Feeding Intensity Research Articles

Strategies for feeding tilapia in smallholder aquaculture systems: a study of aquaculture feed access and availability in two districts of Northern Province, Zambia

ABSTRACT Aquaculture contributes to agricultural development, economic growth, and poverty reduction initiatives in Zambia. In northern and rural regions of the country, development requires improvements to the management of local and farm-generated inputs. Fish farmers have limited market access and purchasing power, which restricts access to commercial inputs, such as formulated feeds and chemical fertilizers. Instead, farmers adopt integrated-agriculture-aquaculture (IAA) practices, whereby on-farm resources supply essential inputs. Research is limited regarding local systems of resource management, and this hinders development of innovations for improving efficiency. This mixed-methods study explored local patterns of resource utilization. Smallholders (n = 63) participated in surveys and interviews regarding resource management. A series of quantitative analyses, including descriptive statistics, principal component analyses (PCA), and multinomial logistic regression (logit) models, identified relationships between these resources, household livelihood, and system productivity. Qualitative analyses explored farmer perceptions regarding seasonal resource availability. Results suggested that livestock manure, homemade feeds comprising maize and soybean, garden residues, and kitchen wastes were the most frequently selected inputs. Logit models of feeding intensity found that income, proximity to Luwingu Town, experience in aquaculture, and access to extension services were key household resources. High-intensity feeding strategies incorporated commercial feeds, chemical fertilizers, as well as home-made feeds containing a wide diversity of both high- and low-quality ingredients. Low-intensity feeding strategies incorporated only farm waste and termites. Potential innovations that may improve system performance in resource poor contexts are considered and recommendations for additional research are offered.

Seasonal changes in the dietary patterns and reproductive aspects of deep-water rose shrimp Parapenaeus longirostris in the central Adriatic Sea

The deep-water rose shrimp Parapenaeus longirostris is one of the most important commercial species caught by trawl fisheries in the Mediterranean Sea. The species has a warm-water affinity and its landings are increasing in the whole basin. Detailed information on its trophic ecology, niche width and role within demersal food webs is dated or lacking in the Adriatic Sea, the most exploited basin of the Mediterranean. Therefore, to detect seasonal changes in diet and trophic levels, seasonal samplings were conducted in this area from January to December 2021, collecting 400 specimens, and integrating information from both stomach content analysis and stable isotope analyses (SIA). To identify potential links between the biological cycle of the species and fluctuations in energy demand, seasonal changes in fullness, as a proxy of feeding intensity, and gonadosomatic and hepatosomatic indices were assessed. The deep-water rose shrimp is confirmed to be a benthic feeder that predominantly preys on molluscs, crustaceans, fish, echinoderms and polychaetes. According to SIA, P. longirostris is defined as a secondary consumer. Observed seasonal dietary changes precede the reproductive periods, likely due to the need for more energetic prey.

Morphological characteristics of the species Gaidropsarus Mediterraneus (Linnaeus, 1758) population from the Romanian Black Sea coast in the year 2023

The aim of this study is to provide an overview of the Gaidropsarus mediterraneus (Linnaeus, 1758) species growth pattern from the Romanian Black Sea Coast, in the year 2023. This has been accomplished by studying the length-weight relationship, an important parameter, which helps to know the growth pattern of fish populations, Fulton’s condition factor (kc) (useful index for monitoring feeding intensity) from 180 shore rockling individuals caught by pots. Average lengths and weights were 24.4 ± 2.6 cm and 101.46 ± 38.11 g, respectively, the minimum and maximum values were 18.3 and 33.8 cm, 36 and 319 g in weight. The value of exponent b in the equation is 3.197, indicating that the increasing weight is allometrically positive and mean value of the Fulton was 0.68 ± 0.08, with the minimum and maximum values of 0.47 to 0.90. The obtained results can have useful effects on the stock management of this fish species.

