Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R² = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R² > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.

The biodetoxification pathway for the reduction of the fermentation inhibitor furfural was utilized to produce furfuryl alcohol using both a commercial Bakers’ yeast and six other native strains, selected for their high tolerance towards the inhibitory effects of furfural. This study explores the potential of the microbial method as an environmentally-benign alternative to the conventional catalytic hydrogenation process for producing furfuryl alcohol used extensively in industry. The microbial method for furfuryl alcohol production provides the benefit of a homogeneous biochemical conversion, devoid of chemical catalysis, in conjunction with other carbohydrate-based processes (e.g. production of ethanol). Results showed that the yields of furfuryl alcohol using the laboratory yeast strains exceeded 90% of the theoretical yield at a furfural concentration of 25 g l⁻¹, which are comparable to yields obtained using the catalytic process. Furfuryl alcohol yields progressively declined as the furfural concentration was increased up to 65 g l⁻¹, where the yields averaged over 37%. Piecing together novel high-yield conversion processes for furfural and furfuryl alcohol, an integrated biorefinery model based on the production of furans has been envisioned. Such a facility bypasses the need for high pressure hydrogenation using copper chromite catalysts and hydrogen and azeotropic distillation of furfural to produce dilute streams of both notable platform chemicals.