fertilizer Efficiency Enhancers on Nitrogen Use Efficiency and Greenhouse Gas Emissions in Agriculture

by Xiaoyu Shi, Lingli Wang, Zhanbo Wei, Lei Zhang and Qiang Gao

Agronomy 2025, 15(2), 459; https://doi.org/10.3390/agronomy15020459 - 13 Feb 2025

Cited by 1 | Viewed by 643

Abstract

Nitrogen (N) fertilizer incorporation of efficiency enhancer is a well-established practice aiming at reducing N loss while enhancing crop yield. However, the effect of different kinds of fertilizer efficiency enhancer on N use efficiency (NUE) and gas loss are rarely compared and poorly [...] Read more.

Nitrogen (N) fertilizer incorporation of efficiency enhancer is a well-established practice aiming at reducing N loss while enhancing crop yield. However, the effect of different kinds of fertilizer efficiency enhancer on N use efficiency (NUE) and gas loss are rarely compared and poorly comprehended. Here, we conducted a field experiment involving the combination of urease and nitrification inhibitor (NI), the biological inhibitor eugenol (DE) and the bioploymer poly-glutamic acid (PG) and their combinations (NI + PG, NI + DE, PG + DE) to evaluate their effects on crop yield, NUE, NH3 volatilization and greenhouse gas emissions (GHGs). Results indicated that NI, DE, PG and their combinations significantly enhanced the crop yield, N uptake and NUE. NI, DE and PG are all effective in reducing NH₃ volatilization and N₂O emission, averagely decreased by 11.13%, 6.83%, 8.29%, respectively, and by 11.15%, 4.32%, 8.35%, respectively, while have no significant effects on CO₂-C and CH₄-C fluxes, except PG significantly increases CO₂-C emission and thus global warming potential. The combination of these three efficiency enhancers has no multiply effect on maize yield, NUE and gas loss. These findings help to screen the fertilizer efficiency enhancer that can be more effectively utilized in agricultural practices and contribute to their application strategies within agricultural systems. Full article

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20 pages, 11693 KiB

Open AccessEditor’s ChoiceArticle

Long-Term Annual Changes in Agricultural Carbon Footprints and Associated Driving Factors in China from 2000 to 2020

by Xingyuan Xiao, Xuanming Hu, Yaqun Liu and Changhe Lu

Agronomy 2025, 15(2), 453; https://doi.org/10.3390/agronomy15020453 - 13 Feb 2025

Cited by 1 | Viewed by 695

Abstract

China is one of the world’s largest agricultural producers, and its agricultural carbon footprint (CF) is a major contributor to global warming. However, the long-term annual changes in its agricultural CF and the underlying driving factors remain largely unknown, compromising the scientific basis [...] Read more.

China is one of the world’s largest agricultural producers, and its agricultural carbon footprint (CF) is a major contributor to global warming. However, the long-term annual changes in its agricultural CF and the underlying driving factors remain largely unknown, compromising the scientific basis for effective carbon reduction and sustainable agriculture management. To this end, we used the life cycle assessment (LCA) method and statistical data to calculate long-term annual agricultural CFs in China. We then adopted the linear regression slope and the Moran’s I method to analyze the temporal trends and spatial clustering characteristics and revealed the correlations between the main drivers and agricultural CFs. The results showed that the total (TCF) and farmland-averaged carbon footprint (FCF) of crop production both increased first and then decreased in China from 2000 to 2020, with a turning point in 2015. Overall, the TCF increased by 6.82% (3022.16 × 10⁴ t CO₂ eq), while the FCF slightly decreased by 0.004% (0.01 t CO₂ eq/ha). Both the TCF and the FCF showed spatial heterogeneity, with high values in the east and low values in the west, and the spatial clustering of the TCF and its components has weakened over time. Fertilizer (39.26%) and paddy (27.38%) were the main contributors to TCF. Driver analysis found that grain yield was positively correlated with TCF in most provinces, indicating that the continuous yield increase has brought greater pressure on agricultural carbon emission reduction in China. Agricultural stakeholders should optimize crop planting structures and patterns and improve resource-use efficiencies through technological and management innovation to adapt to these threats and achieve low-carbon agriculture. The findings of our research can aid the scientific research on spatiotemporal estimation and driver analysis of agricultural CFs and provide decision-making support for sustainable agricultural practices. Full article

(This article belongs to the Special Issue Sustainable Agricultural Systems: Enhancing Efficiency and Reducing Environmental Impact)

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45 pages, 1708 KiB

Open AccessEditor’s ChoiceReview

The Role of Ligninolytic Enzymes in Sustainable Agriculture: Applications and Challenges

by Agnieszka Gałązka, Urszula Jankiewicz and Sławomir Orzechowski

Agronomy 2025, 15(2), 451; https://doi.org/10.3390/agronomy15020451 - 12 Feb 2025

Cited by 4 | Viewed by 2694

Abstract

The most important ligninolytic enzymes in lignin degradation include laccases and peroxidases (lignin peroxidase, manganese peroxidase, versatile peroxidase). White-rot fungi (e.g., Cerrena sp., Phlebia sp. or Trametes sp.) are their main source in nature. The ability of ligninolytic enzymes to degrade both phenolic [...] Read more.

