Profitability and Risk Analysis of Soybean Planting Date by Maturity Group

Boyer, Christopher N.; Stefanini, Melissa; Larson, James A.; Smith, S. Aaron; Mengistu, Alemu; Bellaloui, Nacer

doi:10.2134/agronj15.0148

Cited by 20 publications

(19 citation statements)

References 37 publications

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“…The main plots were four mature groups, two subplots were seeding rates, and sub‐subplots were seven different planting windows ranging from 17 April to 10 August. There were no differences in yields across mature groups and seeding rates, thus, yields were combined (Boyer et al., 2015). There were a total of 672 observations.…”

Section: Methodsmentioning

confidence: 99%

“…More information about the soybean planting date experiment can be found in Boyer et al. (2015). For all these experiments fertility, seeding rates, herbicide, and all other practices were held constant.…”

Section: Methodsmentioning

confidence: 99%

“…To reflect yield loss due to late planting, a quadratic response function was used to estimate the yield response to planting date, which is a common approach in the literature (Boyer et al., 2015; Boyer & Smith, 2019). The yield response to planting date was estimated for corn, cotton, and soybean following

y_{t j} = β_{0} + β_{1} P D_{j} + β_{2} P D_{j}^{2} + z_{t} + e_{t j}

where

y_{t j}

is the yield in year t for plot j

; β_{0}

, β 1 , and β 2 are coefficients to be estimated; PD is the Julien planting date;

z_{t} \sim N false(0, σ_{z}^{2} false)

is the random year effect; and

e_{t} \sim N false(0, σ_{e}^{2} false)

is the error term.…”

Section: Methodsmentioning

confidence: 99%

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Analyzing corn and cotton producers optimal prevented planting decision on moral hazard

et al. 2020

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The prevented planting provision U.S. crop insurance programs protects producers from financial losses and risk associated with delayed planting. This provision has recently undergone some changes due to concerns about moral hazard in this provision. However, little is known about the likelihood of moral hazard in prevented planting and how these changes impact this possibility. We compared the net returns and variability of net returns for the four prevented planting options available for a corn (Zea mays L.) and upland cotton (Gossypium hirsutum L.) producer assuming soybean [Glycine max (L.) Merr.] as a viable second crop. We determine the optimal prevented planting option using the current prevented planting coverage factor at three crop insurance coverage levels. We also show how reductions in the prevented planting coverage factor would change the optimal selection and moral hazard in prevented planting. Data were collected from non-irrigated corn, upland cotton, and soybean planting date experiments located in Tennessee. Yield response functions to planting date for corn, cotton, and soybean were estimated and incorporated into simulation models to generate distributions of net returns. Risk neutral, profit maximizers would choose a late planting option over the full prevented planting payment, but as risk aversion increases, the full prevented planting payment was preferred. Reduction in prevented planting coverage factors will likely reduce the likelihood an insured producer chooses to not produce a crop and take the full prevented planting indemnity payment, despite planting during the late planting period or switching crops being more profitable options.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

y_{t j} = β_{0} + β_{1} P D_{j} + β_{2} P D_{j}^{2} + z_{t} + e_{t j}

where

y_{t j}

is the yield in year t for plot j

; β_{0}

, β 1 , and β 2 are coefficients to be estimated; PD is the Julien planting date;

z_{t} \sim N false(0, σ_{z}^{2} false)

is the random year effect; and

e_{t} \sim N false(0, σ_{e}^{2} false)

is the error term.…”

Section: Methodsmentioning

confidence: 99%

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Analyzing corn and cotton producers optimal prevented planting decision on moral hazard

et al. 2020

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“…This unsuccessful discrimination seems to be attributable to the negligible crop canopies at the time point of the image data acquisition on 12 June 1992. In the region, soybean is planted from May to mid-June (Boyer et al, 2015) whereas corn is conventionally planted in May (Schenk and Barber, 1980). When the image was captured, the plants were so small that the differences in leaf colour contributed to the discrimination only slightly (Fig.…”

Section: Resultsmentioning

confidence: 99%

Improved discrimination among similar agricultural plots using red-and-green-based pseudo-colour imaging

