Agriculture doi: 10.3390/agriculture16222506

Authors: Mia Seitz Isabell Pappert Tran Quoc Khanh Ralf Kaldenhoff

The ability to continuously monitor plant status is a prerequisite for data-driven automation in controlled-environment agriculture. Extracellular electrophysiology provides a non-destructive, scalable measurement approach. However, it remains unclear whether recorded signals contain reproducible, biologically meaningful response features linked to environmental stimuli. Here we present a proof-of-concept framework for investigating light-dependent electrophysiological responses in lettuce (Lactuca sativa). Plants were exposed to repeated light–dark transitions across different photon flux densities and spectral compositions while leaf–stem voltage was recorded continuously. White, red, green, and blue light were evaluated over photon flux densities between 13 and 130 μmol m−2 s−1, whereas violet light was evaluated over a lower range of 7–70 μmol m−2 s−1. Dark controls and non-biological channels supported the interpretation that stimulus-locked voltage changes were associated with biological activity within the plant tissue. Characteristic population-level responses were extracted using stimulus-aligned intra- and interplant averaging, reducing biological variability and circadian effects. Response amplitudes increased with photon flux density and saturated at high intensities. Spectral comparisons revealed wavelength-dependent response patterns: red and green light produced multiphasic profiles similar to white light, whereas blue and violet light elicited lower-amplitude responses with qualitatively different temporal dynamics. Prior exposure to multiple light spectra reduced spectral differentiation, suggesting that electrophysiological profiles may reflect both current light conditions and spectral history. These findings indicate that extracellular electrophysiological recordings can capture reproducible, light-dependent response patterns and establish a methodological foundation for quantitative feature analysis and future adaptive environmental control strategies.

Agriculture doi: 10.3390/agriculture16222505

Authors: Jan Buczek Beata Michalska-Klimczak Elżbieta Harasim Cezary A. Kwiatkowski

Rye is an important European crop utilized primarily for human consumption due to its dietary fiber and bioactive compound content. Its environmental and agrotechnical requirements, combined with the genetic potential of its cultivars, allow for cultivation under both conventional and integrated systems, the latter minimizing agriculture’s negative environmental impact and limiting the use of pesticides and fertilizers. A three-year field experiment with winter rye was conducted between 2020 and 2023 at the University of Rzeszów’s Experimental Station in Krasne (50°03′ N, 22°05′ E; altitude 220 m), near Rzeszów, in south-eastern Poland. The experiment followed a split-plot design, with three replications. The study examined two factors: farming systems (conventional and integrated) and four winter rye cultivars (cvs.), three hybrid cultivars (KWS Serafino abbr. Serafino, SU Performer abbr. Performer, Tur) and one population cultivar (cv.) (Dańkowskie Dragon abbr. Dragon). Cultivation under the conventional system resulted in higher grain yields (by 0.39 t·ha−1) and more favorable leaf area index (LAI) and chlorophyll content (SPAD) values. Additionally, improvements were observed in gas exchange parameters, specifically photosynthetic rate (Pn), transpiration rate (E), and stomatal conductance (Gs), alongside reductions in weed count and air-dry weed biomass (by 7.8% and 26.3%, respectively). Cv. Serafino was characterized by the highest grain yield, higher LAI, SPAD, and gas exchange parameter values (particularly Pn and E), as well as comparable yield levels under both the integrated (8.05 t·ha−1) and conventional (8.31 t·ha−1) systems. The integrated system resulted in lower LAI, SPAD, Pn, E, and Gs values, as well as the lowest grain yields for the cvs. Dragon and Tur. Grain produced under the integrated system exhibited more favorable values for falling number, fiber, and ash content. Farming systems did not affect protein content, with the highest levels observed in cv. Dragon. Grain of cv. Performer was characterized by a more favorable chemical composition (fat, fiber, and ash) in both systems, despite having the lowest protein content and falling number. Despite differences in quality parameters, grain from all rye cultivars grown under either the conventional or integrated system met the requirements for grain intended for human consumption. The appropriate selection of rye cultivars within an integrated system, which combines environmental protection, sustainable fertilization, and plant protection, enables the production of high-quality grain, while greater weed competitiveness can contribute to yield stability.

Agriculture doi: 10.3390/agriculture16222503

Authors: Luming Shen Ling Wan Zijuan Ding Jiaxing Xia Bing Sui Xiaohong Fan Hongxia Xie Yujie Feng

Timely early rice mapping is essential for crop-area estimation and agricultural management in double-cropping regions. Multitemporal synthetic aperture radar (SAR) provides reliable observations under persistent cloud cover, but existing SAR time-series classifiers often emphasize center-pixel temporal signatures while making limited use of local field structure. Moreover, the serialization of spatial neighborhoods for recurrent encoding has received insufficient attention. To address these limitations, we construct a multitemporal Sentinel-1 SAR dataset and propose a Target-Centered Spiral-Scan Bidirectional Long Short-Term Memory network (TSS-BiLSTM) for early rice classification. The dataset contains ten dual-polarization acquisitions and 2109 spatiotemporal neighborhood samples derived from 793 reference parcels and regions interpreted from unmanned aerial vehicle (UAV) imagery in Yueyang County, China. TSS-BiLSTM employs target-centered spiral serialization to organize neighborhood pixels along a locally continuous path. A spatial BiLSTM captures within-date neighborhood dependencies, while a temporal BiLSTM models backscatter evolution across acquisition dates. Wavelet-based VV/VH fusion and temporal attention further integrate complementary polarization responses and emphasize discriminative observations. Across six region-level spatial cross-validation folds, TSS-BiLSTM achieved a mean accuracy of 96.26%, a mean recall of 97.28%, and a mean F1-score of 96.90%. Scan-strategy comparisons and component ablations further supported the contributions of spatial ordering, polarization fusion, and temporal weighting. These results demonstrate the potential of center-referenced spatial–temporal modeling for early rice mapping in fragmented agricultural landscapes.

Agriculture doi: 10.3390/agriculture16222504

Authors: Sina Cosmulescu

Local seedling-derived populations of Persian walnut (Juglans regia L.) represent valuable sources of phenotypic variation for germplasm conservation and breeding. This study aimed to characterize phenotypic diversity and multivariate structure in 52 genotypes from three local germplasm sets originating in southwestern Romania. Six nut and kernel traits were evaluated: nut length, width, thickness and weight, kernel weight, and kernel percentage. Genotypic means were used for integrated analyses. Phenotypic variability was assessed using descriptive statistics and exploratory comparisons among germplasm sets, while trait relationships were examined using Pearson correlations calculated separately for each set. Multivariate structure was investigated using principal component analysis and hierarchical clustering. Substantial phenotypic variability was observed, particularly for nut and kernel weight. Nut weight and kernel weight were consistently positively correlated, whereas nut weight and kernel percentage were negatively associated, indicating that heavier nuts did not necessarily have a higher kernel percentage. The first two principal components explained 88.84% of the total variance and reflected two major dimensions of phenotypic diversity related to nut size–weight and kernel efficiency. These findings highlight contrasting and complementary phenotypic profiles that may support germplasm conservation and the prioritization of material for further evaluation in walnut breeding programs.

Agriculture doi: 10.3390/agriculture16222502

Authors: Shipeng He Junying Han

Counting oilseed flax seedlings accurately is challenging because the plants are small, densely distributed, and often partially occluded against complex field backgrounds. We developed CWT-DEIM, a detector based on DEIM-HGNetv2-N, to detect and count plants at the fir-tree stage in quadrat images. Three modules were incorporated into the neck: a Cross-layer Direct Feature Pyramid Network (CDFPN) for multi-scale feature interaction, Wavelet-Guided Adaptive Candidate Downsampling (WGACD) for detail preservation, and Three-region Difference–Consistency Relationship Modulation (TDCRM) for feature discrimination under occlusion. In the name CWT-DEIM, C, W, and T refer to CDFPN, WGACD, and TDCRM, respectively. On a multi-location dataset of flax image tiles, CWT-DEIM achieved an mAP50:95 of 58.8%, an mAP50 of 95.4%, and an APsmall of 49.4%, exceeding the baseline by 3.0, 1.1, and 4.6 percentage points, respectively. On 30 randomly sampled quadrat images from Jingtai, the model achieved a mean absolute counting error of 2.03 plants and a coefficient of determination of 0.983. These gains came with increased model complexity and inference latency. These results indicate improved localization of small, densely distributed flax plants and suggest potential applications in seedling surveys and field management.

Agriculture doi: 10.3390/agriculture16222501

Authors: Chongtao Chen Chunyan Li

This study adopts a cross-country panel research framework covering 2010–2025 and constructs composite indices of digital agriculture development and sustainable agricultural development. A two-way fixed-effects model is employed to examine the sustainability effects of digital agriculture and its underlying mechanisms. Country-level cluster-robust standard errors are used for statistical inference. Interaction term models and Wald tests are employed to examine differences in estimated coefficients across country groups and digital agriculture dimensions. The results show that digital agriculture is significantly positively associated with sustainable agricultural development. The baseline regression coefficient is 0.306 and is significant at the 1% level. Mechanism analysis indicates that improvements in agricultural production efficiency and green technological innovation are potential transmission channels through which digital agriculture affects sustainable agricultural development. The moderating effect of institutional quality is significant at the 10% level, whereas the moderating effect of agricultural policy support does not reach statistical significance. Heterogeneity analysis reveals that digital agriculture exhibits significant positive associations in both high-income and low- and middle-income countries. However, the difference in coefficients between income groups is not statistically significant (p = 0.148). The difference in coefficients across institutional quality groups is also not statistically significant (p = 0.157). Further analysis shows that digital infrastructure conditions, digital application capabilities, and agricultural digital integration are all significantly positively associated with sustainable agricultural development. In the standardized joint model, the estimated coefficient for agricultural digital integration is 0.045, which is significantly higher than those for digital infrastructure conditions and digital application capabilities. The joint Wald test is significant at the 1% level. The findings indicate that promoting the sustainable development of digital agriculture requires coordinated efforts in digital infrastructure, digital technology applications, and agricultural industry integration. Attention should also be paid to the synergistic effects of technology application efficiency and the institutional environment.

Agriculture doi: 10.3390/agriculture16222499

Authors: Zhiheng Lu Daer Qin Shusheng Yu Qiyong Huang Long Ouyang Wang Yang

Sugarcane is an important economic and energy crop cultivated globally. Pre-cut sugarcane planters have advantages such as seed saving and high efficiency, but irregularly shaped sugarcane setts can impair seed metering performance. Therefore, sugarcane setts must be screened by phenotypic parameters before planting. Manual sorting is labor-intensive, and existing studies rarely integrate parameter acquisition with sorting systems. To address this, we propose a machine vision-based method for estimating sugarcane sett phenotypic parameters. The sugarcane variety used in this study was Guitang 40. First, improved YOLOv8-obb and YOLOv8-seg algorithms are used to establish detection and segmentation models for sugarcane setts and nodes, achieving mAP@0.75 of 87.8% and 93.4%, respectively. Second, node number is calculated from rotated bounding boxes based on affiliation relationship between nodes and sugarcane setts, with 98% accuracy. Length, diameter, and curvature are estimated from sugarcane sett contours. Length is derived from the longer side of the minimum bounding rectangle, diameter from the two long edges after corner partitioning, and curvature by fitting Bezier curves. The mean errors and root mean square errors (RMSE) for length and diameter are 3.14 mm, 0.9 mm and 3.6 mm, 1.01 mm, respectively, and the RMSE for curvature is 0.042. Third, a sorting system is established to conduct experiments. The sorting success rates for sugarcane setts unqualified in node number, length, diameter, and curvature are 96.6%, 91.7%, 85%, and 80%, respectively. After sorting, the multiple-seeding rate and miss-seeding rate of the seed metering device were 3.54% and 2.55%, respectively. Under the tested mixing conditions, sorting reduced both the multiple-seeding rate and the miss-seeding rate, indicating that the proposed method can improve metering performance when unqualified sugarcane setts are present.

Agriculture doi: 10.3390/agriculture16222500

Authors: He Du Huajun Xu Zhongqing Yang Junqian Yang Yang Zhang Junwei Xiang

Residual maize roots alter soil adhesion and tillage resistance in wet loessal soil. A zoned, coupled biomimetic plough surface was developed by combining a shark shield-scale-inspired serrated structure (SSS) on the ploughshare with an antlion-inspired spherical convex hull structure (SCH) on the mouldboard. A coupled soil–root–plough DEM model incorporated Hertz–Mindlin with JKR and bonding contact models and a flexible root model. A Box–Behnken design optimized SCH height, lateral spacing, and longitudinal spacing using soil adhesion mass and tillage resistance as responses. Both regression models were highly significant (p < 0.0001), with R2 values of 0.9873 and 0.9861, respectively. The selected dimensions were 14, 17, and 14 mm. In the DEM surface-configuration comparison, the coupled structure reduced simulated adhesion mass by 69.30% relative to the smooth baseline, with the SCH providing the dominant anti-adhesion contribution. In a separate simulated root-condition comparison, the biomimetic surface reduced adhesion mass by 49.89% in root-free soil and 56.96% in root-containing soil relative to the corresponding smooth-surface treatments. For the same surface configuration, root inclusion reduced adhesion but increased tillage resistance. The optimized design increased the simulated soil-disturbance cross-sectional area by 5.3% and the number of broken interparticle bonds from 5.32 × 105 to 6.03 × 105. Soil-bin experiments at a soil water content of 16 ± 1% and a tillage depth of 250 mm measured reductions of 59.80% in adhesion mass and 12.20% in mean tillage resistance relative to the smooth surface.

Agriculture doi: 10.3390/agriculture16222498

Authors: Orken Zhandybayev Kanat Kulymbet Bakhytbek Amirov Sultan Bazarbayev

Apple (Malus domestica Borkh.) mineral nutrition reflects interactions among soil nutrient availability, rootstock traits, rhizosphere processes, internal transport and remobilization, water supply, and fruit demand. This review examined research trends and synthesized physiological and agronomic evidence using an audited corpus of 847 Scopus-indexed publications from 1995 to 2025. Bibliometric analyses quantified publication patterns and keyword co-occurrence, while qualitative synthesis examined nutrient acquisition, allocation, and management across experimental settings. Publication activity increased markedly, with 62.3% of included publications appearing during 2016–2025. The keyword network comprised 60 terms, 869 links, and five thematic clusters; nitrogen, yield, and fruit quality were among the most frequent concepts. The literature increasingly connected individual nutrient responses with rootstock-mediated acquisition, soil–rhizosphere interactions, fertigation, fruit mineral allocation, molecular regulation, and precision monitoring. Across these themes, the synthesis highlights that nutrient availability alone does not determine tree nutritional status or fruit mineral composition; physiological transport, phenology, rootstock characteristics, and water availability also matter. Interpretation is constrained by heterogeneous study designs and the absence of formal risk-of-bias appraisal. Future research should integrate multi-season orchard experiments, cultivar- and rootstock-specific diagnostic approaches, and independently validated sensing methods to support site-specific nutrient management and assessment of environmental outcomes.

Agriculture doi: 10.3390/agriculture16222497

Authors: Haodong Liu Wei Cheng Minghua Li Chao Feng Ning Yang

Agricultural sensing systems must convert variable soil, plant and product signals into reliable information under constraints on energy, response time and maintenance. This review presents a task-driven framework linking sensor chips, analog readout, edge computing and system integration across crop production and agricultural product testing. It covers electrochemical and biochemical sensors, microfluidic and MEMS devices, optical and spectral systems, edge processors and accelerators, and board-level to heterogeneous integration. The synthesis identifies three recurring design patterns. Soil nutrient monitoring depends strongly on sensor selectivity, reference stability and calibration. Image-based plant phenotyping requires coordinated acquisition, data movement and computing. In low-abundance pathogen detection, sample handling and signal acquisition may dominate the workflow before inference performance becomes decisive. These patterns guide hardware specialization toward the dominant measurement or processing constraint. The synthesis also identifies complete-system energy, long-term stability and field validation as priorities for translating laboratory demonstrations into agricultural applications. We propose task-specific hardware configurations and evaluation criteria covering real-sample error, end-to-end response time, measured system energy, data completeness, calibration retention and life-cycle cost. Together, these outputs provide practical guidance for aligning hardware selection and integration with agricultural measurement needs.

Agriculture doi: 10.3390/agriculture16222496

Authors: Yujie Ren Enkai Cao Lining Liu Rui Zong Zeqiang Sun Yan Li Changjian Ma

Although integrating organic amendments with chemical fertilizers is widely advocated to maintain crop productivity and soil health under reduced synthetic inputs, the optimal substitution ratio within such combined systems is still undetermined. Nevertheless, the optimal substitution ratio and the mechanisms underlying its effects in the North China Plain remain poorly understood. A three-year field trial (2021–2024) was therefore conducted to evaluate how deficit irrigation combined with organic fertilizer substitution influences grain yield, resource use efficiency, and soil quality. A split-plot design was employed. The main plots received two water-limited treatments (I1 = 30 mm and I2 = 60 mm), and the subplots included three fertilization regimes (T1, T2, T3). These regimes utilized organic materials to replace 20%, 40%, and 60% of the inorganic fertilizer inputs. According to the results, the maximum levels of soil Available N, Available P, and Available K in the 0–20 cm layer were recorded under I2T2. This treatment also produced grains with the highest N, P, and K concentrations. Compared with I1T1, I1T2 gave 43.1%, 39.8%, and 47.9% greater grain yield. Conversely, the evapotranspiration of I1T1 was the lowest. Relative to I2T2, the WUE under I1T1 declined by 27.1%, 26.5%, and 25.3% across the three seasons. This indicates that a moderate organic substitution rate can partially offset the WUE penalty induced by restricted irrigation. The structural equation model further revealed the driving mechanism of yield improvement: water is the fundamental regulatory factor for yield formation, having a significant positive impact on soil nutrient status (0.89) and soil water use efficiency (0.58); among them, soil water use efficiency had the strongest direct contribution to yield (0.59), and soil nutrients also showed a small but significant positive impact on yield (0.16*). This study proves that partially substituting traditional chemical fertilizers with organic fertilizers can increase wheat yield and improve the effectiveness of soil nutrients. Among the rates tested, 40% organic substitution gave the highest yield under both deficit irrigation levels. The results of this study provide an experimental basis for achieving water-saving and yield-increasing through optimizing the proportion of organic fertilizer substitution under conditions of insufficient irrigation.

Agriculture doi: 10.3390/agriculture16222495

Authors: Wilson Vergara Vergara Andrea del Pilar Uribe Díaz David Vergara Cruz

Classical cattle-cycle theory was developed primarily for temperate capital-intensive livestock systems, leaving its applicability to tropical extensive production less well established. Using Colombian monthly bovine slaughter data for 1954–2024 and a census-anchored herd reconstruction for 2008–2022, this study examines cyclical persistence, structural change, price relationships, and selected external indicators through complementary time-series and state-space methods. Spectral evidence is multimodal, with concentrations around 4.4–5.0 and 7.1–7.8 years. The reference turning-point procedure yields an average cycle duration of approximately 6.1 years, although estimates increase under more restrictive dating rules. Structural-break tests strongly reject parameter stability; a seven-break segmentation is retained under the reference specification and major-gap exclusion checks, although several dates are specification-sensitive. Conventional system-wide Granger tests identify predictive associations within the multivariate system, but pairwise Toda–Yamamoto tests do not establish robust short-run price–slaughter predictability. The selected ENSO, monetary policy, and real-exchange-rate indicators provide no robust incremental predictive content under the specifications examined. Retrospective state-space smoothing identifies a herd contraction of 4.3–5.2 million head between 2010 and 2014, depending on the replacement specification, followed by recovery through 2022. The evidence is consistent with retention–liquidation dynamics while distinguishing long-run slaughter-cycle evidence from the shorter reconstructed inventory record.

