Agriculture

21 pages, 12422 KB

Open AccessArticle

Bio-Regulatory Mechanisms of Straw Incorporation in Haplic Phaeozem Region: Soil Ecosystem Responses Driven by Multi-Factor Interactions

by Yucui Ning, Zhipeng Chen, Rui Xu, Yu Yang, Shuo Wang and Dongxing Zhou

Agriculture 2025, 15(21), 2195; https://doi.org/10.3390/agriculture15212195 (registering DOI) - 22 Oct 2025

Abstract

With the increasing global food production year by year, the effective return of crop straw to the field has become an urgent problem to be solved. This study examined the impact of straw decomposition under different return methods on soil ecosystems, focusing on [...] Read more.

With the increasing global food production year by year, the effective return of crop straw to the field has become an urgent problem to be solved. This study examined the impact of straw decomposition under different return methods on soil ecosystems, focusing on changes in soil biological characteristics. Simulating modern mechanized agricultural practices, an orthogonal experiment was conducted in the haplic Phaeozem region of Northeast China. The factors studied included the amount, length, and burial depth of straw returning. A comprehensive analysis model was built using path analysis, factor analysis, and response surface methodology to investigate the response of soil ecosystem during straw decomposition. This was assessed from four aspects: soil basic nutrients, organic carbon pool, enzyme activity, and microbial community structure. The study found evidence of a strong synergistic relationship between the soil enzyme system and straw decomposition. Notably, during the mid-phase of straw return (60 days), phosphatase and particulate organic carbon (POC) acted as “mirror” antagonistic indicators. Catalase, soil nitrate nitrogen, and POC were identified as key response indicators in the soil ecosystem post-straw return. The appropriate supplementation of nitrogen during the early (0–45 days) and late (75–90 days) stages of straw return was found to facilitate straw decomposition. These findings provide experimental evidence for the return of corn straw in cold haplic Phaeozem regions and offer scientific support for sustainable agricultural practices and national food security. Full article

(This article belongs to the Special Issue Innovative Conservation Cropping Systems and Practices—2nd Edition)

19 pages, 1235 KB

Open AccessArticle

Experimental Evaluation of Anti-Rain Agricultural Nets: Structural Parameters and Functional Efficiency

by Greta Mastronardi, Roberto Puglisi, Sergio Castellano, Pietro Picuno, Audrey Maria Noemi Martellotta, Giacomo Scarascia Mugnozza and Ileana Blanco

Agriculture 2025, 15(21), 2194; https://doi.org/10.3390/agriculture15212194 (registering DOI) - 22 Oct 2025

Abstract

Plastic agrotextiles are increasingly used in modern agriculture to protect crops from adverse climatic events, such as excessive rainfall, wind, and solar radiation. Among these, anti-rain nets represent a promising solution to mitigate rain-induced disorders, such as fruit cracking, especially in crops sensitive [...] Read more.

Plastic agrotextiles are increasingly used in modern agriculture to protect crops from adverse climatic events, such as excessive rainfall, wind, and solar radiation. Among these, anti-rain nets represent a promising solution to mitigate rain-induced disorders, such as fruit cracking, especially in crops sensitive to water excess. This study investigates the structural and functional properties of eight agrotextiles, including both anti-rain and anti-insect nets. The analysis focuses on geometric characteristics (porosity, thread diameter, mesh density) and on functional performance through experimental evaluation of air and rainwater permeability under different slope conditions. Air permeability was assessed using a wind tunnel, while rainwater permeability was tested via a rainfall simulation bench. The results demonstrate a stronger correlation between the air permeability index (K_a) and the rainwater permeability index Φ_rw (R² = 0.95–0.99), across different net slopes (10° and 30°), than between the net porosity and Φ_rw (R² = 0.86–0.92). These findings emphasize the greater explanatory power of the dynamic performance indicator K_a as a predictor of rainwater permeability, over purely geometric descriptors like porosity, since it inherently accounts for the dynamic performance of the air flow through the net. This contributes to the development of more effective and sustainable net-based crop protection systems tailored to specific environmental and agronomic needs. Full article

(This article belongs to the Section Agricultural Technology)

