The Milking Profile of Dairy Cattle Farms in Central Macedonia (Greece)

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1 The Milking Profile of Dairy Cattle Farms in Central Macedonia (Greece) Ioannis Mitsopoulos 1*, Athanasios Ragkos 1, Vasilios Dotas 1, Vasilios Skapetas 1, Vasileios Bampidis 1, Vasiliki Lagka 1, Zaphiris Abas 2 1 Department of Animal Production, School of Agricultural Technology, Alexander Technological Educational Institute (ATEITHE), Thessaloniki, Greece 2 Department of Agricultural Development, Democritus University of Thrace (DUTH), N. Orestiada, Greece Abstract The purpose of this paper is to provide insights of the profile of the dairy farms of Central Macedonia (Greece), in terms of their milking practices. The analysis is based on data from a random sample of 123 dairy farms, obtained by means of a survey. The employment of the Categorical Principal Component Analysis on the 14 variables initially used to describe milking practices and of the Two-Step Cluster Analysis led to the grouping of the 123 farms to three clusters. Farms of the first cluster, named Innovative, use state-of-the-art equipment, automatic systems and innovative milking techniques (31.1% of the sample farms). Peasant farms (11.4%) are mainly extensive, using mainly bucket plants. The third and most abundant group, the Modernizing farms (54.5%) are use equipment of reasonable standards and some of them are on the process of renewing it. The results of a Multinomial Logit model verify that Innovative farms are large and achieve high yields, while the Modernizing ones are smaller, producing milk of lower quality and they are owned by relatively older dairy farmers. An interesting profile is depicted for Peasant farms, as they achieve satisfactory economic performance, combined with adequate milk quality. The analytical framework included the reduction of analysis variables to a smaller group of dimensions, using the Categorical Principal Component Analysis (CatPCA), based on which farms were clustered to alternative profiles, by employing a Two-Step Cluster (TSC) Analysis. Differences in elements of milk quality and in the social profile of farms and farmers were examined among alternative profiles through the estimation of Multinomial Logit Models. Keywords: Central Macedonia, dairy cattle, milking practices, multinomial logit, two-step cluster analysis 1. Introduction The main objective of dairy farms is to increase their productivity, as well as the quality of milk, in order to ameliorate their economic performance. Under the influence of breeding and of improved nutrition and management, milk production and its quality have been considerably increased during the past 40 years. On the other hand, this modernization entails the concentration of * Corresponding author: Dr. Ioannis Mitsopoulos, E- mail: gmitsop@ap.teithe.gr, Tel./Fax: production to fewer farms of larger size [1], generating a pattern to which Greece does not constitute an exception [2]. These farms are able to ameliorate their infrastructure (buildings and machinery) and adopt innovative milking practices, which can ensure that milk production is maximized and that milk is produced under healthy and controlled conditions. Milking in dairy farms is ameliorated through the introduction of milking machines; the use of milking machines improves the quality and hygiene of milk, increases labour efficiency and ameliorates the working conditions in the farm, as long as the proper cleaning and sanitization 412

2 methods are used. The proper functioning of milking machines has been found to have positive effects on the health of the udder [3], which, in turns, heavily affects milk quality. Furthermore, milking is the most crucial and time-consuming chore within the milk-production chain [4], since it requires an estimated 33%-55% of the total labour in dairy farms [5,6,7]. Consequently, the efficient management of milking improves costeffectiveness and animal welfare as well [8]. The purpose of this study is to generate a typology of dairy farms in Central Macedonia, Greece, in terms of their milking practices, and to investigate the characteristics of each group. 2. Materials and methods The data for the empirical analysis were gathered from a sample of 123 dairy farms in Central Macedonia, Greece. The choice of this particular study area is due to the fact that almost half of the cow milk produced yearly in Greece comes from this region. According to data from the Greek Organization of Milk and Meat (Elogak), during , the sector in Central Macedonia comprised 1,539 dairy farms which produced 335,600 tons of milk; these account for 33.6% of Greek dairy farms and for 48.0% of total cow milk production in the country [2]. The sample size was determined through a random stratified sampling [9]. The survey was conducted from September 2009 to August 2010 by means of a carefully designed questionnaire, through personal interviews which took place on-farm. The sampled farms accounted for all types of farms typically operating in the region, from small family farms to modern ones. The analytical framework employs established methodological tools. First, the Categorical Principal Component Analysis method is used in order to reduce the original set of variables describing milking practices into a smaller set of uncorrelated components that represent most of the information found in the original variables [10]. Using the components generated by means of the CatPCA, the sampled farms are grouped to clusters/groups with common characteristics in terms of milking practices, using the Two-Step Cluster Analysis (TSCA) method. TSCA constitutes an extension of a typical cluster analysis aiming at the determination of clusters which share common characteristics based on categorical and/or continuous variables. For each cluster, the main characteristics of the farmers, as well as economic and milk quality characteristics of farms are investigated through the estimation of Multinomial Logit Models (MNL) [11]. MNL models enable a regression analysis when the dependent variable is categorical. 3. Results and discussion Table 1 presents the results of the CatPCA. The 19 variables were grouped into four dimensions, which account for 60.36% of the total variance. Each one of the four dimensions is characterized by the variables with the highest loadings (denoted in bold) and is then named accordingly. Hence, Dimension 1 is named The milking parlour, as the variables with the highest loadings account for basic characteristics of the milking machines and milking parlour. Variables with the highest loadings in Dimension 2 describe automatic systems which improve the milking process, so it is named Milking automations. Similarly, Dimension 3 is named Treatment automations. Variables with the highest loadings in Dimension 4 describe common milking practices, which point to assigning the name Maintenance and milk delivery. The results of the TSCA are reported in Table 2. The 123 sampled dairy farms are grouped in three clusters, which constitute three different profiles. Of these, the first includes 42 dairy farms (34.1%), the second 14 farms (11.4%) and the third 67 farms (54.5%). The examination of the means of variables formulating each cluster along with the frequency analysis of variables characterizing each Dimension (see Table 3) are necessary in order to identify each profile. The first cluster is mainly described by Dimensions 1, 2 and 3. Farms of this cluster are, thus, endowed with a separate milking parlour with permanent milk and vacuum hoses (97.6%) and with herringbone-shaped milking machines with two rows (73.8%), which serve the most cows per hour (64.45). The automatic systems used include recording the milk production, the fall of teats and door-opening (54.8%, 64.3% και 42.6% respectively), while automatic feeding and udder wash are not available. These farms are named Progressive as they adopt modern milking infrastructure and equipment. 413

