







































 
 

 

42 
© 2025 by the authors; licensee Asian Online Journal Publishing Group 
 

Agriculture and Food Sciences Research 
Vol. 12, No. 1, 42-48, 2025 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/aesr.v12i1.6570 

© 2025 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 

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

Ankrumah Emmanuel1   

Ogbonna Peter Ejimofor2   

Onyia Vincent Nduka3   

Omoigui Lucky4   

Kamara Alpha Yahya5   

Ndifon Elias M’jaika6   

 

 
( Corresponding Author) 

 
1,6Department of Crop Science, Alex Ekwueme Federal University Ndufu-Alike, Ebonyi State, Nigeria. 
1Email: ankrumahemmanuel@gmail.com   
6Email: emndi4nn@yahoo.com  
2,3Department of Crop Science, University of Nigeria Nsukka, Enugu State, Nigeria. 
2Email: ogbonna@unn.edu.ng   
3Email: Onyia@unn.edu.ng  
4,5Project-Seed System, International Institute of Tropical Agriculture, Kano Station, Sabo Bakin Zuwo Road 
Kano, Kano State, Nigeria. 
4Email: L.Omoigui@cgiar.org  
5Email: A.Kamara@CGIAR.ORG   

 
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. 

 

Keywords: Breeding program, Correlation coefficient, Cowpea, Grain yield, Path analysis, Physiological traits, Planting date, Yield 
partitioning. 
 

Citation | Emmanuel, A., Ejimofor, O. P., Nduka, O. V., Lucky, O., 
Yahya, K. A., & M’jaika, N. E. (2025). Understanding cowpea yield: 
A comprehensive analysis of physiological traits’ contribution 
through path analysis. Agriculture and Food Sciences Research, 12(1), 
42–48. 10.20448/aesr.v12i1.6570 

History:  
Received: 22 November 2024 
Revised: 26 March 2025 
Accepted: 7 April 2025 
Published: 9 April 2025 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 

Funding: This study received no specific financial support.    
Institutional Review Board Statement: Not applicable. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study; that no vital features of the 
study have been omitted; and that any discrepancies from the study as 
planned have been explained. This study followed all ethical practices during 
writing. 
Competing Interests: The authors declare that they have no competing 
interests. 
Authors’ Contributions: All authors contributed equally to the conception 
and design of the study. All authors have read and agreed to the published 
version of the manuscript. 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 43 
2. Materials and Methods ................................................................................................................................................................... 43 
3. Results and Discussion ................................................................................................................................................................... 44 
4. Conclusion ......................................................................................................................................................................................... 47 
References .............................................................................................................................................................................................. 47 
 

mailto:ankrumahemmanuel@gmail.com
mailto:emndi4nn@yahoo.com
mailto:ogbonna@unn.edu.ng
mailto:Onyia@unn.edu.ng
mailto:L.Omoigui@cgiar.org
mailto:A.Kamara@CGIAR.ORG
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/aesr.v12i1.6570
https://orcid.org/0000-0001-9983-2453
https://orcid.org/0000-0003-4434-7922
https://orcid.org/0009-0004-9446-251X
https://orcid.org/0000-0001-8473-2775
https://orcid.org/0000-0002-1844-2574
https://orcid.org/0000-0001-6027-4714


Agriculture and Food Sciences Research, 2025, 12(1): 42-48 

43 
© 2025 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Contribution of this paper to the literature 
This study is original in its use of path coefficient analysis to quantify the direct, indirect, and 
total contributions of physiological traits, such as LAI, IPAR, chlorophyll content, and gas 
exchange parameters to cowpea grain yield. It provides new insights into trait interactions, 
supporting targeted breeding for higher, stress-resilient yields. 

