Analysis of the Market Potential of Syrian Organic Fruit and Vegetables for Exports to Germany

Syria has been a major producer and exporter of fresh fruit and vegetables (FFV) in the Arabic region. Prior to 2011, Syrian FFV were mainly exported to the neighbouring countries, the Gulf States and Northern Africa as well as to Eastern European countries. Although the EU is potentially one of the most profitable markets of high quality FFV (such as organic ones) in the world, Syrian exports of FFV to Western European countries like Germany have been small. It could be a lucrative opportunity for Syrian growers and exporters of FFV to export organic products to markets such as Germany, where national production is limited to a few months due to climatic conditions. Yet, the organic sector in Syria is comparatively young and only a very small area of FFV is certified according to EU organic regulations. Up to the author’s knowledge, little was known about Syrian farmers’ attitudes towards organic FFV production. There was also no study so far that explored and analysed the determining factors for organic FFV adoption among Syrian farmers as well as the exports of these products to the EU markets. The overarching aim of the present dissertation focused on exploring and identifying the market potential of Syrian exports of organic FFV to Germany. The dissertation was therefore concerned with three main objectives: (i) to explore if German importers and wholesalers of organic FFV see market opportunities for Syrian organic products and what requirements in terms of quality and quantity they have, (ii) to determine the obstacles Syrian producers and exporters face when exporting agricultural products to Germany, and (iii) to investigate whether Syrian farmers of FFV can imagine converting their farms to organic production as well as the underlying reasons why they do so or not. A twofold methodological approach with expert interviews and a farmer survey were used in this dissertation to address the abovementioned objectives. While expert interviews were conducted with German and Syrian wholesalers of (organic) FFV in 2011 (9 interviews each), the farmer survey was administrated with 266 Syrian farmers of FFV in the main region for the production of FFV (i.e. the coastal region) from November 2012 till May 2013. For modelling farmers’ decisions to adopt organic farming, the Theory of Planned Behaviour as theoretical framework and Partial Least Squares Structural Equation Modelling as the main method for data analysis were used in this study. The findings of this dissertation yield implications for the different stakeholders (governmental institutions and NGOs, farmers, exporters, wholesalers, etc.) who are interested in prompting the Syrian export of organic products. Based on the empirical results and a literature review, an action plan to promote Syrian production and export of organic products was developed which can help in the post-war period in Syria at improving the organic sector.

