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This report provides the first estimates of the genetic basis of all key breast muscle myopathies (BMM) in broiler chickens [Deep pectoral myopathy, wooden breast, white striping and spaghetti breast] and their relationship with body weight and breast yield. Data from a pure bred high yielding commercial broiler line were analysed to estimate the genetic parameters using a multivariate animal model with the appropriate fixed effects and permanent environmental effect of the dam. Heritabilities of the BMM ranged from 0.04 to 0.25 and the genetic correlation of the BMM with body weight and breast yield ranged from-0.06 to 0.41. Here we highlight that the genetic variance of BMM accounts for a low proportion of the phenotypic variance and the BMM have a low genetic relationship with performance traits. The large contribution of residual variance to the phenotypic variance for the BBM was >71.5% which indicates the importance of the non-genetic effects on BMM. The data presented also show that the moderate to low genetic influence for the development of BMM can be used, through balanced selection, to reduce the myopathy incidence in the long term. The impact of genetic selection against BMM was tested empirically by comparing the incidence of WB and % breast yield of a commercial broiler with a high generation (HG) broiler. The HG broiler used represents 2 years of genetic improvement compared to the commercial broiler; the HG broiler had an 18.4% relative decrease in WB and a 1.02% relative increase in breast yield compared to the commercial broiler. This paper describes the relationship between the genetic and non-genetic factors influencing BMM highlighting the importance of understanding the non-genetic effects on myopathy incidence. It also shows that the genetic component of BMM can be reduced whilst at the same time improving breast yield as part of balanced breeding goals.

Several equipments and methodologies have been developed to make available precision agriculture, especially considering the high cost of its implantation and sampling. An interesting possibility is to define management zones aim at dividing producing areas in smaller management zones that could be treated differently, serving as a source of recommendation and analysis. Thus, this trial used physical and chemical properties of soil and yield aiming at the generation of management zones in order to identify whether they can be used as recommendation and analysis. Management zones were generated by the Fuzzy C-Means algorithm and their evaluation was performed by calculating the reduction of variance and performing means tests. The division of the area into two management zones was considered appropriate for the present distinct averages of most soil properties and yield. The used methodology allowed the generation of management zones that can serve as source of recommendation and soil analysis; despite the relative efficiency has shown a reduced variance for all attributes in divisions in the three subregions, the ANOVA did not show significative differences among the management zones. KEYWORDS:Precision agriculture, spatial variability, fuzzy clustering, management zones, autocorrelation, cross-correlation. DEFINIÇÃO DE UNIDADES DE MANEJO USANDO ATRIBUTOS QUÍMICOS E FÍSICOS DO SOLO EM UMA ÁREA DE SOJA RESUMO:Diversos equipamentos e metodologias vêm sendo desenvolvidos para tornar a agricultura de precisão disponível, especialmente considerando o alto custo de sua implantação e de amostragem. Uma possibilidade interessante é definir a área em unidades menores de produção que podem ser tratadas de maneira diferente, servindo como fonte de recomendação e análise. Assim, o presente estudo utilizou propriedades físicas e químicas do solo e de produtividade visando à geração de unidades de manejo, a fim de identificar se estas podem ser usadas como recomendação e análise. Unidades de manejo foram geradas pelo algoritmo Fuzzy C-Means, e sua avaliação foi realizada por meio da redução da variância e realização de testes de comparação de médias. A divisão da área em duas unidades de manejo foi considerada adequada, e as médias apresentaram-se distintas da maioria das propriedades do solo e produtividade; a metodologia utilizada permitiu a geração de unidades de manejo que podem servir como fonte de recomendação e análise do solo. Apesar de a eficiência relativa demonstrar que houve redução da variância para todos os atributos na divisão em três sub-regiões, a ANOVA não apresentou diferenças significativas entre as unidades de manejo. PALAVRAS-CHAVE: