According to Steffan-Dewenter (2007), cocoa farming under high canopy trees helps to maintain species diversity as well as balance economic gain and environmental conservation. Two types of data can be used to assess the impact of the situation.
Qualitative data- Described as categorical data that are not expressed in numbers. Description is by means of natural language description. In the case above, qualitative data can be useful in the identification of effects of sustainable land use (Steffan-Dewenter, 2007)). Effects on soil erosion and deforestation can be deduced from analysis of qualitative data.
Quantitative data- data is expressed in numbers. For example, the economic gain can be measured by assessing cocoa output of farmers per year, counting each tree species that remains after partial clearance among others. This kind of data gives actual impact of the situation numerically. When the canopy cover by shade trees is reduced from 80% to 40%, the farmers’ income doubles while maintaining biodiversity and ecosystem services on a similar level (Steffan-Dewenter 2007).
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The major importance of collecting these kinds of data is to enable objective analysis of a situation. Data collected accurately provides an unbiased and error-free analysis. For example, market based incentives are needed to encourage canopy cocoa farming. Favorable data on market based incentives that project high yields will encourage farmers. Farmers need to build trust in this kind of farming before they apply it.
Data will be useful in planning. Using the available data, farmers can be able to make informed decisions. The Government can also use the data to control deforestation and protect forest cover while at the same time allow prosperity of contributors of economic growth like farming (Steffan-Dewenter, 2007).
Deforestation clearing of forests and thereafter converting the land to different uses like farming, building homes, construction of roads or even creating urban centers.