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Sources of innovation in family olive farms: the case of Bejaia province in Algeria
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This study aims to check for the contribution of knowledge and information to innovation activity in family olive farms in Bejaia province Algeria , and looks for the most efficient sources and channels of knowledge and information flows. To do this, we have first computed an innovation index for a sample farms, whereupon we have carried out a statistical analysis using linear regression method to find out about the most significant determinants of innovation. Nevertheless, it turns out that farmers seem not take advantage of the operational knowledge that spills over from most of other knowledge and information institutions and organisations. Donwload PDF. Olive growing in Algeria is characterized by the duality of the farming system, which dates back to the colonial period. A modern system is predominant in western Algeria and is intended for the production of table olives. In the other hand, traditional olive growing system, mostly devoted to olive oil extraction, prevails in mountain regions, essentially in Kabylia, a northern region of Algeria. There, olive growing, specifically olive oil, is of great identity and cultural value. Olive growing is mainly a family and subsistence farming, but some rural households get an extra income from the sold part of olive oil. Thus, trees are poorly maintained, harvesting is mostly done by hand, olives are stored in large plastic bags sometimes for a long time, before moving them to an olive oil mill, generally a traditional one. Paradoxically, while the extracted olive oil does not meet international standards mainly because of its high acidity, it is particularly appreciated by the local community. Hadjou et al. Through this study, we aim to check for the contribution of knowledge and information to innovation activity in family olive farms. We also look for the most efficient sources and channels of knowledge and information flows. Innovation is a process through which farmers improve their production and farm management practices. This may involve planting new crop varieties, combining traditional practices with new scientific knowledge, applying new integrated production and post-harvest practices or engaging with markets in new, more rewarding ways. But innovation requires more than action by farmers alone. The public sector — working with the private sector, civil society, farmers and their organizations — must create an innovation system that links these various actors in order to foster the capacity of farmers to innovate. FAO, This article is structured as follows. We shall first provide an overview of the theoretical framework of this study. We then present a summary review of the evolution of agricultural policy in Algeria since independence. Afterwards we proceed with a presentation of the applied methodology, followed by a description of the data that will be used later in a statistical analysis. We shall conclude with a discussion of results. Knowledge is conceived as an essential resource for innovation. From the perspective of the Neo-Schumpeterian evolutionary theory, innovations consist, in large part, of new combinations of existing routines. The knowledge that underlies a skilful performance is in large measure tacit knowledge, in the sense that the performer is not fully aware of the details of the performance and finds it difficult or impossible to articulate a full account of those details. However, innovative firms need specialized knowledge, as well as more types of knowledge often available only outside the firm itself. Nelson and Winter, ; Morone and Taylor, Moreover, information about novelties flows more easily among agents located within the same area, thanks to social bonds that foster reciprocal trust and frequent face-to-face contacts. Breschi, In other words, any firm exists within a more or less complex network of other firms, suppliers and customers, as well as a range of organizations engaged in the production, distribution, and management of knowledge. Smith, Olive-growing, like the overall agricultural sector, is supplier-dominated. In such a subsector, a relatively high proportion of innovative activities are directed toward process innovations. Moreover, one would expect a relatively high proportion of the process innovations used in olive growing to be produced by other sectors. Indeed, farms make only a minor contribution to their process or product innovations. Most innovations come from suppliers of equipment and materials, although in some cases large customers and government-financed research and extension services also make a contribution. Pavitt, ; Dosi, Possas et al. Public institutional sources , comprising universities, research institutions and public research enterprises. They run basic research activities; technology development and transfer. Private sources related to agro-industries. They comprise agricultural product processing industries. Private sources, collectively organized and non-profit oriented , include producer cooperatives and associations whose main purpose is to develop and transfer new seed varieties and agricultural practices such as new planting methods, fertilizer and pesticide dosage, methods for pest control, irrigation, crop storage, etc. Private sources related to services supply , such as firms selling technical support services, planning and production management and services related to grain production, crop and storage. Farm production units , through which new knowledge is established in the learning process which sometimes can be translated into innovations. However, this participatory or cooperative economy did not take long to