Validating Technologies and Evaluating the Technological Level in Avocado Production Systems: A Value Chain Approach

Abstract

In agriculture, technologies support the productivity and competitiveness of production systems in value chains. In the last decade, the Colombian avocado sub-sector has expanded. However, little is known about its technological level (TL). The objectives of this study were (i) to understand the perception of value chain experts in terms of technological supplies and demands, (ii) to evaluate the TL in avocado production systems and (iii) to discover which socioeconomic characteristics impact the TL. The three stages were consultation with experts and parameterization of the TL, evaluation of the TL using multivariate methods and analysis of variables of the production system. The technological supply “By-products from seed” was of great importance, while “Branch girdling” was the least relevant. A total of 161 demands were identified, “Environmental management and sustainability” showing greater relevance. The analysis showed a low correlation between the qualification of experts and the use of technology by producers. The postharvest supplies had the lowest frequency of use. Producers were characterized according to TL: high (34.4%), medium (47.2%) and low (18.4%). A relationship was found between the TL and some variables of the production system. The gap found should be the basis for designing science and technology policies for the avocado sub-sector in Colombia.

1. Introduction

Technology is a large part of daily activities, from educational processes to agricultural work [1,2]. The concept of technology has been commonly applied to artifacts, tools or machinery, which are called hard technologies [3]. However, knowledge and its management are also classified as technologies, which seek to facilitate, optimize or improve processes. This has been called soft technology [4].

In agriculture, technologies have been used to intensify food production [5,6], where soft technologies are implicitly linked to management practices. Some relevant technologies include selecting varieties, sustainable soil management and smart farming for resource management. Recently, information and communication technologies have entered production systems [7]. In terms of technology, there are gaps and resources that give rise to sustainable and comprehensive processes [8]. This information facilitates the execution of improvement actions. Although technology is always present, technology users can decide to use it or not, which is called adoption [9].

Technology can support agriculture through business models, improved production, cost management, business diversification, energy efficiency, process optimization, etc. [7,10,11,12]. However, the target group must be known to allow technology transfer as well as pertinent and efficient research [13]. Some production systems, nevertheless, have technological lags as a result of low or null technological adoption, which generates limitations in productivity and economic results, dependence on intermediaries, modest innovation in the value chain, abandonment of productive activity and loss of labor [14,15]. Adamashvili et al., [16] affirm that state institutions are relevant to generating specific policies and financing research, that is applicable to production systems, thus, contributing to the development of agriculture.

Avocado (Persea americana Mill) cultivation has been very dynamic in recent years. The international trade in fruit increased from 770,084 tons to 2,745,842 tons worldwide between 2010 and 2020 [17] due to its high nutritional value [18]. In the tropics, Colombia’s avocado cultivation had the highest growth, going from 21,592 ha to 78,578 ha between 2010 and 2020 [19]. Exports from Colombia amounted to 40,906 tons for 2020 [17]. Despite this growth, some technological gaps persist in this crop in different geographic areas [20,21].

These technological gaps are associated with inequalities in access to technology, obsolescence and lack of knowledge on the part of producers. Amare et al. [22] affirm that greater participation in exportation processes is required by small avocado producers. On the other hand, Reints et al. [23] indicate that efforts should be made in the adoption of technologies related to efficient water management in avocado cultivation. Likewise, Baidhe et al. [24] draw attention to the need to investigate opportunities for the processing of by-products products to strengthen the industrial development capacities of this crop. In this sense, Ramírez-Gil et al. [14] found a relationship between the technological level (TL) of the production systems and the incidence of diseases in avocado agricultural systems in the neotropics.

Understanding the technological level (TL) in agricultural systems as well as the strengths and weaknesses in the technology adoption process allows us to respond to the needs of research and knowledge transfer. The World Intellectual Property Organization, WIPO [25] proposed measuring the TL at the country level based on five pillars: (i) human capital and research, (ii) market sophistication, (iii) sophistication of business, (iv) knowledge and technology products and (v) creative outlets. García-Muñiz et al. [26] established the TL based on quantitative and qualitative data. Additionally, Barrios et al. [27] proposed a technological adoption index, based on a pre-grouped system by panel of experts. Additionally, weighting of the technological degree has been reported in avocados based on the weight of different parameters or practices carried out in crops related to yield and quality variables and factors involved in decision-making by producers [14,28]. Additionally, socioeconomic characteristics have a high impact with respect to the TL in agriculture [27,29,30,31].

