Effect of two housing systems on performance and longevity of dairy cows in Northern Italy

ArticleThe objective of the current study was to evaluate and compare performance of dairy cows housed in compost-bedded pack barns (CBP) and free stall barns, with a focus on longevityrelated parameters. Study included 30 commercial dairy farms located in the Po Valley, Italy. Twenty farms had free stall barns, among which 10 used rubber mattresses (FSM) and 10 used deep straw bedding (FSS). The remaining 10 farms had CBP. Monthly dairy herd records were obtained from the Italian DHI association for each farm included in the study over a period of one year. All farms were visited to measure characteristics and dimensions of housing facilities. Linear mixed models were used to evaluate the association between housing system and the outcome variables. In CBP total available area was larger than both in FSM and FSS. However, space per cow over the bedded pack area in CBP (6.8 ± 2.4 m2 cow-1 ) was relatively low for this housing system. Milk production was similar among housing systems but somatic cell count and mastitis infection prevalence resulted to be higher in CBP than in FSM and FSS. Calving interval was lower in FSS compared with both FSM and CBP while no differences were found in number of services per pregnancy. Cows housed in CBP were older and had higher parities than those in FSM and FSS while no significant differences in herd turnover rate were detected among housing systems. Results confirm that CBP housing system may improve longevity of dairy cows, which is reported to be one of the most important motivations for building this kind of housing. Nevertheless, CBP housing can pose some challenges in achieving adequate udder health and high milk quality, especially with low space per cow

Criteria of design for deconstruction applied to dairy cows housing: a case study in Italy

ArticleThis work aims at presenting the design process of a new barn for dairy cows. Project embraces several concepts that are rather new to the dairy industry and will deeply affect its environmental, economic and social sustainability. The barn will be built o n a green field site located in Cervasca (CN) in the region of Piedmont. Building has been designed applying the emerging principle of "design for deconstruction" extensively. A series of constructive solutions was developed allowing for complete end - of - li fe disassembly and reuse of building materials. Structural system will consist of locally sourced timber connected by steel joints. Foundations will be realized by means of chestnut wood piles driven into the ground. The employment of an alternative housin g system for dairy cows based entirely on cultivated pack will allow limiting the use of cast - in - place concrete, which is largely employed in conventional dairy barns. The cultivated pack needs a large space per cow leading the building to be particularly extended. The large covered area combined with the high snow load of the building site posed several challenges. Accumulation of snow on the roof would increase dramatically the structural load and therefore construction costs. Therefore, the building will consist of several 12m - large modules with 4m free space between them. Given the unusual shape of the barn and the limited use of concrete for flooring, the development of efficient systems for livestock management required the study of dedicated solutions . A first module, already realized to collect useful information for final design, is described

Validation of a commercial collar‐based sensor for monitoring eating and ruminating behaviour of dairy cows

The use of sensor technologies to monitor cows’ behavior is becoming commonplace in the context of dairy production. This study aimed at validating a commercial collar-based sensor system, the AFICollar® (Afimilk, Kibbutz Afikim, Israel), designed to monitor dairy cattle feeding and ruminating behavior. Additionally, the performances of two versions of the software for behavior classification, the current software AFIfarm® 5.4 and the updated version AFIfarm® 5.5, were compared. The study involved twenty Holstein-Friesian cows fitted with the collars. To evaluate the sensor performance under different feeding scenarios, the animals were divided into four groups and fed three different types of feed (total mixed ration, long hay, animals allowed to graze). Recordings of hourly rumination and feeding time produced by the sensor were compared with visual observation by scan sampling at 1 minute intervals using Spearman correlation, concordance correlation coefficient (CCC), Bland–Altman plots and linear mixed models for assessing the precision and accuracy of the system. The analyses confirmed that the updated software version V5.5 produced better detection performance than the current V5.4. The updated software version produced high correlations between visual observations and data recorded by the sensor for both feeding (r = 0.85, CCC = 0.86) and rumination (r = 0.83, CCC = 0.86). However, the limits of agreement for both behaviors remained quite wide (feeding: −19.60 min/h, 17.46 min/h; rumination: −15.80 min/h, 15.00 min/h). Type of feed did not produce significant effects on the agreement between visual observations and sensor recordings. Overall, the results indicate that the system can provide farmers with adequately accurate data on feeding and rumination time, and can be used to support herd management decisions. Despite all this, the precision of the system remained relatively limited, and should be improved with further developments in the classification algorithm