 
1. Introduction 

Cowpea (Vigna unguiculata L. Walp) is widely cultivated in the tropical and subtropical regions of Africa, 

primarily for its edible seeds, green pods, and nutritious leaves, which are consumed as vegetables. It serves as a 

crucial source of calories and protein for millions of people in developing countries [1, 2]. Additionally, cowpea is 

an affordable and important source of protein, vitamins, and minerals in the human diet, and it also serves as 

healthy fodder for livestock feed [3, 4]. Cowpea is grown on 14.13 million hectares globally, yielding 4.51 million 

metric tons with an average productivity of 387.45 kg/ha [5]. Africa, particularly West Africa, accounts for over 

90% of the world’s production, with Nigeria being the leading producer and consumer in the region, generating 3.6 

million tons in 2021 [6]. 

Cowpea is an important grain legume that is highly adapted to various agro-ecological environments [7]. Its 
resilience under marginal environmental conditions and its capacity for symbiotic nitrogen fixation make it a 
critical food security crop in regions prone to environmental stress [8]. Despite these attributes, improving 
cowpea yield remains a significant challenge, especially due to climate variability and soil nutrient limitations [3]. 
Cowpea yield is an intricate trait that is affected by multiple yield-contributing traits controlled by different genes 
and environmental influences [7].  

A correlation study was conducted to examine the relationships between different traits and their respective 
contributions to yield [9]. Several researchers have explored the connection between these traits to identify key 
factors for selecting high-yielding varieties [10]. The yield of grain legumes is a complex quantitative trait 
influenced by the interaction of various growth and physiological processes [11]. 

Therefore, cowpea yield improvement requires an understanding of the physiological traits that influence yield 
performance. Furthermore, it was noted that physiological traits, including the Leaf Area Index (LAI), the 
Intercepted Photosynthetically Active Radiation (IPAR), the concentration of chlorophyll, and the gas exchange 
parameters (stomatal conductance, photosynthetic rate, and transpiration rate), have a major impact on the 
accumulation of biomass and the potential yield of grain legumes [12, 13]. However, there is inadequate 
information on the complex relative contributions of these traits, whether direct or indirect, to grain yield. 

The path coefficient analysis is essential for use as a criterion for enhancing yield prediction, management, and 
selection in the improvement of grain yield in cowpea. This could aid in the strategic selection of desirable traits for 
breeding. This study could fill this gap and gather information on the contributions of various physiological 
parameters associated with grain yield and yield-related traits. Therefore, this study on path analysis was carried 
out to dichotomize the direct, indirect, and total contributions of key physiological traits on cowpea grain yield, 
which is an essential approach to identify the critical traits driving yield improvement and to provide an 
understanding of their interconnectedness. 

 

2. Materials and Methods 
2.1. Experimental Site 

Field studies were conducted during the 2021 and 2022 main cropping seasons at the Teaching and Research 
Farm of the Department of Crop Breeding and Genetics, Joseph Sarwun Tarka University (JST-UAM) in Makurdi 

(7°44ˈN, 8°30ˈE, 686 m ASL) in the Northern Guinea Savanna. Prior to the establishment of the field trial, soil 
samples were taken from the location and characterized according to the analytical procedures of the soil 
laboratory of the Department of Soil Science, University of Nigeria, Nsukka. Weather information was collected 
from the AccuWeather Stations installed at the trial site. 
 

2.2. Cowpea Varieties, Sowing Date and Experimental Design 
Ten (10) cowpea varieties and three sowing dates were evaluated. The experimental design was a randomized 

complete block in a split-plot arrangement with three replications. The main plot consisted of the sowing dates of 
early August, late August, and early September. The cowpea varieties were assigned to the subplot. Four medium-
maturing and semi-determinate varieties (IT99K-573-1-1, UAM14-126-L33, UAM14-126-L6, and UAM09-1051-
1, which mature within 75–80 days), and six indeterminate varieties (IT89KD-288, UAM14-122-17-7, UAM14-
123-18-3, UAM14-127-20-1-1, UAM14-130-20-4, UAM09-1046-6-1, which mature within 85–90 days) were used. 
Eight of these varieties were developed by the JST-UAM, while the other two were developed by the IITA. The 
subplots were 3 × 4 m and consisted of four ridges with 75 cm spacing between the ridges and 20 cm between plant 
stands on each ridge. 