The most important ligninolytic enzymes in lignin degradation include laccases and peroxidases (lignin peroxidase, manganese peroxidase, versatile peroxidase). White-rot fungi (e.g., Cerrena sp., Phlebia sp. or Trametes sp.) are their main source in nature. The ability of ligninolytic enzymes to degrade both phenolic and non-phenolic compounds has found its application in sustainable agriculture. In recent years, ligninolytic enzymes’ important role has been demonstrated in the biodegradation of lignin, a poorly degradable component of plant biomass, and in removing hazardous environmental pollutants that threaten human health. These enzymes can be successfully used in waste management, composting, improving soil health and fertility, or bioremediation. The challenges of applying lignin-degrading enzymes such as laccases and peroxidases include their stability and resistance to harsh conditions. Still, the rapid development of biotechnological technologies offers the tools to overcome them. Applying biological solutions in agricultural systems involving microorganisms and their metabolic products will significantly reduce the environmental impact and develop a circular economy. Full article

(This article belongs to the Special Issue Advances in Biotransformation of Agricultural Waste: Opportunities and Challenges)

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24 pages, 7672 KiB

Open AccessEditor’s ChoiceReview

Turning Waste Wool into a Circular Resource: A Review of Eco-Innovative Applications in Agriculture

by Francesca Camilli, Marco Focacci, Aldo Dal Prà, Sara Bortolu, Francesca Ugolini, Enrico Vagnoni and Pierpaolo Duce

Agronomy 2025, 15(2), 446; https://doi.org/10.3390/agronomy15020446 - 11 Feb 2025

Cited by 1 | Viewed by 2531

Abstract

Agriculture significantly impacts the environment in terms of greenhouse gas emissions, soil nutrient depletion, water consumption, and pollution and waste produced by intensive farming. Wool has great potential and can be a valuable resource for agriculture due to its high nitrogen, carbon, and [...] Read more.

Agriculture significantly impacts the environment in terms of greenhouse gas emissions, soil nutrient depletion, water consumption, and pollution and waste produced by intensive farming. Wool has great potential and can be a valuable resource for agriculture due to its high nitrogen, carbon, and sulfur content and good water absorption and retention properties, benefiting soil carbon storage and fertility, as well as decreasing the risk of water contamination due to the slow decomposition and nitrogen release. This review aims to provide an overview of bio-based solutions that can benefit agroecosystems as a circular bioeconomy practice. Raw wool and wool hydrolysate are the most common applications, but also wool pellets, wool compost, and wool mats are interesting treatments for plant growing. Waste wool showed positive effects on soil fertility by primarily increasing nitrogen and sulfur content. Improved water retention capacity and microbial activity were also recorded in several studies. The use of wool as mulching is effective for weed control. Attention to the plant species tested aimed at identifying the most promising cultivations in terms of treatment efficiency, possibly lowering environmental impact on the agroecosystem. To eco-design and scale-up processes that strengthen the circular use of wool into widespread practices, further research should be encouraged in conjunction with environmental impact assessments and economic evaluations. Full article

(This article belongs to the Special Issue Organic Improvement in Agricultural Waste and Byproducts)

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19 pages, 6474 KiB

Open AccessEditor’s ChoiceArticle

Improved Lightweight YOLOv8 Model for Rice Disease Detection in Multi-Scale Scenarios

by Jinfeng Wang, Siyuan Ma, Zhentao Wang, Xinhua Ma, Chunhe Yang, Guoqing Chen and Yijia Wang

Agronomy 2025, 15(2), 445; https://doi.org/10.3390/agronomy15020445 - 11 Feb 2025

Cited by 4 | Viewed by 1406

Abstract

In response to the challenges of detecting rice pests and diseases at different scales and the difficulties associated with deploying and running models on embedded devices with limited computational resources, this study proposes a multi-scale rice pest and disease recognition model (RGC-YOLO). Based [...] Read more.

