Identification of mega-environments for grain sorghum in Brazil using GGE biplot methodology.

The performance of genotypes in a wide range of environments can be affected by extensive genotype × environment (G × E) interactions, making the subdivision of the testing environments into relatively more homogeneous groups of locations (mega-environments) a necessary strategy. The genotype main effects + genotype × environment interaction biplot method (GGE) allows identification of megaenvironments and selection of stable genotypes adapted to specific environments and mega-environments. The objectives of this study were to identify mega-environments regarding sorghum [Sorghum bicolor (L.) Moench] grain yield and demonstrate that the GGE biplot method can identify essential locations for conducting tests in different mega-environments. A total of 22 competition trials of grain sorghum genotypes were conducted over three crop seasons across several production locations in Brazil. A total of 25, 22, and 30 genotypes were evaluated during the first, second, and third crop seasons, respectively. After identifying the presence of G × E interactions, the data were subjected to adaptability and stability analyses using the GGE biplot method. A phenotypic correlation network was used to express functional relationships between environments. The GGE biplot was found to be an efficient approach for identifying three mega-environments in grain sorghum in Brazil, selecting representative and discriminative environments, and recommending more adaptive and stable grain sorghum genotype

Reshaping the global agricultural landscape: perspectives from Brazil

This article highlights the main changes observed in Brazilian agriculture and analyzes the connections of the observed changes in global agriculture. My approach to the analysis focuses the main drivers of changes, where institutions play a central role. Three driving forces are are considered: first, the effects of global demand for food, fiber, and energy; second, the sustainability debate; and third, the bio-energy paradigm. Each driver presents both local as well as global effects. The article does not emphasize the impact changes in Brazil had on the global agricultural landscape but argues that the impacts run from local and global changes, which cannot be discussed separately. Copyright (c) 2010 International Association of Agricultural Economists.