Carlos Amiama

Estimating efficiency in automatic milking systems

Milking data of 34 single automatic milking system (AMS) units on 29 Galician dairy farms were analyzed to determine the system capacity in each farm under actual working conditions. Number of cows, milk yield, milkings per cow per day, actual milking time, rejected milking time, cleaning time, and machine downtime were used to determine the number of cows milked per AMS unit to obtain the optimal values of milkings per cow and milk production. Multiple linear regression data analysis was used to model the linear relationship between the dependent variable, milk yield per AMS per year, and the predictor variables: number of cows per AMS, milkings per cow per day, milk flow rate, and rejections per AMS per year. An AMS unit milked 52.7±9.0 cows daily at 2.69±0.28 milkings per cow, with a total milking downtime of 1,947±978 h/yr and a milk yield of 549,734±126,432 kg/yr. The predictor variables cow and milk flow rate had a greater level of influence on the milk yield per AMS than milkings per cow and rejections, and explained the 87% of the variation. The AMS in Galician dairy farms could facilitate an increase of 16±8.5 cows per AMS without impairing milking performance; in this way, the quantity of milk obtained per robot annually could be increased (185,460±137,460 kg). This would make it possible to recoup the cost of the system earlier. In the present situation, the daily milking throughput could be maximized at 2.4 to 2.6 milkings per cow.

Typologies of Dairy Farms with Automatic Milking System in Northwest Spain and Farmers’ Satisfaction

The aim of this study was to determine the characteristics of the dairy farms that installed an automatic milking system (AMS). A survey of 38 dairy farms with AMS, in Galicia (Spain), collected information on quantitative and qualitative variables. Following elimination of redundant variables, categorical principal component analysis identified 4 factors accounting for 43.7% of the total variance. Using these factors, the farms studied were subjected to hierarchical cluster analysis which differentiated 4 types of farms: (A) farms with more leisure and quality of life where the AMS covered the expectations of farmers (29%); (B) farms that removed cows more often due to AMS and farmers with more stress (34%); (C) farms with little leisure and farmers with no successor (21%); (D) large farms with many fulltime employees (FTE) where the AMS had covered farmer’s expectations the least (11%). Generally the farms were based on a family structure with a high percentage of FTE. With the adoption of AMS these farms sought to increase milk production, save labour and have more flexibility. With 87% of farms with free cow traffic the activity that took the most of the farmer’s time was fetching cows for milking (1 h/day). Nearly 58% of farmers were completely satisfied with their AMS, although this value reached 91% in farms with herd sizes below the average which were better adapted to the use of one AMS.

Modelling corn silage harvest logistics for a cost optimization approach

Simulation software is usefully tool to the strategic planning of complex systems.To adjust correctly the number of trucks is crucial to avoid overrun at harvest.Harvest is more sensitive to changes in packing capacity than to the number of trucks. Harvesting corn silage requires balancing the capacities for harvest, transport, and storage operations to eliminate bottlenecks.The overall goal of this paper is to simulate the silage harvest system in order to provide the technicians with a decision support tool. This tool will be useful when performing both the strategic planning at the beginning of the harvest season, and in daily decision making, in order to determine the right combination of resources according to fields to harvest. A model was constructed to evaluate the handling system comprising the harvest, transport and packing of forage intended for corn silage.In order to provide a real example of the usefulness for strategic planning by the tool developed, the harvesting of 590 fields of corn silage in a region of NW Spain was simulated. The model obtained provided a value of 27 trucks to obtain lower harvesting costs if 6-row SPFH were used and 33 trucks with 8-row SPFH. The proper packer capacity at the silo was 3.35tmin-1. The impact that the matching of equipment has on the costs and on the length of the harvest season for each of the harvesters analyzed was more significant with 8-row SPFH. If the number of trucks is less than 30, a 6-row SPFH is more cost efficient than an 8-row SPFH. On the other hand if the number is greater, then the use of the 8-row SPFH would incur lower costs. The harvesting process is more sensitive to changes in the packing capacity than to the number of trucks used relative to the optimum value determined.

Spatial decision support system for the route management for milk collection from dairy farms

In this research, a new spatial decision support system (SDSS) was developed, which solves the milk collection problem in two stages. First it applies an algorithm, employing heuristic techniques, generating solutions in a short period of time. In a second step, a graphic interface has been developed, which allows interaction and changes to be carried out in a rapid and intuitive way on the routes generated by the routing algorithm. The route manager can also carry out a broad range of “What if” simulations to find the solution that minimizes cost. With the use of this tool, significant savings have been obtained in terms of the collection time and kilometers covered by freight, while still keeping the current fleet of vehicles. Sensitivity analysis shows that the total cost of the process is more sensitive to increasing truck capacity than duplicating vehicle working shifts. The existence of farms with difficult access increases the collection costs substantially.

Metodología para la Evaluación de las Tecnologías de Información en la Docencia de Proyectos de Ingeniería

In this work the basic characteristics of an e-learning project done at the University of Santiago de Compostela-Spain are presented, and a methodology for the evaluation of this type of teaching support technology is validated. The results obtained in an evaluation performed by the students on an application developed for virtual teaching, are statistically analyzed. The results are related to the qualifications obtained by the students, using multivariate and correlation analysis techniques, and discriminate factorial analysis. Based on the results, conclusions are drawn about the usefulness of these techniques to make inference of future results, based on previous assessments made by students.

Long-term variability of bulk milk somatic cell and bacterial counts associated with dairy farms moving from conventional to automatic milking systems

Abstract When a farm that was using a conventional milking system introduces an automatic milking system (AMS) possible risk factors can affect milk quality. The aim of the study was to investigate the influence of milking with automatic milking systems on milk quality variables over a long time-period post-installation. Bulk milk total bacterial count (BMTBC) and somatic cell count (BMSCC) were analysed and compared from 2 years before introduction of automatic milking until 4 years after. Differences regarding these quality parameters were contrasted using t-test and one-way analysis of variance (ANOVA) and post hoc comparisons were performed. A significant increase in BMTBC was observed during the first three months after introduction of AMS, counts then declined to equivalent levels pre-AMS installation, from 25,000 to 50,000 cfu mL−1. Although differences were significant for the first two years post-installation, they became non-significant during the following two years. The difference in BMSCC was not statistically significant between pre and post-AMS installation time periods, but by grouping data into annual periods, significantly higher values of BMSCC were found during the first year after introduction. Nevertheless, these values decreased over time and even showed a significant improvement in the third year with respect to pre-introduction. The data show that the installation of AMS had a marked impact on milk quality. However, as soon as farmers become accustomed to managing the new equipment and the adaption of cows is real, a level of milk quality which can be maintained over time is achievable.