The field was disc-harrowed and ridged before planting. The trial was planted as per the scheduled sowing 
dates: on the 5th of August, 19th of August, and 2nd of September, 2021, respectively. In 2022, it was done on the 4th 
of August, the 18th of August, and the 5th of September, respectively. Seeds of the cowpea varieties were planted at a 
depth of 3 cm. Four seeds were planted and later thinned to two stands per hill. Thinning was performed 14 days 
after sowing. At sowing, the recommended fertilizer rate for legumes in the Nigerian savannas of 50 kg of P2O5 in 

the form of SSP was applied. A mixture of pendimethalin (500 g L−1) and Gramoxone (1:1-dimethyl-4,4-

bipyridinium dichloride), at a rate of 1 L ha−1 each, was applied immediately after sowing using a knapsack sprayer. 
This was followed by hoe weeding 4 weeks after sowing. 
 
 
 



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2.3. Data Collection and Evaluation 

The two middle ridges were used for data collection. At the vegetative stage, Leaf Area Index (LAI) and 
intercepted photosynthetically active radiation (IPAR) were measured simultaneously at the full bloom stage using 
the AccuPAR model LP-80 PAR/LAI Ceptometer, following the procedure reported by Kamara et al. [7]. The net 
photosynthetic rate, stomatal conductance, and transpiration rate were measured simultaneously at the full 
flowering and podding stages of cowpea using a portable photosynthesis system, model LI-6400XT (LI-COR 
Biosciences, USA), after calibration according to the manufacturer’s instructions. Relative leaf chlorophyll content 
was measured at the full bloom and podding stages using the Konica Minolta SPAD 502 chlorophyll meters, where 
a minimum of five different measurements was conducted on different plants, and average readings were recorded. 

At pod maturity, the two middle ridges were used for measuring yield components. Pods from the two middle 
ridges (net rows) were harvested, threshed, and seed weight per plot was obtained. The moisture content of grain 
samples from each plot was determined using a V-Tech 3.5 to 40% grain moisture tester, Model SF00000885. 
Grain yield was extrapolated from the total seed weight in grams per plot and adjusted based on moisture content 
(12%) to obtain grain yield in kilograms per hectare (kgha-1) using the relation below. 

Grain Yield (kg haˉ1) =
𝑆𝑒𝑒𝑑𝑠 𝑤𝑒𝑖𝑔ℎ𝑡 𝑝𝑒𝑟 𝑝𝑙𝑜𝑡 (𝑔)

1000
∗

100 − moisture (%)

88%
∗

10000m²

4.5𝑚²
 

Simple correlation coefficients between the grain yield (Y) and physiological characters (X) and within the 
physiological traits themselves were worked out using the following equation after [14]. 

𝑟𝑥ʏ =
𝑛(∑XY) − (∑X)((∑Y)

√[𝑛∑Х2 − (∑Х)2][𝑛∑Ү2 −  (∑Ү)2] 
 

Where: rxʏ = Correlation coefficient between variables X and Y, n = Number of observations, ΣXY = Sum of 

the product of X and Y values, ΣX, ΣY = Sums of X and Y values, respectively, ΣX2, ΣY2 = Sums of squared values 
for X and Y.  

The calculated coefficients were further used to develop the following simultaneous equations, from the general 
path equation using the Poolman Equation, in order to partition the correlations into cause and effect by working 
out the path coefficients (Pi). For the dependent variable Y (grain yield), the path equation is: Y = PXi X1 + PX2X2 

+ PX3X3 + ⋯ + PXnXn + Residual. Where: PXi is the path coefficient (direct effect) of variable Xi on Y. Xi is the 
independent variables, and Residual (PR) is the unaccountable variance by the model. 

