Agricultural Landscapes Research Articles

The herpetofauna of the Sporades Islands (Aegean Sea, Greece): New discoveries and a review of a century of research

The Sporades are one of the most biologically important archipelagos in the Aegean Sea (Greece) and have received priority conservation over the last 50 years. However, despite numerous early efforts, its herpetofauna is only partially described, resulting in many distributional gaps that have prevented adequate understanding and management of the resident species communities. We review one century of bibliography from the Northern Sporades and combine this information with a review of museum specimens and insights from numerous extensive field surveys over the last near-decade to provide for the first time a comprehensive picture of the reptiles and amphibians of the archipelago. We report here on 26 new island records and find that the herptile communities of the region are largely derived from the herpetofauna of the nearby Thessaly mainland, with only a few introduced taxa. There is also a small but significant set of endemic taxa in the archipelago. Island species richness declines with decreasing island size and increasing duration of island isolation. Herptile communities on smaller islands are progressively nested subsets of the communities on larger islands. The presence of reptile species depends sensitively on the condition and management of native ecosystems. While non-aquatic species maintain largely healthy populations, most populations are under pressure from the combined effects of rampant tourist development, the destruction and degradation of rare wetland habitats, and the abandonment of traditional agricultural landscapes. We provide recommendations regarding sustainable management of the local reptile and amphibian populations.

Reintroduction strategies for rare arable weeds in agricultural practice and their influence on yield

AbstractAgricultural intensification and changing landscape structure led to decreasing numbers of arable flora and fauna during the last decades. To counteract these declines, arable plant diversity needs to be restored. It is important to promote regional plant communities including endangered species to ensure adaption to agricultural landscapes. Therefore, this study examines the development of arable plant diversity, crop yield and the reintroduction success of rare arable plants (RAPs) under different practical farming strategies in cereals from 2019 to 2021 in southwestern Germany. Farming strategies varied regarding weed control intensity (none, mechanical, herbicide with and without efficiency gap), fertilisation (mineral and none) and row spacing (12.5 cm and 20 cm). Additionally, two different seed mixtures containing RAP and common species were used for reintroduction: one commercially available and another self‐composed. Reintroduction success was quite high with more than 80% establishment rate. Species numbers doubled in all treatments over time, except the one with herbicide application, with on average 8–10 species emerging from the soil seed bank. Reintroduction of RAP increased arable plant species richness further up to 37 species. The site‐adapted mixture showed in the end a site‐typical weed community. Yield was significantly lower in treatments with reintroduction via sown mixtures and no weed control, losing on average 65% yield. The reintroduction of RAP was successful and accompanied by an increasing floral diversity, however, with a simultaneous yield loss. Financial incentives or subsidies are therefore still necessary to compensate yield loss for reintroduction and conservation efforts.

Revolutionizing Agriculture: Innovative Techniques, Applications, and Future Prospects in Precision Farming

Precision agriculture (PA) represents a transformative approach to farming, employing advanced technologies to enhance productivity, efficiency, and sustainability. This review article provides an in depth analysis of the latest innovations in PA techniques, their diverse applications, and future directions. Precision agriculture is revolutionizing the agricultural landscape by integrating sophisticated tools such as GPS, remote sensing, Internet of Things (IoT), and big data analytics. These technologies enable farmers to monitor and manage variability in crop production meticulously, optimize the use of inputs, and enhance overall farm management practices. The key innovations in PA include the development and application of Geographic Information Systems (GIS) and Global Positioning Systems (GPS), which facilitate accurate mapping and variable rate technology (VRT) for site specific input management. Remote sensing technologies, encompassing both satellite imagery and UAVs (unmanned aerial vehicles), provide critical insights into crop health, soil conditions, and weather patterns, allowing for proactive and informed decision making. The integration of IoT in agriculture involves deploying sensors and connected devices to monitor soil moisture, temperature, and other environmental parameters in real time. This integration supports precision irrigation, climate monitoring, and efficient resource utilization. Big data analytics further enhances PA by processing vast amounts of data to generate actionable insights, enabling predictive analytics and decision support systems (DSS) that aid in optimizing farming operations. The article explores the applications of these advanced techniques in crop management, resource use optimization, and environmental stewardship. Examples include variable rate application of fertilizers, precision irrigation systems, and automated machinery such as drones and robotic harvesters. These innovations lead to significant improvements in crop yields, resource efficiency, and sustainability. Moreover, the review addresses the challenges associated with the adoption and implementation of PA technologies. These challenges include data management complexities, high initial costs, limited accessibility, and the need for technical expertise. The article discusses potential solutions such as cloud computing, machine learning algorithms, government subsidies, collaborative models, and comprehensive training programs to mitigate these barriers, the review highlights the integration of advanced technologies like artificial intelligence (AI), blockchain, and enhanced connectivity through 5G networks as pivotal developments that will further revolutionize precision agriculture. AI and machine learning will enhance predictive modeling and automated decision making, while blockchain will ensure transparency and traceability in supply chains. Enhanced connectivity will facilitate real time monitoring and collaborative platforms, driving efficiency and innovation in farming practices.

