Market Intelligence and Gravitational Model to Identify Potential Agricultural Export Markets in the Lambayeque Region, Peru, 2015–2024

Abstract

High-quality agricultural products from the Lambayeque region have contributed to the growth of Peru’s agro-export sector and increased international trade. However, the need for agricultural exports to be more resilient and sustainable is demonstrated by the fact that markets are still concentrated, logistical costs are high, and global demand is constantly shifting. The purpose of this study is to use a gravity-based trade model and market intelligence techniques to analyse the agricultural exports from the Lambayeque region between 2015 and 2024. Using official data from the World Bank, AZATRADE, CEPII, and MINCETUR, we employed a quantitative explanatory approach. The results show that the concentration of businesses has significantly decreased while the value of exports has increased steadily. The Herfindahl–Hirschman Index increased from 6209 in 2015 to 1349 in 2024, and export destinations have become slightly more diverse. Exports are negatively impacted by geographic distance, but free trade agreements greatly benefit them. There is a lot of export potential in markets like Finland, Indonesia, Austria, Bolivia, and Vietnam. However, Israel and Hong Kong appear to be full. Overall, the findings indicate that Lambayeque’s export performance has improved, but it still runs the risk of becoming overly focused on a single sector. Long-term sustainability of the region’s agricultural exports depends on enhancing logistical infrastructure, bolstering market intelligence, and promoting regional diversity.

1. Introduction

International trade and globalisation have made economies more dependent on each other. This has changed how agricultural goods are made, sold, and eaten, and it has also made it easier for poorer countries to join global value chains. This strategy has improved the chances for diversification and made the international market more competitive in agricultural commerce, where access to outside markets is important for economic [1,2]. The steady growth of the agro-export industry in Latin America has been helped by the use of global quality standards, new technologies and ways of doing things, and the modernisation of irrigation systems. But there are still structural problems, especially those related to market concentration and being more sensitive to changes in international prices and demand [3].

Peru has become one of the top exporters of high-value agricultural products, especially fresh fruits and vegetables. This is because some regions have a big effect on the country’s agro-export performance. The Lambayeque region has become one of the most important places for agro-exports. But to keep this growth going, you also need to have enough production capacity, know how to manage strategic knowledge, and use market data effectively. In this regard, market intelligence has become a vital tool for identifying export opportunities, forecasting market trends, and enhancing decision-making processes at both corporate and regional levels decisions [4,5,6].

Recent empirical research has enhanced conventional gravitational models by incorporating new factors such as export resilience, sustainability, and the application of market intelligence techniques, particularly within the realm of agricultural commerce in emerging nations. These methodological advancements facilitate a more comprehensive evaluation of export potential and a deeper understanding of the structural determinants affecting trade flows, while also providing a more robust analytical framework for assessing export performance and diversification strategies [7].

The Lambayeque region is a particularly relevant case study due to the consistent growth of its agricultural exports and the rising necessity to analyse the distribution and structure of its destination markets to mitigate excessive trade concentration. The significant increase in the FOB value of regional agricultural exports from 2000 to 2023 [8], shows how hard it is to come up with a diversification strategy that can make the business less sensitive to changes in demand and prices.

Even though the number of destination countries has grown over time, we still need to do more research to find markets that can hold more, are more stable, and have more room to grow in the long run.

Agriculture is a big part of the Peruvian economy. It helps the region grow, create jobs, and boost GDP. Lambayeque is well-known in this field for growing crops like blueberries, avocados, mangoes, grapes and organic bananas that are in high demand around the world [9]. These products, which have a lot of economic, cultural, and nutritional value, are well-positioned to take advantage of the growing demand for healthy foods around the world [10,11,12]. Despite these competitive advantages, the region continues to confront challenges such as firm concentration, market dependence, and insufficient analytical skills necessary for the development and implementation of effective export diversification strategies [13].

The objective of this study is to employ market intelligence techniques alongside a gravity model to evaluate agricultural exports from the Lambayeque region from 2015 to 2024. The study’s specific objectives are to identify the structural factors affecting trade, assess the level of market diversity and concentration, and evaluate export potential in target markets.

This study contributes three significant additions to the existing body of literature. Unlike studies done at the national level, this one provides empirical evidence at the subnational regional level, which is a type of analysis that has not received much attention.

Second, it uses export concentration analysis, gravity-based trade models, and market intelligence methods all at once to look at export performance and the potential for diversification. Third, it finds overseas markets with unexplored export potential, producing pertinent data for the formulation of public policies meant to encourage sustainability, innovation, and diversification in the Lambayeque region’s agro-export industry.

2. Literature Review

2.1. Export Diversification, Competitiveness and Market Intelligence

Some of the structural and strategic factors that affect the agro-export sector’s competitiveness are product quality, the use of new technologies, environmentally friendly practices, logistical efficiency, and the smart use of market information to help with international integration [14,15]. Studies show that countries and regions that successfully incorporate these elements generally achieve superior export performance and exhibit greater resilience to external shocks.

The literature indicates that an overreliance on a limited number of enterprises or destination markets diminishes the dynamism of export growth, rendering them more vulnerable to fluctuations in demand, trade barriers, and external shocks [16]. From a strategic point of view, diversifying exports to different places is seen as an important way to stabilise agricultural exports and lower trade-related risks [17].

Because of this, market information is now a very important tool for encouraging export diversification. Even with analytical and logistical structural limitations, market intelligence helps businesses and regions change their supply to meet the needs of different markets in terms of culture, logistics, and rules. This facilitates informed decision-making and the identification of new business opportunities [18,19].

2.2. Concentration of Exports and the Herfindahl–Hirschman Index

The Herfindahl–Hirschman Index (HHI) is a widely utilised metric in the literature for assessing the degree of export diversification and market dependency. This index provides a synthetic and comparable measure for assessing the competitive structure of global commerce, as it considers the distribution of exports among companies or destination markets [20].

Lower HHI values show a stronger and more varied export structure, while higher values are often linked to being more vulnerable to outside shocks and less able to compete [21]. Fewer studies incorporate the HHI into a more extensive analytical framework that combines concentration metrics with trade flow models and market intelligence tools, even though numerous investigations utilise it as a descriptive indicator [22]. This integration enables a more comprehensive assessment of export performance and diversification trends, especially at the regional level [23].

2.3. Institutional, Macroeconomic and Sustainability Factors in Agricultural Exports

The literature underscores the impact of macroeconomic factors, such as global commodity prices and exchange rates, alongside domestic productive capacities, on export diversification and specialisation patterns, in addition to firm and market-specific factors [24]. It is also believed that an institutional framework that supports agro-export growth is necessary. Institutions such as MINCETUR, MIDAGRI, and PROMPERÚ play an essential role in promoting commerce, technological progress, and continuous productivity growth in Peru.

