Greenhouse gas calculator at farm level addressed to the growers

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.