Francesco Maria Tangorra

Methods of estimating liner compression

The aim of this study was to compare 2 methods of measuring overpressure (OP) using a new test device designed to make OP measurements more quickly and accurately. Overpressure was measured with no pulsation (OP np) and with limited pulsation (OP lp) repeatedly on the same cow during a single milking. Each of the 6 liners (3 round liners and 3 triangular liners) used in this study were tested on the same 6 experimental cows. Both OP np and OP lp were measured on all 4 teats of each experimental cow twice for each liner. The order of OP np and OP lp alternated sequentially for each cow test. The OP results for the 6 liners were also compared with liner compression estimated on the same liners with a novel artificial teat sensor (ATS). The OP lp method showed small but significantly higher values than the OP np method (13.9 vs. 13.4 kPa). The OP lp method is recommended as the preferred method as it more closely approximates normal milking condition. Overpressure values decreased significantly between the first and the following measurements, (from 15.0 to 12.4 kPa). We recommend performing the OP test at a consistent time, 1 min after attaching the teatcup to a well-stimulated teat, to reduce the variability produced by OP changing during the peak flow period. The new test device had several advantages over previously published methods of measuring OP. A high correlation between OP and liner compression estimated by the ATS was found, but difficulties were noted when using the ATS with triangular liners.

Evaluation of the Fourier Frequency Spectrum Peaks of Milk Electrical Conductivity Signals as Indexes to Monitor the Dairy Goats’ Health Status by On-Line Sensors

The aim of this study is a further characterization of the electrical conductivity (EC) signal of goat milk, acquired on-line by EC sensors, to identify new indexes representative of the EC variations that can be observed during milking, when considering not healthy (NH) glands. Two foremilk gland samples from 42 Saanen goats, were collected for three consecutive weeks and for three different lactation stages (LS: 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total amount of 1512 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define the health status of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as NH. For each milk EC signal, acquired on-line and for each gland considered, the Fourier frequency spectrum of the signal was calculated and three representative frequency peaks were identified. To evaluate data acquired a MIXED procedure was used considering the HS, LS and LS × HS as explanatory variables in the statistical model.Results showed that the studied frequency peaks had a significant relationship with the gland’s health status. Results also explained how the milk EC signals’ pattern change in case of NH glands. In fact, it is characterized by slower fluctuations (due to the lower frequencies of the peaks) and by an irregular trend (due to the higher amplitudes of all the main frequency peaks). Therefore, these frequency peaks could be used as new indexes to improve the performances of algorithms based on multivariate models which evaluate the health status of dairy goats through the use of gland milk EC sensors.

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Intramammary infection affects the quality and quantity of dairy goat milk. Health status (HS) and milk quality can be monitored by electrical conductivity (EC). The aim of the study was to determine the detection potential of EC when measured on-line on a daily basis and compared with readings from previous milkings. Milk yields (MYs) were investigated with the same approach. To evaluate these relative traits, a multivariate model based on fuzzy logic technology – which provided interesting results in cows – was used. Two foremilk samples from 8 healthy Saanen goats were measured daily over the course of six months. Bacteriological tests and somatic cells counts were used to define the HS. On-line EC measurements for each gland and MYs were also considered. Predicted deviations of EC and MY were calculated using a moving-average model and entered in the fuzzy logic model. The reported accuracy has a sensitivity of 81% and a specificity of 69%. Conclusions show that fuzzy logic is an interesting approach for dairy goats, since it offered better accuracy than other methods previously published. Nevertheless, specificity was lower than in dairy cows, probably due to the lack of a significant decrease of MY in diseased glands. Still, results show that the detection of the HS characteristics with EC is improved, when measured on-line, daily and compared with the readings from previous milkings.

Evaluation of an electronic system for automatic calving detection on a dairy farm

Precise calving monitoring is important for reducing the effects of dystocia in cows and calves. The C6 birth control system is an electronic device that detects the time of the expulsion phase during calving. Several 53 Holstein were fitted on Day 280 ± 5 of gestation with the C6 birth control system, which was left in place until confirmation of calving. Sensitivity and PPV of the system were calculated as 100 and 95%, respectively. The partum events occurring at the group fitted with the system where compared with the analogous occurred at 59 animals without device. When alarmed by the system farm staff were in the calving barn during the expulsion phase in 100% of cases. On the contrary the cows without the device were assisted only in 17% of cases (P < 0.001).

Signal spectral analysis to characterize gland milk electrical conductivity in dairy goats

Intramammary infection affects quality and quantity of milk. Having as final target the improving of animal health’ monitoring, this research studied the gland milk electrical conductivity (EC) signal in order to identify a possible parameter more representative of the EC variations that can be observed, during a milking, when not healthy (NH) glands are considered. Two foremilk gland samples, from 40 Saanen goats, were acquired for three weeks and lactation stages (LS: 0-60 Days In Milking; 61-120 DIM; =>120 DIM), for a total amount of 1440 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define glands health status. In case of negative bacteriological analyses and SCC <1,000,000 cells/mL, glands were classified as healthy; alternatively, when bacteriological analyses were positive or SCC higher than 1,000,000 cells/mL, for two or more consecutive days, glands were classified as NH. A spectral analysis, to calculate the frequency spectrum and the bandwidth length of the milk EC signal, was performed. To validate data acquired, A MIXED procedure was used considering the HS, LS and the LS x HS as explanatory variables of the statistical model. Results showed that spectral analysis allows characterizing the milk EC variations thorough the bandwidth length parameter. This parameter has a significant relationship with the gland health status and it provides more accurate information than other traits, like the statistical variance of the signal. Therefore, it could be useful to improve the performances of multivariate models/algorithms that detect dairy goat health status.

