SciELO - Scientific Electronic Library Online

 
vol.16 issue2Governance in the integral management of water resources in the Rio Sabinal and Cañon Del Sumidero watersheds in Chiapas, MexicoRestrictions for results orientation of rural development programs in Mexico author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Agricultura, sociedad y desarrollo

Print version ISSN 1870-5472

agric. soc. desarro vol.16 n.2 Texcoco Apr./Jun. 2019  Epub Feb 25, 2020

https://doi.org/10.22231/asyd.v16i2.1006 

Articles

Factors that influence the adoption of innovations by orange producers in Álamo, Veracruz

Fidelia Mercado Escamilla1 

Alma Velia Ayala Garay2 

Arturo Flores Trejo1 

Evelia Oble Vergara1 

Gustavo Almaguer Vargas1  * 

1Departamento de Fitotecnia, Universidad Autónoma Chapingo. Km. 38.5. Carretera México-Texcoco. Chapingo, Estado de México, 56230. México. (nollys_31@yahoo.com.mx, almaguervargas@hotmail.com).

2Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Campo Experimental Valle de México, Km.13.5 de la Carretera los Reyes-Texcoco, Coatlinchán, Texcoco, México, México (ayala.alma@inifap.gob.mx)


ABSTRACT

In Veracruz, the yields per surface unit have been practically stable for more than 34 years and when the factors that influence this were determined, it was found that a deficient adoption of innovations is the most important issue. With the aim of improving this adoption, an initial baseline survey was applied to 100 citrus producers from ten localities. Later, integral extension work was provided for one year through the use of monitoring logbooks and at the end, the final baseline survey was applied. An increase of 30.5 % in average was attained in the adoption of technological innovations, since fertilization and control of pests and diseases were improved. To determine the factors that influence the adoption of innovations, simple regression models were generated, using as dependent variable the increase in the innovation adoption index and as independent variables the specific attributes of the producer and his production units. The factor that had significant correlation with the innovation adoption index was the income of the producer. Factors such as age, years of experience and schooling did not influence decision making for the adoption of technologies.

Key words: adoption of innovations; technology transference; income

RESUMEN

En Veracruz, los rendimientos por unidad de superficie se han mantenido prácticamente estables por más de 34 años y al determinar los factores que influyen en esto se encontró que la deficiente adopción de innovaciones es lo más importante. Con la finalidad de mejorar dicha adopción se aplicó una encuesta de línea base inicial a 100 citricultores de diez localidades. Posteriormente se brindó extensionismo integral durante un año mediante el uso de bitácoras de seguimiento y al terminar se aplicó la encuesta de línea base final. Se logró incrementar la adopción de innovaciones tecnológicas en 30.5 % en promedio, ya que se mejoraron la fertilización y control de plagas y enfermedades, principalmente. Para determinar factores que influyen en la adopción de innovaciones se generaron modelos de regresión simple, utilizando como variable dependiente el incremento en el índice de adopción de innovaciones y como variables independientes los atributos específicos del productor y de sus unidades de producción. El factor que tuvo correlación significativa con el índice de adopción de innovaciones fue el ingreso del productor. Los factores como edad, años de experiencia y escolaridad no influyeron en la toma de decisiones para adoptar tecnologías.

Palabras clave: adopción de innovaciones; ingreso; transferencia de tecnología

INTRODUCTION

Veracruz is the primary orange producing state at the national level; in 2014, it contributed 52 % of the production in Mexico; Tamaulipas, 13 %; San Luis Potosí, 10 %; Nuevo León, 6.8 %; and Puebla, 4.9 % (SIACON-SAGARPA, 2015).

The surface of oranges harvested in Veracruz went from 78 049 ha for the five-year-period of 1980-1984, to 160, 537.26 ha for the five-year-period of 2010-2014, and in terms of the production volume it is reported that it increased from 862 568 to 1 980 490 tons for the same period of time (SIACON-SAGARPA, 2015).

One of the main problems that orange production faces in Veracruz is low yields, with an average of 13.1 tons per hectare, which have remained practically the same since 1980. It has been suggested that the low production levels, low competitiveness and even the inefficiency of agricultural and livestock production units are explained largely by the deficient adoption of innovations (Feder et al., 1985; Jasso, 2005; Bozoinn and Ceyhan, 2007; Hartwich et al., 2007; García et al., 2011). In Tlapacoyan, Veracruz, it was quantified that lime producers applied only 15 out of 100 innovations, influencing the low yield and a benefit/cost relationship of 1.55 (Almaguer and Ayala, 2014).

