Factors influencing the competitiveness of 21 agricultural extension projects were identified and measured. The data used are from a total of 21 extension services projects carried out by Project INCAGRO in the regions of Amazonas, San Martín, Loreto and Ucayali. The factors that explain the variability of the competitiveness with 83.5% probability are: F1 = X1 which integrates CONTRIBUTIONS (month's duration), X2 (contributions INCAGRO) and X3 (strategic alliance); F2 = TECHNICAL grouping X10 (services offered to customers) and X11 (technical teams involved); and F3 = Impact factor that integrates X4 (economic return) and X9 (environmental impact). The multiple linear regression equation Y = 3.429 + F2 + 0.107F3 0.031F1 0317, explains the variability of the competitiveness of extension services projects with approximately 74% of probability; where factor F2 TECHNICAL [X10 (services offered to customers) and X11 (technical teams involved)] has the highest score and for every point increase, competitiveness will increase on average by 0.317, if the scores of other factors remain constant. With F3 factor IMPACT [X4 (economic return) and X9 (environmental impact)] is estimated that for every additional point of this factor is estimated to increase competitiveness on average 0.107, remaining constant, the scores of the other factors. Finally, for each additional point in F1 score CONTRIBUTIONS Factor X1 [(month duration of the project), X2 (contributions INCAGRO) and X3 (strategic alliance)], is estimated to increase competitiveness on average only 0.031, remaining constant the scores of the other factors. The values ​of the coefficients of Spearman nonparametric correlation shows that the competitiveness of extension services projects is significantly correlated with environmental impact variables (X9), services offered to customers (X10) and technical equipment (X11), in other words, enhancing the values ​of these variables, ignoring the presence of other variables allows for an improvement in the level of competitiveness. The results also support the conclusion that the degree of competitiveness has a weak association with the X2 variables (input INCAGRO), X3 (contributions strategic alliance), X4 (economic return), X7 (effectiveness), X1 (months), X5 (benefit cost INCAGRO), X6 (benefit-cost strategic alliance) and X8 (efficiency). All this allows us to conclude that to improve the competitiveness of Extension Services projects is essential to provide good services to customers, technical teams of dedicated extension technicians, good economic returns of the technologies offered and low environmental impact.