Corn quality factors affecting dry milling performance

Paulsen, Marvin R; Hill, Lowell D.

doi:10.1016/0021-8634(85)90092-7

Cited by 57 publications

(50 citation statements)

References 1 publication

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“…However, Blandino et al (2010) performed dry milling only on typical, flat-shaped, whole kernels from the middle part of the ear to reduce variability in kernel size. In general, DME appears to be higher with grain of high test weight as shown by this study and other work (Blandino et al, 2010;Kirleis and Stroshine, 1990;Lee et al, 2007b;Pan et al, 1996;Paulsen and Hill, 1985;Pomeranz and Czuchajowska, 1987). Many of the hybrids tested by Rausch et al (2009) and Blandino et al (2010) had test weights greater than 75 kg hL −1 , which is not typical of most yellow dent hybrids in the US Corn Belt.…”

Section: Dry-milling Efficiencysupporting

confidence: 63%

“…Our FGY prediction model provides the first insight to improving both grain yield and DME simultaneously. Based on the findings of Wu and Bergquist (1991), Kirleis and Stroshine (1990), and Paulsen and Hill (1985), we suggest that including breakage susceptibility, kernel density, or both in the regression model may improve the prediction of FGY.…”

Section: Predicting Performancementioning

confidence: 78%

“…Furthermore, because of the previous lack of small-scale processing methods, it was not feasible to evaluate a large number of genotypes, which is necessary to understanding the genetic basis for the trait and required to facilitate selection. In the past, DME was estimated indirectly by assessing grain quality traits found to be related to DME such as test weight (Blandino et al, 2010;Kirleis and Stroshine, 1990;Lee et al, 2007a;Pan et al, 1996;Paulsen and Hill, 1985;Pomeranz and Czuchajowska, 1987), kernel hardness (Blandino et al, 2010;Kirleis and Stroshine, 1990;Lee et al, 2007a;Litchfield and Shove, 1990;Narváez-González et al, 2006), kernel density (Kirleis and Stroshine, 1990;Wu and Bergquist, 1991), breakage susceptibility (Paulsen and Hill, 1985), and protein content (Dorsey-Redding et al, 1991;Jackson et al, 1988;Lee et al, 2005Lee et al, , 2007aPhilippeau et al, 2000;Yuan and Flores, 1996). Ideally, in maize grown for dry-milling end use, selection for DME would result in the maximizing the amount of flaking grits produced per unit of land (FGY).…”

Section: Genetic Factors Underlying Dry-milling Efficiency and Flakinmentioning

confidence: 99%

“…Several previous studies explored methods to select for drymilling qualities applying prediction models. Paulsen and Hill (1985) proposed a prediction equation for DME that used test weight and breakage susceptibility, accounting for 82% of the phenotypic variation (R 2 = 0.82). Kirleis and Stroshine (1990) found that kernel density was highly correlated (r = 0.88) with a milling evaluation factor, calculated by combining the amount of endosperm that remains on the 3.5 mesh wire, 5 mesh wire, and 7 mesh wire after dry milling.…”

Section: General and Specific Combining Ability Heritability And Gementioning

confidence: 99%

“…The models reported by Paulsen and Hill (1985), Kirleis and Stroshine (1990), and Lee et al (2007a) are time consuming and labor intensive; furthermore, they require specialized equipment and the destruction of grain samples to obtain measurements to predict DME. Our model was based on physical kernel traits that could rapidly be collected soon after harvest without specialized equipment to predict flaking-grit productivity.…”

Section: Predicting Performancementioning

confidence: 99%

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Genetic Factors Underlying Dry‐Milling Efficiency and Flaking‐Grit Yield Examined in US Maize Germplasm

et al. 2016

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A small but important proportion of US maize (Zea mays L.) grain is channeled to create breakfast cereals and other food products; yet, little focus has been devoted to genetic improvement of corn hybrids to meet needs of dry millers and other end users in the cereal pipeline. This study was designed to evaluate a broad range of US maize germplasm for key dry‐milling traits: dry‐milling efficiency (DME), the proportion of flaking grits produced from dry‐milled maize grain, and flaking‐grit yield (FGY), the amount of flaking grits produced per unit land. Genetic parameters for DME and FGY were characterized based on grain produced over 3 yr, and the associations between dry‐milling, agronomic, ear, and kernel traits were assessed. Means for DME among experimental hybrids ranged from 24.0 to 36.0%. The DME was negatively correlated with grain yield and positively correlated with test weight; associations with other ear and kernel traits were weak despite being statistically significant in some cases. The DME is moderately heritable (h2 = 0.53) and controlled largely by additive gene action. A moderate amount (31%) of the variation in FGY was explained by multiple linear regression of grain yield and simple physical kernel properties: test weight, kernel thickness, and 100‐kernel volume. Genetic variation for DME in US maize germplasm was demonstrated, which could be exploited to develop new maize hybrids with improved DME and, ultimately, FGY. However, more work is needed to develop indirect selection approaches to predict DME and FGY at or before harvest.

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Section: Dry-milling Efficiencysupporting

confidence: 63%

Section: Predicting Performancementioning

confidence: 78%

Section: Genetic Factors Underlying Dry-milling Efficiency and Flakinmentioning

confidence: 99%

Section: General and Specific Combining Ability Heritability And Gementioning

confidence: 99%

Section: Predicting Performancementioning

confidence: 99%

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Genetic Factors Underlying Dry‐Milling Efficiency and Flaking‐Grit Yield Examined in US Maize Germplasm

et al. 2016

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Influence of the protein distribution of maize endosperm on ruminal starch degradability

Philippeau

Landry

Michalet-Doreau

2000

J. Sci. Food Agric.

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Production response of Illinois farmers to premiums for low-temperature dried corn

1988

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Low-temperature drying methods result in improved corn quality, which is of some importance to all users but is particularly important to dry millers. Several low-temperature drying methods exist, but there are few economic incentives to encourage their use. A supply function based on farm survey data was estimated to determine responses to price premiums. Factors other than price that influence decisions to purchase grain dryers were identified through ordinary least squares procedure and logit models. The significant coefficients in the models indicate that farmers will shift to low-temperature drying at premiums as low as one cent per bushel.

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Corn quality factors affecting dry milling performance

Cited by 57 publications

References 1 publication

Genetic Factors Underlying Dry‐Milling Efficiency and Flaking‐Grit Yield Examined in US Maize Germplasm

Genetic Factors Underlying Dry‐Milling Efficiency and Flaking‐Grit Yield Examined in US Maize Germplasm

Influence of the protein distribution of maize endosperm on ruminal starch degradability

Production response of Illinois farmers to premiums for low-temperature dried corn

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