17 pages, 6250 KB

Open AccessArticle

An Interpretable Machine Learning Approach to Remote Sensing-Based Estimation of Hourly Agricultural Evapotranspiration in Drylands

by Qifeng Zhuang, Weiwei Zhu, Nana Yan, Ghaleb Faour, Mariam Ibrahim and Liang Zhu

Agriculture 2025, 15(21), 2193; https://doi.org/10.3390/agriculture15212193 (registering DOI) - 22 Oct 2025

Abstract

Obtaining evapotranspiration (ET) estimates at high spatiotemporal resolution is a fundamental prerequisite for clarifying the patterns and controlling factors of agricultural water consumption in drylands. However, most existing ET products are provided at daily or coarser spatial–temporal scales, which limits the ability to [...] Read more.

Obtaining evapotranspiration (ET) estimates at high spatiotemporal resolution is a fundamental prerequisite for clarifying the patterns and controlling factors of agricultural water consumption in drylands. However, most existing ET products are provided at daily or coarser spatial–temporal scales, which limits the ability to capture short-term variations in crop water use. This study developed a novel hourly 10-m ET estimation method that combines remote sensing with machine learning techniques. The approach was evaluated using agricultural sites in two arid regions: the Heihe River Basin in China and the Bekaa Valley in Lebanon. By integrating hourly eddy covariance measurements, Sentinel-2 reflectance data, and ERA5-Land reanalysis meteorological variables, we constructed an XGBoost-based modeling framework for hourly ET estimation, and incorporated the SHapley Additive exPlanations (SHAP) method for model interpretability analysis. Results demonstrated that the model achieved strong performance across all sites (R²= 0.86–0.91, RMSE = 0.04–0.05 mm·h⁻¹). Additional metrics, including the Nash–Sutcliffe efficiency coefficient (NSE) and percent bias (PBIAS), further confirmed the model’s robustness. Interpreting the model with SHAP highlighted net radiation (Rn), 2-m temperature (t2m), and near-infrared reflectance of vegetation (NIRv) as the dominant factors controlling hourly ET variations. Significant interaction effects, such as Rn × NIRv and Rn × t2m, were also identified, revealing the modulation mechanism of energy, vegetation status and temperature coupling on hourly ET. The study offers a practical workflow and an interpretable framework for generating high-resolution ET maps, thereby supporting regional water accounting and land–atmosphere interaction research. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

16 pages, 1755 KB

Open AccessArticle

Coffee Farming in the Sierra Norte Region of Puebla, Mexico: A Multivariate Analysis Approach to Productive Dedication

by Zayner Edin Rodríguez-Flores, Cesar San-Martín-Hernández, Victorino Morales-Ramos, Victor Hugo Volke-Haller, Juliana Padilla-Cuevas and Carlos Hernández-Gómez

Agriculture 2025, 15(21), 2192; https://doi.org/10.3390/agriculture15212192 (registering DOI) - 22 Oct 2025

Abstract

Puebla is Mexico’s third largest coffee-producing state, supporting more than 40,000 families in the Sierra Norte region alone. In this area, the heterogeneity of production, which ranges from traditional subsistence methods to technified models, and a significant difference in the level of dedication [...] Read more.

Puebla is Mexico’s third largest coffee-producing state, supporting more than 40,000 families in the Sierra Norte region alone. In this area, the heterogeneity of production, which ranges from traditional subsistence methods to technified models, and a significant difference in the level of dedication to production represent major challenges for the sustainability of coffee farming. This study aimed to classify coffee producers in the Tlaxcalantongo ejido, Xicotepec, Puebla, according to their level of productive dedication, using multivariate techniques such as hierarchical clustering, non-metric multidimensional scaling (NMDS), and Random Forest. Data were obtained from a structured questionnaire with 102 questions administered in person to 50 active producers. The cluster analysis found patterns and differences in the productive dedication of coffee growers that allowed them to be differentiated into two groups. Group 1 (8%) showed minimal fertilization practices and low operating expenses, reflecting significant differences in resource management. In contrast, producers in group 2 (92%) had a profile characterized by intensive fertilization practices, greater investment in inputs, and structured agronomic management. In the NMDS analysis, dimension 1 was significantly associated with the group of producers with low productive dedication and dimension 2 was significantly associated with the group with greater dedication, while the third dimension showed no clear differentiation between the groups. The variables that determined the productive dedication profiles were fertilization application, division, type, and expenditure. Full article