A gap was found in the evaluation of the technological degree in the entire value chain. This is the result of an expert consensus validated with field data for avocado production in the tropics. Ramírez-Gil et al. [20] affirm that, in order to achieve an improvement in the avocado value chain in Colombia, it is necessary to establish the technological level of production systems and to unify technological criteria that allow producers greater profitability. To respond to the gap in knowledge and provide tools to improve the avocado value chain, three objectives were proposed: (i) to understand the perception of value chain experts regarding technological supplies and demands, (ii) to evaluate the TL in avocado production systems and (iii) to discover which socioeconomic characteristics impact the TL. The hypotheses were that value chain experts give equal importance to all technologies, regardless of the link in the chain wherein they are located; that avocado production systems in Colombia have a low to medium TL; and that the TL is related to both the socioeconomic characteristics of the producers and the production systems. The results of this research will serve as input for measuring the TL of the avocado sub-sector. It will be the starting point in establishing improvement actions for the generation of technology that responds to the needs of the sub-sector and the planning of assertive knowledge communication strategies.

2. Materials and Methods

Avocado production in Colombia is distributed throughout the country. The main production areas are Tolima, Caldas, Valle del Cauca and Antioquia. Currently, there are some 13,000 avocado producers growing a wide variety of avocado, but that variety that shows the greatest growth in planted area is Hass, due to its export potential [32].

This research establishes a methodological proposal for the avocado value chain and constitutes a first approach is appraising the technological level of the industry. To respond to the objectives, the research was undertaken in three parts: (i) Consultation with experts regarding existing supplies and demands, (ii) Evaluation of the technological level and (iii) Correlation of the technological level with socioeconomic characteristics.

2.1. Consultation with Experts and Existing Demands in Avocado Production Systems

The opinion of experts in the avocado value chain (producers, researchers, technical assistants and marketers) was elucidated with an instrument built with four groups and a number of technological supplies: (i) Propagation of plant material and nursery (20), (ii) Production (47), (iii) Postharvest (25) and (iv) Management and marketing (22). Their opinion was solicited for the importance of technological supplies and demands in this value chain. The Likert scale [33] was used, with five response options: 5—very important, 4—important, 3—moderately important, 2—low importance and 1—unimportant. The participants were selected from a bibliometric analysis based on the following criteria: (i) research with data capture and (ii) validation of the research through data analysis processes and under field conditions.

Researchers with postgraduate training who reported more than 10 publications on avocado on the ScienTI platform of the Ministry of Science, Technology and Innovation, were sought. To contact marketers, a web search of the main avocado marketing agents in Colombia was carried out. Producers identified themselves through associations and social networks. Technical assistants were contacted through Linkata and social networks. Experts’ responses were not categorized according to role, due to the relevance of the experience and expertise of each role in enriching the analysis of this value chain [34].

The evaluation of technological demands was based on the report on the SIEMBRA platform (http://www.siembra.gov.co/Offers/Oferta/Reporte, accessed on 10 September 2022) and the search was made 30 June 2021. This platform is responsible for knowledge management in science, technology and innovation. It condenses information on supplies, demands, technological capabilities and future or ongoing projects [35], prioritized with the aim of focusing research needs and making efficient use of resources [36]. The result of lawsuits on the platform was validated according to the perspective of the experts. The area used for querying demands is general and the results are presented descriptively.

For the data, reliability of each instrument was evaluated with the alpha (α) criterion of Cronbach [37] with the psych library [38]. A cluster analysis was performed to identify whether some technologies were considered to be of greater or lesser importance by the experts, using the cluster library [39] with the K-means method [40]. The result of the cluster analysis was validated with the Silhouette coefficient for internal analysis (Si > 0) [41] and the relationship between the variance between groups (BSS) and the total variance (TSS). The plotly library was used for the graphs [42]. Subsequently, a Confirmatory Factor Analysis (CFA) was carried out [43] to validate the questionnaires and scales, wherein supplies were indicators and the groups of supplies were factors. The null hypothesis for this statistical analysis was that the proposed model fits the data, in which each indicator contributes to the factor additively and in equal proportion [44,45,46].