Innovation is a change factor in all sectors of the economy, society and daily life (Paz et al., 2013). It should be mentioned that, according to Cuevas et al. (2013), innovation is the successful introduction of new knowledge and technologies into social and productive processes; it is an application that the enterprise or the producer performs through the transformation of an idea, whether in a new or improved product, which is introduced into the market and also generates wealth. Innovation is affected by various factors, among which there is perception of the final user, his characteristics and resources available.

The adoption of new technologies by agricultural and livestock producers has always been affected by diverse factors, such as the absence of credit, age, aversion to risk, surface cultivated, schooling (Feder et al., 1985), their relationship with agents of change (Galindo, 1995), exposure to means of communication (Galindo, 1992), and education (Reimers and Klasen, 2013).

Muñoz et al. (2007) consider that traditional methodologies for producers to adopt innovations, such as linear extension work, have been inefficient because they are based on the transference of knowledge as a recipe from service providers to the producer. Linear extension work considers that the transference of technology will be adopted only by showing or proving the increase in yields, without taking into account that there are other aspects such as the development of abilities, human and social capital, type of consultancy, among others. Likewise, most of the production units are in a state of decapitalization and investing in technology that they do not master implies a cost, additional to the investment. Therefore, the adoption of innovations is not automatic in the Mexican farmland.

Almaguer and Ayala (2014) developed a methodology of integral extension work, based on the use of logbooks. It consists of offering workshops and consultancy with andragogy, beginning with a diagnosis of the needs and interests of adult apprentices. Later, recommendations are established where competencies to be achieved are considered, based on these needs and interests; highly significant learning experiences are designed that stem from the experiences of producers and which achieve the competencies; this design is executed by selecting materials, methods and resources oriented at the solution of daily life and production problems, and monitoring and evaluation are performed of the results from the learning experiences, particularly in the adoption of organizational, technological and administrative innovations.

Taking into consideration that there are multiple aspects that affect the adequate adoption of technological innovations by producers, the objective of this study was to analyze some of the factors that influence the adoption of innovations in orange producers, from Álamo Temapache, Veracruz, using the method of integral extension work with the use of logbooks.

METHODOLOGY

Geographic localization: the municipality of Álamo Temapache is located in the northern zone of the state of Veracruz, on coordinates 20° 55’ Latitude North and 97° 41’ Longitude West and an altitude of 40 masl. It limits north with Tepetzintla, Cerro Azul and Tamiahua, east with Tuxpan, south with Tihuatlán, Castillo de Teayo and the state of Puebla, southwest with Ixhuatlán de Madero and wast with Chicontepec; the 100 producers with whom the study was done belong to the communities of the municipality of Álamo Temapache: Llano Grande, Adalberto Tejeda, Lucio Blanco, Vara Alta, Loma Larga, Toaco, Buena Vista, Macario Cortes, Tumbadero del Águila and Ampliación Reforma. In average, there were 10 producers per community.

Decomposition of growth factors of production

To determine whether the lack of increase in the orange yield in Veracruz is because of the lack of adoption of innovations, due to technical obsolescence or to the increase in surface, the Venezian and Gamble (1969) formula was applied, modified by Contreras (2000) and Zarazúa et al. (2009), with data from SIACON-SAGARPA (2015), which indicates that:

Pt=Y0At-A0+A0Yt-Y0+At-A0Yt-Y0

where P t : Total increase of production for the period of analysis; Y 0(A t -A 0): Quantifies the contribution of the surface; A 0(Y t -Y 0): Quantifies the contribution of the yield; (A t A 0)(Yt-Yo): Quantifies the combined effect of surface and yield; A o : Area of orange harvested during the five-year-period of 1980-1984; A t : Area of orange harvested during the five-year-period of 2010-2014; Y 0: Average yield of orange in ton /ha during the five-year-period of 1980-1984; Yt: Average yield of orange in ton /ha during the five-year-period of 2010-2014.