Agriculture doi: 10.3390/agriculture16222494

Authors: Zeus Javier Marquez-Chavez Eduardo Morales-Jerrett Daniel Martín-Collado Juan Manuel Mancilla-Leytón Yolanda Mena

Autochthonous breed dairy goat farming plays a key socio-ecological role in Andalusia (Spain); however, census and farm numbers have declined sharply over five decades (1974–2023). This study analyzes the drivers of this trajectory by combining official census statistics with a participatory workshop to conceptualize qualitative direct causal paths. The sector experienced structural restructuring, where technical efficiency gains were offset by farm-gate price setting asymmetries and supply cost volatility, aggravated by recurring droughts and reduced forage autonomy. Common Agricultural Policy direct payments were perceived merely as short-term liquidity supports. Crucially, a lack of generational renewal—driven by rural decline, quality-of-life concerns, heavy workloads, and low social recognition—constitutes one of the main risks to sector continuity. Participant scores reflect perceived factor importance rather than direct closure metrics. To overcome vulnerability, future resilience would require transformative strategies rather than incremental adjustments, such as farm automation to improve labor conditions, enhanced forage self-sufficiency, knowledge transfer on autochthonous breeds, and local value added through short supply chains and artisanal cheesemaking.

Agriculture doi: 10.3390/agriculture16222493

Authors: Gabriela Ignat Lilia Șargu Ioan Prigoreanu Mihaela-Cătălina Froicu Nicu Șargu Andrian Ulinici

The Common Agricultural Policy (CAP) 2023–2027 replaces compliance-based oversight with a performance-based implementation model, supported by the Performance Monitoring and Evaluation Framework (PMEF), the Integrated Administration and Control System (IACS), and remote sensing-based monitoring. This shift primarily concerns the relationship between the European Commission and the Member States; conditionality and administrative controls at the farm level remain in place. This narrative synthesis, guided by PRISMA principles, examines how these governance and compliance mechanisms relate to farmers’ attitudes, trust, and behavior. It is based on a search of the Web of Science Core Collection and includes 81 studies. The studies point to a major institutional shift, the benefits of which are contingent upon an incomplete coherence across governance levels, while control and audit mechanisms are frequently described as sources of administrative costs and complexity. Institutional trust and perceived fairness emerge as explanations for farmers’ reactions; however, the evidence is largely attitudinal, pertains to earlier periods of the CAP, and does not test trust as a mediator. Findings regarding the erosion of intrinsic motivation are mixed. Digitalization may reduce administrative burdens, but its effect on the perceived opacity of compliance verification has not yet been examined among farmers. We propose longitudinal, farm-level research and comparisons between Member States in Central and Eastern Europe and other Member States.

Agriculture doi: 10.3390/agriculture16222491

Authors: Zhu Zheng Zhu Xiang

Central China is a highly concentrated area of urban expansion and a major grain-producing region that accounts for 28.75% of the country’s total grain yield. This study investigates the spatiotemporal relationships between urban expansion and grain production in Central China from 2000 to 2025 using spatial database construction, grid-based analysis, GeoDetector, geographically weighted regression (GWR), and structural equation modeling (SEM). The main findings are as follows: (1) From 2000 to 2025, urban expansion accelerated substantially, with the urban population increasing by 205.86% and construction land expanding by 169.15%. This expansion was accompanied by the loss of 8659.63 km2 of cultivated land. Nevertheless, total grain production increased by 43.44% during the same period. (2) Three major pathways linking urban expansion and grain production were identified. Urbanization was negatively associated with grain production through cultivated land loss, while economic development, technological progress, and agricultural investment were positively associated with grain production. Meanwhile, growing grain demand was associated with adjustments in grain cropping structure and multiple cropping intensity. (3) Agricultural investment played an important connecting role between economic development and grain production by supporting technological innovation, mechanization, and cropping structure adjustments, thereby helping sustain grain production despite declining cultivated land area. (4) The six major metropolitan areas experienced the greatest cultivated land losses but also substantial grain production growth. Despite intensified land-use pressures, spatial gains in grain production exceeded spatial losses. Improvements in grain production per unit of sown area accounted for 58.91% of the total production increase in these areas, highlighting the importance of enhanced agricultural production capacity in sustaining grain production growth.

Agriculture doi: 10.3390/agriculture16222492

Authors: Hui Guo Guangyuan Zhang Yuande Dong Dailin Zhu Jianglin Liu

Safflower filaments grow in random directions, whereas rigid-cutter picking end-effectors have large front ends and a fixed approach posture. To address these problems, a posture-adjustable cable-based clamping–cutting end-effector for safflower filaments was designed. Carried by a Delta parallel robot, it uses a two-axis deflection platform to align its axis with the flower growth direction and replaces rigid jaws and blades with twisted cable clamping and staggered twin-wire cutting. Twisted-string clamping and cutting-stroke models were established to determine the parameters of the guide orifice and the clamping and cutting mechanisms, and an end-effector–flower axis-angle test and a field picking test were conducted. The projected front-end area was 69.1% smaller than that of the previous-generation rigid-blade picking head. In single-attempt picking of 136 safflowers in the field, the complete clamping rate was 95.6%, the cutting success rate after complete clamping was 99.2%, and the clamping–cutting success rate was 94.9%. With the axes aligned (0–10°), the mass picking rate was 90.39%, within the range of values in the literature for rigid-blade end-effectors; at 30–40°, it decreased to 62.07%. The cable-based end-effector reliably clamps and cuts filaments with a small front end, and the end-effector–flower axis angle significantly affects the mass picking rate. These results provide a reference for the development of selective safflower picking equipment.

Agriculture doi: 10.3390/agriculture16222490

Authors: Wenhao Dong Jiaxi Zhang Yichao Wang Jinming Li Xiaorong Du

Changes in the moisture content of cotton stalks during harvest complicate the selection of airflow velocities for suspending and spreading stalk fragments. Stable suspension velocities were measured for cotton stalk fragments in four moisture-content ranges. Contact parameters were determined using cylinder-lifting, inclined-plane, and drop tests. The ability of the discrete element method (DEM) model to reproduce dynamic piling was assessed by comparing simulated and measured dynamic angles of repose in rotating-drum tests. Two-way coupled computational fluid dynamics–discrete element method (CFD–DEM) simulations were used to predict suspension velocities and investigate spreading through a curved discharge chamber. The equal-weight mean suspension velocity across the four operating conditions increased from 7.82 m/s for the 15–25% moisture-content group to 11.85 m/s for the 45–55% group. The relative errors between the CFD–DEM predictions and the corresponding experimental four-condition means ranged from 3.38% to 4.85%. At an operating airflow velocity of 1.5 times the corresponding suspension velocity, mean spreading uniformity across the four moisture-content groups reached its highest measured value of 89.96% in the bench tests. The mean relative error between simulated and measured spreading uniformity was 2.88%. These findings provide a basis for estimating the suspension velocity of cotton stalk fragments at different moisture contents and selecting the operating airflow velocity for the tested curved discharge chamber.

Agriculture doi: 10.3390/agriculture16222489

Authors: Tzong-Haw Lee Yu-You Liou Hung-Hao Chang

Wholesale livestock markets close regularly, yet little is known about how scheduled closures reshape trading and market performance. This study examines how these closures affect trading in Taiwan’s largest hog auction market, which closes every Sunday and on special days added for festivals and supply adjustments. Using transaction data from 2020 to 2026, we compare the trading days around each closure with reference days in the same week; compare special closures with regular closures in an event difference-in-differences model; and estimate effects on the mean and volatility of daily sales value, head count, and average price. Around the regular Sunday closure, trading is lower before Sunday and higher on Monday, and volatility does not change. Relative to weeks with only the regular closure, festival closures lower quantities by about 12% before and after the closure, raise prices only before it, and raise volatility sharply on the last trading day before the closure. Supply-adjustment closures, imposed when hogs are in short supply, have little effect, and festival closures have much larger effects than supply-adjustment closures of equal length. These effects remain relative to festivals on which the market stays open; however, demand may differ between the two kinds of festivals. Fewer suppliers trade before special closures, and fewer buyers trade before and after them.

Agriculture doi: 10.3390/agriculture16222488

Authors: Loreto P. Thomas Álvaro G. Morales

Dairy farms increasingly face labor-availability challenges, yet evidence centered on hired milkers remains limited. This study aimed to provide a worker-centered assessment of dairy farm milking employment in Chile by characterizing active milkers’ sociodemographic and occupational profiles, work organization, employment conditions, and professional development, and by examining associations between worker and farm characteristics and employment-related outcomes. A cross-sectional survey was conducted between May and July 2024, and responses from 101 active milkers were analyzed using descriptive statistics and cumulative-link ordinal logistic regression. Permanent contracts and full-time employment were reported by 90.1% and 98.0% of participants, respectively, while 73.3% received additional bonuses. Participants had a median of 10 years of experience, had worked on a median of three dairy farms, and had a median tenure of three years in their current or most recent job. Regular training opportunities were reported by 22.8%, whereas 28.7% reported none. Herds of ≥801 cows were associated with longer milking duration compared with herds of ≤300 cows (OR: 8.44), postsecondary education with higher salary categories (OR: 3.75), and salaries above 1,000,000 Chilean pesos (CLP; approximately USD 1079) with greater satisfaction with remuneration compared with salaries ≤CLP 750,000 (approximately USD 809) (OR: 15.27). The findings indicate that dairy labor constraints may involve not only recruitment, but also worker retention, work organization, and access to professional development.

Agriculture doi: 10.3390/agriculture16222486

Authors: Xinyu Shi Abdugheni Abliz Jinting Liu Xu Li Bingqi Dang Jialin Li

Recent studies have demonstrated that microplastics (MPs) in soils can alter soil physicochemical properties and microbial community structures, thereby disrupting ecological processes and threatening food security and human health. The occurrence and characteristics of microplastic-like particles (MLPs) were investigated in farmland soils from Midong District, Urumqi, Xinjiang, a representative arid region of China. MLPs were extracted and presumptively identified by microscopic observation. An integrated framework combining diversity indices and the Pollution Load Index (PLI) as screening-level indicators was employed to characterize MLP contamination and its spatial patterns. Spatial heterogeneity and the spatial association between MLP abundance and diversity were evaluated using Empirical Bayesian Kriging and local bivariate Moran’s I. Results indicated that (1) MLP abundance ranged from 500 to 9850 items/kg, with fibers dominating; transparent and black were the predominant colors, and medium-sized particles (100–500 µm) were most abundant. (2) Size and color diversity exceeded shape diversity. Size diversity was significantly higher toward the south, whereas relatively high color diversity was observed in parts of the north, although no significant north–south trend was detected in color diversity. (3) The mean PLI indicated a slight-to-moderate contamination level, although heavily contaminated areas occurred in the southern and central regions. Bivariate spatial autocorrelation revealed a significant positive coupling between MLP abundance and particle diversity (Moran’s I = 0.193, p = 0.001), with two high–high (H–H) clusters in the central-southern region, indicating potential contamination hotspots.

Agriculture doi: 10.3390/agriculture16222487

Authors: Xiaodong Zhang Deshen Zhang Chuandong Guo Shifang Song Xiangyu Han Zonghua Leng Yixue Zhang

The growing demand for rapid and nondestructive sensing in precision and smart agriculture, particularly for continuous crop monitoring in controlled environment agriculture (CEA), has stimulated interest in terahertz (THz) technologies. Owing to their sensitivity to water, low-frequency molecular vibrations, and dielectric properties, THz waves provide a promising approach for probing agricultural materials. This review summarizes advances in THz sensing for agricultural engineering. The principles and characteristics of THz time-domain spectroscopy, frequency-domain spectroscopy, and imaging are first introduced, together with the major interaction mechanisms between THz waves and agricultural materials. Advances in THz data analysis are then discussed, focusing on model selection, reliability under small-sample conditions, multi-source information fusion, and mechanism-informed analysis. Applications are subsequently reviewed in seed quality and germplasm identification, soil property assessment and contaminant detection, crop water-status monitoring, stress and disease detection, and agricultural product quality and safety assessment. Key challenges include complex-matrix interference, weak target responses, limited model generalization across scenarios, and system integration. Future research should emphasize high-sensitivity sensing, mechanism-data-integrated modeling, and miniaturized online detection systems to facilitate the practical deployment of THz technologies in agriculture, including greenhouses and other controlled production environments.

Agriculture doi: 10.3390/agriculture16222485

Authors: Mingyue Yang Chao Chen Yangjie Shi Guangning Shen Shiwei Dai Yunsong Yan

Rice pest detection in natural field images is affected by weak visual cues and interference from complex crop backgrounds. To improve the use of local appearance and surrounding contextual information, this study proposes LCIC-YOLO, a lightweight detector based on YOLO11n. An ICGSimConv module is introduced into the high-resolution P3 pathway to extract Local and Context features and regulate them independently using energy-based spatial responses before fusion. A six-class RP6 dataset containing 2637 images and 4312 annotated instances was curated from multiple public sources using duplicate detection and visual-family-based partitioning. Across three training seeds under the RP6 evaluation protocol, LCIC-YOLO achieved an mAP50 of 0.8826 ± 0.0051 and an mAP50--95 of 0.5902 ± 0.0047, with 2.60 M parameters and 6.57 GFLOPs. Its mean mAP50--95 exceeded YOLO11n by 2.67 percentage points. The control with matched gating and the fusion ablations support the contribution of Local information when integrated through branch-specific response estimation and independent Local–Context regulation. These results indicate that LCIC-YOLO improves the use of complementary spatial information while retaining the lightweight characteristics of the baseline detector.

Agriculture doi: 10.3390/agriculture16222484

Authors: Seung-Yong Seo Md Rayhan Mahmud Ki-Dong Park Min-Young Hwang Jae-Min Lee Jin-Yeong Lee Seung-Yun Baek Deok-Hyeon Ko Ryu-Gap Lim

Agricultural machinery has extensive blind zones, and reliable proximity alerts need distance information that no single sensor provides across the working range. We fused a depth camera and a 16-channel LiDAR by distance-adaptive inverse-variance weighting, with weights derived from nominal, specification-based error models that cross at 3.87 m. YOLO11m, trained on five classes, detected objects, and the pipeline ran at 26 frames per second on an edge computing platform. Distances to Person and Tractor targets were evaluated against real-time kinematic GNSS in 2521 samples from front and rear tractor approaches at nominal speeds of 3, 5, and 7 km/h. Fusion reduced the root mean square error (RMSE) to 0.211 m, 30.9% below that of the depth camera alone, with the largest reduction (35.8%) at 7–12 m and no change within 7 m. The LiDAR alone was more accurate (0.167 m) but valid in only 82.7% of frames. With both sensors valid, the fused RMSE (0.141 m) was lower than that of either sensor. For detected targets, alert levels agreed with the reference in 99.0% of frames, but a Person recall of 0.72 limits overall reliability. Target geometry and LiDAR resolution strongly affect the benefit of fusion, which may guide sensor selection for other agricultural machines.

Agriculture doi: 10.3390/agriculture16222483

Authors: Kai Xu Yali Du Haibin Jiang Jinming He Yuling Liu Anna Brandorf Jingyu Hao Xiaoyin Deng Qingsheng Niu

Overwintering performance in the Apis mellifera directly affects colony survival, spring buildup, and pollination services. Although honeybees with different genetic backgrounds have evolved distinct overwintering strategies, the genetic basis and molecular regulation of these strategies remain poorly understood. We investigated the Hunchun bee, a black landrace of Apis mellifera distributed in Jilin Province, using four Apis mellifera subspecies as comparison populations. Whole-genome resequencing and genome-wide scans identified 209, 122, 305, and 388 genes under selection in the four pairwise comparison groups between the Hunchun bee and Apis mellifera ligustica, Apis mellifera carnica, Apis mellifera caucasica, and Apis mellifera mellifera, respectively. Transcriptomic sequencing identified differentially expressed genes (DEGs) in thoracic tissues at distinct stages of overwintering. The largest set of common DEGs was detected during mid-overwintering (617 genes), which was enriched in oxidative phosphorylation, tricarboxylic acid cycle, and ribosome-related pathways. Expression of genes that encode β-octopamine receptors was higher during early overwintering but was lower during the middle and late stages. Twelve annotated genes were consistently differentially expressed across all three overwintering stages and were common to all four pairwise comparisons. Of these, 11 showed higher expression in the Hunchun bee and were associated with flight muscle contraction, structural support, RNA processing, or post-transcriptional regulation. Separately, integrative analysis of mid-overwintering DEGs and signatures of selection identified two candidate genes, LOC408595 (locomotion-related protein Hikaru Genki) and LOC413025 (peroxidasin). These two genes contained 26 coding-sequence SNPs, including two missense variants in LOC413025 (p.D947N and p.Y1275N). Taken together, these results identify putative candidate genes and variants for overwintering adaptation in the Hunchun bee, which establishes a theoretical foundation for investigating its molecular regulatory mechanisms.

Agriculture doi: 10.3390/agriculture16222482

Authors: Anrui Ning Limin Jiang Hao Wang Meiqi Xiang Weizhi Feng Daping Fu Gang Wang Cong Qu Jinyou Yu Jingli Wang

Soil degradation caused by soil salinization is an important factor affecting global food security. The soda saline–alkali soils in the Songnen Plain suffer from poor soil structure, hard topsoil and alkalized horizons, and high pH and Na+ concentrations, which pose a major threat to sustainable agricultural development. However, the development of demand–adapted agricultural machinery and agronomic technologies integrating physical and chemical amelioration remains a challenge for soda saline–alkali soil improvement under rain–fed dryland conditions. Based on the water solubility of aluminum sulfate, this study integrated novel equipment and technologies for efficient, engineering–oriented amelioration of soda saline–alkali soils. Laboratory incubation experiments with aluminum sulfate showed that an application rate of 0.6% soil dry weight was a suitable addition level for ameliorating soil pH, SAR, and ESP. Verification tests demonstrated that the soil disturbance coefficient, soil loosening degree, and coefficient of variation for liquid application uniformity among rows were 53.75%, 13.50%, and 4.83%, respectively; these operational performance indicators met the requirements of relevant industry standards. Comparisons of soil physicochemical properties under different amelioration treatments revealed that subsoiling combined with ameliorant spraying (SAS) outperformed subsoiling alone (S), surface ameliorant application (A), and the control (CK). Relative to CK, the SAS treatment increased soil water infiltration rate (WIR) by an average of 63.11%, reduced bulk density (BD) by 0.14 g cm−3, decreased pH by 0.86 units, lowered soil salt content (SC) by 0.71 g kg−1, decreased the exchangeable sodium percentage (ESP) by 7.61%, and decreased the sodium adsorption ratio (SAR) by 4.01 (mmolc L−1)1/2. Field trials on alfalfa cultivation demonstrated that SAS improved the alfalfa seedling emergence rate, dry matter yield and overwintering rate, with increases of 20.59%, 26.06%, and 20.29%, respectively, relative to CK. This study provides equipment and technical references for the comprehensive improvement and utilization of soda saline–alkali land.

