Measuring farm sustainability using data envelope analysis with principal components: The case of Wisconsin cranberry

Journal of Environmental Management

Volumn 147, Issue , 2015, Pages 175-183

  Dong, Fengxia ^a   Mitchell, Paul D ^a   Colquhoun, Jed ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Common weight data envelope analysis; Non negative principal component analysis; Polychoric principal component analysis; Sustainability metric

Indexed keywords

AGRICULTURAL LAND; ALTERNATIVE AGRICULTURE; DATA ENVELOPMENT ANALYSIS; HETEROGENEITY; MANAGEMENT PRACTICE; PRINCIPAL COMPONENT ANALYSIS;

ARTICLE; CONTROLLED STUDY; CORRELATION ANALYSIS; CRANBERRY; DATA ANALYSIS; ENVIRONMENTAL SUSTAINABILITY; EVALUATION STUDY; FARMING SYSTEM; NONHUMAN; PERFORMANCE; PRINCIPAL COMPONENT ANALYSIS; UNITED STATES; AGRICULTURE; ENVIRONMENTAL PROTECTION; HUMAN; PROCEDURES; THEORETICAL MODEL;

UNITED STATES; WISCONSIN;

VACCINIUM MACROCARPON;

AGRICULTURE; CONSERVATION OF NATURAL RESOURCES; HUMANS; MODELS, THEORETICAL; PRINCIPAL COMPONENT ANALYSIS; VACCINIUM MACROCARPON; WISCONSIN;

EID: 84907495032     PISSN: 03014797     EISSN: 10958630     Source Type: Journal    
DOI: 10.1016/j.jenvman.2014.08.025     Document Type: Article

References (58)