Scale dependency of taxonomic and functional diversity in pristine and recovered loess steppic grasslands

Widespread campaigns on forest restoration and various tree planting actions lower the awareness of the importance of grasslands for carbon sequestration and biodiversity conservation. Even lower attention is given to the conservation of biodiversity and ecosystem functioning in remnants of ancient, so-called pristine grasslands. Pristine grasslands generally harbour high biodiversity, and even small patches can act as important refuges for many plant and animal species in urbanised or agricultural landscapes. Spontaneous succession of grassland is frequently viewed as a cost-effective tool for grassland restoration, but its applicability is strongly dependent on many local to landscape-scale factors, and the recovery is often slow. It is therefore essential to monitor the success of grassland restoration projects that rely on spontaneous succession. We compared the species diversity and functional attributes of pristine and recovered grasslands by studying the taxonomic and functional diversity in thirteen (8 pristine and 5 recovered) loess steppic grasslands using differently sized sampling plots from 0.01 to 100 m2. Our results indicate that there are remarkable differences in taxonomic and functional diversity between pristine and recovered grasslands. We also found that during secondary succession there is a likely functional saturation of the species assembly in the first few decades of recovery, and while patterns and structure of recovered grasslands became quite similar to those of pristine grasslands, species richness and diversity still remained much lower. Pristine grasslands support considerable plant diversity, and species composition is slow to recover if destroyed by agricultural land use. This underlines the importance of preserving existing pristine grassland remnants, which might serve as sources of species for future restoration measures.

Invasive neophytes alter ecological functions and services of riparian hardwood forests

Riparian hardwood forests, now rare in Europe, occur in small patches within agricultural landscapes and are important biodiversity hotspots protected by the EU Habitats Directive and monitored. They play a vital ecological role and provide essential ecosystem services (ES). Recent studies indicate their unfavourable conservation status with a negative trend reported by EU Member States. One of the most visible symptoms of the degradation of riparian hardwood forest is the invasion of alien plant species which can radically change ecosystem functions. In this work, we use plant functional trait analysis to develop proxies and quantify an impact of invasive alien species, specifically invasive neophytes, on specific functions of riparian hardwood forests and their potential to provide five relevant regulating services. Data from 249 phytosociological relevés across 83 Natura 2000 habitat monitoring sites in Poland were analyzed.Our findings indicate that invasive neophytes negatively affect the potential for maintaining nursery populations and habitats, as well as regulation of the chemical condition of freshwaters by these forests. On the other hand, they do not significantly influence the local climate conditions and the forests’ potential to produce antimicrobial compounds. We recommend integrating an ES assessment based on plant functional traits into habitat monitoring. This approach is cost-effective, leveraging existing data, and its results could be used in some cases as a premise for initiating additional environmental monitoring based on direct field measurements.

