Competitiveness of productive chains

In March 2008, FAO and SAGARPA presented a definition of the competitiveness of an agri-food chain production system, based on that conceived by Esterhuizen (2006), and Monke and Pearson (1989): The competitiveness of an agri-food production system can be defined as the capacity of said system ?given its current technology, its inputs and products prices, and the interventions of national and international policies and programs (subsidies and taxes)? to achieve net gains greater than zero, a cost-benefit ratio (RBC) greater than 1, and to be equal or superior to that of other production systems that compete for the use of the same factors of production (water, land, capital, labor) (^{Sagarpa-FAO, 2008}).

According to ^{Romo and Rivas (2012)}, competitiveness is related to productivity, so it is necessary to emphasize that a company can be productive and not be competitive, although for a company to be competitive it must be productive. This implies that competitiveness is a more complex concept and, for practical purposes, it can be said that productivity is one of its components.

As ^{Sánchez and Avilés (2012)} comment, competitiveness depends especially on the quality of the product and the price level. These two factors in principle are related to productivity, innovation and inflation. It follows from this approach that efforts should be concentrated on increasing productivity and incorporating and developing technology, which is not always achieved by reducing costs or devaluations.

^{Schwentesius and Gómez (1997)} established that the analysis of production costs is one of the methods used to estimate the level of competitiveness between products from two or more regions. The studies that were prepared in preparation for the North American Free Trade Agreement were based, therefore, on this analysis. The results indicated that Mexico, in most cases, had higher costs than the United States of America.

When studying the competitiveness of productive chains, we highlight the influence of the use of limited resources such as water resources. With regard to efficient use of water, we should refer to studies conducted by Donald M. Tate who presents the principles in Table 1.

The principles for the efficient use of water allow highlighting that, as the main user of the resource, the agricultural sector must take actions that enable its optimal use and contribute to a collective benefit.

Kind of investigation

Considering how the data was analyzed, the scope of the research is causal correlational, since the purpose is to show how the variables relate to a given phenomenon and to explain the phenomenon in our context (^{Hernández, Fernández, & Baptista, 2010}). It seeks to explain how the variable of efficient water use has a direct impact on the degree of competitiveness.

Research design

The research presents a non-experimental, cross-sectional or transverse design, with a correlational-causal analysis and a quantitative empirical approach (^{Grajales, 2004}), since it is intended to objectively measure the variables involved and as accurately as possible.

Population and sample

With regard to the study population, it should be noted that the study unit is the agricultural companies in the San Quintin Valley, Baja California, Mexico. The population was determined according to the information provided by SAGARPA in the Rural Development Support Center (CADER), with a total of 97 agricultural companies. Once the population was determined, the sample was selected, which is a part of the population or universe to be studied (^{Gómez, 2006}). To calculate the sample, a proportion of agricultural enterprises was considered, with a confidence level of 95% and 5% error, by applying the probabilistic method with a stratified sampling based on the size of the agricultural enterprises (micro, small companies, medium and large) as classified by the Ministry of Economy, according to the agreement establishing the stratification of micro, small and medium enterprises (^{SE, 2009}).

Initial model of the measuring instrument

The instrument that was used to collect the data for the pilot test was developed in order to obtain specific information from the study group. For the selection of some of the items in the instrument, the existence of some statistical data regarding the efficient use of water was reviewed. The instrument that was developed for the study consists of a data section with the characteristics of the agricultural companies and another section with 51 items related to the dimensions of the variables involved.

Characteristics of the measuring instrument

The data collection instrument developed by this research consists of 2 parts. One corresponds to sociodemographic characteristics (15 items) and the other consists of 48 items corresponding to the measurement instrument, with a Likert scale of five options, on Availability, Management, Performance, Irrigation Technology, Price-cost, Productivity and Technological Innovation, which are the dimensions of the efficient use of water and competitiveness variables. The response options range from totally disagreeing, disagreeing, neither agreeing nor disagreeing, agreeing and totally agreeing, assigning values ​​from 1 as totally disagreement to 5 as totally agreeing.