The factorial model was applied by group of technologies in a general way (all groups) to review the importance given by experts within each group and to the technology individually using the Lavaan [47], factoMineR [48] and factoextra libraries [49]. For the confirmatory factor analysis, the estimator proposed by Muthén [50] “Diagonally Weighted Least Squares—DWLS” was used considering that the sample size was small and the data did not present normally. The fit of the proposed model was measured with the Comparative Fit Index (CFI > 0.95), the Tucker-Lewis index (Tucker-Lewis index—TLI > 0.90) and the mean square error (Root Mean Square Error—RMSE < 0.10) [51]. Factor loadings obtained from this analysis were subsequently used to calculate the TL. The open questions were analyzed based on the frequency of use of terms, with a word cloud graph as a visualization tool, in which the size of the terms reflect the frequency with which they were used by the experts.

2.2. Evaluation of the Technological Level in Avocado Production Systems

The input generated from the expert consultation was used to design an instrument to evaluate the frequency of use of 82 technologies (in groups (i), (ii) and (iii)) by avocado producers (Appendix A). The number of technologies evaluated in each group was different: Propagation of plant material and nursery (16), Production (43) and Postharvest (23). Producers were asked whether they knew the technology (1) or not (0) and the frequency of use, according to a Likert-type scale [33], with five response options: 5—always, 4—almost always, 3—sometimes, 2—hardly ever and 1—never. The experts stated that some of the variables related to group IV were general and therefore did not differentiate the supplies. The study, therefore, evaluated the use or non-use of the technologies. In addition, the perception of the producers regarding the technologies of this group were considered.

A total of 125 producers were surveyed in the following municipalities: Cundinamarca (Silvania and Anolaima), Caldas (Belalcazar, Aguadas, Anserma, Manzanares, Manizales, Marquetalia, La Merced, Pácora, Pensilvania, Riosucio, Risaralda, Salamina, San José, Victoria and Villamaría), Antioquia (Sonsón, Abejorral and San Vicente Ferrer), Risaralda (Guática and Quinchía) and Tolima (Ibagué). The survey was applied in person to each producer during events developed by institutions, guilds, associations, or commercial houses.

This research poses two scales, the value chain (expert consultation) and production systems (producers). Considering the above, it was decided to use the methodology proposed by Ramírez-Gil et al. [14], which establishes the weighting of pre-established technologies and is related to the frequency of their use at a given time, since this study evaluates the TL and not the process of technological adoption. The technologies evaluated in each group were previously assessed by experts in the production chain. The result of the factor loadings was used as a weight to calculate the TL in each group of technologies. The equation proposed by Ramírez-Gil et al. [14] was modified according to the objective of the research, evaluating the frequency of use of technologies (Equation (1)). After the TL measurement, a principal component analysis (PCA) was performed using the factoextra library [49]. The PCA results were used for a cluster analysis to generate an objective classification of the producers according to the TL (low, medium and high) following the previously mentioned methodology. For this analysis, the groups of technological supplies: (i), (ii) and (iii) were included.

$$TL={\sum }_i^n\left[WT\times FT\right]$$

(1)

where “TL” is technological level, “WT” is equal to the weighting of the technology according to experts based at the factorial load and “FT” is the frequency of use of the technology.

2.3. Characteristics of Producers and Production Systems

In the instrument used for data collection, information on the characteristics of the producers and the production system was collected. The characterization variables were correlated with the TL. Correlation of the characteristics of the producers and the production systems was carried out with the Pearson coefficient and the p-value was reported by using the rstatix library [52]. Subsequently, a mixed data analysis such as the combination between a PCA and multiple correspondence analysis (MCA) was used considering that the variables were heterogeneous (continuous and categorical) [53]. The resulting dimensions were analyzed, adding the contributions for each variable and those with the greatest contribution (>70%) were selected to reduce their complexity. Based on the selected variables, a multinomial logistic regression was proposed to predict the TL, which has previously been used to model other social characteristics [54,55]. The data matrix was divided into training data (30%) and validation data (70%) to verify overfitting. In addition, the residual deviance and AIC metrics were used to verify the fit of the model [56,57]. To perform this analysis, the ipred [58] and nnet [59] libraries were used.