(This article belongs to the Section Agricultural Systems and Management)

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15 pages, 240 KB

Open AccessArticle

Assessment of the Impact of Biodegradable Coated Fertilizers on Corn Yield

by Łukasz Rusek, Marzena Sylwia Brodowska, Paulina Bogusz and Piotr Rusek

Agriculture 2025, 15(21), 2191; https://doi.org/10.3390/agriculture15212191 (registering DOI) - 22 Oct 2025

Abstract

The aim of the study was to assess the impact of fertilizer type (urea, compound fertilizer), biodegradable coating type (linseed oil or hemp oil based) and nitrogen dose (135 and 180 kg N·ha⁻¹) on the yield of corn intended for silage. [...] Read more.

The aim of the study was to assess the impact of fertilizer type (urea, compound fertilizer), biodegradable coating type (linseed oil or hemp oil based) and nitrogen dose (135 and 180 kg N·ha⁻¹) on the yield of corn intended for silage. A three-year field experiment was conducted using a randomized block design with three replicates. The test plant was corn intended for silage. The field experiment was conducted in a factorial design comprising three experimental factors: fertilizer type (two levels), coating type (two levels), and fertilizer dose (two levels). Controlled-release fertilizers (CRF) based on biodegradable coatings are an emerging solution in sustainable nitrogen management, yet their field-scale performance remains insufficiently validated. This study investigated how biodegradable coatings based on linseed and hemp oils affect nutrient release dynamics and maize yield under three-year field conditions. The study represents the first field validation phase translating laboratory coating characteristics into agricultural performance metrics. Statistical analysis (ANOVA, Tukey’s test) showed that in the first year of the study, the greatest impact on plant height and corn yield was observed in the case of type of fertilizer used (η²_p up to 17.83%), type of coating (η²_p up to 63.15%) and their interaction (η²_p up to 11.92%). The symbol η²_p (partial eta squared) represents a measure of effect size in analysis of variance (ANOVA). The largest plant size (average 307–310 cm) and the highest yield (107.33 t·ha⁻¹) were obtained in the case of yields in which compound fertilizer or urea with coatings were used in relation to the series in which fertilizers without coatings were applied (differences up to 11 t·ha⁻¹). Statistical analysis using repeated measures ANOVA confirmed a significant time effect, with fertilizer effectiveness declining in subsequent years of the experiment (p < 0.05). In the experiment, no effect of the tested factors on the number of corn cobs was found (η²_p < 2.27%). The highest fresh matter yield for silage production was obtained with coated NPK compound fertilizer (98.80 t·ha⁻¹), representing a 48% increase compared to the unfertilized control (66.90 t·ha⁻¹). The results of the study indicate that the use of coated compound fertilizers—NPK has the most beneficial effect on yield and biometric parameters of plants in the first growing season after their soil application. The enhanced nutrient release from biodegradable coatings provided greatest benefits in the first growing season, with diminishing effects in subsequent years due to coating degradation and residual soil nutrient accumulation. Full article

(This article belongs to the Section Crop Production)

17 pages, 6068 KB

Open AccessArticle

Characterization of Drought-Responsive miRNAs in Peanut Through Integrated Transcriptomic Approaches

by Xin Zhang, Rui Zhang, Zhenbo Chen, Xiaoyu Zhang, Xiaoji Zhang, Yuexia Tian, Yunyun Xue, Huiqi Zhang, Na Li and Dongmei Bai

Agriculture 2025, 15(21), 2190; https://doi.org/10.3390/agriculture15212190 (registering DOI) - 22 Oct 2025

Abstract

Drought stress severely limits peanut productivity, highlighting the urgent need to understand the molecular mechanisms that underlie drought adaptation. While microRNAs (miRNAs) are known to play essential roles in plant stress responses, their functional contributions in polyploid crops like peanut remain insufficiently explored. [...] Read more.