In response to the challenges of detecting rice pests and diseases at different scales and the difficulties associated with deploying and running models on embedded devices with limited computational resources, this study proposes a multi-scale rice pest and disease recognition model (RGC-YOLO). Based on the YOLOv8n network, which includes an SPPF layer, the model introduces a structural reparameterization module (RepGhost) to achieve implicit feature reuse through reparameterization. GhostConv layers replace some standard convolutions, reducing the model’s computational cost and improving inference speed. A Hybrid Attention Module (CBAM) is incorporated into the backbone network to enhance the model’s ability to extract important features. The RGC-YOLO model is evaluated for accuracy and inference time on a multi-scale rice pest and disease dataset, including bacterial blight, rice blast, brown spot, and rice planthopper. Experimental results show that RGC-YOLO achieves a precision (P) of 86.2%, a recall (R) of 90.8%, and a mean average precision at Intersection over Union 0.5(mAP50) of 93.2%. In terms of model size, the parameters are reduced by 33.2%, and GFLOPs decrease by 29.27% compared to the base YOLOv8n model. Finally, the RGC-YOLO model is deployed on an embedded Jetson Nano device, where the inference time per image is reduced by 21.3% compared to the base YOLOv8n model, reaching 170 milliseconds. This study develops a multi-scale rice pest and disease recognition model, which is successfully deployed on embedded field devices, achieving high-accuracy real-time monitoring and providing valuable reference for intelligent equipment in unmanned farms. Full article

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

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13 pages, 1382 KiB

Open AccessEditor’s ChoiceArticle

Evaluating the Level of Total Mercury Present in the Soils of a Renowned Tea Production Region

by Jinghua Xu, Ruijia Xie, Liping Liu and Zhiqun Huang

Agronomy 2025, 15(2), 435; https://doi.org/10.3390/agronomy15020435 - 10 Feb 2025

Cited by 1 | Viewed by 834

Abstract

Total mercury pollution in oolong tea garden soils was comprehensively investigated in this study. Soil samples were collected from 146 villages in a famous oolong tea production area. The total mercury content in the soils ranged from 0.025 to 0.296 mg/kg, with a [...] Read more.

Total mercury pollution in oolong tea garden soils was comprehensively investigated in this study. Soil samples were collected from 146 villages in a famous oolong tea production area. The total mercury content in the soils ranged from 0.025 to 0.296 mg/kg, with a median of 0.105 mg/kg. According to the Soil Accumulation Index Method, 67.81% of samples were pollution-free, 31.51% had pollution levels from none to moderate, and 0.68% were moderately polluted. The PMF model revealed that natural geochemical processes were the main mercury source, contributing 72.4%, with some from transportation, coal combustion, and industrial activities. Most values were below the HQ threshold, suggesting low non-carcinogenic risk from mercury in most soils. Further research is needed to understand mercury’s bioaccumulation in tea leaves and assess short- and long-term exposure risks for a better understanding of its long-term impacts on the tea industry and human health. Full article

(This article belongs to the Special Issue Heavy Metal Pollution and Prevention in Agricultural Soils)

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26 pages, 12201 KiB

Open AccessEditor’s ChoiceArticle

MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping

by Limin Xie, Jun Jing, Haoyu Wu, Qinguan Kang, Yiwei Zhao and Dapeng Ye

Agronomy 2025, 15(2), 432; https://doi.org/10.3390/agronomy15020432 - 10 Feb 2025

Cited by 1 | Viewed by 930

Abstract

The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the [...] Read more.

The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the grasping force based on individual mushroom sizes. To address this, we propose an improved method that integrates visual feedback to dynamically adjust the execution end, enhancing cut precision. Our approach enhances YOLOv8n-seg with Star Net, SPPECAN (a reconstructed SPPF with efficient channel attention), and C2fDStar (C2f with Star Net and deformable convolution) to improve feature extraction while reducing computational complexity and feature loss. Additionally, we introduce a mask ownership judgment and merging optimization algorithm to correct positional offsets, internal disconnections, and boundary instabilities in grasping area predictions. Based on this, we optimize grasping parameters using an improved centroid-based region width measurement and establish a region width-to-PWM mapping model for the precise conversion from visual data to gripper control. Experiments in real-situation settings demonstrate the effectiveness of our method, achieving a mean average precision (mAP50:95) of 0.743 for grasping area segmentation, a 4.5% improvement over YOLOv8, with an average detection speed of 10.3 ms and a target width measurement error of only 0.14%. The proposed mapping relationship enables adaptive end-effector control, resulting in a 96% grasping success rate and a 98% qualified cutting surface rate. These results confirm the feasibility of our approach and provide a strong technical foundation for the intelligent automation of enoki mushroom cutting systems. Full article

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

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15 pages, 9987 KiB

Open AccessEditor’s ChoiceArticle

Characterizing Optimum N Rate in Waterlogged Maize (Zea mays L.) with Unmanned Aerial Vehicle (UAV) Remote Sensing

by Bhawana Acharya, Syam Dodla, Brenda Tubana, Thanos Gentimis, Fagner Rontani, Rejina Adhikari, Dulis Duron, Giulia Bortolon and Tri Setiyono

Agronomy 2025, 15(2), 434; https://doi.org/10.3390/agronomy15020434 - 10 Feb 2025

Cited by 1 | Viewed by 849

Abstract

High soil moisture due to frequent excessive precipitation can lead to reductions in maize grain yields and increased nitrogen (N) loss. The traditional methods of computing N status in crops are destructive and time-consuming, especially in waterlogged fields. Therefore, in this study, we [...] Read more.

