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JEL Code: O13, Q12.
Tomás Bragulat, Elena Angón, Alberto Giorgis, & José Perea (2020). Typology and characterization of the pampean beekeeping systems, Esic Market Economics and Business Journal, 51(2), 299-318. Doi: 10.7200/esicm.166.0512.2Download in PDF Format
Beekeeping in Argentina is an activity that is practiced even before the arrival of the European bee. The aboriginal settlers collected honey and wax made by autochthonous meliponas and trigonas bees (Bierzychudek, 1979). The first Apis mellifera hives were introduced in Mendoza and Buenos Aires in the mid-19th century. The productive advantages of this bee compared to the native ones originated a rapid expansion process (Salizzi, 2014). In 1895 beekeeping was already a consolidated activity according to Ardissone (1931), who analysed the second agricultural census of the Argentine Republic. Towards the middle of the 20th century, the sector became a main exporter of honey (Von Kotsch, 1944). Since then, the export role has intensified, which has been accompanied by a continuous process of increasing production (FAOSTAT, 2017). Currently, Argentina is the third largest honey producer in the world and the second main exporter, allocating more than 95% of production to the foreign market (Blengino, 2015). Probably, the main challenge for Argentine beekeeping is to continue maintaining its export quota in an increasingly competitive and demanding market, especially in matters of food safety and traceability. Facts such as the complaint of dumping by the United States in 2001 or the detection of nitrofurans in 2003 in Great Britain had important consequences for Argentine exports, which, together with a poorly developed internal market, caused significant instability in the sector (Mogni et al., 2008). The beekeeping chain was one of the first sectors with a traceability regulation in Argentina, due to the demands of the European market. However, Argentine honey is traced the extraction room, so the market does not know what happens in the production units (Mogni et al., 2008). In the near future, traceability will most likely include the beekeeper; however, the sector may not be prepared for this change. Firstly, because in Argentine beekeeping an informal culture predominates that does not perceive traceability as an opportunity (Estrada, 2015). On the other hand, there is little scientific information on what happens at the production level. Bragulat et al. (2018) analyzed the influence of the managerial capacity of the beekeeper on the viability in the province of La Pampa. Cozzarin and Díaz (2016) evaluated the socioeconomic situation in the province of San Luís. Estrada (2015) reviewed the types of Argentine beekeepers. Ulmer et al. (2011) evaluated beekeeping in the province of Santa Fe. However, in other regions that compete in the same markets, different beekeeping typologies have been developed, such as in Brazil (Fachini et al., 2010) or Mexico (Contreras et al., 2013; Castellanos-Potenciano et al., 2015; Vélez-Izquierdo et al., 2016). These works have identified and characterized the different production systems, helping to understand the production process and the relevant factors. This information is the basis of any intervention policy or recommendations that help to improve the future of the sector. Among the techniques used to construct livestock typologies, multivariate methods are relevant (Toro-Mújica et al., 2012; Rivas et al., 2015). The main advantage of these techniques is that the classification is based on the degree of similarities and differences of the farms with respect to a set of classification criteria, and not with respect to external criteria subject to the subjectivity of the researcher. Furthermore, it is not necessary to establish a priori the number of categories, but rather arises the variability of the farms (Köbrich et al., 2003). For these reasons, the aim of this study was to identify and characterize the beekeeping production systems in La Pampa through multivariate techniques. Taking the typology as a starting point, the main factors that affect the systems are analyzed.
Materials and methods
Data acquisition The study was carried out in the province of La Pampa, which is located between 63 ° and 65 ° W and 35 ° and 39 ° S. The approximate surface is 32,467 km2 and has a population of 1,500 farms (RENAPA, 2014). Benign winters and mild summers predominate in this area, with concentrated seasonal rains in spring. The average annual precipitation is 724 mm and the average temperature is 15 ° C (Directorate General of Cadastre, 2014). The information was collected in 2013 through face-to-face interviews with producers during a visit to the farm. The same person did all the surveys. The sample consisted of 80 beekeepers and was obtained through simple random sampling, in which each beekeeper had an equal probability of being selected to represent the population. The sample represents 5% of the studied population and is made up of those beekeepers willing to supply the information. The questionnaire was based on the Rivas et al. (2015) adapted to beekeeping; with items related to the productive and patrimonial structure of the farms, socioeconomic aspects, production, performance and business management. Systems characterization The classification and description of the systems was