Drought stress severely limits peanut productivity, highlighting the urgent need to understand the molecular mechanisms that underlie drought adaptation. While microRNAs (miRNAs) are known to play essential roles in plant stress responses, their functional contributions in polyploid crops like peanut remain insufficiently explored. This study provides the first integrated transcriptomic analysis of drought-responsive miRNAs in tetraploid peanut (Arachis hypogaea). We performed high-throughput sRNA sequencing on a drought-tolerant cultivar Fenhua 8 under PEG6000-simulated drought stress, identifying 10 conserved drought-responsive miRNAs. Among these, ahy-miR398 and ahy-miR408 were significantly downregulated under drought conditions. Degradome sequencing revealed that ahy-miR398 targets copper chaperones for superoxide dismutase (CCSs), potentially reducing SOD activation and amplifying oxidative stress. In contrast, ahy-miR408 targets laccase 12 (LAC12), P-type ATPase copper transporters (COPAs), and a blue copper protein-like (PCL) gene. These targets are involved in copper homeostasis and the regulation of reactive oxygen species (ROS), suggesting that ahy-miR408 plays a role in oxidative stress management. Functional validation in transgenic Arabidopsis lines overexpressing ahy-miR398 or ahy-miR408 showed significantly reduced drought tolerance, with impaired seed germination, shorter primary roots, and exacerbated growth suppression during water deprivation. Taken together, these findings highlight a novel miRNA-mediated regulatory network in peanut drought adaptation, centered on copper-associated oxidative stress management. This study provides new insights into miRNA-based regulation in polyploid crops and offers potential molecular targets for breeding climate-resilient peanut varieties, especially in arid regions where yield stability is crucial. Full article

(This article belongs to the Topic Tolerance to Drought and Salt Stress in Plants, 2nd volume)

22 pages, 10686 KB

Open AccessArticle

A Vision Navigation Method for Agricultural Machines Based on a Combination of an Improved MPC Algorithm and SMC

by Yuting Zhai, Dongyan Huang, Jian Li, Xuehai Wang and Yanlei Xu

Agriculture 2025, 15(21), 2189; https://doi.org/10.3390/agriculture15212189 (registering DOI) - 22 Oct 2025

Abstract

Vision navigation systems provide significant advantages in agricultural scenarios such as pesticide spraying, weeding, and harvesting by interpreting crop row structures in real-time to establish guidance lines. However, the delay introduced by image processing causes the path and pose information relied upon by [...] Read more.

Vision navigation systems provide significant advantages in agricultural scenarios such as pesticide spraying, weeding, and harvesting by interpreting crop row structures in real-time to establish guidance lines. However, the delay introduced by image processing causes the path and pose information relied upon by the controller to lag behind the actual vehicle state. In this study, a hierarchical delay-compensated cooperative control framework (HDC-CC) was designed to synergize Model Predictive Control (MPC) and Sliding Mode Control (SMC), combining predictive optimization with robust stability enforcement for agricultural navigation. An upper-layer MPC module incorporated a novel delay state observer that compensated for visual latency by forward-predicting vehicle states using a 3-DoF dynamics model, generating optimized front-wheel steering angles under actuator constraints. Concurrently, a lower-layer SMC module ensured dynamic stability by computing additional yaw moments via adaptive sliding surfaces, with torque distribution optimized through quadratic programming. Under varying adhesion conditions tests demonstrated error reductions of 74.72% on high-adhesion road and 56.19% on low-adhesion surfaces. In Gazebo simulations of unstructured farmland environments, the proposed framework achieved an average path tracking error of only 0.091 m. The approach effectively overcame vision-controller mismatches through predictive compensation and hierarchical coordination, providing a robust solution for vision autonomous agricultural machinery navigation in various row-crop operations. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

24 pages, 832 KB

Open AccessArticle

Co-Application of Arbuscular Mycorrhizal Fungi and Silicon Nanoparticles: A Strategy for Optimizing Volatile Profile, Phenolic Content, and Flower Yield in Rosa damascena Genotypes

by Nasrin Gharaei, Ali Nikbakht, Mehdi Rahimmalek and Antoni Szumny

Agriculture 2025, 15(21), 2188; https://doi.org/10.3390/agriculture15212188 (registering DOI) - 22 Oct 2025

Abstract

This study investigated the individual and synergistic impacts of arbuscular mycorrhizal fungi (AMF) inoculation and foliar-applied silicon nanoparticles (SiNPs) on the yield parameters, volatile profile, and phenolic composition of two Rosa damascena genotypes (D231 and C193). Experiments were conducted using a split–split–plot design, [...] Read more.