Development of HW and SW solutions for milk traceability

Fontina cheese is a DOP (Designation of Protected Origin) cheese produced in Aosta Valley (Western Alps, North Italy) by dairy farms and cheese factories members of Fontina Cheese Producers’ Consortium. Since 2003 Fontina Consortium has developed an identification and branding system to trace the cheese from the factories to the consumers, but till now no operative instruments have been developed for the traceability of the milk produced by the farms members of the Consortium. This lack is mainly due to the traditional dairy cattle breeding system of Aosta Valley that is based on small family-operated dairy farms characterized by a low technological level. In these farms cows are housed in stall barns during winter, while in spring and summer livestock are herded to upland pastures located at the highest elevations (up to 2300 m a.s.l.). The average number of lactating cows is lower than 20 and they are milked by mobile milking units, bucket or pipeline milking systems. Following the European Union Regulation (EC Reg. 178/2002), a traceability system of the milk was developed for the dairy farms located in mountain areas. The project has been articulated in three sequential phases: • development of a database model (Entity and Relationship model) to identify all the farm data (livestock treatments, feed registration, external foodstuff records, etc) required and useful to trace the milk yield; • implementation of the database model by a user friendly software application to simplify the storage and the management of all the farm data; • development of a special mobile milking unit to record milking data (cows production and milk electric conductivity). The mobile milking unit was equipped with: a RFID antenna to identify the cows by transponders (ruminal bolus); a mastitis detection device, based on the measurement of the milk electrical conductivity, to monitor the quality of the milk produced by each cow; a milk meter to measure the milk yield of each cow; a palm PC with a customized software application to record milking data and then transfer them to the farm database. Presently a field test of the developed system is in progress in a farm in Aosta Valley. Preliminary results showed a fairly good effectiveness of the system in tracing milk production from the stable to the dairy factory nailing the traceability system implemented by Fontina Consortium.

Evaluation of the performance of the first automatic milking system for buffaloes

The objective of this study was to evaluate the response of buffaloes to automatic milking, examining the relationships between milking interval, milk production, and milking time for this species. A total of 7,550 milking records from an average of 40 buffaloes milked by an automatic milking system (AMS) were analyzed during a 3-mo experimental period at a commercial farm with Italian Mediterranean buffaloes in southern Italy. Date and time of animal identification, milk yield, milking duration, milking interval, and average milk flow rate were determined for each milking. The results were also used to predict the maximum number of milkings per day and the optimal number of buffaloes per AMS for different levels of milk production. The average interval period between 2 consecutive milkings was 10.3 h [standard deviation (SD) 3.3]. Overall, 3.4 and 25.7% of the milkings had an interval of ≤ 6 h or >12 h, respectively. Milking duration averaged 8.3 min per buffalo per milking (SD 2.7). The average milk flow rate was 1.3 kg/min (SD 0.5) at a milk yield of 2.8 kg per milking (SD 1.4). Assuming that the milking station is occupied 80% of the time, the number of milkings ranged from 136 to 152 per day and the optimal number of buffaloes per AMS ranged from 59 to 66 when the production level increased from 2 to 5 kg of milk per milking. Automatic milking systems are suitable for buffalo, opening new options for the management of dairy buffalo farms.

Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis.

The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors.

Evaluating an e-nose ability to detect biogas plant efficiency: a case study

Abstract The demand for online monitoring and control of biogas process is increasing, since better monitoring and control system can improve process plants stability and economy. A number of parameters in both the liquid and the gas phase have been suggested as process indicators (pH, alkalinity, VFA and H2, redox potential, biogas production rate, biogas composition, FOS/TAC ratio, COD and/or VS reduction). The present study proposes the use of complex sensors as a possible solution to engineer a consistent control system. Tests were performed to analyze the biogas coming from a biogas plant in which conditions of pre-overloading and overloading were artificially inducted. The different inducted functioning conditions were recognised using a FOS/TAC tester, or the ratio volatile organic acids/alkaline buffer capacity. The FOS/TAC ratio has long been recognized extending as far as the imminent inversion of the digester biology to be detected at an early stage. Data coming from the e-nose were sorted and classified according to FOS/TAC ratio as a reference method. Not all the sensors of the e-nose were reactive to manure digestates, but four of them, sensitive to aromatic compounds, ammonia alkanes and methane, resulted crucial in the samples identification. Results confirmed that the e-nose can discriminate different digestion conditions, demonstrating the possibility to reduce the number of sensors in this innovative tool for biogas control systems. However, this instrument cannot be considered a complete alternative to traditional analysis systems, as, for example, the FOS/TAC titration, but a supporting tool for a quick analysis of the system.

Study and Development of an Integrated Automatic Traceability System for the Bovine Meat Chain

In Italy, the bovine meat production chain is extremely complex both under structural and organizational profile. This is due to: high number of operators involved; large fragmentation in agricultural and industrial phases; existence of remarkable import flow of animals and meat; commercial channels complexity. Traceability can be easily reached in each single separate step of the food chain (breeding, slaughtering, packaging and selling). Problems arise in integrating each productive process subsystem. Aim of the project was to study and develop an integrated automatic traceability system for the bovine meat chain based on RFID technologies.