Luis A.N. Aguirrezábal

Effect of growth temperature on the high stearic and high stearic-high oleic sunflower traits

Abstract. We investigated variability in the response of oil fatty acid composition to temperature among high stearic and high stearic-high oleic sunflower (Helianthus annuus L.) genotypes. Two experiments were conducted with high stearic (including the CAS-3 mutation) and high stearic-high oleic inbred lines (including both the CAS-3 and the high oleic Soldatov mutations). Plants were cultivated in pots with soil, irrigated, and fertilised. Plants were exposed to different day/night temperatures during grain filling: 16/16°C, 26/16°C, 26/26°C, and 32/26°C. Oil fatty acid composition was determined by gas–liquid chromatography in seeds harvested after physiological maturity. Higher temperature during grain filling increased palmitic and oleic acid percentages and reduced stearic and linoleic acid percentages, suggesting some modifications on enzymatic activities. When the high oleic mutation was included, the variation in stearic and oleic acid percentages in response to temperature was reduced but not the variation in palmitic acid concentration. Variations in fatty acid composition in high stearic genotypes were mainly associated with night temperature as reported previously for traditional and high oleic hybrids. Knowing the effect of temperature on oil fatty acid composition in traditional and mutated genotypes is useful for selecting the environment in which to produce grains with the desired oil quality.

Oil yield components and oil quality of high stearic-high oleic sunflower genotypes as affected by intercepted solar radiation during grain filling

Abstract. High stearic-high oleic sunflower oil presents high thermal stability. This oil is an alternative to the hydrogenation process which produces trans fatty acids. The effect of intercepted solar radiation (ISR) per plant during grain filling on oil yield components and oil fatty acid composition was investigated in three sunflower high stearic-high oleic genotypes. Three field experiments were conducted and treatments to modify ISR per plant were applied during grain filling: shading, defoliating and thinning plants. Increasing ISR per plant linearly increased grain number per capitulum, weight per grain and in some cases palmitic and stearic acid percentages. In the hybrid, grain oil percentage and oleic acid concentration increased with a decreasing rate, reaching a maximum value at high levels of ISR per plant. Linoleic acid percentage decreased with a decreasing rate, reaching a minimum value at high levels of ISR per plant. Oil yield components presented heterosis. This information contributes to explain the effects of environment on yield and oil quality in high stearic-high oleic genotypes and could be used to design management practices that optimise these traits.

Sunflower Crop Physiology and Agronomy

Publisher Summary Agronomy is the science and technology of producing and using plants for food, fuel, and fiber. Crop physiology studies the structure and function of crops in relation to productivity and quality product for different uses. This chapter discusses physiology and agronomy together of sunflower crop, giving the basis for the crop management and genetic improvement of sunflower from a physiological point of view. It also discusses some differences among the physiology of sunflower and other crops. Applying ecophysiological knowledge could be helpful to optimize sunflower crop management to obtain high yields and high oil quality; to save soil water and nutrients; and to reduce the application of chemical products. It could be expected that sunflower production and crop system sustainability could be improved by a greater adoption of precision agriculture. Information systems could help to take into account the spatial heterogeneity of soil and crop properties as a decision support for process optimization. Adjustments of sowing date, plant population, and row spacing, as well as the selection of hybrid cycle are important agronomical practices to improve the performance of rain-fed sunflower crops when sown in environments with middle and high probabilities of facing drought periods.

Kinetic Modeling of Sunflower Grain Filling and Fatty Acid Biosynthesis

Grain growth and oil biosynthesis are complex processes that involve various enzymes placed in different sub-cellular compartments of the grain. In order to understand the mechanisms controlling grain weight and composition, we need mathematical models capable of simulating the dynamic behavior of the main components of the grain during the grain filling stage. In this paper, we present a non-structured mechanistic kinetic model developed for sunflower grains. The model was first calibrated for sunflower hybrid ACA855. The calibrated model was able to predict the theoretical amount of carbohydrate equivalents allocated to the grain, grain growth and the dynamics of the oil and non-oil fraction, while considering maintenance requirements and leaf senescence. Incorporating into the model the serial-parallel nature of fatty acid biosynthesis permitted a good representation of the kinetics of palmitic, stearic, oleic, and linoleic acids production. A sensitivity analysis showed that the relative influence of input parameters changed along grain development. Grain growth was mostly affected by the specific growth parameter (μ′) while fatty acid composition strongly depended on their own maximum specific rate parameters. The model was successfully applied to two additional hybrids (MG2 and DK3820). The proposed model can be the first building block toward the development of a more sophisticated model, capable of predicting the effects of environmental conditions on grain weight and composition, in a comprehensive and quantitative way.

Assessing the efficiency of phenotyping early traits in a greenhouse automated platform for predicting drought tolerance of soybean in the field

Conventional field phenotyping for drought tolerance, the most important factor limiting yield at a global scale, is labor-intensive and time-consuming. Automated greenhouse platforms can increase the precision and throughput of plant phenotyping and contribute to a faster release of drought tolerant varieties. The aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform, for predicting the drought tolerance of field grown soybean genotypes. A group of genotypes was evaluated, which showed variation in their drought susceptibility index (DSI) for final biomass and leaf area. A large number of traits were measured before and after the onset of a water deficit treatment, which were analyzed under several criteria: the significance of the regression with the DSI, phenotyping cost, earliness, and repeatability. The most efficient trait was found to be transpiration efficiency measured at 13 days after emergence. This trait was further tested in a second experiment with different water deficit intensities, and validated using a different set of genotypes against field data from a trial network in a third experiment. The framework applied in this work for assessing traits under different criteria could be helpful for selecting those most efficient for automated phenotyping.