2012 Annual Report
1a.Objectives (from AD-416):
Our overall goal is to determine the genetic foundation for morphological and compositional characteristics that enhance agronomic performance and quality of maize grain and stover. We will also develop new tools for measuring economically important traits in maize. For the next five-year research cycle we will:
Objective 1: Identify key morphological phenotypes and the underlying genes and/or genetic systems that have contributed to improvement in grain yield and other important agronomic phenotypes in maize.
Objective 2: Develop new tools for evaluating and identifying maize varieties with superior properties for bioenergy production.
Objective 3: Identify key physiological and biochemical phenotypes and the underlying genetic systems that have contributed to improvement in methionine content and bioenergy potential.
1b.Approach (from AD-416):
Objective 1: We will map genomic regions and attempt to identify candidate genes for morphological phenotypes that may have changed in response to selection for agronomic performance in the Iowa Stiff Stalk Synthetic population. We will focus on three morphological phenotypes, silking-anthesis interval, leaf angle, and number of ears per plant. We will determine the magnitude of selection response for these traits in Iowa Stiff Stalk Synthetic population and determine if any of these are correlated to agronomic traits in the base population. We will then use advanced cycles of selection and the base population to generate a genetic mapping experiment to determine how many regions and what magnitude of effects those regions have for the morphological traits. We will use all of the information collected to determine if response to selection for agronomic traits can be explained by indirect response for morphological characters.
Objective 2: One method of production of biofuel from plant material involves fermentation, which is dependent on the production of sugars from the plant material. We will develop methods for screening varieties for their ability to produce sugars in small scale processes that mimic current methods being used for production of ethanol. Sugars will be quantified using bacterial biosensor strains we will develop using bacterial genes known to respond to sugar levels.
Objective 3: We will characterize the genetic mechanism controlling production of methionine by biochemical analysis of populations selected for high and low methionine. In addition, we will determine if different genetic mechanisms controlling methionine levels are complimentary by combining them genetically and determining the methionine levels in the different genetic combinations.
Experiments were carried out to meet project objectives. Field plot experiments were carried out and evaluated for yield and other agronomic traits as well as grain quality traits including methionine and tryptophan content. Breeding programs for grain quality and agronomic performance were advanced in summer and winter breeding nurseries based on results of data from laboratory analysis or field plot experiments. These experiments included reciprocal recurrent selection for agronomic performance, and recurrent selection for methionine, lysine, and phytic acid. Populations to aid in identification of genes underlying agronomic performance grain quality traits were developed. Experiments to generate preliminary data required for preparation of our next project plan were carried out.
Predicting plant density response to increase crop productivity. ARS researchers at Ames, Iowa improved variety performance prediction using plant density responses. Choosing the best performing variety is an important decision for corn producers. However, relative performance among varieties can vary widely among years and locations making it difficult for producers to identify the highest performing variety for their farms. We have discovered that differences among varieties in their responses to varying plant density were relatively constant across environments. Our data suggest that changes in relative performance among varieties are caused by differences among environments in average corn response to plant density. Because corn varieties are compared at constant densities across environments, they are compared near optimum density in some environments and at densities that are too high or too low in other environments, resulting in comparisons being made under highly variable density-stress levels. Because our data suggest that the differences in density response among varieties are constant across environments, these consistent differences in density response could be combined with knowledge of environment-specific density response to vastly improve our ability to predict relative corn-variety performance. This accomplishment provides a tool that will be used to design much more precise evaluations of corn varieties leading to more precise information, better decision making, and increased production.
Hybrid vigor does not improve corn tolerance to high plant density. ARS researchers at Ames, Iowa examined the effect of hybrid vigor on the ability of plants to grow at high density. Plant density response evaluated in populations and population crosses with substantial variation in response to plant density demonstrated that crosses did not have higher grain yields at higher plant densities than corresponding parents. These results demonstrated that hybrid vigor (heterosis) does not improve the tolerance of corn plants to high plant densities, which is a key factor underlying high grain yield. These results demonstrate what types of phenotypes can be selected in parental lines of corn to improve hybrids which substantially reduces cost and time in developing new corn hybrids.
Edwards, J.W. 2011. Changes in plant morphology in response to recurrent selection in the Iowa Stiff Stalk synthetic maize population. Crop Science. 51(6):2352-2361.
Brekke, B.H., Edwards, J.W., Knapp, A. 2011. Selection and adaptation to high plant density in the Iowa Stiff Stalk synthetic maize (Zea mays L.) population. Crop Science. 51(5):1965-1972.
Brekke, B.H., Edwards, J.W., Knapp, A. 2011. Selection and adaptation to high plant density in the Iowa Stiff Stalk synthetic maize (Zea mays L.) population: II. Plant morphology. Crop Science. 51(6):2344-2351.
Asoro, F.G., Newell, M.A., Beavis, W.D., Jannink, J., Scott, M.P. 2011. Accuracy and training population design for genomic selection in elite north american oats. The Plant Genome. 4:132-144.
Scott, M.P., Byrnes, K., Blanco, M.H. 2012. Dry matter and relative sugar yield from enzymatic hydrolysis of maize whole plants and cobs. Plant Breeding. 131(2):286-292.
Schroder, M.N., Floyd, B.E., Scott, M.P. 2011. Maize transgenes containing zein promoters are regulated by opaque2. Crop Science. 51(6):2716-2720.