Agriculture doi: 10.3390/agriculture16222481

Authors: Meiling Liu Haofeng Sun Chang Sun Yiyang Wu Caixia Jin Chanchan Zhou Mingzhe Zhao Wen Zhang Dezhi Han Xue Ao

Phosphorus is an essential macronutrient for the growth and development of soybeans. PHF1, an endoplasmic reticulum-localized SEC12-like protein mediating intracellular trafficking of phosphate transporters, plays a crucial role in plant responses to low-phosphorus stress. In this study, we analyzed the changes in photosynthetic physiology, transcriptome, and metabolome of soybean plants overexpressing GmPHF1 under low-phosphorus stress conditions. Compared with wild-type plants under low-phosphorus stress conditions, GmPHF1-overexpressing plants exhibited higher photosynthetic capacity, phosphorus content, and acid phosphatase activity. Transcriptome and metabolome analyses revealed that GmPHF1-overexpressing plants exhibited greater numbers of differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) involved in low-phosphorus response. Overexpression of GmPHF1 significantly upregulated key DEGs involved in amino acid and phosphorus transport/metabolic pathways under low-phosphorus stress, and also increased the consumption of amino acid and lipid metabolites, which helped sustain soybean growth under phosphorus-limited conditions. Weighted gene co-expression network analysis (WGCNA) indicated that genes involved in the phosphate transport pathway were significantly upregulated in GmPHF1-overexpressing plants, which may explain the marked increases in phosphorus content and acid phosphatase activity under low-phosphorus stress. Collectively, our findings indicate that GmPHF1 overexpression may confer improved low-phosphorus tolerance in soybean, which is linked to preserved photosynthetic efficiency, elevated leaf phosphorus concentration and acid phosphatase activity, as well as modulated transcript and metabolic profiles associated with phosphate transport, amino acid turnover and phosphorus homeostasis.

Agriculture doi: 10.3390/agriculture16222480

Authors: María del Sol Cuellar-Espejel Evangelina Esmeralda Quiñones-Aguilar Guillermo Alejandro Solís-Sánchez Juan Carlos Mateos-Díaz Melchor Arellano-Plaza Gabriel Rincón-Enríquez

Harpin (Hrp) proteins from Xanthomonas euvesicatoria represent a promising class of eco-friendly biocontrol inputs, yet their success depends on stable and scalable production systems. In this study, we evaluated the pilot-scale production of Hrp proteins (BioFensa) using the BV801 strain across two optimized platforms: solid-state fermentation (SSF; patent MX420963) and liquid-state fermentation (LSF, patent application MX/a/2025/002981). To optimize expression, Capsicum annuum tissue was integrated into both platforms as an eliciting substrate to simulate the natural plant–pathogen interface. Results demonstrated that SSF achieved a 3.28-fold higher biomass-normalized protein yield (Y p/x) and a 1.44-fold higher total protein yield than LSF. While both platforms produced functionally stable Hrp proteins, BioFensa derived from SSF reduced bacterial spot severity in C. annuum by approximately 60% at a low concentration of 0.01 μg mL−1 (p ≤ 0.05). Subsequent field trials confirmed safety and evaluated agronomic performance under natural conditions in the absence of disease pressure. These findings establish the technical feasibility of scaling Hrp protein production and underscore BioFensa as a promising, eco-friendly alternative for managing bacterial spot disease.

Agriculture doi: 10.3390/agriculture16222479

Authors: Feiran Li Yong Zhu Tao Zhou Shida Zhang

Hydraulic transmission and control technology is a core supporting technology for agricultural machinery. Its technical performance directly determines the operational quality, production efficiency, and overall reliability of agricultural machinery. This paper focuses on hydraulic transmission and control technology for agricultural machinery. It systematically reviews the field from three dimensions: key technology analysis, research status review, and development trend outlook. First, it elaborates on the three core technical systems in agricultural machinery hydraulics. In terms of hydraulic components, it analyzes the working principles and application scenarios of power elements, actuating elements, and control elements. In terms of hydraulic systems, it analyzes the technical characteristics and evolution paths of five typical systems used in agricultural machinery. In terms of control technology, it summarizes a multi-level technical framework. This framework includes classical PID and its improved algorithms, advanced control methods, and intelligent control methods. Second, based on the latest domestic and international research literature, it comprehensively reviews the research progress in hydraulic components, hydraulic systems, and control methods. Finally, in response to the transformation and upgrading of smart agriculture and the “dual carbon” strategy, it identifies three major future directions for hydraulic transmission and control technology in agricultural machinery. The findings of this review are expected to offer theoretical references and technical guidance for the R&D and industrial upgrading of high-end hydraulic systems for agricultural machinery.

Agriculture doi: 10.3390/agriculture16222478

Authors: Liyuan Chen Pengfei Wu Yanfeng Li Yanguoer Zhang Sijia Wu Zhaoxuan Ren Maodian Liu Lixi Wang Jie Hou Wenxin Liu

Crop field soils are a major reservoir for polycyclic aromatic hydrocarbons (PAHs), yet how crop type influences PAH accumulation and dietary health risks remain unclear at the national scale, as does the role of agronomic and environmental drivers. This study collected 322 soil samples from crop fields covering over 30 crop species across 31 administrative divisions in mainland China. PAH concentrations differed significantly among the four major crop categories and the 13 subcategories (p < 0.05). However, dietary PAH exposure through cash crop fields posed the highest risk to adults (total: 1.1 × 10−4, unacceptable cancer risk), while food crop fields showed the lowest risk. Children faced a total dietary health risk of 6.0 × 10−5 (potential cancer risk), with vegetable and fruit fields contributing the highest risks. Agronomic practices (tillage frequency, irrigation method, agricultural film removal method), soil properties (total organic carbon, pH, total nitrogen), meteorological conditions (sunshine duration, shortwave radiation, precipitation), and altitude all significantly influenced soil PAH concentrations (p < 0.05). PAH concentrations were positively correlated with plastic-related contaminants (microplastics, phthalic acid esters, and organophosphate esters, p < 0.05). These findings provide scientific evidence for differentiated crop management and safe agricultural production.

Agriculture doi: 10.3390/agriculture16222476

Authors: Ricardo A. Varela-Pardo Miguel Sobarzo Paola Díaz-Navarrete Damián Vega Rosa Hermosa Ricardo Tighe-Neira Claudio Inostroza-Blancheteau Alejandra Fuentes-Quiroz

Conventional agriculture based on intensive agrochemical use threatens human health and worsens food insecurity, making multifunctional biological control agents a strategic priority. Entomopathogenic Metarhizium fungi combine insect pathogenicity with plant growth promotion. This study isolated native entomopathogenic fungi naturalized in La Araucanía, Chile, assessing in vitro growth-promoting traits and tomato (Solanum lycopersicum) physiology under greenhouse conditions. Twenty-five soil samples were collected from agroecological plots in La Araucanía; using Tenebrio molitor larvae as bait, and 59 fungal isolates were recovered using a monosporic culture. Isolates were evaluated in vitro for AlPO4, phytic acid solubilization, indole-3-acetic acid and siderophore production. In total, 22 promising isolates were identified by ITS1–5.8S–ITS2 sequencing and Maximum Likelihood phylogenetics. Six isolates (MM23B, MM29C, G5X, MM26B, G9C, MM30D) were tested on tomato (S. lycopersicum) under a randomized greenhouse design (CRD), measuring growth, gas exchange, chlorophyll fluorescence, water use efficiency (WUE), photosynthetic pigments and lipid peroxidation. Highest AlPO4 solubilization indices (3.24–3.14) were recorded for MM15D, MM17B, MM23D, and G5X. MM16B showed the highest phytic acid solubilization (40.5%) and MM24B the highest siderophore production (88.86%). Under greenhouse conditions, MM30D produced the highest net photosynthesis (131.24% above control), ΦPSII (0.2134), electron transport rate (56.00), and WUE (51.98), while G5X exhibited the highest stomatal conductance (0.39 mol H2O m−2 s−1), transpiration (3.506), and maximum quantum yield (0.6784). All inoculated plants showed higher WUE than the control (19.43). Naturalized Metarhizium isolates from La Araucanía possess multifunctional traits supporting sustainable bioinput formulation.

Agriculture doi: 10.3390/agriculture16222477

Authors: Tina Lešnik Tadeja Kraner Šumenjak Petra Recko Novak Andreja Borec

The loss of landscape elements and the increasing simplification of agricultural landscapes may threaten biodiversity, ecological connectivity, and landscape multifunctionality, while their conservation and establishment largely depend on farmers’ willingness to retain existing elements and introduce new ones. This study examined farmers’ perceptions of landscape elements, their willingness to establish new elements, and associated socio-economic and spatial factors in northeastern Slovenia. Data from an online survey of 1197 farmers conducted in 2023 were analysed using descriptive statistics and multivariable multinomial logistic regression, complemented by spatial analysis using the national Landscape Features Register. More than 90% of farmers recognised benefits for biodiversity and ecosystem stability, while over 80% perceived production-related constraints. With adequate financial compensation, 37.1% were willing to establish new landscape elements, 35.1% were unwilling, and 27.8% were undecided. Willingness was associated with ANC location, attitudes, perceived benefits, and age, while farm size and farming system showed no significant associations. Greater recognition of biodiversity, ecosystem stability, and crop-quality benefits was positively associated with willingness to establish landscape elements, whereas negative attitudes were negatively associated with willingness. The “I do not know” response was more common among farmers operating in Natura 2000 areas, among those reporting existing landscape elements, and particularly among those uncertain about whether landscape elements were present on their farms. Farmers mainly preferred small-scale elements along field margins. Effective landscape management should therefore combine financial incentives, advisory support, and spatially targeted measures reflecting ecological objectives and farm-level constraints.

Agriculture doi: 10.3390/agriculture16222475

Authors: Juehui Luo Huishan Luo Yingpin Yang Boxiong Qin Qiting Huang Yanan Du Zhifeng Wu Xiankun Yang Jinnian Wang

Pineapple is an economically important tropical fruit crop that contributes substantially to agricultural production, international trade, and rural livelihoods in tropical regions. Pineapple can be planted throughout the year under favorable climatic conditions, resulting in strong phenological asynchrony among fields and reducing the effectiveness of conventional fixed-window classification. To address this challenge, this study proposes a phenology-guided dynamic temporal window approach for pineapple mapping using Sentinel-2 time-series observations. A continuous normalized difference vegetation index (NDVI) time series was reconstructed to identify the start of the growing season (SOS) for individual fields, which was then used as a temporal reference to define a key phenological window 12–14 months after the SOS, corresponding to the inflorescence emergence and flowering stages. Nine multispectral bands and seven vegetation indices within this window were summarized and used to train a random forest classifier. Experiments in Xuwen County, China, showed that the proposed method achieved an overall accuracy of 91.67% and a Kappa coefficient of 0.8329, with pineapple producer’s accuracy, user’s accuracy, and F1-score of 90.44%, 92.04%, and 91.23%, respectively. Compared with a conventional fixed-window approach, overall accuracy increased from 85.83% to 91.67%, demonstrating the benefit of phenological alignment. Shortwave-infrared bands and moisture-related indices contributed most strongly to classification. Regional application produced a pineapple distribution map broadly consistent with known planting patterns. These results demonstrate that aligning asynchronously planted fields to comparable phenological stages can improve spectral feature consistency and support accurate regional pineapple mapping.

Agriculture doi: 10.3390/agriculture16222474

Authors: Zhengxin Zhou Yan Huang Jiangrun Zhu Jin Jia Anni Wei Songliang Wang

This study investigated heavy metals (Cd, Pb, Cr, Cu, Zn) in the soil-rice system of Sanming, a typical industrial and rice-growing region in China, to analyze their spatial patterns, sources, and ecological and health risks. Standard deviation ellipses based on the geoaccumulation index (Igeo), single ecological risk index (Er) and comprehensive ecological risk index (RI) were used to visualize spatial disparities of pollution and risk. Cd was the primary pollutant with the widest distribution; its average (Igeo) reached 2.29, and roughly 80% of samples presented moderate to heavy pollution. The highly matched (Igeo) and (Er) ellipses of Cd resulted from its high toxicity and low background value. Zn and Pb exhibited local hotspots: Zn pollution clustered around Shaxian industrial sites, while Pb spread east–west along the Shaxi River basin. The RI centroid was at 26.334° N, 117.379° E. The hazard index (HI) of rice intake for children exceeded 1, indicating notable non-carcinogenic risks, whereas HI values for other populations were below 1. Zn, Cr and Cd dominated non-carcinogenic risks, and pH drove elevated Zn-related health risks. The Unmix model showed mixed industrial and agricultural sources contributed 55% of heavy metals. This study implies concentration-based spatial analysis may underestimate ecological and health hazards of low-toxic metals. These findings support effective soil management and the protection of local residents from dietary heavy metal exposure. Future studies should apply repeated standard deviational ellipse analyses to track pollution and risk centroid shifts and thus support adaptive management.

Agriculture doi: 10.3390/agriculture16222473

Authors: Kritchayan Intarat Teerawong Laosuwan Yannawut Uttaruk Nithima Nuengjumnong Woraman Jangsawang

Rice is one of the world’s most important staple crops, sustaining food security and rural livelihoods across many tropical countries. Accurate mapping of rice crop systems is therefore essential for monitoring agricultural production, supporting irrigation management, and assessing seasonal cultivation patterns. Reliable discrimination between crop systems depends not only on the quality of satellite observations but also on the time-series representation’s ability to preserve crop phenological characteristics. This study compares representative image encodings for rice crop system classification using Sentinel-2 Enhanced Vegetation Index (EVI) time series over Suphan Buri province, Thailand. The EVI profiles were reconstructed into daily observations using a Savitzky–Golay filter and encoded as images by the Continuous Wavelet Transform (CWT), Synchrosqueezing Transform (SST), Gramian Angular Field (GAF), Markov Transition Field (MTF), Recurrence Plot (RP), and Short-Time Fourier Transform (STFT), with the unencoded one-dimensional (1D) series as the baseline. To ensure a fair comparison, all representations were classified using the same convolutional neural network architecture and identical training samples, and evaluated on 200 independent ground truth points (with 50 samples per class). Overall accuracy ranged from 0.788 to 0.899 across the seven representations. STFT was the most accurate at 0.899, followed by CWT at 0.871, with a statistically significant difference between them. Confusion between the two most intensive systems accounted for about seven-tenths of the difference in accuracy between the best encoding and the baseline. Encoding alone was insufficient, since CWT did not significantly exceed the baseline, and SST, RP, GAF, and MTF fell below it. Encodings that preserve the interval between growth cycles are therefore preferable when classes are defined by cycle count rather than by signal amplitude.

Agriculture doi: 10.3390/agriculture16222472

Authors: Jing Li Jiawen Wang Haiting Xiao Panpan Yang Leifeng Xu

The flower color of ornamental tomatoes is predominantly yellow. Developing cultivars with diverse flower colors is critical for enhancing their ornamental value and broadening their application potential. In this study, we cloned the genomic sequence of LdCCS from Lilium davidii var. willmottiae; this sequence spanned 1971 bp and included the promoter and 5′ UTR. The construct was transformed into tomato (Solanum lycopersicum L.) cultivar Micro-Tom, and the resulting transgenic lines exhibited orange pigmentation across the entire flower. No significant difference in total carotenoid content was observed in the flowers of transgenic plants relative to the wild type (WT). Notably, capsanthin and capsorubin were specifically synthesized de novo and accumulated in transgenic flowers, whereas lutein and β-carotene contents were reduced compared with the WT. Collectively, this study provides new genetic resources and a viable strategy for targeted molecular modification of flower color in ornamental tomato, offering new insights for flower color innovation and molecular breeding of ornamental plants.

Agriculture doi: 10.3390/agriculture16222471

Authors: Lorenzo Salis Gian Marco Marrosu Simone Cossu Stefania Fanni Daniele Manca Giovanni Marongiu Paolo Mulè Andrea Pintore Maria Sitzia

A rainfed Italian ryegrass crop (Lolium multiflorum Lam.) was established at the Bonassai experimental farm (NW Sardinia, Italy) to evaluate the effects of different tillage systems and sheep stocking rates on pasture productivity and soil characteristics in an extensive Mediterranean farming system. Three seedbed preparation systems were compared: conventional tillage (CT), minimum tillage (MT), and no-tillage (NT). Each tillage treatment was combined with three grazing management regimes: high stocking rate (HS), low stocking rate (LS), and ungrazed control (UG). During three consecutive growing seasons (2019–2021), pasture herbage mass and botanical composition, together with soil physical, mechanical, chemical, and biological properties, were assessed. Grazing reduced Italian ryegrass herbage dry matter compared with ungrazed plots. In one of the three experimental years, CT produced significantly higher herbage yield than both MT and NT, and also reduced weed abundance in the pasture, particularly Chrysanthemum coronarium. HS two years out of three reduced the abundance of legumes and increased both Asteraceae and bare ground proportion compared with UG. Most soil chemical properties, including macro and micronutrient concentrations, were not significantly affected by either tillage system or stocking rate. However, CT reduced soil electrical conductivity (EC) and exchangeable sodium percentage (ESP) compared with NT in two of the three years. CT also resulted in lower soil compaction, as indicated by reduced bulk density (BD) and penetration resistance (PR), particularly in the upper soil layers, whereas HS increased soil compaction during spring. Finally, NT showed higher values of Biological Quality of Soil (QBS-ar) index in the first year, whereas no clear and persistent pattern emerged during the subsequent years. MT combined with moderate grazing could be considered a balanced approach for Mediterranean extensive systems. Under the environmental conditions examined in this study, these findings highlight the importance of adapting tillage practices and grazing intensity to local soil conditions to balance pasture productivity with soil conservation.

Agriculture doi: 10.3390/agriculture16222469

Authors: Benius Tukahirwa Dirk Schmalowski Elke Bloem Holger Heuer

Plants and local soil microbiota self-organize as holobionts, thereby increasing resistance to pathogens. However, the local factors that contribute to self-organization are not well understood. We hypothesized that top-down control of the rhizobiome by local soil nematode communities could suppress pathogens, depending on how preceding plants condition the nematode community. In five pot experiments, we inoculated fallow soil microbiomes and conditioned nematode communities to tomato or potato seedlings growing in sterile sand, to test the resistance of the assembled holobionts to root-knot nematode invasion. Tomato resistance increased with nematode communities conditioned by tomato plants compared to oilseed rape or fallow soil, while inoculation of nematode communities alone, without a soil microbiome, did not decrease root invasion. In contrast, nematode communities conditioned by tagetes enhanced resistance compared to nematode communities from tomato without a soil microbiome. Nematodes from crotalaria decreased invasion of potato roots more than nematodes from black oat or fallow soil, especially with co-inoculation of a soil microbiome. Solely inoculating the microbiome conferred no resistance. In conclusion, soil nematodes play a role in the local self-organization of crop holobionts depending on the preceding plants. Crop rotation schemes may be used to fortify crops against soil-borne pathogens.

Agriculture doi: 10.3390/agriculture16222470

Authors: Yulong Cai Zepeng Peng Jinlu Deng Fangsiqi Lin Bin Yang

Primary root elongation during seedling establishment is a key determinant of rice (Oryza sativa L.) vigor and productivity, yet its genetic basis remains incompletely understood. Here, a genome-wide association study (GWAS) of 194 diverse rice accessions identified three loci, qPRL3, qPRL6, and qPRL12, associated with primary root length. By integrating local association signals, organ-level expression profiles, and haplotype analysis, we identified OsRPN10, which encodes a regulatory particle non-ATPase subunit of the 26S proteasome, as the most likely candidate gene underlying qPRL3. Natural allelic variation at OsRPN10 was significantly associated with divergence in primary root length, and CRISPR/Cas9-mediated loss of function markedly impaired primary root elongation, confirming its positive role in root growth. Protein–protein interaction prediction and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggested potential associations between OsRPN10 and proteasome-related pathways. Population genetic and geographic analyses revealed strong selective signatures and clear geographic differentiation at the OsRPN10 locus during rice domestication and diversification. Collectively, these findings identify OsRPN10 as a previously unrecognized regulator of primary root elongation in rice, expand current understanding of proteasome-mediated control of root development, and provide a promising molecular target for improving rice root architecture.

