Search Results (911)

Green rice husk dietary fiber (GHDF) is an underutilized agricultural by-product with promising potential for applications in the food industry. This study investigated the effects of incorporating dietary fiber from GHDF at 1%, 3%, and 5% together with different hydrocolloids, including xanthan gum (XG), carrageenan (CC), and guar gum (GG), on the physical and chemical, textural properties, and consumer acceptance of gummy products. The results showed that adding more GHDF increased the nutritional value of the gummies, with total dietary fiber ranging from 1.01 to 5.02 g per 100 g of product. FTIR results also showed that fiber from green rice husk was present in the gummies. The combined addition of GHDF and hydrocolloids also affected the internal gel structure of the products. This interaction made the gel structure stronger, resulting in firmer gummies with greater hardness, gumminess, and chewiness. In addition, higher GHDF levels contributed to reduced syneresis. Among the hydrocolloids tested, xanthan gum produced the strongest gel, while the formulation with 3% GG received the highest consumer liking scores. These results suggest that GHDF could be used as a useful ingredient to develop food products with higher nutritional value and better use of agricultural by-products. Full article

The rapid expansion of the poultry industry has led to the large-scale generation of feather waste, creating serious environmental and public health concerns due to the recalcitrant nature of keratin. Poultry feathers are composed mainly of highly cross-linked keratin proteins stabilized by numerous disulfide bonds, which confer resistance to conventional proteolytic enzymes and natural degradation processes. This review examines the potential of keratinolytic fungi and their enzymes as sustainable, eco-friendly, and value-added strategies for poultry feather waste management and resource recovery. It discusses the environmental and health risks associated with improper feather disposal, such as pathogen proliferation, odor generation, and ecosystem contamination. Conventional management approaches, steam pressure hydrolysis, mechanical grinding, thermal treatment, acid–alkali hydrolysis, and oxidation, are critically evaluated in terms of efficiency and environmental impact. The review further highlights biological degradation pathways mediated by keratinolytic fungi and enzymes, with emphasis on fungal genera such as Aspergillus and Chrysosporium. Key mechanisms of fungal keratin degradation, including sulfitolysis, proteolysis, deamination, hyphal penetration, enzyme secretion, and biofilm formation, are discussed. Finally, industrial, agricultural, and feed applications of keratinases, along with advances in strain improvement, omics technologies, synthetic biology, and associated biosafety and regulatory considerations, are addressed. Full article

Arsenic (As) pollution has become a global concern, and the search for effective and sustainable As remediation methods has attracted much attention. Sustainable and cost-effective technologies for As remediation are essential to protect public health. This study aligns with the United Nations Sustainable Development Goals (SDGs), specifically SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production), by transforming agricultural waste into value-added biochar for environmental remediation. Currently, studies on the remediation of As pollution using iron-modified biochar (Fe-BC) and cerium-modified biochar (Ce-BC) have demonstrated promising application potential. Although there is an established research foundation regarding their remediation performance and mechanisms, comparative studies evaluating their performance and mechanisms under unified experimental conditions remain limited. As in this study, Fe-BC and Ce-BC were prepared and systematically investigated. The As remediation performance and mechanisms of the two biochars were compared and analyzed through material characterization, aqueous adsorption experiments, and soil remediation assessments. The results showed that the specific surface areas of Fe-BC and Ce-BC were 94.380 m²·g⁻¹ and 36.388 m²·g⁻¹, respectively, both higher than that of the original biochar (BC). The Langmuir and Freundlich models adequately fitted the As adsorption processes of all three materials. Fe-BC and Ce-BC exhibited a tendency toward monolayer adsorption for As(III). The Freundlich distribution coefficient K_F of Fe-BC was 0.1604, which was higher than that of BC and Ce-BC, indicating superior adsorption performance for As(III). In the pot experiment, when Fe-BC and Ce-BC were applied at 5%, the As content in ryegrass decreased by 78.38% and 77.15%, respectively. Fe-BC reduced the available As content in soil by 63.1% and decreased As accumulation in ryegrass by 78.38%. The reduction in available As content achieved by Fe-BC was greater than that achieved by Ce-BC. Fe(III) oxides supported on Fe-BC immobilized As through complexation and precipitation mechanisms. Fe⁰ and Fe₃O₄ in the materials altered the redox potential of the local microenvironment, affecting the transformation and stabilization of As species. Ce-BC primarily oxidized As(III) to As(V), and Ce⁴⁺ facilitated the formation of CeAsO₄ precipitates due to its high redox potential. Full article