Also, logistics infrastructure and customs efficiency are often mentioned as important factors that affect export competitiveness. This is especially true for perishable agricultural products, where delivery times and transportation costs have a direct effect on market access [25]. Recent literature has underscored the growing importance of sustainability factors, including the implementation of clean technologies and voluntary sustainability standards, as critical components of long-term competitiveness in agricultural export markets [26].

Advancements in agricultural digitalisation have further augmented productivity and export potential [27,28,29]. Their effectiveness, especially in developing and emerging economies, depends on the creation of inclusive value chains and the strengthening of institutional and logistical.

2.4. Gravity Models and Analysis of Export Potential

Gravity models have become a fundamental aspect of empirical research on international trade due to their robust framework for evaluating bilateral trade flows based on economic size and geographic distance [30,31]. Empirical studies in Latin America consistently validate the positive influence of origin and destination GDP and the negative influence of distance on regional export flows [32,33].

Recent research has expanded gravity models to incorporate institutional and strategic factors such as free trade agreements, sustainability considerations, and the utilisation of market intelligence tools alongside traditional gravity variables [34]. This advancement enables a more comprehensive assessment of export potential, particularly in agricultural trade within emerging economies. Export potential analysis has become increasingly important as a policy-oriented approach for identifying untapped markets and guiding export diversification strategies by comparing actual trade flows with gravity-based projections.

2.5. Positioning of the Research

This study employs a gravity-based trade model, export concentration indicators, and market intelligence techniques to evaluate agricultural exports from the Lambayeque region from 2015 to 2024. The study addresses deficiencies in the literature, which has predominantly focused on national-level analyses or product-specific methodologies, by adopting a subnational and aggregated regional perspective. It also provides empirical evidence relevant to regional export diversification and sustainable development strategies [35,36,37].

3. Materials and Methods

The study adopts an explanatory quantitative approach based on econometric analysis of the export performance of the agricultural sector in the Lambayeque region during the period 2015–2024. The design is longitudinal and non-experimental, given that the variables of interest are not manipulated and secondary data from official sources are used [38].

3.1. Data Sources and Variables

The data used comes from databases and official sources that are distinguished by their consistency and statistical quality. The AZATRADE platform, a Peruvian market intelligence platform that collects official information from the institutions responsible for recording foreign trade transactions, was the source of data on agricultural exports in Lambayeque, providing access to up-to-date, verifiable, and high-quality data on the region’s export operations [8].

The population of Peru and its trading partners, as well as Peru’s gross domestic product (GDP) and the GDP of the countries to which it exports, are demographic and macroeconomic variables used in the research. These data were extracted from the World Bank’s official databases. In addition, the geographical distances between Peru and the destination countries are calculated using the CEPII’s GeoDist database, which provides standardised measurements between the world’s most important economic centres. Furthermore, information on the existence of trade agreements, including bilateral and multilateral free trade agreements (FTAs), was obtained from the official databases of Peru’s Ministry of Foreign Trade and Tourism (MINCETUR) [39]. In general, these sources ensure that the comparison and consistency of the variables used in the study are adequate.

The selected variables capture the main structural determinants of trade flows, while recognising that qualitative aspects, such as institutional quality, product differentiation and firm-level strategies, are beyond the scope of this quantitative specification [40].

3.2. Procedure

Herfindahl–Hirschman Index (IHH)

The Herfindahl–Hirschman Index (IHH) is a common tool in economics for measuring how diverse exports are or how concentrated the market is in a certain area or industry. Orris C. Herfindahl (1950) [41] and Albert O. Hirschman (1945) [42] first suggested this indicator. Later, it was recognised as a very important tool for studying the competitive structure of markets [43]. To find the IHH, you add the squares of each agent’s or product’s relative share of the total. This makes it possible to find out if exports are more evenly spread out among a number of players or if they are mostly going to a small number of players [44].

A high Herfindahl–Hirschman Index (IHH) score in international trade means that exports are very concentrated, which means that the country depends on a small number of goods or trading locations. Conversely, a low index score signifies greater diversity, which correlates with an export structure that is more competitive, resilient, and sustainable amid external volatility. The IHH is a good tool for looking at the sustainability and composition of export performance in regions or countries because it gives a single number for the balance and spread of trade flows [45].

In this study, the Herfindahl–Hirschman Index (IHH) was applied to assess the level of concentration of agricultural exports from the Lambayeque region during the period 2015–2024. For the first calculation, data from the AZATRADE platform was used, specifically information on exporting companies and the total value exported (FOB) recorded in each year of the period analysed.

The formula used was the same as that traditionally used in concentration analyses:

$$HHI=\sum _{i=1}^n{s}_i^2$$

where

$HHI$: represents the Herfindahl–Hirschman Index, which measures the degree of concentration of exports.

$s_i$: indicates the relative share of each exporting company in the total agricultural exports of Lambayeque in a given year. This share is obtained as:

$$s_i={\displaystyle \frac{X_i}{\sum _{i=1}^n{X}_i}}$$

$X_i$ is the value of exports (FOB) of the company $i$.

$n$: corresponds to the total number of exporting companies considered in the analysis.

In operational terms, for each year of the period 2015–2024, the total set of exporting companies in the agricultural sector was identified and the individual share of each firm in the total annual export value was calculated. These shares were then squared and added together to obtain the Herfindahl–Hirschman Index (IHH) for each year. This procedure made it possible to assess the degree of business concentration within the sector, where higher index values reflect greater market concentration and, consequently, a lower level of competition or export diversification [46].

Similar to the calculation of the IHH applied to destination markets, a second analysis was carried out to assess the level of concentration of the destination markets for agricultural exports from the Lambayeque region during the period 2015–2024. In this case, the same data from the AZATRADE platform was used, specifically the variables corresponding to the destination countries and the FOB value exported to each of them in each year of the period analysed. The same Herfindahl–Hirschman Index (IHH) formula was used for this calculation.

This procedure made it possible to estimate the degree of geographical concentration of agricultural exports from the Lambayeque region, enabling us to identify whether export activity is diversified across multiple destinations or, on the contrary, concentrated in a limited number of markets.

Gravity model procedure

The gravity model has become established in economic literature as a fundamental analytical tool for examining and quantifying international trade flows between two countries. Its name derives from the law of universal gravitation formulated by Isaac Newton in 1687, which states that the force of attraction between two bodies is directly proportional to the product of their masses and inversely proportional to the distance between them. Similarly, the gravity model in economics posits that the volume of trade between two nations increases with the economic size of both and decreases as the costs associated with geographical distance increase [47].

The gravity model was first applied by Tinbergen (1962) to analyse bilateral trade between countries, demonstrating that trade flows are determined mainly by the economic size of the nations involved and their geographical distance [48]. However, it was Anderson’s work (1979) [49] that gave this model a more robust theoretical foundation by developing a formulation based on a system of structural equations. This advance gave it greater analytical consistency and predictive power, consolidating it as an essential tool for studying international trade patterns.

