Including Risk in Economic Feasibility Analyses: The Case of Ethanol Production in Texas

Richardson, James W.; Herbst, Brian K.; Outlaw, Joe L.; Gill, Robert Chope

doi:10.22004/ag.econ.62291

Cited by 15 publications

(2 citation statements)

References 11 publications

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“…Monte Carlo simulation can be used to estimate a distribution of possible outcomes by repeatedly sampling from probability functions specified for each uncertain (stochastic) variable. The method estimates the likelihood of a range of outcomes occurring as the result of a decision or scenario (Bizimana and Richardson, 2019;Richardson et al, 2000Richardson et al, , 2007. Simulation analysis is often used for economic evaluation of agricultural practices with considerable risk and uncertainty (Barrett et al, 2018;Evans et al, 2014;Rodriguez et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

Watermelon Planting Decisions with Multiple Risks: A Simulation Analysis

Athearn

Burani-Arouca

Dufault

et al. 2022

hortte

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Watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai] growers choose transplanting dates every year considering multiple risk factors. Earlier harvests linked to earlier planting typically find more favorable markets, but earlier planting has higher risk of freeze damage. Research also indicates that risk of fusarium wilt (caused by Fusarium oxysporum f. sp. niveum) is higher during cooler weather, adding to the risk of planting earlier. Thus, growers need to balance market risk (e.g., getting a low price) and production risk (e.g., lower harvest or higher cost due to freezing temperatures or disease) in selecting a planting date. The objective of this analysis is to examine the effect of planting date on the distribution of potential economic returns and evaluate whether late planting could be a favorable risk-management strategy. Probability distributions are estimated for key risk factors based on input from watermelon growers, published price data, historical freeze data, experiment station trials, and expert discussions. The distribution of economic returns is then simulated for three planting windows (early, middle, and late) using simulation software. Results demonstrate planting date risk–return tradeoffs and indicate that late planting is unlikely to be preferable to middle planting, even when risk of fusarium wilt is high.

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Section: Methodsmentioning

confidence: 99%

Watermelon Planting Decisions with Multiple Risks: A Simulation Analysis

Athearn

Burani-Arouca

Dufault

et al. 2022

hortte

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“…Simulations were chosen over other research methods of N rate analysis, such as examining yield and profit goals, incremental agronomic N efficiency, incremental gross return above fertilizer cost, or production function estimation (Dobermann et al, 2004;Havlin, 2004;Murrell, 2004;Neeteson and Wadman, 1987;Sutherland et al, 1986;Webb, 2009). Simulations allowed us to account explicitly for the risks and producers' risk preferences (Mun, 2006;Richardson et al, 2007). Specifically, we accounted for production risks associated with carrot yield variability and for marketing risks linked to carrot sales and nitrogen fertilizer prices.…”

Section: Methodsmentioning

confidence: 99%

Choosing Nitrogen Application Rate Recommendation Given Florida’s Regulatory Water Policy

Borisova²,

Athearn

et al. 2022

horts

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State and federal policies in the United States focus on agricultural best management practices (BMP)—such as improving nutrient management—to address water quality issues. BMP development is a challenging process as a new BMP may also affect farm profitability. This article explores the economic feasibility of nitrogen (N) management programs, including nitrogen application rates (N rates), given alternative scenarios for current nitrogen use and producer risk perceptions of carrot production in Florida. In this study, eight alternative N rates are ranked to find the economically optimal BMP. Carrot profitability is determined based on carrot yields per hectare, input costs, and carrot sale prices, using data from a 2-year carrot production experiment. The analysis applied stochastic simulation to account for the uncertain factors by using Simetar Add-In for Excel. We found that 224 kg·ha−1 N fertilizer rate is the most preferred by the producers among the eight rates considered. According to Florida’s agricultural water policy, BMP recommendations should balance water quality improvements and agricultural productivity. We consider the potential reduction of nitrogen fertilizer rate BMP from 224 kg·ha−1 to 168 kg·ha−1 and show that the effect of such reduction depends on producers’ current fertilizer application rates and their risk aversion levels. For example, reducing the N fertilizer rate from 336 kg·ha−1 to 168 kg·ha−1 decreases mean net returns by only 2% ($49/ha). In contrast, reducing the nitrogen fertilizer rate from 224 kg·ha−1 to 168 kg·ha−1 reduces the mean net returns by $151/ha, with an almost 10% reduction in the certainty equivalent of the net returns (for extremely risk-averse producers). Overall, if most producers in the region are very or extremely risk-averse, and if most of them operate close to the optimal level of fertilizer use, then setting the more restrictive BMP of 168 kg·ha−1 N can be perceived as undermining their economic profitability and require significant cost-share incentives to ensure targeted 100% adoption of BMP recommendations.

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The long-term viability of U.S. wine grape vineyards: Assessing vineyard labour costs for future technology development

McCorkle

Dudensing

Hanselka

et al. 2019

IJESB

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The motivation for this study centres on the labour-and cost-intensive nature of wine grape production and the potential opportunities for robotic technology. The objectives of this study are to develop cost of production budgets for five representative wine grape vineyards in four US states, assess the economic viability of wine grape production under current operating conditions, evaluate labour costs by production task, and identify common production challenges and tasks that could be augmented with robotic technology development. Investigators have worked with grower panels to develop a production budget for representative vineyards in four states, and to gather input on production tasks that the growers and technology developers feel would be most suitable for robotic technology. A stochastic simulation model was developed to assess baseline pro-forma financial statements for each vineyard size. Combined, the results help in exploring opportunities to strengthen vineyard profitability and competitiveness using robotics.

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Including Risk in Economic Feasibility Analyses: The Case of Ethanol Production in Texas

Cited by 15 publications

References 11 publications

Watermelon Planting Decisions with Multiple Risks: A Simulation Analysis

Watermelon Planting Decisions with Multiple Risks: A Simulation Analysis

Choosing Nitrogen Application Rate Recommendation Given Florida’s Regulatory Water Policy

The long-term viability of U.S. wine grape vineyards: Assessing vineyard labour costs for future technology development

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