Accurate and early prediction of crop yield at the sub-field scale is essential for precision-agriculture and food-system planning. This study evaluates a phenology-based machine learning framework for winter wheat yield prediction using Sentinel-2 satellite imagery, climate reanalysis data, and field-level yield data. Phenological metrics derived from the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and the normalised difference red-edge index (NDRE) were combined with accumulated seasonal rainfall and seasonal potential evapotranspiration, and multiple modelling strategies were assessed using a leave-one-field-out cross-validation (LOFO CV) scheme to ensure spatial generalisation. Among the evaluated models, the Random Forest (RF) algorithm achieved the highest overall performance, explaining up to 73% of the yield variability with a root mean square error (RMSE) of 0.88 t ha⁻¹ at optimal prediction timing (day of year 160–175). Integrating phenological and climatic covariates consistently improved prediction accuracy compared to models based only on phenological variables, while the inclusion of soil properties provided limited additional benefit at the examined spatial scale. Phenological metrics based on red-edge data, particularly the maximum NDRE, were the most influential predictors, highlighting the added value of red-edge spectral information beyond traditional red–near-infrared indices. Uncertainty analysis revealed spatially heterogeneous prediction uncertainty, particularly near field boundaries and in areas of complex spatial patterns. Overall, the proposed framework enables robust, early, and interpretable yield prediction at the sub-field scale, supporting uncertainty-aware decision-making in precision agriculture and offering a scalable foundation for regional crop monitoring. Full article

Servicification—defined as the services value added embodied in goods—has been studied mainly in manufacturing, but its role in agricultural exports is less understood. We measure servicification in agricultural exports and examine how it is associated with export performance, upstream linkages and upgrading-related proxies. Using trade-in-value-added accounting for 80 countries (1995–2022), we estimate two-way fixed-effects panel models with exporter-clustered standard errors. Higher servicification is associated with both larger and intermediate agricultural value-added exports within countries over time. Decompositions show that these relationships are driven by services produced domestically, which are a location-based measure that may include services supplied by foreign-owned affiliates operating locally. Foreign services value added is not systematically related to outcomes. Servicification is also associated with a smaller agriculture-to-economy value-added gap proxy, and embodied financial and Information and Communication Technology (ICT) services appear complementary. Labour-market results for a smaller subsample are suggestive of stronger links with skill-intensive employment shares at lower GDP per capita levels. Because reverse causality cannot be ruled out, the findings are interpreted as conditional associations that motivate future causal identification. Full article

The transition to a circular economy is a strategic direction of the European Union, and the agri-food sector is essential in this transformation through resource consumption, climate impact and an economic role in the food chain. This study analyses the relationship between the circularity of the economy and the sustainable performance of agri-food systems in the EU-27, using Eurostat data for the period of 2014–2023. Circularity is operationalised through a composite index built from the circularity of materials and resource productivity, aggregated through principal component analysis and complemented by an alternative measure based on the average of the standardised components. Sustainable performance is assessed through economic indicators (value added and output in agriculture, value added in the food industry), environmental indicators (greenhouse gas emissions from agriculture) and, complementary, energy indicators (energy intensity in the food industry), the latter being analysed separately on the available observations. The results do not indicate clear aggregate differences in sustainable performance associated with circularity measured at the macro level over the analysed period, underlining the importance of connecting circularity objectives with interventions and indications specific to the agri-food chain for monitoring and policy design at the EU level. Full article