Pilot test

The pilot test refers to the application of the questionnaire in a small sample of respondents to identify and eliminate possible problems. As a general rule, a questionnaire should not be used in a field survey without having been tested. The pilot test should be extensive, all aspects of the questionnaire should be tested, and the respondents should be similar to those that will be included in the actual survey in terms of characteristics, familiarity with the topic, behavioral attitudes and interest (^{Malhotra, 2004}).

The pilot test was carried out with 13 agricultural companies in the study region, and with the data obtained. The database was developed using the Statistics for Social Sciences software (SPSS) version 21.0 for Windows.

Validity and reliability of the instrument

The content validity was assessed by applying the Lawshe model, supplemented by the experience and knowledge of seven experts in the area related to this case. The clarity and relevance of each item was evaluated by the experts. We determined the quantitative index of the content validity based on the model by Lawshe, modified by ^{Tristán (2008)}, which considers the Content Validity Reason (CVR') with a value equal to or greater than 0.5823. Each expert was given the instrument along with the conceptual and operational definition of the variables involved. The results of the evaluation were collected and the data and information were processed with Microsoft Excel 2010 software. The Content Validity Index (CVI) was determined. In order to determine the reliability of the instrument, the questionnaire was analyzed using Cronbach's Alpha coefficient.

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Collection of data

The primary data collection method was through the survey, through the preparation of a questionnaire-type instrument to be administered to selected agricultural companies in the San Quintín Valley, Baja California, Mexico. Data from secondary sources was obtained through statistics, communications, records, and publications, among others, from official sources, government agencies, and relevant related research. This information will provide support to achieve the expected objectives (^{Torres, 2014}).

Descriptive Analysis

The pilot test was conducted in 13 agricultural companies located in the study region and the main indicators of the sample are presented below.

Figure 1 shows that 38.5% of the agricultural companies surveyed are located in the Col. Vicente Guerrero, 23.1% in San Quintín, 15.4% in Colonet and Camalú, and 7.7% in El Rosario. These zones belong to an area containing overexploited wells with saline intrusion problems (^{Conagua, 2013}).

Figure 1 Location of the company. Source: Developed by the authors with data obtained by this study. 

Figure 2 shows the companies surveyed based on the stratification of the companies by the Ministry of Economy (^{SE, 2009}), with those that have 251 or more employees (38.5%) classified as large, those that have between 51 and 250 employees (38.5%) as medium, those with employees from 11 to 50 (15.4%) as small and the remaining (7.7%) with less than 10 employees as microenterprises.

Figure 2 Number of employees in the companies. Source: Developed by the authors with data obtained by this study. 

Figure 3 shows the main crop product in the companies surveyed: 53.8% produce strawberry, 15.4% tomato, 15.4% cucumber, 7.7% onions and the remaining 7.7% produce a different crop, in this case peas.

Figure 3 Main crop product. Source: Developed by the authors with data obtained by this study. 

Figure 4 shows that 76.9% of the companies export their products, and participation in the national, regional and local markets is 7.7% each.

Figure 4 Target Markets for products. Source: Developed by the authors with data obtained by this study. 

Figure 5 shows that 92.3% of the companies use drip irrigation with irrigation tape and 7.7% use sprinkler irrigation. It is notable that most companies use drip irrigation, which is considered more efficient than sprinkler irrigation (^{Conagua, 2013}).

Figure 5 Type of irrigation used in the production process. Source: Developed by the authors with data obtained by this study. 

Figure 6 shows that 46.2% of the companies surveyed do not produce under protected agricultural conditions, 30.8% produce with 61% or more of the cultivated area under protected agricultural conditions, 15.4% grow their products with up to 10% of the surface under protected agriculture and 7.7% report having an area of 41 to 50% under protected agricultural conditions. Producing under protected agriculture conditions increases the efficient use of water, optimizing the use of the resource in the productive process (^{Moreno, Aguilar, & Luévano, 2011}).

Figure 6 Production under protected agricultural conditions. Source: Developed by the authors with data obtained by this study. 

Figure 7 shows that 46.2% of the companies have tunnel-type greenhouses, 30.8% do not have greenhouses, 15.4% have chapel-type greenhouses and 7.7% asymmetric greenhouses. It is important to mention that companies that do not have greenhouses present an area of ​​opportunity to improve the use of water resources in the production process (^{Conagua, 2013}).