Survey of initial and final baseline

The producers were surveyed directly in their productive units, before and after the interventions, with the following information being collected:

Location data. Information was collected with regard to the municipality, locality and telephone of the producer.

Producer’s profile. The variables of sex, years of experience in the activity, age and schooling were recorded.

Characteristics of the productive unit. Information was requested about the surface destined to citrus production that the interview respondents had.

Adoption of innovations. For the calculation of this indicator, the methodology proposed by Muñoz et al. (2007) was used, who suggest defining the innovating capacity of producers through the calculation of the Innovation Adoption Index (InAI). The InAI is obtained when quantifying the number of practices carried out by the producer in a specific moment compared to the number of total practices that must be carried out to reach efficiency. The catalog of innovations is elaborated by experts. Given that the InAI estimates the amount of innovations adopted compared to a possible total, its calculation can be expressed in a general percentage, by category of group of innovations, by producer and by each innovation included in the catalog.

The InAI is one of the most important variables in this study, since it allows understanding which innovations are being used by the producer, both before and after extension work and, based on this information, being able to quantify the impact of the intervention.

Application of the integral extension work methodology: Use of logbooks

Integral extension work was applied during 2012, according to the methodology described by Almaguer and Ayala (2014), which basically consists in applying a diagnosis and, based on the needs of the apprentices, making recommendations, offering workshops and courses, and providing extension work with andragogic techniques. A logbook is used with the aim of collecting information that refers to the implementation of the recommendations of the technical adviser and the workshops, costs, incomes and practices performed every fortnight.

As has been explained, a Survey of Initial Baseline (Encuesta de Línea Base Inicial, ELBI) was applied before the intervention and one year after its operation, which was called Survey of Final Baselinne (Encuesta de Línea Base Final, ELBF), which had the same design and which were helpful to cover the objective of the research of identifying the impact of the intervention in the adoption of innovations.

Increase of the net income

To obtain the net income generated by the sale of orange to the producer who had technical assistance, sections in the design of the logbook were included to gather information related to the costs of activities recommended, which were part of the catalog of adoption of innovations. The total costs of the orange crop included the fixed and variable costs. The net income was obtained from the difference between the total income and the total production costs.

Factors related to the adoption of innovations

To determine the influence of the producer’s profile and his production units in the adoption of innovations, simple regression models were generated, using as dependent variable the increase in the innovation adoption index (InAI) of producers after technological intervention, and as independent variables the following were used: age, years of experience of the producer, and total income of the producer. The information obtained from the surveys was captured and analyzed with the Microsoft Office Excel software.

RESULTS AND DISCUSSION

Decomposition of the production growth factors

Table 1 shows the calculation of the decomposition of orange production growth factors in Veracruz. When the production growth of a region or country is intensive, it means that it is based primarily in the use of technology or adoption of innovations, since the yields increase by surface unit. Instead, when the production growth is based on the increase of the surface, it means that technology is practically not being used, which implies technological obsolescence (Contreras, 2000; Zarazúa et al., 2009).

Table 1 Effect of the surface and the yield on the orange production increase in Veracruz, Mexico. 

Efecto superficie Efecto rendimiento Interacción superficie- rendimiento Total
Valor obtenido 899 122.03 14 8293.10 156 727.69 1 204 142.82
Porcentaje 74.66 12.31 13.01 99.9 %

Source: authors’ elaboration with data from SAGARPA-SIACON, 2015.

Orange production in Veracruz is characterized by a reduced application of technological innovations, since the production increase is given in 74.66 % from the effect of the increase in surface, 13.01 % from a combination of the increase in surface and the use of some technology or innovation, and only 12.31% exclusively from the application of technology or innovations.

Innovation adoption index (InAI)

The average increase in the adoption of technologies for the communities studied was 30. 5%, starting from an InAI of 16.3 %. The analysis of the InAI of the ELBI compared to the ELBF gave the same results by locality:

  • Tumbadero del águila: this community increased the adoption of innovations in 44.7 %. The most accepted technologies were fertilization with soil analysis and pest and disease control.

  • Macario Cortés: with extension work, 35.8 % was obtained in the adoption of technology compared to its traditional production technique. Fertilization and pest and disease control were the most accepted technologies.

  • Buena Vista Molino: this locality increased 68.6 % its adoption of technology, with fertilization and pest and disease control being the technologies that they adopted in more than 90 %.