Hydroponic systems are gaining prominence in sustainable agriculture, yet their nutrient-rich effluents remain an underexplored source of microbial biodiversity with potential biotechnological interest. In this study, shotgun metagenomic sequencing was employed to profile, with a high taxonomic resolution, the photosynthetic microbial community in hydroponic effluent before and after a natural algal bloom, revealing pronounced shifts in microbial composition. Notably, relative abundance increased sixfold for Chlamydomonas reinhardtii and tenfold for Bigelowiella natans. Four dominant microalgal strains (PR1–PR4) were subsequently isolated and characterized through integrative morphological and molecular taxonomy, with phylogenetic analyses based on four genetic markers (18S rRNA, ITS, rbcL and tufA) confirming that each isolate represents a distinct lineage within Chlorophyceae families, including Chlorella sp., Chlamydomonas sp., and Scenedesmus sp. Growth kinetics under three temperature regimes, typical of Greek environmental conditions from spring to autumn (15 °C, 23 °C, 32 °C), demonstrated broad ecological plasticity and rapid biomass production, highlighting strains with strong adaptive resilience. Biochemical profiling of the isolates revealed significant inter-strain differences in primary and secondary metabolite content, including proteins (up to 43% DW), lipids (up to 31% DW), carbohydrates (up to 44% DW), photosynthetic pigments, phenolics, flavonoids, and antioxidant activity. The observed metabolic diversity of autochthonous microalgal strains from hydroponic environments, combined with their high growth rates, underscores their potential for applications in bioremediation, bioenergy, and the development of value-added products within a circular bioeconomy framework. Full article

Crop yield estimation, particularly early-season yield prediction, is highly important for global food security and disaster mitigation. In this study, we utilized deep learning models combined with remote sensing data to develop in-season crop yield estimation models, enabling immediate yield prediction. We employed a convolutional neural network (CNN) for spatial feature extraction and a long short-term memory network (LSTM) for temporal patterns, complemented by Gaussian process regression (GP) that introduced geographical coordinates. Three groups of in-season yield prediction experiments were designed, utilizing four-phase, two-phase, and single-phase data, respectively. The results indicated that under the two-phase training scheme, the LSTM_GP model achieved the highest performance in the sixth period, with an $R^2$ value of 0.61 and a root mean square error ($RMSE$) value of 983.38 kg/ha. When trained on single-phase data at the twelfth phase (approximately mid-to-late July), the LSTM_GP model also performed best, attaining an $R^2$ value of 0.62 and an $RMSE$ value of 969.06 kg/ha. The single-phase prediction model outperformed time-series models in yield prediction accuracy. The periods from mid-to-late July to early-to-mid August represent critical crop growth stages were essential for accurate yield prediction. From our research, we found that adding GP can improve the prediction accuracy, especially for LSTM. Moreover, the proposed single-phase prediction model realized reliable crop yield prediction as well as the silking to early grain-filling stage (mid-to-late July), providing a critical lead time of approximately 2–2.5 months before harvest to support pre-harvest agricultural decision-making. Full article

Mining tailings are considered a significant environmental challenge due to their large quantities and high residual metal content, particularly iron. Recent developments in biogenic technologies offer a sustainable approach to recovering valuable materials from these waste streams. We consider a biogenic iron oxide nanoparticles production process from mining tailings as an environmentally friendly route to magnetic materials. Microorganisms, including iron-oxidizing and iron-reducing bacteria, microalgae, and fungi, can convert soluble and mineral-bound iron into iron oxide nanoparticles (NPs) phases such as magnetite, maghemite, and hematite. These biogenic iron oxide NPs often exhibit specific physicochemical properties, including controlled particle size, high surface area, and engineered magnetic properties, which make them potentially important for applications in environmental remediation, catalysis, and agriculture. The processes behind microbial iron conversion, the parameters governing mineral phase formation, and the approaches for optimizing the process are presented. This strategy supports the circular economy concept by combining biogenic synthesis with various forms of mining waste, thereby reducing environmental threats associated with tailings confinement and providing an environmentally friendly mechanism for the production of value-added magnetic materials. Full article

The growing accumulation of coal beneficiation waste represents a significant environmental and technological challenge while simultaneously creating opportunities for the resource recovery within circular economy frameworks. This study presents the development and process-oriented evaluation of an environmentally safe technology for converting coal beneficiation waste into potassium humate, with the simultaneous recovery of molybdenum compounds via alkaline extraction. The proposed solution is designed to improve resource efficiency, reduce the volume of waste directed to landfilling, and generate a high value-added product for agricultural and technological applications. The process flow includes preliminary characterization and preparation of the waste, determination of moisture, ash, and organic matter content, and the separation of metal-bearing fractions. Alkaline extraction was carried out using potassium hydroxide under controlled temperature and reaction time conditions, followed by purification and concentration of the humate solution. The process management strategy focuses on optimizing key technological parameters, including alkali concentration, solid-to-liquid ratio, temperature, and reaction time, to maximize humate yield while preserving functional groups responsible for biological activity. Comprehensive physicochemical, thermal, and mineralogical analyses confirmed the stability of the aluminosilicate matrix and the suitability of the material for alkaline processing without adverse structural degradation. Biological tests using oat (Avena sativa) demonstrated that potassium humate derived from coal beneficiation waste exhibits higher growth-stimulating effectiveness than a conventional commercial humate. Economic analysis revealed a strong correlation between humic acid content and added value, confirming the feasibility of transforming coal beneficiation waste from an environmental burden into a valuable secondary resource. Full article