become a planning one. Moreover, with the agrarian revolution during the s, unused private farmlands were nationalized and farms became even larger. Bedrani and Bourenane, ; Bedrani, This policy failed and has worsened with the s oil glut which resulted in austerity and an agricultural sector reform from Pluvinage, The reform was mainly characterized by the disengagement of the government from the management of the large farms and their fragmentation to smaller collective and even individual farms. Thus, farmers have gotten back the autonomy of management and thereby the responsibility to make the farms profitable. As a result of the structural adjustment programme SAP engaged by Algeria and the transition to market economy during the s, the loans granted to farms declined sharply, the interest rate has significantly increased, and the agricultural subsidies have been cut off. All this caused an important disinvestment of private actors in the agricultural sector. Djenane, At the end of the s, the SAP takes end and the financial situation of Algeria improved considerably thanks to the increase in oil price. This made it possible to set up an agricultural development programme from the beginning of s. Bessaoud, However, several factors have prevented the success of the development program, including lack of extension services support and low levels of education and agricultural training. Laoubi and Yamao, To remedy these shortcomings, a wider and more ambitious policy has set from For the first time, olive growing has been set as a priority and strategic subsector. MADR, These projects are related to the genetic improvement of the olive, the installation of a pilot processing plant for demonstration and training to improve olive oil quality; and the recycling of olive wastewater and composted olive pomace generated by olive oil production as fertilizers. Another major actor of the institutional environment is the University of Bejaia, mainly through basic research conducted by biology laboratories. Along the olive oil value chain, the upstream side presents no specific characteristics. To the extent that they have sufficient finance capacity to pay the transaction totally or partially in addition to a bank loan , olive-growers can acquire any equipment or authorized input from a local or foreign supplier. On the other hand, the downstream side of the olive oil value chain is very typical. Indeed, farmers undergo very little or no pressure at all from processors to enhance olive quality or meet required standards. This is because olive processors seem to be satisfied with the prevailing market conditions. Not being required to meet standards and regulation exempt them from substantial expenses and investments. Besides, the activity of most of olive processors consists essentially of a service provision. Moreover, end consumers are satisfied by the value for money of the olive oil because of a strong information asymmetry about its purity. The phenomenon of adulterating olive oil on the informal market is indeed common because of a lax product and consumer safety regulation. The present study is based upon cross-sectional data collected from a survey of 60 olive farms in Bejaia province. To select the surveyed farms, we used the stratified sampling method with proportional allocation. The statistical population has been first divided into 52 strata, each corresponding to a municipality of Bejaia province. The number of olive farms surveyed in each municipality then has been determined using the number of olive trees as a weighting parameter. Thus, the number of the surveyed farms in each municipality was proportional to the number of olive trees it has, that is, to its olive potential. To select farms within each municipality, we have randomly drawn them from the local agricultural administration catalogue. Among the attempts of adaptation of the Oslo Manual methodology to measure innovation in agriculture OECD and Eurostat, is that of a research group belonging to a network of universities that studies different aspects of agribusiness firms in Colombia Ariza et al. Following this methodology, the data collected from our survey were subsequently used to compute the value of an innovation index Innov for each farm, as well as to get information about a set of variables that may contribute to the explanation of the variation in this index. The survey was conducted as a face-to-face interview with the farmers. Most of questions were closed with two or more options, but there were also some open questions, specifically designed for identifying innovations from the motivation or the goal of the farmer through their implementation reducing costs; improving performance; improving product quality; saving time; reducing health, environmental and occupational hazards; the penetration of a new market, etc. Saavedra et al. The minimum requirement for an innovation is that the product, process, marketing method or organisational method must be new or significantly improved to the firm. This includes products, processes and methods that firms are the first to develop and those that have been adopted from other firms or organisations». The innovation index Innov matches a unique numerical value for all the innovations of each olive farm. It is computed using the following formula Ariza et al. Frequencies are measured in the interval \]0,1\]. Given the values of the power k j , rare and major innovations are rewarded with the highest contributions to Innov contributions higher than 1. Minor innovations add 1 to Innov , whether they are common or rare. The minimum value of Innov is 0, for a farm with no innovations. The maximum value of Innov is given by the extreme case when all innovations are major, and a single farm implements all possible innovations in the