In recent decades, the intensive use of synthetic pesticides and agrochemicals to maximise crop productivity has severely degraded soil physicochemical properties, contaminated water resources, caused environmental pollution, and reduced food quality and safety. In addition, heavy metals and toxicants released from agricultural activities have accumulated within agroecosystems, posing serious risks to environmental and human health. This review describes sustainable agricultural technologies and integrated management systems that can reduce chemical dependency while improving soil fertility, crop productivity, and environmental sustainability. Eco-friendly approaches, such as natural farming, biofertilisers, plant growth-promoting rhizobacteria (PGPR), biocontrol agents, bioremediation, and phytoremediation, contribute to enhanced nutrient cycling, stress tolerance, contaminant detoxification, and the restoration of ecological balance. Special emphasis is placed on how PGPR, biofertilisers, biocontrol agents, and remediation technologies can be integrated within sustainable management systems to improve agricultural resilience and soil health. Recent advances in microbial ecology, formulation technologies and integrated soil management practices are also explained to highlight their role in sustainable crop production and environmental restoration. It further demonstrates that combining these biological and remediation-based technologies can effectively mitigate the adverse impacts of conventional agricultural practices while promoting climate-resilient and resource-efficient farming systems. Overall, the study concludes that large-scale adoption of integrated sustainable agricultural strategies is essential for improving food security, reducing environmental pollution, restoring degraded ecosystems, and supporting global sustainability goals.
In this study, we induced mutations in the genome of Trichoderma harzianum (NAS-T101) via gamma irradiation to enhance its antagonistic potential against Pythium ultimum. Efficacy testing of mutants and the wild type was conducted in a completely randomised design (CRD) with four replications in the greenhouse on lettuce (Grand Rapid cultivar) at the Nuclear Agriculture Research Institute collection (NSTRI, AEOI) in spring 2018. The experiments included plants treated with T. harzianum NAS-T101 (non-irradiated) and its selected mutant TM4 against the P. ultimum pathogen, and the control plant. Disease incidence (DI), morphological traits (length and dry weight of shoots and roots), and physiological traits (total protein, peroxidase, polyphenol oxidase, chlorophyll a and b, and malondialdehyde activity) were measured. The results showed that T. harzianum (NAS-T101 and TM4) significantly reduced DI and developed the plant root system. T. harzianum (NAS-T101 and TM4) treatments increased the accumulation of proteins of molecular weight associated with peroxidase and polyphenol oxidase enzymes, enhanced their activity, and led to a significant increase in chlorophyll a and b. The TM4 treatment showed a significant increase in antioxidant enzyme activities and protein accumulation compared to the control. This study showed that the mutant (TM4) was significantly more efficient at biocontrol of P. ultimum than its parental, un-irradiated isolate, and that lettuce plants treated with TM4 maintained morphological and physiological processes under disease conditions.
Genetic variability is essential for accelerating the selection of high-yield varieties. Therefore, this study aims to evaluate the genetic variability and agronomic performance of eggplant doubled haploid obtained through anther culture. A total of 18 genotypes (15 doubled haploid lines and 3 commercial varieties) were assessed using a randomised complete block design with 4 replications. Data were analysed for heritability, cluster analysis, phenotypic correlations, and a weighted selection index. Significant variation was observed among genotypes, with stem diameter, plant height, fruit number, fruit weight, and fruit length showing high heritability and extensive genetic diversity. In addition, cluster analysis grouped the genotypes into 3 clusters at a 20% similarity threshold. The results showed that fruit weight per plant exhibited strong positive correlations with fruit weight, diameter, length, stalk length, plant height, and stem diameter. The weighted selection index identified 11 promising doubled haploid lines, including derivatives of Hitavi F1 (AM8H, AM9H, AM10H, and AM11H), Mustang F1 (AM13M and AM14M), and Provita F1 (AM4P, AM6P, AM23P, AM28P, and AM29P). These results indicated the strong potential of the selected doubled haploid lines for eggplant variety development.
