Skip to main content
SpringerLink
Log in

Systems Informatics and Analysis

  • Chapter
  • First Online:
Engineering and Science of Biomass Feedstock Production and Provision

Abstract

Various biomass feedstock production and provision (BFPP) tasks discussed in earlier chapters are highly interconnected. Design and operational decisions for any task impact decisions for most other tasks. In view of such complex interactions, it is critical that we also look beyond an individual task and focus on the techno-economic feasibility of the complete production and provision system. This calls for a holistic view of the BFPP system. Systems theory based approaches that integrate systems informatics and analysis methods are ideally suited to achieve this objective. This chapter reviews the literature on the application of such approaches for BFPP. The basics of informatics, modeling and analysis, and decision support are first discussed. Then their applications for different system classes, namely, crop growth and management systems, on-farm production systems, local biomass provision systems, and regional/national/global systems, are presented. The literature review illustrated that applications of the systems-based tools at the crop growth, establishment, and management levels as well as the local biomass provision level have been numerous. Many of these developments have built on tools already existing for conventional crops. Systems theory applications to the on-farm production scale have been limited, possibly due to lack of field study data as well as limited commercial farming. In contrast, interest in studying the regional/national/global systems has increased in recent years. We conclude that greater efforts are needed to validate these tools and to study issues cutting across multiple scales. We also recommend that seamless integration of informatics, analysis, and decision support tools is necessary to achieve a truly concurrent science, engineering, and technology-based platform for decision making in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shastri YN, Hansen AC, Rodriguez LF, Ting KC (2010) Optimization of Miscanthus harvesting and handling as an energy crop: BioFeed model application. Biol Eng Trans 3(1):37–69

    CAS  Google Scholar 

  2. Von Bertalanffy L (1969) General system theory: foundations, development, applications. G. Braziller, New York, NY

    Google Scholar 

  3. Nygaard K (1986) Basic concepts in object oriented programming. In: Proceedings of the 1986 SIGPLAN workshop on Object-oriented programming, New York, NY

    Google Scholar 

  4. Newman BD, Conrad KW (2000) A framework for characterizing knowledge management methods, practices, and technologies. In: Proceedings of the third international conference on practical aspects of knowledge management (PAKM2000), October 30–31, Basel, Switzerland

    Google Scholar 

  5. Smith PG (2007) Concurrent product-development teams. In: Cleland DI (ed) Field guide to project management. Wiley, Hoboken, NJ, pp 594–608

    Google Scholar 

  6. Ting KC, Fleisher DH, Rodriguez LF (2003) Concurrent science and engineering for phytomation system. J Agric Meteorol 59(2):93–101

    Google Scholar 

  7. Finkelstein A, Kramer J (2000) Software engineering: a roadmap. In: Proceedings of the conference on The future of Software engineering, ACM Press, New York, NY, p 4–11

    Google Scholar 

  8. Liao Y, Rodriguez LF, Shastri Y, Hansen AC, Ting KC (2011) Concurrent science, engineering and technology (ConSEnT) for biomass feedstock production decision support; August 2011. American Society of Agricultural and Biological Engineers, St. Joseph, MI

    Google Scholar 

  9. Sokhansanj S, Kumar A, Turhollow A (2006) Development and implementation of integrated biomass supply analysis and logistics model (IBSAL). Biomass Bioenergy 30:838–847

    Google Scholar 

  10. Keating B, Carberry PH, Hammer GL, Probert M, Robertson M, Holzworth D, Huth N et al (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18:267–288

    Google Scholar 

  11. Modell ME (1998) A professional’s guide to systems analysis. McGraw-Hill, New York, NY

    Google Scholar 

  12. Epplin F (1996) Cost to produce and deliver switchgrass biomass to an ethanol-conversion facility in the southern plains of the United States. Biomass Bioenergy 11(6):459–467