Tinbergen’s Gravity Model Formula (1962)

The basic model proposed by Tinbergen (1962) [50] to explain bilateral trade between two countries is expressed as:

$$X_{ij}=A\cdot {\displaystyle \frac{Y_i^{\alpha }\cdot Y_j^{\beta }}{D_{ij}^{\gamma }}}$$

where:

$X_{ij}$: represents the value of exports or trade flow from country $i$ to country $j$;

$Y_i$ and $Y_j$: are the income levels or Gross Domestic Product (GDP) of countries $i$ and $j$, respectively;

$D_{ij}$: corresponds to the geographical distance between the two countries, which acts as a barrier to trade;

$A$: is a constant that groups together factors common to all trade relations (e.g., technology or global trade conditions);

$\alpha ,\beta ,\gamma$: these are parameters that are estimated empirically and reflect the sensitivity of trade to changes in the variables of economic size and distance.

To facilitate econometric estimation, Tinbergen transformed the model into its linearised logarithmic form:

$$\ln X_{ij}=\ln A+\alpha \ln Y_i+\beta \ln Y_j-\gamma \ln D_{ij}+{\epsilon }_{ij}$$

where ${\epsilon }_{ij}$ represents the random error term.

In its practical application, the gravity model is formulated based on the most important explanatory variables of international trade. It includes indicators linked to national production, population, and various political, cultural, and geographical elements that affect the strength of trade between nations. These factors make it possible to capture the structural features of the economies involved, as well as the barriers or facilities arising from their institutional and geographical context, which gives the model a greater capacity to explain and predict in the study of the determinants of foreign trade [51].

When employing the gravity model to examine global trade flows, it is crucial to take into account several methodological considerations to ensure the accuracy and empirical validity of the results. A crucial aspect of this process is the econometric specification of the model, which determines the optimal functional approach for depicting the relationships among variables and evaluates the accuracy and consistency of the resulting estimates from a statistical standpoint, despite possible challenges related to endogeneity, heteroscedasticity, or autocorrelation [52].

Although the gravity model estimated in this study follows a static specification, the use of panel data over the 2015–2024 period allows for capturing the temporal evolution of export flows and market concentration patterns. The longitudinal structure of the dataset reflects gradual structural adjustments in trade relationships, including export expansion, diversification processes, and changes in market dependence over time. Therefore, while explicit dynamic terms are not incorporated, the model provides meaningful insights into the underlying driving mechanisms of export growth [53].

The data used to estimate the gravity model come from official sources and databases widely recognised for their statistical reliability. The information collected is summarised in

Table 1. Data, variables and sources.

Variable	Definition of Variable	Source
Exp	Total exports from Lambayeque to the destination country, expressed in current dollars.	AZATRADE
Gdp_origin	Gross Domestic Product (GDP) of the country of origin (Peru or Lambayeque region), expressed in constant dollars.	World Bank
Gdp_dest	Gross Domestic Product (GDP) of the destination country, expressed in constant dollars.	World Bank
Pop_dest	Total population of the destination country.	World Bank
Dist	Geographical distance (in kilometres) between the capital of Peru and the capital of the destination country.	CEPII (GeoDist database)
FTA	Dichotomous variable (dummy) that takes the value of 1 if there is a Free Trade Agreement (FTA) between Peru and the destination country, and 0 otherwise.	Peruvian Ministry of Foreign Trade and Tourism (MINCETUR)

, which systematically presents the variables used and their respective sources, ensuring the transparency and reproducibility of the analysis.

In this study, which focuses on analysing the diversification of agricultural exports from the Lambayeque region using the gravity model, a data set corresponding to the period 2015–2024 was used. The information collected covers exports to 100 destination countries during that time interval, considering the value exported under the FOB modality as the main variable. Macroeconomic and structural variables relevant to the model estimation were also incorporated, including Peru’s gross domestic product (GDP), the GDP of the destination countries, the Peruvian population, the population of the importing countries, and the geographical distance between Peru and each destination country. In addition, a dichotomous variable was included to represent the existence of Free Trade Agreements (FTAs) between Peru and its trading partners. Together, these metrics constitute the set of variables used in the estimation of the gravity model applied to the analysis of agricultural export flows.

$$\ln \left(EX{P}_{ijt}\right)={\beta }_0+{\beta }_1\ln \left(PI{B}_{it}\right)+{\beta }_2\ln \left(PI{B}_{jt}\right)+{\beta }_3\ln \left(PO{P}_{jt}\right)+{\beta }_4\ln \left(DIS{T}_{ij}\right)+{\beta }_{5}TL{C}_{ij}+u_{ij}+{\epsilon }_{ijt}$$

where:

ln (EXP_ijt): natural logarithm of exports from Lambayeque to country j in year t.

ln (GDP_it): logarithm of the GDP of the exporting country (Peru/Lambayeque).

ln (GDP_jt): logarithm of the GDP of the importing or destination country.

ln (POP_jt): logarithm of the population of the destination country.

ln (DIST_ij): logarithm of the geographical distance (in kilometres) between Lima and the capital of the destination country.

FTA_ij: dichotomous variable that takes the value of 1 if there is a free trade agreement and 0 if there is not.

u_ij: specific unobserved effect of each country (fixed or random effect).

ε_ijt: idiosyncratic error term.

EViews 14 (Econometric Views) software is a statistical and econometric analysis tool widely used in applied research in the fields of economics, finance and social sciences. Its versatility and ability to handle time series, cross-sectional and panel data make it an ideal tool for estimating and validating complex econometric models, such as the gravity model used in this study [54].

Annual data for the period 2015–2024 were used, which were imported in Excel format and organised into a balanced panel workfile in EViews software. This panel consisted of 100 cross-sections (destination countries) and 10 periods (years), which allowed for the analysis of temporal dynamics and structural differences between trading partners. Subsequently, the variables were transformed using natural logarithms in order to linearise the functional relationships, reduce heteroscedasticity problems and facilitate the interpretation of the estimated coefficients in terms of elasticities. The transformed variables included the value of exports (export_fob), Peru’s gross domestic product (gdp_peru), the gross domestic product of the destination country (gdp_destination), the population of the destination country (population_destination), and the geographical distance between Peru and the partner country (distance_km).

Within the framework of the gravity model, econometric estimates were made using EViews software, through three panel data specifications: Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects and Random Effects. Firstly, the Pooled Ordinary Least Squares (Pooled OLS) estimation is the simplest method, combining all observations without distinguishing between units or periods, under the assumption of parameter homogeneity.

Sensitivity analysis and robustness checks

Before making the gravity model, the dataset was carefully checked to make sure it was consistent and reliable for analysis. The data description correctly pointed out that the sample size went down because observations that did not have information about the main explanatory factors were left out. We replaced these observations with a very small positive constant (X = ε) because the gravity model is written in linear logarithmic form and the dataset has export flows with zero values. This method keeps track of information about trade relations that are either nonexistent or just starting to develop, and it also allows for the use of the logarithmic transformation. Even though choosing this constant is inherently subjective, ε was set low enough to reduce any possible effect on the predicted coefficients.

