Advertisement

Greenhouse gas calculator at farm level addressed to the growers

  • Carmen M. TorresEmail author
  • Assumpció Antón
  • Francesc Ferrer
  • Francesc Castells
LIFE CYCLE ASSESSMENT: A TOOL FOR INNOVATION IN LATIN AMERICA

Abstract

Purpose

When assessing agricultural products using life cycle assessment (LCA), the farmers play a key role as they have first-hand information to understanding the activities involved in the assessed systems. However, the compilation of these data can be tiresome and complicated. To engage farmers in the LCA, a web tool (eFoodPrint Env®) was designed to facilitate their tasks as much as possible, seeking the trade-off between comprehensiveness and time consumption without affecting the quality.

Methods

The model relies on primary data for the specific parcel and growing season; it starts with the ancillary materials extraction and ends with the transport of products to the corresponding cooperative. The model excludes the infrastructure except in the cases of protected crops including greenhouses. To build the inventory, the web tool guides the user through a questionnaire divided in cultivation, machinery, fertilization, plant treatment, and transport. Carbon footprint is computed with global warming potentials of the International Panel of Climate Change following the norm PAS2050. The calculations behind the web tool have the following modules: (1) farming input and output flows; (2) database and default data; (3) greenhouse infrastructure; (4) impact assessment; (5) uncertainty analysis, and (6) results module.

Results and discussion

The web tool is already in use and can be applied to most of agricultural facilities. Examples of estates of corn, nectarine, grape, and tomato are herein showed. The application displays the results distributed in the different stages considered in each product system, and the scores include error bars derived from the uncertainty analysis. Corn production showed the highest carbon footprint per kilogram of product, with a high contribution due to fertilizer production and application. The carbon footprint of tomato production in low-tunnel greenhouse showed nearly 30 % of impact related only to the greenhouse structure. Regarding uncertainty, the worst value is also associated to the corn production for which the most uncertain activities have more influence (fertilizer and transport).

Conclusions

The design of the tool has the objective of meeting the requirements of data quality and comprehensiveness with the reality of the farms. The tool is generic enough to be applied to different cropping systems, enabling the generation of simple reports with the results of the analysis. The modular structures of both data entry and model calculation allow the identification of potential sources of uncertainty and hotspots within the studied life cycle stages.

Keywords

Carbon footprint Crop Greenhouse Online tool Uncertainty 

Notes

Acknowledgments

The authors wish to acknowledge the financial support received from the University Rovira i Virgili, the support from the Spanish Ministry of Education and Science (Project references ECO2013-41917-P , CTQ2012-37039-C02), and the Catalan Agency for the Management of University and Research Grants AGAUR (2012AGEC00023).