    Google Scholar 

  13. Huisman W, Venturi G, Molenaar J (1997) Cost of supply chain for Miscanthus × Giganteus. Ind Crop Prod 6:353–366

    Google Scholar 

  14. Styles D, Thorne F, Jones MB (2008) Energy crops in Ireland: an economic comparison of willow and Miscanthus production with conventional farming systems. Biomass Bioenergy 32(5):407–421

    Google Scholar 

  15. Khanna M, Dhungana B, Clifton-Brown J (2008) Costs of producing Miscanthus and switchgrass for bioenergy in Illinois. Biomass Bioenergy 32:282–293

    Google Scholar 

  16. van Ouweker EN, James DE, Richard TL, Liebman M (2003) A multi-model approach for sustainable agriculture in the US corn belt. In: Proceedings of the 2003 ASAE annual international meeting, July, Las Vegas, NV

    Google Scholar 

  17. Shastri YN, Hansen AC, Rodriguez LF, Ting KC (2011) Development and application of BioFeed model for optimization of herbaceous biomass feedstock production. Biomass Bioenergy 35(7):2961–2974

    Google Scholar 

  18. Shim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C (2002) Past, present, and future of decision support technology. Decis Support Syst 33(2):111–126

    Google Scholar 

  19. Fabbi KP (1998) A methodology for supporting decision making in integrated coastal zone management. J Ocean Coast Manag 39(1–2):51–62

    Google Scholar 

  20. van Ouweker EN (ed) (2008) I-FARM—a tool for farmers and decision-makers. Iowa State University, Ames, IA

    Google Scholar 

  21. Humphris S, Long S (1995) WIMOVAC: a software package for modeling the dynamics of plant leaf and canopy photosynthesis. Comput Appl Biosci 11(4):361–371

    Google Scholar 

  22. Cushman JH, Easterly JL, Erbach DC et al (eds) (2003) Roadmap for agriculture biomass feedstock supply in the United States. U.S. Department of Energy, Energy Efficiency and Renewable Energy. U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Biomass Program, Washington, DC

    Google Scholar 

  23. Lowrance R, Hendrix PF, Odum EP (1984) A hierarchical approach to sustainable agriculture. Am J Altern Agric 1(4):169–173

    Google Scholar 

  24. Kropff MJ, Bouma J, Jones JW (2001) Systems approaches for the design of sustainable agro-ecosystems. Agric Syst 70(2–3):369–393

    Google Scholar 

  25. Surendran Nair S, Kang S, Zhang X, Miguez FE, Izaurralde RC, Post WM et al (2012) Bioenergy crop models: descriptions, data requirements, and future challenges. GCB Bioenergy 4(6):620–633

    Google Scholar 

  26. Miguez F, Dietze M, Kemanian A (2011) Modeling tools and strategies for developing sustainable feedstock supplies. Sustainable feedstocks for advanced biofuels: sustainable alternative fuel feedstock opportunities, challenges and roadmaps for six U.S. regions. Soil and Water Conservation Society, Ankeny, IA

    Google Scholar 

  27. Grassini P, Hunt E, Mitchell RB, Weiss A (2009) Simulating switchgrass growth and development under potential and water-limiting conditions. Agron J 101:564–571

    Google Scholar 

  28. Jager HI, Baskaran LM, Brandt CC, Davis EB, Gunderson CA, Wullschleger SD (2010) Empirical geographic modeling of switchgrass yields in the United States. GCB Bioenergy 2(5):248–257

    Google Scholar 

  29. Williams JR (1990) The erosion-productivity impact calculator (EPIC) model: a case history. Philos Trans Biol Sci 329(1255):421–428

    Google Scholar 

  30. Brown RA, Rosenberg NJ, Hays CJ, Easterling WE, Mearns LO (2000) Potential production and environmental effects of switchgrass and traditional crops under current and greenhouse-altered climate in the central United States: a simulation study. Agric Ecosyst Environ 78(1):31–47

    Google Scholar 

  31. Kang S, Nair SS, Kline KL, Nichols JA, Wang D, Post WM et al (2014) Global simulation of bioenergy crop productivity: analytical framework and case study for switchgrass. GCB Bioenergy 6(1):14–25

