A Case Study to develop and test GIS/SDSS methods to assess the production capacity of a Cocoa Site in Trinidad and Tobago

Abstract: Pritam Kumarsingh A Case Study to develop and test GIS/SDSS methods to assess the production capacity of a Cocoa Site in Trinidad and Tobago Trinidad and Tobago has achieved world-wide affirmation for its unique fine and flavour cocoa (Theobroma cacao). This classification of cocoa commands price premiums on the global market. However, the industry has declined over years. Investors and farmers are concerned about their rate of return, cost of operations and consistency of production. A potential remedy for reversal of this decline is understanding and managing the complex interrelationship among site specific characteristics and accurate analyses of their influence on production. Assessment of how site conditions impact production, enable stakeholders to reliably forecast revenue streams and make informed decisions on crop management and risk mitigation. Traditional evaluation methods are inadequate. Technology-based, Spatial Decision Support System (GIS/SDSS) supported by Geographic Information System provides a sufficiently resourced platform which enables efficient and effective decision making despite the complexity of analyzing the contributing factors. This research project utilizes soil properties and climatic data to develop and test GIS/SDSS methods to forecast production capacity of a traditional cocoa production site consisting of 4,458 hectares in Gran Couva, Trinidad and Tobago. The GIS/SDSS methods incorporate computational methods from various expert sources to determine the level of influence of each factor on cocoa production. The research is based on the chemical and physical properties of the soil. These are point based, site specific data layers and include pH, Cation Exchange Capacity (CEC), texture and drainage. The Length of the Growing Period (LGP) is used as the climate factor for comparison of evapotranspiration rates with rainfall. These data points are converted to continuous surfaces through interpolation. A Multi Criteria Analysis (MCA) using a weighting mechanism is used to ascertain the level of influence of each factor. The assigned weights of each factor are tested for consistency using Analytic Hierarchy Process (AHP) and Sensitivity Analysis. Additionally, Fuzzy Values are derived for the attributes of each factor to determine realistic production values over the site. The productive capacity is computed using mathematical relationships categorized into geographic classifications where each class depicts the level of production using GIS as the platform. The results show that site suitability for cocoa production is divided into moderate and high production classes. 45.8% of the site is classed as moderate production and 46.1% is high production and 8.2% of zero production which suggest that the site, given its current conditions can potentially produce 2,700Mt annually. The results also demonstrate the need for crop management to include adaptation to the effects of climate change in the future. As temperature increases and rainfall decreases, LGP is a critical component of production. This requires that stakeholders develop policy for mitigation as part of the overall crop management. Keywords: Geography, GIS; MCA; Fuzzy; AHP; Cocoa; SDSS; Crop; Management; Climate; Soil; Agriculture; Production; LGP Advisor: Cecilia Akselsson Master degree project 30 credits in Geographical Information Sciences, 2021 Department of Physical Geography and Ecosystem Science, Lund University Thesis nr 132