Streamlining life cycle inventory data generation in agriculture using traceability data and information and communication technologies – part I: concepts and technical basis

Maurel, Véronique Bellon; Short, Michael D.; Roux, Philippe; Schulz, Matthias; Peters, Gregory

doi:10.1016/j.jclepro.2014.01.079

Cited by 31 publications

(21 citation statements)

References 28 publications

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“…Quantitative environmental assessment methodologies such as LCA require significant time and resource inputs during the data acquisition and life cycle inventory (LCI) phase. Approaches to streamlining the LCI data collection process without degrading data quality are therefore required, and is especially true for agricultural products (Bellon-Maurel et al, 2014). The main reason that may explain this is due to environmental and energy aspects for the development of fruit crops were not taken in account by farmers during the last decades, so there is no available data and the existing information that can be found are not reliable data.…”

Section: Introductionmentioning

confidence: 99%

Life Cycle Assessment of multiyear peach production

Vinyes

Gasol

Asín

et al. 2015

Journal of Cleaner Production

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Considering that peach is a significant fruit in the Mediterranean countries, and most publications on environmental impacts of fruit productions are based on one single productive year, this study attempts to perform an environmental analysis of peach production using Life Cycle Assessment, in order to provide new information on peaches, and also introduce a multiyear perspective analysis to identify the variability of the environmental impacts related to annual orchard yield and weather variations. The system studied is a peach orchard (Prunus persica L.) with integrated agricultural practices. The study analyses the cultivation period, as well as the impact of the initial orchard establishment tasks (soil preparation and planting). Data used have been collected directly from an orchard located in the North East of Spain, and covers 15 years of real production. The functional unit adopted was the cultivation of 1kg of peach. Four scenarios have been considered according to the different yield periods of the peach fruit tree: Growth, Low, High and Multiyear. The results of the study reveals that, depending on production scenario considered, the results per kg of peach can vary between 7% and 69% depending on the environmental indicator. If the impact of initial orchard establishment tasks (soil preparation and planting) is not included in the quantification, then 5% of total emissions may be overlooked, but sometimes a lack of data makes it difficult to include these stages. Caution should be taken when the functional unit is related to mass and only when a single year of production is studied, because unproductive years increase impacts on value per functional unit, whereas over-productive years decrease them. According to variability of the results obtained, multiyear approach should be considered in crops with an average life time of twenty years or higher. The present study can be considered a useful methodological framework for providing a deeper understanding of the key environmental impact issues related to fruit production based on peach case study, and how to avoid multiple interpretation of results associated to reporting annual environmental impact variations.Pre-print of: Vinyes, E., et al. "Life Cycle Assessment of multiyear peach production" in Journal of Cleaner Production, Vol. 104 (October 2015) , p. ABSTRACT.Considering that peach is a significant fruit in the Mediterranean countries, and most publications on environmental impacts of fruit productions are based on one single productive year, this study attempts to perform an environmental analysis of peach production using Life Cycle Assessment, in order to provide new information on peaches, and also introduce a multiyear perspective analysis to identify the variability of the environmental impacts related to annual orchard yield and weather variations. The system studied is a peach orchard (Prunus persica L.) with integrated agricultural practices. The study analyses the cultivation period, as well as the impact of the initial orc...

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Section: Introductionmentioning

confidence: 99%

Life Cycle Assessment of multiyear peach production

Vinyes

Gasol

Asín

et al. 2015

Journal of Cleaner Production

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“…Las tipologías son: trazabilidad de rastreo a los proveedores, trazabilidad interna o de proceso y la trazabilidad de seguimiento hacia los clientes. Por su parte Bellon-Maurel, et al [47] dividen la trazabilidad en: interna y externa, donde la trazabilidad externa hace referencia a lo que ocurre a lo largo de la CSA o parte de la misma y la trazabilidad interna es la que ocurre cuando el TRU está en las instalaciones de una empresa. Teniendo en cuenta ambas clasificaciones, se plantea el siguiente esquema: trazabilidad interna y trazabilidad externa, estaúltima comprende la trazabilidad de rastreo a los proveedores y trazabilidad de seguimiento hacia los clientes.…”

Section: Esquemas De Trazabilidadunclassified

Hacia un Marco Conceptual Común sobre Trazabilidad en la Cadena de Suministro de Alimentos

Ballesteros

Ramírez

Castro

2017

Ing.

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Background: The absence of a common conceptual framework on traceability in the food supply chain (SCF), prevents a cohesive development of this concept. The absence has generated confusion and has made it impossible to demonstrate the social and commercial advantages of its implementation. In addition, not having a common framework in countries such as Colombia obstructs the development of public policies.

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“…The ever increasing need for agricultural products led conventional farming to become aggressive with serious medium-and long-term negative effects on crop yields, environmental quality and natural resources (Tilman et al, 2002). The development of a suitable farm traceability system has received much attention lately (Bellon-Maurel et al, 2014, supporting the concept of precision agriculture (Stafford, 2000) and highlighting the need for monitoring technology to aid management (Srbinovska et al, 2015).…”

Section: Introductionmentioning

confidence: 99%