Two producers can achieve similar yields, market comparable quality fruit, and even operate within the same growing region. However, one may have skilled personnel, access to financing, technology, knowledge of their costs, water availability, and diversified marketing channels, while the other faces difficulties in obtaining financing, innovating, or responding to market changes.

Can we say that both are equally competitive? Probably not.

Production, yield, exports, prices, and cultivated area are fundamental indicators for evaluating agricultural performance, but they primarily describe results. Behind these indicators lie the capabilities that help generate, sustain, and respond to changes in the environment.

That question gave rise to a doctoral research project developed in Michoacán with a specific objective: to construct an Agricultural Competitiveness Indicator (ICA) for producers of blueberry.

From 42 items to seven factors

The study combined background information from the literature with information provided by actors linked to the industry of blueberryBased on that work, a quantitative instrument composed of 42 items was constructed and subsequently applied to producers.

Before proceeding with the indicator, both the internal consistency of the instrument and the appropriateness of applying a factor analysis were evaluated. The questionnaire obtained a Cronbach's alpha of 0,947 and a KMO index of 0,875, while Bartlett's test was statistically significant. Based on this background, exploratory factor analysis (EFA) was applied.

The model in figures

• 42 items evaluated
• 0,947 Cronbach's alpha
• 0,875 KMO index
• 7 factors identified
• 78,69% of the variance explained

The AFE identified seven factors, later interpreted in the study as dimensions of competitiveness:

Strategic Capabilities · Markets and Logistics · Governance · Production · Financing · Bureaucracy · Water

According to the author, these results reinforce a central idea of ​​the work: agricultural competitiveness is multidimensional. It depends not only on production levels or natural and commercial conditions, but also on the capabilities of the company and the environment in which it operates.

Source: Author's own elaboration, based on research.

Two methods, different interpretations

To complement the AFE, Random Forest was used, a machine learning algorithm employed in the research to identify which factors provided the most information to differentiate the levels of competitiveness.

Strategic Capabilities had the highest predictive importance, at 31,27%. This was followed by Financing, at 21,06%; Production, at 17,9%; and Water, at 12,61%. Markets and Logistics reached 8,98%, Governance 5,98%, and Bureaucracy 2,2%.

Source: Author's own elaboration, based on research.

In some dimensions, the results of both methods were very close. Strategic Capabilities, for example, represented 32,08% in the EFA and reached 31,27% predictive importance in Random Forest.

The most marked contrast appeared in Water. In the AFE it represented 4,52%, while in Random Forest it reached a predictive importance of 12,61%.

The difference does not necessarily imply a contradiction between the two methods. While EFA allows for the identification of the statistical structure of the factors, Random Forest provides a second perspective on their predictive importance within the model.

For the author, the case of water shows how a factor with less weight within the general structure can acquire considerable relevance by differentiating competitive profiles.

From analysis to indicator

Based on both analyses, the weightings used in the ICA developed for this study were established:

Source: Author's own elaboration, based on research.

With these weightings, the ICA was calculated individually and transformed to a scale of 0 to 100 points.

Same region, different competitive realities

Among the producers analyzed, the average ICA score was 58,24 points, with a median of 62,04. Individual results ranged from 6,78 to 86,93 points, with a standard deviation of 19,70.

Distribution of ICA results among the producers analyzed

Source: Author's own elaboration, based on research.

According to the author, this breadth helps explain why speaking of “Michoacán’s competitiveness” as a homogeneous condition may be insufficient. Within the same region, companies with very different combinations of capabilities and vulnerabilities can coexist.

Knowing the score is not enough. The potential of the ICA lies in explaining why a producer obtained that result.

Two companies could achieve a similar score and yet exhibit very different profiles. One might have strong entrepreneurial capabilities and a vulnerability related to water; the other, good production and financial conditions, but technological or commercial shortcomings.

Therefore, the indicator can be broken down by dimension to identify where strengths and weaknesses are concentrated.

The question is no longer just:

How competitive am I?

and becomes:

What would I need to improve to be more competitive?

Not all of these areas depend exclusively on the producer. Training, innovation, management, and technology adoption can be addressed primarily from within the company. Financing, infrastructure, water availability, regulation, and bureaucracy also involve institutions, associations, and authorities.

New varieties, higher quality requirements, pressure on natural resources, new producing countries and changes in commercial windows require constant adaptation.

Being efficient today does not necessarily guarantee being competitive tomorrow.

Could the ICA move towards international comparisons?

The ICA developed in Michoacán represents a first approach and still requires expanding observations and carrying out external validations before generalizing its results.

That first application leaves one question open:

What would happen if we could use comparable criteria to analyze producers and regions in Mexico, Peru, Chile, the United States, Morocco, China, or other countries?

Extending and validating the methodology in other countries would allow us to assess whether it can be used to compare different production systems.

According to the author, an international comparison could broaden the conversation beyond who produces or exports the most. It would also allow us to observe where capabilities are concentrated, where vulnerabilities emerge, and what differences exist between the various production systems.

The global industry of blueberry It has developed increasingly sophisticated tools to measure production, quality, condition, performance, and trade. Perhaps the time has come to also build tools to measure the capabilities that make those results possible.

Because the true value of measuring competitiveness isn't in knowing who's in first place. It's in discovering what we need to change to compete better.