Fragments maintain similar herpetofauna and small mammal richness and diversity to continuous habitat, but community composition and traits differ

ContextHuman disturbance has transformed ecosystems globally, yet studies of the ecological impact of landscape modification are often confounded. Non-random patterns of land clearing cause differing vegetation types and soil productivity between fragments in modified landscapes and reference areas—like national parks—with which they are compared.ObjectivesWe sought to explore the influence of land modification on herpetofauna and small mammal communities using multiple biodiversity measures—species richness and diversity, individual species abundance, and community composition. We also aimed to investigate the role of traits such as diet, habitat breadth, and litter size in moderating species responses to land modification.MethodsWe established 100 sampling sites to survey herpetofauna and small mammals in 11 fragments in an agricultural landscape compared to 11 ecologically equivalent ‘pseudo-fragments’ in a nearby national park in south-eastern Australia. We selected pairs of fragments and pseudo-fragments of the same size and vegetation type, and used identical survey methods to sample pairs simultaneously, thereby controlling for numerous confounding factors, such as differing vegetation type, weather, and survey effort.ResultsSpecies richness and diversity were similar between fragments and pseudo-fragments. Despite this, we found community composition differed markedly—driven by the varying responses of individual species—indicating a shift in fauna communities associated with land modification. Fossorial habit, omnivorous diet, and broad habitat requirements led to higher abundance in fragments whilst arboreality, carnivorous diet, and narrow habitat requirements led to higher abundance in pseudo-fragments.ConclusionsAlthough fragments hold similar numbers of species to continuous areas, they contain distinct and novel communities, and sustain high abundances of some species. These diverse communities are dominated by native species, including threatened species, and their distinctive composition is shaped by traits conducive to persistence amidst land modification. These novel communities may provide a reservoir of resilience in the face of environmental change and should be viewed as complementary to conservation areas.

Cropping Systems Diversification as an Approach to Enhancing Crop Productivity: A Review

Agricultural intensification increased crop productivity but simplified production with lower diversity of cropping systems, higher genetic uniformity, and a higher uniformity of agricultural landscapes. Associated detrimental effects on the environment and biodiversity as well as the resilience and adaptability of cropping systems to climate change are of growing concern. Crop diversification may stabilize productivity of cropping systems and reduce negative environmental impacts and loss of biodiversity, but a shared understanding of crop diversification including approaches towards a more systematic research is lacking. The current review highlights the use of Cropping Systems Diversification as an Approach to Enhancing Crop Productivity. Crop diversification can be considered as an attempt to increase the diversity of crops through, e.g. crop rotation, multiple cropping or intercropping, compared to specialized farming with the aim to improve the productivity, stability and delivery of ecosystem services. It can be one measure to develop more sustainable production systems, develop value-chains for minor crops. Crop diversification practices can include higher crop diversity, more diverse crop, mixed cropping; cultivation of grain legumes in otherwise cereal dominated systems, perennial leys or grassland and regionally adapted varieties or variety mixtures. Crop diversification and/or additional diversification measures like variation of seeding time or changing cropping patterns have the potential to lead to higher and more stable yields, increase profitability and lead to greater resilience of agro-ecosystems in the long term. These practices have the potential to make cropping systems more diverse in space, time and genetics. Through a balanced portfolio approach to agricultural sustainability, cropping system performance can be optimized in multiple dimensions, including food and biomass production, profit, energy use, pest management, and environmental impacts.

Digital and smart technologies to enhance biodiversity in agricultural landscapes: An analysis of stakeholders’ perceptions of opportunities and challenges for broader adoption

Digital and smart technologies (DSTs1) for agriculture are currently widely discussed in the literature and increasingly included in common farming practices. However, the main agricultural use of DSTs remains yield-increasing and effort-reducing applications focused on the economic advantages of precision. The potential of DSTs to enhance biodiversity in agricultural landscapes has rarely been examined, especially from the stakeholder perspective. In this study, we examined the barriers to and potential for using DSTs to promote biodiversity in agricultural areas in Germany. For this purpose, we conducted a nationwide stakeholder acceptance analysis based on an online survey and an expert discussion. Our analysis revealed the notable potential of DSTs to strengthen biodiversity in agricultural landscapes, which is, however, accompanied by critical barriers to the broad acceptance and regular use of such technologies by farmers. Only if based on adequate legal and financial political framework, which create incentives for solution-focused cooperation among all relevant stakeholders and allow a user-orientated technology development, can DSTs develop their underlying potential and gain acceptance among farmers.