based on the methodology used by Rivas et al. (2015) and Toro-Mújica et al. (2012), which includes three stages: Review and selection of variables, factor analysis (FA) and cluster analysis. Sixty- six variables related to the size and structure of the production unit, production and productivity, diversification, economic results and beekeeping were analyzed. In a first stage, 30 variables were selected, those of greatest interest, with a coefficient of variation greater than 60% (Table 1). The correlation matrix was then analyzed to rule out uncorrelated variables and linearly dependent variables. The adequacy of the data to AF was verified using the Bartlett sphericity test and the KMO index (Gelasakis et al., 2012). By means of this process, the variables marked in bold in Table 1 were selected. In a second stage, FA was used to synthesize most of the variability in a small number of orthogonal variables called factors. Previously, the variables were standardized to mean zero and standard deviation one to avoid the influence of the different scales of each variable. Factors extracted with eigenvalues greater than one were selected. Varimax orthogonal rotation was applied to more easily relate the selected variables to the extracted factors (Gelasakis et al., 2012; Köbrich et al., 2003). In a third stage, the farms were classified into groups using cluster analysis. First, hierarchical groupings were developed based on Ward’s method with Euclidean, Euclidean squared, and Manhatan distances (Köbrich et al., 2003). The optimal number of groups was determined using the Elbow method (Gelasakis et a., 2012). The optimal grouping was determined using discriminant analysis and analysis of variance (Rivas et al., 2015). The group whose discriminant functions correctly classified the highest percentage of production units and generated significant differences in the largest number of original variables was chosen. The missing data on some variables caused the exclusion of 40 production units. The data was analysed using SPSS software version 15.0. The level of significance was assumed at P <0.05. Table 1. Variables used to identify and characterize the types of beekeeping in La Pampa (Argentina) Variable Description Units Colm Number of hives at the beginning of the production cycle number DCol Percentage of hives that do not finish the production cycle % Apia Number of apiaries number Colm/Apia Number of hives per apiary hives/apiary Inver Total investment including land $ Inver/Colm Total investment including land per hive $/hive UTA Number of annual work units (AW) AW UTAfam Percentage of work that is family % Colm/UTA Number of hives per worker hives/AW Miel Honey sold kg Miel/Colm Honey sold by hive kg/hive Miel/UTA Honey sold by worker kg/AW Variable Description Units Exper Producer experience as a beekeeper years Edad Producer’s age years IT/Colm Total income per hive $/hive IM/IT Percentage that the sale of honey supposes on the total income % GT/Colm Total expense per hive $/hive Amor/Colm Amortization per hive $/hive Sum/Colm Supplies spend per hive $/hive Alim/Colm Food expense per hive $/hive GSan/Colm Health expenditure per hive $/hive SPI/Colm Spending on independent professional services per hive $/hive MO/Colm Labor expense per hive $/hive MOfija Percentage of labor expenditure that is fixed $/hive CV Variable cost % CU Unit cost (total cost / honey sold) $/kg RN Net result (total income - total expense) $ FNC/Colm Net cash flow (net result + amortizations) per hive %/hive FNC/UTA Net cash flow per worker $/AW Rent Net result / total investment including land $
Main characteristics of the production system Beekeeping was the main economic activity for 22.5% of beekeepers. For the remaining 77.5%, it was a secondary activity or an income supplement. The beekeeper has an average age of 41.1 years and has 16.5 years of experience in the sector. The average farm size was 427.7 hives and 3.15 apiaries, of which 30.0% are leased. The average labor force was 1.7 work-year units, of which 85.2% are family members. Each worker manages an average of 224.8 hives. The average investment per hive was $ 1,052.3. 52.5% of farms practice transhumance, while the rest follow a permanent production model. 82.5% of farms use external food at some point in the production cycle. The most common nutritional practice is the provision of sugar during the winter (57.5%), which is sometimes accompanied by a protein supplement (12.5%). Feeding averaged $ 7.8 per hive and accounted for 6.6% of the total cost. The exchange of queens was common in 85.0% of the farms, which did not prevent them causing an average of 15.2% in the hives with which the production cycle begins. The average health cost per hive was $ 7.4 and accounted for 7.6% of the total cost. It was common to have implemented a health plan (95.0%) and apply periodic treatments against varroasis (100.0%). Although it was infrequent to resort to specific diagnoses (20.0%) and rotate the drugs used as preventive and / or routine treatments, an activity that is only carried out in 15.0% of the production units. The fixed cost represented an average of 60.7% of the total cost and is mainly formed by the amortizations that, on average, explain 67.7% of the fixed cost. Typically, the extraction of honey and the replacement of wax used external services, since only 15.0% of the farms had sufficient