An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the breeder's experience, leading to feed wastage and environmental pollution. To achieve precision in feeding, it is crucial to adjust the feed according to the fish's feeding state. Existing computer vision-based methods for assessing feeding intensity are limited by their dependence on a single spatial feature and manual threshold setting for determining feeding status constraints. These models lack practicality due to their specificity to certain scenarios and objectives. To address these limitations, we propose an integrated approach that combines computer vision technology with a Convolutional Neural Net-work (CNN) to assess the feeding intensity of farmed fish. Our method incorporates temporal, spatial, and data statistical features to provide a comprehensive evaluation of feeding intensity. Using computer vision techniques, we preprocessed feeding images of pearl gentian grouper, extracting temporal features through optical flow, spatial features via binarization, and statistical features using the gray-level co-occurrence matrix. These features are input into their respective specific feature discrimination networks, and the classification results of the three networks are fused to construct a three-stream network for feeding intensity discrimination. The results of our proposed three-stream network achieved an impressive accuracy of 99.3% in distinguishing feeding intensity. The model accurately categorizes feeding states into none, weak, and strong, providing a scientific basis for intelligent fish feeding in aquaculture. This advancement holds promise for promoting sustainable industry development by minimizing feed wastage and optimizing environmental impact.

Fish feeding behavior quantification and automatic acoustic event detection: algorithmic optimization with deep learning

In recent years, the integration of sensor processing technologies with intelligent feeding control systems has become crucial for enhancing recirculating aquaculture systems (RAS). Intelligent feeding control significantly improves profitability and animal welfare in aquaculture by accurately monitoring feed intake and reducing waste. Acoustic methods have proven effective in automating feed monitoring, particularly in assessing feeding intensity. Automatic Audio Event Detection (AED) holds promise for identifying sound events associated with feeding activities, enabling precise monitoring of feeding behavior and estimation of feed consumption through frequency-based feeding signatures. Advancements in sensor technologies and computational capabilities enable real-time collection, processing, and analysis of acoustic data. However, handling high volumes of data with minimal latency and high accuracy poses challenges. To address these challenges, we propose a systematic approach: (1) Data preprocessing using wavelet transforms enhances efficiency and reduces computational overhead. (2) Data pruning focuses on informative features and reduces input data dimensionality, improving classification accuracy. (3) We adopt a weakly-labeled semi-supervised learning model with Convolutional Neural Networks (CNNs) to detect feeding events, improving classification performance. (4) Ensemble learning combines multiple classifiers to mitigate overfitting and enhance model generalization. Our methodology shows promise in accurately quantifying fish feeding behavior using acoustic signals.

Transforming the feeding regime towards low-input increases the environmental impact of organic milk production on a case study farm in central germany

Abstract Purpose Despite the direct effect of the feeding regime on the environmental impacts of dairy farming systems, its level of intensity, particularly in organic systems, has rarely been investigated. This study compares the environmental impact of a high-input feeding regime with a grassland-based, low-input feeding regime scenario within an organic milk production system conducted on Gladbacherhof, the research farm of Justus Liebig University Giessen, in Central Germany. Methods An integrated Life Cycle Assessment (LCA) analysis was performed from a cradle-to-farm gate perspective to quantify five environmental impacts, namely Global Warming (GW), Non-Renewable Energy Use (NREU), Land Use (LU), Terrestrial Acidification (TA), and Freshwater Eutrophication (FE). All agronomic data of the Gladbacherhof research farm, averaged over the years 2010–2017, were included. When not directly measured on the farm, ecoinvent data were included. Results and discussion Contrary to our hypothesis, the results suggest that a grassland-based low-input system has a higher environmental impact as compared to a high-input system for each of the five impact categories when using fat and protein-corrected milk (FPCM) as the functional unit. A 50% reduction in concentrates and exclusion of maize silage from the feed ration in the modelled low-input production system lead to a 20% drop in milk yield. To balance the energy content in low-input feeding ration, longer grazing period and higher amount of hay, alfalfa, and grass silage are required. This in turn results in higher emissions from enteric fermentation, manure management, and feed production and hence in higher environmental impact, particularly for GW, TA, and FE. Conclusions This study is one of the few that directly explores the environmental impact of feeding intensity in an organic milk production system. Nevertheless, there is a lack of research on consolidated emission factors for several greenhouse gas (GHG) sources in organic livestock and cropping systems to perform more robust carbon footprint calculations that comply with the Intergovernmental Panel on Climate Change (IPCC) Tier 3 GHG reporting guidelines. To generalize the results at the regional or national scale, direct comparisons with a larger number of organic farms representative of high-input and low-input intensities are still essential.

