Search Results (184)

Accurate prediction of forage digestibility is essential for efficient livestock management and feed formulation. This study evaluated the performance of machine learning (ML) models to estimate the in vitro digestibility of leaf and stem components of Brachiaria hybrid cv. Ipyporã, using three datasets composed of pasture management variables, chemical composition variables, and their combination. Artificial neural network (Multilayer Perceptron, MLP), decision trees (REPTree and M5P), Random Forest (RF), and Multiple Linear Regression (LR) were tested. The principal component analysis revealed that 61.3% of the total variance was explained by two components, highlighting a strong association between digestibility and crude protein content and an opposite relationship with lignin and neutral detergent fiber. Among the evaluated models, MLP, LR, and RF achieved the best performance for leaf digestibility (r = 0.76), while for stem digestibility the highest accuracy was obtained with the LR model (r = 0.79; MAE = 2.42; RMAE = 2.87). The REPTree algorithm presented the lowest predictive performance regardless of the input data. The results indicate that chemical composition variables alone are sufficient to develop reliable prediction models. These findings demonstrate the potential of ML techniques as a non-destructive and cost-effective approach to predict the nutritional quality of tropical forage grasses and support precision livestock management. Full article

Accurate forecasting of the ecological footprint (EF) is critical for advancing the Sustainable Development Goals, particularly those related to climate action, responsible consumption and production, and sustainable cities. To address the limitations of conventional machine learning models, such as instability due to random weight initialization and poor generalization, this study proposes a novel hybrid model that integrates the Chinese Pangolin Optimizer (CPO) with the Extreme Learning Machine (ELM). Inspired by the foraging behavior of pangolins, the CPO efficiently optimizes the ELM’s input weights and biases, significantly enhancing prediction accuracy and robustness. Using comprehensive monthly United States data from 1991 to 2020, the model forecasts EF based on key socioeconomic and environmental indicators, including GDP per capita, human capital, financial development, urbanization, globalization, and foreign direct investment. The CPO–ELM model outperforms benchmark models, achieving an R² of 0.9880 and the lowest error metrics across multiple validation schemes. Furthermore, SHAP (Shapley Additive Explanations) analysis reveals that GDP per capita, human capital, and financial development are the most influential drivers of EF, offering policymakers actionable insights. This study demonstrates how interpretable AI-driven forecasting can support evidence-based environmental governance and contribute directly to sustainability targets under the SDG framework. Full article

Birds are important indicators of ecosystem health and provide a range of benefits to society. It is important, therefore, to understand the impacts of agricultural land use changes on bird populations. The cultivation of energycane (EC)—a sugarcane hybrid—for biofuel production represents one form of agricultural land use change in southern Florida. We used passive acoustic monitoring (PAM) to examine bird community use of experimental EC fields and other agricultural land uses at two study sites in southern Florida. We deployed 16 acoustic recorders in different study plots and used the automatic species identifier BirdNET to identify 40 focal bird species. We found seasonal differences in daily avian species diversity and richness between EC experimental plots and reference agricultural fields (corn fields, orchards, pastureland), and between time periods (pre-planting, post-planting). Daily avian species diversity and richness were lower in the EC experimental plots during Fall and Winter months when plants reached maximum height (>400 cm in some areas). Despite seasonal differences in daily measures of species diversity and richness, we found no differences in cumulative species richness, suggesting that there may be little overall (season-long) effects of EC production. These findings could provide insight to avian seasonal habitat preferences and underscore the potential limitations of PAM in areas experiencing dynamic vegetation changes. More research is needed to better understand if utilization of EC cropping systems results in positive or negative effects on avian populations (e.g., foraging habitat quality, predator–prey dynamics, nest success). Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