This study investigated the individual and synergistic impacts of arbuscular mycorrhizal fungi (AMF) inoculation and foliar-applied silicon nanoparticles (SiNPs) on the yield parameters, volatile profile, and phenolic composition of two Rosa damascena genotypes (D231 and C193). Experiments were conducted using a split–split–plot design, involving AMF inoculation (main plot), three SiNPs concentrations (subplot), and two rose genotypes (sub-subplot). The results demonstrated that AMF, SiNPs, and genotype all had significant and interactive effects on flower yield parameters. Foliar application of SiNPs, particularly when combined with AMF inoculation, consistently enhanced flowering parameters, including flower size, number, and weight across both genotypes. High-performance liquid chromatography (HPLC) further confirmed that phenolic acids (vanillic acid and rutin) increased following foliar application of SiNPs and AMF root colonization, particularly in genotype C193. SPME-Arrow analysis revealed that alcohols, ketones, and terpenes were the predominant volatile constituents. Phenethyl alcohol was the most abundant compound, accounting for approximately 84.69% of the total aroma content and contributing significantly to the ‘rose’ aroma. Other major volatiles included 2-undecanone (4.42%), benzyl alcohol (2.97%), and citronellol (1.95%); however, their levels varied depending on treatment and genotype. These findings highlight that the combined application of AMF and SiNPs offers a sustainable approach to enhancing both the quantitative yield and qualitative phytochemical composition (essential oil components and phenolic compounds) of R. damascena, providing a scientific foundation for optimizing its production in organic farming systems. Full article

(This article belongs to the Special Issue Strategies for Resource Extraction from Agricultural Products)

30 pages, 11870 KB

Open AccessArticle

Early Mapping of Farmland and Crop Planting Structures Using Multi-Temporal UAV Remote Sensing

by Lu Wang, Yuan Qi, Juan Zhang, Rui Yang, Hongwei Wang, Jinlong Zhang and Chao Ma

Agriculture 2025, 15(21), 2186; https://doi.org/10.3390/agriculture15212186 (registering DOI) - 22 Oct 2025

Abstract

Fine-grained identification of crop planting structures provides key data for precision agriculture, thereby supporting scientific production and evidence-based policy making. This study selected a representative experimental farmland in Qingyang, Gansu Province, and acquired Unmanned Aerial Vehicle (UAV) multi-temporal data (six epochs) from multiple [...] Read more.

Fine-grained identification of crop planting structures provides key data for precision agriculture, thereby supporting scientific production and evidence-based policy making. This study selected a representative experimental farmland in Qingyang, Gansu Province, and acquired Unmanned Aerial Vehicle (UAV) multi-temporal data (six epochs) from multiple sensors (multispectral [visible–NIR], thermal infrared, and LiDAR). By fusing 59 feature indices, we achieved high-accuracy extraction of cropland and planting structures and identified the key feature combinations that discriminate among crops. The results show that (1) multi-source UAV data from April + June can effectively delineate cropland and enable accurate plot segmentation; (2) July is the optimal time window for fine-scale extraction of all planting-structure types in the area (legumes, millet, maize, buckwheat, wheat, sorghum, maize–legume intercropping, and vegetables), with a cumulative importance of 72.26% for the top ten features, while the April + June combination retains most of the separability (67.36%), enabling earlier but slightly less precise mapping; and (3) under July imagery, the SAM (Segment Anything Model) segmentation + RF (Random Forest) classification approach—using the RF-selected top 10 of the 59 features—achieved an overall accuracy of 92.66% with a Kappa of 0.9163, representing a 7.57% improvement over the contemporaneous SAM + CNN (Convolutional Neural Network) method. This work establishes a basis for UAV-based recognition of typical crops in the Qingyang sector of the Loess Plateau and, by deriving optimal recognition timelines and feature combinations from multi-epoch data, offers useful guidance for satellite-based mapping of planting structures across the Loess Plateau following multi-scale data fusion. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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15 pages, 1311 KB

