Received: 17 May 2025 Accepted: 30 September 2025 DOI: 10.1002/agg2.70252 O R I G I N A L A R T I C L E A g r o s y s t e m s Yield and yield components responses to plant density in cowpea grown in two savannah agro-ecologies in Nigeria Timothy Aku Otsanjugu Namo1 Ifeoluwa Simeon Odesina1,2 Tersur Theophilus Akpensuen3 Grace Obaiya Utoblo1 Ousmane Boukar2 Patrick Obia Ongom2 Gideon Oluwaseye Oyebode2,4 1Cytogonetics and Plant Breeding Unit, Department of Plant Science and Biotechnology, University of Jos, Jos, Nigeria 2International Institute of Tropical Agriculture, Kano, Nigeria 3Net Zero and Resilient Farming, Rothamsted Research, North Wyke, Okehampton, Devon, UK 4Department of Crop Soil and Environmental Sciences, Auburn University, Auburn, Alabama, USA Correspondence Timothy Aku Otsanjugu Namo, Cytogonetics and Plant Breeding Unit, Department of Plant Science and Biotechnology, University of Jos, PMB 2084, Jos, Nigeria. Email: namoa@unijos.edu.ng Assigned to Associate Editor Anuj Chiluwal. Funding information Tertiary Education Trust Fund (TETFUND), Grant/Award Number: 015 Abstract Gap-filling is used to mitigate yield losses in different legumes. There is scanty infor- mation on this mechanism of yield compensation in the cowpea [Vigna unguiculata (L.) Walp]. This study investigated responses of yield and yield components to plant density in some accessions of cowpea at Minjibir and Shika locations. The random- ized complete block design, in a split-plot arrangement in three replicates, was used. The main plots consisted of four plant densities, while the sub-plots consisted of six cowpea accessions. Results showed that plant density and environment affected yield and yield components. Total grain yield increased as plant density increased at both locations and was highest in the accession DANILA (1793.3 kg ha−1) at 99,999 plant ha−1 and lowest in the accession IT98K-205-8 (1100 kg ha−1) at 33,333 plants ha−1. Pod yield was positively correlated with total grain yield at Minjibir (0.267*) and Shika (0.917**) and when data were combined (0.990**). Shelling percentage was negatively correlated with total grain yield when data were combined (−0.610**). Significant positive correlation between total grain yield and 100-seed weight as well as biological yield was observed at Shika. Harvest index was positively cor- related with total grain yield (0.407**) at Minjibir. The study concludes that erect accessions (IT93K-452-1 and IT98K-205-8) and semi-erect accessions (IT99K-573- 1-1 and IT08K-150-27) could be adopted for cultivation at 133,333 plants ha−1, while prostrate accessions (IT89KD-288 and DANILA) could be cultivated at 99,999 plants ha−1 at Minjibir. The accessions IT93K-452-1, IT98K-205-8, IT99K-573-1-1, and IT08K-150-27 could be cultivated at Shika, irrespective of plant density. Abbreviation: IITA, International Institute of Tropical Agriculture. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). Agrosystems, Geosciences & Environment published by Wiley Periodicals LLC on behalf of Crop Science Society of America and American Society of Agronomy. Agrosyst Geosci Environ. 2025;8:e70252. wileyonlinelibrary.com/journal/agg2 1 of 13 https://doi.org/10.1002/agg2.70252 https://orcid.org/0000-0002-4801-0182 mailto:namoa@unijos.edu.ng http://creativecommons.org/licenses/by/4.0/ https://wileyonlinelibrary.com/journal/agg2 https://doi.org/10.1002/agg2.70252 http://crossmark.crossref.org/dialog/?doi=10.1002%2Fagg2.70252&domain=pdf&date_stamp=2025-11-25 2 of 13 NAMO ET AL. Plain Language Summary The ability of a crop to maintain or increase yield under low plant density is referred to as yield compensation. There is limited study on yield compensation in the cow- pea. This study investigated the responses of yield and yield components to plant density in cowpea in two savannah agro-ecologies in northern Nigeria. At 133,333 plants ha−1, semi-erect and erect accessions produced high yield in the Sudan savan- nah. Prostrate accessions performed well at 99,999 plants ha−1 at Minjibir location. Accessions IT93K-452-1, IT98K-205-8, IT99K-573-1-1, and IT08K-150-27 could be cultivated at the Shika location irrespective of plant density. Environment and accession determine the plant density to adopt in the cultivation of cowpea in northern Nigeria. 1 INTRODUCTION Cowpea [Vigna unguiculata (L.) Walp] is a staple legume, which is consumed by millions of people. It has a high poten- tial for food and nutritional security in sub-Saharan Africa; it is produced for grain, immature green pods, fresh leaves, or fodder due to its nutritional composition (Boukar et al., 2015; Gerrano et al., 2015; Gerrano et al., 2017; Omoigui et al., 2020). Cowpea can be determinate or indeterminate in growth habit and can either be prostrate, spreading, climbing, bushy, semi-erect, or erect in architecture. Cowpea production has gained global attention over the years, particularly in the African continent, which is known to be one of the largest producing hubs of cowpea worldwide (Kebede, 2020). Nigeria and Republic of Niger are reported to be the largest producing countries in the sub-Saharan Africa (Kebede & Bekeko, 2020). The two countries account for about 80% of the cowpea production in West Africa (Aboki & Yuguda, 2013; Boukar