Yield component analysis of cowpea varieties in competition with weeds

There is great interest in varieties with greater competitive ability against weeds. This can be facilitated by path analysis, which involves the statistical evaluation and interpretation of the relationship between yield and its components. In this analysis, the occurrence of multicollinearity results in inconsistent estimates of the coefficients, and overestimates of the direct effects of the explanatory variables on the response variable. The aim of this study was to identify the characteristics with the greatest direct effect on pod yield and green and dry bean yields in traditional cowpea varieties, evaluated in competition with weeds in two experiments. In addition, the presence of multicollinearity was investigated in the analyses. In Experiment-1, twelve varieties with the highest bean yields in a preliminary evaluation were assessed in a randomized block design with five replications. In Experiment-2, six varieties, selected in the preliminary evaluation, were assessed using two methods of weed management: three of the most productive and three of the least productive. Randomized blocks and split plots were used, with five replications. Multicollinearity, indicated by the number of conditions and the variance inflation values, was greater in Experiment-2. In the six cases under study (three yields x two experiments), the number of pods per plant had the greatest direct effect on yield.

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Publication Details

Journal
Ciência Agronômica/Revista ciência agronômica
Published
2024-09-03
DOI
https://doi.org/10.5935/1806-6690.20250017
Primary Topic
Agricultural pest management studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Yield component analysis of cowpea varieties in competition with weeds

Paulo Sérgio Lima e Silva, Júlio César DoVale, Isis Fernanda Silva Medeiros, Jaeveson da Silva et al.
Ciência Agronômica/Revista ciência agronômica
Agricultural pest management studies
article

Yield component analysis of cowpea varieties in competition with weeds

Paulo Sérgio Lima e Silva, Júlio César DoVale, Isis Fernanda Silva Medeiros, Jaeveson da Silva, Vianney Reinaldo de Oliveira
article en

Abstract

There is great interest in varieties with greater competitive ability against weeds. This can be facilitated by path analysis, which involves the statistical evaluation and interpretation of the relationship between yield and its components. In this analysis, the occurrence of multicollinearity results in inconsistent estimates of the coefficients, and overestimates of the direct effects of the explanatory variables on the response variable. The aim of this study was to identify the characteristics with the greatest direct effect on pod yield and green and dry bean yields in traditional cowpea varieties, evaluated in competition with weeds in two experiments. In addition, the presence of multicollinearity was investigated in the analyses. In Experiment-1, twelve varieties with the highest bean yields in a preliminary evaluation were assessed in a randomized block design with five replications. In Experiment-2, six varieties, selected in the preliminary evaluation, were assessed using two methods of weed management: three of the most productive and three of the least productive. Randomized blocks and split plots were used, with five replications. Multicollinearity, indicated by the number of conditions and the variance inflation values, was greater in Experiment-2. In the six cases under study (three yields x two experiments), the number of pods per plant had the greatest direct effect on yield.

Ciência Agronômica/Revista ciência agronômicaVol. 56
Brazilian Agricultural Research Corporation (BR), Universidade Federal do Ceará (BR), Universidade Federal Rural do Semi-Árido (BR)
Universidade Federal Rural do Semi-Árido
Zero hunger
Openalex Percentile: Top 92%
Agricultural pest management studies
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Yield component analysis of cowpea varieties in competition with weeds — Paulo Sérgio Lima e Silva, Júlio César DoVale, et al. · Ciência Agronômica/Revista ciência agronômica (2024) | TGRS Research Map | TGRS