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Woody rangelands are important forage resources for grazing goats in the Mediterranean basin. Goats are well adapted in these heterogeneous and complex ecosystems due to their particular anatomic characteristics, metabolic efficiency and browsing ability. However, the mechanism of grazing behavior and forage selection in goats is quite complex and depends on several factors. The present study deals with the seasonal changes of chemical composition in the selected forages and ingested diets as well as their effects on milk yield and composition of goats grazing in a Mediterranean woody rangeland. Hand plucked forage samples similar to those consumed by the goats were collected for chemical analysis. The nutritive value of consumed forages, in combination with the different feeding and grazing system per trial period, influenced milk quality and performance. The replacement of feed supplementation with an increase of the contribution of grazing in the rangeland could maintain the milk yield and quality at the late stage of lactation. In addition, this paper focuses on the influence of the chemical composition of selected forages on the diet selection of goats using a modeling approach. The model showed a high interpretative accuracy and explanatory value (R2 = 0.923) and was able to distinguish the goats' most important criteria for selecting among the forage plant species in relation to several factors. The results indicate that the dietary preferences of goats were influenced, not only by the seasonality of nutritive value and availability of forages in the rangeland, but also by the plant origin of each nutrient source. The selection of Quercus frainetto was highly correlated with its NDF content. Herbaceous species, Cistus creticus and other woody species were mainly selected due to their CP content. The ADL content seemed to have the lowest influence of all nutritive variables on the selection among groups of plant species. The modeling approach, apart from the understanding of grazing behavior of goats, offers additional information that can be useful to the shepherds in order to manage more effectively the feeding and grazing system of their goat's flock.

The grazing behavior and the diet selection of goats are affected by numerous factors associated with animal characteristics, climate conditions and rangeland features as well as their interactions. Moreover, the special feeding behavior of goats due to their particular anatomic and physiologic characteristics, allow them to be highly selective, to consume lignified forages and to utilize woody plant species. The study of above traits is of great interest especially when goats browse in a complex Mediterranean environment with high seasonal changes. The objectives of the present study were to investigate the grazing behavior, the forage selection and the diet composition of goats browsing in a Mediterranean woody rangeland of Northern Greece. Grazing behavioral data were recorded using the direct observation method of a flock of local breed goats and the measurements were performed during three seasons (spring, summer and autumn) for two consecutive years. The purpose was to evaluate the seasonal differences taking into account not only the different seasons but also the different climate parameters within the two years. According to the results, Quercus frainetto was the most important part of the diet of goats despite the fact that they were able to utilize various nutritive resources such as herbaceous species and acorns. Goat is a particular versatile herbivore that is well adapted to the seasonality of complex Mediterranean landscape.

Goats are characterized as selective feeders due to their specific feeding behavior, thus the study of their seasonal preferences in a Mediterranean oak-juniper ecosystem is of great interest. The current research was conducted in an open oak forest in Megalo Dereio region, northeastern Greece which was grazed mainly by a flock of 650 local breed goats. Grazing behavior data as the number of bites per plant species were recorded using a direct observation method in late spring (May), middle of summer (July) and late autumn (November) of 2010 and 2011. Additionally, a modeling approach was followed in order to study the factors influencing the dietary preferences of goats. Inference – by adopting the Bayesian paradigm – was based on the best multinomial response logistic regression model, selected by the suitable model assessment criteria. According to the results, seasonal and yearly effects were both important in explaining the grazing behavior of goats, with season being the dominant factor. The model’s estimation accuracy was additionally assessed by the comparison of the calculated model-based dietary preferences with the corresponding observed values. The model could be a useful tool to estimate the seasonal changes of dietary preferences of grazing goats.