3 Table 1. Dimensions and component loadings (Results of the CatPCA) Variables Dimensions Communalities Maintenance of milking machine Udder mollification Number of milkers (persons) Automatic washing of milking machine Automatic recording of milk production Duration of milking Automatic fall of teats Numbers of seats in milking parlour (Availability of) Energy reserve Type of milking machine Automatic door opening Milk tank Cows milked per hour Separate milking parlour Milking infrastructure Feeding during milking Automatic udder wash Automatic feeding Frequency of milk deliveries Cronbach s a * Variance accounted for * *Overall model Cronbach-a and total variance accounted for Table 2. Results of Two-Step Cluster Analysis Dimensions Analysis variables Cluster size 1. The milking 2. Milking 3. Treatment 4. Maintenance Clusters parlour automations automations and milk delivery Number Percentage of farms Mean St. Dev. Mean St. Dev. Mean St. Dev. Mean St. Dev. Progressive * * * Peasant * * Modernizing * * * *denotes the variables (dimensions) participating in the formulation of each cluster The second cluster includes extensive dairy farms, which use mobile milking machines, or machines of which only a small portion provide Milking automations (see also Table 3). When it comes to Maintenance and milk delivery, more than half of the farms report infrequent maintenance of their milking machines, while milk is delivered to traders every two days by 42.8% of farms, which is the highest percentage among the three clusters. Considering the aforementioned characteristics, the farms of this cluster are named Peasant. Farms of the third group use milking machines which accommodate cows per hour, which is considerably less than for Progressive farms. The milking equipment is permanently installed in the milking parlour, while the stations of the milking machines are organized in straight (19.4%) or in double-row (56.7%) of single-row (16.4%) herringbone shape (Table 3). Furthermore, only very few of these farms use milking machines with milking and treatment automations, while they engage one or two workers for milking (23.9% and 71.6% of the farms respectively). The farms of this group are, thus, named Modernizing ; they are typically older than 10 years and have not yet undertaken investments in order to renew their milking equipment. 414

4 Table 3. Frequency analysis of variables describing milking practices Variables Cluster 1 Cluster 2 Cluster 3 Progressive Peasant Modernising Total Dimension 1 The milking parlour Cows milked per hour Mean Standard deviation Type of milking machine Parallel seats Seats in straight position Herringbone Double row Herringbone Single row Mobile milking machine Other Milking infrastructure Permanent milk hoses and vacuum pipelines in the stable Permanent milk hoses and vacuum pipelines in the milking parlour Other Separate milking parlour Yes No Dimension 2 Milking automations Automatic recording of milk production Yes No Automatic door opening Yes No Automatic fall of teats Yes No Dimension 3 Treatment automations Automatic feeding Yes No Automatic udder wash Yes No Number of milkers (persons) One (1) Two (2) Three (3) Dimension 4 Maintenance and milk delivery Maintenance of milking machine Frequent Infrequent Frequency of milk deliveries Every day Every two days In order to investigate the characteristics of each group, a MNL model was estimated. The dependent variable accounted for the cluster in which each farm participated, while the 415