Doi

2016

International Agrophysics

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The effects of a pseudo-colour imaging method were investigated by discriminating among similar agricultural plots in remote sensing images acquired using the Airborne Visible/Infrared Imaging Spectrometer (Indiana, USA) and the Landsat 7 satellite (Fergana, Uzbekistan), and that provided by GoogleEarth (Toyama, Japan). From each dataset, red (R)-green (G)-R-G-blue yellow (RGrgbyB), and RGrgby−1B pseudo-colour images were prepared. From each, cyan, magenta, yellow, key black, L*, a*, and b* derivative grayscale images were generated. In the Airborne Visible/Infrared Imaging Spectrometer image, pixels were selected for corn no tillage (29 pixels), corn minimum tillage (27), and soybean (34) plots. Likewise, in the Landsat 7 image, pixels representing corn (73 pixels), cotton (110), and wheat (112) plots were selected, and in the GoogleEarth image, those representing soybean (118 pixels) and rice (151) were selected. When the 14 derivative grayscale images were used together with an RGB yellow grayscale image, the overall classification accuracy improved from 74 to 94% (Airborne Visible/Infrared Imaging Spectrometer), 64 to 83% (Landsat), or 77 to 90% (GoogleEarth). As an indicator of discriminatory power, the kappa significance improved 1018-fold (Airborne Visible/Infrared Imaging Spectrometer) or greater. The derivative grayscale images were found to increase the dimensionality and quantity of data. Herein, the details of the increases in dimensionality and quantity are further analysed and discussed.

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“…h Base and lower bound assumes no soybean revenue loss due to delayed soybean planting because of pennycress harvest. Upper bound assumes a 28-day soybean planting delay from mid-May to Mid-June with an expected yield reduction of 8 percent or 269 kg ha −1[29]. The yield loss is treated as an opportunity cost to the pennycress enterprise and is valued using a soybean price of $0.3814 kg −1 , the average for 2002/03-2016/17 with the high and low values dropped and expressed in 2019 dollars using the Implicit GDP Price Deflator[25,30].…”

mentioning

confidence: 99%

Cost and Profitability Analysis of a Prospective Pennycress to Sustainable Aviation Fuel Supply Chain in Southern USA

Trejo‐Pech¹,

Larson²,

English³

et al. 2019

Energies

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This study evaluates biorefinery bio-oil feedstock costs at the plant gate for a prospective field pennycress (Thlaspi arvense L.) to sustainable aviation fuel (SAF) supply chain. The biorefinery would supply SAF to the Nashville, Tennessee international airport. Supply chain activities include pennycress production, transporting oilseed to a crushing facility, processing of oilseed into bio-oil, and transporting bio-oil to the biorefinery. The analysis shows profit potential for economic agents in the prospective supply chain. Estimated breakeven cost (profit = 0) of growing, harvesting, and transporting oilseed to a crushing facility is 17.7 ¢ kg −1 . A crushing facility can pay up to 23.8 ¢ kg −1 for pennycress oilseed during the first year of production and provide investors 12.5% annual rate of return. Therefore, a profit margin of up to 6.1 ¢ kg −1 is available for the crushing facility to induce prospective pennycress producers to supply oilseed for SAF production. However, the estimated profit margin was sensitive mainly to uncertain oilseed yields, changes in field production costs, and pennycress meal and bio-oil prices. A spatial biorefineries sitting model, the Biofuels Facility Location Analysis Modeling Endeavor, estimated that the least-cost supply chain configuration is to establish three crushing facilities located in Union City, Huntington, and Clarksville, TN, to supply bio-oil to the biorefinery, with the biorefinery sited in an industrial park about 24.14 km from the Nashville international airport aviation fuel storage. Estimated total costs of bio-oil at the biorefinery plant gate are between 83 and 109 ¢ kg −1 if crushing facility oilseed procurement costs are between 17.7 and 23.8 ¢ kg −1 for oilseed.

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Profitability and Risk Analysis of Soybean Planting Date by Maturity Group

Cited by 20 publications

References 37 publications

Analyzing corn and cotton producers optimal prevented planting decision on moral hazard

Analyzing corn and cotton producers optimal prevented planting decision on moral hazard

Improved discrimination among similar agricultural plots using red-and-green-based pseudo-colour imaging

Cost and Profitability Analysis of a Prospective Pennycress to Sustainable Aviation Fuel Supply Chain in Southern USA

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