    Google Scholar 

  32. Kiniry J, Williams J, Gassman P, Debaeke P (1992) A general, process-oriented model for two competing plant species. Trans ASAE 35(3):801–810

    Google Scholar 

  33. Williams J, Jones C, Kiniry J, Spanel DA (1989) The EPIC crop growth model. Trans ASAE 32(2):497–511

    Google Scholar 

  34. Kiniry J, Sanderson M, Williams JR, Tischler C, Hussey M, Ocumpaugh W et al (1996) Simulating Alamo Switchgrass with the ALMANAC model. Agron J 88(4):602–606

    Google Scholar 

  35. Kiniry JR, Tischler CR, Van Esbroeck GA (1999) Radiation use efficiency and leaf CO2 exchange for diverse C4 grasses. Biomass Bioenergy 17:95–112

    Google Scholar 

  36. Kiniry JR, Cassida KA, Hussey MA, Muir JP, Ocumpaugh WR, Read JC et al (2005) Switchgrass simulation by the ALMANAC model at diverse sites in the southern US. Biomass Bioenergy 29(6):419–425

    Google Scholar 

  37. McLaughlin S, Kiniry J, Taliaferro C, De La Torre Ugarte D (2006) Projecting yield and utilization potential of switchgrass as energy crop. Adv Agron 90:267–296

    Google Scholar 

  38. Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50(4):1211–1250

    CAS  Google Scholar 

  39. Kim H, Parajuli PB (2012) Economic analysis using SWAT-simulated potential switchgrass and miscanthus yields in the Yazoo river basin. Trans ASABE 55(6):2123–2134

    Google Scholar 

  40. Nelson RG, Ascough JC II, Langemeier MR (2006) Environmental and economic analysis of switchgrass production for water quality improvement in northeast Kansas. J Environ Manage 79(4):336–347

    CAS  PubMed  Google Scholar 

  41. Baskaran L, Jager HI, Schweizer PE, Srinivasan R (2010) Progress toward evaluating the sustainability of switchgrass as a bioenergy crop using the SWAT Model. Trans ASABE 53(5):1547–1556

    Google Scholar 

  42. Sarkar S, Miller SA, Frederick JR, Chamberlain JF (2011) Modeling nitrogen loss from switchgrass agricultural systems. Biomass Bioenergy 35(10):4381–4389

    CAS  Google Scholar 

  43. Clifton-Brown J, Stampfel PF, Jones M (2004) Miscanthus biomass production for energy in Europe and its potential contribution to decreasing fossil fuel carbon emissions. Glob Chang Biol 10:509–518

    Google Scholar 

  44. Clifton-Brown J, Neilson B, Lewandowski I, Jones M (2000) The modeled productivity of Miscanthus × giganteus (GREEF et DEU) in Ireland. Ind Crop Prod 12:97–109

    Google Scholar 

  45. Parton WJ, McKeown B, Kirchner V, Ojima D (eds) (1992) CENTURY User’s manual. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins

    Google Scholar 

  46. Parton W, Hartman M, Ojima D, Schimel D (1998) DAYCENT: its land surface submodel: description and testing. Glob Planet Chang 19:35–48

    Google Scholar 

  47. Lee J, Pedroso G, Linquist BA, Putnam D, van Kessel C, Six J (2012) Simulating switchgrass biomass production across ecoregions using the DAYCENT model. GCB Bioenergy 4(5):521–533

    Google Scholar 

  48. Miguez FE, Zhu X, Humphries S, Bollero GA, Long SP (2009) A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus × giganteus: description, parameterization and validation. GCB Bioenergy 1(4):282–296

    Google Scholar 

  49. Miguez FE, Maughan M, Bollero GA, Long SP (2012) Modeling spatial and dynamic variation in growth, yield, and yield stability of the bioenergy crops Miscanthus × giganteus and Panicum virgatum across the conterminous United States. Glob Chang Biol Bioenergy 4(5):509–520

