Analysis of production factors determining agricultural productivity in Cuispes
Abstract
This study aimed to evaluate the impact of productive factors – Capital, Labour, Knowledge, Technology, Management, and Land – on agricultural productivity in the district of Cuispes. A sample of 50 producers was analysed using a quantitative approach and PLS-SEM models, and further assessed across four productive groups through R statistical software, using ordinary least squares (OLS) and analytic hierarchy process (AHP) models. The results indicate that land fertility plays a fundamental role in the production process. PLS analysis reveals that Management, Technology, and Knowledge exhibit moderate and low positive correlations of 0.680, 0.632, and 0.341, respectively, whereas Capital and Labour show negative correlations of 0.252 and 0.400 with productivity. Group B excels in Land, Capital, and Technology (AHP: 0.44), demonstrating significant productive potential. OLS results confirm that the combination of Technology and Land is critical to agricultural success. Group D performs well in Knowledge and Technology (AHP: 0.25), and OLS identifies it as the second most significant group in terms of Technology use. Groups A and C score lower (AHP: 0.10 and 0.25), with a negative impact according to OLS; these groups require improvements in production methods and management practices to become more competitive in the market. It is concluded that Group B is the most productive sector, followed by Group D, both representing the most profitable activities in the district. Certain production factors should therefore be developed further, and public or private institutions should strengthen agricultural productivity through targeted public policies
Keywords
PLS analysis; analytic hierarchy process; ordinary least squares; capital; management; technology
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