structure to take on both activities. The average production was 7,399 kg per year, representing a productivity of 13.5 kg per hive and 3,656 kg per worker. The sale of honey was the only income in 87.5% of the farms. The remaining 12.5% also sell nuclei, which represent around 44.2% of total income. The average income per hive was $ 75.8, while the average expense amounted to $ 121.9. This assumes an average net result of - $ 46.1 per hive. However, the average net cash flow was $ 3.8 /hive. The average unit cost was $ 33.9 /kg of honey, while the price of honey ranged $ 6 to $ 7 /kg in 100.0% of farms. Factorial analysis FA resulted in two factors with eigenvalues greater than one, which jointly explain 66.23% of the variance (Table 2, Figure 1). The KMO index was 0.713, while the Bartlett sphericity test was significant (p <0.05); therefore, the adequacy of the data to FA is confirmed (Köbrich et al., 2003). Factor 1 explained 56.30% of the variability and was mainly correlated with variables related to productive and economic performance, which is why it is called “performance”. Factor 2 explained 9.93% of the variability and was positively correlated with the variables related to the size of the production unit, which is why it is called “size”. Table 2. Results factor analysis after applying the varimax rotation Variable F1 Performance F2 Size Miel/Colm 0.758 0.166 Miel/UTA 0.780 0.350 FNC/UTA 0.779 0.372 Colm/UTA 0.681 0.440 CV 0.663 0.009 CU -0.622 -0.070 IT/Colm 0.612 0.099 RN 0.615 0.412 Colm/Apia 0.521 0.220 Colm 0.431 0.722 Apia 0.078 0.706 Miel 0.425 0.565 UTA 0.082 0.508 Eigenvalue 7.320 1.291 Variance (%) 56.30 9.93 Figure 1. Correlations of the original variables with the two factors extracted factor analysis after applying the varimax rotation (• variables mainly correlated with factor 1, • variables mainly correlated with factor 2) Miel/Colm Miel Colm Colm/Apia Apia UTA Miel/UTA Colm/UTA CU CV IT/Colm FNC/UTA RN -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 F2 Farm typology The cluster analysis with the most significant results was the solution of three groups with the Ward’s method, based on the Euclidean distances (Figure 2). Table 3 and Table 4 show the main characteristics of each type of production unit. Group I. Subsistence Beekeeping Subsistence beekeeping grouped 55.0% of the farms and were characterized mainly by being very small in size and obtaining low productive and economic yields. The mean size was the smallest of all the groups with 124.1 hives, 1.3 apiaries and 1.3 jobs. The ratio of hives per worker was the lowest of all the groups (89.3 hives/AW), while the investment per hive was the highest of all the groups ($ 1,411.4 /hive). Although the total investment was the lowest of all the groups ($ 130,878). Production (872.3 kg) and average productivity, both per hive (7.90 kg/hive) and per worker (615.2 kg/.), were the lowest of all the groups. Economic performance was also the worst of all groups, both net result (-7,247.2 $) and profitability (-2.38%). Average unit cost and cost per hive reached $ 53.24 /kg and $ 136.08 /hive and were the highest of all groups. While the income per hive was the lowest of all the groups with $ 54.04 /hive. The amortizations ($ 63.79 /hive) and the expense in supplies ($ 26.80 /hive) were high compared to the other groups, while the health ($ 6.79 /hive), the independent professional services ($ 1.16 /hive) and labor ($ 7.16 / hive) represented a lower average cost per hive than the other groups. Figure 2. Euclidean distances between the three types of beekeeping identified in La Pampa I Subsistencia II Industrial III Comercial 20 25 30 35 40 45 Distancia euclídea Group II. Industrial beekeeping Industrial beekeeping grouped 15.0% of the farms and were mainly characterized by their large size and high productive and economic yields. The mean size was the largest of all the groups, with 1,466.7 hives in 8.8 apiaries and 2.7 jobs. The ratio of hives per worker was the highest (570.0 hives /AW), while investment per hive was the lowest of all the groups ($ 538.2 / hive). Although the total investment was the highest of all the groups ($ 561,638). Production (26,650.0 kg) and production per worker (10,747.2 kg /AW) were the highest. The economic performance was also the best of all the groups, both the net result ($ 2,2018.2) and profitability (10.42%). The average cost per hive ($ 102.84 / hive) and the average income per hive ($ 102.16 / hive) were high compared to group I; while the unit cost was intermediate to the other groups ($ 14.54 / kg). Amortizations ($ 29.24 / hive), supplies ($ 17.45 / hive) and health ($ 6.70 / hive) represented a lower average cost per hive than the other groups; while labor ($ 25.99 / hive) and independent professional services ($ 2.70 / hive) were the highest of all the groups. Table 3. Main technical characteristics of the three types of beekeeping identified in La Pampa Variable Mean I II III SEM1 P value2 n 40 (100.0%) 22 (55.0%) 6 (15.0%) 12 (30.0%) Colm 427.7 124.1a 1.466.7c 465.0b 95.3 0.000 DCol (%) 15.2 12.0 21.7 17.73 4.06 0.666 Apia 3.1 1.3a 8.8b 3.7b 0.68 0.000 Colm/Apia 138.0 92.9a 225.7b 141.0b 13.3 0.001 Inver ($) 450,068 130,878a 561,638a 291,792a 42,258 0.000 Inver/Colm ($) 1,052.3 1,411.4b 538.2a 650.9a 187.6 0.001 UTA 1.7 1.3a 2.7b 1.7a 0.14 0.003 UTAfam (%) 85.2 88.6 68.3 87.5 3.6 0.144 Colm/UTA 224.8 89.3a 570.0c 300.7b 38.2 0.000 Honey (kg) 7,399.1 872.3a 26,650.0c 9,738.3b 2,173.4 0.000 Honey/Colm (kg) 13.5 7.9a 18.8b 21.0b 1.69 0.000 Honey/UTA (kg) 3,656.2 615.2a 10,747.2b 5,684.6b 1,011.1 0.000 Exper (años) 16.5 17.0a 23.8b 15.1a 1.12 0.043 Age (years) 41.1 41.1 43.5 40.0 1.79 0.833 1 Mean standard error. 