Search Results (601)

A virtual training environment offers clear advantages for agricultural robotics. It provides a safe setting in which perception, navigation, and control algorithms can be evaluated without risking damage to either the robot or the crop. It also supports efficient data generation: large volumes of training data can be collected under diverse environmental conditions that would be costly, slow, and often season-dependent in real-world deployments. This broader variability improves model adaptability, reduces the risk of overfitting, and leads to more robust operation. In this paper, we argue that digital twin technology should therefore be understood not merely as a passive mirror of a physical robot, but as an active training environment in which multiple sensor-related subprocesses can be developed, tested, validated, and refined jointly. This paper is based on our experiences with digital twin technology used in the development of a vineyard robot, including a self-driving rover, sensor simulation, procedural map generation, and agriculture-specific movement models. Our contribution is threefold: we reinterpret the digital twin as a training space, propose a layered framework for training agricultural robots in virtual environments, and explain why agriculture is a particularly strong use case, given variable field conditions, expensive real-world experimentation, and persistent labor scarcity. To validate this framework, we present the simulation-based evaluation of an autonomous reinforcement learning agent. The agent has been trained entirely in this virtual environment, which successfully navigated to 155 out of 161 target points in a simulated vineyard demonstration environment. Full article

Modeling the unsaturated soil hydraulic conductivity function (SHCF) is essential for understanding water movement in unsaturated zones and supporting effective agricultural and environmental management. Accurate estimation of SHCF parameters, particularly the α and n parameters of the van Genuchten–Mualem (VGM) model, remains a challenging endeavor due to the complex interplay of soil physical properties. Tree-based machine learning methods have shown promising capabilities in this area. To further assess and compare the performance of tree-based approaches, this study aimed to evaluate the efficiency of three algorithms, Cubist, RF, and light gradient boosting machine (LightGBM), in the parametric estimation of SHCF using 196 soil samples from the UNSODA database. Input variables, including sand, clay, soil bulk density (BD), field capacity (FC), and permanent wilting point (PWP), were structured into four progressively complex pedotransfer functions (PTFs). Results indicate that Cubist demonstrated the best overall generalization during testing, achieving the lowest average RMSD (7.165) across the four PTFs compared to RF (7.602) and LightGBM (8.068), although RF and LightGBM achieved marginally better performance on individual PTF-metric combinations. All three algorithms achieved high coefficients of determination (R² ≥ 0.95) across all PTFs. Specifically, in PTF4, the best-performing model, Cubist achieved a 6.8% lower RMSD than RF and a 12.4% improvement over LightGBM. Shapley additive explanations (SHAP) conducted via XGBoost surrogate models, suggested that FC and PWP were the most influential predictors of SHCF among the variables examined. These findings suggest that Cubist is a viable approach for estimating SHCF, particularly when input data are limited to basic soil properties. Full article

Climate resilience has evolved and transitioned from a concept focused on disaster risk to a strategic development paradigm. It has become a core area of focus for researchers, professionals, and policymakers due to the increasing frequency and severity of climate change hazards. The academic landscape persists in a fragmented state in spite of its significant prominence due to diverse conceptual frameworks, various definitions, and a lack of precise indicators to assess climate resilience across sectors. The crucial objective of this research is to conduct a comprehensive bibliometric analysis of the academic literature on climate resilience, measure the scientific influence, and identify gaps and opportunities. This bibliometric review was conducted using data from Web of Science, consisting of 1096 articles published between 2015 and 2025. Vosviewer represents the main software used to evaluate the network of leading authors, journals, international collaborations, and the dominant countries. Terms such as climate change, resilience, and indicators received particular attention, representing the main conceptual connections. This study reveals an overview of the field’s progression, themes, trends, and challenges. The results reveal a sustained increase in research output and a heterogeneous landscape organized around key domains, including urban resilience, ecosystem dynamics, agricultural systems, governance, climate impacts, and sustainability transitions. Resilience is assessed using diverse, context-specific indicators, with governance, vulnerability, and adaptive capacity frequently identified as core dimensions. However, measurement approaches remain inconsistent and lack standardization. Scientific production is concentrated in a limited number of countries, although international collaboration is gradually expanding. These findings underscore the multidimensional and evolving characteristics of climate resilience research, with no clear movement toward a unified measurement framework. Full article

