Cultivar × Management Interaction to Reduce Lodging and Improve Grain Yield of Irrigated Spring Wheat: Optimising Plant Growth Regulator Use, N Application Timing, Row Spacing and Sowing Date

Peake, Allan; Bell, Kerry L.; Fischer, Ronald P.; Gardner, Matthew; Das, Bianca T.; Poole, Nick; Mumford, Michael

doi:10.3389/fpls.2020.00401

Cited by 31 publications

(18 citation statements)

References 47 publications

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“…A number of other studies have described the importance of G × E × M interactions and their contributions to crop yield productivity (Cooper et al 2001 ; Duvick et al 2004 ; Messina et al 2009 ; Hatfield and Walthall 2015 ; Beres et al 2020 ; Peake et al 2020 ; Rotili et al 2020 ; Snowdon et al 2020 ). Beyond the hybrid-by-density maize example above, a few studies have demonstrated effective improvement and utilisation of positive G × M interactions: improvement of sorghum yield for drought-prone environments in Australia (Hammer et al 2014 , 2020 ; Rodriguez et al 2018 ); improvement of wheat for drought-prone and irrigated environments in Australia (Hunt et al 2019 ); and improvement of maize hybrids for drought-prone environments of the US corn-belt by targeting selection for traits contributing to effective water use (Messina et al 2009 , 2018 ; Cooper et al 2014a , Gaffney et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

et al. 2021

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Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

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Section: Introductionmentioning

confidence: 99%

Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

et al. 2021

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“…This decision was supported by several other studies (Berry & Spink, 2012; Fischer & Stapper, 1987). In addition, an Australian study found severe lodging of wheat plants in early planting (Peake et al., 2020). As higher TGW strongly supported the higher grain yield, we took this trait as a positive trait in the idiotype design.…”

Section: Resultsmentioning

confidence: 99%

Multi‐trait selection of bread wheat ideotypes for adaptation to early sown condition

et al. 2022

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Genotypes developed for a particular mega-environment or management condition may not suit different management or specific local environments. Planting earlier than the recommended planting time is currently a new avenue to improve genotypic performance, supporting horizontal yield increases in the region when winter is shortening. In India, wheat growers are looking at early planting because it can use residual soil moisture from monsoons and escape terminal heat stress. A best linear unbiased prediction (BLUP)-based multi-environmental stability analysis was conducted on three sets of genotypes across three consecutive years (2017, 2018, and 2019) under early and timely planting dates to identify genotypes for further breeding. A number of traits were studied: phenological (early ground cover, days to booting [DTB], days to heading, days to maturity [DAYSMT], boosting to heading days [BTH], and grain filling duration [GFD]), plant stature (plant height, height up to spike base, and spike length [SpkLng]), flag leaf (flag leaf length, flag leaf width, and flag leaf area [FLGLFA]), and yield traits (thousand grain weight [TGW] and grain yield). A significant genotypic effect was observed for all traits in the single environment analysis. A genotype-environment interaction (GEI) was observed in the mixed-effect model, except for FLGLFA in Season 2 and SpkLng in Season 3. Residual components of variation were found to increase under early planting for all studied traits due to exposure of genotypes to early heat and a prolonged growing period. A higher GEI was observed in dissected phenological events such as BTH and GFD. Among phenological traits, it was found that DTB, GFD, and DAYSMT were strongly supporting selection gain throughout all seasons under early planting.The genotypes with a more extended vegetative period and grain filling period tended to have higher grain yield and TGW under early planting.

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“…Generally, it is believed that the application of PGRs increase grain yield (e.g., Reference [31]). Peake et al [36] reported that PGR application improved grain of most wheat varieties in well-irrigated fields, which has sufficient N supply. However, differently from the above findings, some studies reported that the PGRs application reduced the grain yield [32,37].…”

Section: Yield and Protein Contentmentioning

confidence: 99%

Application of Plant Growth Regulators on Soft White Winter Wheat under Different Nitrogen Fertilizer Scenarios in Irrigated Fields

Qin

Noulas²,

Wysocki

et al. 2020

Agriculture

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Lodging in cereal crops can result in yield loss and harvesting difficulties for growers. Application of plant growth regulator (PGR) has been an indispensable management practice to reduce lodging problems that are often exacerbated during high wind growing conditions and/or high nitrogen (N)/water environments, but the data is limited in the Columbia Basin of Oregon. The objective of this research was to evaluate the effect of two PGR products (chlormequat chloride-CC, trinexapac-ethyl-TE) at different rates and application timings on two soft white winter wheat varieties (ORCH-102 and SY Ovation). Crop growth (stem height and thickness), yield-related (spike density as ears m−2, seeds per spike, grain weight) and quality parameters (test weight, protein) were measured for two cropping seasons from October 2017 to July 2019 following the application of the two PGR products at tillering (GS21-26), stem elongation (GS30-32), and/or flag leaf (GS37-39) stages under a high-N fertilizer scenario. In both growing seasons, no lodging problems were recorded for any treatments. The plant height was reduced after PGR application, but the impact on stem thickness was limited. PGR application slightly affected wheat yield, yield components, testing weight, and protein level in both growing seasons. Our results suggested that the effect of PGR application is relatively limited if no lodging problem occurred.

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Cultivar × Management Interaction to Reduce Lodging and Improve Grain Yield of Irrigated Spring Wheat: Optimising Plant Growth Regulator Use, N Application Timing, Row Spacing and Sowing Date

Cited by 31 publications

References 47 publications

Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

Multi‐trait selection of bread wheat ideotypes for adaptation to early sown condition

Application of Plant Growth Regulators on Soft White Winter Wheat under Different Nitrogen Fertilizer Scenarios in Irrigated Fields

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