Open AccessArticle

Hot Pots: Container Color Has a Greater Cooling Effect than Micro-Sprinkler Frequency in Nursery Production

by Lloyd Nackley, Dalyn McCauley, James Owen, Jr., Jacob Shreckhise and Jeb Fields

Agriculture 2025, 15(21), 2185; https://doi.org/10.3390/agriculture15212185 (registering DOI) - 22 Oct 2025

Abstract

Container production systems are critical for the horticulture industry but are particularly vulnerable to temperature extremes. This study investigated the effects of pot color (black vs. white) and irrigation frequency (single vs. cyclic) on root zone temperature and the growth of Hydrangea paniculata [...] Read more.

Container production systems are critical for the horticulture industry but are particularly vulnerable to temperature extremes. This study investigated the effects of pot color (black vs. white) and irrigation frequency (single vs. cyclic) on root zone temperature and the growth of Hydrangea paniculata ‘Limelight’, one of the most popular and widely grown container-grown perennial shrubs in North America. The experiment was conducted at the North Willamette Research and Extension Center in Aurora, Oregon, USA (45°16′51″ N, 122°45′04″ W). A total of 160 Hydrangea paniculata ‘Limelight’ plants were divided into two groups of 80 and potted in black or white 11.4 L nursery containers filled with a bark-based potting mix. Pots were randomly assigned to one of two irrigation treatments based on irrigation frequency: single or cyclic. In the single irrigation treatment, pots received one irrigation event at 07:00 h. In the cyclic irrigation treatment, the same total irrigation volume was divided into three equal applications delivered at 08:00, 12:00, and 16:00 h. Results showed that black pots reached significantly higher root zone temperatures than white pots. Cyclic irrigation effectively reduced the peak root zone temperatures in black pots, cooling by as much as 6 °C during hot afternoons compared with single irrigation. Plants in black pots experienced 6×–7× more hours above critical root-zone temperature thresholds (>38 °C) compared with those in white pots. Although the literature indicates that prolonged exposure above 35–38 °C can inhibit photosynthesis and slow growth, and that root growth may cease at ~38 °C, in our study, plant growth was not significantly affected by pot color, irrigation regime, or their interaction (all p > 0.05). This study emphasizes the importance of optimizing pot color and irrigation practices to address vulnerabilities to extreme temperatures in container production systems. Full article

(This article belongs to the Special Issue Abiotic Stress Responses in Horticultural Crops)

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19 pages, 1621 KB

Open AccessArticle

Generally Recognized as Safe Salts for a Natural Strategy to Managing Fungicide-Resistant Penicillium Strains in the Moroccan Citrus Packinghouse

by Meriem Hamrani, Lamyaa Zelmat, Seyed Mehdi Jazayeri, Mohamed El Ammari, Najiba Brhadda, Rabea Ziri, Jawad Aarrouf and Mohammed El Guilli

Agriculture 2025, 15(21), 2184; https://doi.org/10.3390/agriculture15212184 (registering DOI) - 22 Oct 2025

Abstract

The extensive application of fungicides in citrus packinghouses to mitigate economic losses has resulted in the emergence of fungicide-resistant biotypes of Penicillium spp. Furthermore, many countries have implemented strict monitoring of fungicide residues to protect consumer health and the ecosystem. Maximum residue limits [...] Read more.

The extensive application of fungicides in citrus packinghouses to mitigate economic losses has resulted in the emergence of fungicide-resistant biotypes of Penicillium spp. Furthermore, many countries have implemented strict monitoring of fungicide residues to protect consumer health and the ecosystem. Maximum residue limits (MRLs) have been established in accordance with Codex Alimentarius standards, which present challenges for exports, as exceeding MRLs may restrict market access. This study aimed to identify fungicide-resistant strains of Penicillium spp. in a Moroccan citrus packinghouse and to assess the efficacy of GRAS (Generally Recognized As Safe) salts as eco-friendly alternatives for controlling these resistant strains through in vitro and in vivo tests. A total of 31 Penicillium isolates, labeled H₁ to H₃₁, were collected; 10 were identified as P. digitatum and 21 were identified as P. italicum. Resistance to thiabendazole (61.3%) and imazalil (58.1%) was notable, with some isolates showing dual resistance. In vitro, potassium sorbate, sodium benzoate, and sodium tetraborate salts were highly effective at inhibiting the mycelial growth of resistant isolates, at a concentration of 0.3% (p < 0.0001). In vivo tests on ‘Nadorcott’ fruits demonstrated that 2% and 4% salt solutions effectively prevented the development of green and blue molds caused by Penicillium spp. and showed strong curative effects, resulting in nearly 100% inhibition of most fungal isolates. Additionally, preventive salt treatments increased the accumulation of phenolic and flavonoid compounds, while in fruits treated with sodium benzoate, chitinase and peroxidase activities were significantly enhanced. Full article