High soil moisture due to frequent excessive precipitation can lead to reductions in maize grain yields and increased nitrogen (N) loss. The traditional methods of computing N status in crops are destructive and time-consuming, especially in waterlogged fields. Therefore, in this study, we used unmanned aerial vehicle (UAV) remote sensing to evaluate the status of maize under different N rates and excessive soil moisture conditions. The experiment was performed using a split plot design with four replications, with soil moisture conditions as main plots and different N rates as sub-plots. The artificial intelligence SciPy (version 1.5.2) optimization algorithm and spherical function were used to estimate the economically optimum N rate under the different treatments. The computed EONR for CRS 2022 was 157 kg N ha⁻¹ for both treatments, with the maximum net return to N of USD 1203 ha⁻¹. In 2023, the analysis suggested a lower maximum attainable yield in excessive water conditions, with EONR pushed up to 197 kg N ha⁻¹ as compared to 185 kg N ha⁻¹ in the control treatment, resulting in a lower maximum net return to N of USD 884 ha⁻¹ as compared to USD 1019 ha⁻¹ in the control treatment. This study reveals a slight reduction of the fraction of NDRE at EONR to maximum NDRE under excessive water conditions, highlighting the need for addressing such abiotic stress circumstances when arriving at an N rate recommendation based on an N-rich strip concept. This study confirms the importance of sensing technology for N monitoring in maize, particularly in supporting decision making in nutrient management under adverse weather conditions. Full article

(This article belongs to the Special Issue Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture)

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18 pages, 2778 KiB

Open AccessEditor’s ChoiceArticle

Characterization of Neopestalotiopsis Species Associated with Strawberry Crown Rot in Italy

by Greta Dardani, Ilaria Martino, Francesco Aloi, Cristiano Carli, Roberto Giordano, Davide Spadaro and Vladimiro Guarnaccia

Agronomy 2025, 15(2), 422; https://doi.org/10.3390/agronomy15020422 - 7 Feb 2025

Cited by 1 | Viewed by 1504

Abstract

Different Pestalotiopsis-like species have been reported in strawberry worldwide, as agents of leaf spot, root rot, and crown rot. The identification of Pestalotiopsis-like fungi is based on both molecular and morphological analyses to discriminate between species and clarify phylogenetic relationships. Recent [...] Read more.

Different Pestalotiopsis-like species have been reported in strawberry worldwide, as agents of leaf spot, root rot, and crown rot. The identification of Pestalotiopsis-like fungi is based on both molecular and morphological analyses to discriminate between species and clarify phylogenetic relationships. Recent studies have provided robust multi-locus analyses, which reclassified most Pestalotiopsis-like isolates associated with strawberry root and crown rot diseases as Neopestalotiopsis spp. Numerous disease outbreaks have been observed in strawberry fields in Italy in recent years, showing that Neopestalotiopsis is an emerging pathogen. A survey was conducted in Northern Italy during 2022–2023 to investigate the diversity and distribution of Neopestalotiopsis species. Morphological and phylogenetic characterization, based on ITS, tef1 and tub2, led to the identification of four species: Neopestalotiopsis rosae, N. iranensis, N. hispanica (syn. vaccinii) and N. scalabiensis. Based on our results from multi-locus phylogenetic analysis, N. hispanica and N. vaccinii were grouped in the same cluster; thus, they were confirmed to be the same species. Pathogenicity tests with representative isolates of each species were conducted on strawberry ‘Portola’ transplants. All isolates were shown to be wound pathogens in strawberry, causing crown rot, and were successfully re-isolated. Neopestalotiopsis rosae was confirmed to be the most dominant and virulent species associated with these symptoms, as well as the most dominant among the obtained isolates. To the best of our knowledge, this work represents the first report of N. scalabiensis being associated with the crown rot of strawberry in Italy and the first report of N. iranensis in association with the crown rot of strawberry worldwide. Full article

(This article belongs to the Special Issue Enhancing Crop Production: Unveiling the Vital Role of Plant Roots and Their Dynamic Interplay with Soil and the Environment)

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18 pages, 3489 KiB

Open AccessEditor’s ChoiceArticle

Plastic Film Residue Reshaped Protist Communities and Induced Soil Nutrient Deficiency Under Field Conditions

by Ge Wang, Qian Sun, Maolu Wei, Miaomiao Xie, Ting Shen and Dongyan Liu

Agronomy 2025, 15(2), 419; https://doi.org/10.3390/agronomy15020419 - 7 Feb 2025

Cited by 1 | Viewed by 780

Abstract

The use of plastic agricultural mulching films presents a “double-edged sword”: while these films enhance crop yields, they also lead to the accumulation of plastic film residues in the soil, creating new pollutants (microplastics). Our understanding of the “plastisphere”, a niche formed by [...] Read more.