Statistical evaluation of texture analysis from the biocrystallization method

The consumers are becoming more concerned about food quality, especially regarding how, when and where the foods are produced (Haglund et al., 1999; Kahl et al., 2004; Alföldi, et al., 2006). Therefore, during recent years there has been a growing interest in the methods for food quality assessment, especially in the picture-development methods as a complement to traditional chemical analysis of single compounds (Kahl et al., 2006). The biocrystallization as one of the picture-developing method is based on the crystallographic phenomenon that when crystallizing aqueous solutions of dihydrate CuCl2 with adding of organic solutions, originating, e.g., from crop samples, biocrystallograms are generated with reproducible crystal patterns (Kleber & Steinike-Hartung, 1959). Its output is a crystal pattern on glass plates from which different variables (numbers) can be calculated by using image analysis. However, there is a lack of a standardized evaluation method to quantify the morphological features of the biocrystallogram image. Therefore, the main sakes of this research are (1) to optimize an existing statistical model in order to describe all the effects that contribute to the experiment, (2) to investigate the effect of image parameters on the texture analysis of the biocrystallogram images, i.e., region of interest (ROI), color transformation and histogram matching on samples from the project 020E170/F financed by the Federal Ministry of Food, Agriculture and Consumer Protection(BMELV).The samples are wheat and carrots from controlled field and farm trials, (3) to consider the strongest effect of texture parameter with the visual evaluation criteria that have been developed by a group of researcher (University of Kassel, Germany; Louis Bolk Institute (LBI), Netherlands and Biodynamic Research Association Denmark (BRAD), Denmark) in order to clarify how the relation of the texture parameter and visual characteristics on an image is. The refined statistical model was accomplished by using a lme model with repeated measurements via crossed effects, programmed in R (version 2.1.0). The validity of the F and P values is checked against the SAS program. While getting from the ANOVA the same F values, the P values are bigger in R because of the more conservative approach. The refined model is calculating more significant P values. The optimization of the image analysis is dealing with the following parameters: ROI(Region of Interest which is the area around the geometrical center), color transformation (calculation of the 1 dimensional gray level value out of the three dimensional color information of the scanned picture, which is necessary for the texture analysis), histogram matching (normalization of the histogram of the picture to enhance the contrast and to minimize the errors from lighting conditions). The samples were wheat from DOC trial with 4 field replicates for the years 2003 and 2005, “market samples”(organic and conventional neighbors with the same variety) for 2004 and 2005, carrot where the samples were obtained from the University of Kassel (2 varieties, 2 nitrogen treatments) for the years 2004, 2005, 2006 and “market samples” of carrot for the years 2004 and 2005. The criterion for the optimization was repeatability of the differentiation of the samples over the different harvest(years). For different samples different ROIs were found, which reflect the different pictures. The best color transformation that shows efficiently differentiation is relied on gray scale, i.e., equal color transformation. The second dimension of the color transformation only appeared in some years for the effect of color wavelength(hue) for carrot treated with different nitrate fertilizer levels. The best histogram matching is the Gaussian distribution. The approach was to find a connection between the variables from textural image analysis with the different visual criteria. The relation between the texture parameters and visual evaluation criteria was limited to the carrot samples, especially, as it could be well differentiated by the texture analysis. It was possible to connect groups of variables of the texture analysis with groups of criteria from the visual evaluation. These selected variables were able to differentiate the samples but not able to classify the samples according to the treatment. Contrarily, in case of visual criteria which describe the picture as a whole there is a classification in 80% of the sample cases possible. Herewith, it clearly can find the limits of the single variable approach of the image analysis (texture analysis).

A Modeling Analysis of Changing Global Land Use as affected by Changing Demands for Wood Products

The 21st century has brought new challenges for forest management at a time when globalization in world trade is increasing and global climate change is becoming increasingly apparent. In addition to various goods and services like food, feed, timber or biofuels being provided to humans, forest ecosystems are a large store of terrestrial carbon and account for a major part of the carbon exchange between the atmosphere and the land surface. Depending on the stage of the ecosystems and/or management regimes, forests can be either sinks, or sources of carbon. At the global scale, rapid economic development and a growing world population have raised much concern over the use of natural resources, especially forest resources. The challenging question is how can the global demands for forest commodities be satisfied in an increasingly globalised economy, and where could they potentially be produced? For this purpose, wood demand estimates need to be integrated in a framework, which is able to adequately handle the competition for land between major land-use options such as residential land or agricultural land. This thesis is organised in accordance with the requirements to integrate the simulation of forest changes based on wood extraction in an existing framework for global land-use modelling called LandSHIFT. Accordingly, the following neuralgic points for research have been identified: (1) a review of existing global-scale economic forest sector models (2) simulation of global wood production under selected scenarios (3) simulation of global vegetation carbon yields and (4) the implementation of a land-use allocation procedure to simulate the impact of wood extraction on forest land-cover. Modelling the spatial dynamics of forests on the global scale requires two important inputs: (1) simulated long-term wood demand data to determine future roundwood harvests in each country and (2) the changes in the spatial distribution of woody biomass stocks to determine how much of the resource is available to satisfy the simulated wood demands. First, three global timber market models are reviewed and compared in order to select a suitable economic model to generate wood demand scenario data for the forest sector in LandSHIFT. The comparison indicates that the ‘Global Forest Products Model’ (GFPM) is most suitable for obtaining projections on future roundwood harvests for further study with the LandSHIFT forest sector. Accordingly, the GFPM is adapted and applied to simulate wood demands for the global forestry sector conditional on selected scenarios from the Millennium Ecosystem Assessment and the Global Environmental Outlook until 2050. Secondly, the Lund-Potsdam-Jena (LPJ) dynamic global vegetation model is utilized to simulate the change in potential vegetation carbon stocks for the forested locations in LandSHIFT. The LPJ data is used in collaboration with spatially explicit forest inventory data on aboveground biomass to allocate the demands for raw forest products and identify locations of deforestation. Using the previous results as an input, a methodology to simulate the spatial dynamics of forests based on wood extraction is developed within the LandSHIFT framework. The land-use allocation procedure specified in the module translates the country level demands for forest products into woody biomass requirements for forest areas, and allocates these on a five arc minute grid. In a first version, the model assumes only actual conditions through the entire study period and does not explicitly address forest age structure. Although the module is in a very preliminary stage of development, it already captures the effects of important drivers of land-use change like cropland and urban expansion. As a first plausibility test, the module performance is tested under three forest management scenarios. The module succeeds in responding to changing inputs in an expected and consistent manner. The entire methodology is applied in an exemplary scenario analysis for India. A couple of future research priorities need to be addressed, particularly the incorporation of plantation establishments; issue of age structure dynamics; as well as the implementation of a new technology change factor in the GFPM which can allow the specification of substituting raw wood products (especially fuelwood) by other non-wood products.