(This article belongs to the Special Issue Advances in Plant Disease Resistance and Plant–Pathogen Interactions)

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1 pages, 123 KB

Open AccessCorrection

Correction: Katre et al. How Can Middle-of-the-Chain Organizations Improve Farmer Livelihoods and Reduce Food Insecurity? Agriculture 2025, 15, 251

by Aparna Katre, Brianna Raddatz, Britta Swanson and Taylor Turgeon

Agriculture 2025, 15(21), 2183; https://doi.org/10.3390/agriculture15212183 (registering DOI) - 22 Oct 2025

Abstract

Since the publication of the original article [...] Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

24 pages, 4476 KB

Open AccessArticle

Successive Harvesting Interval and Salinity Level Modulate Biomass Production and Nutritional Value in Sarcocornia fruticosa and Arthrocaulon macrostachyum

by Tesfaye Asmare Sisay, Jaykumar Patel, Kusum Khatri, Babita Choudhary, Dominic Standing, Zai Du Nja, Muki Shpigel, Luísa Margarida Batista Custódio, Ilya Gelfand and Moshe Sagi

Agriculture 2025, 15(21), 2182; https://doi.org/10.3390/agriculture15212182 (registering DOI) - 22 Oct 2025

Abstract

Halophyte bio-saline agriculture can supplement conventional farm methods in salinized soils and salty water. The current study compares the yield and nutritional value of new Sarcocornia fruticosa ecotypes (Shikmona, Megadim, Naaman, and Ruhama) to those of the current ecotype (VM). Additionally, Arthrocaulon macrostachyum [...] Read more.

Halophyte bio-saline agriculture can supplement conventional farm methods in salinized soils and salty water. The current study compares the yield and nutritional value of new Sarcocornia fruticosa ecotypes (Shikmona, Megadim, Naaman, and Ruhama) to those of the current ecotype (VM). Additionally, Arthrocaulon macrostachyum, phenotypically similar to Sarcocornia, was compared to Sarcocornia ecotypes, and the effects of the harvesting regime and irrigation water salinity on yield and nutritional value were studied. At both salinity levels (50 and 150 mM NaCl), 30-day harvesting intervals over a 210-day growth period increased plant yield compared to a 21-day regime. It also tended to improve electrical conductivity (EC) and total soluble sugars (TSS), lower malondialdehyde levels (a marker of toxic stress), and enhance radical inhibition activity in most ecotypes. Compared to VM, the Sarcocornia ecotypes Ruh and Naa exhibited much higher biomass with similar radical inhibition activity but lower total protein content. Higher salinity improved fresh biomass, shoot diameter, relative water content, chlorophyll level, TSS, and EC and tended to increase anthocyanin and carotenoid levels. In contrast, lower salinity tended to increase total flavonoids, polyphenols, and radical inhibition activity. In the 30-day harvest regime, A. macrostachyum exhibited the highest and second-highest yields at high and low salinity, respectively; the highest shoot diameter, total flavonoids, and radical inhibition activity; and one of the lowest malondialdehyde levels. The current study highlights the importance of optimizing harvest frequency and the advantages of employing A. macrostachyum and the Sarcocornia ecotypes Ruhama, Naaman, and Megadim with a 30-day harvesting regime under higher-salinity conditions. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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12 pages, 1477 KB

Open AccessArticle

Pollution Status, Risk Assessment, and Source Identification of Heavy Metals in Farmland Topsoil of Mining Area Along the Yangtze River, East China

by Xinzhan Sun, Bin Wang and Zhitao Li

Agriculture 2025, 15(21), 2181; https://doi.org/10.3390/agriculture15212181 - 22 Oct 2025

Abstract

This study evaluates the pollution status, ecological risks, health risks, and sources of heavy metals from farmland in a mining city located in the Yangtze River basin, East China. A total of 2361 samples of topsoil were collected and analyzed for the concentration [...] Read more.

