Yield response of rice (Oryza sativa L.) genotypes to drought under rainfed lowlands 4. Vegetative stage screening in the dry season

Screening for drought resistance of rainfed lowland rice using drought score (leaf death) as a selection index has a long history of use in breeding programs. Genotypic variation for drought score during the vegetative stage in two dry season screens was examined among 128 recombinant inbred lines from four biparental crosses. The genotypic variation detected for drought score in the dry season was used to examine the reliability of the dry season screening method to estimate relative grain yield of genotypes under different types of drought stress in the wet season. Large genotypic variation for drought score existed in two experiments (A and B). However, there was no relationship between the drought scores of genotypes determined in these two experiments. Different patterns of development and severity of drought stress in these two experiments, i.e. slow development and mild plant water deficit in experiment A and fast development and severe plant water deficit in experiment B, were identified as the major factors contributing to the genotypes responding differently. Larger drought score in the dry season experiments was associated with lower grain yield under specific drought stress conditions in the wet season, but the association was weak to moderate and significant only in particular drought conditions. In most cases, a significant phenotypic and moderate genetic correlation between drought score in the dry season and grain yield in the wet season existed only when both drought score and grain yield of genotypes were affected by similar patterns and severity of drought stress in their respective experimental environments. The dry season environments used to measure genotypic variation for drought score should be managed to correspond to relevant types of drought environment that are frequent in the wet season. The efficiency of using the drought score as an indirect selection criterion for improving grain yield for drought conditions was lower than the direct selection for grain yield, and hence wet season screening with grain yield as a selection criterion would be more efficient. However, using drought score as a selection index, a larger number of genotypes can be evaluated than for wet season grain yield. Therefore, it is possible to apply higher selection intensities using the drought score system, and the selected lines can be further tested for grain yield in the wet season. (C) 2004 Elsevier B.V. All rights reserved.

Skeletal muscle and nuclear hormone receptors: Implications for cardiovascular and metabolic disease

Skeletal muscle is a major mass peripheral tissue that accounts for similar to 40% of the total body mass and a major player in energy balance. It accounts for > 30% of energy expenditure, is the primary tissue of insulin stimulated glucose uptake, disposal, and storage. Furthermore, it influences metabolism via modulation of circulating and stored lipid (and cholesterol) flux. Lipid catabolism supplies up to 70% of the energy requirements for resting muscle. However, initial aerobic exercise utilizes stored muscle glycogen but as exercise continues, glucose and stored muscle triglycerides become important energy substrates. Endurance exercise increasingly depends on fatty acid oxidation (and lipid mobilization from other tissues). This underscores the importance of lipid and glucose utilization as an energy source in muscle. Consequently skeletal muscle has a significant role in insulin sensitivity, the blood lipid profile, and obesity. Moreover, caloric excess, obesity and physical inactivity lead to skeletal muscle insulin resistance, a risk factor for the development of type II diabetes. In this context skeletal muscle is an important therapeutic target in the battle against cardiovascular disease, the worlds most serious public health threat. Major risk factors for cardiovascular disease include dyslipidemia, hypertension, obesity, sedentary lifestyle, and diabetes. These risk factors are directly influenced by diet, metabolism and physical activity. Metabolism is largely regulated by nuclear hormone receptors which function as hormone regulated transcription factors that bind DNA and mediate the pathophysiological regulation of gene expression. Metabolism and activity, which directly influence cardiovascular disease risk factors, are primarily driven by skeletal muscle. Recently, many nuclear receptors expressed in skeletal muscle have been shown to improve glucose tolerance, insulin resistance, and dyslipidernia. Skeletal muscle and nuclear receptors are rapidly emerging as critical targets in the battle against cardiovascular disease risk factors. Understanding the function of nuclear receptors in skeletal muscle has enormous pharmacological utility for the treatment of cardiovascular disease. This review focuses on the molecular regulation of metabolism by nuclear receptors in skeletal muscle in the context of dyslipidemia and cardiovascular disease. (c) 2005 Published by Elsevier Ltd.

A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia

Sorghum is the main dryland summer crop in NE Australia and a number of agricultural businesses would benefit from an ability to forecast production likelihood at regional scale. In this study we sought to develop a simple agro-climatic modelling approach for predicting shire (statistical local area) sorghum yield. Actual shire yield data, available for the period 1983-1997 from the Australian Bureau of Statistics, were used to train the model. Shire yield was related to a water stress index (SI) that was derived from the agro-climatic model. The model involved a simple fallow and crop water balance that was driven by climate data available at recording stations within each shire. Parameters defining the soil water holding capacity, maximum number of sowings (MXNS) in any year, planting rainfall requirement, and critical period for stress during the crop cycle were optimised as part of the model fitting procedure. Cross-validated correlations (CVR) ranged from 0.5 to 0.9 at shire scale. When aggregated to regional and national scales, 78-84% of the annual variation in sorghum yield was explained. The model was used to examine trends in sorghum productivity and the approach to using it in an operational forecasting system was outlined. (c) 2005 Elsevier B.V. All rights reserved.