Crop–weed competition markedly reduces cereal yield. Integrative weed management approaches, involving the use of humic acid (HA) and seaweed extract (SWE), have gained attention as herbicide efficacy declines and environmental concerns grow. However, potential synergistic effects between HA and SWE have not yet been investigated. We evaluated the effects of HA, SWE, and their combination (HA+SWE) on the growth, yield, and competitive ability of cereals against wild weed beets (Beta vulgaris L.). A single-season field experiment was conducted using a split-plot design within a randomised complete block to assess the effects of treatment amendments on wheat, barley, and oats. The results showed that HA and HA+SWE organic amendments consistently improved grain yield and biomass across crop species. SWE responses varied across species, indicating species-dependent sensitivity. In addition, HA enhanced barley weed suppression, highlighting its dual roles in improving crop vigour and reducing weed proliferation. In contrast, SWE modestly increased spike length in oats, emphasising its effect on crop growth characteristics. Overall, these preliminary findings support targeted biostimulant use to enhance cereal yield and integrate weed management into sustainable cropping systems.

Although herbicides are essential for global agriculture and controlling weeds, they impact soil microbial communities and CO₂ emissions. However, the effects of herbicides, tillage systems, and nitrogen fertilisation on CO₂ emissions under different environmental conditions are poorly understood. This review explores how various agricultural practices and inputs affect CO₂ emissions and addresses the impact of pest-management strategies, tillage systems, and nitrogen fertiliser usage on CO₂ emissions using multiple databases. Key findings indicate that both increased and decreased tendencies in greenhouse gas (GHG) emissions were observed, depending on the herbicide type, dose, soil properties, and application methods. Several studies reported a positive correlation between CO₂ emissions and increased agricultural production. Combining herbicides with other methods effectively controls emissions with minimal chemical inputs. Conservation practices like no-tillage were more effective than conventional tillage in mitigating carbon emissions. Integrated pest management, conservation tillage, and nitrogen fertiliser rate optimisation were shown to reduce herbicide use and soil greenhouse gas emissions. Fertilisers are similarly important; depending on the dosage, they may support yield or harm the soil. Fertiliser benefits are contingent on appropriate management practices for specific soil and field conditions. This review highlights the significance of adaptable management strategies that consider local environmental conditions and can guide future studies and inform policies to promote sustainable agriculture practices worldwide.

Field experiment conducted at the Agricultural Research Station, College of Agriculture, University of Basrah, (30°34'11"N 47°45'05"E) during the winter season of 2023–2024. The aim was to determine the influence of sowing date (20 October, 05 November and 20 November) on physiological growth characteristics and grain yield of four triticale varieties, (Admiral, Farah, Amal 7 and Almohanad). The experiment was conducted in a split-plot arrangement according to a randomized complete block design using three replicates. Sowing dates were applied at main plots, while, varieties were occupied the subplots. The results revealed significant differences between triticale varieties in all most parameters. Farah variety exhibited superior performances in terms of plant height (126.25 cm), flag leaf area (51.69 cm2), leaf area index (4.01), leaf area duration (116.35 day), crop growth rate (23.11 g m-2 day-1), and grain yield (5.53 t ha-1). Generally, earlier sowing dates associated with improved plant growth, resulting in increased yields, so highest average of grain yields obtained from sowing date of 20 October, by 4.93 t ha-1. For the interaction, the results showed that, Farah variety at sowing date of 05 November gave highest grain yield by 6.28 t ha-1. Thus, Farah variety was recommended for cultivate at Basrah, Iraq.

Maize productivity and weed suppression are influenced by the combined use of tillage, nitrogen, and herbicide practices, but integrated evidence on crop and weed community responses to these management factors across growing seasons and tillage contexts remains limited. This study aimed to assess variation in total weed biomass, dicot and monocot weed abundance, dominant weed species, maize aboveground biomass, harvest index, and grain yield under conventional tillage (CT) and minimum tillage (MT), two nitrogen rates (74 and 147 kg N ha⁻¹), and five herbicide programs differing in active ingredient composition and application strategy, and to determine whether the relative importance of nitrogen and herbicide management varied with growing season and tillage context. Rainfed field trials were conducted in northwestern Hungary during the 2023 and 2025 growing seasons, and treatment-associated patterns were evaluated using exploratory analyses conducted separately for each year × tillage combination. In 2023, higher mean grain yield was observed at 147 than at 74 kg N ha⁻¹ under both CT (from 12.25 to 14.96 t ha⁻¹) and MT (from 11.95 to 15.41 t ha⁻¹). In 2025, exploratory ANOVA indicated herbicide-program-associated variation in grain yield under both tillage systems, and the highest yields were observed under the dicamba + nicosulfuron + prosulfuron treatment, reaching (21.02 t ha⁻¹) under CT and (14.82 t ha⁻¹) under MT. Total weed biomass also showed herbicide-program-associated variation in the exploratory analyses in 2025, with the lowest biomass observed under tembotrione in CT (17.56 g m⁻²) and dicamba + nicosulfuron + prosulfuron in MT (22.68 g m⁻²). Patterns of dicot and monocot abundance associated with herbicide program also differed among year × tillage contexts, and the dominant weed species differed between years and tillage systems. Crop biomass and harvest index varied less consistently and depended on year and tillage system. Nitrogen × herbicide interaction terms had exploratory p-values ≥ 0.05 for all response variables. Because treatments were not independently replicated, these non-significant interaction terms should not be interpreted as evidence that biological interactions were absent. Taken together, the observed treatment-associated patterns were linked more closely to nitrogen management in 2023 and herbicide program in 2025, while weed-group responses also varied with year and tillage system. By examining together crop performance and weed community responses, the study suggests that observed responses to nitrogen and herbicide management depended on context rather than being consistent across seasons and tillage systems.

Background.The research focuses on using the DSSAT model (v4.8.2) to simulate spring wheat (Triticumturgidum L.)performance in Russian climatic conditions, addressing the need to optimize farming strategies amongst agroecological challenges. Crop models are effective tools to predict crop productivity in different management options and climatic conditions. Purpose.The study aimed to forecast spring wheat yield by evaluating the impact of four nitrogen (N) levels (0, 15, 30, 45 kg/ha) on leaf area index (LAI), stem weight, leaf weight, and tops weight for two spring wheat varieties (Radmira and Kanyuk) to predict the enhancement of agricultural productivity. Materials and methods. Field experiments at the Research Institute of Agrochemistry in Barybino during summer 2024 utilized the DSSAT model (v4.8.2) to simulate spring wheat (Triticum turgidum L.) performance. Statistical analyses (t-tests, RMSE, Std. Dev.) compared observed and simulated data, confirming the model’s accuracy and significance. Results.The DSSAT model accurately simulated outputs within observed ranges: LAI ranged from 0.42 (0 kg N/ha, V1) to 1.25 (30 kg N/ha, V1), and tops weight varied from 1704 kg/ha (45 kg N/ha) to 4248 kg/ha (30 kg N/ha). Simulated values showed less variability (LAI: 0.78–1.10; tops weight: 1749–3412 kg/ha). Model performance was strong for tops weight (r² = 0.88–0.95) and stem weight (r² = 0.90–0.95), but weaker for LAI (r² = 0.796–0.847) and leaf weight (r² = 0.74–0.90). Nitrogen rates significantly affected all parameters (p 0.05), with Kanyuk (V2) outperforming Radmira (V1) at higher N levels. The findings highlighted the importance of advanced DSSAT crop modeling for predicting and optimizing nitrogen use andvarietal selection to improve agricultural productivity under varying agroecological conditions.