    Google Scholar 

  50. Feyereisen GW, Wilson BN, Sands GR, Strock JS, Porter PM (2006) Potential for a rye cover crop to reduce nitrate loss in Southwestern Minnesota. Agron J 98(6):1416–1426

    CAS  Google Scholar 

  51. Feyereisen GW, Camargo GGT, Baxter RE, Baker JM, Richard TL (2013) Cellulosic biofuel potential of a winter rye double crop across the U.S. Corn-Soybean Belt. Agron J 105(3):631–642

    Google Scholar 

  52. Wullschleger SD, Davis EB, Borsuk ME, Gunderson C, Lynd LR (2010) Biomass production in switchgrass across the United States: database description and determinants of yield. Agron J 102:1158–1168

    Google Scholar 

  53. Jain A, Khanna M, Erickson M, Huang H (2010) An integrated biogeochemical and economic analysis of bioenergy crops in the Midwestern United States. Glob Chang Biol Bioenergy 2(5):217–234

    Google Scholar 

  54. Adler PR, Del Grosso SJ, Parton WJ (2007) Life-cycle assessment of net greenhouse-gas flux for bioenergy cropping systems. Ecol Appl 17(3):675–691

    PubMed  Google Scholar 

  55. Bastiaans L, Kropff MJ, Kempuchetty N, Rajan A, Migo TR (1997) Can simulation models help design rice cultivars that are more competitive against weeds? Field Crop Res 51(1–2):101–111

    Google Scholar 

  56. Burrows WC, Siemens JC (1974) Determination of optimum machinery for corn-soybean farms. Trans ASABE 17(6):1130–1135

    Google Scholar 

  57. Lal H, Jones JW, Peart RM, Shoup WD (1992) FARMSYS–-a whole-farm machinery management decision support system. Agric Syst 38:257–273

    Google Scholar 

  58. Sogaard HT, Sorensen CG (2004) A model for optimal selection of machinery sizes within the farm machinery system. Biosyst Eng 89(1):13–28

    Google Scholar 

  59. Glen JJ (1987) Mathematical model in farm planning: a survey. Oper Res 35(5):641–666

    Google Scholar 

  60. Francis ME (1996) Cost to produce and deliver switchgrass biomass to an ethanol-conversion facility in the southern plains of the United States. Biomass Bioenergy 11(6):459–467

    Google Scholar 

  61. Duffy M (ed) (2007) Estimated costs for production, storage and transportation of switchgrass. Iowa State University—University Extension, Ames, IA

    Google Scholar 

  62. Schnitkey G (ed) (2003) Estimated costs of crop production in Illinois, 2003. Department of Agricultural Consumer and Economics, University of Illinois, Urbana, IL: Farm Business Management, University of Illinois

    Google Scholar 

  63. Cundiff JS, Grisso RD, McCullough D (2011) Comparison of baler operations for smaller production fields in the southeast. ASABE Annual International Meeting 2011, Paper Number: 1110922, American Society of Agricultural and Biological Engineers, St. Joseph, MI

    Google Scholar 

  64. Venturi P, Gigler J, Huisman W (1999) Economic and technical comparison between herbaceous (Miscanthus × Giganteus) and woody energy crops (Salix Viminalis). Renew Energy 16:1023–1026

    Google Scholar 

  65. Venturi P, Huisman W, Molenaar J (1998) Mechanization and costs of primary production chains for Miscanthus × Giganteus in The Netherlands. J Agric Eng Res 69:209–215

    Google Scholar 

  66. Camargo GGT, Ryan MR, Richard TL (2013) Energy use and greenhouse gas emissions from crop production using the farm energy analysis tool. Bioscience 63(4):263–273

    Google Scholar 

  67. Rotz CA, Corson MS, Coiner CU (eds) (2007) The integrated farm system model. Pasture Systems and Watershed Management Research Unit, Agricultural Research Service, United States Department of Agriculture, Washington DC

    Google Scholar 

  68. Matthew TW (2011) The economics and logistics of the dual harvest of grain and biomass in a single-pass. MS thesis, University of Nebraska, Lincoln, NE