3. Results

3.1. Consultation with Experts and Existing Demands in Avocado Production Systems

The first objective of this research included the participation of 57 experts and was the basis for validating the proposed supplies. The data collection instrument presented high reliability (α = 0.98; p < 0.05; average r = 0.35). In addition, 97% agreed with the proposal to divide the existing supplies for avocado cultivation into the four groups.

The general model showed a good fit (p < 0.05; CFI = 0.953; TLI = 0.952; RMSE = 0.052). For the entire group of technologies, the highest factorial load was in the post-harvest group and was “Obtaining by-products from the seed”. On the other hand, the technology with the lowest factorial load was in the production group and was “Branch girdling”.

When contrasting the calculated factor loadings of each group against the general loadings, a positive linear correlation of 0.91 was observed (Figure 1). Three clusters were found in the analysis, in which the technologies were classified according to their low, medium or high importance (Si = 0.45; BSS/TSS = 76.1%; p < 0.05) (Appendix B). “Postharvest” presented the greatest number of technologies classified as highly important. This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation and the experimental conclusions that can be drawn.

Factorial analyses by group showed acceptable values at the level of the statistics used. The RMSE value, however, was higher than 0.1 (Figure 2). “Propagation of plant material and nursery” had 16 technologies, where the greatest weight corresponded to “Size of the bags for the seedlings growth”. On the other hand, the lowest weight was in “Plant material from a registered nursery” (p < 0.05; CFI = 0.985; TLI = 0.983; RMSE = 0.114).

The “Production” factor had the highest number of technological supplies with 45 indicators (p < 0.05; CFI = 0.989; TLI = 0.989; RMSE = 0.121). In this group, the indicator with the highest estimate was “Taking leaf samples”. The “Banding” technology presented the lowest value in this group, as seen in the general analysis. For this technological supply, the experts stated that this practice may present more associated risks in relation to the benefits. For the “Production” group, the experts stated that commercial houses should consider maximum residual limits but not within the framework of production.

“Postharvest” was the third group, with 23 indicators (p < 0.05; CFI = 0.979; TLI = 0.977; RMSE = 0.145), which presented a higher estimated value for the technology “Obtaining by-products from the seed”. This behavior was also observed in the general analysis of technologies. On the other hand, the variable with the lowest factorial load in this group was “Measurement of dry matter”.

The group “Management and marketing” had 22 indicators (p < 0.05; CFI = 0.993; TLI = 0.992; RMSE = 0.223) and presented the highest RMSE value. The indicator with the highest estimator was “Technical records” and the one with the lowest was “Digital tools for information management”.

A total of 161 demands were found in different areas, divided by region, on the SIEMBRA platform. Demands were classified by research area, with 12 areas for avocado. The area with the highest number of demands was “Harvest, post-harvest and transformation management” followed by “Planting material and genetic improvement”. The area with the highest average priority was “Environmental management and sustainability”. The confirmatory factor analysis (p < 0.05; CFI = 0.994; TLI = 0.992; RMSE = 0.064) showed that the most important latent variable was “Environmental management and sustainability”, which coincides with the SIEMBRA platform. However, the importance assigned to other demands of the evaluated group did not coincide with the platform (

Table 1. Technological demands to avocado value chain in SIEMBRA, organized by importance according to expert consultation.

Research Area	Regions	No. Demands by Area	Priority *	Importance Experts **	Variance ***
Environmental management and sustainability	Antioquia, Bolívar, Boyacá, Caldas, Cauca, Quindío, Risaralda, Tolima, Valle del Cauca	9	1	1	0.57
Soils and water management	All regions	12	9	2	0.23
Production system management	All regions	18	10	3	0.42
Socioeconomics, marketing and business development	All regions	13	2	4	0.04
Plant physiology and nutrition	Antioquia, Bolívar, Boyacá, Caldas, Cauca, Norte de Santander, Quindío, Risaralda, Tolima, Valle del Cauca	11	6	5	0.26
Technology transfer, technical assistance and innovation	All regions	13	12	6	0.08
Harvest management, postharvest and transformation	All regions	23	11	7	0.14
Planting material and genetic improvement	All regions	22	5	8	0.30
Sanitary and phytosanitary management	All regions	12	8	9	0.30
Strengthening of technical and functional capacities	Antioquia, Boyacá, Caldas, Cauca, Quindío, Risaralda, Santander, Tolima	9	7	10	0.45
Information systems, zoning and georeferencing	All regions	8	3	11	0.17
Quality and safety of supplies and products	Antioquia, Bolívar, Boyacá, Caldas, Cauca, Quindío, Risaralda, Tolima, Valle del Cauca	11	4	12	0.25

* General classification according to priority to SIEMBRA. ** Classification by importance according to factorial load, result of expert consultation. *** Variance of each latent variables (demand).

