ADOPTION AND ECONOMIC IMPACTS OF IPM TECHNOLOGIES IN POTATO PRODUCTION IN CARCHI, ECUADOR

ADOPTION AND ECONOMIC IMPACTS OF IPM TECHNOLOGIES IN POTATO PRODUCTION IN CARCHI, ECUADOR

Department of Agricultural and Applied EconomicsVirginia Tech

Vanessa Carrión, George Norton, Jeff Alwang, Victor Barrera

April, 2013

Twenty six percent (26%) of the total labor force is employed in the agricultural sector.

Farmers in Ecuador use large quantities of pesticides and chemical fertilizers. Potato is a crop with relatively high input requirements and also a very important staple in the average Ecuadorian diet.

Carchi is currently the most important potato production area of the country. It has specialized farmers who cultivate 43% of the production using only 13% of the total national area dedicated to this crop.

Agriculture and potato production in Ecuador

In 1997, Ecuador became a host country for IPM CRSP, funded by the United States Agency for International Development (USAID). After performing a base line study (1998) to prioritize pest problems, researchers tested several IPM practices in farmers’ fields and then began introducing IPM practices to the potato farmers in Carchi, in part through Farmer Field Schools (FFS).  

The Integrated Pest Management Collaborative Research Support Program (IPM CRSP)

1. Determine the factors that affect a farmer’s decision to adopt, not adopt, or continue to use IPM technologies.

2. Assess the economic impact of IPM adoption (profits, yields, pesticide use).

Objectives

Personal interviews were conducted in June 2012 , with a sample of 404 farmers from the four potato-producing municipalities within Carchi province. Two hundred fifteen (215) farmers had some type of formal training. One hundred eighty nine (189) were untrained.

Data

Methodology

Methodology (continued)

Methodology (continued)

INDEP. VARIABLES DESCRIPTIONFAGE Farmer’s age

FEDUC Years of formal education

FMWORK_I Number of family members working in the farm

WEALTHI Wealth indexINFDIF1 FFS (Farmer Field School)INFDIF2 Field DaysINFDIF3 Observation visitsINFDIF4 Extension agent visitINFDIF5 From other farmersINFDIF6 Other methods

Methodology (continued)

The approach to be used to assess the impact of IPM adoption focuses on farm-level economic impacts. To assess such impacts, we will evaluate farmers profits, crop yields and pesticide use using an instrumental (IV) variable approach.

Summary Statistics

VARIABLE MEAN STD. DEV. MIN MAXFAGE 46.58 13.05 18.00 83.00

FEDUC 6.66 3.08 0.00 18.00FMWORK_I 2.88 1.35 1.00 8.00

WEALTHI 0.00 1.59 -2.46 6.03INFDIF1 0.18 0.39 0.00 1.00INFDIF2 0.17 0.37 0.00 1.00INFDIF3 0.06 0.25 0.00 1.00INFDIF4 0.15 0.35 0.00 1.00INFDIF5 0.35 0.48 0.00 1.00INFDIF6 0.02 0.15 0.00 1.00

Results

How farmers learn about IPM?

Farmers by degree of adoption 2003 vs 2012

Why farmers stop adopting IPM?

Ordered Probit Results for Adoption RatesADOP_CTG Coef. P>z

FAGE -0.007 0.119FEDUC 0.022 0.287

FMWORK_I 0.039 0.341WEALTHI 0.071* 0.059INFDIF1 1.170*** 0.000INFDIF2 0.866*** 0.001INFDIF3 0.734** 0.015INFDIF4 0.623** 0.015INFDIF5 0.815*** 0.000INFDIF6 0.534 0.209

Number of obs. 404 LR chi2(10) 35.02000 Prob >chi2 0.00010 Pseudo R2 0.04030

*, **, *** indicate corresponding coefficients are significant at the 10%, 5% and 1% level, respectively.

Marginal effects of significant variables on Adoption Rates

VARIABLEDegree of Adoption

Category 3 (25%-50%) Category 4 (50%-75%)

WEALTHI (Wealth index) 1.48 (0.060) 1.12 (0.064)

INFDIF1 (FFS) 24.39 (0.000) 18.53 (0.000)

INFDIF2 (Field days) 18.05 (0.001) 13.71 (0.001)

INFDIF3 (Observation) 15.29 (0.015) 11.62 (0.019INFDIF4 (Extension Agents) 12.99 (0.015) 9.86 (0.020)

INFDIF5 (Other farmers) 16.98 (0.000) 12.90 (0.001)

Conclusions

Information sources have a positive effect on farmer adoption of IPM. FFSs had the greatest impact on high and medium levels of adoption, followed by field days, exposure to other farmers, and observation visits. Extension agents visits had the least effect on farmer adoption.

Farmer characteristics (socio-economic factors) did not play a significant role in affecting adoption rates. Apart from information effects, the only other significant variable in the model was the wealth index where wealthier farmers adopted more IPM.

Aknowledgements

This project was funded by the IPM CRSP/USAID

Dr. George W. Norton, AAEC Virginia Tech Dr. Jeff Alwang, AAEC Virginia Tech Dr. Victor Barrera, INIAP Dr. Catherine Larochelle, AAEC Virginia Tech

Thanks!