𝑟𝑌1 =  𝑃𝑌1 + 𝑃𝑌2 × 𝑟12 + 𝑃𝑌3 × 𝑟13 + 𝑃𝑌4 × 𝑟14 + 𝑃𝑌5 × 𝑟15 + 𝑃𝑌6 × 𝑟16       (1) 

𝑟𝑌2 =  𝑃𝑌1 × 𝑟21 + 𝑃𝑌2 + 𝑃𝑌3 × 𝑟23 + 𝑃𝑌4 × 𝑟24 + 𝑃𝑌5 × 𝑟25 + 𝑃𝑌6 × 𝑟26        (2) 

𝑟𝑌3 =  𝑃𝑌1 × 𝑟31 + 𝑃𝑌2 × 𝑟32 + 𝑃𝑌3 + 𝑃𝑌4 × 𝑟34 + 𝑃𝑌5 × 𝑟35 + 𝑃𝑌6 × 𝑟36        (3) 

𝑟𝑌4 =  𝑃𝑌1 × 𝑟41 + 𝑃𝑌2 × 𝑟42 + 𝑃𝑌3 × 𝑟43 + 𝑃𝑌4 + 𝑃𝑌5 × 𝑟45 + 𝑃𝑌6 × 𝑟46        (4) 

𝑟𝑌5 =  𝑃𝑌1 × 𝑟51 + 𝑃𝑌2 × 𝑟52 + 𝑃𝑌3 × 𝑟53 + 𝑃𝑌4 × 𝑟54 + 𝑃𝑌5 + 𝑃𝑌6 × 𝑟55        (5) 

𝑟𝑌6 =  𝑃𝑌1 × 𝑟61 + 𝑃𝑌2 × 𝑟62 + 𝑃𝑌3 × 𝑟63 + 𝑃𝑌4 × 𝑟64 + 𝑃𝑌5 × 𝑟65 + 𝑃𝑌6        (6) 
From the above Equations PY1, PY2, PY3, PY4, PY5, and PY6 are the path coefficients (Direct effect) 

representing LAI, IPAR, Chlorophyll content, Stomatal conductance SC, Transpiration rate (TR), and 
Photosynthetic rate (PR), while PY2r12, PY3r13, PY4r14, PY5r15, PY6r16, ……. PY4r64, PY5r65 are the indirect effects, 
while r12 …… r65 are the correlation coefficients. The individual percentage contributions are computed using. 

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝐷𝑖𝑟𝑒𝑐𝑡 𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 = ( 
Pi

𝑅2
)

2

𝑋 100 

Where, Pi = Direct path coefficient of the ith independent variable. 𝑅2 = Coefficient of determination (sum of the 
direct and indirect contributions of all variables). While the percentage combined contribution (%) of any two 
characters were also computed using the following relation. 

𝐶𝑜𝑚𝑏𝑖𝑛𝑒𝑑 𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 = ( 
(𝑃ᵢ + 𝐼ᵢ)²

𝑅²
) 𝑋 100 

Dewey and Lu [15] Where: Iᵢ is the sum of the indirect effects of the ith variable on the dependent variable 

through other variables. Pi + Iᵢ is the total effect (direct + indirect) of the independent variable. The residual effect 

(Re) was calculated using the relation: 𝑅ₑ =  √1 −  𝑅²  R2 is the coefficient of determination, which measures the 
proportion of the total variance in the dependent variable that is explained by the independent variables. 

3. Results and Discussion 
3.1. Correlation Coefficients (r) between Grain Yield and Physiological Traits in Cowpea 

The results of the correlation analysis revealed some highly significant (p < 0.01) positive associations between 
the physiological components assessed and the grain yield of cowpea (Table 1). LAI showed a strong positive 
correlation with stomatal conductance (r = 0.746) and photosynthetic rate (r = 0.720), which indicates that 
increased leaf area contributes to better gas exchange and photosynthetic efficiency. This is in agreement with the 
studies by Oluwasemire and Odugbenro [16], who demonstrated that higher LAI enhances the plant’s ability to 
intercept light and perform photosynthesis, leading to improved growth and development. However, the negative 
correlation with transpiration rate (r = -0.561) suggests that as the leaf area increases, water loss through 
transpiration decreases, likely due to a more efficient water use system or stomatal regulation. This aligns with the 
findings of Bouranis et al. [17], who discussed the trade-offs between leaf area expansion and water use efficiency 
in crops. The positive correlations between chlorophyll content and both stomatal conductance (r = 0.495) and 
photosynthetic rate (r = 0.353) are expected, as higher chlorophyll content is indicative of better light-harvesting 
capabilities and enhanced photosynthetic activity [18]. The weak negative correlation with transpiration rate (r = 
-0.329) might suggest that plants with higher chlorophyll content manage water more efficiently, similar to 
findings in studies on drought-tolerant varieties of crops [19]. The strong positive association recorded between 
stomatal conductance and photosynthetic rate (r = 0.838) undoubtedly aligns with the physiological role of stomata 

in regulating CO₂ uptake for photosynthesis. As stomatal conductance increases, more CO₂ is available for 