sample and the remaining farms do not implement any one. A simple mathematical demonstration given by the original authors allows finding that this value is:. It is quite important to point out that our survey asked the farmers about innovation activities during the last five years. Therefore, Innov only captures the innovation activities of the farm in this time frame This is besides its main limitation. However, the choice of such period has at least two reasons. First, the five-year period is generally the one observed in obtaining the first significant productions of a new olive grove IOC, The second reason is that beyond this period, it would have been difficult to the famers to remember fairly accurately the changes occurred in their activities. The survey allowed us to identify 29 innovations implemented in the olive farms see Table 1. As mentioned above, innovations were also sorted out according to their technological degree. This refers to those features of the innovation implemented by a farm, in terms of their distance with respect to the knowledge frontier, but also in terms of efficiency of the innovation according to the goal it was introduced for. Among the variables defined as possible explanatory variables for variation in the innovation index the independent variable Innov , the following six are numerical:. On the second row corresponding to the variable Innov , we note a clear discrepancy between the median value 7. Such discrepancies may be due to the presence of possible outliers. By an outlier we mean there any value lower than Q 1 — 1. Indeed, the values of the variable Innov corresponding to observations No 9;11;12;21; and 42 are outside this interval. We have thus got a new series named Winsor. Innov cleaned from outliers. On Table 2, note that winsorisation has been applied on Area and Disp series too. On the other hand, 15 dummy variables, plotted on Graph. It reflects vertical integration and scope. It reflects physical infrastructure facilities. Before performing a multiple regression, we have first made a pre-selection among the possible explanatory variables. To do this, we have tested for correlation between the dependent variable Winsor. Innov and each of the other five numerical variables. Then, we checked the output using a univariate regression between the same couples of variables. Area is highly correlated with Winsor. Area will be the unique numerical va riable to be included in the multivariate regression as a possible explanatory variable. Table 4 — Univariate Regression output with Winsor. Innov as the dependent variable. As regards the dummy variables, we have carried out the Wilcoxon Rank-Sum Test for each variable in order to check if the value of Winsor. From the outputs reported in Table 5, it appears that only the last eleven variables should be tested for their contribution to the variation of the innovation index value using the multivariate regression. Innov as the numerical variable. Since the Wilcoxon rank sum test p-values are greater than our significance level of 0. Since the Wilcoxon rank sum test p-values are less than our significance level of 0. To select the fittest multiple regression model, we first used the stepwise model selection procedure Stowell, ; R Core Team, , then we enhanced the selection after some trial and error attempts. Before interpreting the selected model Table 6 , we have first assessed its fit by carrying out some procedures as model diagnostics. Thus, to test for the normality of the error distribution, we performed Shapiro—Wilk test upon the standardized residuals series. From the output shown on Table 7 cf. Annex , we can see that the p-value for the test is 0. As this is greater than our significance level of 0. Thus, the errors normality assumption of linear regression is met. To test for errors homoscedasticity, we carried out the two versions of Breusch—Pagan test. The outputs reported on Table 8 cf. Annex show that the p-values of both tests are greater than our significance level of 0. Moreover, the output of Bonferroni outlier test on Table 9 cf. Annex exhibits no extreme observations. These results tell us that the model is a good fit to the data and includes a good combination of explanatory variables. Given the output of the Fisher test, the null hypothesis that all the coefficients of the model are null is rejected. The output of the regression points out that only three of the twelve possible predictors have coefficient estimates significantly different from 0, namely: Winsor. Thus, the null hypothesis that one of these coefficients estimates is null has been rejected. According to the taxonomy reported above Possas et al. Moreover, farmers having an agricultural high education or interactions with university researchers do not show a higher innovation performance either. So ultimately, farmers seem not take advantage of the operational knowledge that spills over from most of knowledge and information institutional sources. The generic reason we could put forward is that farmers are not enough motivated to look for new and up-to-date knowledge and information, because there is not enough incentives to decide them to introduce change in their farms. Thus, disregarding the shortcomings of the agricultural research, extension, education and training system, and assuming that efficient knowledge and information are available and freely accessible, the fact remains that a no less serious problem lies in the lack of incentives among farmers to look for innovative knowledge and information. According to the literature mentioned above Pavitt, ; Dosi, , one should have expected that most innovation in olive growing will consist essentially of new input and equipment. But for the family farms to introduce innovations, they need sufficient financial