Maize Stand Density Yield Response of Parental Inbred Lines and Derived Hybrids

Amelong, Agustina; Hernández, Fernando; Novoa, Adriana; Borrás, Lucas

doi:10.2135/cropsci2016.02.0083

Cited by 13 publications

(17 citation statements)

References 32 publications

(55 reference statements)

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“…Transition to higher populations in combination with stagnation in plant yield potential resulted in hybrids characterized as density dependent displaying high and narrow spectrum of optimum density (Tokatlidis, 2001, 2013). Due to density dependence, hybrids respond dramatically different to diverse density conditions (Amelong, Hernández, Novoa, & Borrás, 2017; Cox & Cherney, 2012; Dhaliwal & Williams, 2020; Sangoi, Gracietti, Rampazzo, & Bianchetti, 2002). Paradoxically, although crop yield potential is attainable at high densities, profit‐maximizing may be lower than yield‐maximizing densities (Popp, Edwards, Manning, & Purcell, 2006).…”

Section: Introductionmentioning

confidence: 99%

Improved plant yield efficiency alleviates the erratic optimum density in maize

Mylonas

Sinapidou

Remountakis³

et al. 2020

Agronomy Journal

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Plant yield efficiency (PYE) reflects the ability of the single-plant to respond to additional inputs and is fully expressed at the nil-competition regime (an ultra-low density to preclude inter-plant interference for inputs). The purpose of this study was to determine if PYE could prevent the erratic optimum plant density-yield interaction effect in maize (Zea mays L.). Seven hybrids were evaluated across five environments at four densities, under both the normal-input regime (NIR) and low-input regime (LIR). Plant yield efficiency was measured at the lowest density approaching the nil-competition regime (0.74 plants m -2 ), while crop (per area) yield potential was estimated at the highest density corresponding to the typical farming density in the NIR (8.89 plants m -2 ). In terms of optimum density, the hybrids varied extensively in the NIR (6.64-8.81 plants m -2 ) but performed similarly in the LIR (5.11-5.61 plants m -2 ). The hybrid displaying the highest PYE also had high harvest index (HI) and low anthesis to silking interval (ASI) and was proved the most stable according to various stability statistics including the genotype and genotype by environment (GGE) biplot model. In conclusion, crop yield by density interaction is a matter of hybrid. Hybrids with low PYE have inconsistent optimum density, which is a causal factor of yield loss in rainfed maize. High PYE improves hybrid flexibility and performance at low densities ultimately enhancing crop resilience to extremely fluctuating environments.Abbreviations: AMMI, additive main effect and multiplicative interaction; ASI, anthesis to silking interval; G × E, genotype by environment interaction; GGE, genotype and genotype × environment; HI, harvest index; LIR, low-input regime; NIR, normal-input regime; PYE, plant yield efficiency.

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Section: Introductionmentioning

confidence: 99%

Improved plant yield efficiency alleviates the erratic optimum density in maize

Mylonas

Sinapidou

Remountakis³

et al. 2020

Agronomy Journal

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“…Phenotypic plasticity was assessed at the reproductive and full maturity stages (Table 2; Figure 2); the former is an indicator of biological processes leading to the formation of grain yield [48]; while the latter is an indicator of the reproductive biology process determining spike fertility and spike harvest index [73,74]. Wheat developmental patterns play an important role in determining spike fertility index and kernels m −2 ; both yield components can be determined accurately by sampling a small number of individual spikes at crop maturity, thereby allowing for timely evaluation of many genotypes or treatments [34].…”

Section: Growth Stagesmentioning

confidence: 99%

“…A steady increase in population density has been a driver of maize- [74], sorghum- [68], but not wheat-yield improvements, where higher-yielding environments have maximum yields at higher population density. Despite the negative relationship between population density and fertile tillers [32,69], their plasticity estimates covaried positively ( Figure 3); thus, confirming that a trait and its plasticity can be independent, and PP is under its own genetic control under a specific abiotic stress [28].…”

Section: Abiotic Stress Treatmentsmentioning

confidence: 99%

“…A steady increase in population density has been a strong driver of higher grain yield in corn [74]; however, higher population density of wheat may not achieve maximum grain yield in higher-yielding environment [64,75]. A density-dependent biomass partitioning strategy is a fundamental concept in plant population ecology that is applicable in wheat production [30].…”

Section: Abiotic Stress Treatmentsmentioning

confidence: 99%

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Statistical Modeling of Phenotypic Plasticity under Abiotic Stress in Triticum durum L. and Triticum aestivum L. Genotypes