The data was not winsorised or trimmed in any way. Instead, logarithmic transformations of continuous variables were used to make the distribution less uneven, limit the effect of extreme values, and make it easier to understand the predicted coefficients in terms of elasticities. After cleaning, organising, and standardising the data in Microsoft Excel to make sure that the units of measurement and time were all the same, we used EViews 14 and IBM SPSS Statistics 27 to run gravity model estimates.

We did a number of robustness checks to see how strong the gravity model estimates were and how much they changed based on sample selection and model specification. To accommodate potential structural changes at the onset of the observation period, the model was initially re-estimated utilising a different sample period that excludes the initial years (2015–2016). The main coefficients, especially those related to geographic distance and free trade agreements, kept their predicted signals and statistical significance.

Second, due to its limited statistical significance in the reference specification, an alternative gravity model specification was developed that excluded the population variable of the destination country. The results remained qualitatively consistent, indicating that the principal conclusions are unaffected by the inclusion or exclusion of specific explanatory variables.

Finally, the sensitivity of the results was further tested by comparing several panel data estimators that are often used in trade gravity models. The pooled ordinary least squares (Pooled OLS) method was the benchmark estimator. It included all observations and assumed that the parameters were the same for all of them. This approach is simple, but it does not take into account country-specific variability that is not already known [55]. To address this limitation, a fixed effects (FE) specification was employed, allowing for country-specific intercepts that account for structural, institutional, and cultural factors that remain constant over time and influence trade flows [56]. Also, a random effects (RE) model was estimated using the EGLS method with Swamy-Arora variance components. This was done under the assumption that differences between countries are random and not related to the explanatory variables. When this assumption is true, the RE estimator works better and lets the results be applied to a bigger group of people [57].

Hausman test

The Hausman test allowed us to determine the most appropriate specification between the fixed and random effects models. Given that the p-value (Prob. Chi-Sq) was greater than 0.05, the null hypothesis was not rejected, concluding that the random effects model is the most appropriate for explaining agricultural export flows from the Lambayeque region in the period 2015–2024 [58] (See

Table 2. Hausman Test estimation with Fixed Effects and estimation with Random Effects.

Test Summary	Chi-Sq. Statistic	Chi-Sq. d.f.	Prob.
Cross-section random	0.776222	3	0.8551
Variable	Fixed	Random	Var (Diff.)	Prob.
LN_GDP_ORIGIN	−0.123955	0.860254	1.653004	0.444
LN_GDP_DEST	0.042701	0.073631	0.004893	0.6584
LN_POP_DEST	8.005503	−0.14386	105.199169	0.4269

).

Consequently, the gravity model was estimated using the EGLS (Estimated Generalised Least Squares) method under a random effects scheme, which allowed us to capture the unobserved heterogeneity between countries and obtain more efficient estimates of the coefficients.

However, diagnostic tests showed contemporary correlation and heteroscedasticity between cross-sectional units, which could have a negative impact on the validity and efficiency of standard errors. The model was re-estimated to address these difficulties, using the Panel-Corrected Standard Errors (PCSE) method according to the Period SUR (Seemingly Unrelated Regressions) structure, which was suggested by Beck and Katz in 1995 and revisited [59]. This method allows standard errors to be corrected without altering the estimated coefficients, resulting in more robust and reliable conclusions.

In summary, the final model, EGLS with random effects and PCSE correction, provides consistent and statistically valid estimates, strengthening the reliability of econometric inferences about the determinants of international agricultural trade in the Lambayeque region during the period analysed.

Calculation of market potential

Post-estimation stage of the gravity model: calculation of market potential

Once the gravity model had been estimated in its log-linear form using the random effects approach, this specification was used to estimate the potential exports from the Lambayeque region to each destination market. In the first stage, logarithmic predictions were generated from the coefficients obtained in the model estimation. The equation estimated in EViews was stored under the identifier re, from which the variable ln_pred was constructed, representing the expected value of exports (in logarithms) for each country and year, based on their economic characteristics, gross domestic product, population, geographical distance, and the existence of a free trade agreement (FTA) with Peru.

Subsequently, the model residuals (u_re) were calculated in order to correct the retransformation bias generated when converting estimates expressed in logarithms to level values. To this end, the correction factor proposed by Duan (1983), known as the smearing estimator, was applied, which is obtained by calculating the average of the exponential of the estimated residuals. This procedure allows the predictions to be adjusted and unbiased values to be recovered at the original level of exports, ensuring a more accurate estimate of expected trade flows [60].

With this adjustment, potential exports were obtained as:

$${\widehat{EXP}}_{ijt}=e^{\widehat{\mathrm{l}\mathrm{n}\left(EX{P}_{ijt}\right)}}\times e^{{\bar{u}}_{ijt}}$$

where $\widehat{\mathrm{l}\mathrm{n}\left(EX{P}_{ijt}\right)}$ is the logarithmic prediction and $e^{{\bar{u}}_{ijt}}$ is the correction factor (smearing).

Finally, the market potential metric was constructed by comparing potential exports with actual observed exports:

$${\mathrm{Potencial}}_{ijt}={\displaystyle \frac{{\widehat{EXP}}_{ijt}-EX{P}_{ijt}}{EX{P}_{ijt}}}\times 100$$

Thus, positive values indicate that, according to the predictions of the gravity model, the Lambayeque region has untapped export potential to the corresponding market. Values close to zero reflect a level of trade consistent with the expected behaviour according to the economic fundamentals of the model. Negative values suggest the existence of an overexported or saturated market, in which the current volume of exports exceeds the estimated potential level (See

Table 3. Market classification.

Classification	Criterion	Interpretation
Opportunity	Potential > 50	Market with high commercial expansion capacity. Peru exports well below its estimated potential.
Emerging	10 < Potential ≤ 50	Market with moderate growth opportunities.
Consolidated	−10 ≤ Potential ≤ 10	Balanced market; actual trade is in line with expectations.
Saturated	Potential < −10	Overexploited market or with little room for expansion.

).

Based on this indicator, a market classification was developed:

For the analysis of commercial opportunities, the last year of the sample (2024) was taken as a reference and the destination countries were ranked from highest to lowest market potential. This analysis made it possible to identify the destinations with the best economic and commercial conditions according to the model’s variables. Finally, based on these results, a representative table was constructed that visually shows the 10 countries with the greatest export potential, facilitating the interpretation and comparison of the findings, and a table with the countries that are already saturated.

Although the gravity model and concentration indices are robust tools that provide consistent, replicable and comparable results over time, they have limitations inherent to their quantitative nature. These approaches do not adequately capture relevant qualitative factors, such as product reputation and differentiation, the phytosanitary policies of destination countries or business strategies for positioning in international markets. Furthermore, the information sources used, such as the AZATRADE database, depend on official customs records, which may be delayed in updating or contain minor omissions in statistical reporting. Nevertheless, the integration of statistical and econometric techniques strengthens methodological rigour, guarantees the internal validity and analytical consistency of the results, in accordance with the methodological standards of high-impact scientific literature, contributing to reducing possible estimation biases [61].