resources. However, none of the farmers questioned during our survey have got a bank loan. So, it was not possible to get information about the self-financing capacity of farmers. Nevertheless, we can argue that most of families have low or moderate incomes. Consequently, farmers would find it too high the cost of opportunity of time and money to be engaged in olive growing innovation activity. Public subsidies would overcome this constraint, but it appears from our study that they do not motivate farmers to innovate more. The third factor our study showed to impact olive farms innovation level in Bejaia province is scale. Because large farms could make economies of scale, they are therefore more likely to invest in innovation, and by the same token, more motivated to look for new knowledge and information. Thus, a second reason of the lack of incentives among farmers to innovate is the small size of most of family olive farms. Moreover, the olive sector in Bejaia province is characterized by an acute lack of cooperation, competition and regulation. Despite the well known strength of social links and face-to-face communication in Kabylia, cooperation is still limited to the traditional collective harvesting, though even this becomes more and more rare. The study we have just carried out showed that overall, olive growers in Bejaia province seem not take advantage of their interactions with most of knowledge and information institutional sources. Indeed, our study showed that farmers who have direct interactions with researchers from this institution have, in general, a better innovation capacity. However, policy interventions must be as far as possible independent from budget fluctuations for a sustainable development trajectory of olive sector. Beyond the availability and efficiency of public institutions sources of agricultural knowledge and information, the issue of olive sector innovation in Bejaia province should be tackled from the incentives perspective too. Thus, policy interventions must focus not only on the reinforcement of agricultural knowledge institutions, but also on levers likely to enhance both cooperation and competition along the overall olive oil value chain. For guidance, one lever that decision makers could use as a catalyst of competition and innovation dynamics is regulation. On the other hand, to enhance cooperation in the olive sector, policy intervention must promote agricultural cooperatives as an appropriate form of organisation to mutualise physical but also human assets, and overcome the small size constraint of most of family farms thanks to economies of scale. This study has been carried out with the help of experts from the local agricultural administration, as well as farmers and other members of professional associations and organizations. Ariza C. The Electronic Journal of Knowledge Management , 11 3 : Bedrani S. Bessaoud O. Boudi M. Breschi S. Knowledge Spillovers and local innovation systems: A critical survey. Industrial and Corporate Change , 10 4 : Djenane A. Ajustement structurel et secteur agricole. Dosi G. Sources, procedures, and microeconomic effects of innovation. Journal of Economic Literature , 26 3 : The state of food and agriculture: Innovation in family farming. Guaitero B. Innovation and transactional models in agricultural firms. Hadjou L. New Medit , 12 2 : IOC, Production techniques in olive growing. Madrid: International Olive Council. Lamani O. New Medit , 15 3 : Laoubi K. The challenge of agriculture in Algeria: are policies effective? Bulletin of Agricultural and Fisheries Economics , 12 01 : Lundvall B. In Dosi G. Technical Change and Economic Theory. London: Pinter Publishers, Le renouveau agricole et rural en marche: revue et perspectives. Morone P. Proximity, knowledge integration and innovation: An agenda for agent-based studies. Journal of Evolutionary Economics , 22 1 : Nelson R. An evolutionary theory of economic change. OECD and Eurostat, Oslo Manual: Guidelines for collecting and interpreting innovation data. OECD online bookshop. Pavitt K. Sectoral patterns of technical change: Towards a taxonomy and a theory. Research Policy , 13 6 : Pluvinage J. Porter M. Journal of Economic Perspectives , 9 4 : Possas M. An evolutionary approach to technological innovation in agriculture: Some preliminary remarks. Research Policy , 25 6 : R Core Team, R: A language and environment for statistical computing. Saavedra D. How to ask Colombian farmers for innovation: a methodological approach. Smith K. Economic Infrastructures and Innovation Systems. In Edquist C. Systems of Innovation: Technologies, Institutions and Organizations. London: Routledge, New institutional economics and agricultural co-operatives: a Hungarian case study. Naousa, Thessaloniki, Greece, May All Journals. Submit your article Referee access Guidelines for authors. This site uses functional and statistical cookies, necessary for a better browsing experience, and third-party cookies. In case of acceptance, your data will be stored in compliance with art. For further information you can consult our. 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The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes. Accept Deny Preferences Save preferences Preferences. Manage consent. Table 1 — Innovations introduced by the surveyed family olive farms. Table 2 — Summary statistics of numerical variables. Table 6 — Multivariate Linear Regression output using R software. Residual standard error:. Multiple R-squared:.
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Sources of innovation in family olive farms: the case of Bejaia province in Algeria
Bejaia buy weed
Bejaia buy weed
Sources of innovation in family olive farms: the case of Bejaia province in Algeria
Bejaia buy weed
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