Sampling error in US field crop unit process data for life cycle assessment

  • Joyce Smith CooperEmail author
  • Ezra Kahn
  • Robert Ebel



The research presented here was motivated by an interest in understanding the magnitude of sampling error in crop production unit process data developed for life cycle assessments (LCAs) of food, biofuel, and bioproduct production. More broadly, uncertainty data are placed within the context of conclusive interpretations of comparative bioproduct LCA results.


Data from the US Department of Agriculture's Agricultural Resource Management Survey were parameterized for 466 crop–state–year combinations, using 146 variables representing the previous crop, tillage and seed operations, irrigation, and applications of synthetic fertilizer, lime, nitrogen inhibitor, organic fertilizer, and pesticides. Data are described by Student's t distributions representing sampling error through the relative standard error (RSE) and are organized by the magnitude of the RSE by data point. Also, instances in which the bounds of the 95 % confidence intervals are less than zero or exceed actual limits are identified.

Results and discussion

Although the vast majority of the data have a RSE less than 100 %, values range from 0 to 1,600 %. The least precision was found in data collected between 2001 and 2002, in the production of corn and soybeans and in synthetic and pesticide applications and irrigation data. The highest precision was seen in the production of durum wheat, rice, oats, and peanuts and in data representing previous crops and till and seed technology use. Additionally, upwards of 20 % of the unit process, data had 95 % confidence intervals that are less than or exceed actual limits, such as an estimation of a negative area or a portion exceeding a total area, as a consequence of using a jackknife on subsets of data for which the weights are not calibrated explicitly and a low presence of certain practices.


High RSE values arise from the RSE representing a biased distribution, a jackknife estimate being nearly zero, or error propagation using low-precision data. As error propagates to the final unit process data, care is required when interpreting an inventory, e.g., Monte Carlo simulation should only be sampled within the appropriate bounds. At high levels of sampling error such as those described here, comparisons of LCA bioproduct results must be made with caution and must be tested to ensure mean values are different to a desired level of significance.


Error Inventory data Life cycle assessment Meta data Parameterization Uncertainty 



The US Department of Agriculture (USDA) National Agricultural Library (agreement number 58-8201-0-149) funded this research. The views expressed are those of the authors and should not be attributed to the Economic Research Service or the US Department of Agriculture. The views expressed in this article are those of the authors and do not necessarily reflect those of the U.S. Department of Agriculture or Economic Research Service.

Supplementary material

11367_2012_454_MOESM1_ESM.docx (85 kb)
ESM 1 (DOCX 84 kb)


  1. Cooper JS, Noon M, Kahn E (2011) Parameterization in life cycle assessment inventory data: review of current use and the representation of uncertainty. Int J Life Cycle Assess. doi: 10.1007/s11367-012-0411-1
  2. Dieck RH (2007) Measurement uncertainty—methods and applications, 4th edn. International Society of Automation, Research Triangle ParkGoogle Scholar
  3. Dubman RW (2000) Variance estimation with USDA's farm costs and returns surveys and agricultural resource management study surveys. US Department of Agriculture, Economic Research Service Resource Economics DivisionGoogle Scholar
  4. Kim CS, Hallahan C, Lindamood W, Schaible G, Payne J (2004) A note on the reliability tests of estimates from ARMS data. Agr Resource Econ Rev 33(2):293–297Google Scholar
  5. Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment. A survey of quantitative approaches. J Ind Ecol 11(1):161–179CrossRefGoogle Scholar
  6. Sommer JE, Hoppe RA, Green RC, Korb PJ (1998) Structural and financial characteristics of US farms, 1995: 20th Annual Family Farm Report to Congress. Retrieved from Accessed 15 Feb 2012
  7. Spiegel MR, Schiller JJ, Srinivasan RA, Alu R (2009) Schaum's outlines—probability and statistics, 3rd edn. McGraw-Hill, New YorkGoogle Scholar
  8. US Department of Agriculture (2011) 2007 Census of agriculture: history volume 2 subject series Part 7. National Agricultural Statistics Service. Retrieved from: Accessed 15 Feb 2012
  9. Weidema BP, Wesnæs MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Cleaner Prod 4(3–4):167–174CrossRefGoogle Scholar
  10. Weidema BP, Bauer C, Hischier R, Mutel C, Nemecek T, Vadenbo CO, Wernet G (2011) Overview and methodology: data quality guideline for the ecoinvent database version 3 (final draft_revision 1) ecoinvent report no. 1(v3), Accessed 15 Feb 2012

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Design for Environment LaboratoryUniversity of WashingtonSeattleUSA
  2. 2.Economic Research ServiceUS Department of AgricultureWashingtonUSA

Personalised recommendations