Learning achievements of farmers during the transition to market-oriented organic agriculture in rural Uganda

Organic agriculture requires farmers with the ability to develop profitable agro-enterprises on their own. By drawing on four years of experiences with the Enabling Rural Innovation approach in Uganda, we outline how smallholder farmers transition to organic agriculture and, at the same time, increase their entrepreneurial skills and competences through learning. In order to document this learning we operationalised the Kirkpatrick learning evaluation model, which subsequently informed the collection of qualitative data in two study sites. Our analysis suggests that the Enabling Rural Innovation approach helps farmers to develop essential capabilities for identifying organic markets and new organic commodities, for testing these organic commodities under varying organic farm management scenarios, and for negotiating contracts with organic traders. We also observed several obstacles that confront farmers’ transition to organic agriculture when using the Enabling Rural Innovation approach. These include the long duration of agronomic experimentation and seed multiplication, expensive organic certification procedures and the absence of adequate mechanism for farmers to access crop finance services. Despite prevailing obstacles we conclude that the Enabling Rural Innovation approach provides a starting point for farmers to develop entrepreneurial competences and profitable agro-enterprises on their own.

The European organic food market - how to increase its transparency?

Der Europäische Markt für ökologische Lebensmittel ist seit den 1990er Jahren stark gewachsen. Begünstigt wurde dies durch die Einführung der EU-Richtlinie 2092/91 zur Zertifizierung ökologischer Produkte und durch die Zahlung von Subventionen an umstellungswillige Landwirte. Diese Maßnahmen führten am Ende der 1990er Jahre für einige ökologische Produkte zu einem Überangebot auf europäischer Ebene. Die Verbrauchernachfrage stieg nicht in gleichem Maße wie das Angebot, und die Notwendigkeit für eine Verbesserung des Marktgleichgewichts wurde offensichtlich. Dieser Bedarf wurde im Jahr 2004 von der Europäischen Kommission im ersten „Europäischen Aktionsplan für ökologisch erzeugte Lebensmittel und den ökologischen Landbau“ formuliert. Als Voraussetzung für ein gleichmäßigeres Marktwachstum wird in diesem Aktionsplan die Schaffung eines transparenteren Marktes durch die Erhebung statistischer Daten über Produktion und Verbrauch ökologischer Produkte gefordert. Die Umsetzung dieses Aktionsplans ist jedoch bislang nicht befriedigend, da es auf EU-Ebene noch immer keine einheitliche Datenerfassung für den Öko-Sektor gibt. Ziel dieser Studie ist es, angemessene Methoden für die Erhebung, Verarbeitung und Analyse von Öko-Marktdaten zu finden. Geeignete Datenquellen werden identifiziert und es wird untersucht, wie die erhobenen Daten auf Plausibilität untersucht werden können. Hierzu wird ein umfangreicher Datensatz zum Öko-Markt analysiert, der im Rahmen des EU-Forschungsprojektes „Organic Marketing Initiatives and Rural Development” (OMIaRD) erhoben wurde und alle EU-15-Länder sowie Tschechien, Slowenien, Norwegen und die Schweiz abdeckt. Daten für folgende Öko-Produktgruppen werden untersucht: Getreide, Kartoffeln, Gemüse, Obst, Milch, Rindfleisch, Schaf- und Ziegenfleisch, Schweinefleisch, Geflügelfleisch und Eier. Ein zentraler Ansatz dieser Studie ist das Aufstellen von Öko-Versorgungsbilanzen, die einen zusammenfassenden Überblick von Angebot und Nachfrage der jeweiligen Produktgruppen