Interactions between rumen epithelium-associated microbiota and host immunological and metabolic adaptations in response to different milk replacer feeding intensities in dairy calves

The milk replacer feeding regime in dairy calves has a great impact on metabolic and immunological functioning and affects animal welfare and lifetime performance. The feeding regime influences the rumen microbial composition, and epithelium-associated microbes may interact with the immune system of the host. We examined the correlations between blood leukocyte counts and the rumen epithelium-associated microbiome in dairy calves fed 2 different milk replacer feeding intensities and if these factors related to metabolic traits. Fourteen newborn female dairy calves were allocated to a group receiving either 10% (n = 7) or 20% (n = 7) milk replacer of their body weight (on average 41 kg) and provided ad libitum access to grass hay and concentrate pellets. At 3 weeks of life, all calves were fitted with a rumen cannula. Calves were weaned at 12 weeks of life and received a total mixed ration for ad libitum intake. Pre- (8 to 10 weeks of life) and post-weaning (21 to 23 weeks of life), methane production was measured in respiration chambers, and rumen epithelium and blood were sampled for 16S rRNA sequencing and leukocyte analyses, respectively. Pre-weaning, the reduced milk replacer feeding intensity was accompanied with higher concentrate intake but lower growth performance (P < 0.001), a higher abundance of amylolytic and lower abundance of cellulolytic epimural microbes. The group fed a low milk replacer intensity had also greater portions of monocytes (P = 0.031), CD8+ (P < 0.001), and CD14+ (P = 0.044) leukocytes, suggesting elevated inflammatory conditions. Correlations between CD8+ T cells and rumen methanogens, Ruminococcaceae, and Lachnospiraceae were recorded, but these were not consistent throughout maturation. Post-weaning, differences in feed intake and rumen microbial composition converged among milk replacer groups, while differences in growth performance (P = 0.040) and CD8+ cells (P < 0.001) were still present. In conclusion, a reduced milk replacer feeding intensity in dairy calves compromised growth performance and immunity and this effect persisted in the long-term. Significant correlations between the proportion of leukocytes and distinct epimural microbe taxa indicated an interplay between rumen epimural colonization and immune functioning of the host. However, further research is required addressing this interplay between rumen epimural microbes and immune functioning in dairy calves.

Diet, feeding, and niche overlap of west coast steenbras (Lithognathus aureti) and silver kob (Argyrosomus inodorus) in the northern Benguela.

