Intra-specific competition in maize: Ear development, flowering dynamics and kernel set of early-established plant hierarchies

Pagano, Eleonora Samanta; Cela, S.; Maddonni, Gustavo Ángel; Otegui, Marı́a Elena

doi:10.1016/j.fcr.2007.03.013

Cited by 48 publications

(28 citation statements)

References 39 publications

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“…Conservation of the relationships under mild water stress is consistent with proportionate reductions in cell length and width reported in Tardieu et al (2000). By contrast, the differences in coefficients of equations where interplant competition occurred, are consistent with changes of carbohydrate storage pattern (Turgut et al, 2005;Pagano et al, 2007) and competitive effects causing etiolation (lengthening) of cells and organs at the expense of width (Bos et al, 2000;Andrieu et al, 2006).…”

Section: Allometric Relationships Between Organ Morphology and Its Bisupporting

confidence: 54%

Allometric Relationships of Maize Organ Development under Different Water Regimes and Plant Densities

Song

Birch

Rui

et al. 2015

Plant Production Science

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Section: Allometric Relationships Between Organ Morphology and Its Bisupporting

confidence: 54%

Allometric Relationships of Maize Organ Development under Different Water Regimes and Plant Densities

Song

Birch

Rui

et al. 2015

Plant Production Science

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“…In sunflower, florets in the outer radius were more likely to set than florets in the centre of the flower (Alkio et al, 2003). In maize, there was a considerable variation in the number of kernels per plant, related to dominating and dominated plants (Pagano et al, 2007).…”

Section: Observationsmentioning

confidence: 99%

Stochastic Dynamic Simulation of Fruit Abortion: A Case Study of Sweet Pepper

Wubs¹,

Heuvelink²,

Hemerik³

et al. 2011

Acta Hortic.

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Abortion of reproductive organs diminishes yields in many crops. In indeterminate greenhouse crops, alternating periods of fruit abortion and fruit set exist, resulting in fluctuations in fruit yield. Factors affecting the level of abortion are e.g., the supply and demand for assimilates (source and sink strength, respectively), temperature and cultivar. However, simulation of fruit abortion is still a weak part of crop simulation models. Variation in fruit abortion exists between plants, which results in differences in the timing and the number of set fruits. Therefore, simulating fruit abortion with variation could give more realistic simulation results. The probability of a fruit to abort should be related to factors like source strength and sink strength. The more favourable the circumstances are for fruit abortion, e.g., low source strength or high sink strength, the more likely it is that the fruit aborts. Survival analysis estimates parameters quantifying the influence of explanatory variables on the abortion rate. Time-varying explanatory variables can be used in the analysis. In a case study, we used survival analysis to analyse a data set with observations on flowering, fruit abortion and fruit harvest for sweet pepper. Source and sink strength were used as explanatory variables. The resulting equation determining the probability of abortion per day was implemented in a simple simulation model to simulate fruit set. The model output, as an average of 100 plants, showed similar timing in the fluctuations in fruit set as the observations, although the amplitude of the fluctuations was in some cases underestimated. The percentage fruit set was simulated correctly.

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“…Increasing crop density is one of the more efficient weed management strategies that allows for more soil surface coverage and more light capture to compete with weeds. Crop density may change the grain number per ear and grain weight (Dastfal et al, 1999;Pagano et al, 2007). Effects of manipulating the corn crop density are likely to vary with weed density, but no such information is available, especially for this part of the world.…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, seasonal competition from a mixture of weeds, before the R1 stage, has been shown to reduce corn yield by reducing kernel number (Maddonni and Otegui, 2004;Cox et al, 2006). Competition among corn plants because of higher crop population, even in weed-free conditions, began at about the V4 to V6 stages, reducing crop growth rate, grain number per ear, and, ultimately, yield of grain (Pagano et al, 2007). When weeds were not suppressed in corn until later vegetative stages, grain yield was reduced due to a 2-day delay in silking and reductions in leaf area index (LAI) and kernel number (Cox et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…A comparison of yield components showed that ear number per plant, weight per seed, and kernels per ear declined with the duration of weed interference, but the effects were not always significant (Evans et al, 2003;Iftikhar-ud-Din et al, 2011;Memon et al, 2012). The final number of grains appears to be determined not by the number of florets per ear, but by resource and environmental conditions that regulate apportioning of late pollinated silks (Pagano et al, 2007). ) and X. strumarium densities (0, 2, 4, 6, 8, 10, and 12 plants m -2 ).…”

Section: Introductionmentioning

confidence: 99%

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