Analysis of covariance in agronomy and crop research

YangRong-Cai,; JuskiwPatricia,

doi:10.4141/cjps2010-032

Cited by 22 publications

(16 citation statements)

References 15 publications

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“…The effect of planting date on yield was further evaluated with an analysis of covariance (ANCOVA) following the method developed by Yang and Juskiw (2011). The implementation of ANCOVA reduced the error mean square, accounted for missing data, and served to increase the precision of the resulting regression analysis.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Planting date was considered a covariate and classification variable by generating a second column of data (s) identical to the planting date to be used as the covariate. Type 1 sums of squares was specified via the METHOD statement in PROC MIXED (Yang and Juskiw, 2011). Direct regression variables (covariates) s and s*s represent linear and quadratic responses to planting date and are part of the MODEL statement.…”

Section: Statistical Analysesmentioning

confidence: 99%

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The Integration of Spring and Winter Wheat Genetics With Agronomy for Ultra-Early Planting Into Cold Soils

et al. 2020

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Early seeding has been suggested as a method of increasing the grain yield and grain yield stability of wheat (Triticum aestivum L.) in the Northern Great Plains. The point at which early seeding results in a decrease in grain yield has not been clearly identified. Changes in climatic conditions have increased frost-free periods and increased temperatures during grain filling, which can either be taken advantage of or avoided by seeding earlier. Field trials were conducted in western Canada from 2015 to 2018 to evaluate an ultra-early wheat planting system based on soil temperature triggers as opposed to calendar dates. Planting began when soil temperatures at 5 cm depth reached 0°C and continued at 2°C intervals until 10°C, regardless of calendar date. Conventional commercial spring wheat genetics and newly identified cold tolerant spring wheat lines were evaluated to determine if ultra-early wheat seeding systems required further development of specialized varieties to maintain system stability. Ultra-early seeding resulted in no detrimental effect on grain yield. Grain yield increased at sites south of 51°latitude N, and was unaffected by ultraearly seeding at sites north of 51°latitude N. Grain protein content, kernel weight, and bulk density were not affected by ultra-early seeding. Optimal seeding time was identified between 2 and 6°C soil temperatures. A greater reduction in grain yield was observed from delaying planting until soils reached 10°C than from seeding into 0°C soils; this was despite extreme environmental conditions after initial seeding, including air temperatures as low as −10.2°C, and as many as 37 nights with air temperatures below 0°C. Wheat emergence ranged from 55 to 70%, and heads m −2 decreased with delayed seeding while heads plant −1 did not change. Cold tolerant wheat lines did not increase stability of the ultra-early wheat seeding system relative to the conventional spring wheat check, and are therefore not required for growers to adopt ultra-early seeding. The results of this study indicate that growers in western Canada can successfully begin seeding wheat earlier, with few changes to their current management practices, and endure less risk than delaying seeding until soil temperatures reach 10°C or greater.

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Section: Statistical Analysesmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

The Integration of Spring and Winter Wheat Genetics With Agronomy for Ultra-Early Planting Into Cold Soils

et al. 2020

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“…This result corroborates Yang et al (2004) which asserts that the RCBD has a low efficiency in experiments containing a large number of treatments, showing that the analysis of variance autoregressive is able to around this problem by reducing the variability of the block factor. Yang and Juskiw (2011) say that in RCBD, proper blocking can reduce error by maximizing the difference between blocks and maintaining the plot-to-plot homogeneity within blocks, but blocking is ineffective if heterogeneity between plots does not follow a definite pattern (e.g., spotty soil heterogeneity; unpredictable pest incidence after blocking). In addition, when block size is large (>8-12 plots per block), intra-block heterogeneity is inevitable.…”

Section: Resultsmentioning

confidence: 99%

Autoregressive Analysis of Variance for Experiments With Spatial Dependence Between Plots: A Simulation Study

Rossoni

Lima

2019

Rev. Bras. Biom.

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The analysis of variance remains one of the most appreciated techniques of field experiment, even despite almost a hundred years of its first proposal. However, in many cases, its application can be several impaired due the fact of lack – or even forgotten - of assumptions. In several experiments, the researchers make use of blocks to control the local heterogeneity, nevertheless, in some cases, only this it cannot be enough, especially in experiments where the data have some kind of spatial dependence. Therefore, to increase the accuracy of comparisons between treatments, an alternative is to consider the study of the spatial dependence of the variables in the analysis. With the knowledge of the relative positions of the plots (referenced data), the spatial variability can be used as a positive factor, collaborating with the experimental results. To develop this study we used data generated by simulation. The data was generated according a Randomized Complete Block Design (RCBD), with eighteen and five treatments per block; and several scenarios of spatial dependence in the error. We compared the non-spatial analysis (which considers the errors independent) with spatial analysis (analysis of variance considering the autoregressive model - ANOVA-AR). The use of spatial statistical tools in the analysis of data increased the precision of the analysis, through the reduction of the Mean Squared Error. We also noticed a reduction of Mean Squared Block and Mean Squared Treatment. The greater reduction was notice in ANOVA-AR3 for great part of the simulated scenarios, mainly in those with strong spatial dependence. The experiments with a small number of treatments per block did not present a reduction of Mean Squared Error, however, the reduction of Mean Squared Block and Mean Squared Treatment, ally to the fact that data are spatial dependent justify the use of ANOVA-AR.

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“…Before analysis, the data was subjected to a normality test (of the total length distribution data) using the Shapiro-Wilk's W-test (Shapiro et al, 1968). Thereafter, Analysis of Covariance (ANCOVA) was employed to determine the effect of hook size, season and sampling sites on the size of fish caught during the study period, using the method described by Yang and Juskiw, (2011). All tests were considered significant at the 95% confidence level (α = 0.05).…”

Section: Discussionmentioning

confidence: 99%

Hook size selectivity in the artisanal handline fishery of Shimoni fishing area, south coast, Kenya

Ontomwa

Fulanda

Kimani

et al. 2019

West Ind. Oc. J Mar. Sci.

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Aims and scope: The Western Indian Ocean Journal of Marine Science provides an avenue for the wide dissemination of high quality research generated in the Western Indian Ocean (WIO) region, in particular on the sustainable use of coastal and marine resources. This is central to the goal of supporting and promoting sustainable coastal development in the region, as well as contributing to the global base of marine science. The journal publishes original research articles dealing with all aspects of marine science and coastal management. Topics include, but are not limited to: theoretical studies, oceanography, marine biology and ecology, fisheries, recovery and restoration processes, legal and institutional frameworks, and interactions/relationships between humans and the coastal and marine environment. In addition, Western Indian Ocean Journal of Marine Science features state-of-the-art review articles and short communications. The journal will, from time to time, consist of special issues on major events or important thematic issues.

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Analysis of covariance in agronomy and crop research

Cited by 22 publications

References 15 publications

The Integration of Spring and Winter Wheat Genetics With Agronomy for Ultra-Early Planting Into Cold Soils

The Integration of Spring and Winter Wheat Genetics With Agronomy for Ultra-Early Planting Into Cold Soils

Autoregressive Analysis of Variance for Experiments With Spatial Dependence Between Plots: A Simulation Study

Hook size selectivity in the artisanal handline fishery of Shimoni fishing area, south coast, Kenya

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