liefern. Folgende Schlüsselvariablen werden untersucht: Öko-Produktion, Öko-Verkäufe, Öko-Verbrauch, Öko-Außenhandel, Öko-Erzeugerpreise und Öko-Verbraucherpreise. Zudem werden die Öko-Marktdaten in Relation zu den entsprechenden Zahlen für den Gesamtmarkt (öko plus konventionell) gesetzt, um die Bedeutung des Öko-Sektors auf Produkt- und Länderebene beurteilen zu können. Für die Datenerhebung werden Primär- und Sekundärforschung eingesetzt. Als Sekundärquellen werden Publikationen von Marktforschungsinstituten, Öko-Erzeugerverbänden und wissenschaftlichen Instituten ausgewertet. Empirische Daten zum Öko-Markt werden im Rahmen von umfangreichen Interviews mit Marktexperten in allen beteiligten Ländern erhoben. Die Daten werden mit Korrelations- und Regressionsanalysen untersucht, und es werden Hypothesen über vermutete Zusammenhänge zwischen Schlüsselvariablen des Öko-Marktes getestet. Die Datenbasis dieser Studie bezieht sich auf ein einzelnes Jahr und stellt damit einen Schnappschuss der Öko-Marktsituation der EU dar. Um die Marktakteure in die Lage zu versetzen, zukünftige Markttrends voraussagen zu können, wird der Aufbau eines EU-weiten Öko-Marktdaten-Erfassungssystems gefordert. Hierzu wird eine harmonisierte Datenerfassung in allen EU-Ländern gemäß einheitlicher Standards benötigt. Die Zusammenstellung der Marktdaten für den Öko-Sektor sollte kompatibel sein mit den Methoden und Variablen der bereits existierenden Eurostat-Datenbank für den gesamten Agrarmarkt (öko plus konventionell). Eine jährlich aktualisierte Öko-Markt-Datenbank würde die Transparenz des Öko-Marktes erhöhen und die zukünftige Entwicklung des Öko-Sektors erleichtern. ---------------------------

Yield gap analysis and assessment of climate-induced yield trends of irrigated rice in selected provinces of the Philippines

This study describes a combined empirical/modeling approach to assess the possible impact of climate variability on rice production in the Philippines. We collated climate data of the last two decades (1985-2002) as well as yield statistics of six provinces of the Philippines, selected along a North-South gradient. Data from the climate information system of NASA were used as input parameters of the model ORYZA2000 to determine potential yields and, in the next steps, the yield gaps defined as the difference between potential and actual yields. Both simulated and actual yields of irrigated rice varied strongly between years. However, no climate-driven trends were apparent and the variability in actual yields showed no correlation with climatic parameters. The observed variation in simulated yields was attributable to seasonal variations in climate (dry/wet season) and to climatic differences between provinces and agro-ecological zones. The actual yield variation between provinces was not related to differences in the climatic yield potential but rather to soil and management factors. The resulting yield gap was largest in remote and infrastructurally disfavored provinces (low external input use) with a high production potential (high solar radiation and day-night temperature differences). In turn, the yield gap was lowest in central provinces with good market access but with a relatively low climatic yield potential. We conclude that neither long-term trends nor the variability of the climate can explain current rice yield trends and that agroecological, seasonal, and management effects are over-riding any possible climatic variations. On the other hand the lack of a climate-driven trend in the present situation may be superseded by ongoing climate change in the future.