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ADOPTION AND ECONOMIC IMPACTS OF IPM TECHNOLOGIES IN POTATO PRODUCTION IN CARCHI, ECUADOR. Department of Agricultural and Applied Economics Virginia Tech Vanessa Carri ó n, George Norton, Jeff Alwang, Victor Barrera April, 2013. Agriculture and potato production in Ecuador. - PowerPoint PPT Presentation
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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 StatisticsVARIABLE MEAN STD. DEV. MIN MAX
FAGE 46.58 13.05 18.00 83.00FEDUC 6.66 3.08 0.00 18.00
FMWORK_I 2.88 1.35 1.00 8.00WEALTHI 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!