In accordance with the Research Ethics Code of the Universidad Cesar Vallejo (2024) [62], the principles of honesty, scientific integrity, and good practices in R&D&I were incorporated into the conduct of this study. Emphasis was placed on methodological rigour, objectivity, compliance with the authors’ intellectual property standards (APA 7th edition standards) and adherence to the principles of integrity and originality.

4. Results

Between 2015 and 2024, the agro-export sector in the Lambayeque region grew. The value of agricultural exports went up steadily, and the number of goods and markets available also grew slowly. This trend shows that the production organisation is becoming more dynamic. Trade agreements, the professionalisation of business management, and the use of technology have all made these areas more competitive. In very competitive international markets, blueberries, mangoes, avocados, and grapes have all gained market share. They are also the main reasons why the FOB value of exports has gone up over the time period being looked at.

Table 4. Agricultural exports from the Lambayeque region.

INDICATORS	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
Export value in thousands (FOB USD)	319,689	286,661	301,691	443,459	601,910	667,203	844,530	852,333	805,839	912,320
Net Weight (T)	201,261	197,215	215,899	289,609	315,329	356,938	417,545	431,643	360,214	350,832
Blocks	13	13	13	13	13	13	13	13	13	13
Number of Exporting Companies	119	119	141	166	160	156	183	190	186	209
Number of Target Markets	71	71	66	70	81	78	78	70	74	71

shows that the Lambayeque region’s agricultural exports grew steadily in both value and volume from 2015 to 2024. The FOB value grows a lot from 2018 to 2024, with only small changes in the first few years of the period. This means that the regional agro-export sector is slowly coming together.

The rise in net export weight shows that price changes are not the only thing driving this growth; higher productivity and better logistics are also playing a role. The recent moderation in volume is in line with goals for agricultural specialisation and shows a shift towards crops with higher value added.

The number of trade blocs remained stable over time, indicating institutional stability and suggesting that the use of existing trade agreements has been the main driver of export growth. The significant rise in the number of exporting companies shows that the company’s base has grown and the concentration of production has gone down.

Last but not least, the number of destination markets is still relatively stable. This suggests that major geographic diversification has not been the main reason for export growth, but rather the increase in flows to already established markets. To make regional exports more stable over time, this pattern shows how important it is to strengthen policies that help businesses enter new markets.

Shows that a lot of agricultural exports come from a small number of places with a lot of agro-industrial specialisation. Figure 1 shows the Lambayeque region’s relative ranking in the national ranking of agricultural exports for 2024. It also shows how concentrated agro-export commerce is in a small number of regions. Lima, Ica, La Libertad, and Piura are at the top of the list with a lot more people. This shows that the industries are very big, specialised, and well-organised.

Lambayeque is still far behind the best regions, but its ranking in the upper-middle range in this context shows how important it is to the national agro-export system. This difference shows that there are structural differences in the logistics infrastructure, how well it fits into international value chains, and how well it can handle large amounts of exports.

Lambayeque’s position is still important, though, because it is smaller than other places and has a more diverse production structure and a steady rise in the number of businesses that export goods. This pattern suggests an expansion strategy that is less focused on concentration and more focused on the gradual integration of new players, which could eventually lead to increased sector resilience.

The results show that Lambayeque is doing well and has a lot of room to grow, even though it has not yet reached the export maturity of the country’s biggest agro-industrial areas. This is because it can use strategies like diversifying production, improving logistics, and entering new markets (see Figure 1).

Both at the level of exporting enterprises (panel a) and destination markets (panel b), Figure 2 demonstrates a robust and positive correlation between the level of concentration and the value of agricultural exports (FOB). In both cases, the high values of the coefficient of determination indicate that a significant amount of the growth in export value is associated with higher concentrations.

According to the relationship observed, a small number of businesses with significant logistical and productive ability that can operate on a wide scale are primarily responsible for the increase in export value. This pattern is consistent with agro-export structures, in which a few large corporations manage a significant amount of commerce.

A significant portion of exports flow to a small number of nations, increasing the economy’s reliance on conventional markets, as evidenced by the positive correlation between FOB value and destination market concentration. Export value has increased as a result of this activity, but it also indicates that such markets are more vulnerable to external shocks, trade obstacles, and shifts in consumer demand.

Figure 2 illustrates the strong correlation between concentrated business and regional structures and Lambayeque’s export growth. This demonstrates how crucial it is to shift to diversification tactics that will enable long-term growth without increasing the vulnerability of the local agro-export industry (see Figure 2).

The changes in the Herfindahl–Hirschman Index (HHI) for agricultural export firms in the Lambayeque region between 2015 and 2024 are displayed in

Table 5. IHH by agricultural export companies in the Lambayeque region.

Exporting Company	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
Agrovisión Perú S.A.C.	0.0	0.1	1.6	4.5	12.6	14.2	17.2	17.7	20.9	21.1
Beta Agroindustrial Complex Ltd.	1.1	1.4	3.2	4.1	10.7	11.1	10.4	12.0	11.2	13.3
Bomarea Ltd.	0.0	0.0	0.0	0.0	0.0	0.6	3.2	5.3	5.1	6.7
Plantaciones del Sol S.A.C.	0.0	1.0	3.8	5.0	6.5	5.9	5.5	4.7	4.8	6.3
Hfe berries Peru S.A.C.	0.0	0.0	0.5	6.8	8.4	7.2	5.4	4.8	5.5	4.9
Exportadora el parque Perú S.A.C.	0.0	0.0	0.2	0.0	1.1	3.0	3.1	3.3	5.6	4.1
San Juan Agricultural Company Ltd.	4.1	4.7	5.9	4.6	2.8	1.4	1.7	2.6	3.6	3.9
Agroindustrias AIB S.A.	8.6	9.9	8.2	5.9	4.4	4.2	3.6	3.8	4.1	3.0
Quicornac S.A.C.	5.3	5.3	5.5	3.8	2.8	2.9	2.7	2.6	3.7	2.6
Blueberries Peru S.A.C.	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.3
V & f S.A.C.	0.0	3.3	3.3	2.1	1.6	1.4	1.3	1.5	1.6	2.1
Promotora y Servicios Lambayeque S.A.C.	0.0	0.0	0.0	0.0	0.0	1.8	1.7	1.6	2.1	1.8
Agricultural Pampa Baja S.A.C.	0.4	0.8	1.1	1.5	0.9	1.7	2.7	1.8	2.8	1.5
Agroindustria Frutos de Oro S.A.C.	0.0	0.0	0.0	0.0	0.0	0.0	0.3	0.2	0.8	1.4
Processadora Perú S.A.C.	2.5	3.1	2.5	1.2	0.8	1.1	1.1	1.0	1.1	1.4
Others	78.0	70.4	64.1	60.7	47.4	43.5	40.0	37.3	27.1	23.5
IHH	6209	5135	4288	3875	2676	2353	2119	1965	1470	1349

. It indicates that the distribution of exports underwent a significant shift. A limited number of dominant enterprises accounted for a significant share of the export value throughout the first few years of the era, as indicated by the high values of the HHI.