  • Ampliación Reforma: the InAI of the survey of initial baseline was very low with only 10 %; however, the InAI of the ELBF increased 50 %.

  • Toaco: it can be observed that fertilization is the technology that is best accepted with 50 %, while the other technologies were not of great impact. The index increased 11 % in average.

  • Loma Larga: it is a locality with more participative producers, and the technology adoption index managed increase by 28 %, fertilization was the most accepted technology with more than 60 % compared to the first evaluation (ELBI).

  • Lucio Blanco: it is one of the localities with most work within the group, in the second evaluation of the InAI they increased 26 % compared to the first evaluation.

  • Vara Alta: it is the locality with highest number of producers in the group; however, it is among the localities with low increase in the adoption of technology, with 9.8 %. Fertilization was the best accepted technology with 50 % (IAIC).

  • Adalberto Tejeda: the InAI of the ELBI is higher compared to the other localities; however, after the second evaluation, the InAI increased 32.3 %. In this case, fertilization and pest and disease control are the most accepted.

  • Llano Grane: it is another locality where there was good adoption of technology with 48 % compared to 100 % of all the activities evaluated in the ELBI and in the second evaluation it increased 25 %; the most accepted technology was pest and disease control.

Muñoz et al. (2007) mention that adoption quantifies the result of the decision of producers to use or not to use a technology and, therefore, it is a measure of the innovating ability. In this regard, the locality that developed the highest innovating ability was Buena Vista Molino, with 68.6 % of adoption, because the group participants were interested in the theme; there were strong blood ties between group receptors (family members or trustworthy friends); high schooling (87.5 % are literate); the amount of surface that they have (7.33 ha); and the citrus income is among the highest of the zone ($5,77.70 per year per hectare).

Instead, the locality of Vara Alta obtained an InAI of 9.8 % in the increase of technology adoption. In this case, it was a large group that showed lack of interest in participating in integral extension work, as well as difficulty for self-organization. Toaco also had low adoption of innovations, attributable to its reduced experience in orange cultivation, advanced age and small surface.

When strawberry agro-businessmen from Michoacán were diagnosed in terms of their adoption of technological innovations (Zarazúa et al., 2011), it was found that their initial InAI was 55.56 % (there was no extension work), which is considered very high in relation to other groups of producers since, for example, Almaguer and Ayala (2014) quantified the initial adoption of innovations for lime in Veracruz in 15 %. In another study about adoption of innovations in maize, small-scale producers had an InAI of 12.55 % (Zarazúa et al. 2012). Muñoz et al. (2007) found that the maize network of Estado de México was quite disarticulated because it had 30 % loose nodes, an innovation adoption index (InAI) of 13.3 %, and a centralization index of 14 %, which is why they concluded that there were several structured actors and diffusers with low influence ability.

Such great differences are because strawberry is a crop that is very demanding of technology, since it is mainly for export. In the cases of maize and lime producers, the initial percentage of adoption was similar to the one obtained with orange, although the difference was the application of an efficient extension work methodology.

It should be mentioned that in Veracruz, Mexico, the initial yield in lime was 5.24 t ha1 and the benefit/cost rate of 1.55, attributable to the reduced percentage of adoption of innovations, was 15 % (survey of initial baseline). After starting a process of integral extension work for two years, the percentage of adoption of innovations increased 45 %; the income, 45 %; and the benefit/cost rate, 68 %, in addition of forming a network for input purchase (Almaguer and Ayala, 2014).

Factors that influence the adoption of technology

Results from the simple linear regression models showed that some aspects of the producer’s profile and of the characteristics of their production units influence the adoption of innovations.

In the Table 2, factors are presented that affected the adoption of technological innovations.

Table 2 Social factors of orange producers from Álamo Temapache, Veracruz. 

Localidad Edad (años) Ingreso Citrícola ($) Experiencia como citricultor (Años)
A. Reforma 50.83 55 000 16.5
T. Águila 60.09 32 090 15.9
Macario Cortez 52.2 35 666 13.3
Buena Vista 54.6 57 777 20.8
Toaco 54.3 21 291 12.6
Loma Larga 58.3 26 415 14.4
Vara Alta 50.87 14 615 19.5
Lucio Blanco 58.78 31 464 23.5
A. Tejeda 61.8 49 000 21.3
Llano Grande 58.787 24 500 10.0
Promedio 56.1 35 711.4 16.8

Age of producers. The average age of producers was 56.1 years. The locality of Adalberto Tejeda was the one that had the oldest producers, of 61.8 years, while Ampliación Reforma had the lowest average, of 50.8 years. Age can be a decisive factor in the adoption of innovations (Rogers, 2003).

