Meat sheep farming systems according to economic and productive indicators: A case study in Southern Brazil

ABSTRACT This study aimed to characterize 24 representative sheep production farms from five mesoregions in the state of Paraná, Southern Brazil, so that economic and productive improvement strategies could be proposed. The representative farms for each region were defined at meetings with sheep farmers and technicians via the rapid appraisal methodology and represent 65% of the state’s flock. The information of each representative farm was collected between March 2015 and February 2016. Principal component analysis was used to verify the relationships among the different variables that characterized the farms. These characteristics were: number of ewes (V1), total cost per kilogram of revenue-generating product (V2), feeding costs (V3), labor costs (V4), facility and equipment depreciation costs (V5), and gross margin in the production cycle (V6). Cluster analysis was performed, resulting in three distinct groups: one including eight, another seven, and the third, nine farms. The results aided in classifying the farms into groups with similar characteristics, such as production scale, reproductive efficiency, technical and managerial control, appreciation of cooperatives, and availability of continuous technical assistance. The production scale, reproductive and productive efficiency, adoption of technologies, and cooperative organizational structure can be emphasized as positive performance benchmarks and were the most important aspects to achieve positive economic results.

摘要 本研究旨在对巴西南部巴拉那州五个中观区域的24家代表性绵羊养殖农场开展特征刻画，以期提出经济与生产优化策略。各区域的代表性农场通过快速评估法，联合绵羊养殖户及技术人员研讨确定，其存栏规模占该州绵羊总存栏量的65%。各代表性农场的相关信息采集工作于2015年3月至2016年2月期间完成。本研究采用主成分分析（Principal Component Analysis）对表征农场特征的各类变量间的关联关系进行验证分析，涉及的特征变量包括：母羊数量（V1）、单位创收产品总成本（V2）、饲养成本（V3）、人力成本（V4）、设施与设备折旧成本（V5）以及生产周期毛利（V6）。随后开展聚类分析，最终将样本农场划分为3个不同组别，分别包含8家、7家与9家农场。研究结果可将农场按特征相似性进行分组，分组维度涵盖生产规模、繁殖效率、技术与管理管控、合作社认可度以及持续技术援助可及性。其中，生产规模、繁殖与生产效率、技术应用以及合作社组织结构可作为正向绩效标杆，亦是实现正向经济成果的核心关键要素。