Genetic and Environmental Predictors for Determining Optimal Seeding Rates of Diverse Wheat Cultivars

Mehring, Grant H.; Wiersma, J. V.; Stanley, Jordan D.; Ransom, Joel K.

doi:10.3390/agronomy10030332

Cited by 10 publications

(13 citation statements)

References 30 publications

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“…Planting dates within environments had differing yield responses, so data from each planting date were partitioned into high (>5000 kg ha −1 ) and low (<5000 kg ha −1 ) yielding environment datasets by considering individual planting dates as a single environment for a total of nine environments similar to Mehring et al [52]. Yield within an environment was evaluated by standardizing the distribution of each, using z-scores.…”

Section: Methodsmentioning

confidence: 99%

“…Wheat yield components are considered plastic and compensate for one another and their relative contributions to yield may vary in high or low yielding environments. Therefore, the data were separated into four high (>5000 kg ha −1 ) and five low (<5000 kg ha −1 ) yield environments, similar to Mehring et al [52]. The environments ranged from 5202 to 5923 and 3846 to 4633 kg ha −1 for high and low yield environments, respectively.…”

Section: Yield Componentsmentioning

confidence: 99%

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Seeding Rate Effects on Hybrid Spring Wheat Yield, Yield Components, and Quality

Schmitz

Ransom

2021

Agronomy

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Agronomic practices, such as planting date, seeding rate, and genotype, commonly influence hard red spring wheat (HRSW, Triticum aestivum L. emend. Thell.) production. Determining the agronomic optimum seeding rate (AOSR) of newly developed hybrids is needed as they respond to seeding rates differently from inbred cultivars. The objectives of this research were to determine the AOSR of new HRSW hybrids, how seeding rate alters their various yield components, and whether hybrids offer increased end-use quality, compared to conventional cultivars. The performance of two cultivars (inbreds) and five hybrids was evaluated in nine North Dakota environments at five seeding rates in 2019−2020. Responses to seeding rate for yield and protein yield differed among the genotypes. The AOSR ranged from 3.60 to 5.19 million seeds ha−1 and 2.22 to 3.89 million seeds ha−1 for yield and protein yield, respectively. The average AOSR for yield for the hybrids was similar to that of conventional cultivars. However, the maximum protein yield of the hybrids was achieved at 0.50 million seeds ha−1 less than that of the cultivars tested. The yield component that explained the greatest proportion of differences in yield as seeding rates varied was kernels spike−1 (r = 0.17 to 0.43). The end-use quality of the hybrids tested was not superior to that of the conventional cultivars, indicating that yield will likely be the determinant of the economic feasibility of any future released hybrids.

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Section: Methodsmentioning

confidence: 99%

Section: Yield Componentsmentioning

confidence: 99%

Seeding Rate Effects on Hybrid Spring Wheat Yield, Yield Components, and Quality

Schmitz

Ransom

2021

Agronomy

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“…Wheat grain yield consists of four components, plant density, spikes per plant, kernels per spike and kernel weight (Slafer et al., 2014; Wang et al., 2003). Plant density is primarily driven by seeding rate, but final plant density achieved is also influenced by growing season conditions such as moisture and pest pressure (Mehring et al., 2020). Wheat will compensate for low plant stands by producing additional stems per plant, called tillers, to capitalize on resources and space available (Bastos et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Maximizing spring wheat productivity in the eastern Canadian Prairies: I. Yield, yield components, and lodging risk

et al. 2022

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Spring wheat (Triticum aestivum L.) yields across the Canadian prairies are rising, however, a yield gap remains between average yield and yield potential, which can be explored further under Prairie conditions. The high rates of nitrogen (N) fertilizer required for current high-yielding cultivars may increase lodging risk adding to this yield gap. The objectives of this study were to determine the influence of intensive management through N fertilization and plant growth regulator (PGR) application, on grain yield, yield components, and their relationships, in addition to lodging risk.Field trials were conducted at two locations in 2018 and 2019 in Manitoba, Canada, to evaluate three high-yielding spring wheat cultivars, three N management strategies, and the application of a PGR. Kernels per spike had the strongest overall linear relationship with grain yield (r 2 = .47), while the response of spikes per plant was highly related to yield response with alterations in N and PGR management practices (r 2 = .98). Splitting N fertilizer applications showed potential to reduce lodging risk of spring wheat, while maintaining yield and increasing protein. The PGR applications reduced lodging and increased grain yield by 91 kg ha −1 mainly through increases in kernels per spike. To achieve consistent yield increases, management practices should maximize early season N availability to target increases in spikes per plant and kernels per spike, thus a PGR application may be required to balance increased lodging risk. INTRODUCTIONWheat (Triticum aestivum L.) accounts for approximately 35% of the total Canadian cropland in annual production each year Abbreviations: CCC, chlormequat chloride; CNHR,

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“…After these new cultivars are subsequently tested in multi-year seeding rate studies, the actual OSR can greatly differ from the original extension recommendation. These differences can reveal 2 + years of reduced yields and economic losses due to genotype x management (GxM) interactions (Mehring et al, 2020). Although this reinforces the importance of proper seeding rate selection, with the continued release of new cultivars (and discontinuation of older cultivars), determining OSR for each cultivar is expensive, time-consuming, and repetitive research.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods of splitting of datasets can be used to minimize these errors when conducting statistical analyses (Crowley, 1992). A prior HRSW seeding rate study conducted in ND and MN produced regression models predictive for grain yield by dividing the original dataset into two subsets (Mehring et al, 2020). This method represents the validation set approach.…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments

et al. 2020

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Seeding rate in hard red spring wheat (HRSW; Triticum aestivum L.) production impacts input cost and grain yield. Predicting the optimal seeding rate (OSR) for HRSW cultivars can eliminate the need for costly seeding rate research and growers using OSRs can maximize yield and seeding efficiency. Data were compiled from seeding rate studies conducted in 32 environments in the Northern Plains United States to determine the OSR of HRSW cultivars grown in diverse environments. Twelve cultivars with diverse genetic and phenotypic characteristics were evaluated at five seeding rates in 2013-2015, and nine cultivars were evaluated in 2017-2018. OSR varied among cultivar within environments. Cultivar x environment interactions were explored with the objective of developing a decision support system (DSS) to aid growers in determining the OSR for the cultivar they select, and for the environment in which it is sown. A 10-fold repeated cross-validation of the seeding rate data was used to fit 10 decision tree models and the most robust model was selected based on minimizing the value for model variance. The final decision tree model for predicting OSR of HRSW cultivars in diverse environments was considered the most reliable as bias was minimized by pruning methods, and model variance was acceptable for OSR predictions (RMSE = 1.24). Findings from this model were used to develop the grower DSS for determining OSR dependent on cultivar straw strength (as a measure of lodging resistance), tillering capacity, and yield of the environment. Recommendations for OSR ranged from 3.1 to 4.5 million seeds ha −1. Growers can benefit from using this DSS by sowing at OSR relative to their average yields; especially when seeding new HRSW cultivars.

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Genetic and Environmental Predictors for Determining Optimal Seeding Rates of Diverse Wheat Cultivars

Cited by 10 publications

References 30 publications

Seeding Rate Effects on Hybrid Spring Wheat Yield, Yield Components, and Quality

Seeding Rate Effects on Hybrid Spring Wheat Yield, Yield Components, and Quality

Maximizing spring wheat productivity in the eastern Canadian Prairies: I. Yield, yield components, and lodging risk

A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments

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