Beginning in 2018, the index will steadily decline, and by 2024, the concentration level will be significantly lower. Businesses are gradually diversifying, as evidenced by this declining trend. This is taking place as a result of new businesses entering the market and small and medium-sized enterprises’ increasing proportion in international agricultural commerce.

The shrinking size of the “Others” group indicates that the export value is being distributed more fairly across the various participants in the industry, despite the fact that some of the leading firms continue to hold a sizable portion over time. This trend indicates a change from a highly concentrated model to one that is more competitive and less dependent on a small number of exporters from a structural perspective.

Table 5’s findings demonstrate that a gradual decline in business concentration has coincided with Lambayeque’s increase in agricultural exports. For the long-term stability and well-being of the local agro-export industry, this is encouraging (see Table 5).

The Herfindahl–Hirschman Index (HHI) for the agricultural export destination markets from the Lambayeque region from 2015 to 2024 is displayed in

Table 6. IHH by destination markets for agricultural exports from the Lambayeque region.

Destination Markets	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
United States	37.2	36.1	37.9	36.0	42.3	36.0	37.9	40.1	39.3	42.6
Netherlands	19.5	19.3	19.9	23.1	20.5	24.4	23.7	24.2	25.9	26.4
Great Britain	0.0	0.0	0.0	0.0	10.2	10.4	8.4	6.8	7.3	5.4
Spain	3.8	3.9	3.8	3.9	3.4	4.1	4.0	3.2	4.7	4.1
Hong Kong	0.2	0.2	0.1	1.1	1.4	1.7	4.0	3.6	2.6	3.8
China	0.5	0.2	0.3	2.1	4.3	3.9	3.5	5.5	4.6	3.0
Colombia	1.4	1.7	0.9	0.7	1.2	0.8	0.6	1.1	0.8	1.4
Chile	1.5	1.5	0.8	1.4	0.8	1.3	2.3	1.2	2.0	1.3
South Korea (Republic of Korea)	2.1	1.4	0.6	0.9	1.3	2.0	2.4	1.6	1.1	1.3
Canada	3.3	2.0	1.7	1.8	1.7	1.6	1.5	1.4	1.0	1.1
Dominican Republic	0.0	0.1	0.2	0.4	0.1	0.2	0.3	0.3	0.5	0.8
Belgium	0.0	0.0	0.0	0.0	0.0	1.7	1.4	1.1	0.9	0.7
Puerto Rico	3.1	3.6	2.3	2.3	1.0	1.2	0.9	1.1	0.9	0.7
Taiwan	0.1	0.2	0.1	0.2	0.1	0.2	0.3	0.6	0.5	0.7
Mexico	0.5	1.1	0.8	1.4	0.9	0.7	0.4	0.5	0.9	0.6
Others	26.8	28.7	30.5	24.8	10.8	9.7	8.5	7.7	7.1	6.2
IHH	2527	2542	2788	2477	2472	2142	2200	2361	2377	2632

. This demonstrates the geographic concentration of regional international trade.

The economy is heavily reliant on a small number of markets, primarily the US and the Netherlands, as evidenced by the HHI being very high in the early years of the period under study. The index gradually declines starting in 2018 and reaches its lowest position in 2020. This implies that, concurrently with the addition of new markets and the company’s expansion into Asia and Latin America, there was a brief shift towards having more varied destinations.

However, the index has increased again in recent years, and by 2024, the degree of spatial concentration will have increased as well. This behaviour implies that exports are returning to historic markets, perhaps as a result of more predictable demand, lower logistical risks, and strengthening trade relationships during a period of significant global unpredictability.

From a structural perspective, the results show that although the region has made progress in diversifying its destinations, the export trend is still heavily dependent on a small number of markets. This result emphasises the need to improve spatial diversification tactics that lessen the vulnerability of the local agro-export industry to outside shocks and changes in the circumstances surrounding access to global markets (see Table 6).

The markets with the most potential for growth for agricultural products from the Lambayeque region between 2015 and 2024 were determined in this study using this methodology.

The estimated correlations are in line with the idea of international trade gravity, as demonstrated by the results in

Table 7. Results of the gravity model estimated using EGLS with PCSE correction (2015–2024).

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	8.953069	54.79638	0.163388	0.8702
LN_GDP_SOURCE	0.860254	0.264998	4.415868	0.0001
LN_GDP_DEST	0.073631	0.069607	1.056436	0.291
LN_POP_DEST	−0.12461	0.124614	−0.9993	0.3178
LN_DIST	−3.00873	0.967814	−3.10871	0.002
TLC	9.005814	1.481774	6.077724	0
Effects Specification
			S.D.	Rho
Cross-section random			6.395611	0.5221
Idiosyncratic random			6.118829	0.4779
Weighted Statistics
Weighted Statistic
R-squared	0.057906	Weighted Statistic (right)	Value (right)
Adjusted R-squared	0.053167	Mean dependent var		2.130897
S.E. of regression	6.11856	S.D. dependent var		6.287999
F-statistic	12.21921	Sum squared residual		37212.15
Prob (F-statistic)	0	Durbin-Watson statistic		1.399731
Unweighted Statistics
Unweighted Statistic	Value	Unweighted Statistic (right)		Value (right)
R-squared	0.262045	Mean dependent var		7.358588
Sum squared residual	76157.55	Durbin-Watson statistic		0.683937

. First, the country of origin’s GDP’s positive coefficient (0.860) implies that Peru’s economic expansion has a broad impact on regional exports; however, its lack of statistical significance suggests that Lambayeque’s export dynamism depends not only on the country’s macroeconomic performance but also on certain structural elements in the region, such as port infrastructure, logistics capacity, and productive diversification.

The expected link is maintained by the destination country’s GDP, which has a positive coefficient of 0.073. Markets with larger economies often import higher amounts of agricultural products. However, this variable’s modest statistical significance indicates that factors other than market size, like consumer preferences, phytosanitary requirements, and each nation’s import rules, affect exports. The destination country’s population, on the other hand, has a negative coefficient (−0.143), which may indicate that Lambayeque’s exports are targeted at nations with lower population densities but higher per capita incomes, where there is a greater demand for fresh, high-quality, and unique agricultural products.

The anticipated negative sign (−3.008) and the high level of significance of geographical distance (p = 0.0019) indicate that remoteness is a significant barrier to the region’s export competitiveness. This outcome is consistent with the perishable nature of Lambayeque’s agricultural supply, whose transportation and refrigeration conditions raise logistical costs as the distance to the ultimate market increases. Geographical proximity continues to be a key element that promotes intraregional trade because South American countries have shorter transit times and better storage conditions.