Net blotch, caused by Pyrenophora teres Drechsler, is a major constraint on barley production. We assembled a panel of 276 spring barley genotypes from 18 countries and evaluated resistance to net form net blotch (P. teres f. teres) using lesion scoring after controlled artificial inoculation of detached leaf segments. Inoculum identity was verified by species-specific PCR, confirming the presence of P. teres f. teres (378 bp) and the absence of P. teres f. maculata. To track inoculum performance and normalise lesion scores, three reference barley genotypes (‘Pax’, ‘P22-SP’, ‘Polo’) were included in every inoculation run. Across 26 inoculation time points, lesion severity in reference barley genotypes remained within a narrow, biologically consistent range, indicating stable yet discriminatory infection pressure. Two independent datasets of 276 barley genotypes showed similar distributions of relative lesion score (paired non-parametric test: no shift in central tendency). At the genotype level, categorical agreement between datasets was high: 62.0% exact matches, 37.7% one-step differences and 0.4% ≥2-step differences. Rank concordance was moderate (Spearman ρ = 0.619; Kendall τ_b = 0.584; both p < 0.001), and chance-corrected agreement was fair (Cohen’s κ ≈ 0.36 – 0.40), driven mainly by Susceptible ↔ Intermediate reclassifications. Thirty-four genotypes were consistently classified as resistant or highly resistant in both datasets; three (‘Romans’, ‘SK-3212’, ‘Svit’) showed particularly low within-genotype variability across replicates, indicating stable expression of resistance. These results demonstrate the robustness and reproducibility of the phenotyping pipeline and identify promising germplasm for resistance breeding against net form net blotch.
The sequestration and release of carbon in soil is a crucial aspect of agricultural production studies, involving numerous small-plot trials and modelling processes. Small-scale heterogeneity in soil properties can influence measured carbon dioxide (CO2) fluxes. This study aimed (i) to compare CO2 emission from two neighbouring sandy soil plots managed with identical agricultural practices; (ii) to identify the key factors influencing soil CO2 emissions; and (iii) to examine the effect of soil water content (SWC) and soil temperature (Ts) on the results. Two plots located approximately 30 m apart and differing in terms of their humus depths and contents were selected for investigation. Continuous SWC and Ts measurements were taken. Portable devices were used to determine CO2 emissions, penetration resistance (PR) and vegetation cover. The humus layer in plot A was 55 cm thicker than in plot B, while the soil organic carbon (SOC) content was 18% and 163% higher in the 0–30 cm and 30–90 cm soil layers, respectively. Vegetation cover was nearly twice as high in plot A, and the mean soil CO2 emissions were 36% higher than those measured in plot B. SWC showed an opposite trend, with plot B exhibiting values that were 9.7% and 17.7% higher than those of plot A in both the top and deepest soil layers, respectively. These findings emphasize the importance of including small-scale spatial heterogeneity when parameterizing or interpreting biogeochemical models, particularly when model inputs are based on limited soil measurements from specific locations.
Reliable joint detection of mango fruits and stems is an essential upstream perception task for robotic harvesting, but remains challenging because stems are small, slender, frequently occluded, and visually degraded by illumination variation. This study proposes MangoNET, a YOLOv11n-based framework for joint mango fruit and stem detection in complex orchard environments. A P2 high-resolution detection head preserves fine spatial information for small targets, while SPPF-ELAN aggregates local and contextual features for partially visible objects. SENet recalibrates channel responses under illumination variation, and WIoU v3 regulates bounding-box samples with different localization qualities. A dataset containing 1782 original images of Tainong and Jinhuang mangoes was collected from two orchards and data augmentation was applied only to the training set, increasing its size from 1172 to 2886 images through rotation, contrast adjustment, and Gaussian noise addition. MangoNET achieved fruit and stem F1-scores of 0.920 and 0.916, respectively, with mAP50 and mAP50–95 values of 0.941 and 0.690. Compared with YOLOv11n, mAP50 and mAP50–95 increased by 1.6 and 2.9 percentage points, respectively, while stem recall increased from 0.877 to 0.906. Source-image-independent five-fold cross-validation yielded mean mAP50 and mAP50–95 values of 0.944 and 0.711, respectively. Pilot evaluations using images acquired by a UAV and an RGB-D camera in a geographically distinct orchard suggested that MangoNET could maintain detection performance in a different orchard environment. MangoNET supplies fruit and stem candidate regions for subsequent association, harvesting-point localization, and robotic manipulation.