Occupational Risk Factors and Hypertensive Disorders in Pregnancy: A Systematic Review

Hypertensive disorders in pregnancy (HDP), including gestational hypertension (GH) and preeclampsia (PE), characterize a major cause of maternal and prenatal morbidity and mortality. In this systematic review, we tested the hypothesis that occupational factors would impact the risk for HDP in pregnant workers. MEDLINE, Scopus, and Web of Knowledge databases were searched for studies published between database inception and 1 April 2021. All observational studies enrolling > 10 pregnant workers and published in English were included. Un-experimental, non-occupational human studies were excluded. Evidence was synthesized according to the risk for HDP development in employed women, eventually exposed to chemical, physical, biological and organizational risk factors. The evidence quality was assessed through the Newcastle–Ottawa scale. Out of 745 records identified, 27 were eligible. No definite conclusions could be extrapolated for the majority of the examined risk factors, while more homogenous data supported positive associations between job-strain and HDP risk. Limitations due to the lack of suitable characterizations of workplace exposure (i.e., doses, length, co-exposures) and possible interplay with personal issues should be deeply addressed. This may be helpful to better assess occupational risks for pregnant women and plan adequate measures of control to protect their health and that of their childre

Opportunities and challenges of nanotechnology in the green economy

In a world of finite resources and ecosystem capacity, the prevailing model of economic growth, founded on ever-increasing consumption of resources and emission pollutants, cannot be sustained any longer. In this context, the "green economy" concept has offered the opportunity to change the way that society manages the interaction of the environmental and economic domains. To enable society to build and sustain a green economy, the associated concept of "green nanotechnology" aims to exploit nano-innovations in materials science and engineering to generate products and processes that are energy efficient as well as economically and environmentally sustainable. These applications are expected to impact a large range of economic sectors, such as energy production and storage, clean up-technologies, as well as construction and related infrastructure industries. These solutions may offer the opportunities to reduce pressure on raw materials trading on renewable energy, to improve power delivery systems to be more reliable, efficient and safe as well as to use unconventional water sources or nano-enabled construction products therefore providing better ecosystem and livelihood conditions.However, the benefits of incorporating nanomaterials in green products and processes may bring challenges with them for environmental, health and safety risks, ethical and social issues, as well as uncertainty concerning market and consumer acceptance. Therefore, our aim is to examine the relationships among guiding principles for a green economy and opportunities for introducing nano-applications in this field as well as to critically analyze their practical challenges, especially related to the impact that they may have on the health and safety of workers involved in this innovative sector. These are principally due to the not fully known nanomaterial hazardous properties, as well as to the difficulties in characterizing exposure and defining emerging risks for the workforce. Interestingly, this review proposes action strategies for the assessment, management and communication of risks aimed to precautionary adopt preventive measures including formation and training of employees, collective and personal protective equipment, health surveillance programs to protect the health and safety of nano-workers. It finally underlines the importance that occupational health considerations will have on achieving an effectively sustainable development of nanotechnology

Machine learning based prediction of insufficient herbage allowance with automated feeding behaviour and activity data

peer-reviewedSensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support farmers in many decision-making processes. This article thus considers the performance of a set of RumiWatchSystem recorded variables in the prediction of insufficient herbage allowance for spring calving dairy cows. Several commonly used models in machine learning (ML) were applied to the binary classification problem, i.e., sufficient or insufficient herbage allowance, and the predictive performance was compared based on the classification evaluation metrics. Most of the ML models and generalised linear model (GLM) performed similarly in leave-out-one-animal (LOOA) approach to validation studies. However, cross validation (CV) studies, where a portion of features in the test and training data resulted from the same cows, revealed that support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) performed relatively better than other candidate models. In general, these ML models attained 88% AUC (area under receiver operating characteristic curve) and around 80% sensitivity, specificity, accuracy, precision and F-score. This study further identified that number of rumination chews per day and grazing bites per minute were the most important predictors and examined the marginal effects of the variables on model prediction towards a decision support system