Estimating daily green leaf area index for corn in Virginia
Ebodaghe, Denis Abumere
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A model to predict the daily green leaf area index (GLAI) for corn has been developed for Indiana conditions. Using daily maximum and minimum temperatures the GLAI was predicted for the vegetative stage, reproductive and grain filling stage, and the leaf senescing stage of corn. Predictions of GLAI for corn can be made on a daily basis from the day corn is planted until it is harvested for grain. The GLAI model was tested under Virginia conditions using green leaf area measurements collected from corn plants grown on Davidson silty clay loam, Davidson silty clay, and Mayodan sandy loam soils in the Piedmont region of the State. Maximum and minimum temperature data were also collected at the three sites. Measurements were made for two growing seasons using corn hybrid Pioneer 3369A, three plant population densities and two irrigation schedules. Short duration temperature data were also collected to compare with the daily maximum and minimum temperature data for the Mayodan site. Also a combination of soil temperature at 10 cm depth and air temperatures were used for the temperature functions accumulated from date of planting at the Mayodan site. Results of this study show that the predicted and measured GLAI values compare favorably under irrigated conditions on the Davidson soil. The results were not as favorable on the irrigated corn on the Mayodan soil. When the corn is subjected to severe moisture stress on either soil, GLAI cannot be predicted with this model. Short duration temperature data resulted in a better prediction of GLAI on the Mayodan soil. When applying nitrogen fertilizer to the corn through the irrigation system through the grain filling stage, the measured GLAI values compared favorably with the predicted GLAI values. However, the application of nitrogen and sulfur fertilizer together resulted in GLAI being maintained above that predicted for a longer period of time during the grain filling stage before its decline.
- Doctoral Dissertations