Exploring optimal features and image analysis methods for crop type classification from the perspective of crop landscape heterogeneity

Agricultural landscape structure (e.g., the shape of fields, crop diversity, and landscape heterogeneity) greatly influences the selection of methods for large-scale crop mapping using remote sensing data. However, in-depth assessments of its impacts on crop mapping remain infrequent in the existing literature. This study investigated the optimal crop identification features and image analysis methods including pixel- and object-based approaches on crop classification, through the integration of spectral and textural features across various quantitative agricultural landscapes. In the experiments, crop fields were initially delineated into four distinct landscapes using the K-means clustering algorithm based on analyzing 13 selected landscape metrics such as PLAND, LSI and SHDI. Both pixel- and object-based approaches were then employed to conduct crop classification was then conducted using 48 selected features including 9 band reflectance, 23 vegetation indices (VIs), and 16 textures) and two image analysis methods. Specifically, five classification schemes for the different combinations of feature datasets and image analysis methods were explored to assess the impacts of crop heterogeneity on crop classification. Results indicated the five landscape metrics (e.g., SPLIT, SHEI, Average distance, etc.) performed best in assessing crop heterogeneity. In general, spectral bands and VIs had a higher contribution in the compositional heterogeneity, while textural features and VIs played a more important role in the configurational heterogeneity. VIs in the object-based approach and texture features in the pixel-based approach can improved crop classification accuracy in configurational landscapes. The findings provide a theoretical basis on selecting optimal features and image analysis methods for crop classification in complex agricultural landscapes.

Towards sustainable landscapes: Implementing participatory approaches in contract design for biodiversity preservation and ecosystem services in Europe

Agricultural landscapes are the result of the long-term, complex, and intermingled interfaces between nature and culture. Among the stakeholders involved in the production of agri-environmental and climate services, farmers play a crucial role, and their contribution should be duly acknowledged to promote sustainable land management. Therefore, this research aims to contribute to the participatory design of agri-environmental contracts to incentivize landscape and biodiversity practices. To achieve this goal, 13 innovation labs with multi-actor perspective were established across nine different European countries. These groups envisioned a future sustainable landscape characterized by diversity and balance of economic, socio-cultural, and environmental components. Trust-based networks and effective communication channels emerge as vital components for the success of sustainable local production systems. Practitioners emphasize the significance of European-level policies in effecting transformative change and influencing farmers'willingness to contribute to both food production and environmental public goods. They defined concrete contract features such as public funding, hybrid payments, and the presence of intermediaries for the potential of agri-environmental measures. Notably, practitioners perceive a wide range of benefits associated with the implementation of agri-environmental measures, extending beyond economic compensation. Gaining a deeper understanding of practitioners' perceptions of their territories and agri-environmental measures is crucial for policymakers to design tailored and appealing programs that resonate with practitioners'needs.

Wood Turtle Use of Agricultural Hayfields and Their Behavior During Hay Harvest: Implications for Wood Turtle Management in Agricultural Landscapes

Wood Turtle Use of Agricultural Hayfields and Their Behavior During Hay Harvest: Implications for Wood Turtle Management in Agricultural Landscapes

Agricultural Landscapes: A Pattern-Process-Design Approach to Enhance Their Ecological Quality and Ecosystem Services through Agroforestry

Agricultural landscapes are currently suffering and generating severe ecological issues. This is especially true in intensively managed alluvial contexts, where biodiversity is declining and ecosystem services (ES) delivery capacity is being depleted. The aim of our study is to set up and test a synthetic analytical methodology that allows us to: understand current agricultural landscape ecological quality drivers (structural and functional traits); identify context-specific strategies to correct current negative trends (landscape ecology design approach); and assess the changes in the landscape ecological behavior provided by design scenarios. The applied methodology is low-cost and low-time-demanding and is based on multi-scale landscape ecology and land-use-based ES assessment; it implements a pattern-process-design approach. Analyses are applied to four northern Italian alluvial agricultural landscape systems. We specifically address landscape biodiversity support functions (landscape ecology indicators) and landscape multifunctionality (ES spatial assessment). We test the agroforestry approach (landscape feature insertions and crop diversification) as a key strategy to enhance ecological quality and ES, and we account for its contributions to context-specific design scenarios. This analytical toolkit might serve for future applications on similar case studies.