Financial vulnerability in agriculture is shaped by both enterprise-specific factors and the agricultural systems in which firms operate, challenging financial distress prediction models based exclusively on firm-level financial metrics. This study assesses the predictive performance of logistic regression (LR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and category boosting (CatBoost), examines whether country-level agricultural environment characteristics improve prediction beyond traditional financial indicators, and uses Shapley additive explanations (SHAP) to analyze predictor contributions. The empirical analysis is based on 28,745 firm-year observations of agricultural enterprises in the Visegrad Group (V4) countries over 2019–2024. Adding agricultural environment variables produced a modest but consistent improvement in predictive performance, increasing AUC from approximately 0.854–0.855 in firm-level boosting models to 0.864–0.865 in integrated specifications. All three gradient boosting algorithms outperform conventional LR, although differences among XGBoost, LightGBM, and CatBoost remain statistically insignificant. SHAP analysis identifies liquidity, leverage, profitability, and firm size as the primary predictors while revealing nonlinear and threshold-dependent relationships not captured by conventional linear models. Integrating agricultural environment characteristics with explainable machine learning (ML) therefore supports more comprehensive, transparent, and context-aware early-warning systems for agricultural financial distress.
Whether the extracts of Fomes fomentarius, Ganoderma applanatum and Trametes versicolor can inhibit the growth of Fusarium graminearum in vitro, reduce the severity of Fusarium head blight (FHB) and reduce fungal colonization of wheat seeds artificially infected with this pathogen were the questions that initiated this research. The mycelial growth of F. graminearum was significantly reduced after treatment with ethanol extracts of the studied white-rot fungi, from 43.1% with F. fomentarius to 53.2% with T. versicolor. Likewise, these extracts significantly reduced disease development in artificially infected wheat seeds. Disease severity was quantified using the Disease Severity Index (DSI). In fact, after artificial infection with F. graminearum of lines carrying effective resistance (Fhb7 locus) from an alien source and their treatment with mushroom extracts, disease severity was negligible, and seedling fitness was very high. The highest efficacy of 99.49% was recorded in the resistant line R6-1, carrying the Fhb7 allele from Thinopyrum elongatum (7E) following treatment with the T. versicolor extracts. Slightly lower efficacy values were obtained after treatment with G. applanatum (97.83%) and F. fomentarius (97.47%). The obtained results showed that the selected mushroom extracts can be used in F. graminearum control and support successful wheat cultivation in FHB-prone areas.
Fixed full-boundary headlands create redundant non-working space in irregular fields, while working direction and swath offset jointly affect coverage quality and inter-swath turning cost. This study proposes a dynamic-headland-aware coverage path planning framework that allocates headland space according to swath endpoints and turning demand and generates parallel working swaths and Bézier U-turns for each direction–offset candidate. Field-specific empirical cumulative distribution function (ECDF)-midrank normalization converts turning distance, coverage error, and headland ratio into relative quality scores, and DQN-SHADE searches the resulting non-smooth discrete evaluation landscape. In geometric simulations on 24 actual field boundaries, DQN-SHADE achieved the lowest mean gap to the discrete reference optimum (0.0034) and the highest threshold success rate, SR5×10−3, of 80.83% among six stochastic optimizers under a common candidate-evaluation budget. Relative to fixed full-boundary headlands, dynamic headland allocation reduced the mean headland ratio from 13.30% to 7.52%, increased retained working area by 16,378.23 m2 per field, and maintained 98.16% mean coverage. The proposed framework improves field-space utilization while maintaining coverage quality and geometric feasibility under the evaluated simulation conditions.