).

The individual opinions showed that research has not responded to demands in the sub-sector, such as planting material and genetic improvement, physiology and nutrition, soil-water management and socioeconomics. In addition, although the technologies are known or available, they have not gone through transfer processes that allow producers to use them for decision-making. This is the case with geographic information systems, zoning and environmental aspects. Likewise, there are difficulties in cultivation areas with a high slope degree, transformation aspects, generation of added value and adaptability to climate change, which are highly important demands (Figure 3). The topics with greater weight among the expert comments included “Information systems”, “Zoning”, “Planting material”, “Georeferencing” and “Genetic improvement” (Figure 3).

3.2. Technological Level in Avocado Production Systems

The instrument for measuring the frequency of use of the technologies applied to avocado producers presented high reliability (α = 0.92; p < 0.05; average r = 0.09). When contrasting the average importance given to each technology by the experts and the average use of these technologies by the producers, a correlation of 0.37 was found. The “Postharvest” supplies had the lowest value of frequency of use, followed by Production and Propagation of plant material and nursery.

A correlation was found between the technological levels of each group. The production and post-harvest groups were the most highly correlated (0.70), followed by the correlation between Propagation of plant material and nursery and Production (0.33). The lowest correlation was between the groups Propagation of plant material and nursery and Postharvest (0.08). Based on the PCA and cluster analyses, three technological levels were found: high (34.4%), medium (47.2%) and low (18.4%) (Si = 0.57; BSS/TSS = 80.6%, p < 0.05). The “Production” technologies presented the greatest contribution when performing the cluster analysis and those belonging to the “Propagation of plant material and nursery” group had the least application by producers (Figure 4).

In the TL analysis, 63% of the producers surveyed in the region of Antioquia were located in medium TL, while in Caldas 40% were located in high TL. In the other region, the number of people surveyed was lower. Notably, Cundinamarca had 82% of the producers between medium and high TL. In Risaralda, there was no low TL; all were located between medium and high TL. On the other hand, the producers surveyed in the region of Tolima were located in a high TL. For the genetic material, 55.5% of the production systems that cultivate the Hass variety had a high to medium TL, while the other varieties (Fuerte, Lorena, Semil, Booth, Reed, Colinred, Choquette, Augustus, Semil and Santana) were indistinctly distributed among the technological levels.

3.3. Characteristics of Producers and Production Systems

It was possible to establish relationships between the technological level and socioeconomic characteristics of avocado producers. Significant correlations were found between the variables evaluated to characterize the surveyed producers (Appendix C). For TL, the variables academic training (0.32; p < 0.001), registration of export farm (0.28; p < 0.01), global GAP certification (0.22, p < 0.01), completion of records (0.19; p < 0.05), size of the production system (0.19; p < 0.05) and the presence of technical assistance (0.19; p < 0.05) stood out with a positive relationship. Similarly, municipalities presented a high correlation (0.22; p < 0.05), where Manizales, Marquetalia, Pácora, Risaralda and Salamina were the ones with the highest production systems located at the medium and high technological levels. On the other hand, the higher the percentage of product rejection, the lower the technological level was (0.21; p < 0.05). The crop yield was higher with medium and high TL (0.21; p < 0.05). The type of employment relationship in the production system was correlated with the TL (0.21; p < 0.05), producers who employed family labor had a lower TL than those who hired external labor.

The multivariate analysis showed that 42 dimensions explained at least 70% of the variance of the collected data. These dimensions obtained the contributions for the qualitative and quantitative variables, indicating high variability and little or no self-correlation between the predictors (Figure 5).

The prioritization of variables according to the PCA and MCA methods obtained eight variables that were detrimental to the TL (Figure 5B, Equation (2)). The number of sources that each producer had access to for the technological aspects had an important contribution (67%). Among the categories of academic training, technological training presented the greatest contribution, as shown in the correlations (Figure 5B, Equation (2)).