References

  1. Antón A, Torrellas M, Raya A, Montero JI (2014) Modelling the amount of materials to improve inventory datasets of greenhouses infrastructures. Int J Life Cycle Assess 19:29–41CrossRefGoogle Scholar
  2. Branca G, Lliper L, McCarthy N, Jolejole MC (2013) Food security, climate change, and sustainable management. A review. Agron Sustain Dev 33:635–650CrossRefGoogle Scholar
  3. BSI British Standards, Carbon Trust and Department for Environment Food and Rural Affairs (2011) Specification for the assessment of the life cycle greenhouse gas emissions of goods and services. BSI, LondonGoogle Scholar
  4. Butchart SHM, Walpole M, Collen B, van Strien A, Scharlemann JPW, Almond REA, Baillie JEM, Bomhard B, Brown C, Bruno J, Carpenter KE, Carr GM, Chanson J, Chenery AM, Csirke J, Davidson NC, Dentener F, Foster M, Galli A, Galloway JN, Genovesi P, Gregory RD, Hockings M, Kapos V, Lamarque J-F, Leverington F, Loh J, McGeoch MA, McRae L, Minasyan A, Hernández Morcillo M, Oldfield TEE, Pauly D, Quader S, Revenga C, Sauer JR, Skolnik B, Spear D, Stanwell-Smith D, Stuart SN, Symes A, Tierney M, Tyrrell TD, Vié J-C, Watson R (2010) Global biodiversity: indicators of recent declines. Science 328:1164–1168CrossRefGoogle Scholar
  5. Ciroth A, Fleisher G, Steinbach J (2004) Uncertainty calculation in life cycle assessments—a combined model of simulation and approximation. Int J Life Cycle Assess 9:216–226CrossRefGoogle Scholar
  6. Colomb V, Bockel L, Chotte JL, Martin S, Tinlot M, Bernoux M (2013) Selection of appropriate calculators for landscape-scale greenhouse gas assessment for agriculture and forestry. Environ Res Lett 8:1. doi: 10.1088/1748-9326/8/1/015029 CrossRefGoogle Scholar
  7. Denef K, Paustian K, Archibeque S, Biggar S and Pape D (2012) Report of greenhouse gas accounting tools for agriculture and forestry sectors (Interim Report to USDA under Contract No. GS23F8182H 140). http://www.usda.gov/oce/climate_change/techguide/Denef_et_al_2012_GHG_Accounting_Tools_v1.pdf. Accessed 18/December/2015
  8. Ecoinvent Centre (2013) Ecoinvent data v3.1. Swiss Centre for Life Cycle Assessment, DuebendorfGoogle Scholar
  9. eFoodPrint (2015)—Agrifood Software and Solutions. http://efoodprint.com/efoodprint-env/. Accessed 29/September/2015
  10. Frischknecht R, Jungbluth N, Althaus HJ, Doka G, Dones R, Heck T, Hellweg S, Hischier R, Nemecek T, Rebitzer G, Spielmann M (2005) The Ecoinvent Database: overview and methodological framework. Int J Life Cycle Assess 10:3–9CrossRefGoogle Scholar
  11. IPCC Intergovernmental Panel on Climate Change (2006) IPCC Guidelines for National Greenhouse Gas Inventories. Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K. Prepared by the National Greenhouse Gas Inventories Programme. IGES, JapanGoogle Scholar
  12. ISO International Organization for Standardization (2013) ) ISO 14067:2013. Greenhouse gases—carbon footprint of products—requirements and guidelines for quantification and communication. International Organization for Standardization, Geneva, SwitzerlandGoogle Scholar
  13. Itten R, Frischknecht R, Stucki M (2013) Life Cycle Inventories of Electricity Mixes and Grid. ESU-services Ltd. Uster (Switzerland): Paul Scherrer Institut (PSI)Google Scholar
  14. JRC EC – Joint Research Center European Commission (2010) ILCD (International Reference Life Cycle Data System) Handbook. General guide for Life Cycle Assessment – Detailed guidance. http://eplca.jrc.ec.europa.eu/uploads/ILCD-Handbook-General-guide-for-LCA-DETAILED-GUIDANCE-12March2010-ISBN-fin-v1.0-EN.pdf. Accessed 29 September 2015
  15. LCAFood (2015) http://www.lcafood.dk/. Accessed 29 September 2015
  16. Lloyd SM, Ries R (2007) Characterizing, propagating and analyzing uncertainty in life-cycle assessment—a survey of quantitative approaches. J Ind Ecol 11:161–179CrossRefGoogle Scholar
  17. OECD (2001) Environmental indicators for agriculture. OECD, ParisGoogle Scholar
  18. Pidd M (1996) Five simple principles of modelling. The 1996 Winter Simulation Conference, CaliforniaGoogle Scholar
  19. REE – Red Eléctrica Española (2014) Informe del sistema eléctrico español 2014. Red Eléctrica Española, www.ree.es/es/publicaciones/sistema-electrico-espanol/informe-anual/informe-del-sistema-electrico-espanol-2014. Accessed 29 September 2015
  20. Romero-Gámez M, Antón A, Soriano T, Suárez-Rey E, Castilla N (2009) Environmental impact of greenbean cultivation: comparison of screen greenhouses vs. open field. J Food Agric Environ 7(3&4):132–138Google Scholar
  21. Torrellas M, Antón A, López JC, Baeza EJ, Pérez Parra J, Muñoz P, Montero JI (2012) LCA of a tomato crop in a multi-tunnel greenhouse in Almeria. Int J Life Cycle Assess 17:863–875CrossRefGoogle Scholar
  22. Torrellas M, Antón A, Montero JI (2013) An environmental impact calculator for greenhouse production systems. J Environ Manage 118:186–195CrossRefGoogle Scholar
  23. Vermeulen SJ, Campbell BM, Ingram JS (2012) Climate change and food systems. Annu Rev Environ Resour 37:195–222CrossRefGoogle Scholar
  24. Weidema BP, Bauer C, Hischier R, Mutel C, Nemecek T, Reinhard J, Vadenbo CO, Wernet G (2012) Data quality guideline for the Ecoinvent database version 3. The Ecoinvent Center, St. GallenGoogle Scholar
  25. Williams AG, Audsley E, Sandars DL (2006) Determining the environmental burdens and resource use in the production of agricultural and horticultural commodities. Cranfield University and Defra. Defra Research Project IS0205. Available from http://www.silsoe.cranfield.ac.uk and http://www.defra.gov.uk

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Carmen M. Torres
    • 1
    Email author
  • Assumpció Antón
    • 2
  • Francesc Ferrer
    • 3
  • Francesc Castells
    • 1
  1. 1.Departament d’Enginyeria QuímicaUniversitat Rovira i VirgiliTarragonaSpain
  2. 2.IRTABarcelonaSpain
  3. 3.Centre d’Assessoria LabFerrerLleidaSpain

Personalised recommendations