Figure 1 indicates that there is much variation in terms of the correlation between adoption of innovations and age, since there were young producers with a low InAI, compared to older producers who had a higher acceptance of the new practices. It can be observed that age (for this region) is not an important factor; the correlation was 15 %.

Figure 1 Behavior of the InAI regarding age in Álamo, Veracruz. 

In this regard, Rogers (2003) pointed out that in approximately 114 studies performed there was evidence that age influences the attitude that older adults show when acquiring new knowledge and practices, and taking on risks. In our case, age did not influence the adoption of innovations, possibly because of the availability of resources to invest, low human capital or because most of the producers are older and there are practically no young people.

Experience of the citrus producer. The results showed that orange producers from Veracruz have been devoted to citrus culture for 16.8 years in average. The locality of Lucio Blanco is the one that presents most experience in citrus production, with 23.5 years devoted to this activity compared to the community of Llano Grande with only 10 years.

The years of experience as citrus producer was not a key factor for the adoption of innovations, since there was great dispersion between both variables; in addition, the correlation found was, same as in age, of only 15 %; the producers need to understand and see results to evaluate the possible adoption of technology (Figure 2).

Figure 2 Relation of the InAI compared to years of experience as citrus producers in Álamo, Veracruz. 

Allub (2001) mentions that the decisive force that directs the decision making process of peasant units is aversion to risk or uncertainty, and not the principle of maximization of utilities or experience. Lipton (1968) states that this is why attachment to “traditional techniques” are not irrational attitudes, but rather proven ways of minimizing uncertainty to avoid total loss and, as consequence, their disintegration as productive units (Cáceres, 1994). During this whole process, economic agents (peasants) behave as people who minimize risk and not as stubborn players, despite the potential benefits that they could obtain if they were to choose them (Feder et al., 1985).

Being able to test things (experience) gives it points in their favor to be able to make accurate decisions that benefit them; on the other hand, the more attached they are to their traditional practices, it is more difficult to instill new ways of doing things, unless it is proven to them that they work.

A factor that may have influenced the deficient correlation of experience and adoption of innovations was the low schooling of orange producers. Most (55.5) did not conclude primary school, while 24.2% of the producers did not study any school grade; the producers with complete or incomplete secondary school represent 16 %, and 3 % of producers have high school. Only 0.7 % of the producers interviewed have a technical degree.

Various studies point at variables that influence the adoption of technology; age, schooling, relation with change agents (Cuevas et al., 2013), and when they are scarcely developed there cannot be a high innovation index (Feder et al., 1985; Reimers and Klasen, 2013).

Income and adoption of innovations. The income that orange producers have in the region is variable. The regional average is $3478.20 per year; however, the inhabitants of Buena Vista Molino presented a higher income with $5777.00 annually, while Vara Alta is the community of lowest citrus income with $1461.5 annually (Figure 3).

Figure 3 Income of citrus producers in the region of Álamo, Veracruz. 

The citrus income was a significant factor for the adoption of innovations. The correlation for this case was statistically significant, of 0.36. Figure 4 shows that producers who had more income showed higher adoption of innovations. Allub (2001), Binswanger and Sillers (1983), Feder and O’Mara (1982) mention that economic resources determine the scale or level of adoption of a technology.

Figure 4 Behavior of the InAI with regard to the citrus income in Álamo, Veracruz. 

On the other hand, resistance to change in individuals in order to have a higher technological level will depend on the benefits that the change entails compared to the costs that it implies; that is, the individual will be motivated to innovate while the benefits exceed the costs of opportunity. Testing a new technology entails incurring in time, energy, money and land costs, which may be assumed only by those farmers that have sufficient economic resources for small trials, adjusting the scale upwards or downwards, in the direction toward non-adoption, to the extent where there is advancement in knowledge and trust about the results obtained.