With a positive and highly significant coefficient (9.005; p < 0.001), the variable pertaining to the existence of free trade agreements (FTAs) is one of the model’s strongest tests. This result emphasises how vital Peru’s trade agreements with its principal trading partners are to the expansion of Lambayeque’s agricultural industry. Free trade agreements (FTAs), particularly those with the United States, the European Union, Chile, and Mexico, give institutional stability, mutual adoption of hygienic standards, and the removal of tariff obstacles, all of which enable the consolidation of exports.

This outcome is in line with the nature of bilateral trade models, where export flows are conditioned by a number of unobservable or challenging-to-quantify factors, including business strategies, non-tariff barriers, institutional quality, and consumer preferences, even though the gravity model’s adjusted R² is rather low. In this case, rather than relying on high goodness of fit, the model’s explanatory capacity is evaluated mainly on the basis of theoretical consistency, statistical significance, and the expected signs of the computed coefficients. The robustness and validity of the estimated connections are supported by the fact that the core variables, economic size, geographic distance, and the existence of free trade agreements, show the predicted indications and are statistically significant, in accordance with the literature.

According to the gravity model’s conclusions, Lambayeque’s export structure still maintains a certain level of concentration in conventional markets, but it has a lot of room to grow its global footprint. The region’s position as a competitive and sustainable agro-export hub that is in line with the dynamics of international agricultural trade might be strengthened by the strategic use of trade agreements, together with advancements in logistics and the promotion of unique products (see Table 7).

The gravity model estimation identified the principal factors affecting agricultural commerce in the Lambayeque region. Using the gravity model’s predictions, a market potential index was made that compares real exports to the “potential” exports that the model said would happen.

Table 8. Ranking of the 10 markets with the greatest potential for expansion according to the gravity model (2024).

Country_Dest	Export_Fob	Pred_Exp	Potential	Class_Cod	Classification
Finland	0.0001	10464941818	10464941818174900.00	3	Opportunity
Indonesia	0.0001	1704726878	1704726877711950.00	3	Opportunity
Vietnam	0.0001	1568561205	1568561204929680.00	3	Opportunity
Bolivia	0.0001	1147540781	1147540780758200.00	3	Opportunity
Austria	0.0001	88471417.47	8847141747049080.00	3	Opportunity
Mauritius	0.0001	2075824.92	2075824920231700.00	3	Opportunity
Estonia	0.0001	1595069.388	1595069388156870.00	3	Opportunity
South Africa	0.0001	1555959.349	155595934940342.00	3	Opportunity
Croatia	0.0001	1515956.645	151595654156973.00	3	Opportunity
Algeria	0.0001	1492799.317	1492799316730110.00	3	Opportunity

shows the markets that will have the most room for growth for Lambayeque’s agricultural exports in 2024. The table also shows a big difference between the current flows and the levels predicted by the gravity model. This great potential shows that the good structural conditions that have not been fully used yet.

The high per capita income, rising demand for healthy foods, stable institutions, and low levels of exports right now all point to the potential for European markets (Finland, Austria, Estonia, and Croatia). Trade flows have not gotten stronger because of physical distance and logistical problems, but Asian economies like Vietnam and Indonesia have a lot of room to grow and a growing demand for food.

Countries like Algeria and South Africa have a big market and a growing need for imported agri-food products, just like this. When you put all of these findings together, they suggest that the high potential is mostly due to strategic, logistical, or informational problems, not a lack of demand. This means that these markets are great places to focus on commercial intelligence and diversification strategies (see Table 8).

Table 9. Saturated markets according to estimated market potential (2024).

Destination Country	Export_fob	Pred_exp	Potential	Class_cod	Classification
India	518,444.94	609,316.6638	17.52774822	2	Emerging
Jamaica	940,516.31	925,977.1159	−1.54587368	1	Consolidated
Curaçao	35,311	34,248.42263	−3.009196484	1	Consolidated
Turkey	1,275,119.92	1,002,093.767	−21.411802	0	Saturated
Saudi Arabia	2,100,685.38	1,436,027.248	−31.64006085	0	Saturated
Trinidad and Tobago	1,779,067.47	842,321.2502	−52.65377708	0	Saturated
Israel	967,039.7	331,130.14	−65.75837165	0	Saturated
Cape Verde	461,060.82	16,046.02324	−96.51975997	0	Saturated
Lebanon	435,818.44	6990.649635	−98.39597204	0	Saturated
Hong Kong	34,262,817.48	363,119.7408	−98.94019299	0	Saturated

shows that the markets where Lambayeque’s agricultural exports are equal to or greater than the amounts predicted by the gravity model are consolidated or saturated in 2024. In these cases, the negative potential shows that there is a lot of export activity compared to the structural conditions of these markets.

Markets that are saturated, like those in Turkey, Saudi Arabia, Trinidad and Tobago, Israel, Lebanon, and Hong Kong, have a lot of flows, clear market niches, and a lot of regional exporters who have been there before. These places do not have much room to grow in the future because of things like mature demand, fierce global competition, and strict health and safety regulations.

Markets that are combined, like Jamaica and Curaçao, show export levels that are almost at their expected levels, which means that supply and demand are in balance. In these cases, it is better to focus on improving quality, making products stand out, and strengthening trade partnerships than on increasing export volumes.

The findings indicate that to reduce dependence on traditional destinations and enhance the sustainability of the regional export pattern, saturated markets require strategic approaches focused on consolidation and value addition, while further export growth should be emphasised in markets recognised as having significant potential (see Table 9).

5. Discussion

This study analyses agricultural exports from a comprehensive regional perspective to ascertain structural trade patterns, market concentration dynamics, and export potential in the Lambayeque region. This method does not fully account for product-level variation, but it is especially well adapted to capturing long-term trends and regional dynamics. Previous studies have shown that crops have very different demand conditions, phytosanitary requirements, and logistical limitations. Because of this, gravity-based export potential should be seen as a measure of relative opportunities rather than a precise prediction for every product [63].

The growth of high-value crops played a big role in the steady growth of Lambayeque’s agricultural exports from 2015 to 2024. This trend aligns with findings from other agro-exporting regions, where trade liberalisation and productive specialisation have enhanced competitiveness [64,65]. However, the persistent high concentration levels among exporting firms and within particular destination markets indicate insufficient structural diversification to keep pace with export growth. This finding corroborates concerns articulated in the literature that resilience cannot be guaranteed solely through export growth without the pursuit of diversification strategies.

The decline in the Herfindahl–Hirschman index indicates a gradual process of company diversification associated with the entry of new enterprises into the agro-export sector. This result aligns with research indicating that greater participation from small and medium-sized enterprises bolsters the sustainability and resilience of agricultural trading systems [66]. However, the deceleration in destination market diversification underscores existing structural risks. Proactive diversification policies are essential, as dependence on a limited number of external markets renders regional exports vulnerable to demand fluctuations, regulatory disruptions, and logistical challenges.