Towards sustainable agricultural landscapes: Lessons from an interdisciplinary research-based framework applied to the Saint Lawrence floodplain

Floodplains are unique environments that provide a dynamic link between terrestrial and aquatic systems. Intensification of human activity – particularly agriculture and urbanisation – has resulted in the degradation of floodplains worldwide. Restoration and sustainable management of floodplains requires holistic assessment and compromise between stakeholders to successfully balance environmental, economic, and social benefits. Yet, understanding these complex systems sufficiently to provide evidence-based recommendations is a challenge. We present the lessons learned from establishing an interdisciplinary research-based framework on the agricultural floodplain of Lake Saint Pierre, Québec, Canada, whose mandate was to a) understand and define key environmental, agricultural, and socioeconomic attributes of the landscape, b) quantify the trade-offs and synergies between these attributes across different agricultural practices, regions, and land uses, and c) explore novel agri-environmental management practices to assess their role in sustainable floodplain management. Within this manuscript, we explore the benefits that such an approach offers in evaluating sustainable floodplain land use. We found that an interdisciplinary research-based approach demonstrated important benefits such as knowledge transfer, more efficient use of resources (e.g., personnel, funding), and a flexible yet robust research framework. A framework of individual research projects connected to broader interdisciplinary themes allowed a more holistic synthesis of the floodplain systems and assessment of agri-environmental practices. By implicitly considering spatial and social scales, we conceptualised not just how redistribution of the land use types can meet sustainable management objectives, but also explored how compromises within existing uses can optimise socio-economic, agricultural and environmental dimensions and move towards a sustainable multifunctional landscape.

Landscape effects on dispersal‐related traits in a sedentary butterfly in agricultural landscapes

Abstract Agricultural intensification causes the loss and fragmentation of natural habitats, which negatively impacts farmland biodiversity. The concomitant isolation of essential resources may favour increased dispersal ability in flying insects, resulting in differences in flight morphology and dispersal propensity across landscapes. We collected the butterfly Coenonympha pamphilus in four replicated landscape pairs, each consisting of (a) a highly fragmented and intensified ‘modern’ and (b) a less fragmented ‘traditional’ agricultural landscape. We compared condition (i.e. storage reserves), flight morphology, dispersal capacity and propensity of the collected butterflies between both landscape types. We also tested for the impact of landscape composition and configuration at different spatial scales on butterfly traits. No differences between landscape types per se could be found, but a variety of landscape features affected butterfly traits, indicating complex interactions between landscape heterogeneity and dispersal. In particular, landscapes with a high proportion of crop fields appeared to be selected for phenotypes enhancing dispersal ability. Since dispersal is a key factor for the persistence of metapopulations in fragmented landscapes, we argue that limitations on dispersal in relation to compositional and configurational landscape heterogeneity should be considered in the debate on insect declines in agricultural landscapes.

Surface Flux Patterns of Nutrient Concentrations and Total Suspended Solids in Western Carpathian Stream within Agricultural, Forest, and Grassland Landscapes

The intricate processes of surface water erosion are vital for ecological systems and river-scale management; yet, understanding them comprehensively remains a challenge. Forested agricultural catchments, especially in the Carpathian region, face significant degradation, potentially leading to inorganic nutrient leaching and total suspended solid (TSS) flux. Continuous rainwater inundation of soils in river valleys exacerbates this issue. Utilizing innovative tools like SWAT+, studies have revealed higher concentrations of inorganic nutrients in main watercourses from flysch catchments, with agricultural use linked to N-NO3− concentrations and pasture use linked to anion P-PO43−. Maintaining detailed records is crucial for researchers comparing data. SWAT+ proves valuable for studying TSS washing out and inorganic nutrient leaching, informing collaborative watershed management policies involving stakeholders from agriculture, conservation, and water management sectors. The insights on nutrient leaching, particularly phosphorus (P) and nitrogen (N), are instrumental for shaping policies targeting nutrient pollution within pasture land use for EU agriculture. These findings can guide policy frameworks focused on sustainable practices, especially for eco-schemes, and encourage collaborative watershed management efforts.