Figure 7 Type of greenhouses in the companies. Source: Developed by the authors with data obtained by this study. 

Figure 8 shows that 53.8% of the companies do not have a desalination plant for their production, 23.1% have a large desalination plant, 15.4% have a small desalination plant and 7.7% have a medium desalination plant.

Figure 8 Desalination plants in agricultural enterprises. Source: Developed by the authors with data obtained by this study. 

Content validity

Table 2 shows the results of the content validity ratio procedure according to the model by Lawshe and modified by ^{Tristán (2008)}. This demonstrates that the global validity of the 51-item instrument is 83%. Most of the instrument items are acceptable, 3 items were eliminated for having values lower than the acceptable minimum of 0.5823, which were 2, 30 and 33, leaving an instrument with 48 validated items.

The overall validity of the instrument (CVI) (Table 2), considering the opinion of seven experts in the area, is 83%, so the instrument is acceptable, eliminating items 2, 30 and 33 increased this to 85%, according to ^{Tristán (2008)}.

Analysis of the items

The item analysis was done by conducting a correlation analysis and determining its effect on the Cronbach Alpha coefficient, eliminating items that did not meet the 0.30 requirement to be acceptable.

In Table 3, the majority of the items have correlation indexes above 0.3, considered acceptable. The items that have values lower than this reference are numbers 1, 2, 3, 4, which correspond to water availability, 5, 6, 9, 11, 12, which correspond to water management, 13 which corresponds to performance, 16 and 19 which correspond to irrigation technology, and 26 and 27 which correspond to prices and costs. However, when eliminating the effects from the instrument they do not result in a significant increase in the Cronbach's alpha, remaining at the same level of reliability. And since the information that could be collected for the items is useful they would not be eliminated.

Items 2 and 16 have a negative correlation, nevertheless, their effect on the level of reliability when eliminated is minimal, and since the information gathered is of importance to the research, it was decided to keep the items in the instrument.

Reliability analysis

The general Cronbach's Alpha presented in Table 4 is 0.91 for the 48 items, which is considered an acceptable level since it is greater than 0.7. The 13 agricultural companies surveyed (100%) were included for its determination.

Construct validity by Factorial Analysis

Construct validity assesses the degree to which the instrument reflects the theory of the phenomenon or concept it measures. The validity of the construction of the instrument ensures that the measures resulting from the responses to the questionnaire can be considered and used as a measure of the phenomenon that we want to measure. The most frequently used calculation methods are factor analysis and a multimethod matrix (^{Arribas, 2004}).

In this investigation, the construct validity was assessed after the content validity was determined by the experts. The instrument’s dimensions were determined through the exploratory factorial analysis by means of the SPSS software, using the principal components extraction technique and the orthogonal rotation method (Varimax), resulting in the determination of four factors that explain 75.69% of the variance. The internal consistency of the scale was also analyzed for each of the factors determining the Cronbach's Alpha coefficients (Table 5).

Determination of the number of factors to rotate

Table 5 shows the explained variance of the factors in order to determine the best factorial solution for the instrument. In this case it was determined that four factors explain 75.686% of the variance, which is considered adequate for the investigation since this exceeds the minimal acceptable percentage of 60%.

Sedimentation graph

The sedimentation graph is a method for extracting factors, where the sum of the eigenvalues is equal to the explained variance. The purpose is to identify the values whose associated eigenvalue is large enough to be considered.

Figure 9 shows the sedimentation graph with the eigenvalues ​​associated with the efficient water use and competitiveness variables for the agricultural enterprises in the San Quintín Valley, Baja California, Mexico.

Figure 9 Sedimentation graph of the determined factors. Source: Developed by the authors based on data. 

Extraction of factors

The extraction of factors was done using the extraction method in the main components analysis and the rotation method used was that of Kaiser's Varimax in the SPSS System (Table 6).

Determination of the internal consistency of the scale

Table 7 shows the coefficients determined by the extraction method corresponding to the principal components analysis of the competitiveness variables. The items that did not meet the correlation requirements were eliminated and a final instrument with 38 items was obtained, with a global Cronbach alpha of 0.926.