Characterization and typologies of dairy goat farms in the Mediterranean region: A case of Italy and Turkey

Gökdai, Arzu; Magrin, Luisa; Sakarya, Engin; Contiero, Barbara; Gottardo, Flaviana

doi:10.1016/j.smallrumres.2020.106196

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…The farming system was based on intensive/semi-intensive medium to large farms with owned land area. Milk was mostly transformed in-farm and cheese directly sold to consumers [ 31 ]. Anthelmintic treatments were less frequent compared to other European countries [ 32 , 33 , 34 ], confirming what has already been reported in Northern Italy by Lambertz et al [ 4 ], Manfredi et al [ 7 ] and Zanzani et al [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

Survey on Endoparasites of Dairy Goats in North-Eastern Italy Using a Farm-Tailored Monitoring Approach

Maurizio

Stancampiano

Tessarin

et al. 2021

Veterinary Sciences

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With the spread of anthelmintic resistance (AR), endoparasite monitoring consolidates its role for a more sustainable targeting of treatments. A survey on endoparasites in dairy goat farms of north-eastern Italy was conducted to test a monitoring approach based on a farm-tailored sample size. Farm management and parasites control practices were investigated in 20 farms through a questionnaire survey. Further, fecal samples were collected (November 2018–September 2019) from 264 animals from 13 farms and were analyzed individually with a modified McMaster method and subsequently pooled to perform a coproculture. Coccidia (78.4%), gastrointestinal strongyles (37.9%), Strongyloides (28.4%), Skrjabinema (18.9%), Trichuris (8.0%) and Nematodirus/Marshallagia (0.4%) were identified. Abundances were higher for coccidia and gastrointestinal strongyles. Haemonchus (71%) was the dominant gastrointestinal nematode. Pasture and age class resulted in the main risk factors at the multivariable analysis through a negative binomial regression model. Results from farm monitoring indicate that our approach can be a cost-effective decision tool to target treatments more effectively, but farmers need to be educated about the importance of parasitological testing, which is currently scarcely implemented, against the risk of AR.

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Section: Discussionmentioning

confidence: 99%

Survey on Endoparasites of Dairy Goats in North-Eastern Italy Using a Farm-Tailored Monitoring Approach

Maurizio

Stancampiano

Tessarin

et al. 2021

Veterinary Sciences

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“…This result was due to biophysical and socio-economic conditions. Gökdai et al [ 31 ] also revealed that Turkish and Italian goat farms tend to cluster correspondingly to their location.…”

Section: Discussionmentioning

confidence: 99%

“…The cluster analysis (CA) is an unsupervised method used to find sub-groups or “clusters” in a dataset, according to their similarities among variables (i.e., PC) [ 36 ]. It was used in many herd applications in the characterization and classification of farms [ 37 ], usually in terms of their structural [ 27 ], techno-economic [ 13 , 16 , 19 , 38 , 39 ], and socio-economic aspects [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

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Characteristics of Buffalo Farming Systems in Turkey Based on a Multivariate Aggregation of Indicators: A Survey Study

Öztürk

Koçak

Gökdai

et al. 2022

Animals

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This study aimed to determine the heterogeneity that exists in water buffalo husbandry systems in Marmara Region, Turkey. A questionnaire containing a total of 60 indicators was submitted to 52 farmers. A Principal Component Analysis was performed to reduce original variables into a simplified and latent structure, which was characterized by six orthogonal components: milk productivity, economic efficiency, roughage management, dual-purpose farming, concentrate supply, and fodder production. An ANOVA model was applied to the six components to investigate the effects of the province, investment levels, grazing type, milk production, and profitability. Differences in milk productivity, roughage management, concentrate supply, and fodder management were significant according to the province and grazing type, which indicated a difference in intensification levels among the cities. Economic efficiency and dual-purpose farming differed significantly for milk production levels as well as milk productivity, and economic efficiency differed for profitability levels. We found a tendency regarding the impact of roughage management on profitability. The results conclude that profitability was associated with improving the milking traits of buffaloes and roughage management of the farms.

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“…Considering that addressing heterogeneity depends on the nature of variables used to aggregate farms [55] and the process of variable selection [30,48], we adopted a four-step procedure to classify and characterize farms. First, a set of variables were derived from the literature on farm typology, including farm size, number of dairy cows, number of lactating cows, milk yield, labor intensity, stocking rate, amount of concentrates used, feeding/grazing systems used, herd size, farmer's experience, age at first mating, farmers' age, farm facilities and equipment, farm income, household size, hired labor, keeping animal records, milking frequency, farmer education level, use of artificial insemination, lactation period, and calving interval [37,42,49,50,[56][57][58][59][60][61][62][63].…”

Section: Variable Selectionmentioning

confidence: 99%

Context Specificity and Time Dependency in Classifying Sub-Saharan Africa Dairy Cattle Farmers for Targeted Extension Farm Advice: The Case of Uganda

et al. 2021

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Despite the huge potential for milk production, interventions to improve productivity in sub-Saharan Africa (SSA) are barely based on specified farm classifications. This study aimed to develop robust and context-specific farm typologies to guide content of extension farm advice/services in Uganda. From a sample of 482 dairy farmers, we collected data on farmer socio-demographics, farm management practices, ownership of farm tools and facilities, willingness to pay for extension services, milk production, and marketing. Farm typologies were obtained based on principal component and cluster analyses. Thereby, of the three dairy production systems that emerged, small-scale, largely subsistence yet extensive and low productive farms were more prominent (82.6%). Farms that were classified as large-scale, less commercialized yet extensive with modest productive systems were more than the medium-scale commercial farms with intensive and highly productive systems. However, the later were considered to potentially transform dairy farming in Uganda. It was also predicted that the validity of our farm classification may persist until half of the farms have moved between clusters. The study gives new insights on dairy production systems in Uganda, which can be used to organize more targeted research on farmers’ extension needs for facilitating delivery of relevant and effective extension services and designing appropriate extension policies.

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Characterization and typologies of dairy goat farms in the Mediterranean region: A case of Italy and Turkey

Cited by 8 publications

References 19 publications

Survey on Endoparasites of Dairy Goats in North-Eastern Italy Using a Farm-Tailored Monitoring Approach

Survey on Endoparasites of Dairy Goats in North-Eastern Italy Using a Farm-Tailored Monitoring Approach

Characteristics of Buffalo Farming Systems in Turkey Based on a Multivariate Aggregation of Indicators: A Survey Study

Context Specificity and Time Dependency in Classifying Sub-Saharan Africa Dairy Cattle Farmers for Targeted Extension Farm Advice: The Case of Uganda

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