Some Considerations in Estimating Assembly Cost Functions for Agricultural Processing Operations

French, Ben C.

doi:10.2307/1235111

Cited by 41 publications

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“…Regular hexagons, in particular, have been shown to be the most efficient market shapes for the Euclidean distance norm [2,11,12,39,60,861. However, for other distance measures such as the rectilinear norm, or "Manhattan metric," diamondshaped market areas are more efficient [34,57). Triangular market areas seem to be -of little interest, and we shall not consider them further.…”

Section: (D)mentioning

confidence: 99%

The General Optimal Market Area Model

Erlenkotter¹

1988

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for their helpful comments. AbstractMarket area models determine the optimal size of market for a facility. These models are grounded in classical location theory, and express the fundamental tradeoff between economies-of-scale from larger facilities and the higher costs of transport to more distant markets. The simpler market area models have been discovered and rediscovered, and applied and reapplied, in a number of different settings. We review the development and use of market area models, and formulate a General Optimal Market Area model that accommodates both economies-of-scale in facilities costs and economies-of-distance in transport costs as well as different market shapes and distance norms. Simple expressions are derived for both optimal market size and optimal average cost, and also the sensitivity of average cost to a non-optimal choice of size. The market area model is used to explore the implications of some recently proposed distance measures and to approximate a large discrete location model, and an extension to price-sensitive demands is provided. i ' 1 "Accession Fo, NTIS GRA&I DTIC TAB

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Section: (D)mentioning

confidence: 99%

The General Optimal Market Area Model

Erlenkotter¹

1988

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“…Production and processing data for each production period are collected as some crops, such as rice and watermelon are planted multiple times per year. Transportation, plant construction, and operation and feedstock storage costs are calculated based on French's [21] and McCarl et al's [6] studies.…”

Section: Datamentioning

confidence: 99%

Nonparametric Forecasting for Biochar Utilization in Poyang Lake Eco-Economic Zone in China

Chang

Kung

2014

Sustainability

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Abstract:Agriculture is the least profitable industry in China. However, even with large financial subsidies from the government, farmers' living standards have had no significant impact so far due to the historical, geographical, climatic factors. The study examines and quantifies the net economic and environmental benefits by utilizing biochar as a soil amendment in eleven counties in the Poyang Lake Eco-Economic Zone. A nonparametric kernel regression model is employed to estimate the relation between the scaled environmental and economic factors, which are determined as regression variables. In addition, the partial linear and single index regression models are used for comparison. In terms of evaluations of mean squared errors, the kernel estimator, exceeding the other estimators, is employed to forecast benefits of using biochar under various scenarios. The results indicate that biochar utilization can potentially increase farmers' income if rice is planted and the net economic benefits can be achieved up to ¥114,900. The net economic benefits are higher when the pyrolysis plant is built in the south of Poyang Lake Eco-Economic Zone than when it is built in the north as the southern land is relatively barren, and biochar can save more costs on irrigation and fertilizer use.

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“…The capture radius for a plant in location l is a function of the feedstock demand (i.e., plant size) and local biomass supply characteristics including producer participation rate. We model capture radius following French (1960) for a circular biomass supply area with a square road grid:…”

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“…8 French (1960) provides a flexible framework for modeling alternate transportation systems; the conversion coefficient of can be adjusted for different transportation systems (i.e., capture radius vs. average hauling distance, circular vs. square supply plane, road grid, etc.). French's general framework has been adapted by several others to analyze biomass transportation (e.g., Beach et al 2012, Kung et al 2013, McCarl et al 2000.…”

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Modeling biomass procurement tradeoffs within a cellulosic biofuel cost model

2016

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We develop a long-run cellulosic biofuel cost model that minimizes feedstock procurement and processing costs per gallon. The distinguishing feature of the model is that it accounts for the procurement tradeoff between the intensive margin (biomass producers' participation rate) and extensive margin (biomass capture region). To investigate the extent to which this procurement trade-off affects processors' cost-minimizing decisions, we apply the model to switchgrass ethanol production in U.S. crop reporting districts. Results suggest that location characteristics will determine the extent to which processors can reduce their total procurement costs by offering a higher biomass price to increase participation near the plant and reduce transportation costs. Modeling Biomass Procurement Tradeoffs within a Cellulosic Biofuel Cost Model AbstractWe develop a long-run cellulosic biofuel cost model that minimizes feedstock procurement and processing costs per gallon. The distinguishing feature of the model is that it accounts for the procurement tradeoff between the intensive margin (biomass producers' participation rate) and extensive margin (biomass capture region). To investigate the extent to which this procurement trade-off affects processors' cost-minimizing decisions, we apply the model to switchgrass ethanol production in U.S. crop reporting districts. Results suggest that location characteristics will determine the extent to which processors can reduce their total procurement costs by offering a higher biomass price to increase participation near the plant and reduce transportation costs.

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Some Considerations in Estimating Assembly Cost Functions for Agricultural Processing Operations

Cited by 41 publications

References 0 publications

The General Optimal Market Area Model

The General Optimal Market Area Model

Nonparametric Forecasting for Biochar Utilization in Poyang Lake Eco-Economic Zone in China

Modeling biomass procurement tradeoffs within a cellulosic biofuel cost model

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