    Google Scholar 

  69. Bochtis DD, Dogoulis P, Busato P, Sørensen CG, Berruto R, Gemtos T (2013) A flow-shop problem formulation of biomass handling operations scheduling. Comput Electron Agric 91:49–56

    Google Scholar 

  70. Hwang S, Epplin FM, Lee B, Huhnke R (2009) A probabilistic estimate of the frequency of mowing and baling days available in Oklahoma USA for the harvest of switchgrass for use in biorefineries. Biomass Bioenergy 33(8):1037–1045

    Google Scholar 

  71. Smeets EM, Lewandowski IM, Faaji AP (2009) The economical and environmental performance of Miscanthus and switchgrass production and supply chains in a European setting. Renew Sust Energ Rev 13(6–7):1230–1245

    Google Scholar 

  72. Hess JR, Wright CT, Kenney KL (2007) Cellulosic biomass feedstocks and logistics for ethanol production. Biofuels Bioprod Biorefin 1(3):181–190

    CAS  Google Scholar 

  73. Kumar A, Sokhansanj S (2007) Switchgrass (Panicum vigratum, L.) delivery to a biorefinery using integrated biomass supply analysis and logistics (IBSAL) model. Bioresour Technol 98(5):1033–1044

    CAS  PubMed  Google Scholar 

  74. Sokhansanj S, Mani S, Turhollow A, Kumar A, Bransby D, Lynd L et al (2009) Large-scale production, harvest and logistics of switchgrass (Panicum virgatum L)—current technology and envisioning a mature technology. Biofuels Bioprod Biorefin 3:124–141

    CAS  Google Scholar 

  75. Ravula P, Grisso R, Cundiff J (2008) Comparison between two policy strategies for scheduling trucks in a biomass logistic system. Bioresour Technol 99:5710–5721

    CAS  PubMed  Google Scholar 

  76. Ravula PP, Grisso RD, Cundiff JS (2008) Cotton logistics as a model for a biomass transportation system. Biomass Bioenergy 32(4):314–325

    Google Scholar 

  77. Mukunda A, Ileleji KE, Wan H (2006) Simulation of corn stover logistics from on-farm storage to an ethanol plant. ASABE International Meeting 2006, Paper Number: 066177, American Society of Agricultural and Biological Engineers, St. Joseph, MI

    Google Scholar 

  78. Soldatos PG, Lychnaras V, Asimakis D, Christou M (2004) Bee—biomass economic evaluation: a model for the economic analysis of biomass cultivation. In: Second world conference and technology exhibition on biomass for energy, industry and climate protection, 10–14 May

    Google Scholar 

  79. Monti A, Fazio S, Lychnaras V, Soldatos P, Venturi G (2007) A full economic analysis of switchgrass under different scenarios in Italy estimated by BEE model. Biomass Bioenergy 31(4):177–185

    Google Scholar 

  80. De Mol R, Jogems M, Van Beek P, Gigler J (1997) Simulation and optimization of the logistics of biomass fuel collection. Neth J Agric Sci 45:219–228

    Google Scholar 

  81. Turhollow A, Sokhansanj S (2007) Costs of harvesting, storing in a large pile, and transporting corn stover in a wet form. Appl Eng Agric 23(4):439–448

    Google Scholar 

  82. Nilsson D (2000) Dynamic simulation of straw harvesting systems: influence of climatic, geographical and biological factors on performance and costs. J Agric Eng Res 76(1):27–36

    Google Scholar 

  83. Jenkins BM, Arthur JF (1983) Assessing biomass utilization options through network analysis. Trans ASABE 26(5):1557–1560

    Google Scholar 

  84. Grado SC, Strauss CH (1995) Using an inventory control model to establish biomass harvesting policies. Sol Energy 54(1):3–11

    Google Scholar 

  85. Cundiff J, Dias N, Sherali HD (1997) A linear programming approach for designing a herbaceous biomass delivery system. Bioresour Technol 59:47–55