In this study, we described and compared the diet, monthly feeding intensity, and condition of west coast steenbras (Lithognathus aureti) and silver kob (Argyrosomus inodorus) caught at a unique habitat in the northern Benguela. Stomach contents of 179 west coast steenbras and 114 silver kob caught from October 2020 to September 2022 were investigated. The peak in feeding intensity of west coast steenbras appeared to be opportunistic during winter and summer periods depending on food availability. The fish condition, however, peaked at the beginning (October) and at the end (April) of the austral summer spawning period, with the hepatosomatic index (HSI) at 1.5% and the condition factor (CF) at 0.022%. Seven prey taxa were found in the diet of west coast steenbras (bivalves, bony fishes, other mollusks, algae, crustaceans, cnidaria, and polychaetas) and six taxa in the diet of silver kob (bivalves, crustaceans, bony fishes, algae, starfish, and zooplankton), indicating generalist feeding behavior in both the species. The bivalves were the most important prey items in the diet of west coast steenbras (95.9% index of relative importance [IRI]). The most important prey items in the diet of silver kob were crustaceans (83.1% IRI) and bony fishes (16.0% IRI). Crustaceans were most important in the diet of small-to-medium-sized silver kob, whereas bony fishes were most important in the diet of larger silver kob(>75 cm), with significant differences of IRI% by size class. Schoener's index of niche overlap indicated a relatively low overall niche overlap (0.11) between west coast steenbras and silver kob. This allows them to coexist as their feeding habits allow them to occupy unique niches in the coastal reef and sandy habitat and reduce competition for resources.

The intensity of supplementary feeding in an urban environment impacts overwintering Mallards (Anas platyrhynchos) as wintering conditions get harsher

Although urbanization poses various threats to avifauna, some bird species, including Mallards (Anas platyrhynchos), choose towns and cities as their wintering habitats, possibly due to favourable temperatures and abundant anthropogenic food. In this study, we investigated how population dynamics changed in relation to winter harshness and intensity of supplementary bird feeding in Mallard, a dabbing duck species well adapted to an urban environment. We surveyed 15 city ponds for five consecutive years, counting overwintering individuals and incidents of bird feeding conducted by the citizens of Gdańsk. Number of Mallards observed in the studied area fluctuated both annually and within seasons, with on average 327 (± SD = 108.5) individuals and a male-biased sex ratio reported. We observed a gradient of feeding intensity on the surveyed ponds, with number of feeding incidents ranging from 0 to 30 in a day. The results indicated that the number of Mallards increased with the bird feeding intensity, independently of the size of the studied ponds, and both males and females were similarly attracted by food provisioning. More severe wintering conditions, expressed by the percentage of the ice cover of studied ponds resulted in more birds wintering in the urban area. This effect was more pronounced in females compared to males, possibly due to females having a lower ability to withstand severe wintering conditions, forcing them to relocate towards urban areas. Our results confirm that urbanized areas may serve as wintering grounds for birds, due to additional food resources available there and milder wintering conditions compared to surrounding rural areas.

Harnessing multimodal data fusion to advance accurate identification of fish feeding intensity

Accurately identifying the fish feeding intensity plays a vital role in aquaculture. While traditional methods are limited by single modality (e.g., water quality, vision, audio), they often lack comprehensive representation, leading to low identification accuracy. In contrast, the multimodal fusion methods leverage the fusion of features from different modalities to obtain richer target features, thereby significantly enhancing the performance of fish feeding intensity assessment (FFIA). In this work a multimodal dataset called MRS-FFIA was introduced. The MRS-FFIA dataset consists of 7611 labelled audio, video and acoustic dataset, and divided the dataset into four different feeding intensity (strong, medium, weak, and none). To address the limitations of single modality methods, a Multimodal Fusion of Fish Feeding Intensity fusion (MFFFI) model was proposed. The MFFFI model is first extracting deep features from three modal data audio (Mel), video (RGB), Acoustic (SI). Then, image stitching techniques are employed to fuse these extracted features. Finally, the fused features are passed through a classifier to obtain the results. The test results show that the accuracy of the fused multimodal information is 99.26%, which improves the accuracy by 12.80%, 13.77%, and 2.86%, respectively, compared to the best results for single-modality (audio, video and acoustic dataset). This result demonstrates that the method proposed in this paper is better at classifying the feeding intensity of fish and can achieve higher accuracy. In addition, compared with the mainstream single-modality approach, the model improves 1.5%–10.8% in accuracy, and the lightweight effect is more obvious. Based on the multimodal fusion method, the feeding decision can be optimised effectively, which provides technical support for the development of intelligent feeding systems.

