Understanding cowpea yield: A comprehensive analysis of physiological traits' contribution through path analysis

This study investigates the physiological processes affecting the grain yield of cowpea (Vigna unguiculata), a key protein, vitamin, and mineral source in human diets. Gaining an understanding of these mechanisms can be crucial for developing high-yielding cowpea varieties in breeding programs. A field experiment was conducted with 30 treatments, including three sowing dates (Early August, Late August, Early September) and ten cowpea genotypes (UAM09-1051-1, UAM09-1046-6-1, UAM14-126-L33, IT99K-573-1-1, IT89KD-288, UAM14-126-L6, UAM14-122-17-7, UAM14-123-18-3, UAM14-127-20-1-1, and UAM14-130-20-4). These treatments were arranged in a split-plot design within a Randomized Complete Block Design, replicated three times. Key physiological traits like Leaf Area Index (LAI), Intercepted Photosynthetically Active Radiation (IPAR), Stomatal Conductance, Photosynthetic Rate, Transpiration Rate, and Chlorophyll Content were measured. Data collected were analyzed using correlation and path coefficient methods; the results showed significant positive correlations between grain yield and traits like LAI, stomatal conductance, and photosynthetic rate. In contrast, the transpiration rate negatively correlated with yield. Path analysis revealed that the net photosynthetic rate had the most direct impact on grain yield, highlighting its role in photosynthesis and grain filling. The study suggests that cowpea breeding efforts should focus on improving photosynthetic efficiency and optimizing traits like LAI and stomatal conductance to boost cowpea grain yields.

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Publication Details

Journal
Agriculture and Food Sciences Research
Published
2025-04-09
DOI
https://doi.org/10.20448/aesr.v12i1.6570
Citations
2
Primary Topic
Agricultural pest management studies
Type
article
Field-Weighted Citation Impact
3.50
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article

Understanding cowpea yield: A comprehensive analysis of physiological traits' contribution through path analysis

Elias Mjaika Ndifon, P. E. Ogbonna, Alpha Y. Kamara, Omoigui Lucky et al.
2 citations
Agriculture and Food Sciences Research
Agricultural pest management studies
3.50
article

Understanding cowpea yield: A comprehensive analysis of physiological traits' contribution through path analysis

Elias Mjaika Ndifon, P. E. Ogbonna, Alpha Y. Kamara, Omoigui Lucky, Ankrumah Emmanuel, Onyia Vincent Nduka
article en
2 citations

Abstract

This study investigates the physiological processes affecting the grain yield of cowpea (Vigna unguiculata), a key protein, vitamin, and mineral source in human diets. Gaining an understanding of these mechanisms can be crucial for developing high-yielding cowpea varieties in breeding programs. A field experiment was conducted with 30 treatments, including three sowing dates (Early August, Late August, Early September) and ten cowpea genotypes (UAM09-1051-1, UAM09-1046-6-1, UAM14-126-L33, IT99K-573-1-1, IT89KD-288, UAM14-126-L6, UAM14-122-17-7, UAM14-123-18-3, UAM14-127-20-1-1, and UAM14-130-20-4). These treatments were arranged in a split-plot design within a Randomized Complete Block Design, replicated three times. Key physiological traits like Leaf Area Index (LAI), Intercepted Photosynthetically Active Radiation (IPAR), Stomatal Conductance, Photosynthetic Rate, Transpiration Rate, and Chlorophyll Content were measured. Data collected were analyzed using correlation and path coefficient methods; the results showed significant positive correlations between grain yield and traits like LAI, stomatal conductance, and photosynthetic rate. In contrast, the transpiration rate negatively correlated with yield. Path analysis revealed that the net photosynthetic rate had the most direct impact on grain yield, highlighting its role in photosynthesis and grain filling. The study suggests that cowpea breeding efforts should focus on improving photosynthetic efficiency and optimizing traits like LAI and stomatal conductance to boost cowpea grain yields.

Agriculture and Food Sciences ResearchVol. 12(1)
University of Nigeria (NG), Funai Electric (Japan) (JP), International Institute of Tropical Agriculture (NG)
Openalex Percentile: Top 9%
Agricultural pest management studies
3.50
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