2 Means with different letters significantly differs. Group III. Commercial beekeeping Commercial beekeeping grouped 30.0% of the farms and were mainly characterized by high productivity with intermediate farm sizes. The number of hives was 465.0, in 3.7 apiaries and 1.7 jobs. The ratio of hives per worker was intermediate (300.7 hives/AW). The investment per hive was similar to group II ($ 650.9 /hive), while the total investment was similar to group I ($ 291,792). Production (9,738.3 kg) was intermediate to the other groups; however, the average productivity, both per hive (21.0 kg / hive) and per worker (5,684.6 kg/ AW), were similar to those obtained by group II. The net result ($ 231.9) was also intermediate compared to the other groups, although the profitability was similar to that of group II (6.16%). The unit cost was the lowest of all the groups, with an average of $ 8.14 /kg, while the average cost per hive ($ 105.57 /hive) and the average income per hive ($ 102.60 /hive) were similar to group II. Amortizations ($ 34.72 /hive), labor ($ 7.34 /hive) and supplies ($ 26.81 /hive) assumed an average expense per hive similar to group I; while the average health expenditure ($ 9.25 /hive) was the highest of all the groups. Table 4. Main economic characteristics of the three types of beekeeping identified in La Pampa Variable Mean I II III SEM1 P value2 RN ($) -46.1 -7,247.2a 2,2018.2b 231.9a 3,178.31 0.004 Rent (%) 2.1 -2.4a 10.4b 6.2b 1.59 0.003 IM/IT (%) 87.5 94.4 92.1 95.8 2.5 0.901 CV (%) 39.3 32.8a 43.8ab 48.8b 2.88 0.035 CU ($/kg) 33.9 53.2b 14.5a 8.1a 8.51 0.037 FNC/UTA ($) 5,466.5 -1,071.4a 22,401.3c 8,985.2b 2,212.5 0.000 FNC/Colm ($) 3.8 -18.2a 28.6b 31.7b 6.03 0.000 IT/Colm ($) 75.8 54.0a 102.2b 102.6b 8.85 0.020 GT/Colm ($) 121.9 136.1a 102.8b 105.6b 9.23 0.023 Amor/Colm ($) 49.9 63.9b 29.2a 34.7a 5.08 0.006 Sum/Colm ($) 25.4 26.8b 17.4a 26.8b 3.77 0.021 Alim/Colm ($) 7.8 7.6 6.7 8.8 1.52 0.903 GSan/Colm ($) 7.4 6.8a 6.0a 9.2b 0.60 0.011 SPI/Colm ($) 1.8 1.2a 2.7b 2.5b 0.24 0.008 MO/Colm ($) 10.0 7.2a 26.0b 7.4a 3.06 0.008 MOfija (%) 71.7 60.6 84.3 85.7 9.40 0.486 1 Mean standard error. 2 Means with different letters significantly differs.
FA explained 66.23% of the variation between farms. This value is acceptable considering that it is usual to take into account solutions that represent at least 60% of the total variance (Jiménez and Aldás, 2005). Vélez-Izquierdo et al. (2016) obtained a similar value in a typology of Mexican beekeepers. The typology has identified three systems in La Pampa: Subsistence beekeeping, commercial beekeeping and industrial beekeeping. There are few typologies and/ or characterizations of systems, making it difficult to make comparisons with other regions. Ulmer et al. (2011) studied a sample of 18 representative beekeeping farms in the province of Santa Fe (Argentina). There are no important differences with La Pampa in terms of structure (depreciation: $ 54.1 /hive), size (448 hives), family profile (83.2% family labor) and beekeeper characteristics (44% beekeeping main activity, 47 years and 17.5 years of experience). However, in La Pampa a lower mortality rate has been obtained (42% difference hives), hives are more productive (7.1 kg of honey per hive), fewer apiaries (7 apiaries) and use of external inputs (81%of expenses was food). At economic level, results Santa Fe (-24% profitability) was less favourable than those obtained La Pampa. In the province of San Luis, Cozzarin and Díaz (2016) reported a larger and more intensive beekeeping, still more productive and profitable than in La Pampa and Santa Fe. The comparison shows a marked variability. On the one hand, the interrelationships of beekeeping with the agro-ecosystem are dynamic and complex, and also depend to a large extent on the particular conditions of each farm (orography, altitude, etc.) and on poorly controllable factors such as the climate. On the other hand, farms tend to have marked structural and technological differences (Contreras et al., 2013; Magaña-Magaña et al., 2012). In intensive systems the processes follow standards, while beekeeping is adapted to the conditions of each environment and beekeeper. All this explains that typological models constitute a good starting point in the analysis of beekeeping. Typologies are useful because they help to understand the different alternatives of the systems and, above all, because they offer a more appropriate evaluation framework, the production units are already classified according to their main characteristics in specific systems (Vélez-Izquierdo et al., 2016). Subsistence beekeeping is the most common type of beekeeping in La Pampa and corresponds to the so-called subsistence models, which are the most frequent in the developing world (Affognon et al., 2015; Fachini et al., 2010; Güemes - Ricalde et al., 2003; Kalanzi et al., 2015; Mujuni et al., 2012). This system is very similar to the one developed by small beekeepers with a low technological level in Morelos in Mexico (Vélez - Izquierdo et al., 2016) and the traditional system in Veracruz (Mexico) described