P .1 Study Approach were incorporated in our goal-setting process. We used SiD as a framework for combining these two sets of tools. Prior to conducting the SiD analysis, we interviewed various parties in the greenhouse sector in order to better understand the latest technological advances, real costs, and on-the-ground issues in greenhouse production. In this phase we also began assembling the required data for the subsequent analysis: a large data library on crop, livestock, mushroom, and aquaculture production. We then used this data library as a foundation for the SiD analysis. The basic steps in any SiD analysis involve: 1. Goal setting. Establishing the performance criteria of the final design - in this case the performance goals of the Polydome greenhouse.1 2. Mapping the system. Identifying the key elements that make up the system of interest and defining their interconnections. In this process, system mapping consisted of two key parts: defining system boundaries (what crop and livestock elements are we going to include, and why?) and then examining their key interrelationships. 3. Synthesizing knowledge. Combining all the data from the various system mapping 1 These criteria are listed on pages 20 -21 of this document exercises, gaining an overview of key leverage points. 4. Optimizing the system. Positioning key elements in the most beneficial way relative to one another and scaling them appropriately. Considering effects in time, space, and context. 5. Evaluating and iterating. In this phase we check our results against the goals we set out in the beginning, and ideally would return to earlier steps as necessary. In this case, we only completed one round of iteration because this is a preliminary study without a specific application in mind. Our primary concern in this design was to create a functional ecosystem through the careful selection of crops and livestock. The Polydome Ecosystem Design Method In a natural ecosystem, plants and animals arrange themselves through a continuous process of trial and error. A seed may fall in a certain location, only to die a few weeks after sprouting because the spot was too wet, dark, or exposed to predators. Microbes also form colonies based on their preferred conditions, creating diverse, invisible communities of around one billion individuals per gram of soil. Likewise, animals travel from habitat to habitat as they search for areas with adequate food, shelter, and potential mates. Because living creatures are constantly traveling, reproducing, and dying out, their patterns of distribution can adjust to changing conditions. Natural ecosystems are constantly in flux, facing climactic fluctuations, physical habitat changes, and varying concentrations of food availability. During periods of relative stability, species settle in patterns that take maximum advantage of the current conditions. Anyone who has explored a natural environment has probably encountered such patterns. For example, along any rocky coastline, plants and animals arrange themselves in bands dependent on distance from the water. Areas that remain under water even at the lowest tides harbor the most water-dependent species, such as fish and anemones. As we move towards the shore, to areas that may dry out towards the end of the low tide period, we start finding species that are slightly less water-dependent and more mobile, such as sea stars, crabs, and snails. Even further towards the shore, we start to see areas densely packed with creatures that can seal themselves off from the dry air, giving themselves several safe hours of life without water, such as mussels and barnacles. This kind of stratification is based on the needs and survival capacities of each individual species. Our task in designing the Polydome ecosystem was to artificially imitate the phenomenon seen in such a rocky shoreline: create an optimized spatial distribution for each species in the system. This assignment was complicated even further by our 52 Pagina 51
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