In this study, the level of technical efficiency of 58 sheep farms rearing the Chios breed in Greece was measured through the application of the stochastic frontier analysis method. A Translog stochastic frontier production function was estimated using farm accounting data of Chios sheep farms and the impact of various socio-demographic and biophysical factors on the estimated efficiency of the farms was evaluated. The farms were classified into efficiency groups on the basis of the estimated level of efficiency and a technical and economic descriptive analysis was applied in order to illustrate an indicative picture of their structure and productivity. The results of the stochastic frontier model indicate that there are substantial production inefficiencies among the Chios sheep farms and that these farms could increase their production through the improvement of technical efficiency, whereas the results of the inefficiency effects model reveal that the farm-specific explanatory factors can partly explain the observed efficiency differentials. The measurement of technical inefficiency and the detection of its determinants can be used to form the basis of policy recommendations that could contribute to the development of the sector.

Dairy farming, despite its indisputable contribution to the production of edible products consumed worldwide, more than often poses threats to the environment. The purpose of this study is to provide a typology of the dairy farms situated in Central Macedonia, Greece, based on their environmental management practices. The empirical analysis uses data from a survey of 123 dairy farms in the study area. The farms are categorized into alternative profiles, using a two-step cluster analysis; the clustering is based on the results of a Categorical Principal Component Analysis, by means of which the variables describing environmental management practices are grouped into dimensions. Then, the characteristics of farmers of each category are explored by estimating a Multinomial Logit Model. The results illustrate that when it comes to the environment, dairy farmers in Central Macedonia adopt diverse farming practices, some of which are detrimental, while others promote environmental protection and, at the same time, ensure acceptable incomes for dairy farmers.

In this study the level of technical efficiency of 58 Chios sheep farms is estimated with the implementation of Data Envelopment Analysis. This level of technical efficiency is used as a classification criterion and the main technical and economic indices of the farms are analyzed in order to investigate their contribution to the improvement of technical efficiency. Next, the structural and economic characteristics of the farms that utilize the existing technology efficiently are described and compared with the average farm. The results show the presence of substantial inefficiencies in the allocation of the existing resources among farms that rear Chios sheep in Greece, which indicates that there is considerable room for development. The estimated mean level of technical efficiency is 0.76, suggesting that the average farm could increase its value of production by 24%, given the level of inputs and the existing production technology. Furthermore, larger farms are associated with higher levels of efficiency. A technical and economic description indicates that the efficient farms, compared to the average Chios breed farm, use less land and labor, rear a smaller number of ewes, utilize less variable and fixed capital and achieve a higher income in order to produce the same level of output.

Sheep herds are a significant capital of dairy industry in Greece, but as dairy farming becomes more complex and intense the need to provide assistance to dairy producers on milk quality is critical. In this study, the quality of raw milk produced in sheep farms of NE Greece was surveyed and the role of various factors was explored. In total, 21 dairy ewe's farms from the regions of Xanthi and Evros, in the north-eastern Greece were monitored. Milk samples were sampled after the morning milking every 15 days throughout the dairy period (March-June). For the study, a questionnaire was also filled by personal interview with the owners in order to collect information about herd characteristics, health status, handling practices etc. From each farm, air was also sampled for microbiological analysis. Milk samples were examined for chemical components: fat content, protein, lactose, non-fat dry matter (NFDM) and somatic cells count (SCC). Microbiological examination involved the estimation of Total Bacterial Counts (TBC), coliform count (CC), Staphylococcus aureus, Streptococcus sp., and preliminary incubation count (PIC). The possible correlation among different bacterial species and their interaction with SCC and chemical components of milk was also considered. It was examined whether farm management practices could influence the hygiene and the quality of milk. Our results show that as an average TBCs were 5.48 log cfu/ml, SCC: 6.05 log cells/ml, CC: 4.49 log cfu/ml, S. aureus: 3.94 log cfu/ml, Streptococcus sp.: 4.95 log cfu/ml and PIC: 5.7 log cfu/ml. The mean fat, protein, lactose and NFDM were 6.17%, 5.28%, 4.73% and 10.95% respectively. The study revealed significant positive correlation between TBC and PIC (0.825), while SCC was marginally positive correlated with protein and NFDM. No statistically significant correlations observed among SCC with any of the bacterial species. Herd size and farm management practices had considerable influence on SCC and bacterial species.