Maize silage can play a key role in policies aimed at stabilising local energy systems, as it constitutes a critical renewable feedstock for European biogas plants. By providing a dense and predictable source of chemical energy, it supports balance and reliability in the agricultural energy sector. To convert this potential into stable energy production, operators require kinetic models that translate routine silage quality indicators into concrete guidance for digester operation and control. Therefore, the aim of this article was to evaluate the batch kinetics of anaerobic digestion (AD) of maize silage and to select an adequate model for describing biochemical methane potential (BMP) profiles and associated energy recovery in the context of start-up, organic loading rate (OLR), hydraulic retention time (HRT) and feedstock preparation. Ten batches of silage (A–J) were examined, covering a realistic range of pH, electrical conductivity (EC), dry and volatile solids, ash, protein–fat–fibre fractions, fibre composition (NDF, ADF and ADL), derived fractions (hemicellulose, cellulose, and residual organic matter (OM)), C/N ratio and macro-/micronutrient profiles, including trace elements relevant to methanogenesis (Ni, Co, Mo, and Se). BMP tests were carried out in batch mode, and the resulting curves were fitted using the modified Gompertz and a first-order kinetic model. Methane yields of approx. 100–120 m³ CH₄/Mg fresh matter (FM) and 336–402 m³ CH₄/Mg volatile solids (VS), with CH₄ contents of 52–57% v/v, were typical for energy-grade maize silage. Kinetic and energetic behaviours were governed mainly by residual OM and hemicellulose (shortening the lag phase and increasing the maximum methane production rate), the ADL/cellulose ratio (controlling the slower hydrolytic tail), EC and Na/Cl/S (extending the lag phase), and C/N together with Ni/Co/Mo/Se (stabilising methanogenesis). The modified Gompertz model reproduced BMP curves with a pronounced lag phase and asymmetry more accurately (lower error and better information criterion values), and its parameters directly support start-up design, OLR ramp-up and energetic performance optimisation in bioenergy reactors. The novelty of this work lies in combining batch BMP tests, comparative kinetic modelling and detailed silage characterisation to establish quantitative links between kinetic parameters and routine maize silage quality indicators that are directly relevant for biogas plant operation and renewable energy production. Full article

Volunteer tourism is garnering growing attention across various fields, allowing tourists to both consume and co-produce tourism services. In agriculture, however, this remains underexplored, despite a worsening farm labour shortage due to ageing populations and a lack of successors, particularly in industrialised nations. This issue threatens farm productivity and food security. This paper addresses this research gap by examining paid volunteer tourism platforms in Japan. It presents a framework highlighting the co-creation of local tourism demand and analyses wage determinants across 138 farms. Results show that corporate farms engaged in direct sales offer higher wages, especially when prices are elevated or locations are remote, suggesting wage premiums reflect labour shortages. Accommodation and Wi-Fi provision depend on farm finances and unused facilities. Organic and GAP-certified farms offer lower wages, likely due to higher production costs, despite producing value-added goods. As platform-based paid volunteer tourism meets the needs of both farmers and volunteers, its prevalence is expected to increase. Full article