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photosynthetic reactions, which has been well-documented in studies on plant gas exchange [13]. The significant 
negative relationship between stomatal conductance and transpiration rate (r = -0.684) reflects the role of stomata 
in balancing water loss and carbon uptake, a common feature in plants adapted to water-limited environments 
[12]. A strong negative correlation between transpiration rate and photosynthetic rate (r = -0.642) revealed the 
potential for cowpea varieties with lower transpiration rates to maintain higher photosynthetic efficiency. This 
finding is supported by research into the water-use strategies of leguminous crops, where reduced water loss 
through transpiration helps sustain photosynthesis under water stress [12, 16]. There were significant 
correlations between LAI (r = 0.759), chlorophyll content (r = 0.490), stomatal conductance (r = 0.684), 
photosynthetic rate (r = 0.645), and grain yield, suggesting that traits improving carbon assimilation and water use 
efficiency directly contribute to yield formation. This agrees with several studies emphasizing the importance of 
these traits in yield improvement under various stress conditions [16, 19-21].  

Conversely, the negative correlation between transpiration rate and grain yield (r = -0.572) further reinforces 
the idea that efficient water use is critical for optimizing yield in cowpea, a finding supported by studies on the 
physiological responses of cowpea to drought [21]. However, the lack of a significant relationship between IPAR 
and grain yield in this study contrasts with some literature that highlights the importance of IPAR in determining 
crop yield potential [18, 22, 23]. Nevertheless, the weak negative association with chlorophyll content suggests 
that under certain conditions, higher radiation interception does not always translate to better photosynthetic 
performance, especially if other limiting factors, such as nutrient or water availability, constrain growth. 

 
Table 1. Matrix of correlation coefficients (r) showing the association between grain yield and some physiological parameters of cowpea. 

Parameters LAI IPAR 
Chlorophyll 

content 
Stomatal 

conductance 
Transpiration 

rate 
Photosynthetic 

rate 
Grain 
yield 

LAI 1 
      

IPAR -0.087 1 
     

Chlorophyll 0.487** -0.271** 1 
    

Stomatal conductance 0.746** -0.057 0.495** 1 
   

Transpiration rate -0.561** 0.001 -0.329** -0.675** 1 
  

Photosynthetic rate 0.720** -0.038 0.353** 0.838** -0.642** 1 
 

Grain yield 0.759** -0.060 0.490** 0.684** -0.572** 0.645** 1 
Note: ** Correlation is significant at the 0.01 level.   IPAR: Intercepted photosynthetic rate. LAI: Leaf area index 

 

3.2. Direct, Indirect and Total Contributions of Some Physiological Characters to Grain Yield in Cowpea 
The results of the path coefficient revealed that Leaf Area Index (LAI) and Intercepted Photosynthetically 

Active Radiation (IPAR) recorded weak to moderate negative direct effects (-0.187 and -0.330, respectively), but 
strong positive total contributions to grain yield (0.759 and 0.760, respectively) (Table 2). The negative direct 
effects suggest that dense canopy architecture and suboptimal light interception can cause yield reduction as a 
result of mutual shading and resource competition [20]. However, their high indirect contributions through 
Photosynthetic Rate emphasize the importance of canopy architecture in driving efficient utilization of light 
intercepted [20]. Even though the negative direct effect (-0.217) of chlorophyll content on grain yield exists, its 
strong positive total association (0.890) suggests that its contribution is highly effective (0.822) via indirect paths, 
mainly through improvement in Photosynthetic Rate. This finding is in line with the role of chlorophyll as the 
primary pigment for light absorption during photosynthesis [24]. This means that increasing chlorophyll content 
alone may not directly increase the grain yield of cowpea; however, maintaining optimal chlorophyll levels is vital 
for optimizing light interception and carbon assimilation [19]. The study also revealed that Stomatal Conductance 