Optimizing Fish Feeding with FFAUNet Segmentation and Adaptive Fuzzy Inference System

Efficient and optimized fish-feeding practices are crucial for enhancing productivity and sustainability in aquaculture. While many studies have focused on classifying fish-feeding intensity, there is a lack of research on optimizing feeding, necessitating a precise and automated model. This study fills this gap with a hybrid solution for precision aquaculture feeding management involving segmentation and optimization phases. In the segmentation phase, we used the novel feature fusion attention U-Net (FFAUNet) to accurately segment fish-feeding intensity areas. The FFAUNet achieved impressive metrics: a mean intersection over union (mIoU) of 89.39%, a mean precision of 95.07%, a mean recall of 95.08%, a mean pixel accuracy of 95.12%, and an overall accuracy of 95.61%. In the optimization phase, we employed an adaptive neuro-fuzzy inference system (ANFIS) with a particle swarm optimizer (PSO) to optimize feeding. Extracting feeding intensity percentages from the segmented output, the ANFIS with PSO achieved an accuracy of 98.57%, a sensitivity of 99.41%, and a specificity of 99.53%. This model offers fish farmers a robust, automated tool for precise feeding management, reducing feed wastage and improving overall productivity and sustainability in aquaculture.

Trapping efficacy of invasive crows is affected by environmental factors and deployment history

AbstractHouse crows (Corvus splendens) are considered an invasive species and are prevalent in parts of Europe, Africa, and Southeast Asia. Existing crow management measures include food limitation, habitat modification, and direct population control. The latter method provides a more immediate solution, particularly in nesting locations where crow attacks occur more frequently. Crow ladder traps are widely adopted to capture crows, but a thorough examination of the environmental conditions that optimize trapping efficacy is lacking. We assessed factors affecting crow trap efficacy in Singapore to better advise future deployment strategies of crow traps. We obtained data from 170 crow trapping operations including the identities of the contractors conducting the trapping operations, operation start date, crow density, intensity of bird feeding, various land use cover proportions, and an index to quantify the spatial‐temporal proximity to previous trapping operations. We used a spatial gamma generalized linear mixed model (GLMM) to determine the factors affecting the daily number of crows captured and a spatial binomial GLM to determine the factors affecting the probability that crows will be captured. The number of bird‐feeding incidents had a negative effect on crow trap efficacy suggesting that crows are probably less inclined to enter traps if proximate anthropogenic food abundance is high. Trapping efficacy increased if traps were placed in high crow‐density areas, likely because more crows could detect the traps. The probability of capture also declined if the traps were placed closer to prior trapping locations or with a shorter time interval from previous trapping operations. Capture rates of crows can be increased by placing traps in high crow density areas, limiting bird‐feeding incidences, and lengthening the spatial and temporal proximities to previous trapping operations. The deployment of ladder traps is a viable means to control the population of house crows and consideration of environmental conditions and trapping history can improve capture rates.

Feeding habits of the deep-sea shrimp Plesionika quasigrandis Chace, 1985 (Decapoda: Caridea: Pandalidae) in the southeastern Arabian Sea

ABSTRACT Species of PlesionikaSpence Bate, 1888 holds significant importance in the commercial harvesting of deep-sea shrimps along the southern coasts of India. Despite Plesionika quasigrandisChace, 1985 being a crucial resource on the southwest coast of India, there is a need for comprehensive information on its food and feeding habits. We investigated the diet of P. quasigrandis in relation to sex, size, and season. The species exhibited a diet primarily consisting of crustaceans, foraminiferans, and detritus. Both males and females showed a noteworthy reliance on detritus, as indicated by an index of preponderance values exceeding 45.8. The species displayed active scavenging behaviour alongside predatory dietary habits. We observed a decrease in feeding intensity from pre-monsoon to post-monsoon in both males and females. Permutational multivariate analysis of variance (PERMANOVA) revealed significant differences in the diets of P. quasigrandis, considering the combined effects of size and sex (P &amp;lt; 0.01). No significant distinctions were noted in the diet composition between males and females in relation to season.

