RESUMO
This study was conceived with the aim of exploring applications of the circular economy (CE) principles in the olive oil sector, with the lens of Life Cycle Assessment (LCA). To that end, the authors performed a systematic literature review (SLR), from a pre-determined set of keywords that were searched for in the two most comprehensive databases of peer-reviewed journals, namely Scopus and Web-of-Science. From the screening process provided by the PRISMA model, a total of fifteen papers were selected that formed the final review sample, most of which included research on production systems in the Mediterranean region. To facilitate a comparative analysis of the findings from those studies, the latter were grouped into clusters, considering their characteristics and methodological approaches. Five articles were classified as dealing with 'closed-loop' systems wherein the resources from the valorisation of by-products were reintegrated into the same production system. The remaining articles were categorised as related to 'open loop' systems since by-products were utilised in processes and systems outside olive oil production. Notably, the 'closed-loop' systems showed the best LCA outcomes. Identified hotspots within the sector included the agricultural and packaging phases. Although comparing LCA applications is challenging due to the inherent nature of the method and researcher autonomy in selecting basic characteristics, valuable best practices emerged from the analysis of the current state of the art. These practices included valorisation of olive pomace (OP) by converting it into biogas to meet the energy needs of the system processes themselves, the collection of waste cooking oil to convert it into biodiesel, and the use of organic farming techniques in olive production. OP oil extraction emerged as a widespread practice enhancing system sustainability. Moreover, increasing industrial symbiosis by promoting proximity amongst plants was documented by this SLR to be a key factor in strengthening system sustainability.
Assuntos
Azeite de Oliva , Agricultura/métodosRESUMO
In the field of precision livestock farming, many systems have been developed to identify the position of each cow of the herd individually in a specific environment. Challenges still exist in assessing the adequacy of the available systems to monitor individual animals in specific environments, and in the design of new systems. The main purpose of this research was to evaluate the performance of the SEWIO ultrawide-band (UWB) real time location system for the identification and localisation of cows during their activity in the barn through preliminary analyses in laboratory conditions. The objectives included the quantification of the errors performed by the system in laboratory conditions, and the assessment of the suitability of the system for real time monitoring of cows in dairy barns. The position of static and dynamic points was monitored in different experimental set-ups in the laboratory by the use of six anchors. Then, the errors related to a specific movement of the points were computed and statistical analyses were carried out. In detail, the one-way analysis of variance (ANOVA) was applied in order to assess the equality of the errors for each group of points in relation to their positions or typology, i.e., static or dynamic. In the post-hoc analysis, the errors were separated by Tukey's honestly significant difference at p > 0.05. The results of the research quantify the errors related to a specific movement (i.e., static and dynamic points) and the position of the points (i.e., central area, perimeter of the investigated area). Based on the results, specific information is provided for the installation of the SEWIO in dairy barns as well as the monitoring of the animal behaviour in the resting area and the feeding area of the breeding environment. The SEWIO system could be a valuable support for farmers in herd management and for researchers in the analysis of animal behavioural activities.
Assuntos
Agricultura , Comportamento Animal , Feminino , Animais , Bovinos , Análise de Variância , Sistemas Computacionais , FazendasRESUMO
Due to technological developments, wearable sensors for monitoring the behavior of farm animals have become cheaper, have a longer lifespan and are more accessible for small farms and researchers. In addition, advancements in deep machine learning methods provide new opportunities for behavior recognition. However, the combination of the new electronics and algorithms are rarely used in PLF, and their possibilities and limitations are not well-studied. In this study, a CNN-based model for the feeding behavior classification of dairy cows was trained, and the training process was analyzed considering a training dataset and the use of transfer learning. Commercial acceleration measuring tags, which were connected by BLE, were fitted to cow collars in a research barn. Based on a dataset including 33.7 cow × days (21 cows recorded during 1-3 days) of labeled data and an additional free-access dataset with similar acceleration data, a classifier with F1 = 93.9% was developed. The optimal classification window size was 90 s. In addition, the influence of the training dataset size on the classifier accuracy was analyzed for different neural networks using the transfer learning technique. While the size of the training dataset was being increased, the rate of the accuracy improvement decreased. Beginning from a specific point, the use of additional training data can be impractical. A relatively high accuracy was achieved with few training data when the classifier was trained using randomly initialized model weights, and a higher accuracy was achieved when transfer learning was used. These findings can be used for the estimation of the necessary dataset size for training neural network classifiers intended for other environments and conditions.
Assuntos
Algoritmos , Redes Neurais de Computação , Feminino , Bovinos , Animais , Aprendizado de Máquina , Comportamento Alimentar , AcelerometriaRESUMO
In the livestock sector, the monitoring of gas concentrations plays a relevant role in the implementation of mitigation strategies, as well as the improvement in the indoor air quality for both workers and animals in barns. In the present study, measurements of gas concentrations (NH3, CH4, and CO2) at different locations were carried out in an open dairy barn using a commercial photo-acoustic spectroscopy system. Measurement variability for different datasets was computed to contribute to the evaluation of the sampling strategy in the measurement campaign. The results showed that the position of sampling locations (SLs) significantly influenced (p < 0.001) the variability in the gas distribution. Specifically, the SLs located on the perimeter of the building had the highest variability. The number of SLs utilised for determining the mean value of gas concentration in the barn had a significant influence on NH3 (p < 0.001), CO2 (p < 0.001), and CH4 (p < 0.001) in both the central zone and the perimeter one. At least three SLs were necessary to obtain a mean value of gas concentration that reduced the variability to under the 10% in the central zone and 16% in the perimeter one. Moreover, the NH3 concentrations obtained as the mean value of the gas measurements at two SLs with a 10 m distance had a lower variability compared to those recorded at two SLs located at 5 m.
RESUMO
Measurement of gas concentrations constitutes basic knowledge for the computation of emissions from livestock buildings. Although it is well known that hot climate conditions increase gas emissions, in the literature the relation between gas concentrations from open barns and animal-related parameters has not been investigated yet. This study aimed at filling this gap by evaluating daily gas concentrations within an open-sided barn in hot Mediterranean climate. The influence of microclimatic parameters (MC) and cow behavior and barn management (CBBM) were evaluated for ammonia (NH3), methane (CH4), and carbon dioxide (CO2) concentrations. Results showed that both MC and CBBM affected concentrations of NH3 (p < 0.02), CH4 (p < 0.001), and CO2 (p < 0.001). Higher values of NH3 concentration were detected during the cleaning of the floor by a tractor with scraper, whereas the lowest NH3 concentrations were recorded during animal lying behavior. Measured values of CO2 and CH4 were highly correlated (C = 0.87-0.89) due to the same sources of production (i.e., digestion and respiration). The different management of the cooling systems during the two observation periods reduced significantly CH4 concentrations in the barn when the cooling system in the feeding area was switched off. Based on methodological choices due to the specific barn typology, parameters related to animals can provide information on the variation of gas concentrations in the barn environment in hot climate conditions.