Jaradat

2018

Agronomy

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Future challenges to the role of durum and bread wheat in global food security will be shaped by their potential to produce larger yields and better nutritional quality, while increasingly adapting to multiple biotic and abiotic stresses in the view of global climate change. There is a dearth of information on comparative assessment of phenotypic plasticity in both wheat species under long-term multiple abiotic stresses. Phenotypic plasticities of two durum and bread wheat genotypes were assessed under increasing abiotic and edaphic stresses for six years. Combinations of normal and reduced length of growing season and population density, with or without rotation, generated increasing levels of competition for resources and impacted phenotypic plasticity of several plant and yield attributes, including protein and micronutrients contents. All the phenotypic plasticity (PPs) estimates, except for the C:N ratio in both genotypes and grain protein content in T. aestivum genotype, were impacted by abiotic stresses during the second stress phase (PS II) compared with the first (PS I); whereas, covariate effects were limited to a few PPs (e.g., biomass, population density, fertile tillers, grain yield, and grain protein content). Discrimination between factor levels decreased from abiotic phases > growth stages > stress treatments and provided selection criteria of trait combinations that can be positively resilient under abiotic stress (e.g., spike harvest and fertility indices combined with biomass and grain yield in both genotypes). Validation and confirmatory factor models and multiway cluster analyses revealed major differences in phenotypic plasticities between wheat genotypes that can be attributed to differences in ploidy level, length of domestication history, or constitutive differences in resources allocation. Discriminant analyses helped to identify genotypic differences or similarities in the level of trait decoupling in relation to the strength of their correlation and heritability estimates. This information is useful in targeted improvement of traits directly contributing to micronutrient densities, yield components, and yield. New wheat ideotype(s) can be designed for larger grain yield potential under abiotic stress by manipulating yield components that affect kernels m −2 (e.g., number of tillers, number of florets per spikelet, and eventually spike fertility and harvest indices) without impacting nutrient densities and kernel weight, thus raising harvest index beyond its current maximum.

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“…Maize hybrid response to seeding rate change for grain yield can differ (Sangoi et al, 2002; Berzsenyi and Tokatlidis, 2012; Edwards, 2016: Amelong et al, 2017), but hybrid improvement to increase grain yield per plant at constant plant populations under well‐watered conditions has not been obtained (Duvick, 2005). Tokatlidis et al (2015) documented hybrid differences with 44,400 plants ha −1 under 50% deficit irrigation.…”

mentioning

confidence: 99%

Seeding Rate Influence on Rainfed Maize Grain Yield and Components in the Western Corn Belt

Kruger

Mason

Kmail

et al. 2018

Agrosystems Geosci & Env

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Core Ideas• Maize seeding over 20,000 kernels ha -1 seldom increased yield in water-limited western Nebraska.• Increased seeding rate increased the number of ears m -2 , which was compensated for by other yield components to reduce or maintain grain yield in western Nebraska.• Primary yield components had greater effect than secondary yield components in water-limted western Nebraska, compared with other studies under wellwatered conditions. ABSTRACTGrain yield components were analyzed to determine the influence of seeding rate on maize (Zea mays L.) yield under rainfed conditions in the Western Corn Belt. The objectives were to compare maize yield and yield components at four seeding rates in Brule, Ogallala, and North Platte, NE, in 2012 and 2013. Research was conducted by planting multiple maize hybrids at 20,000 to 57,000 kernels ha -1 . Grain yield, ears m -2 , rows ear -1 , kernels ear -1 , kernels row -1 , 100-kernel weight, and bulk grain density were determined. Path and regression correlation analyses was used to understand yield determination with seeding rates, and determine yield component relative importance. Seeding rates >20,000 kernels ha -1 only increased maize yield in one environment, whereas decreasing yield by approximately 1 Mg ha -1 in three environments. In the hot, dry 2012 growing season, ears m -2 , kernels ear -1 , and 100-kernel weight had positive direct effects on yield for sites with silt loam and loam soil textures, but only the kernels ear -1 for the sandy soil site. In the higher rainfall environments of 2013, positive direct effect of ears m -2 with yield were present, but in the lower rainfall site, ears m -2 had negative direct effect with yield. Based on the magnitude of the direct effects, the yield component ears m -2 was most important for yield determination followed by the kernels ear -1 , and lastly kernel weight. Maize should be planted at 20,000 kernels ha -1 in the Western Corn Belt to optimize grain yield and the number of ears m -2 produced.

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Maize Stand Density Yield Response of Parental Inbred Lines and Derived Hybrids

Cited by 13 publications

References 32 publications

Improved plant yield efficiency alleviates the erratic optimum density in maize

Improved plant yield efficiency alleviates the erratic optimum density in maize

Statistical Modeling of Phenotypic Plasticity under Abiotic Stress in Triticum durum L. and Triticum aestivum L. Genotypes

Seeding Rate Influence on Rainfed Maize Grain Yield and Components in the Western Corn Belt

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