Agriculture doi: 10.3390/agriculture16222467

Authors: Matheus Koengkan Feroz Khan Marco Cuono Di Leva José Alberto Fuinhas

Crop diversification is widely proposed as a resilience strategy, but whether regional crop diversity buffers crop-specific productivity after wildfire exposure remains uncertain. This study examines seven Portuguese agricultural regions using exposure years 2012–2024 and crop-specific productivity at t + 1. The primary sample comprises 972 Region × Crop × Year observations nested in 91 Region × Year cells. Pre-fire diversity is measured by a one-year-lagged Shannon index based on 11 crop groups. Models include Region × Crop and Year fixed effects, a lagged crop-area control, and few-cluster inference based on null-imposed wild cluster restricted bootstrap tests with Webb weights. Neither wildfire exposure nor crop diversity shows a statistically supported main association with subsequent productivity. The wildfire × diversity interaction is positive but unsupported at t + 1 (β = 0.0931; p = 0.491), t + 2 (β = 0.0623; p = 0.699), and t + 3 (β = 0.0348; p = 0.778). The interaction changes sign in a supplementary region-specific linear-trend specification (β = −0.0410), indicating sensitivity to regional trajectories; none of the preferred hypothesis tests support the proposed buffering mechanism. The results do not establish regional crop diversity as a wildfire-specific buffer for crop-specific productivity in this design, while remaining consistent with potential diversification benefits for other outcomes and spatial scales.

Agriculture doi: 10.3390/agriculture16222468

Authors: Carlos Mestanza-Ramón Fidel Márquez-Sánchez Ider Alfonso Morán Caicedo Guido Omar Macas-Acosta

Ecuador is the world’s leading banana exporter, yet substantial productivity disparities persist across its provinces. This study applies Functional Data Analysis (FDA) to examine provincial banana yield dynamics during 2021–2025. Using 3034 producer-level observations aggregated into 90 province-year observations across 23 provinces, we estimate smoothed yield trajectories using B-splines and apply Functional Principal Component Analysis (FPCA), functional clustering, function-on-scalar regression (FOSR), and hierarchical functional spatial autoregressive (SAR) models. The first functional principal component explains a substantial proportion of the total variability (ranging from 91.0% to 98.4% depending on the smoothing parameter, with a baseline estimate of 93.3%), indicating that provincial differences primarily reflect yield levels rather than temporal trajectory shapes. Functional clustering identifies three distinct productivity groups: high-yielding (four provinces, 39.6 t/ha), intermediate-yielding (four provinces, 22.8 t/ha), and low-yielding provinces (12 provinces, 5.6 t/ha). Irrigation and producer education are positively associated with yields (both p < 0.001). The hierarchical SAR specification identifies significant dependence associated with Guayas, Ecuador’s principal banana export hub (p = 0.024), whereas geographic contiguity is not statistically significant (p = 0.53). These findings are consistent with the potential importance of export-oriented value chains in technological diffusion, although the underlying transmission mechanisms require further verification. The results highlight irrigation investment, producer education, and stronger productive linkages as priorities for reducing territorial productivity disparities. We hypothesize that: (H1) interprovincial heterogeneity is primarily driven by yield levels rather than temporal shapes; (H2) irrigation and education are the main internal determinants; and (H3) spatial dependence is driven by value-chain hubs rather than geographic proximity.

Agriculture doi: 10.3390/agriculture16222466

Authors: Cristina Hidalgo Eva Contreras Juan Manuel Mancilla-Leytón Laura Serrano Antonia Jiménez-Rodríguez

Eutrophication in the lower Guadalquivir River estuary is often attributed to runoff from riverside rice paddies. We tested whether dissolved unreactive phosphorus (DUP)—the difference between total dissolved phosphorous (TDP) and dissolved reactive phosphorous (DRP)—can discriminate urban wastewater from agricultural P inputs. Because DUP is derived from two fractions measured with the standard molybdenum-blue method, it requires no specialized equipment and adds little cost to routine monitoring. In total 288 surface-water samples from 31 reference, agricultural, urban and mixed estuarine stations were analyzed, plus effluents from the five wastewater treatment plants (WWTPs) serving Seville. DUP showed a sharp contrast: it was present in all WWPT effluents (0.03–0.25 mg P L−1) and in 86% of urban samples (median 0.09 mg P L−1), but remained below 16 µg P L−1 in reference waters and rice-paddy drainage outlets, and rare (<10%) in other agricultural channels, confirmed at station level. Regarding agricultural water resources, the monitored rice-paddy circuit did not enrich irrigation water in dissolved P: TDP decreased by 10–52% between intake and drainage outlets on 9 of 10 dates. Urban effluents therefore appear to be the main source of dissolved P in the estuary. DUP is a robust, low-cost, context-dependent indicator that helps managers target urban P sources and avoid misattributing them to agricultural water users.

Agriculture doi: 10.3390/agriculture16222465

Authors: Luna Lilás Henriques Ildefonso Natália dos Santos Renato Nathália Domingues Silva Dilson Novais Rocha Marcio Arêdes Martins Alisson Carraro Borges

The reliance on synthetic fertilizers in agriculture contributes significantly to greenhouse gas emissions and environmental degradation, highlighting the need for sustainable alternatives. This study assessed the environmental impacts of producing a biofertilizer based on the microalga Scenedesmus obliquus BR003 at pilot scale using Life Cycle Assessment (LCA). The cradle-to-gate system boundary included cultivation, harvesting, and processing. The functional unit was 1 kg of biofertilizer, with impacts quantified using the ReCiPe 2016 Midpoint (H) method in OpenLCA. Water consumption and global warming were the most impactful categories, with electricity as the main hotspot; the global warming potential was 17.6 kg CO2 eq/kg biofertilizer. A separate assessment was conducted to compare the environmental performance of six maize treatments based on their production of dry matter, extending the system boundary to cradle-to-farm-gate. T2 (microalgae biofertilizer supplemented with inorganic P and K) and T4 (conventional NPK fertilizer) showed the best agronomic performance and were selected for comparison. Under the deterministic model, T2 showed lower impacts than T4 across all categories evaluated, including a net negative global warming potential (−0.649 kg CO2 eq) versus a positive value for T4 (0.057 kg CO2 eq), reflecting urea replacement and increased soil carbon sequestration. These findings support microalgal biofertilizer as a lower-impact alternative to conventional fertilizers under the modeled conditions.

Agriculture doi: 10.3390/agriculture16222464

Authors: Yoshinori Matsuda Koji Kakutani Hideyoshi Toyoda

Integrated weed management requires environmentally friendly alternatives to conventional herbicides. We previously developed a pulse-charged expanded metal (PCEM) system that provides dual-action weed control depending on installation height: direct killing of young seedlings at lower heights and growth suppression at higher heights. This study elucidated the underlying biophysical mechanisms using electrostatic modeling, pulse-current measurements, and multi-species experiments under greenhouse and field conditions. Needle-to-plane simulations demonstrated that emerging shoot apices strongly enhance local electric fields through a lightning-rod effect. Current measurements revealed distinct electrical responses between dicotyledonous and monocotyledonous seedlings. Dicotyledonous seedlings progressively developed high electrical impedance during structural maturation (45–95 mm height), limiting discharge currents (attenuated to 28.9–46.6 μA) and achieving 100% height-dependent growth suppression without foliar injury. Conversely, 100% of monocotyledonous seedlings were lethally electrocuted across all stages, driven by pointed apex field concentration and conductive guttation droplets (inducing peak currents up to 491.5 ± 17.8 μA). Three-year field trials across 60 multi-habitat installations demonstrated 100% prevention of weed canopy penetration and stable species-selective weed suppression under natural environmental fluctuations.

Agriculture doi: 10.3390/agriculture16222463

Authors: Jiayi Ma Lei Pang Jianhao Sun Jianhua Zhao Wenhao Ma Yanting Wang Haofei Zheng Jianlong Lu Hongyu Huang Khuram Shehzad Khan

Maize/soybean intercropping systems improve resource use efficiency through interspecific complementarity. However, the regulatory mechanisms by which the partial replacement of chemical nitrogen fertilizer by organic fertilizer and biofertilizer affects crop growth and productivity in maize/soybean systems remain unclear. A two-year field experiment (2024–2025) was conducted in the Hexi oasis irrigation district of Zhangye, Northwest China. The main plots were assigned to fertilization regimes (conventional nitrogen, N1; reduced nitrogen, N2; organic substitution, ON2; and biofertilizer substitution, BN2), and the subplots were assigned to cropping systems (maize monoculture, soybean monoculture, and maize/soybean intercropping). A logistic growth model was used to simulate dry matter accumulation, and the land equivalent ratio (LER), net effect (NE), complementarity effect (CE), and selection effect (SE) were employed to evaluate intercropping advantage. Mantel analysis was applied to analyze relationships between crop growth parameters and system grain yield. Study results showed that cropping system fertilization regime, year, and their multiple interactions had significant effects on maize, soybean, and system grain yields (p < 0.001). Under all fertilization treatments, the LER of the maize/soybean intercropping system was greater than 1, indicating that intercropping had higher land-use efficiency than monoculture. Compared to the weighted mean of monocultures, ON2 enhanced intercropping grain yield by 25.30% over two years driven primarily by positive SE. Compared to conventional N practice, ON2 increased intercropped maize grain yield by 19.98% and 12.40% in 2024 and 2025, respectively, while BN2 enhanced intercropped soybean yield by 22.60% in 2025. Together, system grain yield followed the rank ON2 > BN2 > N1 > N2. Mantel tests further revealed that maize maximum biomass potential (Ymax) was highly significantly and positively correlated with system grain yield (p < 0.01), and maximum growth rate (Imax) was significantly positively correlated (p < 0.05), whereas soybean growth parameters showed no significant correlation with system yield. Overall, partial substitution of chemical fertilizer with organic fertilizer enhances maize biomass accumulation and fosters positive interspecific interactions, thereby increasing grain yield in maize/soybean intercropping. These findings provide a practical basis for reducing chemical fertilizer use while maintaining productive and sustainable cropping systems.

Agriculture doi: 10.3390/agriculture16222462

Authors: Long Ming Yu Zheng Yang Wang Jiayi Sun Meng Sun Chunlan Zhang Wei Cheng Bing Zhang

Soil degradation caused by continuous cropping has become a major constraint on stable Ganoderma lingzhi production, while biochar–mineral amendments are commonly used to restore degraded soils. However, the specific effects of continuous cropping on Ganoderma lingzhi production and soil functions, as well as the potential of co-application to restore its yield and quality, remain unclear. A Ganoderma lingzhi microcosm system was established with first-cropping, continuous-cropping, and different biochar–dolomite treatments. Soil physicochemical properties, enzyme activities, microbial communities, yield, quality traits, and heavy metals were evaluated. Continuous cropping significantly reduced soil pH, organic carbon, total nitrogen, available nutrients, microbial diversity, and Ganoderma lingzhi yield. Fruiting-body dry weight and triterpenoid content declined by 20.09% and 24.62%, respectively. Biochar–dolomite co-application effectively alleviated the adverse effects of continuous cropping. Compared with other co-application treatments, the 30% biochar + 70% dolomite treatment showed strong overall recovery capacity. Compared with continuous cropping, this treatment increased fruiting-body biomass by up to 54.81% and triterpenes by up to 75.57%. Different co-application ratios produced distinct ecological regulation patterns: the dolomite treatment favored soil pH elevation, available nutrient and S-NPH accumulation, and sporulation; the biochar treatment showed advantages in S-UE activity and heavy metal control; and the 70% biochar + 30% dolomite and 50% biochar + 50% dolomite treatments regulated carbon transformation processes and microbial community recovery, respectively. Co-application also increased microbial richness and diversity and reshaped community structure. Structural equation modeling indicated that the amendments strengthened positive links among soil nutrients, the ionic environment, microorganisms, enzyme activities, and Ganoderma lingzhi quality, revealing a potential nutrient and ionic environment-microorganism-enzyme-Ganoderma lingzhi quality pathway. In summary, biochar–dolomite co-application alleviates the continuous cropping obstacle of Ganoderma lingzhi and improves its yield and functional quality by improving the soil physicochemical environment, promoting humus accumulation, and reshaping microbial community structure, providing a theoretical basis for quality improvement of medicinal fungi and soil ecological regulation under continuous cropping.

Agriculture doi: 10.3390/agriculture16222460

Authors: Chung-Liang Chang Chen-En Lee Yu-Hsin Chen

Errors in temporal behavior annotations can change decisions about when behavioral events occur. This study examined how six types of annotation errors affect downstream event decisions. A temporal event decision model (TEDM) was evaluated using 100 synthetic reference sequences and manually annotated sow–piglet ethograms. Its results were compared with those of three rule-based methods under controlled perturbations. In repeated five-fold cross-validation on synthetic data, TEDM and a smoothing-based method achieved micro-averaged Event F1 scores of 0.9293 and 0.9300, respectively, without a statistically significant difference (Holm-adjusted p = 0.4570). Category errors and omitted events caused the largest reductions in event detection, whereas small timing shifts mainly affected temporal alignment. In the held-out manual ethogram evaluation, TEDM and the smoothing-based method achieved Event F1 scores of 0.822 and 0.796, respectively. These results showed that the effects of annotation errors depended on the error type and the temporal evidence required by each event criterion. Further evaluation using larger datasets and outputs from trained behavior recognition models is needed.

Agriculture doi: 10.3390/agriculture16222461

Authors: Zhongliang Bao Xinyu Li Jun Xie Guanglian Ma Yichi Zhang Boyuan He Dan Bai Xinyu Jiang Xun Hu Lihua Liu

Plant spacing and row configurations alter air circulation, light penetration and other conditions within the crop canopy, thereby affecting soil micro-environmental conditions. However, in cold-region rice cultivation, it remains unclear whether bacterial and fungal communities respond differentially to different planting configurations and how such microbial changes are associated with soil organic carbon fractions. This study conducted a two-year field experiment with three planting spacing treatments, including uniform row spacing of 30 cm × plant spacing of 12 cm (CK), (33 cm wide row + 17 cm narrow row) × plant spacing of 14 cm (A), and (33 cm wide row + 17 cm narrow row) × plant spacing of 12 cm (B). Soil samples were collected at the rice maturity stage in 2025 to evaluate treatment effects on soil chemical properties, microbial communities, and rice yield, and to explore the relationship between microbes and yield components. Results showed that treatments A and B significantly increased pH, soil organic carbon (SOC), readily oxidizable carbon (ROC), and water-soluble organic carbon (WSOC). Total nitrogen and available nitrogen levels significantly increased under treatment A but reduced under treatment B. In terms of microbial communities, treatments A and B enriched functional microbes involved in carbon and nitrogen cycling, while the community structures of both bacteria and fungi remained stable. Treatment A outperformed treatment B in enriching beneficial saprophytic fungi and suppressing pathogenic fungi. Compared with CK, treatments A and B increased yields by 7.37% and 2.64%, respectively, in the first year of the experiment and by 7.57% and 3.47%, respectively, in the second year of the experiment.

Agriculture doi: 10.3390/agriculture16222459

Authors: Viktor Szentpéteri Ákos Juhász Katalin Posta

While numerous plant growth-promoting (PGP) microorganisms have been described, the identification and characterization of novel indigenous strains with multiple PGP traits and strong tolerance to abiotic stress remain important for the development of sustainable agricultural bioinoculants. We isolated 90 bacterial strains from drought-affected soils across Hungary and screened them for key PGP traits, including phosphate solubilization, nitrogen fixation, indole-3-acetic acid production, 1-aminocyclopropane-1-carboxylate deaminase activity and antifungal activity. Furthermore, stress tolerance against osmotic stress induced by polyethylene glycol and growth on varying pH levels were evaluated. Among the isolates screened, we focused on those that exhibited multiple plant growth-promoting characteristics together with high tolerance to drought and narrowed down the number of candidates to ten, which were examined in a seed germination test. Based on 16S rRNA gene sequencing, these ten isolates were identified and found to be predominantly Pseudomonas sp. Isolate 1379 was particularly noteworthy as it has exhibited the ability to perform all of the examined PGP properties. Moreover, isolates 0210, 0526, 0736, 1163, 1166, and 1272 displayed four of the five tested traits. It should be mentioned, however, that while maximum biomass was observed with isolate 1379, no significant differences were detected between seeds treated with any of the isolates compared to the uninoculated control during the maize germination test. Consequently, further validation of isolates will be performed under greenhouse and field conditions, with the ultimate goal of formulating an effective bioinoculant to enhance crop drought resilience.

Agriculture doi: 10.3390/agriculture16222458

Authors: Chenxu Zhao Hongguang Yang Fengwei Gu Yanqin Zhang Peng Chen Jiayong Pei Feng Wu

To address the issues of high missed-pickup loss caused by ground undulations and the high labor intensity of manual adjustment during peanut pickup harvesting, this study proposed a profiling pickup control system for peanut pickup harvesters based on the synergy of multi-sensor fusion and a fuzzy PID control algorithm. To ultimately determine the optimal operating height, a wheeled profiling device equipped with an angle sensor was utilized to detect the ground clearance of the pickup header, corrected in combination with an inclination sensor. A mechanical–electrical–hydraulic co-simulation was conducted using Amesim and Matlab/Simulink to comparatively analyze the control performance of conventional PID and fuzzy PID. The simulation results demonstrated that fuzzy PID control exhibited superior performance in reducing height errors and enhancing overall operational stability, showing better adaptability. Field trials were conducted across multiple sets of working conditions, operating speeds, and pickup heights. The experimental results indicated that under fuzzy PID control, the average response speeds for lifting and lowering the pickup header were 0.29 m/s and 0.23 m/s, respectively, representing increases of 52.6% and 76.9% compared to conventional PID control. Across different forward speeds and operating conditions, the average control accuracy of fuzzy PID control reached 95.2%, an improvement of 1.2 percentage points over PID control. The average peanut pickup rate reached 99.3%, 0.3 percentage points higher than that achieved by manual adjustment. This control system demonstrates high reliability and provides technical support for the intelligent, low-loss pickup harvesting of peanuts.

Agriculture doi: 10.3390/agriculture16222457

Authors: Chi Sun Xiaojin Chen Jun Wang Jinshang Wang Yong Wu

Intensive agricultural areas across China suffer severe nitrogen surplus, yet its spatiotemporal drivers remain unclear. Using the nutrient balance method, this study quantified cropland nitrogen surplus and its intensity in 47 prefecture-level cities in Beijing, Tianjin, Hebei Province, Shandong Province, and Henan Province from 2010 to 2022. The Geographically and Temporally Weighted Regression (GTWR) model was applied to analyze six driving factors. Nitrogen surplus decreased from 6.71 million t to 4.11 million t, representing a reduction of 38.7%, with a marked decline occurring after 2016. Surplus intensity dropped by 30.7% from 29.13 t/km2 to 20.20 t/km2, and nitrogen use efficiency rose from 46% to 60%. The GTWR model proved to perform well (R2 = 0.749). Both the multiple cropping index and the agricultural industrial structure shifted from positive to negative effects. Specifically, the effect of the multiple cropping index declined from 0.325 to −0.183, showing the largest absolute average annual change. Urbanization rate, agricultural mechanization level, and per capita cropland area exerted negative effects, whereas the agricultural planting structure varied among regions. The multiple cropping index was the most critical factor. Therefore, optimizing cropping systems is an effective pathway to reduce nitrogen surplus. Future research should focus on incorporating climate factors and exploring the interactive effects among driving factors at finer spatial scales (e.g., county level), to achieve more precise nitrogen management.

