Informe agroclimático 2018 - Situación a Enero.
MAGGnet: an international network to foster mitigation of agricultural greenhouse gases.
Research networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA).
MAGGnet:an international network to foster mitigation of agricultural greenhouse gases.
Research networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA).
SIGRAS App:climate, vegetation and soil information for support systems for decision making in agricultural production through smart devices.
ABSTRACT. One of the most important adaptation measure to Climate Change and Variability is the development of tools and systems to support agricultural activities management and preservation of natural resources. The GRAS Unit of the National Institute of Agricultural Research (INIA Uruguay), elaborate and make available tools and products to contribute to these goals (www.inia.uy/gras).
SIGRAS App: climate, vegetation and soil information for support systems for decision making in agricultural production through smart devices.
ABSTRACT. One of the most important adaptation measure to Climate Change and Variability is the development of tools and systems to support agricultural activities management and preservation of natural resources. The GRAS Unit of the National Institute of Agricultural Research (INIA Uruguay), elaborate and make available tools and products to contribute to these goals (www.inia.uy/gras).
Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency.
Abstract: Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.).