Regional Corn Planting Date Recommendations for Iowa

Abendroth, Lori; Marlay, Stephanie; Myers, Anthony J.W.; Elmore, Roger W.; Foster, R. E.

doi:10.31274/farmprogressreports-180814-2554

Cited by 51 publications

(78 citation statements)

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“…Non‐destructive measurements included the following: (1) emergence date (date when 50% of the plants emerged and were visible), (2) number of plants at approximately 6th leaf stage (Abendroth et al., 2011)—plant counts were on average 97% of the target population, (3) number of green collared leaves and number of senesced leaves (>50% leaf area yellow) at each harvest date, (4) dates when 50% of the plants in the center two rows were shedding pollen and 50% of plants had visible silks, (5) physiological maturity dates determined by a fully formed black layer according to Hunter et al. (1991), and (6) number of plants stalk and root lodged in the center two rows prior to harvest.…”

Section: Methodsmentioning

confidence: 99%

“…Each time, we harvested a 1‐m section from one of the two center plot rows. Harvest dates were designed to capture key growth stages, namely V8 (8 collared leaves), R1 (silking), R2 (blister), R4 (dough), and R6 (physiological maturity) (Abendroth et al., 2011). At each harvest, plants were cut at ground level, and the number of harvested plants was recorded.…”

Section: Methodsmentioning

confidence: 99%

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Calibration of a crop growth model in APSIM for 15 publicly available corn hybrids in North America

2023

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Application of crop growth models (CGMs) in plant breeding is limited by the large number of candidate cultivars that breeders work with and the large number of CGM parameters that affect cultivar performance. The objectives of this study were to (1) calibrate 15 publicly available maize hybrids in Agricultural Production Systems sIMulator and quantify prediction accuracy in modeling physiological trait differences (yield, biomass, phenology, etc.) among genotypes; (2) better understand minimum phenotypic data requirements for CGM cultivar calibration to inform breeding efforts; and (3) quantify simulated genotype by environment interactions (G × E) across years for five traits. We calibrated hybrids with two years of multi‐trait, temporal field measurements. The R2 of simulated versus observed phenotypes was 0.89 for grain yield and over 0.80 for half of all other simulated traits. Phenology parameters accounted for nearly half of the variability in grain yield. Average (across traits) normalized root mean square error was reduced from 35% to 30% with calibration based on phenological measurements and was reduced to 20% with inclusion of physiological and nitrogen‐related measurements such as radiation use efficiency and grain nitrogen. Long‐term simulations demonstrated distinct G × E among the hybrids which accounted for 2%–29% of the total genetic variation across traits. Parameter values derived in this work will provide insight regarding important physiological traits for further phenotyping, selection, and understanding of G × E. These calibrations are for publicly available hybrids, which are currently lacking.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Calibration of a crop growth model in APSIM for 15 publicly available corn hybrids in North America

2023

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“…Maize growth stage was determined according to the collar method (Abendroth et al., 2011). A handheld active crop sensor, RapidSCAN CS‐45 (Holland Scientific Inc.) was used to obtain normalized difference red‐edge (NDRE) values from maize canopies at different growth stages.…”

Section: Methodsmentioning

confidence: 99%

Coal char effects on soil chemical properties and maize yields in semi‐arid region

Panday

Mikha

Sun

et al. 2021

Agrosystems Geosci & Env

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Soil amendments with high carbon (C) content can be effective in semi‐arid regions where soils are characterized by low C. A field study was conducted in 2016–2018 to evaluate the effect of char on soil chemical properties and irrigated maize (Zea mays L.) yields in sandy loam fertilized with urea or composted manure. Carbon‐rich char used was a product of coal combustion residue from a local factory in western Nebraska. The experiment was arranged in a split‐plot randomized complete block design in four replications with char (0, 6.7, 13.4, 20.1, and 26.8 Mg C ha−1) as main and N treatment (0, 90, 180, and 270 kg urea‐N ha−1 and 33.6 and 67.2 Mg ha−1 of composted manure) as subplot factors. A handheld spectral sensor was used to determine normalized difference red edge (NDRE) at growth stages (V6, V8, V10, and R1) in 2017 and 2018. After 2 yr, char increased Fe, reduced pH at lower rates, and increased K and Mg at higher rates in top 20 cm soil but did not affect crop yields. Char applied at ≥13.4 Mg C ha−1 increased soil organic C by ≥8% and composted manure increased soil P and K compared to the control. There was a strong correlation of NDRE with N rates and grain yields at V8 and V10. This study found no adverse effect of char on soil properties. However, more site‐specific research is needed before char can be used as a regular soil amendment in semi‐arid regions.

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“…The comparison of the respective detailed values for each trait in each environment and landrace in 2017 and 2018 are illustrated in the (S1 Table ). Vi in phenotypic traits represents the vegetative growth stage when i leaf collars are visible based on the leaf collar method of the corn growth [42]. Early vigour at V3 stage (EV_V3), female flowering (FF) and root lodging (RL) were not phenotyped in all four environments for both years.…”

Section: Data Used For Analysismentioning

confidence: 99%

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks

et al. 2023

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The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this regard, epistasis models have shown to be capable of increasing the prediction accuracy while their high computational load is challenging. In this study, we investigated the predictive ability obtained in additive and epistasis models when utilizing haplotype blocks versus pruned sets of SNPs by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The prediction accuracies of bivariate GBLUP, ERRBLUP and sERRBLUP were assessed with eight phenotypic traits for 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot. The results indicate that the obtained prediction accuracies are similar when utilizing a pruned set of SNPs or haplotype blocks, while utilizing haplotype blocks reduces the computational load significantly compared to the pruned sets of SNPs. The number of interactions considered in the model was reduced from 323.5/456.4 million for the pruned SNP panel to 4.4/5.5 million in the haplotype block dataset for Kemater and Petkuser landraces, respectively. Since the computational load scales linearly with the number of parameters in the model, this leads to a reduction in computational time of 98.9% from 13.5 hours for the pruned set of markers to 9 minutes for the haplotype block dataset. We further investigated the impact of genomic correlation, phenotypic correlation and trait heritability as factors affecting the bivariate models’ prediction accuracy, identifying the genomic correlation between years as the most influential one. As computational load is substantially reduced, while the accuracy of genomic prediction is unchanged, the here proposed framework to use haplotype blocks in sERRBLUP provided a solution for the practical implementation of sERRBLUP in real breeding programs. Furthermore, our results indicate that sERRBLUP is not only suitable for prediction across different locations, but also for the prediction across growing seasons.

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Regional Corn Planting Date Recommendations for Iowa

Cited by 51 publications

References 0 publications

Calibration of a crop growth model in APSIM for 15 publicly available corn hybrids in North America

Calibration of a crop growth model in APSIM for 15 publicly available corn hybrids in North America

Coal char effects on soil chemical properties and maize yields in semi‐arid region

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks

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