Monitoring of agroecosystem desertification processes according to satellite data: experience and perspectives

Desertification is a major environmental threat that affects the lives of millions of people around the world. In order to effectively prevent this effect in agro-ecosystems at local and regional levels, it is necessary to have accurate information about its spatial distribution. Remote sensing plays a crucial role in this process due to its cost-effectiveness, ability to cover large areas, and use of a variety of sensors and data analysis methods. The combination of GIS, mathematical models and ground observations allows for more accurate data on land degradation. The purpose of this study is to summarize the experience of both Ukrainian and foreign researchers in the use of remote sensing for monitoring and assessment of desertification and land degradation processes at the global and national levels, as well as to identify areas for further improvement of remote sensing methods for integrated monitoring of desertification in agroecosystems and land degradation. This article reviews the experience of using satellite remote sensing data, mathematical models of desertification, and geographic information systems to identify areas prone to degradation in different parts of the world. Special attention is paid to the adaptation of these models to local conditions using additional parameters. The article also highlights the achievements of Ukrainian scientists in the field of satellite monitoring of agricultural landscapes, their classification and assessment based on remote sensing data. Prospects for further research are outlined. The use of an integrated approach combining geographic information systems, satellite data, analysis of environmental indicators, and mathematical models of desertification adapted to regional characteristics proves to be effective. The main directions remain the adaptation of models to regional conditions, the development of forecasting methods using remote sensing data, and the expansion of international data and experience exchange. It is important to continue to improve remote monitoring methods for the early detection of degradation processes and to strengthen research at the national level for the implementation of appropriate measures.

Lumpy Skin Disease Detection in Cattle by a Robust Approach using Advanced Convolutional Neural Networks

Background: Lumpy skin disease (LSD) is a significant health concern for cattle globally and poses economic threats by affecting various aspects of cattle health. Integrating artificial intelligence (AI) and machine learning (ML) with visual inspections and biosensor data has shown promise in enhancing disease detection and diagnosis. The present study harnesses the potential of Convolutional Neural Networks (CNN) and image processing for detecting LSD. Methods: Using images from the agricultural landscape, this study highlights the significance of convolutional neural networks that identify the lumpy skin disease (LSD) in animals. Images are categorized into two groups: LSD (infected skin) and non-LSD (normal skin). This is achieved by applying a deeply designed deep learning model carefully built to fulfill this particular need. Evaluation metrics assess the model’s performance, including accuracy, loss and a confusion matrix. Result: A CNN-based model trained for 50 epochs to classify skin conditions, achieved an 86.54% accuracy. The study underscores the potential of CNN in early LSD detection, paving the way for practical applications in veterinary medicine. Future work involves addressing dataset limitations, refining model parameters, reducing image noise, exploring different feature extraction methods and investigating additional animal skin conditions.

Peasant perception of beekeeping constraints and practices in large honey production areas in Burkina Faso

BackgroundIn recent decades, agricultural landscapes have been profoundly modified due to the intensification of agriculture, therefore leading to significant disturbances in all components of biodiversity. A survey on the knowledge of beekeeping realities and the use of phytosanitary products in areas of high honey production in Burkina Faso was carried out. Beekeeping realities design the state of beekeeping activities in the study localities.MethodsThe objective of this survey was to characterize Beekeeping operations and to assess the level of knowledge of beekeepers on the effects of the use of phytosanitary products through different beekeeping and agricultural practices. In this sense, 113 farmer beekeepers from the Boucle du Mouhoun, Hauts-Bassins and Nord regions in Burkina Faso were surveyed about their different beekeeping practices.ResultsThe results obtained indicated that beekeeping is a secondary activity (96.47%) and is mainly practiced by men (90.27%). The respondents have mostly an average of 22 traditional hives. The majority of beekeepers have not received training (84.07%) on the hazards of plant protection products on their beekeeping farms. However, a large amount of beekeepers (70.73%) acknowledged that the use of plant protection products could be harmful to their activity. Hives are usually installed in or near the fields. The plant protection products used for crop protection are herbicides (27%), insecticides (23%), fungicides (8%), but especially mixed (42%).ConclusionThe results show that beekeeping in Burkina Faso remains traditional and is practiced for sociocultural reasons. The use of pesticides close to beekeeping could play a role in bee colony collapse taking place in these regions. Training beekeepers on the dangers of the chemicals they use in fields near hives is therefore essential.

