Spatio‐Temporal Differentiation of Life Cycle Assessment Results for Average Perennial Crop Farm: A Case Study of Peruvian Cocoa Progression and Deforestation Issues

Abstract

Summary The application of spatially and temporally explicit information to increase result precision is gaining momentum in Life Cycle Assessment (LCA) studies. It is vital for the assessment of environmental impact of perennial crops with non‐productive years, grown in combination with shade crops. Available studies rely on differentiated life cycle inventory data for the inputs in LCA or application of adapted impact assessment methodologies. This study uses the identification of greenhouse gas emissions (GHG) hotspots (statistically significant clusters of farms with either high or low GHG emission values) estimated from average LCA results and assesses a relative deforestation risk in such hotspots. A total of 1892 farms in the Tocache province of San Martin region of Peru were evaluated between the year 2008 and 2010. Combination of average LCA results with farm size, age and deforestation progression allowed for the identification of areas and farms with a high relative risk of environmental impacts and potential deforestation. It was estimated that farms belonging to high‐GHG emission hotspots were twice more likely to expand their agricultural frontier and cause deforestation than farms in low‐GHG emission hotspots. Combining LCA with geo‐information systems and geostatistics is a viable path to explore the differentiation of assessment results, which might lead to faster, more accurate, and resource‐efficient ways to tackle environmental impacts while also accounting for important environmental impacts such as deforestation. Further research on the application of suggested approaches with other perennial crops and other geographical areas is needed.

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