The age of the producer presenting the greatest relevance to the TL was 18–20 years. The distance to the municipal seat of 21–30 km presented the greatest contribution for this variable type. The source of bank credit and seniority in the association of producers from 5 to 10 years presented high importance. On the other hand, the marketing channel, intermediary and the once-a-year frequency of technical assistance also had a greater contribution. From this process, equation 2 was used as a suggested model (Figure 5B, Equation (2)).

$$TL=Aap+Bdmc+Csc+Dat+Eta+Fmc+Gsi+Hfta$$

(2)

where “TL” is technological level, “ap” is age of the producer, “dmc” is distance to the municipality, “sc” is source of credit, “at” is academic training, “ta” is a time to be associated, “mc” is a marketing channel, “si” is a source of information on technological aspects and “fta” is the frequency of technical assistance, each multiplied by an assigned factor.

The calculated model explained 53.7% of the variance of the data and an adequate adjustment (AIC = 91.8) (

Table 2. Variables and categories used for the multinomial logistic regression model and coefficients assigned by the model.

Variable	Categories	Coefficient	Standard Error
Age	18–20; 21–30; 31–40; 41–50; 51–60; 61–70; >70 years	0.50	±0.60
Distance to the municipality	<5; 6–10; 11–20; 21–30; >30 km	0.20	±0.47
Credit source	I do not use credit; banks; association; cooperative; state entity; lender	2.44	±1.51
Academic training	Primary; secondary; technical; technologist; professional; postgraduate	−0.96	±0.84
Time to be associated	Not associated; <1; 1–5; 5–10; >10 years	0.59	±0.79
Way of marketing	Association; central wholesaler; exporter; department stores; intermediary	−0.3	±0.70
Source of information on technological aspects	Fairs; academic events; association; technical assistance; neighbors; commercial house; UMATA *; internet; social networks; television	−0.08	±0.55
Technical assistance frequency	I do not receive technical assistance; weekly; monthly; bimonthly; quarterly; six-monthly; annually	0.81	±0.74

* Stands for Unidades Municipales de Asistencia Técnica Agropecuaria (Municipal Agricultural Technical Assistance Units).

). When using all variables, this model explained 0.012% of the variance and presented a lower fit (AIC = 148). Among the eight variables, the source of credit had the greatest weight in the proposed model, followed by academic training.

4. Discussion

This study confirmed that technologies can be classified into groups of low, medium or high importance, regardless of the group (i, ii, iii, iv). The perception of the technologies used in the avocado value chain demonstrated the presence of strong and weak links. However, they varied according to the region, avocado variety and stage of the production system.

Arías García et al. [32] stated that the demands of the Hass variety are mainly concentrated in the areas of “harvest and post-harvest” and “planting and genetic improvement”. This study found that the demands of the avocado sub-sector are generally linked to the production process. Supply in the harvest and post-harvest stages has been met. In contrast to the first hypothesis, despite the relevance of the management and marketing aspects, this group presented the least adjustment in the proposed model, which may be associated with characteristics of low use or little knowledge on the part of the value chain despite the importance it has gained recently [4]. This indicates that the dissemination of management as a relevant topic for the sector is still in process and requires strengthening for use in agriculture [60,61].

The more important supplies were transformation of the fruit and search for added value from seeds, where a large amount of waste is generated, which may have high potential [62,63,64,65]. Likewise, the size of the bag in the nursery stood out, a sensitive variable with a high influence on the quality of the plant material [66]. Environmental considerations have been highly relevant given the association with the use of resources because of water and carbon footprints [67,68,69]. The foregoing agrees with the consultation with experts, according to which demands related to environmental aspects presented the greatest factor loads. Like previous geographic information systems (GIS), technology for data management in space and time allows multiple applications [70,71,72,73]. Additionally, genetic resources and the possibility of predicting the response of plants to biotic and abiotic factors is an advantage for avocado production systems [74,75]. On the other hand, the importance of foliar analysis for decision-making and monitoring fertilization management was reflected in the expert consultation [76,77].

Although there are technologies at different stages of the chain, not all of them are considered useful or appropriate for the local context. For example, girdling is used in various fruit trees including avocado crops and this practice implies some commercial advantages [78,79]. However, when testing this practice in tropical areas, the results have not been as expected [80], while dry matter is used for decision-making for harvest [81]. Despite this, the value chain did not consider it relevant, though it is currently the most important harvest criterion used by producers [82].

