Year of Publication

2010

Degree Name

Doctor of Philosophy (PhD)

Document Type

Dissertation

College

Agriculture

Department

Soil Science

First Advisor

Dr. Ole O. Wendroth

Abstract

The study was aimed to provide information on local biomass development during crop growth using ground based optical sensors and to incorporate the local crop status to a crop growth simulation model to improve understanding on inherent variability of crop field. The experiment was conducted in a farmer’s field located near Princeton in Caldwell County, Western Kentucky. Data collection on soil, crop and weather variables was carried out in the farm from 2006 December to 2008 October. During this period corn (Zea mays L.) and winter wheat (Triticum sp) were grown in the field. A 450 m long representative transect across the field consisting of 45 locations each separated by 10 m was selected for the study. Soil water content was measured in a biweekly interval during crop growth from these locations. Measurements on crop growth parameters such as plant height, tiller count, biomass and grain yield were able to show spatial variability in crop biomass and grain yield production. Crop reflectance measured at important crop growth stages. Soil water sensing capacitance probe was site specifically calibrated for each soil depth in each location. Various vegetation indices were calculated as proxy variables of crop growth. Inherent soil properties such as soil texture and elevation were found playing a major role in influencing spatial variability in crop yield mainly by affecting soil water storage. Temporal persistence of spatial patterns in soil water storage was not observed. Optimum spatial correlation structure was observed between crop growth parameters and optical sensor measurements collected early in the season and aggregated at 2*2 m2 sampling area. NDVI, soil texture, soil water storage and different crop growth parameters were helpful in explaining the spatial processes that influence grain yield and biomass using state space analysis. DSSAT was fairly sensitive to reflect site specific inputs on soil variability in crop production.

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