The Ag Budget Operations Table presents a detailed compilation of operations along with their corresponding parameters and material inputs for both conventional and energy crops used in the Billion Ton 2023 study. This dataset encompasses a range of activities, including land preparation, planting, fertilization, pest management, land maintenance, and harvesting.
Key fields within the dataset include equipment data, fertilizer and chemical application details, and seed information. Additionally, the dataset contains cost metrics such as purchase costs and labor costs, enabling users to effectively analyze the financial aspects of crop production.
To enhance understanding of the data, a supplementary spreadsheet is provided, containing field definitions that clarify the terminology and metrics used throughout the dataset.
biomass feedstocks
This dataset contains data on agricultural crop and residue production by county from 2022 to 2041. The agricultural crop in this dataset includes barley, biomass sorghum, corn, cotton, energy cane, eucalyptus, grain sorghum, hay, miscanthus, oats, pine, poplar, rice, soybean, switchgrass, wheat, and willow, and the agricultural residue includes barley straw, corn stover, oats straw, sorghum stubble, and wheat straw. The dataset was obtained from the database of the BT23 (Davis et al., 2024) for the mature-market medium scenario with biomass market prices of up to $70 per dry ton.
For access to this dataset, please use teh contact form and indicate the dataset by name.
This dataset contains data on agricultural crop and residue production by county in 2041. The agricultural crops in this dataset include barley, corn, cotton, grain sorghum, hay, oats, rice, soybeans, and wheat. The agricultural residues include barley straw, corn stover, oats straw, sorghum stubble, and wheat straw. The dataset was obtained from the database of the BT23 (Davis et al.,2024) for the near-term scenario with biomass market prices of up to $70 per dry ton.
For access to this dataset, please use the contact form and indicate this dataset by name.
Abstract: Distributed storage and pre-processing of biomass feedstock at satellite storage locations (storage
depots) has been proposed in literature to reduce costs and improve efficiency of the supply system. The performance of such a system, however, has not yet been rigorously quantified and compared with conventional alternatives. This work presents such an analysis using the BioFeed optimization model. BioFeed is a system-level model that optimizes the important feedstock production activities and determines the optimal system configuration on a regional basis. The BioFeed model was first modified to enable modeling of mechanical pre-processing, such as pelletization and grinding, at the input or the output of storage facilities, which can be mandatory or optional. The model was used to study different Miscanthus production scenarios in southern Illinois. The results showed that making storage pre-processing mandatory increased the total cost by up to 16–53% as compared to the base case. However, it reduced the farmers’ share of the total cost by up to 13–39%. The exact values depended on the particular pre-processing technology and scenario modeled. The most cost-effective system consisted of a combination of pre-processing on the farms as well as at the storage facilities. The study recommended that biomass output from a hammer mill should be the biorefinery delivery specification; the hammer mills should be installed at the input of the storage facilities, but pre-processing at the storage facility should be optional. This led to the minimum total cost of 46.4 $ Mg−1.