displayed a strong positive direct effect (0.572) on cowpea grain yield (Table 2), indicating its critical role in CO₂ 
and water vapor exchange regulation between the leaves and the atmosphere [25]. The current study aligns 
closely with the findings of Chikov and Akhtyamova [19], who reported that efficient stomatal conductance can 

improve photosynthetic rates by ensuring adequate CO₂ uptake while minimizing water loss, which is crucial in 
maintaining photosynthesis in varying environmental conditions. The highly strong indirect effect through the net 
Photosynthetic Rate (0.915) confirmed the independent report by Boukar et al. [8], who stated that the 
reinforcement in stomatal conductance indirectly impacts crop yield performance by optimizing the photosynthetic 
machinery. However, under extreme drought conditions, high stomatal conductance may lead to high transpiration 
rates and a potential negative impact on grain yield, as reported by Bouranis et al. [17]. 

The transpiration rate had a moderate positive (0.339) direct effect; however, it had a strong negative (-0.572) 
total contribution to grain yield (Table 2). This conflicting effect highlights the dual responsibility of transpiration 
in plant physiology. While transpiration is necessary for cooling and nutrient uptake, according to Bouranis et al. 
[17], a higher transpiration rate can deplete water resources, especially under drought conditions. The highly 
strong negative (-0.701) indirect contribution through the photosynthetic rate emphasizes that high transpiration 

may affect stomatal regulatory function by closure, leading to a decline in the influx and uptake of CO₂, thus 
resulting in a decrease in photosynthesis.  

The path analysis showed that the Photosynthetic Rate has the highest (1.092) direct effect on grain yield 
(Table 2). This finding aligns with the central role of photosynthesis in biomass accumulation and grain filling, as 
it is the fundamental process converting light energy into chemical energy [26]. Previous studies have shown that 
enhancing photosynthetic efficiency can significantly increase yield, especially in C3 plants like cowpea, which often 
suffer from photorespiration losses [10, 22]. Additionally, the strong indirect effects of the Photosynthetic Rate 
through physiological traits like LAI (0.786) and Stomatal Conductance (0.915) indicate the importance of 
strengthening photosynthetic capacity while optimizing canopy architecture and efficiency in gaseous exchange to 
achieve higher cowpea grain yields. 
 
 
 
 



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Table 2. Path analysis for direct (Diagonal), indirect (Off diagonal) and total contributions effect of some physiological traits to grain yield 
of cowpea planted during 2021 to 2022 rainfed seasons. 

Parameters LAI IPAR 
Chlorophyll 

content 
Stomatal 

conductance 
Transpiration 

rate 
Photosynthetic 

rate 
Total 
corr. 

LAI -0.187 0.029 -0.105 0.426 -0.190 0.786 0.759** 
IPAR 0.016 -0.330 0.059 0.318 0.000 0.697 0.760** 
Chlorophyll content -0.091 0.090 -0.217 0.397 -0.112 0.822 0.890** 
Stomatal conductance -0.139 -0.184 -0.150 0.572 -0.229 0.915 0.784** 
Transpiration rate 0.105 0.000 0.071 -0.386 0.339 -0.701 -0.572 
Photosynthetic rate -0.134 -0.211 -0.163 0.479 -0.218 1.092 0.845** 

Note: LAI = Leaf area index. IPRA = Intercepted photosynthetic active radiation. ** significant probability at 0.01 level. 