by Castellanos - Potenciano et al., (2015). Other similar systems have been described in Serbia (Marinkovic and Nedic, 2019), Croatia (Cvitkovic et al. 2009), Ethiopia (Kumsa and Takele, 2014) or India (Agrawal, 2014). The low barriers to entry and the low need for capital favor the expansion of this type of beekeeping (Travadelo et al., 2012). In Argentina, in addition, it has been incentivized through apicultural development plans, with the aim of absorbing surplus labor other sectors of the economy (Crisanti et al., 2009). This process is transforming beekeeping into a micro-enterprise format, characterized by low levels of production and productivity. In La Pampa this system concentrates 55% of the farms, although they produce less than 7% of the honey. In Serbia, 98% of beekeepers produce 60% of honey (Marinkovic and Nedic, 2019). Similar results have been described in other regions of Ibero-America (eg Mexico, Contreras - Escareño et al., 2013; Chile, Leal - Méndez, 2012; Ecuador, Marín - Palma, 2017). The main characteristic of this type of system is its low productivity. In La Pampa it was 7.9 kg of honey per hive, comparable to the 6.64 kg of Saudi Arabia (Adgaba et al., 2014), 9.5 of Ethiopia (Kumsa and Takale, 2014) or 8.5 of India Agrawal (2014). Low productivity is mainly related to a low technological and managerial level (Rege et al., 2001). This can be seen in the level of investment and spending on independent professional services, which mainly corresponds to technicians and advisers. In addition, most farms practice a fixed beekeeping. According to Freitas et al. (2004), beekeeping productivity depends mainly on management technologies, which are related to better beekeeping management and farm organization. The reduced profit margin and the small scale make difficult to access to the financial and technological markets (Freitas et al., 2004; Güemes et al., 2003). Furthermore, under the subsistence logic, it is very difficult to carry out investments that commit the producer in the long term, such as technological ones (Rangel et al., 2017). When subsistence logic changes and technological or management improvements are introduced, small systems can achieve yields comparable to large apiculture farms. For example, small farms in Turkey or Ethiopia, when improved, obtain an average of 22 and 23 kg of honey per hive, respectively (Saner et al., 2004; Kumsa and Takale, 2014). Productivity is also conditioned by the reduced use of external inputs, which is common in subsistence systems (Oosting et al., 2014). This can be seen in the low level of variable cost and in the reduced expenditure on food or health. Subsistence beekeeping is socially relevant for its contribution to employment and income in rural areas (Magaña et al., 2007). In this context, the work performed by the family in the production unit does not increase the level of costs for the beekeeper (Perea et al., 2014). Consequently, the number of hives can increase or decrease until all the surplus family labor is saturated. However, there is a threshold beyond which it is necessary to increase technology to increase the number of hives per worker. In La Pampa, as in Mexico, when the technological level is low, it is estimated at around 80 - 90 hives (Vélez - Izquierdo et al., 2016). In this regard, there are important differences between producing areas. For example, in Saudi Arabia the average number of hives exceeds 351 while in Ethiopia this system operates 10 to 140 hives (Adgaba et al., 2014; Kumsa and Takale, 2014). The low productivity and the limited number of hives explain why the economic performance is negative. The small scale has a large negative impact, especially on fixed costs (Ramírez - Angulo et al., 2010). This can be seen, for example, in amortizations. Despite being a low investment system, the small size makes investment and amortization per hive triple and double, on average, that of the industrial and commercial systems. The low income per hive is due to its low productivity and the sale price is set by the export market, the pampas beekeeping does not exert any type of influence. Although the economic performance is negative, under the logic of subsistence there are benefits. This is because the beekeeper does not account for depreciation or compensation for family labor as an expense (Perea et al., 2014). According to Güemes et al. (2003), this system will continue to be relevant as long as there are no better employment alternatives in other sectors of the economy. Changes in surplus labor will cause the level of production and the number of farms to increase or decrease. However, in order for these production units to continue in the long term, they have to increase the level of production to a scale that allows them to generate economic advantages (Klaesson, 2001). The growth in the number of hives must be accompanied by technological improvements, especially in the management and advisory area (Rege et al., 2001). In other words, they must abandon the logic of subsistence and change the production system. The system furthest subsistence beekeeping is industrial beekeeping. This type of beekeeping is hardly mentioned in the literature, probably because there are few production units that follow it in each region. However, they are very important because they concentrate most of the production and have a clear export