CFFI-Vit: Enhanced Vision Transformer for the Accurate Classification of Fish Feeding Intensity in Aquaculture

CFFI-Vit: Enhanced Vision Transformer for the Accurate Classification of Fish Feeding Intensity in Aquaculture

Mathematical Model of Calculation of the Surface Intensity of Feed of a Coolant in Aviation Extinguishing of Ground Forest Fires

Mathematical Model of Calculation of the Surface Intensity of Feed of a Coolant in Aviation Extinguishing of Ground Forest Fires

Diet and trophic structure of fishes in the Barents Sea: Effects of size within (ontogenetic) and between species

A large data set on stomach content (over 27,000 individual fish) – collected in the Barents Sea in 2015 – was used to examine ontogenetic and interspecific changes in diet with size for 35 fish species. The analysis combined the use of hierarchical clustering and random forest. Two different diet metrics were used (% weight of prey types per weight of stomach content or per weight of fish, the latter reflecting also feeding intensity), and data were analysed based on average diet of predator groups (species and size groups) across the entire sample series (from the whole Barents Sea in different seasons), or for data broken down by geographical areas and seasons. Similar trophic groups (clusters) were identified for the various data sets suggesting that the results on trophic structure were robust. The trophic groups including size information were broadly similar to groups found earlier with data averaged at species level. Hierarchical clustering produces a hierarchy of trophic groups (or trophic guilds) at various levels of diet dissimilarity. With 12 clusters identified, one cluster had fish as dominant prey, and 5–6 others had either plankton or benthos as dominant prey. The clusters tended to be distinct and homogenous with one dominant prey category in average diet (often > 60 %). This was especially the case for the plankton clusters, with copepods, euphausiids, hyperiid amphipods, or gelatinous zooplankton as dominant prey in each of 4 trophic groups. The benthos clusters tended to be less dissimilar with more overlap in diet composition, with predominance of either hard-shelled forms (echinoderms and molluscs) or softer prey (polychaetes and crustaceans) for groups of clusters. There were clear patterns of ontogenetic shifts in fish diet. Fish that clustered as piscivores at larger size tended to grow from being planktivores when smaller. Smaller species of planktivores or benthivores shifted position among trophic groups but remained within the categories of planktivores or benthivores, respectively. Taxonomy (species) tended to be more important than size for explaining the diet composition of the different clusters of fish predators.

Augmenting Aquaculture Efficiency through Involutional Neural Networks and Self-Attention for Oplegnathus Punctatus Feeding Intensity Classification from Log Mel Spectrograms.

Understanding the feeding dynamics of aquatic animals is crucial for aquaculture optimization and ecosystem management. This paper proposes a novel framework for analyzing fish feeding behavior based on a fusion of spectrogram-extracted features and deep learning architecture. Raw audio waveforms are first transformed into Log Mel Spectrograms, and a fusion of features such as the Discrete Wavelet Transform, the Gabor filter, the Local Binary Pattern, and the Laplacian High Pass Filter, followed by a well-adapted deep model, is proposed to capture crucial spectral and spectral information that can help distinguish between the various forms of fish feeding behavior. The Involutional Neural Network (INN)-based deep learning model is used for classification, achieving an accuracy of up to 97% across various temporal segments. The proposed methodology is shown to be effective in accurately classifying the feeding intensities of Oplegnathus punctatus, enabling insights pertinent to aquaculture enhancement and ecosystem management. Future work may include additional feature extraction modalities and multi-modal data integration to further our understanding and contribute towards the sustainable management of marine resources.