Agriculture faces increasing pressure to meet global food demands while minimizing environmental harm, yet many current practices remain unsustainable. This study develops the EAGLES framework (Environmental Impact of Agriculture using Life Cycle Assessment and Expert System) to address limitations in current sustainability assessment approaches. Life Cycle Assessment (LCA) evaluates environmental impacts but is limited by data availability and usability, especially for new users in agriculture. The objective of this study is to address this gap by developing the EAGLES framework, which organizes agricultural LCA data within an expert system (knowledge-based, rule-based) structure to guide the application of LCA phases. The knowledge base is developed from Phase 1 datasets reported in previous work and additional datasets developed as part of this study. The rule base uses if–then logic to check if the required data are available and to guide movement across the LCA phases. The framework is designed to support multiple scope types, impact categories, and assessment methods within a single structure. The framework was applied to rice production in Mississippi (2021) to assess marine eutrophication and acidification. The case study results show that the framework enables consistent progression across all LCA phases and produces impact results that can be interpreted using normalization and weighting. A second pathway was applied to assess acidification, demonstrating that the framework can handle multiple impact categories within the same system. By organizing data and linking inventory, impact assessment, and interpretation into a single process, the framework provides a structured and transparent approach for conducting agricultural LCA. Full article

Habitat selection is often activity-specific, as animals may use different environments depending on whether they are foraging, breeding, or moving through the habitat. Behavioural studies of nocturnal species are challenging, and conventional methods are limited in their applicability. In this study, we tested a thermal drone in combination with Artificial Intelligence (AI) for focal animal sampling and habitat use of mesopredators. A drone mounted with a thermal video camera recorded the movements and behaviours of red foxes (Vulpes vulpes), European badgers (Meles meles), and Eurasian otters (Lutra lutra), while simultaneously geocoding their position. Additionally, we tested an AI-based analysis, LabGym for species and behaviour detection of video recordings. In Danish agricultural areas, both habitat separation and spatial overlap among the three mesopredators, were observed. Foxes showed a higher degree of versatility in both behaviour and habitat choice compared to badgers and otters. Otters were primarily found near water bodies, while badgers preferred foraging under tree cover and in meadows. The optimised LabGym achieved 80.4% mAP for species identification and successfully classified four behaviours with more than 80% accuracy. Using the thermal drone in combination with geolocation data and AI enables spatial mapping of mesopredator activities, adding valuable insights into predator ecology. Full article

Effective monitoring of insect populations is essential for sustainable pest management and for supporting Integrated Pest Management (IPM) strategies that reduce reliance on chemical pesticides. Bioacoustic methods have recently emerged as a promising approach for monitoring insects by analyzing the sounds and vibrations they produce during activities such as feeding, movement, and communication. This review examines the application of bioacoustics in insect monitoring and pest management, with particular emphasis on recent advances in artificial intelligence (AI) and automated detection technologies. The biological foundations of insect sound production, acoustic monitoring technologies, and AI-based analytical methods are discussed. Machine learning and deep learning models enable automated detection and classification of insect species based on acoustic signals, facilitating early pest detection and biodiversity monitoring. Bioacoustics has been applied to detect and identify insect pests, monitor stored-product insects, and manipulate insect behavior using acoustic and vibrational signals. Despite these advances, challenges remain, including environmental noise interference, limited acoustic datasets, and technical constraints of monitoring systems. Future research should focus on improving datasets, signal processing methods, and the integration of bioacoustics monitoring with precision agriculture and IPM frameworks to support sustainable crop protection. Full article