Transhumance sheep and goat production have been a common and traditional practice in Greece, with its origins dating back to ancient times. Despite the diminishing number of transhumance farms, it remains an essential activity in less-favoured and mountainous areas of the country. This article applies DEA (Data Envelopment Analysis) in a sample of transhumance farms in Greece in order to assess the technical efficiency of sheep and goat transhumance flocks and determine the factors that affect their performance. The effect of EU subsidies on the technical efficiency of transhumance farms is assessed and the type of farms that benefit most is investigated. Results accrued reveal that the overall technical efficiency of transhumance farms in Greece is quite low and it is affected by herd size. EU subsidies have a significant impact on the technical efficiency of only the low-efficiency, small-sized farms.

In situ degradability and in vivo (by difference) digestibility trials were conducted to estimate lower tract residual N digestibility (LTRND) of five protein supplements. Efforts were also made to improve the in situ method of measuring protein degradability. For in situ degradability trials, soybean meal (SBM), corn gluten meal (CGM), cotton seed cake (CSC), wheat bran (WB) and corn gluten feed (CGF) were weighed into Dacron bags and incubated in the rumen of three cannulated Chios ewes. SBM, CGF and WB were degraded significantly, while CGM and CSC were least degraded. Microbial contamination (MC) resulted in a 5.3–28.3% artificially decrease in effective ruminal protein degradation of supplements. Total tract digestibility was measured using five rams in an in vivo, by difference, trial using a 5 × 5 Latin-square design. SBM had higher CP digestibility compared to WB, CGF and CSC, and higher N free extract (NFE) digestibility compared to the other feeds. CGM showed higher CP digestibility compared to WB, CGF or CSC, while CGF had higher organic matter (OM) and crude fibre (CF) digestibility compared to WB. CSC was the protein source with the lowest digestibility of OM, CP and NFE in comparison with the other feeds. LTRND was predicted as 0.928, 0.806, 0.227, 0.540, and 0.498 for SBM, CGM, CSC, WB, and CGF, respectively, or 0.931, 0.803, 0.147, 0.364, and 0.316 when the correction for MC was applied. Lower tract N digestibility could be predicted via a combination of in situ degradability and in vivo apparent digestibility data. This approach yields significant data regarding LTRND estimation of protein supplements, while diminishing animal suffering by avoiding small intestinal fistulation.

The aim of this work was to determine the effect of vacuum level of machine milking on milk yield, milk composition, milking efficiency, somatic cells counts (SCC), and teat end thickness of the Greek mountain breed Boutsiko ewes. Seventy two ewes after a suckling period of 45 ± 5 days were divided into three equal groups of 24 animals for level of milk production, prolificacy and lactation number. The overall experiment lasted 14 weeks. All groups were milked with a pulsation rate of 120/min, pulsator ratio of 50/50, and three vacuum levels of 38, 44 and 50 kPa, respectively, were applied for the three groups. Every two weeks, in a.m. and p.m. milking, measurements were taken for machine milk (MM) and machine stripped milk (MSM), milk composition, SCC, milk emission characteristics (only in a.m. milking) and teat end wall thickness (TEWT) before and after milking. The results have shown that the lower vacuum level of 38 kPa increased ( P P P

The aim of this work was to explore possibilities to build an alarming system based on the results of the application of data mining (DM) techniques in genetic evaluations of dairy cattle, in order to assess and assure data quality. The technique used combined data mining using classification and decision-tree algorithms, Gaussian binned fitting functions, and hypothesis tests. Data were quarterly national genetic evaluations, computed between February 1999 and February 2003 in nine countries. Each evaluation run included 73,000-90,000 bull records complete with their genetic values and evaluation information. Milk production traits were considered. Data mining algorithms were applied separately for each country and evaluation run to search for associations across several dimensions, including bull origin, type of proof, age of bull, and number of daughters. Then, data in each node were fitted to the Gaussian function and the quality of the fit was measured, thus providing a measure of the quality of data. In order to evaluate and ultimately predict decision-tree models, the implemented architecture can compare the node probabilities between two models and decide on their similarity, using hypothesis tests for the standard deviation of their distribution. The key utility of this technique lays in its capacity to identify the exact node where anomalies occur, and to fire a focused alarm pointing to erroneous data.

