Global map of wheat mega-environments. The map show twelve different mega-environments around the world. Mega environments mapped according to: Braun et al., Multi-location testing as a tool to identify plant response to global climate change. In M. P. Reynolds, (ed.) Climate change and crop production. CABI Climate Change Series, U.K. pp. 115-138. 2010.

Global map and shape files of maize mega-environments. The map show six different mega-environments around the world.

El concepto de HUB fue desarrollado en los 2000s por investigadores de CIMMYT que buscaban una solución a la baja adopción que sufría la Agricultura de Conservación (Sayre, 2006), pero ha evolucionado, alineándose a las teorías de sistemas de innovación. Este concepto fue operacionalizado en México en 2008 mediante dos proyectos, uno en la Región Noroeste...

En 2015 y 2016, como parte de la actividad 1.1.4., el Programa de Socioeconomía lleva a cabo la subactividad 1.1.4.2. Medición de indicadores clave del desempeño como parte del programa MasAgro Productor. Dicha subactividad busca generar información que cuantifique el desempeño de los HUBs con relación a los resultados obtenidos y a su avance para...

Replication data for: Identification of QTL for early vigor and stay-green conferring tolerance to drought in two connected advanced backcross populations in tropical maize (Zea mays L.) We aimed to identify quantitative trait loci (QTL) for secondary traits related to grain yield (GY) in two BC1F2:3 backcross populations (LPSpop and DTPpop) under...

Summary results and individual trial results from the International Late White Variety - ILWV, (Tropical Late White Varietal Trial - EVT12) conducted in 2004.

Summary results and individual trial results from the International Late Yellow Hybrid - ILYH, (Elite Tropical Late Yellow Normal and QPM Hybrid Trial - CHTTYA) conducted in 2005.

Summary results and individual trial results from the International Intermediate Yellow Hybrid - IIYH, (Transition Zone Intermediate Yellow Hybrids - CHTHTZIY) conducted in 2005.

Summary results and individual trial results from the International Intermediate Yellow Hybrid Trial - IIYH, (Highland Intermediate Yellow Hybrids - CHTHIY) conducted in 2005.

Summary results and individual trial results from the International Intermediate White Hybrid - IIWH, (Highland Intermediate White Hybrid - CHTHIW) conducted in 2005.

Summary results and individual trial results from the International Late White Variety - ILWV, (Highland Late Normal and QPM Varieties - EVT17HLW) conducted in 2005.

Summary results and individual trial results from the International Intermediate Yellow Hybrid - IIYH, (Subtropical Intermediate Yellow Normal and QPM Hybrid Trial - CHTSY) conducted in 2005.

Summary results and individual trial results from the International Intermediate White Hybrid - IIWH, (Subtropical Intermediate White Normal and QPM Hybrid Trial - CHTSW) conducted in 2005.

Summary results and individual trial results from the International Intermediate Yellow Variety Trial - IIYV, (Subtropical Intermediate Yellow Normal and QPM Varieties - EVT16A) conducted in 2005.

Summary results and individual trial results from the International Intermediate White Variety - IIWV, (Subtropical Intermediate White Normal and QPM Varities - EVT16B) conducted in 2005.

Summary results and individual trial results from the International Late Yellow Hybrid - ILYH, (Advanced Tropical Three-Way and Single CrossesYellow Normal and QPM Late - TTWSCYL) conducted in 2005.

Summary results and individual trial results from the International Late White Hybrid - ILWH, (Advanced Tropical Three-Way and Single Crosses White Normal and QPM Late - TTWSCWL) conducted in 2005.

Summary results and individual trial results from the International Late Yellow Hybrid - ILYH, (Elite Tropical Late Yellow Normal and QPM Hybrid Trial - CHTTY) conducted in 2005.

The marker × environment interaction (M×E) decomposes the marker effects into main effects and interaction environmental-specific effects. The M×E interaction may be modeled through a linear kernel (Genomic Best Linear Unbiased Predictor, GBLUP) or with non-linear Gaussian kernels. In this paper we proposed to use two non-linear Gaussian kernels, one is...

Non-parametric methods have been shown to be effective in genome-enabled prediction, in particular, the multi-layer perceptron (MLP) and the radial basis function neural network (RBFNN). In this study, we evaluated and compared the performance of MLP classifier versus the probabilistic neural network (PNN) classifier, which is a special case of RBFNN, to...