Soil hydrophysical properties play a key role in processes such as water movement through soil and also affect the amount of water available to plants, thus influencing the sustainability of water management in lowland agricultural landscapes. This study investigated whether the application of calcium sulfate dihydrate (gypsum, CaSO₄·2H₂O) can improve selected hydrophysical properties of a heavy clay agricultural soil from the Eastern Slovak Lowland (Slovakia). In a controlled laboratory experiment, topsoil samples (0–15 cm depth) were treated with four rates of gypsum application (0.5, 1, 2.5 and 10 g core⁻¹; ≈2–40 t ha⁻¹ equivalents) and then repacked in 100 cm³ cores. Gypsum caused a marked apparent shift from “clay” to “silt” in the particle-size analysis, consistent with flocculation and incomplete dispersion rather than a real textural change. Increasing the gypsum dose also led to a gradual increase in saturated hydraulic conductivity (from 0.68 ± 0.21 to 2.00 ± 0.66 cm d⁻¹). Water retention near saturation changed little, but water content at the wilting point decreased at higher doses, increasing plant-available water (maximum ~59% at 2.5 g core⁻¹). Under laboratory conditions, gypsum improved the hydraulic function of the soil, and, at selected doses, increased water availability related to drought, supporting its potential as a structural amendment for enhancing the sustainable management of heavy clay soils. Full article

This paper investigates the dynamic linkages and volatility transmission among global food prices, biofuel commodity prices, and crude oil prices, with a focus on the profound disruptions caused by the COVID-19 pandemic. While interdependencies between energy and agricultural markets are well-studied, the specific role of biofuels as a transmission channel and the exacerbating effects of the crisis remain underexplored, especially through a robust multivariate volatility framework. Utilizing A VECM-CCC-GARCH models, this study captures both mean and conditional variance dynamics, allowing for the examination of asymmetric news impacts and volatility spillovers. The analysis employs a comprehensive dataset including the FAO Food Price Index, key biofuel, ethanol, biodiesel, and crude oil prices (Brent and WTI), alongside proxies for the pandemic’s severity. The research hypothesizes that the COVID-19 pandemic significantly amplified the volatility and strengthened the price transmission channels. We expect to find increased co-movement and volatility spillovers, reflecting reduced demand for transport fuels, agricultural supply chain disruptions, and shifting biofuel production incentives. The TARCH component will discern if negative news (e.g., sharp drops in oil demand) had a disproportionately larger impact on volatility than positive news. By providing a nuanced understanding of these complex interdependencies, this study offers valuable insights for policymakers addressing food security, energy transition strategies, and macroeconomic stability in the post-pandemic world, particularly concerning the strategic role of biofuels. Full article

The study addresses the challenge of accurately simulating and visualising the kinematics of agricultural machinery during field operations. The research is motivated by the current lack of comprehensive guidelines for selecting optimal movement and turning modes under varying forward speeds, working widths, and field geometries. A spreadsheet-based environment was utilised to perform simultaneous kinematic simulation and trajectory visualisation. Turning manoeuvres were modelled using smooth composite curves, consisting of straight segments, clothoids, and circular arcs, with trajectories represented in a Cartesian coordinate system through geometric transformations including translation, rotation, and mirror symmetry. Continuity between curve elements was ensured by dimensional chains linking abscissas, ordinates, and direction angles at their start and end points. The influence of key operational factors—forward speed, angular turning velocity, working direction, and field boundaries—was evaluated for a range of turn types, including semicircle, pear-shaped, figure-eight, side exit, U-turn, and P-turn manoeuvres. Field experiments conducted on selected patterns confirmed that the proposed approach can reproduce actual trajectories with sufficient practical accuracy. These results demonstrate that spreadsheet-based kinematic modelling is a robust and accessible tool for optimising tractor–implement movement, enhancing operational planning, and providing a reliable framework for further research into machinery performance under complex field conditions. Full article

Coffee-price volatility is a recurrent external shock for Peru’s small open economy, with potentially uneven consequences across sectors. This study evaluates whether global coffee prices and domestic macro-agricultural indicators share stable long-run equilibria and quantifies the transmission of coffee-price shocks to the terms of trade, nominal exchange rate, consumer prices, agricultural GDP, and total GDP. Using a multivariate vector error-correction model identified via Johansen cointegration, and controlling for major global disruptions and ENSO-related seasonality, we trace dynamic effects through impulse-response analysis. The results indicate economically meaningful cointegration, implying that external prices and domestic aggregates are linked by long-run restrictions. A positive coffee-price shock produces heterogeneous real effects: the response of aggregate GDP is modest and short-lived, while agricultural GDP reacts more strongly and persistently. The shock propagates mainly through external and nominal channels—especially the exchange rate and terms of trade—whereas consumer-price pass-through is present but comparatively moderate. These findings contribute to the commodity-shock literature by providing sector-sensitive evidence for an agricultural export shock and by clarifying the mechanisms through which coffee-price movements propagate to domestic activity and prices in a small open agricultural economy. Full article

Human–elephant conflict (HEC) has emerged as a major conservation and socio-economic challenge across Asia, largely driven by habitat degradation and increasing human pressure within elephant ranges. In India, expanding agriculture, mining activities, and infrastructure development have progressively altered forest landscapes, restricting elephant movement and intensifying interactions with human settlements. This study examines the relationship between landscape dynamics and HEC in the West Singhbhum district, Jharkhand, India. A three-year field investigation (2018–2020) across four forest divisions—Porahat, Chaibasa, Kolhan, and Saranda—was integrated with multi-temporal land-use and land-cover (LULC) analysis from 2000 to 2020 to evaluate habitat changes and their influence on conflict patterns. During the study period, 157 human casualties and extensive crop and property losses were recorded, indicating the severity of the conflict in the region. Landscape analysis revealed a substantial decline in dense forest cover and a reduction of large core forest areas (>500 acres), accompanied by increasing agricultural expansion and forest perforation. NDVI trends further indicated widespread deterioration in vegetation condition, reflecting declining habitat quality. These structural landscape changes have fragmented elephant habitats and displaced movement routes toward human-dominated landscapes and are thus associated with a spatial clustering of conflict events, particularly in the Chaibasa Forest Division. In contrast, the Saranda Forest Division retains relatively intact forest cores and supports more stable elephant habitat conditions. The findings demonstrate that HEC in the region is strongly linked to habitat fragmentation and declining vegetation quality rather than random elephant behaviour. Maintaining large contiguous forest blocks, restoring landscape connectivity, and implementing targeted mitigation strategies are therefore essential for sustaining elephant populations while reducing conflict with local communities. Full article

Prices and quantities in agricultural commodity and food product markets are subject to constant changes due to evolving supply and demand conditions. Big and sudden shifts in supply or demand may lead to price movements that bring negative consequences for food producers or consumers. Factors causing such movements can be of different natures, but substantial changes in the world energy price levels are supposed to be one of the most important. The purpose of the study was to investigate the effect of global energy price shocks on the evolution of food commodities and food consumer prices. Using the World Bank data on the respective price indices, we looked for shocks in these data series by utilizing statistical tools. Having identified three global energy price shocks in the period 2000–2024 induced by the financial crisis of 2008, the COVID-19 pandemic, and the outbreak of war in Ukraine, their influence on the world agricultural commodity prices and food consumer prices was assessed. It was found that the series of energy, food commodity, and food consumer price indices were related in the long term. Also, the occurrence of global energy price shocks to a visible extent translated into global food commodity and food consumer price shocks. Applying various statistical and econometric techniques, including Chow tests and MS-VAR modelling, enables the identification of which breaking points led to regime changes between the analysed variables. The most sensitive to the structural breaking points appeared to be the relation between energy and consumer food prices. This discovery can be considered our major contribution. Full article

Although the ATP-binding cassette (ABC) transporter superfamily of proteins typically facilitates the movement of compounds across cellular membranes, the ABC E subfamily (ABCE) influences protein synthesis via non-transporter roles in ribosome biogenesis. Despite this essential role, our understanding of the impact that ABCE proteins have on insect physiology is limited. Here, we identified and characterized the ABCE gene from Lygus hesperus, a major agricultural pest of crops in North America. LhABCE transcripts were constitutively expressed throughout development and were present in all adult tissues tested. RNA interference (RNAi)-mediated knockdown of LhABCE transcripts in fifth instar nymphs resulted in high nymphal mortality and an incomplete molt. LhABCE knockdown in adults disrupted gametogenesis and reduced longevity. In females, oogenesis was impaired and oocytes did not progress beyond the pre-vitellogenic phase. In males, LhABCE knockdown reduced both spermatozoa abundance and male fertility. LhABCE knockdown, however, had little to no impact on hemolymph protein levels or the levels of circulating vitellogenin. Taken together, the results indicate that LhABCE is critical for the normal progression of processes like molting and gametogenesis that require coordinated bursts of protein synthesis and suggest that ABCE may play an important role in the mechanisms underlying those bursts. Full article

Bees are essential pollinators for agricultural systems, making accurate, automated monitoring of their behavior critical for assessing colony health and ecosystem stability. Recent advances in computer vision and artificial intelligence have enabled large-scale bee traffic monitoring at hive entrances; however, most existing event classification methods focus exclusively on simple entrance and exit events. This simplification overlooks compound movements—such as U-turns and guarding behaviors—that represent a substantial portion of bee activity and can lead to inaccurate trajectory reconstruction and misleading behavioral interpretations. In this work, we systematically analyze existing event classification strategies used in automatic bee traffic monitoring, evaluating their performance on both simple and compound movements. We then propose extended classification methods that explicitly model compound events by incorporating bidirectional movement patterns derived from positional and angular cues. Using a manually annotated dataset of computer-vision-based hive entrance recordings, we compare threshold-based, displacement-based, and angle-based approaches under simple and mixed-event conditions. Our results demonstrate that compound events account for over one-third of all detected movements and that classification methods explicitly designed to handle bidirectional behavior substantially outperform traditional approaches in both accuracy and robustness. In particular, threshold-based bidirectional classification achieves near-perfect performance when full trajectories are available, while displacement-based methods provide a reliable alternative under partial observations. These findings highlight the importance of modeling compound behaviors in automated bee monitoring systems and contribute to more accurate flight reconstruction, behavioral analysis, and AI-driven decision support for precision agriculture and pollinator management. Full article

This paper investigates whether water-related climate stress predicts tail movements in food system assets and whether these spillovers vary across market regimes and investment horizons. Using daily data from January 2012 to January 2026, we examine the relationships among a water-risk proxy, agricultural commodities, agribusiness, and food supply-chain equities, and a fertilizer-related proxy. The analysis combines the cross-quantilogram with quantile spillover analysis in the frequency domain, allowing us to capture directional dependence in the tails of the distribution and short- and long-run connectedness. To account for structural change, we employ data-driven break detection and identify three major regimes: a pre-disruption period, a COVID-related adjustment phase, and a broader food system stress regime from early 2022 onward. The findings indicate that water-related climate stress has its strongest predictive power in the tails, especially for agribusiness and fertilizer-related assets, while the broad agricultural commodity basket is comparatively less sensitive. Lower-tail dependence is predominantly negative and often significant, whereas upper-tail dependence is generally positive, indicating asymmetric transmission under extreme market conditions. The spillover results further show that connectedness in the water–food system is mainly short-run, with agribusiness and fertilizer channels acting as the primary conduits of transmission. From a practical perspective, these findings suggest that investors and risk managers can use water-related market signals as early warning indicators of stress in food system assets, while policymakers can strengthen food system resilience through integrated water management, input market monitoring, and supply chain adaptation measures. The findings suggest that water-related climate stress is not merely an environmental constraint but a systemic source of food system risk with implications for resilience, risk monitoring, and integrated water-agriculture governance. Full article