vocation. In Argentina, it is the system that dominates exports and sometimes plays a relevant role as a honey gatherer other production units (Mogni et al., 2008; Estrada, 2015). Industrial beekeeping in La Pampa concentrates 54% of production in 15% of farms. These are large farms that have few restrictions in financial markets and in access to technology. Consequently, there are important advantages derived scale and technological level. This is seen in different aspects of the system; for example, in the reduced expense on amortization and investment per hive compared to the high investment of the system. Another advantage derived technology is that it increases work efficiency. Industrial beekeeping multiplies by six the number of hives that each worker manages, and by fifteen the honey that each worker produces, compared to the subsistence system. The work efficiency exceeds 500 hives that Ulmer et al. (2011) indicated as optimal in Santa Fe. Productivity per hive increases with respect to the subsistence system and is similar to the commercial system, which derives better management and beekeeping technology, but not a greater use of external inputs. Productivity is comparable to that obtained by small beekeepers in Turkey (Saner et al. 2004) the large beekeepers with an intermediate technological level in Morelos (Vélez-Izquierdo et al., 2016), although lower than the commercial system in Veracruz (Castellanos - Potential et al. 2015) and, in general, the average of the large apicultural regions (Contreras et al., 2013). This is explained, in addition to the edaphoclimatic differences, by a reduced use of external inputs and food compared to other apicultural regions (García-Girou, 2002). The reduced use of external inputs is a characteristic feature of the three beekeeping systems of La Pampa and has to do with the high price of raw materials and the availability of long, honey-quality blooms. Production units tend to use part of the flowering to maintain and reproduce the hives, as opposed to the strategy of optimizing the bee population with external food before flowering (García-Girou, 2002). The strategy followed in La Pampa reduces the productivity of the hives but increases the performance of the workforce by simplifying management. Low productivity is attempted to compensate by increasing the number of hives, which in turn is favored by a greater availability of labor. This is clearly seen in the industrial system when compared to the more competitive systems described in Mexico. The Veracruz commercial system needs 440 hives to produce 12,716 kg of honey; while the industrial system of La Pampa produces more than double but needs more than three times as many hives (Castellanos - Empowerment et al., 2015). However, the yield per worker in the La Pampa industrial system triples that of the Veracruz commercial system. Industrial beekeeping is the system with the highest economic returns, which is mainly related to a more competitive scale. Commercial beekeeping also obtains a positive economic performance, although of lesser value due to the smaller average size of the production units. The main difference is that it operates on a smaller and less competitive scale. This is seen above all in that the work performance is half that obtained by the industrial system and is also related to a lower technological level. This system also has similarities with the one developed by large beekeepers with an intermediate technological level in Morelos de México (Vélez-Izquierdo et al. 2016). According to Bragulat et al. (2018), although the production system conditions the different productive and economic returns, it is not the only factor that explains them. There will be types of beekeeping that facilitate the success of the activity, and others that make it difficult or even impossible to achieve acceptable yields. In this sense, the way in which one intervenes in industrial and commercial beekeeping is the manager’s task and will strongly condition the final success of the operation. Decisions regarding which aspects should be intervened, how, at what time and with what technology, are specific to each production unit. These kinds of questions should also be subject to analysis.
Beekeeping in La Pampa is generally an economic complement or a subsistence activity, with low productivity, heterogeneous and low use of inputs. Three types of beekeeping have been identified, which are mainly differentiated by the yield and size of the production unit: Subsistence beekeeping groups. They are 55% of the production units and are mainly characterized by being very small in size and obtaining low productive and economic returns. It is a socially relevant system for its contribution to family employment and income in rural areas. The low productivity and the limited number of hives explain a low economic performance. This system will remain relevant as long as there are no better employment alternatives in other sectors of the economy. Industrial beekeeping concentrates 54% of production in 15% of farms, which are mainly characterized by their large size and high productive and economic yields. It is the system with the highest economic returns, which is mainly related to a more competitive scale. Commercial beekeeping groups are 30% of the production units and are mainly characterized by high productivity with intermediate farm sizes.