Effects of the Three Gorges Dam on the mandarin fish larvae (Siniperca chuatsi) in the middle reach of the Yangtze River: Spatial gradients in abundance, feeding, growth, and survival

AbstractLarge dams significantly impact river ecosystems by disrupting connectivity, altering physicochemical variables, and modifying flow regimes. These modifications influence the spatial and temporal dynamics of biological processes and species distributions. While much research has focused on potamodromous species, there remains a gap in understanding the recovery gradients of resident species in dam‐altered rivers. This study examines the responses of larvae of a resident species, the mandarin fish (Siniperca chuatsi), to environmental alterations caused by the Three Gorges Dam (TGD) in the middle reach of the Yangtze River. We hypothesized that larval abundance, feeding, growth, and survival would exhibit longitudinal recovery gradients, improving with the increased distance from the TGD. Our results confirm this hypothesis, showing that larvae further from the TGD exhibited higher abundance, increased feeding intensity, enhanced growth rates, improved survival rate, and earlier peak abundance and hatching dates. Key environmental factors, including water temperature and discharge, increased downstream, while transparency decreased. Major tributary inputs significantly contributed to these recovery gradients. The observed longitudinal gradients in larval attributes mirrored environmental changes, underscoring the TGD impact on population recruitment. These results highlight the broader implications of dam‐induced changes on resident species recruitment, potentially affecting entire fish communities. Our study contributes to understanding the distinct spatial patterns of population trends, providing valuable insights for designing more effective conservation and management practices for resident freshwater fishes in large regulated rivers.

Alpine grazing management, breed, and diet effects on coagulation properties, composition, and microbiota of dairy cow milk by commercial mountain-based herds

Cow milk microbiota has received increased attention in recent years, not only because of its importance for human health but also because of its effect on the quality and technological properties of milk. Several studies, therefore, have investigated the effect of various production factors on the microbial composition of milk. However, most of the previous studies considered a limited number of animals from experimental or single farm, which could have biased the results. Therefore, this study aimed to understand the effect of different alpine production systems on the compositional and microbiological quality of milk, considering commercial herds with different feeding intensities and cattle breeds. The results obtained in this work indicated that the month/season of sampling (July for summer or February for winter) more than farm, breed and cow diet exerted significant effects on cow milk parameters and microbiota. In particular, significant differences were observed for urea content in milk between sampling seasons. Differences in milk fat were mainly related to breed specific effects. From a microbiological point of view, statistically significant differences were found in presumptive lactic acid bacteria counts. Based on a culture-independent method, milk obtained in February harbored the highest number of Firmicutes (e.g., Lactobacillus) and the lowest number of Actinobacteria (e.g., Corynebacterium). Moreover, bacterial richness and diversity were higher in July/summer during alpine pasture season indicating a significant effect of pasture feed on the growth of bacterial communities. The results of this study highlighted the effect of month/season mainly related to differences in feeding management (e.g., access to pasture during vegetation period, concentrates supplementation) on composition and microbiota in milk.

Organic or Non-organic Agriculture: Comparison of Organic and Conventional Farming Sustainability

Reduction of the level of greenhouse gas emissions from sector of agriculture are one of the serious issues across European Union. The dairy industry is responsible for generating a significant amount of emissions from enteric fermentation, manure and long-term storage. The amount of emissions produced depends highly on factors such as livestock feeding and manure management systems, feed intensity and quality. Farms that have high milk yield also produce more liquid manure, leading to an increase in overall emissions from manure management.Organic farming has been valued as one of the most suitable solutions to conventional agriculture for the achievement of climate goals. Organic farming can be described as a farming approach based on prevention, ecological processes, and the restriction of pesticides. Restricting pests and lower yields can have a negative impact on production costs and revenues. However, organic farming has lower operating costs and higher product costs, which can increase overall incomes.The research aims to evaluate the environmental, economic and social dimensions of conventional and organic farming, comparing were comparing on their integral environmental, economic and social sustainability. The core element for the sustainability assessment is the construction of the composite sustainability index using data for scientific literature, reports and statistics.