Agriculture doi: 10.3390/agriculture16222456

Authors: Bogdan V. Paliev Pavel Yu. Kroupin Albert V. Shchegolkov Dmitriy E. Zima Gennady I. Karlov Marina V. Klimushina Mikhail G. Divashuk

Variation in segregating soybean populations has rarely been decomposed into between- and within-population components, and not yet for the southern Russian ecotype. We phenotyped 1062 individually harvested plants from seven F2 populations for thirteen quantitative traits in Krasnodar Krai, 2024. The season was extreme on the following four measures: 4415 °C as the sum of active temperatures, 93 days above 30 °C, 30 days above 35 °C (of which 26 were during heat waves lasting 19 and 7 days each), and 215 mm of rainfall. All four lie outside the six preceding years’ ranges. On average, 76.4% of variation resided within populations, ranging from 52.3% for plant height to 93.6% for productive nodes. The negative protein–oil correlation held in all seven populations (ρ −0.36 to −0.73) but differed in strength. Protein was unrelated to seed mass per plant, and the F2 median exceeded the parental values measured in another nursery in one of six crosses. The growing regime was completely confounded with the parental pairs, so the two groups of crosses are not comparable as treatments, making pseudoreplication measurable: twelve traits separate the groups when the plant is the unit, and only one when the cross is the unit. Pooled-threshold selection repeated the error, assigning 42 out of 78 candidates to one population. Within-population selection retained 20 plants from all seven crosses.

Agriculture doi: 10.3390/agriculture16212455

Authors: Zefu Chen Ruiqi Jin Maile Zhou Jianjun Yin

Producing high-density rice straw bales requires a model that captures the anisotropic deformation and breakage of fresh straw during compression. We therefore developed and calibrated a spherocylinder-based discrete element model for rice straw baling. The model represents hollow elliptical cross-sections, distinct stem and node structures, and anisotropic bonding, and its mechanical and breakage responses were evaluated before its application to compression baling device optimization. Calibration via single-factor, Plackett–Burman, steepest ascent, and Box–Behnken experiments using the angle of repose as the target identifies three key parameters: straw–straw rolling friction (0.593), straw–steel rolling friction (0.188), and node Poisson’s ratio (0.411). The calibrated model reproduced the measured angle of repose with a relative difference of 0.39% and reproduced the compression and shear responses within 10% error. Compared with a multi-sphere model, the spherocylinder model reduces the particle count by 90.25%, simulation time by 72.52%, and memory usage by 89.83%. Adams–EDEM co-simulation reproduced the progressive deformation and localized breakage observed during baling, without excessive pulverization. Simulations across different tapered opening angles identified a balanced density–torque configuration, which was validated by field trials with a relative density error of 1.23%. These results demonstrate the model’s accuracy, efficiency, and practicality for simulating rice straw in compression molding machinery.

Agriculture doi: 10.3390/agriculture16212453

Authors: Gabriela Vdoviak Tomyslav Sledevič Dalius Matuzevičius

Defensive behavior among honeybees at the hive entrance, such as colony defense against intruders, appears as a group-level pattern rather than in individual bees. This study investigates its automatic recognition from single frames as a single-class object-detection task suitable for edge deployment. A dataset of 20,000 annotated images was built from 87 videos (13 h) recorded at 20 hive entrances; defense was observed at 6 entrances in 32 instances totaling about 6 min. YOLOv8, YOLO11, and YOLO26 detectors at five scales were evaluated by five-fold cross-validation, with folds grouped by defense instance, and exported to ONNX and NCNN for CPU-only benchmarking on a Raspberry Pi 5. The models reached mAP50 of 0.728–0.800 and mAP50-95 of 0.447–0.541, with YOLO11 and YOLO26 generally more accurate than YOLOv8, although not in every split. Larger models improved mAP50-95 by 4.6–6.4 percentage points, which is consistent across all splits but small relative to the 14–20× increase in time per frame, and export changed accuracy by, at most, 2.5 percentage points on average. NCNN was the fastest and most energy-efficient format, with nano-models reaching about 13–14.5 FPS at 0.4–0.5 J per frame. YOLO11n, YOLO26n and YOLO11s appear to offer the best frame-level accuracy–speed trade-off; generalization beyond one apiary and event-level performance remain to be verified.

Agriculture doi: 10.3390/agriculture16212454

Authors: Liliana G. Alfaro-Sifuentes Raghad Alahmadi Gabriel I. Romero-Villegas Alawi Jifri Akram Filimban Raghdah Malibari Ricardo Erik González-Portela Rahul V. Kapoore Emna Mhedhbi Mohamad Padri Claudio Fuentes-Grünewald

A sustainable biostimulant has been gaining more attention, as it provides eco-friendly agriculture practices and helps achieve sustainable results. This study investigated the effects of microalgae-based biostimulants on tomato seedling vigor, plant growth, and fruit productivity. Four microalgae biomasses from different strains and conditions were tested (Limnospira maxima in seawater and freshwater cultivation, Leptolyngbya sp. and Chlorella sp.) for the early stage of cultivation and later followed by testing in mature stages. Limnospira maxima freshwater was found to significantly improve germination up to 99.3% and reduced T50 from 38.4 to 29.6 h. It was also found to increase the mean germination time (MGT) from 12.6 to 9.96 h. Overall, the germination index (GI) increased from 1.2 to 1.6. Root length was also found to increase significantly in the microalgae biostimulant, similar to the effect of a commercial biostimulant. In terms of productivity, the algal-based biostimulant has higher fruit productivity than the control and commercial biostimulant (2.64 kg, 2.2 kg, and 1.9 kg, respectively). These findings highlight the potential of algal-based biostimulants for sustainable tomato crop cultivation for clean agriculture practices in semi-arid environments. Linking to the carbon footprint, the application also avoided emissions of more than 14,000 kg CO2 equivalent per hectare per crop cycle. These outcomes position locally produced microalgae biostimulants as a green product that couples crop productivity with carbon mitigation and supply chain resilience in semi-arid environments.

Agriculture doi: 10.3390/agriculture16212452

Authors: Zeyu Wang Xiaodong Chang Chenyang Liu

Enhancing rural economic resilience is a critical pathway for unleashing endogenous momentum in rural development, safeguarding national food security, and ensuring macroeconomic stability. Employing panel data from 31 Chinese provinces (autonomous regions and municipalities) spanning 2008–2023, this study employs the FAHP–Entropy Weight–TOPSIS composite method to measure the levels of rural demographic structure transition and rural economic resilience. We systematically integrate two-way fixed effects models, mediation-moderation models, and Spatial Durbin Models to identify the mechanisms, transmission pathways, and spatial spillover characteristics through which rural demographic structure transition affects rural economic resilience. The findings indicate that: ① The spatiotemporal evolution of China’s rural economic resilience is characterized by “stage-differentiated growth, synergistic evolution across dimensions, and regional pattern divergence,” and it currently displays a pronounced “core–periphery” structure. ② Rural demographic structure transition exerts a significant and robust negative effect on rural economic resilience. Heterogeneity analysis reveals that this inhibitory effect is only significant in major grain-producing areas and the central region, primarily weakening the resistance-recovery and innovation-reconfiguration capabilities of the rural economy. ③ The mechanism analysis reveals that the mediating effect of agricultural technological progress is not empirically supported, while the scale of cultivated land operations and rural digital infrastructure further amplify this negative effect. From a spatial perspective, changes in rural population structure significantly inhibit the development of rural economic resilience in the local region while generating a significant positive spillover effect on rural economic resilience in neighboring regions. However, the estimated total effects are highly sensitive to the specification of the spatial weight matrix. These findings should therefore be interpreted with caution. ④ Based on these findings, this paper constructs optimization pathways encompassing differentiated talent cultivation, industrial technology empowerment, scale operation adaptation, digital literacy enhancement, and cross-regional agricultural synergy, providing theoretical reference and policy references for hierarchically enhancing rural economic resilience and promoting high-quality agricultural development in the context of demographic structural transformation in the new era.

Agriculture doi: 10.3390/agriculture16212451

Authors: Xianneng Ai Hui Li Qingyang Xu Xiangyan Qian Weiwei Huang

Agricultural environmental productivity is commonly analyzed using alternative frontier methods and indices, although these approaches may refer to different environmental objects: inputs, production residuals, emissions, ambient conditions, damage, and changes in natural capital. This critical review examines how these objects are constructed and what technological, environmental, welfare, and sustainability conclusions they support. We compare joint production, weak disposability, material balance, and by-production and organize the selected empirical studies according to the environmental variable, its accounting or spatial boundary, and the supported inference. An interpretive framework distinguishes object, boundary, and attribution mismatch and clarifies the additional information required to move from production decisions to environmental outcomes. The reviewed applications primarily demonstrate production-side or material-use performance. Their findings cannot be directly translated into ambient environmental improvement, avoided damage, or sustainable stock management. Technological shadow values are not damage values, and improved environmental-adjusted productivity need not imply greater welfare or sustainability. Meaningful comparison and policy interpretation require alignment among the environmental object, its construction, the production technology, the system boundary, and the intended economic inference.

Agriculture doi: 10.3390/agriculture16212450

Authors: Bo Wang Hongbo Xu Fengwei Gu Hongguang Yang Jiayong Pei Haoyan Peng Zhaoyang Yu Feng Wu

Against the backdrop of increasing demand for intelligent, high-efficiency, and low-loss harvesting, the operational performance of threshing and cleaning devices directly affects the loss rate, grain quality, and operational efficiency of grain combine harvesters. However, continuous variations in crop type, moisture content, feed rate, and material composition make the threshing and cleaning processes highly complex. Existing devices still encounter problems such as grain breakage, threshing losses, cleaning losses, and increased impurity content under high loads and complex operating conditions. This paper systematically reviews the current research status and development progress of threshing and cleaning devices for grain harvesters and analyzes the structural composition and working principles of threshing and cleaning systems. In terms of threshing, particular attention is given to research progress on tangential-flow, axial-flow, and combined tangential- and longitudinal-flow cylinders, as well as fixed, variable-clearance, rotary, and load-adaptive concave structures. The applications of the discrete element method (DEM) and its coupling with multibody dynamics in analyzing material motion and forces and optimizing parameters are also examined. In terms of cleaning, approaches to structural improvement are summarized with respect to vibrating screen structures, fan and air duct configurations, and related aspects. The applications of DEM, computational fluid dynamics (CFD), and coupled CFD–DEM simulations in studies of particle stratification, passage through screen openings, and gas–solid motion are analyzed. Existing cleaning airflow designs have gradually shifted from single-variable airflow rate adjustment toward zoned and multi-source air supply to accommodate material loads in different screen sections. In addition, this paper further reviews technologies for feed rate and operational load sensing, online detection of threshing and cleaning quality, and coordinated control of multiple parameters. Issues concerning material state coupling, simulation model credibility and transferability, and sensing delays are also discussed. Finally, it is proposed that threshing and cleaning devices should evolve from the optimization of individual structures and parameters toward the integration of structural design, numerical simulation, state sensing, and coordinated regulation and control. This review provides a theoretical and technical reference for the low-loss, high-efficiency, and intelligent design of threshing and cleaning devices for grain combine harvesters.

Agriculture doi: 10.3390/agriculture16212449

Authors: Vojtech Ilko Kristina Nakonechna Adéla Kylarová Markéta Berčíková Leoš Uttl Vladimír Vietoris Lukáš Zlatohlávek Marek Doležal

This survey of rapeseed oils purchased on the Central European retail market in 2025 included 31 products: 19 refined oils (including three declared as gently refined and one high-oleic oil) and 12 cold-pressed oils; seven products (six cold-pressed and one refined) came from certified organic farming. The oils demonstrated high quality of technological processing with low values of trans-unsaturated fatty acids, acid value, and peroxide value and also met the limits set by legislation in the content of erucic acid, process contaminants 3-monochloropropane-1,2-diol (3-MCPD) esters and glycidyl esters, and residue of pesticides. Oxidative stability, expressed as the induction period ranging from 2.4 to 5.6 h, predisposes them to cold cooking or short-term frying, while the 6.1 h of the high oleic rapeseed oil (HORO) indicates the possibility of medium-term heat stress. The nutritional benefit is a low content of saturated fatty acids (5.69–8.15%) and a high content of mono- and polyunsaturated fatty acids, with a favorable n-3 α-linolenic acid proportion ranging from 7.04 to 10.33%, and an average vitamin E content of 305 mg/kg oil. Sensory quality ranged from excellent to fair; cold-pressed oils were characterized by a more pronounced color, aroma, and typical nutty flavor, whereas refined oils were sensorially almost neutral. Within the analyzed set of products and the parameters determined, the results indicate that rapeseed oil available on the Central European market is a nutritionally valuable and technologically high-quality commodity. Principal component analysis showed that the processing technology (refined vs. cold-pressed) was the main factor structuring the data, followed by tocopherol content and oxidative status, whereas organic and conventional cold-pressed oils were not differentiated.

Agriculture doi: 10.3390/agriculture16212447

Authors: Ruyi Tian Zhaoqiang Zhou Tangzhe Nie Peng Chen Sicheng Du Yu Han Haijun Liu Hang Guo Zhongxue Zhang

The fate of urea-derived carbon (urea-C) in paddy soils is poorly understood. Although urea-C is generally considered to be rapidly mineralized and released as CO2, a fraction may be retained in soil through physicochemical and microbial processes. Here, we used 13C-labeled urea combined with 13C-phospholipid fatty acid stable isotope probing (13C-PLFA-SIP) to determine the fate of urea-C under contrasting long-term irrigation and straw management practices and to identify the microbial groups involved in its assimilation. Soil CO2 efflux peaked on day 5, whereas the proportion of urea-derived CO2 in total soil CO2 efflux generally peaked on day 3. Both subsequently declined during the observation period. Cumulative urea-C loss as CO2 accounted for 36–60% of the applied C, with straw return stimulating early CO2 release and the greatest loss occurring under controlled irrigation combined with straw return. Nevertheless, a measurable fraction of urea-C remained in the soil, declining to approximately 10% of the applied C by day 21, with greater retention under flooded irrigation. Controlled irrigation combined with straw return increased microbial biomass and the fungal-to-bacterial (F/B) and Gram-positive-to-Gram-negative (GP/GN) ratios. 13C-PLFA-SIP further revealed distinct microbial partitioning of urea-C: flooded irrigation promoted incorporation into general and Gram-positive bacteria, whereas controlled irrigation favored Actinomycetes and fungi. Straw return increased urea-C incorporation into Actinomycetes and decreased its incorporation into Gram-positive bacteria under both irrigation regimes, while enhancing incorporation into Gram-negative bacteria only under controlled irrigation.

Agriculture doi: 10.3390/agriculture16212448

Authors: Yilizhati Yimamu Xuheng Zhao Min Zhou Yi Ding Sumera Anwar Hongsu He

Seed germination in cold deserts is constrained by winter conditions, yet the combined effects of snow cover and freeze–thaw cycles on seed metabolism remain unclear. We investigated their interactive influence on Seriphidium transiliense germination using four snow cover levels (P1: 0%, P2: 50%, P3: natural, P4: 200% natural) and five freeze–thaw cycles (C1: none; C2 to C5: daily fluctuations from −2/8 °C to 8/−2 °C) in a factorial design. Germination traits, physiology, and metabolomics were analyzed. Snow cover and freeze–thaw cycles significantly interacted. P4C5 treatment (200% snow cover, 8/−2 °C freeze–thaw regime) most strongly promoted germination, increasing the germination percentage, potential, index, and vigor index by 67.6–172.5% compared with P1C1. P4C5 treatment increased soluble sugars and proteins but reduced proline and malondialdehyde levels, indicating improved osmotic balance and membrane stability. Antioxidant enzyme activities rose alongside increased indole-3-acetic acid and gibberellin and decreased abscisic acid. Metabolomic profiling revealed reprogramming, with HexCer (17:2;02/16:1;0) as a key metabolite and enrichment in cofactor biosynthesis, ABC transporters, and flavonoid pathways. Greater snow cover combined with mild freeze–thaw regime primes S. transiliense seeds for germination by optimizing the microenvironment, strengthening physiological resilience, and activating coordinated metabolic networks.

Agriculture doi: 10.3390/agriculture16212446

Authors: Danijel Pantelić Jelena Rudić Jasmina Oljača Jasna Savić Ivana Momčilović

This study evaluated the effect of harvest interval on minituber production and characterized plant growth in four potato cultivars grown aeroponically during the autumn–winter season in a plastic greenhouse under a moderate continental climate. The cultivars Agria, Désirée, Dragačevski Mesečar, and Festival were evaluated at two harvest intervals (10-day and 20-day). All four cultivars developed successfully and produced minitubers throughout the autumn–winter season, with high survival rates following acclimatization. Cultivars differed significantly in plant survival, growth characteristics, productivity, tuber size distribution, and aerial tuber formation. Festival achieved the highest minituber productivity, whereas Désirée produced the highest number of tubers per plant. The 10-day harvest interval increased the number of harvested minitubers by 17.7% compared with the 20-day interval (p = 0.023), primarily due to an increase in the number of tubers in the smaller diameter categories. However, harvest interval did not significantly affect total tuber yield (p = 0.918) or the number of tubers >25 mm (p = 0.824), whereas cultivar significantly affected both traits (p < 0.001 for both). Taken together, these results support the feasibility of an additional autumn–winter production cycle for pre-basic seed potato multiplication using greenhouse-based aeroponics under moderate continental climate conditions.

Agriculture doi: 10.3390/agriculture16212445

Authors: Susan B. Harper Mo Salman

Biosecurity in laboratory animal facilities supporting agricultural research is often viewed as a means of preventing the introduction, transmission, or release of infectious agents. These functions remain essential in facilities housing livestock, poultry, aquaculture species, and laboratory animal models used to investigate agricultural priorities. Such research supports the development and evaluation of veterinary medicines, diagnostic methods, feed and nutrition strategies, and interventions intended to improve animal health, food safety, and the management of agricultural environments. Biosecurity failures can therefore affect research animals and personnel, compromise experimental validity, and create pathways by which infectious agents or contaminated materials could reach other animal populations or the surrounding environment. Effective biosecurity requires a broader, system-based perspective. Laboratory animal facilities are complex operational environments in which animals, personnel, biological materials, equipment, facilities, infrastructure, information, and research activities are interdependent. Failure in one area can have consequences for animal health and welfare, worker safety, physical security, facility operations, and research quality and integrity. This manuscript presents an integrated framework for biosecurity in laboratory animal facilities that support agricultural research and distinguishes biosecurity from related concepts, including biosafety and biocontainment. The framework is organized into five interrelated domains: (1) biocontainment, (2) animal health, (3) worker safety (biosafety), (4) physical security, and (5) research quality and integrity. Across these domains, biosecurity involves identifying vulnerabilities, assessing risks and potential consequences, implementing risk-based controls, monitoring their effectiveness, and responding appropriately when controls fail. Examples involving poultry and livestock research illustrate how the framework can be applied to animal procurement and quarantine, personnel and equipment movement, disease surveillance, waste and environmental management, and protection of research resources. This system-based approach integrates biosecurity into facility design and operations; animal procurement and movement; personnel practices; equipment management; animal health surveillance; emergency preparedness; training; incident response; and program oversight. It helps protect research animals and personnel, preserve valuable biological resources, minimize operational disruptions, and reduce preventable sources of experimental variability.