1. Adler N., Golany B. Including principal components weights to improve discrimination in data envelopment analysis. J.Operat. Res. Soc. 2002, 53:985-991.
2. Adler N., Yazhemsky E. Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction. Eur. J. Operat. Res. 2010, 202:273-284.
3. Arledge Keene A., Mitchell P.D. Economic Impact of Specialty Crop Production and Processing in Wisconsin 2010, Agricultural and Applied Economics, University of Wisconsin, Madison WI, Online:, (accessed 02.01.13.). http://www.aae.wisc.edu/pubs/misc/.
4. Ascough G.W., Kiker G.A. The effect of irrigation uniformity on irrigation water requirements. Water SA 2002, 28:235-241.
5. Babakus E. The Sensitivity of Maximum Likelihood Factor Analysis Given Violations of Interval Scale and Multivariate Normality 1985, Unpublished PhD dissertation, The University of Alabama.
6. Banker R., Morey R. Efficiency analysis for exogenously fixed inputs and outputs. Operat. Res. 1986, 34:513-521.
7. Blend J.R., van Ravenswaay E.O. Consumer demand for eco-labeled apples: results from econometric estimation. Am. J. Agric. Econ. 1999, 81:1072-1077.
8. Clemmens A.J., Solomon K.H. Estimation of global irrigation distribution uniformity. J.Irrigat. Drain. Eng. 1997, 123:454-461.
9. Colquhoun J., Johnson H. Sustainable Cranberry Production for a Vibrant Future: the Wisconsin Experience 2010, Horticulture Department, University of Wisconsin, Madison, WI, Online:, (accessed 29.03.12.). http://www.wiscran.org/user_image/pdf_files/SustainableCranberry_FINAL_web.pdf.
10. Cooper W., Seiford L., Tone K., Zhu J. Some models and measures for evaluating performances with DEA: past accomplishments and future prospects. J.Prod. Anal. 2007, 28:151-163.
11. Deng L., Cheng K., Dong J., Griffin J., Chen Z. Non-negative principal component analysis for NMR-based metabolomic data analysis. Chemom. Intell. Lab. Syst. 2012, 118:51-61.
12. Despotis D. Improving the discriminating power of DEA: focus on globally efficient units. J.Operat. Res. Soc. 2002, 53:314-323.
13. Despotis D. Areassessment of the human development index via data envelopment analysis. J.Operat. Res. Soc. 2005, 56:969-980.
14. Duong T., Duong V. Non-negative sparse principal component analysis for multidimensional constrained optimization. Lecture Notes in Artificial Intelligence 2008, vol. 5351:103-114. Springer-Verlag, Berlin.
15. Dyson R., Allen R., Camanho A., Podinovski V., Sarrico C., Shale E. Pitfalls and protocols in DEA. Eur. J. Operat. Res. 2001, 132:245-259.
16. FAO FAO Statistical Yearbook 2012 2012, UN FAO, Rome, Online:, (accessed 26.04.12.). http://faostat.fao.org/site/567/default.aspx#ancor.
17. Gòmez-Limón J.A., Riesgo L. Alternative approaches to the construction of a composite indicator of agricultural sustainability: an application to irrigated agriculture in the Duero basin in Spain. J.Environ. Manag. 2009, 90:3345-3362.
18. Gòmez-Limón J., Sanchez-Fernandez G. Empirical evaluation of agricultural sustainability using composite indicators. Ecol. Econ. 2010, 69:1062-1075.
19. Han H. Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery. BMC Bioinform. 2010, 11(Suppl.1):S1.
20. Hatefi S.M., Torabi S.A. Acommon weight MCDA-DEA approach to construct composite indicators. Ecol. Econ. 2010, 70:114-120.
21. Jenkins L., Anderson M. Multivariate statistical approach to reducing the number of variables in data envelopment analysis. Eur. J. Operat. Res. 2003, 147:51-61.
22. Jollands N., Lermit J., Patterson M. Aggregate eco-efficiency indices for New Zealand - a principal components analysis. J.Environ. Manag. 2004, 73:293-305.
23. Jolliffe I.T. Principal Component Analysis 2002, Springer, New York. second ed.
24. Karsak E., Ahiska S. Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection. Int. J. Prod. Res. 2005, 43:1537-1554.
25. Karsak E., Ahiska S. Acommon-weight mcdm framework for decision problems with multiple inputs and outputs. Lecture Notes in Computer Science 2007, vol. 4705:779-790. Springer-Verlag, Berlin.
26. Kolenikov S., Angeles G. Socioeconomic status measurement with discrete proxy variables: Is principal component analysis a reliable answer?. Rev. Income Wealth 2009, 55:128-165.
27. Lobell D.B., Schlenker W., Costa-Roberts J. Climate trends and global crop production since 1980. Science 2011, 333:616-620.
28. Montgomery D.R. Soil erosion and agricultural sustainability. Proc. Natl. Acad. Sci. U.S.A. 2007, 104:13268-13272.
29. National Research Council Toward Sustainable Agricultural Systems in the 21st Century 2010, The National Academies Press, Washington, DC.
30. Nimon W., Beghin J. Are eco-labels valuable? Evidence from the apparel industry. Am. J. Agric. Econ. 1999, 81:801-811.