A well-established rabbit model of enteropathogenic E. coli (EPEC) disease was used to examine whether vitamin E (VE) nutritional supplementation had an effect on the pathological changes induced in the bowel by EPEC. Quantitative methods were used to evaluate the influence of VE on bacterial colonization, intestinal mucosal architecture and inflammation, and intestinal epithelial proliferation and apoptosis. VE did not affect EPEC colonization and did not give significant protection against EPEC-induced changes and diarrhoea. Although VE had no effect on the EPEC-related increase of enterocyte apoptosis, it clearly contributed to an acceleration of epithelial cell proliferation in the ileal crypts. This finding may explain why ileal morphometry undertaken in this study showed that VE ameliorated somewhat the effects of EPEC on intestinal mucosal architecture. Quantitative studies on inflammatory cells in the intestinal mucosa revealed that VE nutritional supplementation resulted in an increased neutrophilic and mononuclear inflammatory cell response to EPEC infection, which did not contribute, however, to the clearance of infection.

Linear models were developed and evaluated for the estimation of daily (24 h) and total lactation milk yield of dairy sheep from single morning (am) or evening (pm) milking records. A total of 22,908 individual test-day milk records of 5110 ewes of the Chios breed, raised in 46 flocks, were used. Depending on the model, different daily milk yield estimates were derived for each ewe, accounting for lactation number, stage of lactation, season of previous lambing and interval between successive milkings. Daily milk yield was also estimated from doubling the single am or pm record. Actual and estimated yields were compared using root mean square errors (RMSEs), mean absolute differences, an accuracy parameter defined as the ratio of the actual yield variance over the sum of the variances of actual yield and absolute difference, and the product moment correlation between estimated and actual yield. Results were validated on independent datasets. Linear models resulted in less biased and more accurate estimates of the daily milk yield than simply doubling the am or pm record. Root mean square errors decreased by 7–37% and the mean absolute difference was reduced by 1–4% of the average daily yield. Higher accuracy and correlation were obtained from linear model application than doubling. Total lactation milk yield was predicted based on actual daily yield and compared to predictions based on estimated daily yield from am or pm records, as well as directly on single milking records. Root mean square errors obtained when daily yield had been estimated with linear models were 26–35% lower compared to doubling the am or pm yield and 0–13% lower compared to estimating the total lactation yield directly from single milking records. Linear model application also resulted in lower mean absolute difference and higher accuracy and correlation than doubling the am or pm record. Recording the yield of a single milking (am or pm) instead of both can benefit milk recording by reducing its cost and increasing farmer participation. In this context, linear models developed in the present study can be used for the accurate estimation of daily (24 h) and total lactation milk yield from single milking records.

The aim of this study was to test the usefulness of artificial neural networks (ANNs) for predicting lactation as well as test-day milk yield(s) in Chios dairy sheep on the basis of a few (2–4) available test-day records at the beginning of a lactation period. The ANN employed was a neural network-like system with some advantages over other ANNs. No selection of learning coefficients, of the number of hidden layers or of the number of neurons in the layers was required. The effect on the network's predictive ability of the number of records used in the training phase, the number of input variables (i.e. test-day records) and data preprocessing was investigated. Input variables were the county, herd, lactation, lambing month, litter size, milk yield recorder, test day and days in milk (after lambing) when the first milk sample was obtained. Various criteria of goodness of prediction of lactation as well as of test-day yields were used, including Pearson and rank correlations between observed and predicted yields; the average difference between observed and predicted yields; the difference between their standard deviations; the standard deviation of differences between observed and predicted yields, and the ratio between it and the observed mean value. The average difference between observed and predicted yields was generally statistically non-significant (P