Agriculture doi: 10.3390/agriculture16212444

Authors: Zhuoyu Shi Sen Yang Xiangzhou Li

Adapting monocular metric depth to agricultural imagery requires balancing target-domain accuracy against the scope of model updates and embedded execution cost. We evaluate SARE-Depth on CitrusFarm using a frozen teacher-guided Depth Anything V2 Small base. Depth-Preserving Residual Head Refinement (DP-RHR) learns image-level log-scale and gated local corrections, followed by fixed depth-conditioned Range-Expert (RE) refinement. Before adaptation, fixed-checkpoint evaluations show persistent overestimation under both square and aspect-preserving input. On 750 internal-test images with common FP32 references and a 30 m cap, scalar calibration reduces absolute relative error (AbsRel) from 4.782 to 0.476. DP-RHR reaches 0.325 with 93,123 updated parameters; complete metric-head fine-tuning achieves 0.220 ± 0.006 across three runs with 2,728,513 updated parameters. Subsequent RE refinement provides smaller, domain-dependent gains with reduced threshold accuracy, while separate Dingxi examples reveal object-distance underestimation. Cumulative Jetson Orin NX latency increases from 26.87 ms for the base to 31.49 ms with DP-RHR and 35.08 ms for the complete path. These comparisons relate adaptation scope to accuracy and measured execution cost in this agricultural case study.

Agriculture doi: 10.3390/agriculture16212443

Authors: Juxia Li Zhenghang Yu Yanwen Li Bin Wang Fuzhong Li

Existing selective sweet pepper harvesting systems use industrial arms and high-performance computers, which limits adoption on small farms and replication by researchers. We developed a low-cost, open-source modular harvesting robot based on Raspberry Pi 5 and a 3D-printed manipulator. The system has three modules. First, an improved YOLOv13n detector combines GhostConv, ReLU6, and DIoU Loss. It reaches 96.9% mAP@0.5 for fruits and 95.1% for peduncles with 2.35 M parameters. Compared with the YOLOv13n baseline, parameters and GFLOPs drop by 4.1% and 3.2%, while overall mAP@0.5 falls by 1.8 percentage points. Second, an adaptive size measurement and multi-criteria decision strategy uses the correlation between median depth and tilt angle (R2 = 0.824), giving a mean absolute error of 5.0 mm and a 95.4% effective hit rate. Third, a ROS 2-based full-stack platform enables closed-loop perception–decision–execution. Laboratory tests give a selected-target harvesting success rate of 95.4% (103/108) and an overall success rate of 85.8% (103/120), with 1.9% fruit damage. Motion planning and execution consume 83% of cycle time. All hardware designs, ROS 2 packages, and model weights are released.

Agriculture doi: 10.3390/agriculture16212442

Authors: Vasile Adrian Nan Gheorghe Badea Anca Patricia Grădinaru Ana Cornelia Badea Mihai Valentin Herbei Lucian Octavian Dragomir

Accurate crop classification from satellite imagery can substantially enhance agricultural monitoring. Recent advances in deep learning have improved crop classification by employing models that take into account both spatial and temporal information contained in satellite image time series. However, the limited interpretability of these models remains a major challenge for their adoption in agricultural applications. This study presents a one-versus-rest classification framework for five crop types (wheat, maize, rapeseed, sunflower and watermelon and melon), in Ialomița County, Romania, using a CNN-LSTM (Convolutional Neural Network–Long Short-Term Memory) architecture and nine Sentinel-2 images acquired throughout the 2025 growing season. Each image sequence consisted of four spectral bands and NDVI (Normalized Difference Vegetation Index), enabling the exploitation of spectral, spatial and temporal information. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrices and the area under the receiver operating characteristic curve (ROC-AUC). The models achieved high validation accuracy, with values of 96.01% for wheat, 94.33% for maize, 97.48% for rapeseed, 97.06% for sunflower and 97.06% for watermelon and melon classifications. Explainable AI (XAI) techniques such as Integrated Gradients and SHAP (SHapley Additive exPlanations) were employed to quantify the contribution of individual spectral bands and acquisition dates to the classification decision. The Integrated Gradients analysis showed that for some crops, NDVI was the most influential input feature, accounting for 50.78% of the feature importance in wheat classification, 39.30% in rapeseed classification and 40.01% in watermelon and melon classification, while for other crops, the NIR (Near-infrared) spectral band was the most influential feature, accounting for 29.89% of the feature importance in maize classification and 41.00% in sunflower classification. The results demonstrate that the proposed framework achieves high classification performance while providing meaningful explanations of model predictions, highlighting the importance of both vegetation characteristics and seasonal dynamics in crop classification.

Agriculture doi: 10.3390/agriculture16212440

Authors: Hiroto Yonechi Ayasha Akter Kishore Kumar Sarker Chisato Murakami Kenji Osabe Ryo Fujimoto

Brassica rapa includes economically important vegetables such as Chinese cabbage, pak choi, and turnip, whose development and agricultural traits are strongly influenced by environmental conditions. This review summarizes current understanding of epigenetic regulation in B. rapa, an area that has advanced from substantial findings in the model plant Arabidopsis thaliana. Epigenetic mechanisms, including DNA methylation, histone modifications, small RNAs, and chromatin organization, regulate gene expression, transposable-element activity, genome stability, and phenotypic variation. Recent advances in methylome and chromatin profiling have revealed both conserved mechanisms and regulatory features shaped by whole-genome triplication (WGT) in B. rapa. During vernalization in A. thaliana, Polycomb-mediated accumulation of H3K27me3 represses FLOWERING LOCUS C (FLC), while related mechanisms regulate BrFLC paralogs and flowering time in B. rapa. Epigenetic changes are also associated with heterosis and responses to temperature stress and pathogens. However, major challenges remain in establishing their causal roles in B. rapa, particularly given the genomic complexity arising from WGT, the limited functional evidence for causal epialleles, and uncertainties regarding the transgenerational stability of epigenetic marks. Furthermore, transposable elements, which are normally suppressed by DNA methylation, can influence nearby gene expression through local changes. We distinguish mechanisms established in A. thaliana and findings directly supported in B. rapa, and discuss how epigenetic markers, chemically or genetically induced variation, stress priming, and epigenome editing could contribute to future breeding of Brassica.

Agriculture doi: 10.3390/agriculture16212441

Authors: Yushu Chen Shan Sun Zhaowen Chen Yulun Chen Kun Yang Feng Xue Yingchao Wang Shuai Wang Wei Wang Hong Zhang

Fine-grained cotton detection is challenging because targets at different developmental stages exhibit substantial variations in scale and morphology, while visually similar categories, partial occlusion, and complex field backgrounds further increase recognition difficulty. To address these issues, this study proposes WINGS-Det, a lightweight object detector that coordinates spatial, scale, and frequency-aware feature modeling within a unified architecture. Built on YOLO26n, WINGS-Det incorporates three complementary components. C3k2LGL combines large-kernel local aggregation with sparse global self-attention to capture local structures and long-range spatial dependencies. SCMIFF performs selective cross-scale interaction between hierarchical features to strengthen the fusion of spatial details and semantic information. C2WSA introduces wave-aware frequency modeling to preserve global structural information while enhancing high-frequency boundary and texture cues. A four-category cotton dataset containing 13,771 images and 27,352 annotated instances was constructed for evaluation. WINGS-Det achieves 98.6% Precision, 98.2% Recall, 98.9% mAP50, and 75.7% mAP50:95 with only 2.38 M parameters and 5.3 GFLOPs, while achieving 163 FPS on an RTX 4090 with native PyTorch FP32 inference. On the newly constructed four-category cotton dataset, WINGS-Det improves mAP50 and mAP50:95 over the YOLO26n baseline by 4.7 and 3.8 percentage points, respectively. Extensive evaluations across target scales, object densities, image perturbations, network architectures, hardware platforms, and external datasets further demonstrate the robustness, transferability, and generalization capability of WINGS-Det. The results indicate that the proposed framework achieves an effective balance between fine-grained detection accuracy, computational efficiency, and practical deployment.

Agriculture doi: 10.3390/agriculture16212439

Authors: Marinela Enculescu Madalina Mincu-Iorga Antoaneta Roman Marius Constantinescu Dinu Gavojdian

National greenhouse gas inventories commonly apply a single enteric methane emission factor to an entire dairy population, which can obscure meaningful differences between production systems and between feeding seasons. An IPCC Tier 2 assessment was performed for the Romanian dairy herd in the reference year 2024 (1,051,894 cows), stratified into extensive (72.77%), semi-intensive (23.10%), and intensive (4.12%) systems and, for the two grazing-based systems, into a winter indoor and a summer grazing period. Gross energy intake (GE), digestible energy (DE), and the methane conversion factor (Ym) were derived from national feed composition references, with Ym estimated from DE using a regression equation. Season-specific emission factors were combined into day-weighted annual values. GE ranged from 153.60 MJ head−1 day−1 to 387.78 MJ head−1 day−1 and Ym from 5.48% (semi-intensive, grazing) to 6.44% (extensive, indoor). Day-weighted annual emission factors were 144.37 kg CH4 head−1 year−1, 108.17 kg CH4 head−1 year−1 and 81.92 kg CH4 head−1 year−1 for the intensive, semi-intensive, and extensive systems, whereas methane intensity declined in the opposite direction, from 25.32 g CH4 kg−1 FPCM to 19.98 g CH4 kg−1 FPCM. National enteric emissions were estimated at 95,260 t CH4 (2,572,014 t CO2-eq), 65.8% of which originated from the extensive system. Sensitivity analysis showed that national emissions were more sensitive to gross energy intake (±10% change in emissions for a ±10% change in GE intake) than to digestible energy (±4.3–4.8% change for a ±2 percentage-point change in DE). These results suggest that the methane-related environmental ranking of Romanian dairy systems depends strongly on the functional unit considered and that seasonal variation in Ym is comparable in magnitude to differences between production systems.

Agriculture doi: 10.3390/agriculture16212438

Authors: Xiong Zhao Xianghui Xu Ning Mao Xingxiao Ma Jianneng Chen

The limited seedling tray capacity of vegetable transplanters requires frequent manual transportation of seedling trays to replenishment points during greenhouse transplanting, resulting in high labor demand and reduced operational efficiency. To improve operational efficiency, an ultra-wideband (UWB)–vision integrated navigation system was developed for an autonomous seedling supply chassis. When seedling replenishment is required, UWB positioning supports waypoint and target-point generation and guides headland turning, while vision-based navigation guides the chassis along the ridges. These complementary functions enable the seedling supply chassis to navigate autonomously toward the moving transplanter. During integrated navigation, heading error generated when the chassis exits a ridge under vision guidance adversely affects the accuracy of translational headland turning. A headland turning strategy based on a four-wheel Ackermann steering model was proposed to compensate for this heading error. Field experiments were conducted in a 30 m ridged field to evaluate the proposed navigation system. The results showed that, at operating speeds ranging from 0.4 to 0.6 m/s, the absolute heading error at ridge entry was consistently smaller than that at ridge exit, while the mean lateral error remained below 3 cm. Furthermore, the chassis reached the target position with sufficient remaining operating distance for dynamic seedling tray replenishment. The full-process field experiments demonstrated the feasibility of the proposed navigation system for autonomous navigation toward the transplanter under the tested conditions in a 30 m ridged field, providing a basis for subsequent dynamic seedling tray replenishment of vegetable transplanters.

Agriculture doi: 10.3390/agriculture16212437

Authors: Dong Wang Kai Liao Hong Luo Shihui Xiao Lijun Li Jie Tang

Accurate classification of leaf and wood point clouds is essential for individual-tree 3D reconstruction, structural parameter retrieval, and growth dynamics monitoring. The classification of leaf and wood points in tree point cloud data, collected by terrestrial laser scanning (TLS), remains a significant challenge. This study proposes an enhanced XGBoost for efficient leaf–wood classification in Camellia oleifera point clouds. First, point cloud data of Camellia oleifera are collected using TLS, followed by feature extraction to construct the feature matrix. The ISGA-XGBoost classification model is developed by integrating XGBoost with hyperparameter optimization via the improved snow geese Algorithm (ISGA). The ISGA-XGBoost model achieves 96.19% accuracy, 0.9058 G-mean, and 0.8701 MCC. Compared with XGBoost, ISGA-XGBoost improves overall accuracy by approximately 0.69% and increases the F1-score for wood points from 0.8655 to 0.8891. The results indicate that the ISGA-XGBoost model is effective for leaf–wood classification in the investigated Camellia oleifera dataset.

Agriculture doi: 10.3390/agriculture16212436

Authors: Karen Gutter Iván Matus Dalma Castillo Josefa Pacheco Sara Silva Juan Felipe Alfaro-Quezada Fernando Fuentes-Peñailillo Alejandro del Pozo

Quinoa (Chenopodium quinoa Willd.) is a promising crop for drought-prone Mediterranean agriculture, although environmental variability complicates grain-yield selection. This study evaluated the potential of multisensor, multitemporal unmanned aerial vehicle (UAV) phenotyping combined with machine learning to predict environment-specific best linear unbiased estimates (BLUEs) of grain yield across five site–year environments in central Chile. A panel of 196 quinoa accessions was evaluated in replicated α-lattice trials. RGB, thermal, and multispectral imagery acquired during four reference flights provided 60 predictors. Because genotypes differed in phenological development, flight dates represented common acquisition times rather than synchronized developmental stages. Elastic Net (EN) and Random Forest (RF) were evaluated using repeated five-fold cross-validation in four environments with complete predictor data (n = 779 genotype × environment observations), followed by leave-one-environment-out (LOEO) to assess cross-environment transferability. A sensitivity analysis using 57 predictors included all five environments (n = 974). Grain yield varied substantially among environments with mean values ranging from 0.20 to 3.21 t ha−1 and an approximate broad-sense heritability on an entry-mean basis of 0.49. Within-environment predictive performance was moderate, with mean R2 values of 0.49 for EN and 0.51 for RF. In contrast, RF performance under LOEO validation was substantially lower (R2 = −5.96 to 0.11), revealing limited transferability of absolute yield predictions to unseen environments. Environment-specific SHAP analyses identified differences in the relative importance of UAV-derived color, canopy temperature, and vegetation indices, highlighting the environmental dependence of yield-related predictors. Overall, integrating multisensor UAV phenotyping with machine learning provided useful within-environment grain yield predictions in quinoa.

Agriculture doi: 10.3390/agriculture16212435

Authors: Woon Ji Kim Bo-Keun Ha

The transcriptional processes associated with final seed protein content are not fully characterized in soybean, particularly in progeny derived from crosses with wild soybean. To address this knowledge gap, high-protein (HP) and low-protein (LP) BC1F3 progeny were compared at 25, 35, and 45 days after flowering (D25, D35, and D45) using seed-composition measurements, RNA sequencing (RNA-seq), differential expression analysis, temporal expression clustering, functional enrichment, and weighted gene co-expression network analysis (WGCNA). The HP and LP phenotypes became most distinct late in seed development: at D45, the protein content was 9.0% higher in HP, whereas the oil content was 7.3% lower. The number of differentially expressed genes (DEGs) between HP and LP increased from 3386 at D25 to 7269 at D45, and 18,394 genes showed significant Group × Time interactions. Six temporal clusters revealed stage-dependent expression patterns enriched for cell division, photosynthesis, reserve-related functions, translation, protein processing, and redox-associated processes. WGCNA identified pink and salmon modules positively correlated with protein content and a brown module negatively correlated with protein content. Integration of stage-specific DEGs, temporal interactions, and network metrics prioritized candidates including Glyma.11G063900.1, Glyma.06G030400.1, Glyma.07G038500.2, and Glyma.03G065500.2. Modules correlated with oil content were interpreted primarily as developmental modules because the oil content was nearly collinear with the developmental stage. These results provide a stage-resolved framework for prioritizing candidate genes for functional validation and breeding aimed at improving soybean seed protein content.

Agriculture doi: 10.3390/agriculture16212434

Authors: Luofa Wu Helai Liu Chunwang Dong Yanqi Wu Shifu Shu Chun Ye

Mechanized harvesting of bulk tea in unstructured hilly tea gardens requires accurate dynamic profiling of irregular canopies at a constant cutting depth to avoid harvesting coarse and old leaves, missing tender shoots, and reducing yield and quality. This task is complicated by high-frequency vibrations from crawler travel; perception disturbances caused by impacts with hard, thick branches; and actuator saturation and control chattering when abrupt chassis-posture changes occur under motor torque constraints. To address these challenges, this study proposes a cooperative control architecture that integrates spatiotemporal perception reconstruction with adaptive anti-windup control. First, a global inverse-kinematics mapping model that fuses an inertial measurement unit (IMU) with an absolute encoder is developed. The model decouples chassis motion from local terrain measurements and establishes a unified spatial reference for constant-depth actuator profiling. Second, a spatiotemporal perception reconstruction mechanism is designed for composite multifrequency noise. In the amplitude domain, a robust outlier-rejection kernel adaptively suppresses non-Gaussian transient pulses; in the temporal domain, environmental background-noise estimation and abrupt-change activation dynamically adjust the filtering bandwidth and reduce phase lag in absolute elevation reconstruction during terrain steps. Finally, a nonsingular fast terminal sliding-mode control (NFTSMC) law incorporating active feedforward and anti-windup compensation is developed to address the servo motor torque limit. The law uses chassis pitch angular velocity for feedforward compensation and combines a continuous boundary layer with an auxiliary dynamic system to correct the error manifold, thereby reducing integral windup and high-frequency servo chattering caused by torque saturation. Comparative simulations show that, under extreme multifrequency composite conditions, the global root-mean-square (RMS) tracking error is 41.1% and 35.8% lower than those of Kalman filter-proportional-integral-derivative control (KF-PID) and standard moving-horizon-estimation sliding-mode control (SMHE-SMC), respectively. Field evaluations across 30 measurement areas showed that under the Challenging Scenario (combined slope, speed, and density variations), the proposed architecture maintained mean missed-plucking and loss rates of 0.46% and 0.43%, respectively. ANOVA confirmed a significant main effect of the control strategy on the missed-plucking rate (p < 0.001, ηp2>0.9). Additionally, a structured component-wise ablation study was conducted to evaluate the specific contributions of the core modules. The results indicated that the omission of mechanisms such as inverse-kinematics mapping or anti-windup dynamics notably degraded the overall tracking accuracy by inducing uncompensated spatial drift and saturation-induced errors. These results indicate the potential of the proposed architecture for closed-loop profiling control in intelligent agricultural machinery.

Agriculture doi: 10.3390/agriculture16212433

Authors: Shenghai Huang Jiao Liu Huimin Liu Yang Qiao Xiaonan Li Jun Han Jiasheng Wang

Soil tillage is energy-intensive, and the cutting resistance of soil-engaging tools is its largest single loss. Ultrasonic vibration-assisted cutting (UVAC) can reduce this resistance, but quantitative studies have concentrated on longitudinal vibration; transverse vibration, which loads the soil by cyclic lateral squeeze and release, remains largely unexplored. A calibrated discrete element model of a transverse UVAC tool–soil system is developed to determine how amplitude, frequency and cutting speed govern drag reduction and to identify the contact-level origin of the effect. Bench tests at 20 kHz supplement the simulated design space with an additional operating condition and confirm the predicted forces at eight settings—five vibration conditions and three baselines—with a mean relative error of 8.7% and the baselines within 6%; one calibrated parameter set is used throughout. Over 30–70 µm, 25–45 kHz and 0.03–0.07 m s−1, amplitude dominates, raising the drag reduction from 44.10% to 68.18%, whereas frequency acts mainly through its quadratic term and its interaction with amplitude, and cutting speed enters the same coupling. Box–Behnken optimization places the optimum at 70 µm, 25 kHz and 0.07 m s−1, with a DEM-verified reduction of 69.25% against a predicted 69.53%. Contact analysis shows that the vibration shifts load from normal compression to tangential shear and converts bond failure from concentrated rupture into progressive degradation. Amplitude is therefore the primary design variable for transverse UVAC in sandy loam at 9% moisture, with frequency and speed modulating its expression.