5 independent variables included the Total Microbial Count (TMC) of milk, the gross margin per cow, the milk yield (kg per cow), the number of dairy cows reared and the farmer s age. The results of the analysis are presented in Table 4. For each group, the marginal effects of each variable are reported, which stand for the effects of a marginal change in the variable on the probability of participation in this particular cluster. The Likelihood Ratio Test and the McFadden R 2 (40.5%) reveal the goodness-of-fit of the model. According to the results in Table 4, the probability that a farm is Progressive is raised by % for a raise in milk yields by 1kg and by 0.26% for an additional dairy cow reared. Consequently, farmers of this category are the owners of large dairy farms, which achieve high yields, and their managerial practices are efficient, which ensures high productivity. These findings are consistent with previous research (see for instance [12] and [13]) which validates that largescale farms use state-of-the-art milking equipment and attribute high importance to milking, which is directly linked to higher milk productivity. On the contrary, [14] argues that the use of milking machines may be the cause of stress for animals and of a slight decrease in milk yields and quality. When it comes to farms of the second cluster Peasant, the probabilities of participation are higher for farms producing milk with lower TMC ( % for a unit reduction in TMC), achieving higher gross margin per cow ( % for an additional euro ( ) of gross margin) and for younger farmers (0.54% for every year of age). It appears that Peasant farmers are young and capable of high standards of farm management, by achieving high milk quality and satisfactory economic performance, without resorting to high levels of intensification. These findings are in conflict with the results of [3] who examined the relationships between milk quality and hygiene and the use of different types of milking machines; it was demonstrated that higher quality was achieved when milking took place inside a fully-equipped milking parlour, while the opposite was found for the use of mobile machines. The participation in the third cluster ( Modernizing farms) is positively affected by the TMC and the producer s age, but the number of reared cows reduces probability. Indeed, a unit increase in TMC, a raise in the farmer s age by one year and the reduction of the flock size by one cow increase the probability of participation by %, 0.74% and 0.26% respectively. These characteristics describe a dairy farming system predominantly managed by older farmers, who maintain flocks of moderate size; they do not seem to have high managerial skills, as their milk quality is not as satisfactory as the Peasant farms. Table 4. Marginal effects of the MNL model Standard Wald- Coefficient Variables error statistic Progressive farms Constant *** TMC D D Age Cows 0.003*** Yield 0.325D-04** 0.141D Gross Margin 0.441D D Peasant farms Constant 0.325** TMC D-06** 0.174D Age ** Cows D Yield D D Gross Margin 0.393D-05* 0.207D Modernizing farms Constant TMC 0.600D-06*** 0.212D Age 0.007** Cows *** Yield D D Gross Margin D D Log- Likelihood function Likelihood Ratio Test *** (10 d.f.) McFadden R *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level 4. Conclusions The empirical analysis within this study revealed the existence of three different types of dairy farms, when it comes to their milking practices. The range of practices detected within the survey was wide enough to include farmers who follow proper hygiene and maintenance techniques as well as others who do not address such issues. 416

6 Each one of the three groups needs particular strategies in order to improve their performance. Progressive farms need better access to specialized information, as they operate intensively, while the extensive Peasant ones should rather turn to small-scale interventions and seek basic information. The Modernizing ones, on the other hand, should be the main recipients of measures and incentives aiming at the renewal of milking infrastructure, because the technological depreciation of the existing machine equipment is one of the main causes of reduced milk quality. References 1. Baldock, D., Public Goods and Public Intervention in Agriculture. In: Proposal for a New EU Common Agricultural Policy. IEEP Bird Life International et al. (Eds), Elogak (Greek Organisation for Milk and Meat), Home page address: (In Greek) 3. Lenza A. and J., M. Pereira, Relationship between operational parameters of milking machines and hygienic quality of milk in 632 Galician dairy farms pdf. 4. Anonymous, Role of milking machines in modern dairy farming. Home page address: milking machine/ roleof-milking-machines-in-modern-dairy-farming. 5. De Koning, C.J.A., Automatic milking common practice on dairy farms. The First North American Conference on Precision Dairy Management oning.pdf. 6. Nikita-Martzopoulou, C., Livestock production buildings. Planning, Environment, Cow stables, Pig stables, Sheep stables, Greenhouse-type constructions, Giahoudi, Thessaloniki, Taylor, G., van der Sande, L. and Douglas, R., Technical report for smarter not harder: Improving labour productivity in the primary sector, A Joint Dairy InSight and Sustainable Farming Fund Project, 30 April Dairy NZ, Hamilton, New Zealand, Kaya Kuyululu, Ç.Y., İşbilen Κ., Kumlu S. Y., Aral., Structural characteristics and herd management practices of dairy cattle farms registered to pre-herd book and herd book systems. Ankara Üniv Vet Fak Derg, 2013, 60, Farmakis, Ν., Introduction to Sampling, Α & P. Hristodoulidhi, Thessaloniki, (In Greek) 10. SPSS, SPSS Categories A Software Package, Version 11.0, Chicago: SPSS Inc, Greene, H.W., Econometric Αnalysis, 6th Edition, Prentice Hall International Editions, USA, Johnson, A P., A proper milking moutine: Proc National Mastitis Council Annual Meeting, 2000, Date of consultation Oliver, S. P., Best Management Practices to Reduce Mastitis and Improve Milk Quality. Address: Hemsworth, P. H., Human-animal interactions in livestock production, Applied Animal Behaviour Science, 2003, 81,