There is an increasing need for genetic resources to provide ecosystem services in agricultural systems. Brachypodium is a promising grass cover species used in olive and fruit grooves in Mediterranean agroecosystems. In this agricultural context, they provide winter soil protection and moisture retention, and terminate their cycle in early spring, minimizing water competition with the tree crop (Gomez et al. 2020).

In this work, we characterized the performance of four Brachypodium varieties in five environments (both field- and semicontrolled environments) in Southern Spain (IAS-CSIC farm). This would provide data on the effects of climatological conditions and management practices (sowing date, irrigation) on soil cover, biomass, and seed and yield traits using traditional and digital phenotyping tools. A strong genotypic influence was detected for the traits observed, while climatic effects (specially heat and drought) were also detected. This will help to identify the possibilities and ecosystem services provided by this grass cover crop's genetic resources in Agromediterranean conditions.

Acknowledgements: We gratefull acknowledge the financial support from grant TED2021-131496B-C22 (BRACHYCOVER) funded by MICIU/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”, and from the European Union’s Horizon project Soil-O-Live (Grant agreement ID: 101091255).

References

Gómez, J.A., Soriano, M.A. 2020. Evaluation of the suitability of three autochthonous herbaceous species as cover crops under Mediterranean conditions through the calibration and validation of a temperature-based phenology model. Agriculture, Ecosystems and Environment, 291, art. no. 106788.

Despite accounting for a high share of the farming population in Lithuania, rural women remain underrepresented in innovation processes and formal support networks. This study explores how the Living Lab (LL) approach, as applied in the EU-funded GRASS CEILING project, supported women-led agricultural innovation through participatory, context-sensitive facilitation.

The Lithuanian LL brought together a diverse group of eight women farmers with varying levels of experience, farm types, and innovation readiness. Over three years, participants engaged in nine meetings, primarily in person, including site visits to women-led businesses, botanical gardens, and public institutions. Activities followed a structured, process-oriented empowerment journey—from capacity-building and self-reflection to innovation prototyping and testing—supported by expert-led training and peer mentoring.

Qualitative data—diaries, structured reflections, mentoring notes, and a final focus group—revealed several outcomes. First, the LL fostered a psychologically safe and inclusive environment, allowing women to exchange ideas, reflect on barriers, and gain confidence. Second, peer learning emerged as a critical support mechanism, often more valued than formal expertise. Third, innovation was redefined—from high-tech solutions to incremental, low-cost changes such as marketing strategies or new sales channels. Several participants increased visibility through media, awards, and market engagement.

However, systemic challenges such as limited access to childcare, funding, and policy alignment remain. Participants also noted the lack of targeted training for small farms and insufficient integration into broader agricultural policy frameworks.

In conclusion, the Lithuanian LL demonstrates that gender-sensitive, place-based participatory methods can empower rural women, enhance innovation ecosystems, and contribute to inclusive rural transformation in a post-socialist context.

Indoor paddy farming presents a promising solution to food security challenges, offering controlled environments for optimised crop growth. However, maximising yield efficiency requires a thorough understanding of microclimatic factors including carbon dioxide (CO₂). This study employs Computational Fluid Dynamics (CFD) to evaluate the effects of additional CO₂ supply on airflow dynamics, CO₂ distribution, and plant growth conditions in an indoor paddy farming setup. Two settings are examined, one with ambient CO₂ levels and the second with targeted CO₂ enrichment. The CFD model incorporates realistic paddy plant structures and simulates transpiration, energy exchange, and CO₂ absorption processes. By defining precise boundary conditions for temperature, humidity, and gas exchange on the leaf surfaces, the model enables detailed analysis of CO₂ transport inside the cultivation space (paddy beds) with the help of a small-scale controlled air capture device, inspired by direct air capture (DAC) technology. It is indeed promising that the CO₂ enrichment through controlled air capture enhances uniformity in gas distribution as well as optimising concentrations around the rice canopy, which can boost photosynthesis and biomass accumulation. Contrariwise, the second setting (without CO₂ enrichment) displays areas of restricte plant growth which reduces the overall yield. With the exploration of the effects of controlling ventilation on CO₂ retention and distribution, the findings suggest that an integrated CO₂ delivery system with optimised airflow patterns potentially mitigates stratified flow issues, and therefore it further maintains stable concentrations across the paddy beds as well as enhancing evapotranspiration processes, which creates a balanced microclimate for the plants. This research demonstrates the potential for exploring innovative indoor farming design strategies, particularly for maximising yield efficiency of staple agri-foods like rice using CFD analysis. Through CO₂ enrichment and airflow optimisation, the indoor farming technique potentially maximises its profitability through high yield production and simultaneously reduces resource consumption and uncertainties due to external climatic fluctuations.