The use of plastic agricultural mulching films presents a “double-edged sword”: while these films enhance crop yields, they also lead to the accumulation of plastic film residues in the soil, creating new pollutants (microplastics). Our understanding of the “plastisphere”, a niche formed by agricultural film residues in the soil, where unique microbial communities and soil conditions converge remains limited. This is particularly true for protists, which are recognized as key determinants of soil health. Therefore, this study simulated a field experiment to analyze the effects of long-term plastic film residues on the structure of protist microbial communities in the rhizosphere, bulk soil and plastisphere of oilseed rape as well as their effects on soil nutrients. The results revealed that the residual plastic films underwent significant structural and chemical degradations. Protist diversity and co-occurrence network complexity were markedly reduced in plastisphere soils. In addition, soil moisture content, inorganic nitrogen and available phosphorus levels declined, leading to deficiencies in soil nutrients. Functional shifts in consumer protists and phototrophs along with weakened network interactions, have been identified as key drivers of impaired nutrient turnover. Our study underscores the critical role of protist communities in maintaining soil nutrient cycling and highlights the profound adverse effects of plastic film residues on soil ecosystems. These findings provide valuable insights into mitigating plastic residue accumulation to preserve long-term soil fertility and ensure sustainable agricultural productivity. Full article

(This article belongs to the Special Issue The Impact of Mulching on Crop Production and Farmland Environment)

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19 pages, 11739 KiB

Open AccessEditor’s ChoiceArticle

Exploring the Spatial Distribution Characteristics of Urban Soil Heavy Metals in Different Levels of Urbanization

by Jianwei Sun, Mengchan Chen, Jingrou Xiao, Gang Xu, Haitao Zhang, Ganlin Zhang, Fangqin Yang, Chang Zhao and Long Guo

Agronomy 2025, 15(2), 418; https://doi.org/10.3390/agronomy15020418 - 7 Feb 2025

Cited by 1 | Viewed by 946

Abstract

With the development of urbanization and industrialization worldwide, soil heavy metal pollution has become a critical and pressing environmental problem in urban areas. Soil heavy metals exhibit complex and varying spatial aggregation and diffusion processes within diverse urban landscapes, especially in different urban [...] Read more.

With the development of urbanization and industrialization worldwide, soil heavy metal pollution has become a critical and pressing environmental problem in urban areas. Soil heavy metals exhibit complex and varying spatial aggregation and diffusion processes within diverse urban landscapes, especially in different urban areas with varying urbanization levels. However, many existing experimental methods and conventional models overlook the crucial aspects of spatial autocorrelation and heterogeneity between soil heavy metals and influencing factors. This neglect poses significant environmental concerns, as rapid monitoring of soil heavy metals and accurate identification of their determinants become imperative. This study investigated four environmentally sensitive and potentially harmful soil heavy metals, arsenic (As), cadmium (Cd), copper (Cu), and lead (Pb), in two urban areas in China with varying urbanization levels. Enshi (a prefecture-level city) and Wuhan (a provincial capital city) were selected for comparison of the spatially variable relationships between soil heavy metals and their influencing factors. We employed a global stepwise linear regression (STR) model and a local spatial model-geographically weighted regression (GWR) to map the spatial distribution of soil heavy metals based on 121 auxiliary variables, including terrain, geophysical, socioeconomic factors, and remote sensing data. Our results showed that: (1) soil heavy metals exhibited strong spatial aggregation in the prefecture-level city (Enshi) but, nonetheless, have strong spatial heterogeneity in the provincial capital city (Wuhan) due to elevated anthropogenic disturbances; (2) GWR accurately mapped the spatial distributions of As (r = 0.47 and 0.66), Cd (r = 0.74 and 0.53), Cu (r = 0.60 and 0.54), and Pb (r = 0.44 and 0.50) based on auxiliary variables in different cities and also can clearly reveal the spatially variable relationships with main influence factors; (3) human activities were the primary driving factors influencing As and Pb, while natural environment variables were identified as the main potential sources of Cd and Cu. This study demonstrates a methodology to explore spatially variable characteristics of soil heavy metals and their spatial varying relationships with influence factors. The comparative analysis between two cities provides insights that can greatly enhance quantitative source apportionment and support sustainable management strategies for controlling soil heavy metal pollution across varied urban environments. Full article

(This article belongs to the Section Farming Sustainability)

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25 pages, 9665 KiB

Open AccessEditor’s ChoiceArticle

Simulating Soil Moisture Dynamics in a Diversified Cropping System Under Heterogeneous Soil Conditions

by Anna Maria Engels, Thomas Gaiser, Frank Ewert, Kathrin Grahmann and Ixchel Hernández-Ochoa

Agronomy 2025, 15(2), 407; https://doi.org/10.3390/agronomy15020407 - 6 Feb 2025

Cited by 2 | Viewed by 1833

Abstract

Agro-ecosystem models are useful tools to assess crop diversification strategies or management adaptations to within-field heterogeneities, but require proper simulation of soil water dynamics, which are crucial for crop growth. To simulate these, the model requires soil hydraulic parameter inputs which are often [...] Read more.