Agriculture doi: 10.3390/agriculture16212432

Authors: Jehyeong Yeon Jae-Hyung Ahn Joon-hui Chung Dayeon Kim Young-Joon Ko Sihyun An Hang-Yeon Weon

Procymidone is a dicarboximide fungicide; its persistence and the formation of the toxic metabolite 3,5-dichloroaniline (3,5-DCA) pose environmental risks. A microbial strategy was developed to enhance the degradation of procymidone and its intermediates via complementary bacterial metabolism. A procymidone-degrading bacterium, Prescottella equi strain YW-05, was previously isolated from agricultural soil with long-term procymidone application. Strain YW-05 degraded 86% of procymidone (10 mg L−1) within 7 days in mineral medium, exhibiting a half-life of 2.6 days. However, the strain showed a limited ability to degrade the intermediate metabolite 3,5-DCA. Pseudomonas putida strains were screened to degrade dichloroaniline isomers, and Pseudomonas putida KACC 10270T exhibited the highest degradation activity toward 3,5-DCA. Co-inoculation with strain YW-05 and P. putida KACC 10270T enhanced 3,5-DCA degradation, reaching 90.5% removal within 14 days and 92.8% within 28 days. In soil microcosms, co-inoculation accelerated the early removal of 3,5-DCA, with 80.3% removed within 3 days compared with 52.3% in the uninoculated control, and reduced the half-life to 1.4 days. Metabolite analysis using LC-MS/MS revealed 4-chlorocatechol and cis,cis-muconic acid and suggested procymidone degradation proceeded through hydrolytic cleavage, followed by dechlorination and aromatic ring cleavage. These findings highlight the use of complementary microbial consortia as an effective strategy for detoxifying pesticide residues and their toxic intermediates in agricultural soils.

Agriculture doi: 10.3390/agriculture16212430

Authors: Zsófia Kovács Antal Szőke Andrea Kitti Tóth-Lencsés Janka Bedő Bánk Pápai Hussein G. Daood Csilla Mihályfi Ákos Tarnawa Ákos Juhász Gábor Csilléry Anikó Veres

Anthocyanin accumulation has been associated with extended shelf life and improved postharvest quality of tomato; however, whether similar effects occur in pepper remains unclear. Therefore, this study investigated purple and green near-isogenic Capsicum annuum lines at economical and biological ripeness during six weeks of storage at 10 and 25 °C. Physical quality traits, antioxidant-related parameters, individual phenolic compounds, and fruit decay were all evaluated via spectrophotometry and HPLC. Storage temperature and time strongly affected fruit quality, with higher temperature accelerating weight loss and softening. At 25 °C, purple fruits showed greater weight loss than green fruits, whereas firmness declined more strongly in green fruits, although differences between the green and purple fruits’ firmness were not significant. In contrast, the purple phenotype showed a distinct phytochemical profile, with higher total phenolic and flavonoid contents, anthocyanin accumulation, and antioxidant capacity. HPLC analyses showed fruit colour, storage time, and temperature-dependent changes in individual phenolic compounds. Spontaneous rot occurred at 25 °C, but no overall fruit colour effect on decay was detected. These results indicate that, unlike in purple tomato, anthocyanin accumulation in pepper does not necessarily confer a consistent shelf-life advantage but is associated more with possibly more advantageous phytochemical and antioxidant characteristics.

Agriculture doi: 10.3390/agriculture16212431

Authors: Chunyu Jie Yanhui Lu Junce Tian Yao Wu Xiaowei Liu Qiang Li Juying Luo Lin Wang Yajun Yang

The rice leaffolder (Cnaphalocrocis medinalis) is a major migratory pest whose population dynamics are synergistically driven by endogenous biological factors and exogenous meteorological conditions. Developing precise quantification models for its occurrence is a prerequisite for sustainable pest management. This study applied the Inverse Hyperbolic Sine (IHS) transformation to handle non-normally distributed pest population data with five years of continuous monitoring (2020–2024). We construct two dynamic predictive models: a simplified early warning model (Model I) and a multi-factor coupling model (Model II) by employing the Autoregressive Integrated Moving Average model with exogenous variables (ARIMAX) to integrate exogenous meteorological drivers (rainfall level during the monitoring interval, Rlt–1,2), endogenous biological indicator (local population development abundance index, LPDAI), and endogenous population dynamic factor (IHS-transformed daily average trap catch increment of the previous interval, IHS_IIPTn–1). Correlation analysis revealed that the trap catch was highly significant and positively correlated with LPDAI and Rlt–1,2 (p < 0.01). Parameter estimation of the IHS-ARIMAX models indicated that the exogenous rainfall factor (Rlt−1,2, Model I: B = 0.996; Model II: B = 0.947) reached significance levels (p < 0.05) in both models; IHS_IIPTn–1 (B = 1.535) in Model I and LPDAI (B = 0.846) in Model II exhibited strong positive effects. Robust fitting performance was shown by both ARIMAX models (Model I: total R2 = 0.903, stationary R2 = 0.860, RMSE = 8.58; Model II: total R2 = 0.922, stationary R2 = 0.898, RMSE = 8.34). Ljung-Box Q test (Model I: Q(18) = 21.500, p = 0.160; Model II: Q(18) = 13.729, p = 0.619) confirmed that the residuals are independently distributed white noise with no remaining significant autocorrelation. Both models stably captured the outbreak trends of C. medinalis. Crucially, an independent out-of-sample validation using field data from 2025 showed the high robustness and generalization power of Model II. It achieved highly significant trend synchronization with the observed values (Spearman’s ρ = 0.499, p = 0.005) and maintained complete numerical unbiasedness (p = 0.157). The IHS-ARIMAX framework developed here effectively addresses the statistical challenges of autocorrelation and non-linear fluctuations in ecological data and offers a robust tool to potentially support future decision making for sustainable pest management.

Agriculture doi: 10.3390/agriculture16212429

Authors: Florin Daniel Lipșa Florina Stoica Ionuț Dumitru Veleșcu Ioana Cristina Crivei Roxana Nicoleta Rațu Eugen Ulea

Dairy microbial ecosystems cannot be adequately described by total microbial counts, pathogen detection, or taxonomic profiling alone. Milk and dairy products contain interconnected microbial and genetic layers—the microbiome, resistome, mobilome, and phageome—shaped by animal-associated reservoirs, farm and processing environments, starter cultures, technological operations, and storage. This structured critical narrative review integrates these four components within a common microbial–genetic framework and examines how dairy processing reconfigures their interactions. Particular attention is given to cooling, heat treatment, fermentation, ripening, packaging, and storage and to the distinction between molecular detection and biological relevance. Across the reviewed evidence, the significance of microorganisms, antimicrobial resistance genes, mobile genetic elements, and phage-associated sequences depends on viability, functional activity, host assignment, genomic context, transfer potential, persistence, and exposure. Shotgun and long-read metagenomics, metatranscriptomics, metaproteomics, metaviromics, and viability-resolved methods provide increasingly detailed information, but interpretation remains constrained by methodological heterogeneity and limited experimental validation. Future progress requires standardized workflows, longitudinal sampling, stronger ARG–MGE–host resolution, validated phage–host interactions, and studies linking microbial identity and genetic mobility with technological outcomes and dairy safety.

Agriculture doi: 10.3390/agriculture16212428

Authors: Shiqi Yang Kun Ruan Jun Sun Mantao Wang Lan Xiang Jianguo Xia

Plant organ segmentation supports image-based phenotyping, but occlusion, slender structures, and differences between image domains limit performance. This study proposes MVA-PlantNet, an encoder–decoder network incorporating a Multi-view Attention (MVA) block. MVA combines Dynamic Group Depth Attention (DDA), which models channel and within-batch relationships, with Global Spatial Attention (GSA), which uses feature gating. The network was pretrained on synthetic images from the Sweet Pepper Dataset and fine-tuned on real Sweet Pepper images and a Capsicum Dataset containing 30 images, of which six were used for fine-tuning and 24 for validation. Additional fine-tuning experiments used 100 images from the Plant Organ Dataset. MVA-PlantNet achieved a reported mean intersection over union (mIoU) of 71.45% on synthetic Sweet Pepper images, exceeding U-Net and SegFormer by 9.60 and 24.91 percentage points, respectively. The reported mIoUs after fine-tuning were 60.17% on real Sweet Pepper images, 74.16% on the Capsicum Dataset, and 55.52% on the Plant Organ Dataset. MVA-PlantNet had the highest reported mIoU among the evaluated models on these splits. However, stem IoU on the Plant Organ Dataset remained low at 1.32%. The experimental results demonstrate that MVA-PlantNet achieved the best overall segmentation performance among the evaluated models. This showed advantages in segmenting certain small plant organs and adapting to target domains through fine-tuning, providing a feasible approach to addressing segmentation challenges arising from morphological differences among plant organs.

Agriculture doi: 10.3390/agriculture16212427

Authors: Yuchen Sun Guangchuan Zhang Guoqi Shao Hang Liu Jihui Qiao Tong Li

Tobacco leaf lesions vary in size, appearance, and spatial distribution, making detection difficult under field conditions. We developed HCC-RT-DETR (Hierarchical Channel Calibration for RT-DETR), an RT-DETRv2-based detector for four infectious disease categories, the abiotic disorder Weather Fleck, and a Healthy reference class. The module derives channel weights from globally averaged features using a local one-dimensional convolution and sigmoid activation. Learnable residual terms add the reweighted features to the original representations. Calibration strength α is initialized at 0.01 for the high-resolution P3 feature and 0.05 for the P4 and P5 features. On the held-out test split of 780 field images, HCC-RT-DETR achieved a three-seed mean of 64.35% mAP@0.5:0.95 and 28.50% AP_small. These values exceed the baseline by 1.46 percentage points (2.3% relative) and 10.32 percentage points (56.8% relative), respectively. HCC incurred a modest latency increase on the CPU: 1.64% under ORT FP32 and 0.97% under ORT FP16-converted execution. A validation-based sensitivity analysis examined four initializations of P3 calibration. Evaluation was limited to Yunyan 301 at the vigorous growth stage, using clear-weather images collected between 10:00 and 14:00; transfer to other cultivars, seasons, or weather conditions remains untested.

Agriculture doi: 10.3390/agriculture16212426

Authors: Peng Chen Zhaoyang Yu Hongguang Yang Chenxu Zhao Fengwei Gu Feng Wu

Due to the complex physical properties of peanut threshed materials, existing cleaning equipment faces problems such as poor parameter adaptability, low separation accuracy for irregular impurities, and a prominent trade-off between cleaning efficiency and loss, which constrain the industrial application of peanut pickup combine harvesters. In this study, to address these issues, we designed a cleaning device that couples a positive–negative-pressure dual-airflow system with a vibrating screen, and conducted theoretical analysis and parameter design of its key components. Based on gas–solid two-phase flow theory, a numerical simulation model of the coupled positive–negative-pressure dual-airflow system and vibrating screen cleaning device was developed using the CFD–DEM coupling method. With the vibrating-screen vibration frequency, crossflow fan rotational speed, and impurity-suction fan rotational speed as experimental factors, response surface optimization of cleaning performance was carried out. Through multi-response optimization analysis in Design-Expert 13 software, the optimized parameter combination was determined after engineering rounding as follows: crossflow fan rotational speed of 2104 r/min, vibrating-screen vibration frequency of 6 Hz, and impurity-suction fan rotational speed of 1824 r/min. The experimental results showed that under these parameters, the actual impurity content rate of the prototype was 2.06% and the loss rate was 2.33%, and the regression model demonstrated high prediction accuracy and engineering reliability. This study reveals the cleaning and separation mechanism of peanut threshed materials under the coupled action of the vibrating screen and dual-airflow system, providing a theoretical basis for the parameter design and regulation of cleaning devices in peanut pickup combine harvesters.

Agriculture doi: 10.3390/agriculture16212424

Authors: Ramona Cristina Heghedűș-Mîndru Petru Negrea Ducu Sandu Ștef Ileana Cocan Monica Negrea Călin Ionel Jianu Gabriel Heghedűș-Mîndru

Fruits are an important source of essential minerals, but can also contribute to the intake of potentially toxic elements. This study determined the moisture, macroelement (Na, K, Ca, Mg) and trace element (Cu, Mn, Fe, Cd, Pb, Zn, Cr) contents of 15 fruit species purchased from the agri-food market in Reșița, Romania, by flame and graphite furnace atomic absorption spectrometry. The data were evaluated by principal component analysis (PCA), k-means and hierarchical cluster analysis (HCA) and used to assess the health risk. Mean contents followed the orders K > Ca > Mg > Na and Fe > Zn > Mn > Cu > Cr > Pb > Cd, strawberry being the richest in most elements. The first three principal components explained over 93% of the variance and the samples clustered with literature data for the same species. Pb and Cd contents were well below EU and Codex Alimentarius limits. For daily intakes of 200 g (adults) and 175 g (children aged 6–9 years), all hazard quotients were below 1 and the margin of exposure indicated a low risk from Pb; thus, under the evaluated scenarios, the analysed fruits pose no health concern and are a valuable source of essential minerals.

Agriculture doi: 10.3390/agriculture16212425

Authors: Zhengzhao Pi Defu Liu Guoquan Pei Ziran Xu Xueying Qian Baijuan Wang Bing Zhou Bi Zhao Zigao Liu Wendou Wu

Nondestructive assessment of finished black tea is challenged by limited reference samples and highly correlated spectra. This study examined whether compact wavelength selection and selective residual modeling could support the prediction of chemically distinct quality constituents. We developed FB-CARS–PER-BHNet, combining frequency-aggregated, block-constrained competitive adaptive reweighted sampling (FB-CARS), a partial least squares regression (PLSR) backbone, and evidence-gated residual learning. Forty-seven finished black tea samples were represented by 243 visible–near-infrared wavelengths (401.05–999.20 nm) and evaluated by outer leave-one-out validation for water extract, epigallocatechin gallate (EGCG), epicatechin gallate (ECG), and lysine (Lys). Held-out R2 was 0.9068 for water extract, 0.9051 for EGCG, 0.8644 for ECG, and 0.7553 for Lys. FB-CARS reduced spectral redundancy, while compact PLSR models approached full-spectrum accuracy using 20, 25, 15, and five wavelengths, removing 89.7–97.9% of the original variables. Component-wise ablation showed the largest mean R2 decrease when FB-CARS was replaced; PLSR captured the dominant quantitative structure, and evidence gating selectively admitted residual correction. The complete framework achieved higher target-wise R2 point estimates than the evaluated standalone regressors (mean R2 = 0.8579). Spatial projections of five held-out samples illustrated local PLSR responses without establishing within-sample chemical accuracy. The findings support further evaluation of compact spectral screening, while larger independent datasets are needed before routine quality-control use.

Agriculture doi: 10.3390/agriculture16212423

Authors: Carlos Rafael Castillo Hernández Juan Pablo Figueroa Macías Dayana Mesa Tejeda Alicia M. Díaz García Yamilet Coll García Yaneris Mirabal Gallardo

Intensive agricultural production generates significant lignocellulosic waste while contributing to heavy metal pollution, particularly copper (Cu(II)). Synthesizing low-cost nanocomposites from agricultural by-products offers a sustainable circular-economy strategy for water remediation. Here, lignin–silica nanocomposites (LSNCs) were synthesized via a sol–gel method using two abundant Cuban agro-industrial wastes: rice husk and coffee parchment. Physico-chemical characterization (FT-IR, TEM, and TGA) revealed spherical particles (50–60 nm) whose silica contents reflected their parent materials (52% for rice husk vs. 11% for coffee parchment). Batch adsorption experiments showed rapid Cu(II) uptake, with the dissolved Cu(II) concentration exhibiting an apparent plateau from the first sampling point at 10 min onward. The Sips isotherm model best described the process, yielding model-predicted maximum apparent sorption capacity (qm) of 15 mg/g for rice husk-derived LSNC and 12 mg/g for coffee parchment-derived LSNC. These findings demonstrate that valorizing local agricultural waste into functional nanocomposites provides a low-cost platform for heavy metal adsorption. Coffee parchment and rice husk were successfully used as precursors for lignin/silica nanocomposites that reduced dissolved Cu(II) concentrations in batch aqueous experiments; however, the contribution of adsorption could not be separated from possible precipitation.

Agriculture doi: 10.3390/agriculture16212422

Authors: Luana Bettanin Luiz Felipe Zappani Thaís Fernanda de Marco Alexandre Tadeu Paulino Jaqueline Scapinello

2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most widely used herbicides in agriculture, and its occurrence in aquatic environments has prompted the search for efficient removal strategies. In this context, the use of agricultural residues as precursors for adsorbent materials represents a promising approach for the treatment of contaminated water. Therefore, this study evaluated the use of rice husk-derived biochar for the removal of 2,4-D from aqueous solutions, considering adsorption conditions, kinetics, equilibrium, thermodynamics, and desorption. Batch adsorption experiments were performed to investigate the effects of solution pH (2.0, 3.0 and 4.0), adsorbent dosage (0.1 and 0.2 g), contact time (0–360 min), initial 2,4-D concentration (20–240 mg L−1), and temperature (15.0–45.0 °C), and the residual 2,4-D concentration was quantified by HPLC-DAD. Adsorption kinetics were better described by the pseudo-second-order model (R2 = 0.8727), while the Langmuir, Sips, and Redlich–Peterson models satisfactorily described the equilibrium data (R2 ≈ 0.9). Langmuir maximum adsorption capacities ranged from 20.40 to 25.37 mg g−1, while FTIR analysis indicated the involvement of surface functional groups in 2,4-D adsorption. Thermodynamic analysis revealed negative ∆G° values (−34.0 to −37.7 kJ mol−1) and a mildly positive enthalpy change (ΔH° = 12.45 kJ mol−1), indicating a spontaneous and mildly endothermic adsorption process. Desorption close to 100% was achieved using 0.1 M NaOH:methanol (50:50, v/v), demonstrating high desorption efficiency and reversible interactions under the tested conditions. These results demonstrate the potential of rice husk biochar as a sustainable adsorbent for 2,4-D removal from aqueous systems and highlight the valorization of agro-industrial waste.

Agriculture doi: 10.3390/agriculture16212421

Authors: Zhiyuan Ma Chunfang Yue Dachun Chen Qiji Sun

Soil-moisture deficits affect irrigation management in arid irrigated systems, yet their short-term persistence and uncertainty are difficult to quantify at the field scale. This study developed a spatially explicit probabilistic framework combining high-resolution root-zone soil-moisture estimation with Copula-based dependence modeling to quantify relative soil-moisture-deficit persistence in the Daheyanzi Irrigation District, Xinjiang, China. Using Google Earth Engine, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model generated daily vegetation-index and land-surface-temperature products at 30 m resolution, while a Random Forest model estimated daily 0–40 cm soil moisture. A percentile-based relative soil-moisture-deficit index (RSDI) was constructed, and four Copula models were evaluated. The Frank Copula provided the best statistical fit. Kendall’s τ decreased from 0.505 at a 1-day lead to 0.085 at a 7-day lead, indicating that persistence information was concentrated mainly within 1–3 days. When current RSDI exceeded 0.80, the probabilities of future RSDI exceeding 0.90 were 28.0% and 19.0% at 1- and 3-day leads, respectively. Rolling-window validation showed the clearest predictive advantage at 1 day, with improved discrimination at 3 days. The framework provides spatially explicit deficit probabilities to support irrigation-need assessment and field prioritization when short-term irrigation-water availability is limited.