Maize production and consumption in Bangladesh are increasing as an alternative to wheat for sustainable food security and economic growth. An authentic crop production forecasting method is crucial for securing food security through proper agricultural policy-making, especially in developing nations like Bangladesh, where most people depend on agricultural farming. The present study predicted the time-series analysis of maize cultivation area, yield, and production employing the Autoregressive Integrated Moving Average (ARIMA) model. Stationarity assessments were done utilizing the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and Augmented Dickey–Fuller (ADF) tests at a 5% significance level using yearly data from 2009 to 2024 collected from Index Mundi. The fit model was chosen based on the lowest value of Bayesian Information Criterion (BIC), the Corrected Akaike Information Criterion (AICc), and the Akaike Information Criterion (AIC). Ljung–Box and Jarque–Bera tests were utilized to detect autocorrelation. The metrics MAE, RMSE, MASE, and MAPE were used to verify values. After reviewing all criteria, ARIMA (2,1,0) was identified for the production area, ARIMA (0,1,0) for yield, and ARIMA (1,1,1) for production. The forecast specified a steady increase in the production area, with a compound annual growth rate (CAGR) of 8.1%. Yield is anticipated to remain stable at 9.0 tons per hectare, reflecting the ongoing utilization of high-yielding hybrids and advanced agronomic techniques. It is forecasted that by 2029, maize production will reach around 9.04 million metric tons, with a compound annual growth rate (CAGR) of 10.0%. The results underscore the importance of the increasing maize production trend in Bangladesh's food and feed sector, particularly as a vital resource for the dairy, fishery, and poultry sectors. Furthermore, they demonstrate the reliability and predictability of ARIMA models, thereby assessing their relevance in agricultural planning and judicious decision-making in the face of market and climatic uncertainty.

Cocoa production remains a cornerstone of Nigeria’s rural economy and export revenue. However, the excessive use of non-approved pesticides threatens compliance with international Maximum Residue Limits (MRLs), risking market rejection and income losses. To address this, the Nigerian authorities introduced a list of approved pesticides (APs), yet their adoption remains limited. This study investigated the socio-economic and institutional drivers of AP adoption and quantified its causal impact on cocoa productivity and profitability using advanced machine learning methods. Survey data from cocoa farmers in Osun State were analyzed using logistic regression and random forest models to identify adoption and profitability drivers. Key predictors of AP use included education, the farm size, off-farm income, and awareness. Profitability was influenced by age, experience, the farm size, and the input use (fungicides and insecticides). To estimate the causal effects, we employed Causal Forests, revealing that AP adoption increases the log output by 41.6% and the log profit by 44.9%. These findings highlight the transformative impacts of approved pesticide adoption on farm welfare. Promoting awareness and targeted support policies can scale adoption and enhance sustainability. Machine learning methods enriched the analysis by revealing both average and heterogeneous treatment effects, offering evidence-based insights for agricultural policy and development planning.

The discharge of nickel (Ni)-laden wastewater from the cooking oil industry poses significant environmental hazards. This study explores the potential of Eichhornia crassipes and Oedogonium sp. for Ni removal from industrial effluents. A 21-day phytoremediation experiment was conducted using wastewater dilutions with rainwater in ratio of 0%, 50% and 100% along with control, which were then treated individually and with a combination of Eichhornia and Oedogonium. The results showed substantial reductions in biochemical oxygen demand (BOD) and chemical oxygen demand (COD) by 49% and 69%, respectively. Notably, Ni concentration decreased by 81% in combined treatments, with the treated effluent meeting safety standards for agricultural reuse. It was then applied on germinating sunflowers for a period of three months and the Ni bioaccumulation and translocation factor was determined. The findings of this study demonstrate the efficacy of E. crassipes and Oedogonium sp. in Ni removal, offering a sustainable and eco-friendly solution for industrial wastewater treatment.

Climate change and increasing water scarcity necessitate the adoption of efficient water management strategies in agriculture to enhance crop productivity and sustainability. This study comparatively evaluates the growth performance and water-use efficiency of mustard (Brassica juncea) cultivated under three systems: Nutrient Film Technique (NFT), Deep Water Culture (DWC) hydroponics, and conventional Soil-Based (SB) cultivation. Mustard seedlings were transplanted into each system; hydroponic setups were supplied with Hoagland’s nutrient solution (EC 2.0 mS cm⁻¹, pH 6.0), while the SB system utilized standard fertilization and irrigation. Key growth parameters, including number of leaves (NL), stem diameter (SD), shoot length (SL), root length (RL), and plant height (PH), were statistically analyzed alongside total water consumption per plant. Results demonstrated that the NFT system significantly outperformed both DWC and SB systems, achieving the highest plant height (42.2 cm), shoot length (25.8 cm), root length (16.4 cm), and stem diameter (1.8 cm), while using only 3.2 liters of water per plant. DWC showed moderate growth (PH: 35.6 cm; SL: 21.4 cm; RL: 12.3 cm; water use: 5.8 L/plant), while SB showed the lowest growth performance (PH: 29.1 cm; SL: 17.6 cm; RL: 10.1 cm; water use: 8.5 L/plant), primarily due to inconsistent moisture and nutrient availability. These findings confirm the superior water-use efficiency and biomass productivity of the NFT system, underscoring its potential as a sustainable, climate-smart solution for resource-limited agricultural settings.

