Milk Income over Feed Cost Margin, Margin Protection Program, and Farm Finances for a Sample of Wisconsin Dairies in 2000–2017

Skevas, Theodoros; Thompson, Wyatt; Brown, Scott; Cabrera, Víctor E.

doi:10.1002/aepp.13108

Cited by 3 publications

(6 citation statements)

References 33 publications

(41 reference statements)

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“…Since dairy support programs are national in scope, there is an issue regarding whether the national MF margin reflects actual conditions due to substantial regional variation in the MF margin as determined by local market conditions (Mark et al, 2016). Results in a recent study by Skevas et al (2020) show that there is not a strong relationship between the national MF margin and actual MF margins of dairy farms in Wisconsin. The primary objective of this study is to explain why there can be variation in the actual MF margins across regions and over time.…”

Section: Introductionmentioning

confidence: 91%

Change in Hay-to-Milk Price Responsiveness with Dairy Industry Expansion

Hatzenbuehler

Tejeda

Hines

et al. 2021

J. Agric. Appl. Econ.

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The dairy industries in California, Idaho, and New Mexico expanded rapidly during the early 2000s. This study focuses on the expansion effects on milk-to-hay price responsiveness. Dairy industry expansion makes hay markets tighter, with less available marketable supply in most periods. The empirical models account for the expansion effect as well as those from hay exports and low stocks-to-use ratios that also cause changes in hay market demand characteristics. The results show that hay-to-milk price responsiveness increased after dairy expansion in all analyzed states. Low stocks-to-use and high exports dampened the responsiveness, but were not statistically significant for all analyzed states.

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Section: Introductionmentioning

confidence: 91%

Change in Hay-to-Milk Price Responsiveness with Dairy Industry Expansion

Hatzenbuehler

Tejeda

Hines

et al. 2021

J. Agric. Appl. Econ.

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“…As the DMC program is relatively new, only a few studies explicitly examine the program (e.g., Mark et al, 2016;Newton et al, 2016;Nicholson & Stephenson, 2014;Skevas et al, 2021;Wolf et al, 2014). The primary focus of the literature is how the program payments benefit dairy farms.…”

mentioning

confidence: 99%

Do large farms make better choices: Evidence from dairy margin coverage program participation patterns

Gabrielyan²

2023

Applied Eco Perspectives Pol

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Risk management has been a centerpiece of the US farm policy since the Agricultural Act of 2014. We study the Dairy Margin Coverage (DMC) program utilizing a unique operation-level participation data. By investigating the participation and the choice decisions of the dairy operations in US, we find that larger operations are more likely to participate in DMC and also to make margin coverage choices that maximize the net returns.

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“…Exit is found to be negatively affected by farm area (Breustedt and Glauben, 2007;Dong et al, 2016;Ferjani et al, 2015;Kang, 2006;Mishra et al, 2014), livestock units (Skevas et al 2021), farm values (Goetz and Debertin, 2001;Kimhi and Bollman, 1999), and farm sales (Hoppe and Korb, 2006). Not only Goetz and Debertin (2001) but also Skevas et al (2021) show an exception: while they find that the value of land and buildings has a negative coefficient on farm exit rate in the OLS model, their probit model has a positive coefficient. Not surprisingly, the probability of farm exit rates is strongly influenced by other farm characteristics such as farm types and location in the reviewed articles.…”

Section: Farm Exitmentioning

confidence: 99%

“…Prices and payments can matter, as well. The hypothesis that government payment programs keep farms operating is supported (Breustedt and Glauben, 2007;Mishra et al, 2014;Skevas et al, 2021). Breustedt and Glauben (2007) provide empirical evidence that high subsidy payments are likely to lower farm exit rates in western Europe.…”

Section: Farm Exitmentioning

confidence: 99%

“…We address the fixed-effects unobserved heterogeneity problem by using a corrected random effects (CRE) framework (Chamberlain, 1984;Mundlak, 1978;and Skevas et al, 2021). Following Skevas et al (2021), we estimate equation ( 13) that denotes the unobserved farm-specific heterogeneity (ℎ 𝑖 ) as a function of the means of the time-varying explanatory variables.…”

Section: Weathermentioning

confidence: 99%

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Three essays on biofuel policies and aggregate supply curve

Kim¹

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The first essay investigates the impacts of Brazil's new biofuel policy, namely RenovaBio. RenovaBio aims to reduce greenhouse gas (GHG) emissions in the transportation sector through expansion of biofuels by requiring tradable decarbonization credits (CBIOs) associated with the GHG reductions achieved by biofuels. We model the CBIO and fuel markets using a partial equilibrium model to simulate the impacts of RenovaBio. As a result of the implementation of RenovaBio, biofuel consumption increases, and petroleum product use decreases to meet the CBIO compliance targets. Perhaps because of reduced targets during the pandemic, CBIO prices remain low, but our estimates highlight the potential for higher prices and large impacts. Results also show that RenovaBio can cause 7.2 percent of GHG abatement with an annual average compliance cost of R$ 36.3 billion. Total compliance costs are reduced if CBIO compliance targets are relaxed or there is good progress reducing biofuel carbon intensities. The second essay investigates the pass-through of the biodiesel-diesel price spread and BTC to the RIN price and estimate whether the market expectations about the reinstatement of the BTC have affected the RIN prices. The USA biodiesel tax credit (BTC) is a subsidy that could affect the cost of biofuel mandates in that country, but with uncertainty because the BTC is not continuously in place and is otherwise retroactively applicable. The biofuel mandate compliance certificate (RIN) price presumably depends on the price of fuels, such as biodiesel and diesel, as well as the BTC. Our estimated results shows that market participants' responses to the uncertainty of BTC might be different across time. These results could raise questions about whether an inconsistently applied BTC in the context of a blend mandate encourages biofuel expansion. The last essay develops aggregate supply response of Korean rice farms from the results of farm-level estimation. We first estimate supply components using detailed farm-level data. The farming area, land productivity, and farm exit are examined to see how those components are affected by the rice price and other determinants. We also estimate the debt-to-asset ratio that could influence farming area and farm exit. Then, we simulate the short- and long-run supply curves implied by aggregating farm-level responses to price changes. Given the price changes in our simulations, the price elasticities of Korean rice production are between 1.00-1.79 in the short run and 1.57-2.77 in the long run. The decomposed aggregate supply response into its three components shows that supply response might be under-estimated when we focus on only a component of total supply.

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Milk Income over Feed Cost Margin, Margin Protection Program, and Farm Finances for a Sample of Wisconsin Dairies in 2000–2017

Cited by 3 publications

References 33 publications

Change in Hay-to-Milk Price Responsiveness with Dairy Industry Expansion

Change in Hay-to-Milk Price Responsiveness with Dairy Industry Expansion

Do large farms make better choices: Evidence from dairy margin coverage program participation patterns

Three essays on biofuel policies and aggregate supply curve

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