Automatic reseeding in spoon-chain potato planters requires reliable classification of miss-seeding, normal, and multiple-seeding events. Oblique viewing and continuous spoon motion cause scale changes, boundary truncation, blur, and occlusion, which destabilize single-frame predictions. The proposed system treats each seed-spoon passage as one control event. It combines lightweight detection, track-level learning, and multi-frame classification with air-blow clearing and missed-seed reseeding. Based on the target-size distribution, the detector removes the P3 head from YOLOv8n and restores shallow details through space-to-depth rearrangement and gated addition. Frame quality combines boundary distance, sharpness, adjacent-box intersection over union, and box-area stability. High-quality frames form a track prototype, while low-quality frames receive stronger constraints. A quality-gated temporal network classifies each spoon as containing 0, 1, or at least 2 seed potatoes. Each model was trained with three random seeds under the same data split. Compared with YOLOv8n, the detector reduced the parameter count and computational cost by 35.8% and 48.3%, respectively. Track-level learning improved event accuracy by 3.67 percentage points over frame-level training. The seven-frame model achieved 93.41% event accuracy and 93.80% macro-F1. At 0.5 m/s, the closed-loop recognition, air-blow, and reseeding success rates were 93.7%, 96.4%, and 97.1%, respectively. These results provide a practical basis for real-time, event-level planting-quality control in spoon-chain potato planters.
Phosphorus (P) is one of the main limiting factors for sugarcane establishment and longevity in tropical soils, requiring biotechnological strategies to improve fertilizer use efficiency. This study evaluated the effects of inoculation with Bacillus velezensis UFV 3918, alone or combined with reduced monoammonium phosphate (MAP) doses, on the morphological, nutritional, and root nutrient uptake responses of sugarcane grown in a dystrophic Red Latosol under greenhouse conditions. The experiment was conducted in a completely randomized design with six treatments and four replicates: absolute control (AC, without MAP), commercial control (CC, recommended MAP dose), B. velezensis alone (Bv), and Bv combined with 1/3, 2/3, or the full recommended MAP dose. Plant growth responses varied across treatments and stages, with Bv promoting greater leaf area than the CC at 120 and 180 DAP, whereas Bv + 1/3 MAP maintained leaf area comparable to the CC throughout the evaluation period. Principal component analysis (PCA) revealed a clear separation among treatments, with Bv exhibiting the most distinct plant response, primarily associated with greater stalk diameter, root and stalk biomass, higher concentrations of potassium and phosphorus in stalks, sulfur and magnesium in roots, and greater boron-use efficiency. Bv + 1/3 MAP displayed an intermediate multivariate profile, mainly associated with phosphorus uptake per unit root length and nutritional attributes related to phosphorus acquisition under reduced fertilizer supply. Correlation analysis further revealed strong positive associations among growth-related traits, biomass accumulation, and phosphorus-related variables, indicating a close relationship between P acquisition and plant responses to bacterial inoculation. Overall, inoculation with B. velezensis UFV 3918 improved early sugarcane growth and nutrient acquisition, and its combination with 1/3 of the recommended MAP rate maintained plant performance. These findings indicate the potential for reducing mineral P inputs during early sugarcane establishment; however, long-term field trials are required to determine whether these responses can be sustained throughout the crop cycle and under commercial production conditions.
Climate variability can affect forage production through changes in temperature, precipitation, and the frequency of stressful weather conditions. We examined climatic variability and alfalfa (Medicago sativa L.) productivity in three Romanian development regions—South-Muntenia, South-East, and North-East—over 2006–2024. The climatic variables included mean, maximum, and minimum air temperature, annual precipitation, and maximum 24 h precipitation. Regional relationships between climatic variables and alfalfa yield were examined using descriptive statistics, Kendall’s rank-based trend analysis with Sen’s slope, Pearson correlation, and multiple linear regression with diagnostic testing. We also developed an exploratory Alfalfa Climate Resilience Index (ACRI) to compare relative climate-resilience profiles of regional alfalfa production systems. The index combines two performance-related components, normalized mean yield and yield stability, with two climatic-context components, normalized precipitation and inverse normalized mean temperature. Its sensitivity to alternative component weights was also evaluated. Mean annual temperature increased significantly in all regions, with Sen’s slopes ranging from +0.071 to +0.100 °C year−1, whereas annual precipitation showed no significant monotonic trend. Yield slopes were negative in all regions, but only North-East showed a statistically significant decline (τ = −0.794, p < 0.001). Mean annual temperature was negatively correlated with yield in all three regions (r = −0.617 to −0.749), whereas the precipitation indicators showed no significant bivariate relationships with yield. The regional multiple regression models accounted for 64.3–85.1% of the observed variation in annual yield (R2 = 0.643–0.851), with the relative importance and statistical significance of climatic predictors differing among regions. ACRI ranked North-East first (0.760), South-Muntenia second (0.469), and South-East third (0.295), and this ordering remained unchanged across the alternative weighting scenarios examined. These scores represent relative differences among the three regional production systems within the study dataset and should not be interpreted as absolute measures of intrinsic alfalfa resilience. The findings show that climate–yield relationships vary among regions and that temperature was more consistently associated with yield than the precipitation indicators considered here. ACRI is therefore presented as an exploratory, dataset-dependent framework for within-dataset comparison of regional alfalfa production-system climate resilience and requires validation using broader reference datasets, common normalization criteria, and additional management and environmental variables before wider application.