Agro-ecosystem models are useful tools to assess crop diversification strategies or management adaptations to within-field heterogeneities, but require proper simulation of soil water dynamics, which are crucial for crop growth. To simulate these, the model requires soil hydraulic parameter inputs which are often derived using pedotransfer functions (PTFs). Various PTFs are available and show varying performance; therefore, in this study, we calibrated and validated an agro-ecosystem model using the Hypres PTF and the German Manual of Soil Mapping approach and adjusting bulk density for the top- and subsoil. Experimental data were collected at the “patchCROP” landscape laboratory in Brandenburg, Germany. The daily volumetric soil water content (SWC) at 12 locations and above ground biomass at flowering were used to evaluate model performance. The findings highlight the importance of calibrating agro-ecosystem models for spatially heterogeneous soil conditions not only for crop growth parameters, but also for soil water-related processes—in this case by PTF choice—in order to capture the interplay of top- and especially subsoil heterogeneity, climate, crop management, soil moisture dynamics and crop growth and their variability within a field. The results showed that while the impact of bulk density was rather small, the PTF choice led to differences in simulating SWC and biomass. Employing the Hypres PTF, the model was able to simulate the climate and seasonal crop growth interactions at contrasting soil conditions for soil moisture and biomass reasonably well. The model error in SWC was largest after intense rainfall events for locations with a loamy subsoil texture. The validated model has the potential to be used to study the impact of management practices on soil moisture dynamics under heterogeneous soil and crop conditions. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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24 pages, 6085 KiB

Open AccessEditor’s ChoiceArticle

Research on Apple Recognition and Localization Method Based on Deep Learning

by Zhipeng Zhao, Chengkai Yin, Ziliang Guo, Jian Zhang, Qing Chen and Ziyuan Gu

Agronomy 2025, 15(2), 413; https://doi.org/10.3390/agronomy15020413 - 6 Feb 2025

Cited by 1 | Viewed by 900

Abstract

The development of robotic systems for apple picking is indeed a crucial advancement in agricultural technology, particularly in light of the ongoing labor shortages and the continuous evolution of automation technologies. Currently, during apple picking in complex environments, it is difficult to classify [...] Read more.

The development of robotic systems for apple picking is indeed a crucial advancement in agricultural technology, particularly in light of the ongoing labor shortages and the continuous evolution of automation technologies. Currently, during apple picking in complex environments, it is difficult to classify and identify the growth pattern of an apple and obtain information on its attitude synchronously. In this paper, through the integration of deep learning and stereo vision technology, the growth pattern and attitude of apples in the natural environment are identified, and three-dimensional spatial positioning is realized. This study proposes a fusion recognition method based on improved YOLOv7 for apple growth morphology classification and fruit position. Firstly, the multi-scale feature fusion network is improved by adding a 160 × 160 feature scale layer in the backbone network, which is used to enhance the model’s sensitivity in the recognition of very small local features. Secondly, the CBAM attention mechanism is introduced to improve the network’s attention to the target region of interest of the input image. Finally, the Soft-NMS algorithm is adopted, which can effectively prevent high-density overlapping targets from being suppressed at one time and thus prevent the occurrence of missed detection. In addition, the UNet segmentation network and the minimum outer circle and rectangle features are combined to obtain the unobstructed apple position. A depth image of the apple is obtained using an RGB-D camera, and the 3D coordinates of the apple picking point are obtained by combining the 2D coordinates in the RGB image with the depth value. The experimental results show that the recognition accuracy, recall and average recognition precision of the improved YOLOv7 are 86.9%, 80.5% and 87.1%, respectively, which are 4.2, 2.2 and 3.7 percentage points higher compared to the original YOLOv7 model; in addition, the average angular error of the apple position detection method is 3.964°, with an accuracy of 94%, and the error in the three-dimensional coordinate positioning of the apple is within the range of 0.01 mm–1.53 mm, which can meet the demands of apple-picking system operation. The deep-learning-based stereo vision system constructed herein for apple picking robots can effectively identify and locate apples and meet the vision system requirements for the automated picking task performed by an apple-picking robot, with a view to laying the foundation for lossless and efficient apple picking. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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33 pages, 1473 KiB