The transformation of Local Ecological Knowledge (LEK) among minority populations undergoing cultural and linguistic assimilation over time is poorly understood. Arbëreshë communities in Greece, who have preserved Albanian-derived traditions for centuries, offer a unique opportunity to examine how folk plant knowledge adapts over time. This study examines the linguistic labels and culinary uses of wild greens among Arbëreshë (or Arvanites), an ethno-linguistic minority traditionally speaking Arbërisht or Arvanitika, the Tosk dialect of Albanian, who have resided in the Argolis and Corinthia regions of the Peloponnese for several centuries. In 2025, fieldwork was conducted in four rural Arbëreshë villages in the Argolis and Corinthia regions of Greece, combining semi-structured interviews with 24 elderly participants, participant observation, and the collection and identification of botanical specimens. The contemporary dataset was compared with historical ethnobotanical records from the 1970s to assess temporal changes in the use of wild vegetables and folk plant nomenclature. Our results reveal that current Arbëreshë ethnobotanical heritage has undergone profound Hellenisation, with 62% of folk plant names of Greek origin, 14% Albanian, and 24% hybrid, reflecting strong linguistic and cultural assimilation over the past half-century. The traditional boiled green mix (lakra in Arbëreshë, chorta in Greek) remains central to the local cuisine, which is rooted in foraged plants, although its culinary applications have diversified. In total, 37 taxa of wild vegetables across 37 genera and 14 families were documented in 2025, compared with 21 taxa across 21 genera in the filtered 1970 dataset. Core families, such as Asteraceae and Brassicaceae, remained dominant, while new families, like Malvaceae and Portulacaceae, appeared, possibly indicating both ecological and culinary changes. These findings raise questions about whether the Arbëreshë wild vegetable heritage was strongly influenced by the surrounding Greek majority or primarily acquired after migration, potentially facilitated by intermarriages and shared Orthodox Christian affiliation. Overall, our study highlights a largely Hellenised Arbëreshë biocultural heritage and underscores the urgent need for national and regional stakeholders to recognise and celebrate the remaining minority’s linguistic and ethnobotanical diversity. The transformation of local ethnobotanical knowledge over the past fifty years appears influenced by ecological availability, socio-cultural dynamics, and changing taste preferences. Full article

Hungary faces increasing water challenges, including frequent droughts and a growing dependence on irrigation, which necessitate alternative water sources for agriculture. This study evaluated the use of saline aquaculture effluent—characterized by elevated sodium (Na⁺) and chloride (Cl⁻) concentrations—as an irrigation resource for forage sorghum (Sorghum bicolor L.) over four consecutive growing seasons. Three hybrids (‘GK Áron’, ‘GK Balázs’, and ‘GK Erik’) were tested under five irrigation regimes, including freshwater and aquaculture effluent applied via drip irrigation at weekly doses of 30 mm and 45 mm, alongside a non-irrigated control. Effluent irrigation at 30 mm weekly increased biomass yield by up to 61% and enhanced nitrogen uptake by 22% compared to the control. Soil electrical conductivity (EC) values remained below 475 µS/cm, with effluent treatments showing lower EC than non-irrigated plots. The effluent water also supported the recycling of nutrients, especially nitrogen and phosphorus. Unlike conventional saline water, aquaculture effluent contains organic compounds and microbial activity that may improve nutrient mobilization and uptake. Our results highlight how we can reuse aquaculture wastewater in irrigated crop production. The results demonstrate that moderate effluent irrigation (30 mm/week) can optimize crop water use while maintaining soil health, offering a viable strategy for forage sorghum production in water-limited environments. Full article

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work. Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

The greater cane rat algorithm (GCRA) is a swarm intelligence algorithm inspired by the discerning and intelligent foraging behavior of the greater cane rats, which facilitates mating during the rainy season and non-mating during the dry season. However, the basic GCRA exhibits serious drawbacks of high parameter sensitivity, insufficient solution accuracy, high computational complexity, susceptibility to local optima and overfitting, poor dynamic adaptability, and a severe curse of dimensionality. In this paper, a hybrid nonlinear greater cane rat algorithm with sine–cosine algorithm named (SCGCRA) is proposed for resolving the benchmark functions and constrained engineering designs; the objective is to balance exploration and exploitation to identify the globally optimal precise solution. The SCGCRA utilizes the periodic oscillatory fluctuation characteristics of the sine–cosine algorithm and the dynamic regulation and decision-making of nonlinear control strategy to improve search efficiency and flexibility, enhance convergence speed and solution accuracy, increase population diversity and quality, avoid premature convergence and search stagnation, remedy the disequilibrium between exploration and exploitation, achieve synergistic complementarity and reduce sensitivity, and realize repeated expansion and contraction. Twenty-three benchmark functions and six real-world engineering designs are utilized to verify the reliability and practicality of the SCGCRA. The experimental results demonstrate that the SCGCRA exhibits certain superiority and adaptability in achieving a faster convergence speed, higher solution accuracy, and stronger stability and robustness. Full article

As traditional forage crops demand substantial water, exploring alternatives with lower water demands can mitigate the strain on water supplies. This pot study evaluated five annual warm-season forages (forage sorghum (FS) [Sorghum bicolor (L.) Moench], prussic acid-free forage sorghum (PF) [Sorghum bicolor subsp. Drummondii], sorghum x sudangrass hybrid (SS) [Sorghum bicolor x drummondii], sudangrass (SU) [Sorghum sudanense (Piper) Stapf], and pearl millet (PM) [Pennisetum glaucum (L.) R. Br.]) under two different irrigation treatments (40% and 80% ETo). Morphological (leaf area, leaf count, plant height), biomass yield, nutritional content (nitrogen (N), acid detergent fiber, and in vitro true digestibility (IVTD)), and water use efficiency (WUE) parameters were assessed at 35 and 49 days after planting (DAP). Irrigation effects varied with time, more strongly influencing nutritive value at 35 DAP and morphological traits at 49 DAP. WUE was significantly affected by irrigation at both timepoints. No single forage consistently outperformed across all metrics. PF and SU had the most biomass (p < 0.01), while PM had the greatest N content (p < 0.01). However, PF and SU had the highest WUE for biomass and digestible dry matter (p < 0.01). These findings suggest PF and SU may improve forage system sustainability under limited water availability. Full article

