Modeling the Effect of Temperature and Precipitation on Crop Residue Potential for the North Central Region of the United States
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In an effort to advance fuel security in this era of increasing fuel demand and climate change, crop residue can play an important role by serving as an alternative source of biofuel feedstock. Crop grain yield and residue production are tied to the changing climate over regional and global scale. Precipitation and temperature are among the prime climate variables that drive agricultural production across the globe. This study was carried out to understand the effect of temperature and precipitation on spatial distribution of crop residue yield potential at regional scale. Spatial autoregressive models were fitted for county level crop residue yield potential (as a major potential biomass feedstock) in the north central region of the United States using daily mean temperature and total precipitation during the crop growing season. The results of this observational study found the linear increasing trend in crop residue yield potential in most of the states across north central region of USA. Crop residue potential was also identified to have significant spatial variability. The influences of temperature and precipitation on crop residue yield potential exhibited significant interactions. Positive interaction effects were observed in states including Iowa, North Dakota, and Wisconsin. Negative interaction effects of daily mean temperature and total precipitation were observed in states including Illinois and Indiana. These results emphasize that the availability of crop residues for biofuels feedstocks will be sensitive to climatic variability and that these sensitivities will vary geographically.
KeywordsCrop residue Biomass feedstock Time series data Statistical model Spatial variability Interannual variability
This research was supported by funding from the North Central Regional Sun Grant Center at South Dakota State University through a Grant provided by the US Department of Energy Office of Biomass Programs under Award Number DE-FC36-05GO85041.
- 4.Cressie NAC (1993) Statistics for spatial data. Wiley, New YorkGoogle Scholar
- 6.Kendall MG, Stuart A (1963) The advance theory of statistics. Hafner Publishing, New YorkGoogle Scholar
- 19.U.S. Department of Agriculture–National Agricultural Statistical Service (NASS) (2009). Agricultural statistics data base. http://www.nass.usda.gov
- 20.USDOE–USDA (2002a) The vision for bioenergy and biobased products in the United States. Biomass Technical Advisory Committee. DOE–EERE, Washington, DC. www.biomass.govtools.us/pdfs/BioVision_03_Web.pdf. Accessed 30 July 2006
- 21.USDOE–USDA (2002b) Roadmap for biomass technologies in the United States. Biomass Research and Development Technical Advisory Committee. www.biomass.govttools.us/pdfs/mytp.pdf. Accessed 30 July 2005