 

3.3. Direct and Combined Contributions (%) of Yield Components to Grain Yield in Cowpea 
The highest percentage (119.2%) direct contribution of photoassimilate to grain yield of cowpea was exhibited 

by the net photosynthetic rate (Figure 1). This is expected because the capacity to produce high amounts of 
photoassimilates and efficient partitioning of carbon compounds towards harvestable organs has shown a major 
impact on crop yield [18, 20]. An increase in the net photosynthetic rate translates into improved dry matter 
accumulation, translocation, and efficient partitioning of photoassimilates for optimal grain filling [22, 27]. 
However, its negative percentage indirect contribution (-24.7%) may indicate competitive allocation of assimilates 
to vegetative growth and possible potential imbalances in the source-sink dynamics [22]. 

The percentage contribution of stomatal conductance was significant, with a 32.7% direct impact on grain yield 

(Figure 1). This explains the efficient regulatory role of stomatal conductance in optimizing the rate of CO₂ entry 
into the leaf mesophyll membrane and the exit of water vapor, thus influencing photosynthetic efficiency [18, 28]. 
High stomatal conductance is associated with better gas exchange and higher transpiration rates, which often 
improve yield in sufficient moisture ecology [22]. The moderate indirect contributions of about 21.2% suggest the 
interconnectedness of stomatal conductance with other physiological processes in cowpea for the maintenance of 
metabolic gaseous exchange equilibrium and efficient nutrient uptake.  

The contributions of about 11.5% of the transpiration rate (Figure 1) directly into cowpea grain are 
physiologically linked to its role in relatively maintaining leaf temperature and driving nutrient flow through the 
plant [10]. However, about -91.1% negative indirect contribution indicates that under extreme transpiration, 
certain conditions, like drought, especially at critical cowpea developmental phases, can lead to excessive loss of 
water through the stomata, resulting in poor grain filling and a subsequent negative impact on grain yield. 

Even though intercepted photosynthetic active radiation contributed relatively 10.9% modestly directly to 
grain yield, in contrast to about 109% high indirect contribution. Canopy architectural characteristics have been 
reported to primarily affect light interception efficiency of plants [16, 18, 20]. The huge indirect contributions 
recorded imply the magnificent role displayed by IPAR through the synergistic interactions with other 
physiological traits like leaf area and chlorophyll content, which translate to canopy light-use efficiency [16]. 

The percentage direct contribution (4.7%) of chlorophyll was significantly low (Figure 1); however, the very 
high indirect contribution of about 110.7% to the grain yield highlights that chlorophyll concentration alone is not 
a direct driver of grain yield in cowpea, but rather enhances the photosynthetic capacity jointly with other 
physiological traits like LAI and IPAR [16, 18, 20]. A high chlorophyll content has been reported to correspond to 
better quality light absorption by the chlorophyll pigments, which facilitates more efficient carbon assimilation 
[17]. 

The result of the current study showed that LAI contributed the lowest percentage (3.5%) of direct 
contribution to grain yield, but had a strong indirect effect (94.5%) on cowpea grain yield. LAI determines the total 
leaf area available for light capture and gaseous exchange, thus playing a fundamental role in determining crop 
growth potential [10]. Its strong indirect effect could be attributed to its interaction with IPAR and 
photosynthetic traits, which together define the crop’s radiation-use efficiency [29]. 

 

 
Figure 1. Direct and indirect contributions (%) of physiological traits on cowpea grain yield. 

 
Figure 2 presents a summary path diagram illustrating the associations between various physiological traits 

and their effects on cowpeas' grain yield. 
 



Agriculture and Food Sciences Research, 2025, 12(1): 42-48 

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© 2025 by the authors; licensee Asian Online Journal Publishing Group 

 

 

 
Figure 2. Summary path diagram showing association of some effects of physiological traits to grain yield of cowpea. 

 

4. Conclusion 
The simple correlation analysis revealed a highly significant and positive association between and among most 

of the physiological components assessed and grain yield. The path coefficient analysis explained the contribution 
of physiological traits to cowpea grain yield through direct and indirect pathways. Photosynthetic rate and 
stomatal conductance had highly significant direct effects, making them primary targets for breeding programs 
aimed at enhancing yield. On the other hand, traits like LAI, IPAR, and chlorophyll content contribute mainly 
through indirect interactions, suggesting that these traits are critical for improving the overall efficiency of the 
photosynthetic system and canopy structure. 
 

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