Agriculture doi: 10.3390/agriculture16212420

Authors: Hui Guo Dailin Zhu Tianlun Wu Yuande Dong Guangyuan Zhang Wenhao Bo Jianglin Liu

Field safflower harvesting is challenged by target-scale variation, occlusion, and random growth orientations, which can degrade visual perception and end-effector alignment. This study proposes a robotic visual-guidance framework integrating FAPB-Net segmentation, corolla–bud topology, and selective main-auxiliary dual-camera correction. Inclination is estimated from the fitted corolla ellipse, while in-plane orientation is obtained from the directed bud-to-corolla vector. Auxiliary inclination estimates are introduced only when three-dimensional target matching and angular consistency are satisfied. FAPB-Net achieved corolla and bud intersection over union (IOU) values of 90.28% and 70.85% at 28 FPS. On 138 synchronized dual-camera test pairs, inclination mean absolute error (MAE) decreased from 7.5° to 6.3°, while Acc@10°, defined as the proportion of estimates with absolute error below 10°, increased from 86.2% to 90.6%. In field tests with 515 targets, recognition/localization and end-effector arrival rates reached 95.92% and 94.95%, respectively. For 447 targets with complete reference measurements, inclination and orientation MAEs were 6.0° and 4.72°. Pose-guided harvesting achieved a mean harvesting completeness rate (HCR) of 94.63%, compared with 91.40% for position-only guidance. These results indicate the practical value of pose information for reliable safflower harvesting.

Agriculture doi: 10.3390/agriculture16212419

Authors: Shixing Zheng Peng Yuan Wenqi Wang Meng Zhang

Agricultural mechanization reshapes production, yet its relationship with farmers’ subjective well-being remains unclear. Using data from 2079 crop-producing households in the 2020 China Rural Revitalization Survey, this study measures farm machinery use intensity as the mean mechanized area share across five production stages, incorporating household-owned machinery and outsourced services. Ordered logit estimates indicate a positive association that persists across alternative estimators and measures. Based on the average marginal effect, a one-standard-deviation increase in intensity corresponds, as a local approximation, to a 5.1-percentage-point higher probability of reporting “very happy.” Higher machinery use intensity is associated with lower household farm labor input per mu, higher per capita household income, and better self-rated health. Interaction tests support a stronger positive association among larger-scale households but do not establish differences by livestock production or age. These findings extend mechanization assessment from adoption to actual use intensity and highlight household welfare alongside production outcomes.

Agriculture doi: 10.3390/agriculture16212417

Authors: Canser Dolgun Esra Aydoğan Çifci

In the original publication [...]

Agriculture doi: 10.3390/agriculture16212418

Authors: Katheryn L. Vasquez-Gomez Dorila E. Grandez-Yoplac Myryam Yoplac-Navarro Ligia García Jaris Veneros Erick A. Auquiñivin-Silva Lucas D. Muñoz-Astecker Grobert A. Guadalupe

Pajuro (Erythrina edulis) is an underutilized Andean legume with potential for novel food formulations. This study evaluated the risks and benefits of pajuro seed coat and seed kernel flours under theoretical cookie formulation scenarios. Nutritional components and potentially toxic elements were evaluated, and dietary exposure was estimated probabilistically for four age groups using Monte Carlo simulation. Wheat flour replacement with either pajuro flour was modeled using a uniform probability distribution ranging from 5% to 10% of the wheat flour fraction. The highest individual benefit quotients (BQ) were associated with dietary fiber in seed coat flour and potassium in seed kernel flour among children aged 1–6 years. In contrast, hazard quotients (HQ) remained low, with the highest mean values observed for Zn in seed coat flour (0.00590) and Cu in seed kernel flour (0.00586). Aggregate total hazard quotients (THQ), interpreted as screening-level composite indices rather than direct estimates of cumulative health risk, remained below 1 across all age groups, ranging from 0.06 to 0.15 for the seed coat and from 0.08 to 0.16 for the seed kernel. The upper tail of the total benefit quotient (TBQ) reached 0.97 and 0.86 for seed coat and seed kernel, respectively, in children aged 1–6 years. These findings suggest a favorable quantitative risk–benefit profile under the modeled scenarios; however, experimental validation is needed before practical food applications can be established.

Agriculture doi: 10.3390/agriculture16212416

Authors: Jie Wang Chunli Zhao Yanan Chen Feng Zhang Jiquan Zhang Hansheng Liu Guzailinuer Aihemaitijiang

The pronounced spatial heterogeneity of soil heavy metals, coupled with the high costs and intensive resource requirements associated with large-scale sampling, presents substantial challenges for accurate spatial prediction and risk assessment. Although conventional machine learning methods demonstrate strong predictive capabilities, they often struggle to simultaneously address spatial non-stationarity, complex nonlinear relationships, and limited model interpretability. By integrating multi-source datasets with multiple machine learning models, SHAP analysis, Monte Carlo simulations, and bivariate Moran’s I analysis, this study developed an integrated and interpretable framework for spatial prediction and risk assessment. The results revealed that (1) the proposed framework achieved high predictive accuracy, with coefficients of determination (R2) exceeding 0.80 for arsenic (As), cadmium (Cd), copper (Cu), lead (Pb), chromium (Cr), and zinc (Zn). Climatic conditions and soil properties were identified as the dominant factors controlling the spatial distributions of heavy metals; (2) As and Cr were identified as the major contributors to carcinogenic risks, whereas children exhibited higher overall health risks than adults throughout the study area; (3) spatial hotspot analysis revealed significant heavy metal pollution hotspots in Gongzhuling City, northern Yitong County, and Dongliao County. This study provides new insights into soil heavy metal exposure and risk assessment, offering scientific support for targeted environmental management and pollution prevention strategies.

Agriculture doi: 10.3390/agriculture16212415

Authors: Hanjing He Ming Hong Alimu Abulaiti Youwei Jiang Fanfeng Dang

Soil salinization and alkalization are major forms of land degradation, and soil amendments are commonly used to improve the quality of saline–alkali soils. This study aimed to quantify the effects of different amendments on soil physicochemical properties in saline–alkali soils in China, compare single and mixed applications, and identify environmental and management factors associated with variations in their effects. A systematic literature search and screening process identified 223 eligible datasets from studies conducted in China. Data from amended treatments and corresponding unamended controls were synthesized using meta-analysis. The amendments included organic and inorganic materials, such as humic acid, phosphogypsum, and desulfurization gypsum, applied individually or in mixtures. Compared with unamended controls, amendment application significantly decreased soil pH, salt content (SC), exchangeable sodium percentage (ESP), sodium adsorption ratio (SAR), and bulk density (BD) by 2.8%, 12.4%, 27.2%, 44.4%, and 6.6%, respectively; soil nutrient levels increased significantly: available nitrogen (AN), available phosphorus (AP), available potassium (AK), and soil organic carbon (SOC) increased by 33.9%, 42.3%, 20.7%, and 20.5%, respectively. Mixed applications showed greater improvements in six of the nine evaluated soil properties, whereas single applications produced greater increases in available nitrogen and greater reductions in salt content and SAR. Among amendments applied individually, organic amendments generally showed larger increases in soil nutrient availability and organic carbon, whereas inorganic amendments generally showed larger reductions in ESP and SAR. Additionally, random forest analysis identified precipitation as the most important predictor of salt-content responses under single applications and application amount under mixed applications. Application amount was also the most important predictor of soil organic carbon responses under both application strategies. Overall, farmyard manure is a promising option for increasing nutrient availability and SOC and reducing BD, whereas inorganic–organic treatments merit consideration where excessive exchangeable sodium coincides with poor soil fertility. Amendment selection should therefore be based on specific soil conditions and local environmental characteristics.

Agriculture doi: 10.3390/agriculture16212414

Authors: Peiyan Wu Songlin Guo Jun Ma Feifei Yu Yapeng Lin Qingyun Yuan Chunling Chen Tan Liu

Maize Curvularia leaf spot (CLS) severely affects maize yield and quality; the implementation of non-destructive, precise disease grading is crucial for containing the spread of the disease in the field. This study utilised hyperspectral and thermal infrared remote sensing data acquired by unmanned aerial vehicles (UAVs) to extract spectral, textural (TFs) and canopy temperature features. To address the limitations of individual features, the textural-derived indices (CLSTIs) and Normalized Relative Canopy Temperature (NRCT) were developed. Mutual Information-based Recursive Feature Elimination with Cross-Validation (MI_RFECV), Variable Selection Using Random Forests (VSURF), and Variable Combination Population Analysis (VCPA) algorithms were employed to screen for sensitive spectral bands, and these selected bands were fused with the two indices to construct a multidimensional feature set. An ES-EfficientNet model was developed, featuring an MBConv architecture as its backbone with embedded serial multi-scale ECA modules. By utilising depthwise separable convolutions to extract multi-scale spectral features of the disease and employing multi-scale ECA to adaptively optimise channel weights, this model effectively suppresses interference caused by noise and redundant features. The results indicate that the feature combination based on MI_RFECV+CLSTIs+NRCT achieved the optimal classification performance in the ES-EfficientNet model. To ensure the robustness of the results, the model was evaluated across three random seeds (1, 42, and 2024). It maintained a stable performance, achieving an overall accuracy (OA) ranging from 94.17% to 95.33% and a Kappa coefficient up to 94.15%. Its identification performance was superior to that of baseline models using single-source features, and the model maintained stable performance in cross-site transfer within the same provincial-scale study area under the same-year experimental conditions. In summary, this study achieved high-precision, non-destructive grading of maize CLS, providing practical value for field disease monitoring and precise control, while also offering a robust reference framework for remote sensing-based disease identification in other crops.

Agriculture doi: 10.3390/agriculture16212413

Authors: Weizhi Feng Xinping Jia Xinyan Ou Chunrong Li Min Liu Dongyan Huang Gang Wang Daping Fu Wei Song Jingli Wang

A single rolling friction coefficient in discrete element method (DEM) simulations cannot represent differences in rolling resistance among morphology-based peanut seed classes. After shelling, Silihong peanut seeds were classified as upper, middle, or lower based on seed-shape features associated with compression by neighboring seeds. Physical and contact properties were measured, and multi-sphere models were constructed using class-specific geometries and a common geometry based on dimensions weighted by a prescribed 1:2:1 seed-number ratio. Calibration against the measured static angle of repose of 22.280° yielded a baseline seed–seed rolling friction coefficient of 0.02552; rerunning the simulation with this coefficient reproduced the calibration target with a relative error of 0.485%. Shape corrections derived from aspect ratios and single-seed critical angles were 1.1046, 0.9687, and 0.9579, giving class-specific coefficients of 0.02819, 0.02472, and 0.02445 while preserving their weighted mean. At a drum speed of 15 r/min, the three particle models produced similar overall dynamic angles of repose. Coupled simulations yielded qualified, multiple, and missed seeding indices of 92.2%, 6.7%, and 1.1%, respectively, close to the bench-test values; all three mean indices met the cited technical requirements. The parameter set and calibration procedure provide a case-specific reference for the tested Silihong seed batch, particle representation, moisture content, and metering conditions.

Agriculture doi: 10.3390/agriculture16212412

Authors: Jeremiás Máté Balogh Kyuho Lee Inhyuck “Steve” Ha Tamás Mizik

This paper examines how the financial crisis and the COVID-19 pandemic affected wine consumption across European Union (EU) member states, focusing on countries’ trade positions. Using a balanced panel of 28 EU countries from 2000 to 2022, we apply a panel difference-in-differences framework and a complementary event-study analysis to estimate the effects of these shocks. Because year fixed effects absorb shocks common to all countries, the design identifies differences between export-oriented and other countries, not EU-wide effects of the crises. Export-oriented EU countries show a larger, significant decline in measured wine consumption after the financial crisis, at the end of the post-crisis period. The comparison of the impact of COVID-19 is inconclusive because parallel trends are not supported. The estimates are conditional associations and may reflect how consumption is measured and characteristics shared by wine exporting countries. Findings highlight the importance of structural market characteristics for consumer behavior and suggest that global integration may condition the resilience of EU wine markets.

Agriculture doi: 10.3390/agriculture16212411

Authors: Vicente Maass Armando Alfaro-Tapia Gonzalo Ávila Cecilia García Francisco Carvallo Camila C. Santander Emiliano González Daniel Flores Diego Machuca-Díaz Thomas Stowhas Celso Mondlhane Héctor Moya Carlos Huenchuleo Pablo Cornejo Eduardo C. Arellano Daniela Orellana Marta V. Albornoz

Microbial inoculants are increasingly proposed within ecological-intensification strategies, yet their performance under heterogeneous commercial conditions remains uncertain. We evaluated a tripartite consortium of Funneliformis mosseae, Naganishia albida, and Bacillus tequilensis across six tomato production sites in central Chile using a 2 × 2 factorial randomized complete block design (irrigation × microbial management; three blocks per site; 72 plots). Normal irrigation was compared with reduced irrigation designed to receive 38% of the nominal irrigation depth. The same plots were followed across two growing seasons differing in fertilization and crop-management practices. Soil properties, arbuscular mycorrhizal (AM) colonization, enzyme activities, substrate-induced responses, plant physiology, biomass, and yield were assessed. Site and growing-season context strongly structured responses. Consortium application increased total AM colonization from 46.18% to 52.47% (p < 0.001; partial η2 = 0.176) and altered substrate-use composition (p = 0.009). The simple-sugars-to-other-substrates ILR balance increased under consortium application (Holm-adjusted p = 0.005), while the glucose-to-fructose balance decreased (Holm-adjusted p = 0.004), without increasing overall respiratory activity. Total and commercial yield showed no significant consortium response, and reduced irrigation did not enhance agronomic responses. Field evaluation of microbial inoculants should therefore prioritize agronomic outcomes across production contexts rather than colonization alone.

Agriculture doi: 10.3390/agriculture16212410

Authors: Ruijun Wang Lixia Shen Liyong Wang

Organic amendments influence several soil functions simultaneously, but whether material type matters at a fixed carbon (C) input is unresolved. In a rainfed maize field on the Loess Plateau under uniform biodegradable film, peat (MPT), biochar (MBC), fermented pig manure (MPM) and maize straw (MCS) received equal C (1200 kg C ha−1) with an unamended control (MCK) in a randomised complete block design (n = 3). Seventeen indicators formed a six-dimension soil multifunctionality (SMF) index; metagenomes supplied predictors of diversity, connectivity, Y-A-S investment and C-, N- and P-cycling-gene diversity. All four raised SMF 4.0–4.9-fold without differing detectably; yet, their dimensional composition differed (MBC, carbon storage; MPM, nutrient supply and enzymes; MCS, structure; MPT, water regulation and productivity). Functions were predominantly synergistic, forming two weakly linked modules (nutrient–enzyme and carbon–water–biomass) whose separation, though weaker, persisted after treatment adjustment and the few significant negative correlations reflected the reverse orientation of bulk density rather than functional trade-offs. S/Y stress-tolerance investment and connectivity were the strongest predictors, species richness the strongest correlate of SMF (ρ = 0.66), and cycling-gene diversity tracked both SMF and nutrient supply (ρ = 0.53–0.68). Five SMF-positive genera clustered on two phylogenetic branches and the most connected module; none of the 14 hub and SMF-associated genera changed detectably in absolute abundance, yet seven shifted in relative share. At equal C input, material quality reshapes the combination, not the overall level, suggesting function-targeted material choice, with locally available straw and pig manure-favoured pending multi-year, multi-site validation.

Agriculture doi: 10.3390/agriculture16212409

Authors: Fahui He Jingjin Yu Peng Han Xiping Yin Kangting Dong Zhimin Yang

Shade stress is a primary abiotic factor that limits plant growth and development. Its impact is exerted chiefly through photosynthetic pathways, and this effect is especially pronounced in turfgrass species growing under the shade of large trees and buildings. MYB transcription factors play a key role in plant photosynthesis, and several MYB members are involved in plant shade responses. However, the function of MYB44 in shade responses has not yet been reported in plants. In this study, overexpression of AsMYB44 in creeping bentgrass under shade stress increased the plant’s chlorophyll a, chlorophyll b, and carotenoid content, net photosynthetic rate, and antioxidant enzyme activities in creeping bentgrass. Transcriptome analysis revealed that AsMYB44 overexpression significantly upregulated the expression of genes associated with photosynthesis, photosynthesis antenna proteins, and chlorophyll metabolism pathways. Collectively, these results demonstrated that overexpressing AsMYB44 improved shade tolerance in creeping bentgrass. Our findings provides important candidate genes for shade tolerance breeding in plants.

Agriculture doi: 10.3390/agriculture16212408

Authors: Andrea Miceli Dario Gaudioso Niccolò Rimbotti

Bio-based inputs are increasingly promoted as alternatives or complements to conventional agricultural inputs, yet evidence on their adoption remains fragmented across product categories, behavioural outcomes, and farming contexts. This scoping review mapped survey-based and socio-economic evidence on how farmers perceive, evaluate, and use bio-based inputs in crop production systems. Following PRISMA-ScR guidance, Scopus and Web of Science were searched for English-language peer-reviewed survey-based socio-economic studies involving farmers or agricultural input-use decision makers; 1904 records were initially identified; 448 articles were ultimately included. Data were charted using a structured extraction framework and synthesised across four questions addressing perceptions, willingness to adopt or pay, translation into trial or actual use and post-adoption outcomes, and advisory, market, institutional, and policy conditions. Findings reveal tension between perceived sustainability benefits and concerns over agronomic reliability, cost, risk, quality, and compatibility. Willingness was conditional, and favourable attitudes or intentions did not necessarily translate into actual use, highlighting an attitude–adoption gap. The synthesis suggests a context-dependent pathway comprising comparative appraisal, conditional willingness, practical feasibility, and progression towards trial or actual use. Post-adoption evidence remained limited in this exploratory, search-dependent mapping. Overall, adoption emerged as a context-dependent and system-enabled process rather than a direct consequence of favourable attitudes.

Agriculture doi: 10.3390/agriculture16212407

Authors: Ijaz Yaseen Gyuha Kim Beomseon Lee

Korea produces comparatively less summer-cultivated paprika from August to September due to unfavorable environmental circumstances, particularly high temperatures that begin in late June. As a result of this, managing a greenhouse climate well during the hot season is essential to boosting paprika production and ensuring its sustainability in relation to agricultural expansion. This study aims to analyze the efficient use of environmental characters and to minimize prediction errors with the help of data collected from an advanced environmental controlled system installed in a greenhouse, along with frequently used machine learning techniques, as a sustainable method to adopt. Artificial intelligence (AI)-based sustainable methods were used for evaluating the cumulative paprika fruit count (Pn.f) based on the greenhouse environmental data using multiple linear regression (MLR), support vector machine (SVM), ensemble tree bagging (ET Bagged), and Random Forest (RF). The twenty-one potential indicators/input parameters, comprised of relative humidity, humidity deficit and temperature-related variables, were utilized. A number of statistical tests, including R-square (R2), root mean square error (RMSE), and mean absolute error (MAE), were employed to further bolster the performance efficiency of the computed prediction models. To determine the precise impact of the input parameters, parametric and sensitivity tests were also carried out. The findings show that the suggested models meet all the accuracy-related acceptance criteria. SVM and RF, however, perform better than MLR and ET Bagged. The models’ robustness and dependability are asserted not only by simple correlation but also by the physical and logical relationships between the sensitive elements and the dependent variable. The significance of our work lies in the use of SVM and RF models for the efficient handling of environmental factors, and the findings also advise greenhouse growers on how to modify these climatic variables for greenhouses.