Management Options to Maximize the Quality of Food Grade Corn

Food grade corn is a specialty ingredient used for various human consumption products. It is commonly dedicated to dry milling, an industry that produces a  variety of yellow and white corn food ingredients. The main products from dry milling are degermed corn grits (endosperm pieces with embryo removed), corn meal, corn flour, and corn bran. These products are used in everyday foods, such as breakfast cereals, snacks, baked goods, and beer.

Grain quality in food grade corn is determined by kernel hardnessand size. Increased kernel hardness improves the yield of the dry-milling process to produce endosperm pieces and is typically estimated by different metrics such as test weight (a bulk grain density measurement), proportion of hard and soft endosperm (measured by visual inspections), kernel density (measured with a pycnometer), and/or flotation index (measured by the proportion of floating grains in a known solution) (Figure 1). Kernel size is relevant for dry milling because it reflects the graincapacity to yield large endosperm pieces, which are highly valued as a food ingredient. Currently, companies use screen retention to best estimate kernel size, with U.S. dry-milling supply chains using 20/64 round-hole screens as the standard.

The ability to maintain the grain quality demanded by dry milling processors, while maximizing corn yield for growers, is the greatest challenge dry milling supply chains currently face (Borras et al., 2022). For the most part, specialty grain supplychains deal with this challenge by focusing entirely on a genetic centered solution by providing genotype recommendations to farmers through preferred hybrid lists. Other relevant components that can impact grain quality for dry milling are commonly ignored, such as the interaction of genetics, environment, and management.

The few crop management practices commonly advised to farmers by food grade companies are hybrid selection and isolation for purity assurance. Beside these two practices, the farmer optimizes decisions related to other practices such as plant population, fertilization, and/or fungicide/insecticide usage with a focus on maximizing yield. Traits related to grain behavior during dry milling (test weight, kernel density, and screen retention) have a strong genetic component. However, management practices and the environment in which crops are grown also play a crucial role in meeting the quality standards demanded by processors (Duarte et al., 2005; Cirilo et al., 2011; Gerde et al., 2016; Tamagno et al., 2016; Cerrudo et al., 2017).

In recent years, Corteva Agriscience conducted a series of field experiments to test how dry milling grain quality is affected by management practices. For example, Figure 2 describes the results from two contrasting N fertilization rates over commercial hybrids on grain yield, kernel density, screen retention, and test weight. These individual trials demonstrated the significant impact of crop management on the quality of food grade corn, confirming previous academic studies and suggesting a comprehensive assessment on the effects of management and environmental factors on food-grade quality was needed. In the present article, we describe this comprehensive assessment, in which we evaluated what specific management and environmental factors affect corn dry-milling quality traits across a wide range of growing conditions under rainfed production.

A unique data set was compiled from field trials conducted across the central rainfed U.S. Midwest. Grain yield and dry-milling grain quality traits (kernel density, screen retention, and test weight) were measured from 97 current commercial hybrids grown in 86 different environments. These 86 trials were conducted in Iowa, Illinois, and Indiana from 2019 through 2023 with no irrigation (Figure 3). The final dataset comprised around 3,300 observations for each trait. To determine the most important management and environmental factors affecting each grain quality trait we used mixed effects models following Gambin et al. (2016).

Grain yield was expressed at 15% moisture and measured with commercial or small plot research combines. Kernel hardness was estimated by kernel density using a gas pycnometer (Anton Paar, DMA 5001) and corrected by moisture content following Fox and Manley (2009). Test weight and kernel moisture concentration were measured with a Perten system (Perten AM5200). Kernel size was estimated as screen retention after shaking samples for 2 minutes in a 20/64 round holes screen and expressed as percentage (%) of the total sample that was retained above the screen.

All traits relevant to corn dry milling supply chain showed significant variability across field trials. Kernel density ranged from 1.115 to 1.345 g cm^-3, screen retention ranged from 0 to 94%, and test weight ranged from 54.7 to 65.4 lbs bu^-1 in the analyzed database. The most important management and environmental factors explaining each trait variability are described in Table 1.

Kernel density was affected by crop management decisions such as hybrid selection, N fertilization rate, plant population, and fungicide use. The environmental factors that affected kernel density included soil organic carbon, minimum temperatures during the vegetative period, and water balance during the vegetative period. Higher N fertilization, higher soil organic carbon, fungicide application, and a better water balance during the vegetative period (less drought stress) had a positive impact on kernel density. On the other hand, higher plant populations and higher minimum temperatures during the vegetative period (common of later planting dates) had a negative impact on kernel density.

The crop management decisions that affected screen retention were hybrid selection, N fertilization rate, and plant population. Environmental factors like soil organic carbon and the water balance during the grain-filling period also affected screen retention. Higher N availability, soil organic carbon, and a positive water balance during grain filling (less drought stress during this period) positively affected screen retention. Plant population increases had detrimental effects on screen retention.

For test weight, the crop management options that significantly affected the trait were hybrid selection, N fertilization rate, plant population, and planting date. Significant environmental effects that influenced test weight were soil organic carbon and mean maximum temperatures during vegetative and reproductive periods.

Each one of the predictors described in Table 1 had a specific effect on the final trait of interest and were in agreement with management and environmental observations from previous academic studies on food grade corn production. Nitrogen fertilization and plant population also showed in previous studies they are important management options that need to be optimized when considering kernel hardness and size (Borras et al. 2003; Tamagno et al., 2016; Ruiz et al., 2022).

Figure 4 describes with more detail the response to N fertilization rate of kernel density and grain yield, and its interaction with plant population across all hybrids and environments. Kernel density and yield increased as a function of N fertilization rate, but the response depended on the plant population used. In general, a lower plant population resulted in a higher kernel density across all N fertilization rates, and yield was lowest at low N rates and high plant populations and maximized at higher N rates with highest plant populations (as expected based on known interactions between plant population and N fertilization rate). Interestingly, the data showed that maximizing kernel density requires a higher N fertilization rate compared to what is needed to maximize grain yield (221 vs. 171 lbs N acre^-1).

Lastly, we tested the effectiveness of the predictors listed in Table 1 in estimating each grain quality trait using an independent set of data from the region (leave-one-site-out cross-validation methodology). Figure 5 shows the accuracy of these predictions across the region, indicating that knowledge of the genetic, management, and environment factors (GxExM) at each site helped predict final grain quality with adequate accuracy.

When comparing the ability to predict kernel density, screen retention, and test weight using a GxExM model versus a traditional G model (what is traditionally done with hybrid preferred lists), the accuracies (measured as relative root mean square error) were 1.87, 26.7, and 2.0 versus 2.14, 28.9, and 2.2%, respectively. This shows that knowledge of how the crop was managed (in terms of N fertilization rate, planting date, plant population, and fungicide use) and a description of the environmental condition the crop was exposed to, helped explain the final grain quality with a better accuracy than only knowing the specific hybrid grown in a particular field.

When producing food grade corn, it is essential to select a suitable hybrid, as has been traditionally done. Additionally, other crop management decisions that are made by the farmer at each field are important. The amount of applied N and the plant population are relevant for all analyzed traits affecting grain quality for dry milling (kernel density, screen retention, and test weight). Fungicide use and planting date also significantly affected kernel density and test weight. These are all management practices that farmers can optimize in their operations to achieve maximum grain quality while still approaching maximum yields.

As a keynote, maximizing kernel density requires a larger N fertilization rate than the one needed to maximize grain yield. On average, 50 lbs N acre^-1 more were needed across all hybrids and sites tested.

• Borras, L., Caballero-Rothar, Saenz, E., Segui, M., Gerde, J.A. 2022. Challenges and opportunities of hard endosperm food grade maize sourced from South America to Europe. European Journal of Agronomy 140, 126596.
• Cerrudo, A., Martinez, D., Izquierdo, N.G., Cirilo, A.G., Laserna, M.P., Reinoso, L., Valentinuz, O., Balbi, C., Andrade, F.H. 2017. Environment, management, and genetic contributions to maize kernel hardness and grain yield. Crop Science 57, 2788-2798.
• Cirilo, A.G., Actis, M., Andrade, F.H., Valentinuz, O.R. 2011. Crop management affects dry-milling quality of flint maize kernels. Field Crops Research 122, 140-150.
• Gambin, B.L., Coyos, T., Di Mauro, G., Borrás, L., Garibaldi, L.A. 2016. Exploring genotype, management, and environmental variables influencing grain yield of late-sown maize in central Argentina. Agricultural Systems 146, 11-19.
• Gerde, J.A., S. Tamagno, J.C. Di Paola, L. Borrás. 2016. Genotype and nitrogen effects over maize kernel hardness and endosperm zein profiles. Crop Science 56, 1225-1233.
• Ruiz, A., Archontoulis, S., Borras, L. 2022. Kernel weight relevance in maize grain yield response to nitrogen fertilization. Field Crops Research 286, 108631.
• Tamagno, S., I. Greco, H. Almeida, J.C. DiPaola, P. Marti Ribes, Borrás, L. 2016. Crop management options for maximizing maize kernel hardness. Agronomy Journal 108, 1561-1570.

¹Lucas Borras, Ph.D. Corteva Agriscience Senior Research Scientist
²Brian Dahlke, Corteva Agriscience Research Scientist
³Jose Rotundo, Ph.D., Corteva Agriscience Research Scientist
⁴Alejo Ruiz, Ph.D., Corteva Agriscience Research Scientist
⁵Linda Byrum, Corteva Agriscience Associate Investigator