Hierarchical clustering and partitioning to characterize shrimp grow-out farms in northeast Brazil

Abstract

Shrimp farming in northeast Brazil is an important sector of the regional economies, contributing as a source of revenue and poverty alleviation. In recent years, shrimp aquaculture has faced many challenges, ranging from the occurrence of viral diseases to changes in market access. Considering the past and present challenges faced by the shrimp farmers in northeast Brazil there is a current need to identify the present trends in shrimp farming when striving for the sustainable development of this important sector of the Brazilian economy. To this aim, the exploratory data analysis methods of multiple correspondence analysis and hierarchical and partitional clustering were applied to the data collected through a national census: by identifying shrimp farm clusters, we can characterize groups and identify trends that can compromise sustainable production. The present study identified not only trends in shrimp farming but also considered variables excluded from previous publications for the national census data. The general and initially well-known division between the medium and large-scale, highly tecnified shrimp farms and the micro-scale and less tecnified farms, was verified. However, two clusters of micro-scale and less tecnified farms were observed, highlighting differences in farm management and productivity and also how farmer's view shrimp farming, their concerns and their awareness of what compose biosecurity practices and which biosecurity practices they considered most important. The different stakeholders can make use of the results from the analysis and contribute to the improvement of management practices and policy-making for sustainable shrimp farming in northeast Brazil. Statement of relevanceShrimp farming is an important source of income in Brazil's northeast states. Many challenges were faced by this important economic sector and the now extinct Ministry of Fisheries and the Brazilian Association for Shrimp Farming performed a national census of shrimp farms, visiting each farm individually to meet with farmers and record information on many variables. A great deal of the information generated through the census remained unaccounted for in the publication released by these organizations and so multiple correspondence analysis as a data reduction method was used to overcome the challenge of making further use of the data set and that output analyzed through clustering methodology. Trends in shrimp farming can be identified and the information can make for better understanding of shrimp farming to help secure sustainable development through better management and policy making.