Adgaba, N., Shenkute, A. G., Al-Ghamdi, A. A., Ismaiel, S., Al-kahtani, S., Tadess, Y., & Abebe, W., 2014, Socio-economic analysis of beekeeping and determinants of box hive technology adoption in the Kingdom of Saudi Arabia. Journal of Animal and Plant Sciences 24(6), 1876-1884. Affognon, H. D., Kingori, W. S., Omondi, A. I., Diiro, M. G., Muriithi, B. W., & Makau, S., 2015, Adoption of modern beekeeping and its impact on honey production in the former Mwingi District of Kenya: assessment using theory-based impact evaluation approach. International Journal of Tropical Insect Science, 35(2), 96-102. Agrawal, T. J., 2014, Beekeeping industry in India: future potential. International Journal of Research in Applied, Natural and Social Sciences, 2(7), 133-140. Ardissone, R., 1931, Apicultura argentina. Buenos Aires: Imprenta de la Universidad. Bierzychudek, A., 1979, Historia de la apicultura argentina. Buenos Aires: H. J. Mattone. Blengino, C., 2015, Sector Apícola 2014. Alimentos Argentinos. Ministerio de Agroindustria, Presidencia de la Nación. Buenos Aires, Argentina. [http://www. alimentosargentinos.gob.ar/contenido/sectores/otros/apicola/informes/2014.pdf]. Bragulat, T., Angón, E., García, A., Giorgis, A., Barba, C., & Perea, J., 2018, Influencia de la capacidad gerencial del apicultor en la viabilidad de unidades de producción apícola en La Pampa Argentina. Revista Mexicana de Ciencias Pecuarias, 9, 32-47. Castellanos-Potenciano, B. P., Gallardo-López, F., Díaz-Padilla, G., Pérez-Vázquez, A., Landeros-Sánchez, C., & Sol-Sánchez, A., 2015, Apiculture in the humid tropics: Socio-economic stratification and beekeeper production technology along the Gulf of Mexico. Global Journal of Agricultural Economics, Extension and Rural Development, 3, 321-329. Contreras, E. F., Pérez, A. B., Echazarreta, C. M., Cavazos, A. J., Macías, M. J., & Tapia, G. J., 2013, Características y situación actual de la apicultura en las regiones Sur y Sureste de Jalisco, México. Revista Mexicana de Ciencias Pecuarias, 4(3), 387-398. Cozzarin, I. G., & Díaz, J. R., 2016, Evolución socio económica de la producción de miel en San Luis en el periodo 2011 a 2015. Reunión Anual de la Asociación Argentina de Economía Agraria, Mar del Plata, 24 a 26 de agosto de 2016. Crisanti, P., Mateos, M., & Ghezán, G., 2009, Redes socio-técnicas en torno al aseguramiento de la calidad. El caso de los apicultores en el sur de Prov. de Buenos Aires. VI Jornadas Interdisciplinarias de Estudios Agrarios y Agroindustriales. 11-13 de Noviembre de 2009. Centro Interdisciplinario de Estudios Agrarios. Cvitkovi?, D., Grgi?, Z., Matašin, Z., Pavlak, M., Filipi, J., & Gajger I. T., 2009, Economic aspects of beekeeping production in Croatia. Veterinarski Arhiv 79(4), 397-408. Dirección general de catastro, 2014, Cartografía de La Pampa. Gobierno de la Provincia de La Pampa. Argentina. [http://www.catastro.lapampa.gov.ar/]. Estrada, M. E., 2015, Productores apícolas nacionales. Tipificación, desempeño y su rol en el territorio. XLVI Reunión Anual de la Asociación Argentina de Economía Agraria, Tandil, 4 a 6 de noviembre de 2015. Fachini, C., Firetti, R., Cardoso, D. O. E., & Assiz, D. C. A., 2010, Perfil da apicultura em Capão Bonito, estado de São Paulo: aplicação da análise multivariada. Revista Economia Agraria São Paulo, 57, 49-60. FAOSTAT, 2017, ProdSTAT. Organización de las Naciones Unidas para la Agricultura y la Alimentación. [http://www.fao.org/faostat/en/#home]. Freitas, D. F. F., Khan, A. S., & Silva, L. M. R., 2004, Nivel tecnológico e rentabilidade de produçao de mel de abelha (Apis mellifera) no Ceará. RER, Rio de Janeiro, 42, 171-188. García-Girou, N., 2002, Fundamentos de la producción apícola moderna. Ed. Encestando SRL. Bahía Blanca. Buenos Aires. Argentina, 187 p. Gelasakis, A. I., Valergakis, G. E., Arsenos, G., & Banos, G., 2012, Description and typology of intensive Chios dairy sheep farms in Greece. Journal of Dairy Science, 95, 3070-3079. Güemes-Ricalde, F. J., Echazarreta-González, C., Villanueva, R., Pat-Fernández, J. M., & Gómez-Álvarez, R., 2003, La apicultura en la península del Yucatán. Revista Mexicana del Caribe, 16, 117-132. Jiménez, E., & Aldás, J., 2005, Análisis multivariante aplicado. Thompson (Ed) Madrid, España. Kalanzi, F., Nansereko, S., Buyinza, J., Kiwuso, P., Turinayo, Y., & Mwanja, C., 2015, Socio-economic analysis of beekeeping enterprise in communities adjacent to Kalinzu forest, Western Uganda. International Journal of Research in Land Use Sustainability, 2, 81-90. Klaesson, J., 2001, Monopolistic Competition, Increasing Returns, Agglomeration, and Transports Costs. The Annals of Regional Science, 35, 375-394. Köbrich, C., Rehman, T., & Khan, M., 2003, Typification of farming systems for constructing representative farm models: two illustrations of the application of multivariate analyses in Chile and Pakistan. Agricultural System, 76, 141-157. Kumsa, T., & Takele, D., 2014, Assessment of the effect of seasonal honeybee management on honey production of Ethiopian honeybee (Apis mellifera) in modern beekeeping in Jimma Zone. Research Journal of Agriculture and Environmental Management 3(5), 246-254. Magaña-Magaña, M. A., Moguel-Ordóñez, Y. B., Sanginés-García, J. R., & Leyva- Morales, J. E., 2012, Estructura e importancia de la cadena productiva y comercial de la miel en México. Revista Mexicana de Ciencias Pecuarias, 3(1), 49-64. Magaña, M., Aguilar, A., Lara, P., & Sanginés, J., 2007, Caracterización socioeconómica de la actividad apícola en el estado de Yucatán, México. Agronomía, Universidad de Caldas, Colombia. 15, 17-24. Marín Palma, D. C., 2017, La producción artesanal de miel de abeja y su influencia en los ingresos de los apicultores de la comunidad Quimis del cantón Jijipapa. Tesis de Grado. Universidad Estatal del Sur de Manabí. Marinkovi?, S., & Nedi?, N., 2010, Analysis of production and competitiveness on small beekeeping farms in selected districts of Serbia. APSTRACT: Applied Studies in Agribusiness and Commerce, 04, 65-70. Doi: 10.22004/ag.econ.91136 Mogni, F., Palau, H., Sensi, S., & Villlela, F., 2008, La trazabilidad en la apicultura argentina. Elementos para su diseño e implementación. Buenos Aires: Facultad de Agronomía de la Universidad de Buenos Aires (FAUBA), Programa de Agronegocios y Alimentos. [www.agro.uba.ar/agro/agroneg/pdf/AAEA_trazabilidad_miel. pdf]. Mujuni, A., Natukunda, K., & Kugonza, D., 2012, Factors affecting the adoption of beekeeping and associated technologies in Bushenyi District, Western Uganda. Livestock Research Rural Development, 24. Oosting, S., Udo, H., & Viets, T., 2014, Development of livestock production in the tropics: farm and farmers’ perspectives. Animal, 8, 1238-1248. Perea, J., de Pablos-Heredero, C., Angón E., Giorgis, A., Barba, C., & García, A., 2014, Using farmer decision-making profiles and managerial capacity as predictors of farm viability in Argentinean dairy farms (La Pampa). Revista Científica, FCV-LUZ, XXIV, 509-517. Ramírez Angulo, N., Mungaray Lagarda, A., Ramírez Urquidy, M., & Texis Flores, M., 2010, Economía de escala y rendimiento crecientes. Una aplicación en microempresas mexicanas. Economía mexicana Nueva Época, 19, 213-230. Rangel, J., Espinosa, J. A., de Pablos-Heredero, C., Rivas, J., Perea, J., Angón, E., & García-Martínez, A., 2017, Is the increase of scale in the tropics a pathway to smallholders? Dimension and ecological zone effect on the mixed crop-livestock farms. Spanish Journal of Agricultural Research, 15, 1-10. Rege, J. E. O., Marshall, K., Notembaert, A., Ojango, J. M. K., & Okeyo, A. M., 2011, Pro-poor animal improvement and breeding. What can science do? Livestock Science, 136, 15-28. RENAPA, 2014, Ministerio de Agroindustria, Presidencia de la Nación. [https:// renapa.magyp.gob.ar/]. Rivas, J., Perea, J., Angón, E., Barba, C., Morantes, M., Dios-Palomares, R., & García, A., 2015, Diversity in the dry land mixed system and viability of dairy sheep farming. Italian Journal of Animal Science, 14, 179-186. Salizzi, E., 2014, Reestructuración económica y transformaciones en el agro pampeano: la expansión del cultivo de la soja y sus efectos sobre la apicultura bonaerense en los inicios del siglo XXI. Revista de Geografía, 1, 13-46. Saner, G., Engindeniz, S., Tolon, B., & Cukur, F., 2004, The economic analysis of beekeeping enterprise in sustainable development: A case study of Turkey. APIACTA, 38, 342-351. Toro-Mújica, P., García, A., Gómez-Castro, G., Perea, J., Rodríguez-Estévez, V., & Angón, E., 2012, Organic dairy sheep farms in southcentral Spain: Typologies according to livestock management and economic variables. Small Ruminant Research, 104, 28-36. Travadelo, M., Suero, M., Maina, M., Brizi, M. C., Rossler, N., & Caporgno, J., 2012, Las cooperativas apícolas en la provincia de Santa Fe y Este de Córdoba, Argentina: I - Caracterización de las actividades y servicios ofrecidos a los apicultores en su vinculación con los mercados. Ciencias Agronómicas, XIX, 27-33. Ulmer, J., Travadelo, M., Caporgno, J., & Castignani, H., 2011, Identificación y caracterización de los modelos de producción apícola representativos de la Zona Central de la provincia de Santa Fe. Ciencias Agronómicas, XVIII, 43-49. Vélez-Izquierdo, A., Espinosa-García, J. A., Gutiérrez, R. A., & Arechavaleta-Velasco, M. E., 2016, Tipología y caracterización de apicultores del estado de Morelos, México. Revista Mexicana de Ciencias Pecuarias, 7, 507-524. Von Kotsch, R., 1944, Algunos aspectos de nuestra apicultura, en: Apicultura Argentina. Buenos Aires: Instituto Agrario Argentino.