One of the essential parts of contemporary cryptographic systems is s-boxes (Substitution Boxes), which give encryption algorithms more complexity and resilience due to their nonlinear structure. In this study, we propose CSBA (Cuckoo Search–Bees Algorithm), a hybrid evolutionary method that combines the strengths of Cuckoo Search and Bees algorithms, to generate s-box structures with strong cryptographic properties. The initial population is generated with a high-diversity four-dimensional Memristive Lu chaotic map, taking advantage of the random yet deterministic nature of chaotic systems. This proposed method was designed with inspiration from biological systems. It was developed based on the foraging strategies of bees and the reproductive strategies of cuckoos. This nature-inspired structure enables an efficient scanning of the solution space. The resultant s-boxes’ fitness was assessed using the nonlinearity value. These s-boxes were then optimized using the hybrid CSBA algorithm suggested in this paper as well as the Bees algorithm. The performance of the proposed approaches was measured using SAC, nonlinearity, BIC-SAC, BIC-NL, maximum difference distribution, and linear uniformity (LU) metrics. Compared to other studies in the literature that used metaheuristic algorithms to generate s-boxes, the proposed approach demonstrates good performance. In particular, the average value of 109.75 obtained for the nonlinearity metric demonstrates high success. Therefore, this study demonstrates that robust and reliable s-boxes can be generated for symmetric encryption algorithms using the developed metaheuristic algorithms. Full article

Artificial protozoa optimizer (APO), as a newly proposed meta-heuristic algorithm, is inspired by the foraging, dormancy, and reproduction behaviors of protozoa in nature. Compared with traditional optimization algorithms, APO demonstrates strong competitive advantages; nevertheless, it is not without inherent limitations, such as slow convergence and a proclivity towards local optimization. In order to enhance the efficacy of the algorithm, this paper puts forth a multi-strategy fusion artificial protozoa optimizer, referred to as MSAPO. In the initialization stage, MSAPO employs the piecewise chaotic opposition-based learning strategy, which results in a uniform population distribution, circumvents initialization bias, and enhances the global exploration capability of the algorithm. Subsequently, cyclone foraging strategy is implemented during the heterotrophic foraging phase. enabling the algorithm to identify the optimal search direction with greater precision, guided by the globally optimal individuals. This reduces random wandering, significantly accelerating the optimization search and enhancing the ability to jump out of the local optimal solutions. Furthermore, the incorporation of hybrid mutation strategy in the reproduction stage enables the algorithm to adaptively transform the mutation patterns during the iteration process, facilitating a strategic balance between rapid escape from local optima in the initial stages and precise convergence in the subsequent stages. Ultimately, crisscross strategy is incorporated at the conclusion of the algorithm’s iteration. This not only enhances the algorithm’s global search capacity but also augments its capability to circumvent local optima through the integrated application of horizontal and vertical crossover techniques. This paper presents a comparative analysis of MSAPO with other prominent optimization algorithms on the three-dimensional CEC2017 and the highest-dimensional CEC2022 test sets, and the results of numerical experiments show that MSAPO outperforms the compared algorithms, and ranks first in the performance evaluation in a comprehensive way. In addition, in eight real-world engineering design problem experiments, MSAPO almost always achieves the theoretical optimal value, which fully confirms its high efficiency and applicability, thus verifying the great potential of MSAPO in solving complex optimization problems. Full article

Understanding the evolutionary and geographic trajectories of crop wild relatives is vital for enhancing agro-biodiversity and advancing climate-resilient agriculture. This study focuses on ten Vicia L. taxa—comprising five species, four varieties, and one subspecies—of significant agricultural importance in Türkiye. An integrative molecular framework was applied, incorporating nuclear ITS sequence data, ITS2 secondary structure modeling, phylogenetic network analysis, and time-calibrated biogeographic reconstruction. This approach revealed well-supported clades, conserved secondary structural elements, and signatures of reticulate evolution, particularly within the Vicia sativa L. and V. villosa Roth. complexes, where high genetic similarity suggests recent divergence and possible hybridization. Anatolia was identified as both a center of origin and a dispersal corridor, with divergence events estimated to have occurred during the Late Miocene–Pliocene epochs. Inferred migration routes extended toward the Balkans, the Caucasus, and Central Asia, corresponding to paleoenvironmental events such as the uplift of the Anatolian Plateau and the Messinian Salinity Crisis. Phylogeographic patterns indicated genetic affiliations between Turkish taxa and drought-adapted Irano-Turanian lineages, offering valuable potential for climate-resilient breeding strategies. The results establish a molecularly informed foundation for conservation and varietal development, supporting sustainability-oriented innovation in forage crop systems and contributing to regional food security. Full article

