The Coffee Crisis, Fair Trade, and Agroecological Transformation: Impacts on Land-Use Change in Costa Rica

Abstract

This research evaluated the impacts of Fair Trade marketing networks and shade-tree diversification on the reduction of land-use change out of coffee production in the district of Agua Buena, Costa Rica. These resistance strategies were deployed by smallholder coffee farmers in response to the “coffee crisis,” which involved record low coffee commodity prices and record high external input costs. This research found that Fair Trade price premiums were inconsequential in providing support for smallholder resistance to land-use change out of coffee production. In contrast, the adoption of agroecological practices such as shade-tree diversification reduced reliance on costly external inputs, which allowed adopting producers to keep land in coffee production at a significantly higher rate than non-adopters. One conclusion drawn is that when addressing agricultural development crises, the promotion of agroecological practices that cut costs may be as good a strategy or better than one that focuses on enhancing yields or establishing price supports.

KEYWORDS:

• agroecology
• Fair Trade
• land-use change
• impact monitoring
• sustainable agriculture

Notes

1. In Table 2, a Welsh’s one-way analysis of variance test was utilized to compare land use means between the groups. A Welsh’s test was chosen over a standard T test because it allows the unequal variances as well as nonequal standard variations that characterize this dataset.

2. Percentages given in this section correspond to the average area per group dedicated to particular land uses in the given year divided by the average farm-size per group in the given year.

3. Percentages given in this section simply correspond to the 2000 percentages minus the 2009 percentages for each group.

4. Tests of statistical significance and calculations of standard errors were performed by JMP (JMP, Version 9. SAS Institute Inc., Cary, NC 1989–2010). All tables and graphs were produced in MS Excel.

5. Generated using the Sobs (Mao Tau) function of EstimateS 8.2.

6. This variation meant that sample-based species accumulation curves were measures of species density instead of species richness. Curves were transformed to feature individuals instead of quadrants as the x-axis unit of measure using the Coleman rarefaction function of EstimateS 8.2 (Gotelli and Colwell 2001).

7. The Michaelis Menten (MM) richness estimator function of EstimateS 8.2 was utilized.

8. Both the figure and the chi-square test were elaborated using a spreadsheet-based abundance curve calculator tool developed by Dr. James A. Danoff-Burg and X. Chen from Columbia University. The chi-square test evaluated whether the distributions conformed to any of the four most common species distribution models, (geometric, log series, log-normal, and broken stick). This tool can be downloaded from: http://www.columbia.edu/itc/cerc/danoffburg/Biodiversity%20Calculator.xls

9. Species diversity indices were calculated with the software package R2.12.2 (Team 2011).