Biochar application is widely proposed to improve crop yield and fertilizer productivity, yet its performance varies across climatic, soil, and management contexts. This global meta–analysis study of about 705 database entries retrieved from 93 studies analyzed responses across climatic classification, biochar feedstock and properties, soil properties, fertilizer and co–application regimes, and experimental types. Overall, biochar responded at 33%, 34%, and 32% for yield, PFPN, and PFPP. The strongest response occurred at the tropical, humid agro–ecological zone under moderate temperature regimes. Wood-derived biochar with lower pyrolysis temperatures (<400 °C), combined with moderate soil carbon content and nitrogen, produced a greater improvement. Organic + biochar co–applied fertilizer application rates of ≤50 kg N ha–1 and 50–100 kg P2O5 ha–1 increased the response, while excessive fertilizer rates (>200 kg ha) showed reduced response. Economic benefit analysis also showed the average overall highest maximum yield at ≤40–80 t ha–1 biochar rates demonstrating simultaneous improvement in yield and fertilizer productivity under appropriate management.
Stem development determines plant architecture and mechanical support, directly affecting yield, lodging resistance, and ornamental quality. Microtubules are essential components of the plant cytoskeleton, but their role in stem development in herbaceous peony (Paeonia lactiflora Pall.) remains largely unexplored. Here, a β-tubulin gene, PlTUB1, was identified from P. lactiflora. Its coding sequence was 1353 bp in length, encoding a 450-amino-acid protein that localized to microtubules, and its transcript levels declined progressively during stem development. Transient virus-induced gene silencing of PlTUB1 in P. lactiflora, with a silencing efficiency of approximately 55%, enhanced stem development, with increased stem diameter, elevated cellulose accumulation, and thickened secondary walls. Given that cellulose deposition and secondary wall thickening were closely associated with cortical microtubule organization, the role of PlTUB1 in microtubule regulation was further examined. Heterologous overexpression of PlTUB1 in Arabidopsis thaliana disrupted transverse microtubule alignment, reduced cellulose deposition, and impaired secondary wall and xylem development, thereby confirming the mechanistic link between PlTUB1 and microtubule organization. Collectively, these findings demonstrated that PlTUB1 negatively regulates stem development by modulating microtubule organization and secondary wall deposition, providing new insights into the cytoskeletal regulation of stem development in P. lactiflora.
Soil degradation constrains cropland productivity in Northeast China, where conservation tillage has been widely adopted to improve soil structure and sustain maize (Zea mays L.) production. However, previous studies have focused mainly on single soil types or regional-scale assessments, and the applicability of conservation tillage across contrasting cultivated soil types remains inadequately characterized. From 2021 to 2023, field experiments were conducted across geo-ecological zones within mid-temperate sub-humid and mid-temperate sub-arid climatic regions, encompassing seven typical cultivated soil types: Aeolian sandy soil, Bielic, Black soil, Chernozem, Histosol, Inceptisol, and Mollisols. A strip-tillage with straw return treatment (ST) was compared against no-tillage with straw mulching treatment (CK) to systematically evaluate the effects of strip-tillage on (i) soil physical properties (bulk density and porosity), (ii) soil chemical properties (organic matter and total nutrient contents), and (iii) maize yield components. Across the full dataset, ST significantly reduced soil bulk density and increased total porosity relative to CK; however, soil-type-specific comparisons showed that significant responses were detected only in some soil types. By contrast, soil chemical properties were significantly influenced by the treatment × soil type interaction: SOM and STN contents increased significantly in Inceptisol under ST, STP increased significantly in Mollisols, and STK increased significantly in Histosol. A PCA-derived composite physicochemical score, based on six standardized soil indicators, was higher under ST than CK in six of the seven soil types relative to CK, with the most pronounced improvements occurring in Aeolian sandy soil (+1.23), Bielic (+0.87), and Black soil (+0.65); significant maize yield increases under ST were detected in Aeolian sandy soil and Black soil, with mean increases of 7.07% and 10.94%, respectively. The associations between maize yield components and grain yield differed between ST and CK, with 1000-kernel weight showing a stronger association with grain yield under ST. Collectively, these findings indicate that ST can improve soil physical structure, but its effects on soil chemical properties, maize yield, and yield-component associations are soil-type dependent.
