E
Research Supporting a Landscape Vision of Production of Biofuel Feedstock

Recent field-scale precision-management studies (Kitchen et al., 2005; Lerch et al., 2005) provide a practical foundation for the panel’s landscape vision of production of biofuel feedstock. In 1991, authors of those studies implemented a corn–soybean rotation in a 14.5-acre field, using mulch tillage to maintain about 30 percent residue cover. Before initiating the study, they characterized the field on the basis of georeferencing and developed an order 1 soil survey,1 a digital elevation model, electromagnetic induction, and soil-fertility maps. They proceeded by mapping crop yield and producing profitability maps in 1993. The studies showed that the greatest yield-limiting factors were soil texture, topsoil depth, and topography (Lerch et al., 2005). All three of those factors influence soil water-holding capacity and within-field water distribution. Diminished topsoil thickness had an adverse effect on profitability (Kitchen et al., 2005) and was directly related to soil loss via erosion. Reduced soil quality is a result of poor physical and chemical characteristics of an underlying argillic claypan horizon that is not well suited for crop-root growth (Lerch et al., 2005). In a market-driven landscape vision of lignocellulosic feedstock production proposed by this panel, the precision-agriculture system devised by Kitchen et al. (2005) serves as a model because their recommendations to improve sustainability were to add more crop types and crop rotations and to use no-tillage practices tailored to specific management areas in the field based on their long-term, georeferenced database.

In another study, Williams et al. (2008) constructed a method based on

1

Order 1 soil surveys are soil inventories produced for very intensive land uses that require detailed information about soils (USDA-NRCS, 2007).



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E Research Supporting a Landscape Vision of Production of Biofuel Feedstock R ecent field-scale precision-management studies (Kitchen et al., 2005; Lerch et al., 2005) provide a practical foundation for the panel’s land- scape vision of production of biofuel feedstock. In 1991, authors of those studies implemented a corn–soybean rotation in a 14.5-acre field, using mulch tillage to maintain about 30 percent residue cover. Before initiating the study, they characterized the field on the basis of georeferencing and developed an order 1 soil survey,1 a digital elevation model, electromagnetic induction, and soil-fertility maps. They proceeded by mapping crop yield and producing profitability maps in 1993. The studies showed that the greatest yield-limiting factors were soil texture, topsoil depth, and topography (Lerch et al., 2005). All three of those factors influ- ence soil water-holding capacity and within-field water distribution. Diminished topsoil thickness had an adverse effect on profitability (Kitchen et al., 2005) and was directly related to soil loss via erosion. Reduced soil quality is a result of poor physical and chemical characteristics of an underlying argillic claypan horizon that is not well suited for crop-root growth (Lerch et al., 2005). In a market-driven landscape vision of lignocellulosic feedstock production proposed by this panel, the precision-agriculture system devised by Kitchen et al. (2005) serves as a model because their recommendations to improve sustainability were to add more crop types and crop rotations and to use no-tillage practices tailored to specific man- agement areas in the field based on their long-term, georeferenced database. In another study, Williams et al. (2008) constructed a method based on 1Order 1 soil surveys are soil inventories produced for very intensive land uses that require detailed information about soils (USDA-NRCS, 2007). 

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 Liquid Transportation Fuels from Coal and Biomass the geographic information system (GIS) to delineate agroecozones and agroeco- regions that were suitable for various crops. Their procedure relied completely on digital databases and was considered more objective than methods that relied at least in part on expert opinion. The resolution of their procedure, however, was 1 km, and that leaves a spatial-resolution gap between their procedure and the approach used by Kitchen et al. (2005) for an individual field. Yan et al. (2007) described a GIS database-driven method similar to that of Williams et al. (2008). Their study, however, was conducted on a single-field scale to delineate zones requiring different management practices for a single crop. To implement a landscape vision of bioenergy feedstock production, informa- tion should be gathered, on scales of at least 1 mile, of current land tenure and community access, drainage patterns, soil-quality status, crop-rotation and crop- distribution patterns, economic conditions, conservation practices, wildlife and human restrictions and concerns, and other pertinent factors. A potential biofuel- production scheme that increases ecosystem services might include establishing woody species (for example, Populus) near streams as buffers and long-term biomass sources. Next, Miscanthus (Miscanthus x giganteus), reed canarygrass (Phalaris arundinacea), eastern gamagrass (Tripsacum dactyloides), or diverse mixtures of these and similar species, could be used at slightly higher landscape positions to benefit from and reduce leaching of nitrate nitrogen and to sequester carbon as soil organic matter. Slightly higher on the landscape, diverse mixtures of warm-season grasses and cool-season legumes could produce biomass and store organic carbon in soils. In fall, the perennials would be a source of biomass and thus address at least three of the landscape problems—biomass production, car- bon sequestration, and water quality. Moving up the landscape, a diversified rota- tion of annual and perennial crops would be used to meet food, feed, and fiber needs. Erosion could be partially mitigated by using cover crops or living mulches. Intensive row-crop production areas could be established by using best manage- ment practices with the awareness that if fertilizer recovery was less than desired, there would be a substantial buffer (lignocellulosic) production area lower on the landscape to capture residual nutrients and sediment. A step-by-step outline of that process is presented below: 1. Identify landscape characteristics by using georeferenced technologies and methods. 2. Identify the landscape’s most important production and conservation issues.

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Appendix E  3. Delineate critical areas that require different crops and management practices. 4. Identify suites of suitable crops, crop rotations, and conservation prac- tices for each management area. 5. Develop a landscape-scale precision-agriculture system. 6. Apply policies, education, and programs that address social and eco- nomic concerns related to the adoption and implementation of the land- scape-scale precision-agriculture systems. 7. Monitor and document the new system’s performance toward produc- tion and conservation goals. 8. Re-evaluate the system and make adaptive changes to improve its performance. In summary, the important message from the above examples and guide- lines is that the technology needed to implement a sustainable landscape vision of biofuel production exists and that the practices can already be implemented effi- ciently and economically. REFERENCES Kitchen, N.R., K.A. Sudduth, D.B. Myers, R.E. Massey, E.J. Sadler, and R.N. Lerch. 2005. Development of a conservation-oriented precision agriculture system: Crop production assessment and plan implementation. Journal of Soil and Water Conservation 60:421- 430. Lerch, R.N., N.R. Kitchen, R.J. Kremer, W.W. Donald, E.E. Alberts, E.J. Sadler, K.A. Sudduth, D.B. Myers, and F. Ghidey. 2005. Development of a conservation oriented precision agriculture system: Water and soil quality assessment. Journal of Soil and Water Conservation 60:411-421. USDA-NRCS (U.S. Department of Agriculture, Natural Resources Conservation Service). 2007. National Soil Survey Handbook, Title 430-VI. Washington, D.C.: USDA-NRCS. Williams, C.L., W.W. Hargrove, M. Liebman, and D.E. James. 2008. Agroecoregionalization of Iowa using multivariate geographical clustering. Agriculture, Ecosystem and Environment 123:161-174. Yan, L., S. Zhou, L. Feng, and L. Hong-Yi. 2007. Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline soil. Computers and Electronics Agriculture 56:174-186.

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