forecasting wheat yield and quality conditional on weather information and estimating construction costs of agricultural facilities

Two studies were conducted. First study is pre-harvest forecasting of county wheat yield and wheat quality conditional on weather information and second study is improved methods of estimating construction costs of agricultural facilities. The first study estimated wheat regression models to account for the effect of weather on wheat yield, protein, and test weight and to forecast wheat yield and the two wheat quality measures. The explanatory variables included precipitation and temperature for growing periods that correspond to biological wheat development stages. The models included county fixed effects, crop year random effects, and a spatial lag effect. The second study developed and evaluated `Economic Engineering Construction cost templates model' for estimating construction costs of storage facilities. To verify model performance, the regression statistical inferences were used and the predicted costs of the developed cost templates model were benchmarked against previous two projects for grain bin and one example of RSMeans estimating costs for warehouse building.;The results of first study indicated that wheat yield, protein, and test weight level are strongly influenced by weather variables. Study also found that the forecasting power of the yield and protein models was enhanced by adding the spatial lag effect. Out of sample forecasting tests confirm the models' usefulness in accounting for the variations in average wheat yield and qualities. The first study results or prediction information could be widely used and could be particularly important to producers optimizing late season agronomic and marketing decisions and to grain elevators and agribusiness for contracts or purchasing decisions. The results of second study represented the fitting ability of the model is very well and provide information which help to illustrate and quantify the project to project variation in construction costs. It allows producers and agribusiness managers to examine a wide variety of configurations and options and to update their estimates as current RSMeans data becomes available. So, a major contribution of the study is that it develops a method of estimation that can be continuously updated as new RSMeans data is published.