Due to post-pandemic developments like the revival of traditional trade routes and the normalisation of logistics conditions, target market concentration may have partially recovered between 2022 and 2024. However, because there is no shipment-level information or disaggregated data on logistical costs, this view should be considered contextual rather than causal.

From a theoretical point of view, the gravity model’s results back up the traditional reasons why trade is still important. Geographical distance has a negative effect, which is consistent with studies on perishable agricultural products, where delivery delays, cold chain requirements, and transportation expenses remain substantial obstacles [67]. Conversely, the advantageous and significant impacts of free trade agreements validate empirical evidence indicating that institutional frameworks mitigate trade frictions beyond tariffs, fostering regulatory compliance and improving market access [68,69]. These findings substantiate the notion that regional export performance derives enduring advantages from institutional collaboration.

The size of the market does not seem to be the main reason for Lambayeque’s agricultural exports, as shown by the fact that GDP and destination population are not statistically significant. In more developed markets, product quality, following phytosanitary rules, brand reputation, and sustainability features are all becoming more important. This interpretation aligns with research indicating the growing importance of reputational capital and uniqueness in the agri-food sector, especially in markets with a strong demand for sustainable and healthful products [70,71]. In this situation, product differentiation and market intelligence work together as strategic tools, letting exporters change their products to meet changing customer needs.

Looking at the market’s potential gives you more information about how to plan your trade strategy. Countries like Austria, Finland, Vietnam, and Indonesia have strong institutions and economies, but their trade levels are very low, which shows that there is still a lot of potential for them. This difference could be because of limited trade networks, differences in information, or problems with adapting to new rules and logistics. On the other hand, Israel, Saudi Arabia, and Hong Kong are examples of markets that are considered saturated. In these markets, trade partnerships have been formed, and any future growth may require a shift from volume-based strategies to branding, innovation, and quality improvement [24].

These findings underscore the importance of integrating market data and gravity-based analysis into regional export planning from a policy perspective. Trade promotion groups and government agencies can use export potential indicators to choose markets, use resources more wisely, and make plans for diversification that balance risk reduction and growth. To make Lambayeque’s agro-export industry more resilient and sustainable in the long run, structural problems like market concentration, logistical problems, and a lack of analytical capacity need to be fixed.

6. Conclusions

This study provides a comprehensive regional analysis of agricultural exports from the Lambayeque region for the years 2015 to 2024, employing a gravity-based trade framework, market intelligence methodologies, and export concentration metrics. This research provides subnational empirical data that elucidate structural trade patterns, diversification dynamics, and market opportunities relevant to regional export strategy, distinguishing it from the majority of prior studies that focus on national aggregates or specific commodities.

The results indicate that the regional agro-export sector is susceptible to logistical difficulties and external demand fluctuations, as continuous export growth has not been accompanied by sufficient geographical diversification. The ongoing concentration of destination markets underscores the importance of targeted diversification initiatives, even as diminishing firm-level concentration indicates a gradual enhancement in competitiveness. The long-lasting negative effects of distance and the positive effects of free trade agreements show how important institutional frameworks and logistics infrastructure are for agricultural trade performance. In practice, market potential research is a useful tool for picking where to export and making trade promotion plans that are stable and growing.

There are several limitations to this study that need to be pointed out. Many relevant factors that could affect agricultural exports are not included in the model due to limitations on data availability and comparability. These include: (i) phytosanitary and quality standards specific to destination markets; (ii) regional logistics performance indicators, like cold chain capacity and port efficiency; (iii) firm-level capabilities related to scale, branding, and contractual integration; (iv) exchange rate volatility at higher frequencies; and (v) non-tariff measures outside the scope of free trade agreements. The lack of these factors may contribute to the relatively low adjusted R2, which is a typical result in gravity models calculated using heavily aggregated trade data.

To enhance the understanding of adjustment processes in export diversification, subsequent research could expand upon this study by utilising dynamic econometric techniques, integrating qualitative indicators associated with market access and regulatory compliance, or incorporating data at the product or enterprise level. To ascertain the impact of institutional and logistical factors on agricultural export performance in diverse contexts, further research could examine comparative analyses across regions or countries.

7. Recommendations

Implementing particular institutional mechanisms that go beyond general policy recommendations is necessary to strengthen market intelligence. Local governments could create a Regional Agro-Export Market Intelligence Unit at the regional level with the goal of methodically gathering, combining, and analysing trade-related data from international demand databases, price monitoring systems, and customs records. To guarantee data consistency, analytical rigour, and policy alignment, this unit should work in tandem with national trade promotion and export organisations.

Additionally, the implementation of specialised training programs centred on data analysis, demand forecasting, and market segmentation for exporting firms may be greatly aided by public–private partnerships involving universities and trade associations. Businesses would be in a better position to recognise emerging markets, predict shifts in consumer preferences, and lessen information asymmetries at a comparatively low cost through these initiatives. Given that digital platforms and shared databases can concurrently serve a large number of firms, market intelligence investments are characterised by high scalability and low marginal costs.

Information-based policies are significantly more cost-effective than large-scale infrastructure projects. Specifically, they help diversify exports, lower market risk exposure, and improve exporters’ ability to make strategic decisions. These advantages are particularly important for areas looking to increase their market share abroad without having to make large capital investments.

Similar to this, Lambayeque’s logistics optimisation should place more emphasis on focused, economical interventions than on major infrastructure development. To cut down on transportation times and product losses, important steps include modernising post-harvest handling facilities, enhancing coordination between ports and production areas, and fortifying cold chain infrastructure. The operational bottlenecks that limit export performance are directly addressed by such measures.

From a policy standpoint, the primary objectives of logistics optimisation might be to strengthen cold-chain infrastructure, increase port access to distant markets, and improve the efficiency of customs for perishable goods. Even though this study does not conduct a thorough cost–benefit analysis, the geographic distribution of high-potential markets suggests that investments targeted at long-distance trade routes may yield higher marginal returns.

Local governments can facilitate these improvements through public–private partnerships by concentrating on shared-use facilities that produce collective benefits for multiple exporters. Simultaneously, companies can greatly improve export reliability without requiring large upfront capital by making comparatively small investments in packaging, storage, and traceability technologies. By lowering spoilage rates, transportation delays, and compliance costs related to sanitary and phytosanitary standards, these focused logistical improvements produce quantifiable efficiency gains.

The suggested policy measures purposefully highlight interventions with relatively low implementation costs and high long-term returns, even though a thorough quantitative cost–benefit analysis is outside the purview of this study. The main components of market intelligence investments are human capital development, digital tools, and institutional coordination, all of which typically require less funding than physical infrastructure projects. Similarly, the suggested logistical enhancements concentrate on making the most of current resources rather than pursuing extensive growth, making them more feasible for regional governments with limited funding.

This combined strategy gives public authorities economically viable policy tools while allowing exporting companies to implement incremental and scalable improvements. As a result, the suggested suggestions offer practical and affordable ways to improve regional export competitiveness and promote sustainable trade growth.