Adoption of technology according to the surface destined for citrus production. The locality with a greater surface of orange is Adalberto Tejeda, with an average of 11.9 hectares per producer; there is a high income, although schooling is low. Tumbadero del Águila is the locality with lowest average surface (3.42 ha) destined to this crop; they present low schooling and low income. The average for Veracruz is 3.75 hectares (INEGI, 2009).

The localities of Adalberto Tejeda, Vara Alta and Buena Vista Molino exceed the regional average (5.8 ha). At the state level the average surface is five hectares per producer.

Regarding the relation between adoption of technology and surface, the tendency is for the surface not to influence the adoption of technology, since the correlation is low (3 %); in this case, the surface is more related to the income of the citrus producer (48%) and not to the adoption of technology (Figure 5). It should be mentioned that, through a study carried out with 164 citrus producers in the northern zone of Veracruz, COVECA (2002) indicates that 32% of the orange producers are traditional, they use only family labor to carry out the farming tasks, and they have less than five hectares; 34 % corresponds to intermediate producers, who use family and paid workforce to carry out farming tasks and they own between 5 and 20 hectares, and the other 34 corresponds to entrepreneurial producers who use only paid workforce and have more than 20 hectares.

Figure 5 Behavior of the InAI with regard to the surface destined to citrus production in Álamo, Veracruz. 

Increase of the net income. Finally, a calculation of the net income of the producer was made. The costs of the practices that the producer performs are considered, such as soil fertilization once a year, unplanned sanitary pruning, weed control, and application of agrichemical products like insecticides or fungicides (Figure 6). The calculations carried out show that the net income increased by 19 % from 2012 to 2013.

Figure 6 Net income per hectare by orange producers, with survey data. The lines at the end of the bar represent the standard deviation. 

CONCLUSIONS

It was possible to increase the InAI as a result of integral extension work, since an increase in the adoption of technology was observed after the technological intervention during one year, of 57.7% in average, in the localities of Álamo, Veracruz.

The income influences significantly in decision making of the producers; if they do not have sufficient capital to invest in the adoption of new technologies, the process is reduced. This factor had a correlation of 35 %.

The low education (average schooling of primary school), the age (most producers are older adults), and the years of experience as citrus producers did not influence significantly the adoption of innovations by producers.

The best accepted innovations were nutrition and pest and disease control.

LITERATURA CITADA

Allub L., 2001. Aversión al riesgo y adopción de innovaciones tecnológicas en pequeños productores rurales de zonas áridas: Un enfoque causal, Estudios Sociológicos, vol. XIX, núm. 2, mayo-agosto, 2001, El Colegio de México, México. pp: 467-493. [ Links ]

Almaguer V., G., y Ayala G. A. V. 2014. Adopción de innovaciones en limón ‘Persa’ (Citrus latifolia Tan.) In: Tlapacoyan, Veracruz. Uso de bitácora. Revista Chapingo Serie Horticultura, 20(1), 89-100. [ Links ]

Binswanger, H. P., and D. A. Sillers. 1983: Risk Aversion and Credit Constraints in Farmers’ Decision-Making: AR e interpretation, Journal of Development Studies, 20, 5-21. [ Links ]

Bozoinn, M., y Ceyhan, V. 2007. Measuring the technical efficiency and exploring the inefficiency determinants of vegetable farms in Samsun province, Turkey. Agricultural Systems 94 (3): 649-656. [ Links ]

Cáceres D. 1994. Estrategias Campesinas y Riesgo. Desarrollo Agroforestal y Comunidad Campesina, 3(13). pp: 2-6. [ Links ]

Contreras-C., J. M. 2000. La competitividad de las exportaciones mexicanas de aguacate: un análisis cuantitativo. Centro de Investigaciones Económicas, Sociales y Tecnológicas de la Agroindustria y la Agricultura Mundial (CIESTAM). Universidad Autónoma Chapingo (UACh). Reportes de investigación Núm. 46. México, D. F. 42 p. [ Links ]

COVECA. 2002. Diagnóstico de la cadena de naranja en el estado de Veracruz, Gobierno del estado de Veracruz. [ Links ]