31. Nunamaker T. Using data envelopment analysis to measure the efficiency of non-profit organization: a critical evaluation. Manag. Decis. Econ. 1985, 6:50-58.
32. Olsson U. Maximum likelihood estimation of the polychoric correlation. Psychometrika 1979, 44:443-460.
33. Onozaka Y., Mcfadden D.T. Does local labeling complement or compete with other sustainable labels? A conjoint analysis of direct and joint values for fresh produce claims. Am. J. Agric. Econ. 2011, 93:693-706.
34. Pearson K., Pearson E. On polychoric coefficients of correlation. Biometrika 1922, 14:127-156.
35. Perfecto I., Vandermeer J. The agroecological matrix as alternative to the land sparing/agriculture intensification model. Proc. Natl. Acad. Sci. U.S.A. 2010, 107:5786-5791.
36. Reig-Martínez E., Gómez-Limón J., Picazo-Tadeo A. Ranking farms with a composite indicator of sustainability. Agric. Econ. 2011, 42:561-575.
37. Rigby D., Woodhouse P., Young T., Burton M. Constructing a farm level indicator of sustainable agricultural practice. Ecol. Econ. 2001, 39:463-478.
38. Rigdon E., Ferguson C. The performance of the polychoric correlation coefficient and selected fitting functions in confirmatory factor analysis with ordinal data. J.Mark. Res. 1991, 28:491-497.
39. Ronald P. Plant genetics, sustainable agriculture and global food security. Genetics 2011, 188:11-20.
40. Saisana M., Tarantola S. State-of-the-art Report on Current Methodologies and Practices for Composite Indicator Development 2002, European Commission, Joint Research Centre, Institute for the Protection and the Security of the Citizen, Technological and Economic Risk Management Unit.
41. Saisana M., Tarantola S., Saltelli A. Uncertainty and sensitivity techniques as tools for the analysis and validation of composite indicators. J.R. Stat. Soc. A 2005, 168:307-323.
42. Sharpe A. Literature Review of Frameworks for Macro-indicators 2004, CSLS Research Report 2004-03, Centre for the Study of Living Standards, Ottawa, CAN.
43. Singh R.K., Murty H.R., Gupta S.K., Dikshit A.K. An overview of sustainability assessment methodologies. Ecol. Indic. 2009, 9:189-212.
44. Teisl M.F., Roe B., Levy A.S. Eco-certification: why it may not be a 'field of dreams'. Am. J. Agric. Econ. 1999, 81:1066-1071.
45. Tillman D., Balzer C., Hill J., Befort B.L. Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. U.S.A. 2011, 108:20260-20264.
46. U.S.Department of Agriculture Agricultural productivity in the United States Online:, (accessed 02.01.13.). http://www.ers.usda.gov/Data/AgProductivity/.
47. U.S.Department of Agriculture Wisconsin 2011 agricultural statistics Online:, (accessed 02.01.13.). http://www.nass.usda.gov/Statistics_by_State/Wisconsin/Publications/Annual_Statistical_Bulletin/bulletin2011_web.pdf.
48. Van Passel S., Meul M. Multilevel and multi-user sustainability assessment of farming systems. Environ. Impact Assess. Rev. 2012, 32:170-180.
49. Wisconsin State Cranberry Growers Association Cranberry production inWisconsin Online:, (accessed 02.01.13.). http://www.wiscran.org/user_image/pdf_files/CranProduction08.pdf.
50. Wisconsin State Cranberry Growers Association Growing Wisconsin berries, growing Wisconsin's economy Online:, (accessed 02.01.13.). http://www.wiscran.org/about_cranberries_0002/Economic_Impact_0088.html.
51. Wisconsin State Cranberry Growers Association Wisconsin cranberry industry and state ag leaders aim to expand export opportunities Online:, (accessed 02.01.13.). http://wiscran.org/special/libfile_files/media/325.pdf.
52. Wisconsin State Cranberry Growers Association Nutrient management Online:, (accessed 02.01.13.). http://www.wiscran.org/cranberry_grower_information_0006/Whole_Farm_Planning_0048/Nutrient_Management_0009.html.
53. Wisconsin State Cranberry Growers Association Irrigation water management Online:, (accessed 02.01.13.). http://www.wiscran.org/cranberry_grower_information_0006/Whole_Farm_Planning_0048/Irrigation_Water_Management_0011.html.
54. Wisconsin State Cranberry Growers Association Pest management Online:, (accessed 02.01.13.). http://www.wiscran.org/cranberry_grower_information_0006/Whole_Farm_Planning_0048/Pest_Management_0010.html.
55. Zass R., Shashua A. Nonnegative sparse PCA. Advances in Neural Information Processing Systems 2007, vol. 19:1561-1568. MIT Press, Cambridge.
56. Zhou P., Ang B.W., Poh K.L. Comparing aggregating methods for constructing the composite environmental index: an objective measure. Ecol. Econ. 2006, 59:305-311.
57. Zhou P., Ang B.W., Poh K.L. Slacks-based efficiency measures for modeling environmental performance. Ecol. Econ. 2006, 60:111-118.
58. Zhou P., Ang B.W., Poh K.L. Amathematical programming approach to constructing composite indicators. Ecol. Econ. 2007, 62:291-297.