Modeling honeybee flower visitation rates in the fragmented agricultural landscapes based on Lévy-flight behavior

AbstractTypically, honeybees (Apis mellifera L.), rely on waggle dances performed by scout bees to communicate information about fruitful nectar and pollen sources across the landscape. However, when this communication is absent, inaccurate, or when resources become depleted, bees resort to alternative search strategies. Field experiments utilizing harmonic radar have revealed that honeybees follow flight patterns that demonstrate a scale-free (Lévy-flight) behavior, representing an optimal search strategy for relocating the original feeder location. If honeybees adhere to a Lévy flight pattern to discover resources, where would honeybees demonstrate the highest flower visitation rates in agricultural landscapes? We generated simulated landscapes with varying proportions of forest cover scenarios, ranging from 5 to 50% of the total landscape area, along with different levels of fragmentation per se. Subsequently, we constrained the richness of flower farm cells in each landscape. To predict honeybee visitation rates, three different methodologies based on random movement were utilized: (1) moving window, (2) random walk, and (3) Lévy flight. We found that honeybee visitation rates were influenced by the degree of forest fragmentation in each scenario. Across all visitation scenarios, the highest average number of visited flowers per cell was observed in landscapes with maximum fragmentation per se. In landscapes with lower forest cover and higher fragmentation, honeybees were more likely to visit a greater number of flowers due to the increased probability of traversing the landscape and encountering more flower cells. honeybee visitation rates in agricultural landscapes are significantly influenced by the degree of forest fragmentation. The study highlights the importance of considering landscape structure, specifically forest fragmentation, when predicting honeybee visitation rates and underscores the need for further research to better understand the intricate relationship between landscape characteristics and pollinator behavior.

Detection of Disease in Calves using Artificial Intelligence

Background: Livestock farming is experiencing a digital transformation and is becoming more information-driven. However, this type of data is often kept in separate storage towers, making it incapable of practicing its potential to boost animal welfare. Lumpy Skin Disease (LSD) is a serious threat to the health of cattle worldwide and has caused financial problems for many cattle farming enterprises. It has been shown that combining machine learning (ML) and artificial intelligence (AI) with biosensor data and conventional visual inspections can improve the identification and diagnosis of this serious condition. Methods: This study presents an extremely precise livestock farming framework that combines data streams from a wide array of disciplines of dairy cattle to see if ever wider/vast data sources enhance the overall projections for diseases and if using the more complicated prediction models can reimburse for less diverse data to some extent. Using images from the farming landscape, this study highlights the utility of convolutional neural networks (CNNs) in the identification of the Lumpy Skin Disease Virus (LSDV) in animals. Result: An analysis of the relative weight given to individual factors in predicting accuracy shows that disease in dairy cattle results from the intricate interactions between many life domains/parameters, such as housing, nutrition and climate; that prediction performance is enhanced by incorporating a wider range of data sources; and that current information can be repurposed to produce useful information for vaccine development. The study highlights the potential of data-driven dairy interventions, focusing on artificial intelligence for disease prediction in cattle, to improve animal welfare and reduce the risk of disease. A Convolutional Neural Network (CNN) based model effectively classified skin conditions with an overall accuracy of 73.89% after 27 training epochs. This study demonstrates CNN’s useful applications in the field of veterinary medicine by highlighting its potential for early detection of Lumpy Skin Disease (LSD).