Cuevas R. V., Baca M., Julio, Cervantes E, F., Espinosa G., J. A., Aguilar Á. J., y Loaiza M. A. 2013. Factores que determinan el uso de innovaciones tecnológicas en la ganadería de doble propósito en Sinaloa, México. Revista mexicana de ciencias pecuarias, 4(1), 31-46. Recuperado en 21 de julio de 2016, de Recuperado en 21 de julio de 2016, de http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S2007-11242013000100003&lng=es&tlng=es . [ Links ]

Feder, G. and O’Mara, G. T. 1982. On information and innovation diffusion: a Bayesian approach, American Journal of Agricultural Economics 64, 145-147. [ Links ]

Feder, G., R.E. Just, and D. Zilberman. 1985. Adoption of Agricultural Innovations in Developing Countries: A Survey. Economic Development and Cultural Change 33(2):255-98. [ Links ]

Galindo G., G. 1992. Aspectos relacionados con la comunicación agropecuaria entre ejidatarios del Mezquite, Zacatecas. Fitotecnia Mexicana 15: 193-196. [ Links ]

Galindo G., G. 1995. Uso de innovaciones agrícolas en la región central de Zacatecas, México. Fitotecnia Mexicana 18: 140-150. [ Links ]

García S., E. I.; Aguilar Á., J. y Bernal M., R. (2011). La agricultura protegida en Tlaxcala, Méjico: La adopción de innovaciones y el nivel de equipamiento como factores para su categorización. Teuken Bidikay, (No. 2): 193-212. [ Links ]

Hartwich, F.; Monge P., M., Ampuero R, L. y Soto, J. L. 2007. Knowledge management for agricultural innovation: lessons from networking efforts in the Bolivian Agricultural Technology System. In: Knowledge Management for Development Journal, vol. 3 (No. 2): 21-37. [ Links ]

INEGI. 2009. Instituto Nacional de Estadística Geografía e Informática. Censo Nacional Agropecuario y Pesquero. 2007. Aguascalientes, Ags. México. [ Links ]

Jasso V., J. 2005. La dimensión evolutiva de la innovación: un rumbo necesario de la política científica, tecnológica y de innovación. In: Economía y Sociedad, 10(15): 99-119. [ Links ]

Lipton, M. 1968. The theory of the optimising peasant. Journal of Development Studies 4(3), 327-351. [ Links ]

Muñoz R., M., Aguilar Á., J. Rendón M. R., y Altamirano C., J. R. 2007. Análisis de la dinámica de innovación en cadenas agroalimentarias. Universidad Autónoma Chapingo - CIESTAAM / PIIAI. Chapingo, México. 72 p. [ Links ]

Paz, A., Montoya M. P., y Asensio H. R 2013. Escalando innovaciones rurales. Asensio. Lima, IEP; IDRC-CRDI; FIDA, 2013. (Estudios de la Sociedad Rural, 43). [ Links ]

Reimers, M., and Klasen S. 2013. Revisiting the role of education for agricultural productivity. Amer. J. Agr. Econ. 95(1):131-152. [ Links ]

Rogers, Everret. 2003. Difussion of Innovations. 5th ed. New York, USA: The Free Press. [ Links ]

SIACON-SAGARPA (Sistema de información agroalimentaria de consulta. Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación). 2015. Sistema de información agroalimentaria de consulta. México, D. F. [ Links ]

Venezian, E., and Gamble W. 1969. The Agricultural Development of Mexico. Its Structure and Growth Since 1950. Praeger. New York, USA. 305 p. [ Links ]

Zarazúa, J. A., Solleiro, J. L., Altamirano R., Castañón R., y Rendón R. 2009. Esquemas de innovación tecnológica y su transferencia en las agroempresas frutícolas del estado de Michoacán. Revista Estudios Sociales 17(34): 37-71. [ Links ]

Zarazúa E. J. A., G. Almaguer V., y S. R. Márquez B. 2011. Redes de innovación en el sistema productivo fresa en Zamora, Michoacán. Revista Chapingo Serie Horticultura 17: 51-60. [ Links ]

Zarazúa, J.A., Almaguer V.G., y Rendón M.R. 2012. Capital Social. Caso red de innovación de maíz en Zamora Michoacán, México. Cuadernos de desarrollo rural, 9 (68): 105-124. [ Links ]

Received: October 2016; Accepted: September 2017

* Author for correspondence: almaguervargas@hotmail.com

Creative Commons License Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons