Selecting corn hybrids for performance and profit

One of the most important management decisions a corn grower makes each year is the selection of corn hybrids for spring planting. During the past 40 to 50 years, there has been continuous improvement in the genetics of corn hybrids which has contributed to steady increases in grain yield potential ranging from 0.7 to 2.6% per year. To stay competitive growers must introduce new hybrids to their acreage on a regular basis

A Model for Testing New Seed Technologies

Extension specialists from several North Central states recently proposed a new approach to expedite and facilitate evaluation of new genetically modified organism (GMO) hybrids through multi-state testing. As an example of this approach, newly released GMO glyphosate tolerant (GT) corn hybrids were evaluated at multiple locations across five states in 1999 and nine states in 2001. This cooperative testing effort demonstrated that powerful sets of data across a range of production environments could be generated with a minimal amount of input and resource allocation for the individual states

Delivering Timely Extension Information with the Agronomic Crops Team in Ohio

The Agronomic Crops Team was formed in 1995 to provide an Extension program that linked all disciplines together in an attempt to provide an integrated delivery mechanism designed to address all producer needs in a timely fashion. The team provides a newsletter, Web site, agronomy meetings and workshops, and a satellite broadcast to better serve state clientele. To date, the impact of the Agronomic Crops Team has been substantial, reaching managers of over 2.6 million acres of production ground in the state with an economic impact of over $11 million

Agronomic responses of corn to stand reducation at vegetative growth stages

Yield loss charts for hail associated with stand reduction assume that remaining plants lose the ability to compensate for lost plants by mid-vegetative growth. Yield losses and stand losses after V8 – leaf collar system – and throughout the remaining vegetative stages are 1:1 according to the current standards. We conducted field experiments from 2006 to 2009 at twelve site-years in Illinois, Iowa, and Ohio to determine responses of corn to stand reduction at the fifth, eighth, eleventh, and fifteenth leaf collar stages (V5, V8, V11, and V15, respectively). We also wanted to know whether these responses varied between uniform and random patterns of stand reduction with differences in within-row interplant spacing. When compared to a control of 36,000 plants per acre, grain yield decreased linearly as stand reduction increased from 16.7 to 50% (Table 3), but was not affected by the pattern of stand reduction. This rate of yield loss was greatest when stand reduction occurred at V11 or V15, and least when it occurred at V5. With 50% stand loss, yield was 83 and 69% of the control when stand loss occurred at V5 and V15, respectively. With 16.7% stand loss at V5, V8, or V11, yield averaged 96% of the control. Per-plant grain yield increased when stand loss occurred earlier and was more severe. With 50% stand loss at V11 or V15, per-plant grain yield increased by 37 to 46% compared to the control. Corn retains the ability to compensate for lost plants through the late vegetative stages, indicating that current standards for assessing the effect of stand loss in corn should be reevaluated

Seed Tape Effects on Corn Emergence under Greenhouse Conditions

Seed tape has recently received attention as an alternative planting system for smallholder farmers in underdeveloped regions of South America, Africa, China, and India (Mateus, 2014). Seed companies are also developing seed-tape planting systems for germplasm evaluations (Deppermann et al., 2013). Although seed tape has been promoted as a method for ensuring uniform seed spacing and plant density of small-seeded flowers, herbs, and vegetables (Chancellor, 1969), little or no information is available on the use of seed tape for larger-seeded row crops and its effect on crop emergence. The objective of this study was to compare the emergence of corn seed embedded in tape to seeds planted by hand and to determine seed tape effects on rate of corn emergence

Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region

Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed

Selecting corn hybrids for performance and profit

One of the most important management decisions a corn grower makes each year is the selection of corn hybrids for spring planting. During the past 40 to 50 years, there has been continuous improvement in the genetics of corn hybrids which has contributed to steady increases in grain yield potential ranging from 0.7 to 2.6% per year. To stay competitive growers must introduce new hybrids to their acreage on a regular basis.</p

Agronomic responses of corn to stand reducation at vegetative growth stages

Yield loss charts for hail associated with stand reduction assume that remaining plants lose the ability to compensate for lost plants by mid-vegetative growth. Yield losses and stand losses after V8 – leaf collar system – and throughout the remaining vegetative stages are 1:1 according to the current standards. We conducted field experiments from 2006 to 2009 at twelve site-years in Illinois, Iowa, and Ohio to determine responses of corn to stand reduction at the fifth, eighth, eleventh, and fifteenth leaf collar stages (V5, V8, V11, and V15, respectively). We also wanted to know whether these responses varied between uniform and random patterns of stand reduction with differences in within-row interplant spacing. When compared to a control of 36,000 plants per acre, grain yield decreased linearly as stand reduction increased from 16.7 to 50% (Table 3), but was not affected by the pattern of stand reduction. This rate of yield loss was greatest when stand reduction occurred at V11 or V15, and least when it occurred at V5. With 50% stand loss, yield was 83 and 69% of the control when stand loss occurred at V5 and V15, respectively. With 16.7% stand loss at V5, V8, or V11, yield averaged 96% of the control. Per-plant grain yield increased when stand loss occurred earlier and was more severe. With 50% stand loss at V11 or V15, per-plant grain yield increased by 37 to 46% compared to the control. Corn retains the ability to compensate for lost plants through the late vegetative stages, indicating that current standards for assessing the effect of stand loss in corn should be reevaluated.</p