Tropical forage crops vary widely in biochemical composition, resulting in inconsistent silage quality. Understanding how plant traits shape microbial and metabolic networks during ensiling is crucial for optimizing fermentation outcomes. Eight tropical forages—Sorghum bicolor (sweet sorghum), Sorghum × drummondii (sorghum–Sudangrass hybrid), Sorghum sudanense (Sudangrass), Pennisetum giganteum (giant Napier grass), Pennisetum purpureum cv. Purple (purple elephant grass), Pennisetum sinese (king grass), Leymus chinensis (sheep grass), and Zea mexicana (Mexican teosinte)—were ensiled under uniform conditions. Fermentation quality, bacterial and fungal communities (16S rRNA and ITS sequencing), and metabolite profiles (untargeted liquid chromatography–mass spectrometry, LC-MS) were analyzed after 60 days. Sweet sorghum and giant Napier grass showed optimal fermentation, with high lactic acid levels (111.2 g/kg and 99.4 g/kg, respectively), low NH₄⁺-N (2.4 g/kg and 3.1 g/kg), and dominant Lactiplantibacillus plantarum. In contrast, sheep grass and Mexican teosinte exhibited poor fermentation, with high NH₄⁺-N (6.7 and 6.1 g/kg) and Clostridium dominance. Fungal communities were dominated by Kazachstania humilis (>95%), while spoilage-associated genera such as Cladosporium, Fusarium, and Termitomyces proliferated in poorly fermented silages. Metabolomic analysis identified 15,827 features, with >3000 significantly differential metabolites between silages. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment revealed divergence in flavonoid biosynthesis, lipid metabolism, and amino acid pathways. In the sweet sorghum vs. sheep grass comparison, oxidative stress markers ((±) 9-HODE, Agrimonolide) were elevated in sheep grass, while sweet sorghum accumulated antioxidants like Vitamin D3. Giant Napier grass exhibited higher levels of antimicrobial flavonoids (e.g., Apigenin) than king grass, despite both being dominated by lactic acid bacteria. Sorghum–Sudangrass hybrid silage showed enrichment of lignan and flavonoid derivatives, while Mexican teosinte accumulated hormone-like compounds (Gibberellin A53, Pterostilbene), suggesting microbial dysbiosis. These findings indicate that silage fermentation outcomes are primarily driven by forage-intrinsic traits. A “forage–microbiota–metabolite” framework was proposed to explain how plant-specific properties regulate microbial assembly and metabolic output. These insights can guide forage selection and development of precision inoculant for high-quality tropical silage. Full article

Intercropping with green manures is an effective practice for increasing agricultural production and reducing environmental issues. However, the effects of green manure type and intercropping patten on soil nutrient availability and microbial communities remains underexplored. In the present study, the impacts of three green manure–maize intercropping patterns on maize yield, rhizosphere nutrient availability, and soil fungal community were evaluated. Four treatments (three replicate plots for each) were involved, including a monoculture treatment (MC) as a control and three intercropping patterns as follows: maize–ryegrass (Lolium perenne L.) (IntL), maize–forage soybean (Fen Dou mulv 2, a hybrid soybean cultivar) (IntF), and maize–ryegrass–forage soybean (IntLF) intercropping. The results showed that all three intercropping patterns significantly increased maize yield and rhizosphere available phosphorus (AP) compared with MC. Intercropping shifted the dominant assembly process of the maize rhizosphere fungal community from stochastic to deterministic processes, shaping a community rich in arbuscular mycorrhizal fungi (AMF) and limited in plant pathogens, primarily Exserohilum turcicum. AP showed significant correlations with fungal community and AMF, while maize yield was negatively correlated with plant pathogens. In addition, the dual-species green manure intercropping pattern (IntLF) had the strongest positive effects on maize yield, AP content, and fungal community compared with single-species patterns (IntL and IntF). These results illustrate the advantages of planting diversification in boosting crop production by improving nutrient availability and soil health in the rhizosphere and suggest that the maize–ryegrass–forage soybean intercropping system is a potential strategy for improving soil fertility and health. Full article