This paper examines differences in agri-environmental climate and energy performance across the 27 European Union (EU) Member States. An integrated methodological framework was applied, combining the Shannon Entropy Index for objective weighting of indicators with the PROMETHEE–GAIA multi-criteria decision-making approach to rank EU countries according to their relative performance. The analysis focuses on four key indicators: (1) Climate: greenhouse gas emissions from agriculture (GHG) and (2) Energy: (1) gross available energy (GAE), (2) renewable energy primary production (REPP), and (3) gross inland consumption (GIC)—expressed as intensity measures (ktoe per million euro of agricultural gross value added), and covers the period 2017–2023. The results reveal a reduction in cross-country dispersion for greenhouse gas emission intensity, reflected in a decline in entropy values, suggesting partial convergence in climate-related performance. In contrast, energy-related intensity indicators (GAE, GIC, and REPP) remain highly heterogeneous, indicating persistent structural differences in energy efficiency, energy mix and agricultural systems across Member States, despite modest signs of convergence for selected indicators. The PROMETHEE ranking identified Romania, Italy, Greece, Spain and Poland as leading performers, reflecting favourable combinations of lower emission intensity and more efficient energy use per unit of agricultural value added. Conversely, structurally constrained economies such as Malta, Cyprus, and Luxembourg consistently ranked among the lowest-performing countries, primarily due to high energy and emission intensities relative to agricultural output. The findings point to selective and indicator-specific convergence rather than uniform long-term convergence across the EU, underscoring the need for differentiated policy approaches to support a more balanced and sustainable energy transition in agriculture. Full article

Modern agriculture requires optimizing available resources to maximize production while minimizing environmental impact without increasing economic costs. Hydroponic agriculture replaces soil with inert media that provide physical support for plants but do not supply nutrients. In this type of agricultural production, fertilization with nutrient solutions is essential, as they supply the 15 elements necessary for proper plant development. These solutions consist of mixtures of different amounts of fertilizers dissolved in water. In this context, a method based on a simulated annealing algorithm is proposed, a metaheuristic that optimizes fertilizer quantities in grams to achieve target concentrations in parts per million for six macronutrients and nine micronutrients. The algorithm addresses a multi-objective optimization problem, balancing two competing goals: first, maximizing the accuracy of the fertilizer balance to achieve the required nutritional levels, and second, minimizing the total cost of the fertilizer mixture. The algorithm’s fitness function weights the total cost of the fertilizers used and the total relative error between the concentrations obtained and those desired, allowing the relative importance of cost and accuracy in the nutrient solution to be adjusted. The results of three experiments with varying nutrient levels are presented for a 1000-L water tank. The first experiment consisted of three macronutrients and two micronutrients. The second configuration added three macronutrients and two micronutrients, for a total of ten nutrients. Finally, five micronutrients were added to complete the 15 essential nutrients for plants. It is important to note that there are several methods for calculating micronutrients that contribute to precision agriculture, increasing the complexity of finding a solution that meets established nutritional requirements. The nutrient concentrations in parts per million required for tomato cultivation during the vegetative development stage. To balance nutrient accuracy and solution cost, we applied weighting factors of $0.65,0.75,0.85,0.90,0.95,$ and $1.0$ for accuracy. The corresponding weights for cost were calculated as the complement of these values (totaling 1). By favoring nutrient accuracy with a weighting of 1, accuracies of 0.00500, 0.02618, and 0.03077 parts per million were achieved in each experiment, respectively. Meanwhile, the lowest cost is $2.06$, $2.72$, and $2.70$ USD for the aforementioned experiments. Full article

Fermentation, one of the oldest biotransformation processes, has become a key element of contemporary sustainable biotechnology. In modern food systems, it enables the simultaneous resolution of environmental, nutritional, and economic challenges by converting agricultural and food residues into high-value-added products, such as bioactive compounds, organic acids, biofuels, enzymes, and proteins. Consistent with the concept of a circular bioeconomy, fermentation supports resource recycling, waste minimization, and greenhouse gas reduction, contributing to the achievement of selected United Nations Sustainable Development Goals (SDGs). The importance of fermentation extends beyond its environmental aspects—fermented foods and postbiotics support the modulation of the gut microbiome, strengthen immunity, and can act as a preventative measure against metabolic and inflammatory conditions. Simultaneously, the dynamic development of precision fermentation and synthetic biology enables the design of microorganisms that produce specific food ingredients without the use of animals or traditional agriculture, paving the way for more responsible production and consumption. This review presents the categories of organic residues valorized through fermentation, explains their role in circular food and healthcare systems, and identifies key technological and regulatory barriers limiting the scaling of this approach. Collectively, fermentation emerges as a biotechnology platform with significant transformative potential for future sustainable food systems. Full article