Open AccessEditor’s ChoiceReview

Humic Substances: Bridging Ecology and Agriculture for a Greener Future

by Angela Maffia, Mariateresa Oliva, Federica Marra, Carmelo Mallamaci, Serenella Nardi and Adele Muscolo

Agronomy 2025, 15(2), 410; https://doi.org/10.3390/agronomy15020410 - 6 Feb 2025

Cited by 5 | Viewed by 4048

Abstract

Humic substances (HSs) are emerging as multifunctional natural catalysts in sustainable agriculture, offering novel opportunities to enhance soil health, plant productivity, and environmental resilience. This review synthesizes recent insights into the chemical diversity, biological mechanisms, and ecological impacts of HSs, presenting a new [...] Read more.

Humic substances (HSs) are emerging as multifunctional natural catalysts in sustainable agriculture, offering novel opportunities to enhance soil health, plant productivity, and environmental resilience. This review synthesizes recent insights into the chemical diversity, biological mechanisms, and ecological impacts of HSs, presenting a new perspective on their role as dynamic agents in agroecosystems. Derived from decomposed organic matter, HSs regulate critical processes such as nutrient cycling, carbon sequestration, and pollutant detoxification. Unlike plant and microbial biomass, which undergo rapid mineralization due to their active dynamism, HSs exhibit significant resistance to biodegradation, leading to a prolonged residence time in soil that spans years or even centuries. This stability allows HSs to maintain their functional roles over extended periods, contributing to long-term soil health and ecosystem sustainability. Their integration into agricultural systems has demonstrated profound effects, including improved soil structure, increased water retention, and the stimulation of microbial activity, which collectively bolster plant stress tolerance and yield. Notably, it has been proposed that HSs exhibit hormone-like properties, influencing plant signaling pathways to enhance root architecture and nutrient acquisition. Moreover, HSs contribute to environmental remediation by regulating the leaching of heavy metals, mitigating nutrient runoff, and fostering climate resilience. This review highlights the synergistic potential of combining HSs with organic amendments like compost and biochar, positioning HSs as a cornerstone of regenerative farming practices. Addressing challenges such as variability in composition and application methods, the discussion underscores the urgency of developing standardized approaches to harness their full potential. By framing HSs as versatile and adaptive tools, this review paves the way for advancing sustainable agricultural systems while addressing global challenges like food security and climate change. Full article

(This article belongs to the Special Issue Humic Substances: Chemistry and Multidimensional Role in Agricultural Systems and Pollution Management)

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16 pages, 2972 KiB

Open AccessEditor’s ChoiceArticle

Complex Microbial Fertilizer Promotes the Growth of Summer-Sown Short-Season-Cultivated Cotton and Increases Cotton Yield in the Yangtze River Basin by Changing the Soil Microbial Community Structure

by Zhangshu Xie, Xiaorong Wang, Xuefang Xie, Dan Yang, Zhonghua Zhou, Qiming Wang, Aiyu Liu and Xiaoju Tu

Agronomy 2025, 15(2), 404; https://doi.org/10.3390/agronomy15020404 - 4 Feb 2025

Cited by 2 | Viewed by 961

Abstract

The summer-sowing short-season cotton cultivation model is an important method for simplified and mechanized cotton planting in the Yangtze River Basin. However, the effects of microbial fertilizers on cotton growth and soil under this model remain unclear. In 2023, we conducted a systematic [...] Read more.

The summer-sowing short-season cotton cultivation model is an important method for simplified and mechanized cotton planting in the Yangtze River Basin. However, the effects of microbial fertilizers on cotton growth and soil under this model remain unclear. In 2023, we conducted a systematic analysis on the application of microbial fertilizers (compost) at varying levels (CK, MF1, MF2, and MF3) during different growth stages of cotton (bud, flowering, bolling, and boll opening). Results showed that appropriate microbial fertilizer application (MF2 and MF3) enhanced soil bacterial and fungal diversity, enriched beneficial microorganisms (e.g., Acidobacteriota and Candidatus Udaeobacter), improved soil nutrient availability, and increased antioxidant enzyme activity (POD, SOD), while reducing membrane lipid peroxidation (MDA). These effects led to significant improvements in yield traits, such as cotton plant height, number of fruiting branches and bolls, boll weight, and coat weight. The highest microbial fertilizer application level (MF3) resulted in a 54.35% increase in seed yield and a 75.37% increase in lint yield compared to CK. PLS-DA (Partial Least Squares Discriminant Analysis) and multivariate statistical analyses revealed that microbial fertilizer application fine-tuned soil microbial community composition, emphasizing the dynamic balance of the microbial ecosystem. This study provides scientific support for optimizing microbial fertilizer strategies to enhance the yield and quality of summer-sown short-season cotton and promote sustainable agriculture. Full article

