<p>A set of routines that use a genetic algorithm to evolve a best land-use portfolio, in response to expected water availability. While many platforms have genetic algorithm (GA) toolkits available (MATLAB for example), many specialized applications of GA require careful interpretation of fitness functions as well as mutation and crossover. This algorithm is one such particular application where the unit to be optimized is a portfolio of land uses, each with a particular crop rotation and water allocation. In this context, fitness is evaluated as economic product, based on yields that are sensitive to water input (using the FAO yield sensitivity formula). Crossover involves the mixing between two portfolios of different particular crop rotations. Mutation involves point changes within individual rotations of particular crops, or of water allocation. Sample crop data inputs are provided.</p>
<p>Inputs:</p>
<ul>
<li>A time series of water availability data</li>
<li>A set of cost, price, average yield, and water sensitivity (Kc and Ky) data for particular crops of interest</li>
</ul>
<p>Outputs:</p>
<ul>
<li>A land-use portfolio of crop rotations (crops in rotation, land allocated to rotation, water allocated to rotation) that is a best-fitness-fit to the water availability data. This portfolio is an n x 3 cell array, with element 1 describing land allocation to crop rotation i, element 2 describing fraction of total water to crop rotation i, and element 3 containing the rotation itself. The rotation is an m x 2 matrix, with the first element in each row giving the id of the crop, and the 2nd element describing the length (in cycles) of the fallow period before the next crop in the rotation.</li>
</ul