Although some of the experts consulted were also producers, many of the evaluated technologies were not applied in production systems, leading to a gap. Singh et al. [83] found that the technological gap may be related to low prices in the market, low presence of extension agents or technology transfer. The variables with high priority according to the platform designed for this purpose in Colombia (SIEMBRA) were classified by the experts as of low importance. Therefore, the demands and their priority do not respond to the needs of the sector.

In previous experiences, it was found that considering other actors’ vision allows enrichment of the research agendas. In the state of Michoacán, there was a period of closures and restrictions imposed on the exportation of avocado. After years of research and joint work between the avocado trade union and the academy, some restrictions were lifted and they are currently the main producer and exporter of Hass avocado in the world [84]. Adamashvili et al. [16] affirm that the success of agriculture is at the center of technology, collaboration and knowledge, which is strengthened through public policies and financing.

The current technological state of avocado production systems allows the sub-sector to work in those technological groups with identified weaknesses. Focusing technology transfer efforts will allow the levels to be homogeneous despite varieties and geographic location. It is necessary to scale the information collected to other areas to work with regional strategies. Most farmers have some knowledge and positive attitude towards the technologies evaluated, which facilitates adoption processes [85]. On the other hand, Ramírez-Gil et al. [14] found three technological levels in Hass avocado crops in Colombia, linking said level with yields, export capacities and vulnerability to the incidence of soil-borne diseases.

Several authors have mentioned the importance of evaluating the characteristics of producers and production systems when considering the technological level inasmuch as these are decisive in the process of adoption or transfer [86]. In this study, the TL was evaluated and a correlation and causal relationship was found with some variables that have been previously reported. Some authors have stated that age may be a factor related to the technological level since, as producers age, they tend to decrease participation in innovation spaces [87]. Likewise, the distance to markets or population centers has also been related to technological characteristics, innovation, fruit quality and income [29,88,89,90].

On the other hand, Tiruneh et al., [86] found that access to credit is a facilitating condition when deciding to adopt a technology or not, a factor that, in turn, is related to the TL. According to the present study, the determinant was the source of credit. On the other hand, the level of training is a characteristic that has been evaluated and considered positive with respect to the technological level [91]. In addition, the level of training is a characteristic that decreases risk aversion in producers [92]. Technology transfer may be related to the technological gap found. For instance, a report states that the gap between basic and applied research makes it difficult to increase competitiveness [85]. Producer organizations are a channel for the transfer of knowledge through agreements or collective contracts, explaining the high relevance of this variable for the analysis of the TL [92]. On the other hand, the marketing channels, sources of information as well as frequency of technical assistance have also been reported as influential in the TL [93].

The proposed technological groups are a basis for future research because of acceptance by experts. In addition, the technological supplies serve as a basis to evaluate the technological level of the subsector. The perception of the experts regarding the technological supplies showed that the avocado subsector needs to explore the information available to carry out transfer processes. Social and economic characteristics of the avocado production systems were found that have an effect on the technological level and that must be considered in transfer and innovation programs to achieve sustainable results. Finally, the use of the model to predict the TL is an alternative to optimize the processes of characterization and data collection in the field.

5. Conclusions

The methodology used in this work for analyzing and modeling the variables, identifying gaps and defining causal relationships, is new and useful for application in future research. The exploration of categorical data carried out makes it possible to reach specific decisions regarding information collection instruments. On the other hand, this work gives evidence in favor of the use of technological strategies, such as virtual forms for the diagnosis or consultation of actors of value chains in agriculture. For future replications of this work, a larger sample size is recommended in the consultation, including experts and a proportional participation of the actors of the value chain. This work is a tool for the avocado sub-sector with which research, development and innovation strategies can be guided. In addition, the technological state of avocado production systems is recognized for the first time. Supplies evaluated showed high importance; however, no correlation was found with the use of said technologies in the production systems. On the other hand, it was possible to identify the technologies that require more attention and development. The relationships found between TL and socioeconomic characteristics show the need to incorporate these variables into knowledge communication processes. Furthermore, the cohesion of actors in the avocado value chain is one of the main challenges of the sub-sector. For future work, evaluation of the adoption factors as well as the causes of the identified gap is suggested.