Sloping farmland erosion in cold-region black soil zones threatens global agriculture, yet regional-scale quantitative evidence on the erosion mitigation performance and spatial suitability of control measures remains lacking. This study integrated 959 field observation datasets from 132 peer-reviewed publications, and used meta-analysis and Boosted Regression Tree (BRT) models to evaluate tillage, biological, engineering, and combined conservation measures in Northeast China. Results showed that all measures significantly reduced erosion, achieving an average runoff reduction of 73.2% and sediment reduction of 83.6%. Specifically, engineering measures showed the highest runoff reduction, while combined measures achieved more than 95% sediment reduction. Machine learning revealed that runoff reduction was primarily regulated by precipitation, whereas sediment reduction was mainly controlled by soil clay content and bulk density. Spatially, tillage, biological, and engineering measures showed the highest suitability in continuous cultivated plains, plain–hill transition zones, and low hilly and gully regions, respectively. This study moves erosion-control assessment beyond comparisons of average runoff and sediment reduction rates toward environmentally matched spatial allocation, providing a basis for targeted black soil conservation and resilient grain production in cold-region agricultural landscapes.
This study addresses two key challenges in tea nutrient analysis: the limited range of detectable nutrient elements in tea gardens and the interference caused by moisture in fresh tea leaves during spectral data acquisition. To overcome these issues, hyperspectral technology combined with effective spectral intelligent processing algorithms was used to develop quantitative, non-destructive prediction models for four essential nutrients—nitrogen, phosphorus, potassium and carbon—in fresh tea leaves. This study utilises EPO to address the issue of moisture interference in the spectra of fresh tea leaves, and combines it with SG for spectral data processing, namely SG-EPO. Through comparative analysis with traditional pre-processing algorithms, this method was found to effectively reduce moisture interference in fresh tea leaves and enhance prediction accuracy (R2). Finally, based on a multi-stage feature selection strategy involving SG-EPO-VCPA-IRIV-SVM_RFE and SG-EPO-BOSS-SVM_RFE, and in combination with three machine learning models—XGBoost, BP and SVR—quantitative prediction models were developed. The results indicated that the R2 for nitrogen is 0.896; phosphorus, 0.954; potassium, 0.913; and carbon, 0.928. The RMSEP values for N, P, K, and C were 0.062, 0.075, 0.438, and 0.157, respectively. Using this model, nitrogen, phosphorus, potassium and carbon contents were rapidly predicted in tea leaves after the exogenous application of GABA at different concentrations, enabling an assessment of the effects of exogenous GABA application on these nutrient levels. Furthermore, the Shapley Additive Explanation method was employed to identify the feature wavelengths that had the greatest contribution to the XGBoost model, effectively explaining the information underlying the improved model predictions regarding the correlation between spectral and chemical values. Finally, the accuracy of the predictions was confirmed using 20 samples from independent data, demonstrating that the proposed model can achieve rapid, non-destructive detection of multiple nutrient elements in fresh tea leaves under in situ conditions in tea plantations.