    CAS  Google Scholar 

  86. Cundiff J (1996) Simulation of five large round bale harvesting systems for biomass. Bioresour Technol 56:77–82

    CAS  Google Scholar 

  87. Judd JD, Sarin SC, Cundiff JS (2012) Design, modeling, and analysis of a feedstock logistics system. Bioresour Technol 103(1):209–218

    CAS  PubMed  Google Scholar 

  88. Shastri YN, Rodriguez LF, Hansen AC, Ting KC (2012) Impact of distributed storage and pre-processing on Miscanthus production and provision systems. Biofuels Bioprod Biorefin 6(1):21–31

    CAS  Google Scholar 

  89. Shastri YN, Hansen AC, Rodriguez LF, Ting KC (2012) Impact of probability of working day on planning and operation of biomass feedstock production systems. Biofuels Bioprod Biorefin 6(3):281–291

    CAS  Google Scholar 

  90. Lin T, Rodriguez LF, Shastri YN, Hansen AC, Ting KC (2013) GIS-enabled biomass-ethanol supply chain optimization: model development and Miscanthus application. Biofuels Bioprod Biorefin 7(3):314–333

    CAS  Google Scholar 

  91. Leboreiro J, Hilaly AK (2011) Biomass transportation model and optimum plant size for the production of ethanol. Bioresour Technol 102:2712–2723

    CAS  PubMed  Google Scholar 

  92. Zhu X, Li X, Yao Q, Chen Y (2011) Challenges and models in supporting logistics system design for dedicated-biomass-based bioenergy industry. Bioresour Technol 102(2):1344–1351

    CAS  PubMed  Google Scholar 

  93. Zhu X, Yao Q (2011) Logistics system design for biomass-to-bioenergy industry with multiple types of feedstocks. Bioresour Technol 102(23):10936–10945

    CAS  PubMed  Google Scholar 

  94. Sultana A, Kumar A (2011) Optimal configuration and combination of multiple lignocellulosic biomass feedstocks delivery to a biorefinery. Bioresour Technol 102(21):9947–9956

    CAS  PubMed  Google Scholar 

  95. Zuo M, Kuo W, McRoberts KL (1991) Application of mathematical programming to a large-scale agricultural production and distribution system. J Oper Res Soc 42(8):639–648

    Google Scholar 

  96. Mapemba LD, Epplin FM, Huhnke RL, Taliaferro CM (2008) Herbaceous plant biomass harvest and delivery cost with harvest segmented month and number of harvest machines endogenously determined. Biomass Bioenergy 32:1016–1027

    Google Scholar 

  97. An H, Wilhelm WE, Searcy SW (2011) A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas. Bioresour Technol 102(17):7860–7870

    CAS  PubMed  Google Scholar 

  98. Kim J, Realff MJ, Lee JH, Whittaker C, Furtner L (2011) Design of biomass processing network for biofuel production using an MILP model. Biomass Bioenergy 35(2):853–871

    CAS  Google Scholar 

  99. Kim J, Realff MJ, Lee JH (2011) Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Comput Chem Eng 35(9):1738–1751

    CAS  Google Scholar 

  100. Happe K, Kellermann K, Balmann A (2006) Agent-based analysis of agricultural policies: an Illustration of the Agricultural Policy Simulator AgriPoliS, its adaptation and behavior. Ecol Soc 11(1):49

    Google Scholar 

  101. Kempener R, Kaufmann P, Stagl S, Stirling A, Perez K (eds) (2009) The role of agricultural diversity and rural sustainable development: a dynamic systems approach. Austrian Institute for Regional Studies and Spatial Planning, Vienna, Austria

    Google Scholar 

  102. Kempener R, Beck J, Petrie J (2009) Design and analysis of bioenergy networks: a complex adaptive systems approach. J Ind Ecol 13(2):284–305

    Google Scholar 

  103. Shastri YN, Rodriguez LF, Hansen AC, Ting KC (2011) Agent-based analysis of biomass feedstock production system dynamics. Bioenergy Res 4(4):258–275

