Improving the accuracy of kernel set simulation in hybrid seed production

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2005-01-01
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Schneider, Esteban
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Agronomy

The Department of Agronomy seeks to teach the study of the farm-field, its crops, and its science and management. It originally consisted of three sub-departments to do this: Soils, Farm-Crops, and Agricultural Engineering (which became its own department in 1907). Today, the department teaches crop sciences and breeding, soil sciences, meteorology, agroecology, and biotechnology.

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The Department of Agronomy was formed in 1902. From 1917 to 1935 it was known as the Department of Farm Crops and Soils.

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1902–present

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  • Department of Farm Crops and Soils (1917–1935)

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Agronomy
Abstract

Maize (Zea mays L.) is a monoecious plant, having separate staminate and pistillate flowers. Staminate flowers are located at the end of the stem while pistillate flowers arise from nodes located around the mid part of the plant. Because of this anatomical arrangement, cross pollination is the normal for maize to reproduce. Typically male and female parents are crossed to produce hybrid seed. It has been reported that maize pollination is a predictable process that can be quantified and used for modeling kernel set under limited pollen conditions. Simulation models that use flowering characteristics to predict kernel set are an important tool for improving inbred management in seed corn production fields. However, the time needed to generate is an impediment to adoption. It has also been reported that these models overestimate actual kernel number. Typical growing conditions in hybrid seed production define an environment where source and sink limitations occur concurrently, the combination of genotype and growing condition will determine which limitation predominates. The accuracy of kernel set simulations could be improved using a plant growth model that incorporates both sink and source limitations for kernel set. Chapter 2 provides a novel technique for estimating the dynamics of silk exsertion in maize. Chapter 3 extends the use of this technique to determine the minimum number of measurements needed to quantify silking dynamics reliably. Chapter 4 presents possible causes behind differential kernel set across the female block in seed corn production fields. Chapter 5 validates an experimental model that integrates both source and sink limitations to predict kernel set.

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Sat Jan 01 00:00:00 UTC 2005