Spatialization of climate, physical and socioeconomic factors that affect the dairy goat production in Brazil and their impact on animal breeding decisions

Abstract

1073 em melhoramento animal.] O Brasil possui diversidade edafoclimática e realidades socioeconômicas e políticas distintas. Isto contribui para diferenciar as regiões polí-tico administrativas do país. Objetivou-se espacializar os fatores ϐísicos, climáticos e socioeconômicos que melhor discriminam a produção de caprinos leiteiros no Brasil. Foram analisados índice de produção de leite por cabra; ín-dice de produção de caprinos; índice de produção de leite, amplitude da temperatura; temperatura média; precipitação; índice normalizado de diferença vegetativa; umidade relativa do ar; altitude; estabelecimentos agropecuários; estabelecimentos com pastagem nativa; estabelecimentos com pastagens de boa qualidade; estabelecimentos com recursos hídricos; estabelecimentos que recebem orientação técnica; estabelecimentos de agricultura familiar; estabelecimentos de agricultura não familiar e índice de desenvolvimento humano. Brazil has high climate, soil and environmental diversity, as well as distinct socioeconomic and political realities, what results in differences among the political administrative regions of the country. The objective of this study was to determine spatial distribution of the physical, climatic and socioeconomic aspects that best characterize the production of dairy goats in Brazil. Production indices of milk per goat, goat production, milk production, as well as temperature range, mean temperature, precipitation, normalized difference vegetation index, relative humidity, altitude, agricultural farms; farms with native pasture, farms with good quality pasture, farms with water resources, farms that receive technical guidance, family farming properties, non-familiar farms and the human development index were evaluated. The multivariate analyses were carried out to spatialize climatic, physical and socioeconomic variables and so differenciate the Brazilian States and Regions. The highest yields of milk and goat production were observed in the Northeast. The Southeast Region had the second highest production of milk, followed by the South, Midwest and North. Multivariate analysis revealed distinctions between clusters of political-administrative regions of Brazil. The climatic variables were most important to discriminate between regions of Brazil. Therefore, it is necessary to implement animal breeding programs to meet the needs of each region.

A Map Spectrum-Based Spatiotemporal Clustering Method for GDP Variation Pattern Analysis Using Nighttime Light Images of the Wuhan Urban Agglomeration

“…In addition, the pixels with zero values were excluded from the intercalibration. DN't = at × DN 2 t + bt × DNt + ct (2) where DN't is the DN of a pixel after calibration in year t; DNt is the original DN of a pixel in that year; and at, bt and ct are the corresponding model parameters. Figure 3 shows that the intercalibration improved the continuity of nighttime stable light data in study area from 1992 to 2012.…”

“…For instance, to address the inadequacy of census data in reflecting the internal differences of populations in a given area, populations across China were interpolated using nighttime imagery and land use data [1]. To determine the spatial distributions of the physical, climatic, and socioeconomic aspects that best characterize the production of dairy goats and their impact on animal breeding decisions in Brazil, these statistical data were firstly mapped to dasymetric maps [2]. Cao et al [3] estimated the spatial distribution and analyzed the spatiotemporal changes in electricity consumption from 1994 to 2009 in China.…”

Espacialização climática e análise de dados longitudinais de bovinos Nelore criados nos Estados do Maranhão, Pará e Tocantins por meio de técnicas univariadas e multivariadas

“…Assim, é preciso estruturar programas de melhoramento genético animal alicerçado não apenas nos níveis produtivos (McMANUS et al, 2011) mas também em todos os fatores que podem influenciar a produção animal nos diversos ambientes do Brasil (OLESEN; GOREN; GJERDE, 2000;LOPES et al, 2012).…”

“…Esta diversidade de ambientes acarreta oportunidades diferentes de expressão para um mesmo genótipo, o que dificulta a identificação de indivíduos geneticamente superiores para reprodução (LOPES et al, 2008).…”

Reaction norms in weights at 365 days old in nellore bulls in northern Brazil

“…Most of these differences are due to availability of natural resources in its different regions, edaphoclimatic conditions, and economic differences. This diversity provides different opportunities of genotypic expression for animals of the same sire, which makes the identification and proliferation of superior animals all the more difficult (LOPES et al, 2008(LOPES et al, , 2012. In this sense, one of the main goals of livestock improvement seems to be the production of animals with economically consistent performance in specific management and environmental settings (CAMPÊLO et al, 2001).…”