    Google Scholar 

  104. Shastri YN, Hansen AC, Rodriguez LF, Ting KC (2013) Advances in systems informatics and analysis of biomass feedstock production for bioenergy. Pertanika J Sci Technol 21(1):273–280

    Google Scholar 

  105. Domdouzis K, Rodriguez LF, Shastri YN, Hu M, Hansen AC, Ting KC (2009) Systems informatics for biomass feedstock production engineering. ASABE Annual Meeting 2009, Paper Number: 096702, American Society of Agricultural and Biological Engineers, St. Joseph, MI

    Google Scholar 

  106. Domdouzis K, Rodriguez LF, Hansen AC, Ting KC (2010) Development of an Application Programming Interface (API) for biomass feedstock production engineering; June 2010. American Society of Agricultural and Biological Engineers, St. Joseph, MI

    Google Scholar 

  107. Liao YC (2011) Concurrent science, engineering and technology (ConSEnT) for biomass feedstock production decision support. MS thesis, University of Illinois, Urbana-Champaign, IL

    Google Scholar 

  108. Dunnett AJ, Adjiman CS, Shah N (2008) A spatially explicit whole-system model of the lignocellulosic bioethanol supply chain: an assessment of decentralised processing potential. Biotechnol Biofuels 1:13

    PubMed Central  PubMed  Google Scholar 

  109. De La Torre Ugarte, Daniel G, Ray D (2000) Biomass and bioenergy applications of the POLYSYS modeling framework. Biomass Bioenergy 18:291–301

    Google Scholar 

  110. Walsh ME, De La Torre Ugarte DG, Shapouri H, Slinsky SP (2003) Bioenergy crop production in the United States: potential quantities, land use changes, and economic impacts on the agricultural sector. Environ Resour Econ 24(4):313–333

    Google Scholar 

  111. Kszos L, McLaughlin SB, Walsh M (2002) Bioenergy from Switchgrass: reducing production costs improving yield and optimizing crop management. Biomass Research and Development Board’s Bioenergy 2002—Bioenergy for the Environment Conference: report

    Google Scholar 

  112. Khanna M, Chen X, Huang H, Önal H (2011) Supply of cellulosic biofuel feedstocks and regional production pattern. Am J Agric Econ 93(2):473–480

    Google Scholar 

  113. Panoutsou C, Bauen A, Böttcher H, Alexopoulou E, Fritsche U, Uslu A et al (2013) Biomass Futures: an integrated approach for estimating the future contribution of biomass value chains to the European energy system and inform future policy formation. Biofuels Bioprod Biorefin 7(2):106–114

    CAS  Google Scholar 

  114. Panoutsou C, Maniatis K (2013) Biomass futures: estimating the role of sustainable biomass for meeting the 2020 targets and beyond. Biofuels Bioprod Biorefin 7(2):97–98

    CAS  Google Scholar 

  115. Uslu A, van Stralen J, Elbersen B, Panoutsou C, Fritsche U, Böttcher H (2013) Bioenergy scenarios that contribute to a sustainable energy future in the EU27. Biofuels Bioprod Biorefin 7(2):164–172

    CAS  Google Scholar 

  116. Searchinger T, Heimlich R, Houghton RA, Dong F, Elobeid A, Fabiosa J et al (2008) Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319(5867):1238–1240

    CAS  PubMed  Google Scholar 

  117. Kim H, Kim S, Dale B (2009) Biofuels, land use change, and greenhouse gas emissions: some unexplored variables. Environ Sci Technol 43(3):961–967

    CAS  PubMed  Google Scholar 

  118. Wang M (ed) (2001) Development and use of GREET 16 Fuel-Cycle model for transportation fuels and vehicle technologies. Energy Systems Division Argonne National Laboratory, Argonne, IL

    Google Scholar 

  119. Wang M, Wu M, Huo H (2007) Life-cycle energy and greenhouse gas emission impacts of different corn ethanol plant types. Environ Res Lett 2:1–13

    CAS  Google Scholar 

  120. Subramanyan K, Diwekar UM (eds) (2005) User manual for stochastic simulation capability in GREET. Vishwamitra Research Institute, Clarendon Hill, IL

    Google Scholar 

  121. Scown CD, Nazaroff WW, Mishra U, Strogen B, Lobscheid AB, Masanet E et al (2012) Lifecycle greenhouse gas implications of US national scenarios for cellulosic ethanol production. Environ Res Lett 7(1):53

    Google Scholar 

  122. Farrell AE, Plevin RJ, Turner BT, Jones AD, O’Hare M, Kammen DM (2008) Ethanol can contribute to energy and environmental goals. Science 311:506–508

    Google Scholar 

  123. Beccali M, Columba P, D’Alberti V, Franzitta V (2009) Assessment of bioenergy potential in Sicily: a GIS-based support methodology. Biomass Bioenergy 33(1):79–87

    Google Scholar 

  124. Vainio P, Tokola T, Palander T, Kangas A (2009) A GIS-based stand management system for estimating local energy wood supplies. Biomass Bioenergy 33(9):1278–1288

    Google Scholar 

  125. Haddad MA, Anderson PF (2008) A GIS methodology to identify potential corn stover collection locations. Biomass Bioenergy 32(12):1097–1108

    Google Scholar 

  126. Zhang F, Johnson DM, Sutherland JW (2011) A GIS-based method for identifying the optimal location for a facility to convert forest biomass to biofuel. Biomass Bioenergy 35(9):3951–3961

    Google Scholar 

  127. Graham RL, English BC, Noon CE (2000) A Geographic Information System-based modeling system for evaluating the cost of delivered energy crop feedstock. Biomass Bioenergy 18(4):309–329

    Google Scholar 

  128. Kurka T, Jefferies C, Blackwood D (2012) GIS-based location suitability of decentralized, medium scale bioenergy developments to estimate transport CO2 emissions and costs. Biomass Bioenergy 46:366–379

    Google Scholar 

  129. Shastri Y, Miao Z, Rodriguez LF, Hansen AC, Ting KC, Grift TE (2012) Determining the optimal level of size reduction and compression for biomass feedstock using BioFeed optimization model. In: ASABE annual international meeting 2012, American Society of Agricultural and Biological Engineers, St. Joseph, MI

    Google Scholar 

  130. Kenney KL, Smith WA, Gresham GL, Westover TL (2013) Understanding biomass feedstock variability. Biofuels 4(1):111–127

    CAS  Google Scholar 

  131. Jakku E, Thorburn PJ (2010) A conceptual framework for guiding the participatory development of agricultural decision support systems. Agric Syst 103(9):675–682

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400076, India

    Yogendra Shastri Ph.D.

  2. Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, 1304 W. Pennsylvania Avenue, Urbana, IL, 61801, USA

    Alan C. Hansen Ph.D. & K. C. Ting Ph.D.

  3. Department of Agricultural and Biological Engineering, University of Illinois at Urbana- Champaign, 1304 W. Pennsylvania Avenue, Urbana, IL, 61801, USA

    Luis F. Rodríguez B.S., M.S., Ph.D.

  4. Information Trust Institute, Urbana, IL, USA

    Luis F. Rodríguez B.S., M.S., Ph.D.

Authors
  1. Yogendra Shastri Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alan C. Hansen Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luis F. Rodríguez B.S., M.S., Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. C. Ting Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yogendra Shastri Ph.D. .

Editor information

Editors and Affiliations

  1. Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, India

    Yogendra Shastri

  2. Dept. of Ag and Bio Engineering Agricultural Engineering Sciences Bldg, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Alan Hansen

  3. Dept of Ag and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Luis Rodríguez

  4. Dept of Ag and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    K.C. Ting

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Shastri, Y., Hansen, A.C., Rodríguez, L.F., Ting, K.C. (2014). Systems Informatics and Analysis. In: Shastri, Y., Hansen, A., Rodríguez, L., Ting, K. (eds) Engineering and Science of Biomass Feedstock Production and Provision. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-8014-4_8

Download citation

Publish with us

Policies and ethics

Search

Navigation