Abstract
Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agricultural Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red-edge normalized difference vegetation index (RENDVI) had the highest correlation (R2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass (Panicum virgatum) on under-productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg·ha−1 showed reduction of tile NO3-N and sediment exports by 15.9%–25.9% and 25%–39%, respectively. Corresponding reductions in water yields ranged from 0.9% to 2.5%. While further research is warranted, the study demonstrated the integration of remote sensing and hydrologic modeling to quantify the multifunctional value of projected future landscape patterns in a context of sustainable bioenergy crop production.
Highlights
Higher corn grain yields due to the expansion of production acreage and to better production efficiencies have led to an increase in availability of corn stover as a readily available feedstock for cellulosic biofuels.Corn stover consists of leaves and stalks of a corn plant (Zea mays L.) left in the field after grain harvesting.there are constraints on how much corn stover can be removed without negatively impacting subsequent soil productivity [1,2,3,4,5]
The regression analysis of 10 Normalized difference indices (NDIs) and five spectral bands indicated that four variables—red and near infrared (NIR) bands, a NDI using red-NIR combination, and a NDI using red-edge-NIR
The regression model was applied to the pansharpened RapidEye image to map predictive crop yield
Summary
Higher corn grain yields due to the expansion of production acreage and to better production efficiencies have led to an increase in availability of corn stover as a readily available feedstock for cellulosic biofuels.Corn stover consists of leaves and stalks of a corn plant (Zea mays L.) left in the field after grain harvesting.there are constraints on how much corn stover can be removed without negatively impacting subsequent soil productivity [1,2,3,4,5]. Corn stover consists of leaves and stalks of a corn plant (Zea mays L.) left in the field after grain harvesting. Thompson and Tyner [6] used a linear programming model to project that at a price of about $66 per metric ton of corn stover, land use in the U.S Midwest could shift from about 12% to 35% continuous corn. Such cost and farmer supply response projections would lead to more fertilizer use because of the nutrient requirements of a continuous corn agricultural system.
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