(This article belongs to the Special Issue Effect of Agricultural Management Practices on Soil Microbial Community Composition, Diversity and Function)

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22 pages, 2612 KiB

Open AccessEditor’s ChoiceReview

Mechanism of ABA in Plants Exposed to Cold Stress

by Changxia Li, Yuna Pan, Jing Cui, Xuefang Lu and Wenjin Yu

Agronomy 2025, 15(2), 403; https://doi.org/10.3390/agronomy15020403 - 4 Feb 2025

Cited by 2 | Viewed by 1777

Abstract

Abscisic acid (ABA) is a natural hormone produced in plants, which plays an important role in plant growth and development and in response to adversity. Increasing research indicates that ABA is involved in plant response to cold stress and enhances the cold tolerance [...] Read more.

Abscisic acid (ABA) is a natural hormone produced in plants, which plays an important role in plant growth and development and in response to adversity. Increasing research indicates that ABA is involved in plant response to cold stress and enhances the cold tolerance of plants through various pathways. Therefore, the roles, regulator mechanisms and regulator pathways of ABA in plant response to cold stress are summarized. In this paper, we first discuss the mechanism of cold damage in plants. Second, we review the important roles of ABA in enhancing plant cold tolerance, including the interactions between endogenous and exogenous ABA, ABA and other substances, ABA and specific genes and transcription factors, and ABA and phosphorylation. On the whole, the involvement of ABA in the plant’s response to cold stress constitutes a complex and multi-dimensional system. ABA interacts with various factors, including hormones, enzymes, genes and so on, to establish a regulatory network that enhances plant resistance to cold injury. Finally, we also provide some perspectives for future research on plant ABA, and we hope that this paper can provide some lessons for future research on the mechanism of ABA involvement in plant adversity stress. Full article

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

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19 pages, 2343 KiB

Open AccessEditor’s ChoiceArticle

Maintaining Silage Corn Production Under Sodic Irrigation Water Conditions in a Semi-Arid Environment

by Farzam Moghbel, Forough Fazel, Jonathan Aguilar, Behrouz Mostafazadeh-Fard, Abolfazl Mosaedi and Nathan Howell

Agronomy 2025, 15(2), 400; https://doi.org/10.3390/agronomy15020400 - 3 Feb 2025

Cited by 1 | Viewed by 912

Abstract

The Zayandeh-Rud watershed of Iran has had water scarcity for decades, giving rise to pressures toward limiting water allocation for the agriculture sector. Marginal waters can be an alternative source for irrigated agriculture in water-scarce regions if adequately managed. One of the critical [...] Read more.

The Zayandeh-Rud watershed of Iran has had water scarcity for decades, giving rise to pressures toward limiting water allocation for the agriculture sector. Marginal waters can be an alternative source for irrigated agriculture in water-scarce regions if adequately managed. One of the critical hazards for sustainable agriculture and the environment is the accumulated salinity–sodicity problem as a consequence of irrigating with unconventional waters. Applying additional water beyond the crop water requirement, known as leaching application, has been suggested as a solution to this problem. A physical model was built to investigate the effects of the severe sodicity and salinity conditions of irrigation water by creating 250 mm diameter soil columns (27 columns) filled with sandy clay loam soil. The severity of the irrigation water’s sodicity (sodium adsorption ratios (SAR): 5.27, 16.56, and 28.57) and its interactions with various leaching fractions (0%, 15%, and 30%) on critical soil chemical characteristics and corn yield were studied. Implementing a 30% leaching fraction reduced the SAR and salinity in the soil’s first layer (0–10 cm) when irrigating with saline–hyper-sodic water (SAR = 28.57 and ECiw = 9 dS/m). However, an elevated level of sodicity accumulation in the soil profile was observed, emphasizing the importance of adding calcium and magnesium amendments during the irrigation season. A noticeable increase in the efficiency of leaching applications in reducing accumulated salts and the sodicity level in the corn rootzone was detected with higher levels of irrigation water sodicity. The reduction in the accumulated salinity and sodium in the first soil layer due to implementing a 30% leaching fraction resulted in a 223.3% increase in the total biomass of silage corn. Applying a 30% leaching fraction also increased the corn biomass by 58% and 114.56% when irrigating with waters with 5.57 and 16.56 SAR values. The effectiveness of a 15% leaching fraction for enhancing the soil and crop conditions was significantly lower than that of the 30% leaching fraction. Nevertheless, in case of unavailability of sufficient water supply for irrigation purposes, applying a 15% leaching fraction could mitigate the consequences of sodic water irrigation. The results demonstrate that in the absence of the proper calcium amendments, the implementation of leaching management could still be effective in enhancing corn production under sodic water irrigation conditions. Full article

(This article belongs to the Special Issue Effect of Brackish and Marginal Water on Irrigated Agriculture)

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