This study evaluates the pollution status, ecological risks, health risks, and sources of heavy metals from farmland in a mining city located in the Yangtze River basin, East China. A total of 2361 samples of topsoil were collected and analyzed for the concentration of five heavy metals and pH. The Nemerow index was used to assess integrated pollution, while absolute principal component scores-multiple linear regression (APCS-MLR) was used to identify the sources of heavy metals. The results revealed that, excluding Hg and Cr, the concentrations of Cd, As, and Pb in some samples exceeded intervention values, with Cd concentrations in 19.7% of samples surpassing this threshold. Based on the Nemerow index, 68.8% of sites were contaminated, with 27.4% classified as heavily polluted, indicating significant pollution in this area. Cd posed the primary ecological risk, with 19.8% of sites at high risk or above, also presenting carcinogenic risks to adults. Additionally, As exceeded safety thresholds for hazard quotient (HQ = 1) and carcinogenic risk (CR = 1 × 10⁻⁴). APCS-MLR revealed that heavy metals in farmland were mainly influenced by mining, agricultural activities, and natural soil-forming processes. This study offers insights into farmland heavy metal management and highlights industrial pollution sources in mining areas. Full article

(This article belongs to the Section Agricultural Soils)

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29 pages, 6329 KB

Open AccessArticle

Non-Contact Measurement of Sunflower Flowerhead Morphology Using Mobile-Boosted Lightweight Asymmetric (MBLA)-YOLO and Point Cloud Technology

by Qiang Wang, Xinyuan Wei, Kaixuan Li, Boxin Cao and Wuping Zhang

Agriculture 2025, 15(21), 2180; https://doi.org/10.3390/agriculture15212180 - 22 Oct 2025

Abstract

The diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters. Traditional, single-dimensional two-dimensional imaging methods often struggle to balance precision with computational efficiency. This paper addresses the limitations of the YOLOv11n-seg model in the instance [...] Read more.

The diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters. Traditional, single-dimensional two-dimensional imaging methods often struggle to balance precision with computational efficiency. This paper addresses the limitations of the YOLOv11n-seg model in the instance segmentation of floral disk fine structures by proposing the MBLA-YOLO instance segmentation model, achieving both lightweight efficiency and high accuracy. Building upon this foundation, a non-contact measurement method is proposed that combines an improved model with three-dimensional point cloud analysis to precisely extract key structural parameters of the flower head. First, image annotation is employed to eliminate interference from petals and sepals, whilst instance segmentation models are used to delineate the target region; The segmentation results for the disc surface (front) and edges (sides) are then mapped onto the three-dimensional point cloud space. Target regions are extracted, and following processing, separate models are constructed for the disc surface and edges. Finally, with regard to the differences between the surface and edge structures, targeted methods are employed for their respective calculations. Whilst maintaining lightweight characteristics, the proposed MBLA-YOLO model achieves simultaneous improvements in accuracy and efficiency compared to the baseline YOLOv11n-seg. The introduced CKMB backbone module enhances feature modelling capabilities for complex structural details, whilst the LADH detection head improves small object recognition and boundary segmentation accuracy. Specifically, the CKMB module integrates MBConv and channel attention to strengthen multi-scale feature extraction and representation, while the LADH module adopts a tri-branch design for classification, regression, and IoU prediction, structurally improving detection precision and boundary recognition. This research not only demonstrates superior accuracy and robustness but also significantly reduces computational overhead, thereby achieving an excellent balance between model efficiency and measurement precision. This method avoids the need for three-dimensional reconstruction of the entire plant and multi-view point cloud registration, thereby reducing data redundancy and computational resource expenditure. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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Agriculture, Volume 15, Issue 21 (November-1 2025) – 15 articles

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