ABSTRACT.- Random-regression models (RRM) are used for dairy cattle genetic evaluations in many countries. The output of a genetic evaluation with RRM is an estimated breeding value (EBV) for a specific function of the additive genetic random regression coefficients, say, 305-d milk yield and its corresponding reliability. The reliability of an EBV in RRM is calculated from the inverse of the coefficient matrix of mixed model equations (MME). The objective of this study was to develop an efficient method to approximate reliabilities for RRM with ssGBLUP.

Red-banded stink bug Piezodorus guildinii (P. guildinii) has been described as the most damaging stink bug regarding soybean crops, leading to seed injury, low germination percentages and foliar retention, even at low population densities. In this work the first P. guildinii transcriptome from a pool of all developmental stages was assembled, annotated and RNAi components were analyzed.

ABSTRACT.- Random regression models (RRM) with ssGBLUP are used worldwide for dairy cattle genetic evaluations. This study aimed to improve the efficiency of implementing RRM with ssGBLUP for national dairy evaluations using the BLUPF90 software suite and to test a method to approximate reliabilities with genomic information.

ABSTRACT.- With the algorithm for proven and young (APY), the computing cost of ssGBLUP is linear with the number of noncore animals and cubic with the number of core, which is less than 20K for single breed and 50k for multibreed evaluations.

CONTENIDO. -- Síntesis de la situación agroclimática de junio-- Perspectivas climáticas trimestrales elaboradas por el IRI de la Universidad de Columbia (Más información puede encontrarse en el sitio del IRI:http://www.iri.columbia.edu) -- Índice de vegetación (NDVI) -- Precipitaciones -- Porcentaje de agua disponible (PAD) -- Agua no retenida (ANR) -- Índice de bienestar hídrico (IBH) -- Perspectivas climáticas jul-ago-set elaboradas por el IRI de la Universidad de Columbia. -- Destacamos para este mes:INIA Termomín.