Over the past two decades, genetic selection has increased average total litter size in commercial sow herds from approximately 9–10 piglets to more than 14 piglets per farrowing, an achievement that has delivered clear economic benefit but has been accompanied by a parallel rise in pre-weaning mortality (PWM), which is now commonly reported in the range of 12–20% and, by some indicative estimates, is trending upward even in well-managed herds. This review synthesizes the veterinary and reproductive-physiology literature on hyperprolificacy in sows, examining its downstream consequences for individual piglet birth weight, farrowing duration, colostrum access, and passive immunoglobulin G (IgG) transfer. Attention is given to the biological mechanism by which litter size dilutes colostral IgG, including vaccine-induced maternally derived antibody (MDA) against porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2), and to the strength of the supporting evidence with regard to each pathogen, which differs considerably. This evidence is considerably stronger for PCV2, where maternal vaccination has a directly demonstrated effect on offspring antibody titres, than for PRRSV, where litter-size-specific dilution of maternally derived antibody has not yet been directly measured and remains an inferred, biologically plausible mechanism rather than a demonstrated one. The review further appraises management interventions including cross-fostering, split suckling, nurse sows, immunoglobulin supplementation, and structured neonatal triage, together with their documented trade-offs and considers the animal-welfare dimension of continued genetic selection for litter size. A dedicated section evaluates the current state of precision livestock farming (automated farrowing and crushing surveillance, computer-vision piglet weighing, and genomic selection for litter uniformity and robustness), concluding that these tools are promising but largely still at the research or early-adoption stage. We conclude that inadequate transfer of passive immunity behaves as an independent risk factor for mortality, separate from birth weight, and that management responses should be viewed as necessary complements to, rather than substitutes for, a re-balancing of genetic selection indices toward piglet survivability.
Excessive chemical fertilizer and low N use efficiency constrain mulched drip irrigated maize production in China’s Hexi Oasis, while livestock manure is underutilized. Recycling biogas residue and slurry offers a solution, but optimal substitution ratios under reduced N remain unknown. We conducted a two year field experiment with two reduced N rates (15% and 30% relative to the conventional local rate of 360 kg N ha−1) combined with biogas residue replacing basal N at rates of 25%, 50%, or 75% and biogas slurry replacing 50% of topdressed N. The best performing treatment (N125: 15% N reduction + 25% residue substitution) significantly increased LAI (11.77%), SPAD value (10.98%), dry matter (2.51%), leaf and stem N translocation (13.81% and 10.00%, respectively), leading to a 2.14% yield gain. NUE, ANUE, and PFPN improved by 27.4%, 19.5%, and 18.5%. Path analysis revealed that N125 enhanced canopy photosynthesis and dry matter translocation, increasing grain weight. These findings demonstrate that integrated N reduction with biogas substitutes enhances productivity and N efficiency while recycling organic waste. Therefore, under the present experimental conditions, N125 represents a promising strategy for simultaneously improving grain yield and nitrogen use efficiency; however, multisite and long term validation is warranted to substantiate its broader applicability.
Allelopathy is an ecological process in which released chemicals modify the performance of neighbouring organisms, whereas phytotoxicity describes an inhibitory response to a substance under a defined assay and, by itself, does not demonstrate field-level allelopathy. This critical narrative review combines a structured re-screening of the literature with targeted updating through 21 August 2026. Searches used combinations of allelopath*, allelochemical*, phytotox*, bioherbicid*, hormesis, biostimulant*, rhizosphere, microbiome, formulation, resistance, and regulation; peer-reviewed studies were complemented by authoritative regulatory and resistance databases. Evidence was appraised according to experimental context and mechanistic strength, explicitly distinguishing field or whole-plant validation from controlled bioassays, experimentally supported molecular targets from physiological or omics associations, and computational predictions. The synthesis covers chemical diversity, botanical and microbial sources, agro-industrial by-products, mechanisms of phytotoxicity and low-dose responses, rhizosphere interactions, and formulation strategies. Important corrections emerge from this evidence hierarchy: strigolactones are carotenoid-derived apocarotenoid hormones/signals rather than sesquiterpenes; multi-target activity does not preclude resistance evolution; cyanobacterial responses cannot be directly extrapolated to weeds or crops; and hormetic stimulation under controlled conditions is not equivalent to reliable field biostimulant performance. The principal translational gaps are insufficient dose–response standardisation, limited crop-selectivity and field validation, variable biomass chemistry, incomplete carrier and non-target safety data, and regulatory uncertainty for multifunctional products. Allelochemicals therefore represent promising components of integrated crop protection, but their agronomic value depends on rigorous evidence, formulation-specific validation, and context-dependent deployment rather than on laboratory phytotoxicity alone.