Semi-arid regions are particularly vulnerable to climate change, characterized by rising temperatures, altered precipitation regimes, and more frequent droughts. These factors intensify water scarcity and pose significant challenges to agricultural sustainability. To address this challenges, efficient irrigation management is essential to alleviate crop water stress and maintain yields. This study applies the Interactive Canopy Radiative Exchange (ICARE) soil–vegetation–atmosphere transfer (SVAT) model to simulate water and energy fluxes over four winter wheat fields in Morocco's Tensift Basin. Model calibration was performed on a reference field using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), with a focus on a root zone soil moisture (RZSM)-constrained approach to enhance simulation accuracy. Model performance was evaluated across key phenological stages; initial, development, mid-season, and late season by assessing RZSM, surface energy balance components, and evapotranspiration (ET). The model demonstrated strong predictive capability, with optimal simulations achieving RMSE (R²) values of 0.01 m³/m³ (0.88) for RZSM and 0.46 mm/day (0.83) for ET. Simulations also showed high agreement with observed values: 40 W/m² (0.96) for net radiation, 21 W/m² (0.86) for ground heat flux, and 37 W/m² (0.73) for sensible heat flux. A clear phase-dependent behavior was observed, with the RZSM-constrained calibration yielding particularly accurate results during the development and mid-season stages, when plant transpiration is dominant. Validation conducted on three additional fields confirmed the robustness and transferability of the calibrated model, reinforcing its potential as a decision-support tool for improving irrigation efficiency in semi-arid agricultural systems. Overall, this study advances the understanding of soil–plant–atmosphere interactions and offers practical insights for sustainable water resource management under changing climate conditions.

This study examines the effect of awareness of cocoa certification programs on marketing efficiency among Licensed Buying Agents (LBAs) in Osun State, Nigeria. As key intermediaries in the cocoa value chain, LBAs significantly influence cocoa quality and pricing, yet many operate with limited knowledge of certification standards such as UTZ and Rainforest Alliance. A total of 120 LBAs, both certified and uncertified, were selected using multistage sampling. Descriptive statistics were used to assess levels of awareness and socioeconomic characteristics, while the fractional response model (FRM) was employed to estimate the impact of awareness on marketing efficiency. Results show that while 68% of LBAs had heard of certification programs, only 38% possessed operational knowledge of compliance requirements. The result from the fractional response model revealed that awareness about certification and access to credit had a positive effect on marketing efficiency, with marginal effects of 0.033 and 0.0082, respectively, and both were statistically significant at 1%. This shows that certification awareness has a positive and statistically significant effect on marketing efficiency. Certified LBAs exhibited higher average marketing efficiency compared to their uncertified counterparts. The study concludes that awareness of certification programs enhances marketing efficiency and recommends targeted outreach, training, and policy incentives to bridge the knowledge gap and promote wider adoption of certification practices among LBAs.

Water deficiency is a major abiotic stressor that negatively impacts plant growth, physiological functions, and internal biochemical processes. Aquilaria malaccensis (agarwood), globally recognized for its economic and medicinal value, was evaluated in this study to understand its response to drought stress. In this study, the seedlings were exposed to four irrigation treatments, regular irrigation (control) and water withholding for 7, 14, and 21 days, representing mild, moderate, and severe drought stress, respectively. Plant height and collar diameter were recorded at 3 and 6 months of the treatment, while other morphological, physiological, and biochemical parameters were measured after 6 months. At 3 months, drought stress showed no significant effect on height or collar diameter. However, at 6 months, moderate and severe stress significantly reduced plant height, leaf number, specific leaf area, and chlorophyll content. Relative Water Content (RWC) remained above the threshold under mild and moderate stress but declined sharply under severe stress. Root length was significantly affected by severe stress, while a higher root-to-shoot ratio under mild and moderate stress indicated root system adaptation to limited water availability. Stomata remained open in control and mildly stressed plants but mostly closed under moderate and severe stress, leading to reduced stomatal conductance and net photosynthetic rate. Biochemically, hydrogen peroxide (H₂O₂), malondialdehyde (MDA), and proline levels increased under moderate and severe stress, indicating oxidative stress and reduced membrane stability. In response, antioxidant enzymes (POD, CAT, GST, and APX) and secondary metabolites (total phenolics and flavonoids) were elevated, indicating activation of protective mechanisms. Overall, Aquilaria malaccensis exhibited moderate tolerance to short-term drought but was severely affected under prolonged drought stress, both morpho-physiologically and biochemically. Since no prior studies have been carried out on Aquilaria malaccensis under water deficit conditions, further research is required to better understand the effects of water stress.