et al., 2019; FAOSTAT, 2020; Kebede & Bekeko, 2020). The International Institute of Tropical Agri- culture (IITA) has made significant advances in improving the productivity of cowpea in sub-Saharan Africa (Boukar et al., 2019). Boukar et al. (2019) also noted that several varieties have been developed, which combine canopy architecture, different maturity periods, and resistance to diseases, insect pests, and parasitic weeds, as well as good agronomic traits. Cowpea is one of the most preferred crops and a valu- able component in the farming systems of resource-poor rural households in sub-Saharan Africa (Molosiwa et al., 2016). Liu et al. (2008) and Yusuf et al. (2017) noted that yield has been and remains the trait of greatest emphasis by breeders, as it has the greatest impact on farmers’ income. Yield losses in cowpea are associated with poor soil fertility, the use of unimproved varieties or landraces, poor agronomic practices, drought, and other abiotic and biotic stresses. These losses can be mitigated through the use of yield compensation to bridge the gap in cowpea productivity. Yield compensation is defined as the ability of the plant grown under low plant density to produce equal or higher yields when compared with those grown under optimum den- sity. The establishment of an appropriate mechanism of yield compensation could provide insights into the management and phenotypic improvement of different crops (Ball et al., 2000; Liu et al., 2010). Ndiaga (2001) reported that some crops produced higher yields with closer spacing than with wider spacing. However, at very high plant density, the yield per hectare begins to decrease due to decrease in seed weight arising from competition for resources. On the other hand, low plant density has been reported in other crops to result in higher yields than closer spacing. This has been attributed to less competition amongst the plants for light, nutrients, water, and other resources. The minimum plant density at which there is higher or lower yield has not been explicitly demon- strated in the cowpea, and especially the newly developed varieties (Odesina, 2024). Ndiaga (2001) reported that most cowpea cultivars used in breeding programs are cultivated at low plant densities compared to the recommended row spacing, with remarkable impact on yield. Kamara et al. (2016) noted that the yield of cowpea in the Nigerian savannah was low despite the adop- tion of improved varieties. Results of field experiments have shown that the recommended density of 133,333 plants ha−1 adopted by farmers was not optimum for cowpea productiv- ity; it has, therefore, been suggested that smallholder farmers could increase grain and fodder yields at a plant density of 266,666 plants ha−1 (Kamara et al., 2016), even though this might not be applicable in all cases, as high population den- sity could result in decreased yield or yield components (Nur Arina et al., 2021). Yield components are yield-contributing attributes that sum up to the yield value per unit area (Kozak & Mądry, 2006). They are individual plant parts contributing to yield based on their number, size, and weight. Selection indices such as yield and one, or more, morphological charac- 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense NAMO ET AL. 3 of 13 ters, which contribute to yield formation, are considered to increase the inherent yielding ability of cowpea. Adewale et al. (2010) noted that yield improvement of cultivars has been achieved through careful selection of yield components. The evaluation of the yield components allows for clarifica- tion on how variations resulting from genetic, environmental, and management practices affect variation in crop yields. In breeding for increase in yield, breeders need a good knowl- edge of the nature and level of genetic factors that regulate characters that contribute to yield (Edematie et al., 2021). Yield components include pod length, pod width, number of pods, shelling percentage, number of seeds per pod, and aver- age seed weight (Odesina, 2024). Nwofia (2012) noted that pod length and pod width are important components in veg- etable cowpea, as they are known to influence pod yield and grain yield. In many countries, pod length is an important attribute that influences consumer acceptability of vegetable cowpea and is used as a selection index in the breeding pro- gram. A higher shelling percentage indicates that a greater proportion of the weight of pods is comprised of seeds, which directly translates to increased seed yield. This relationship is observed across various crops like maize and peanut (Keno et al., 2024). A higher shelling percentage means more seeds are produced for the same amount of pod weight, directly con- tributing to a higher seed yield. This is because more of the plant’s resources are allocated to seed production rather than pod development or other non-seed components. Shelling percentage is influenced by factors such as genotype, envi- ronment, and management practices like fertilizer application, planting density, and other agronomic practices (Keno et al., 2024). Grain yield in the cowpea has been reported to be positively correlated with pod length (Udensi et al., 2011; Manggoel et al., 2012). Kamara et al. (2016) reported that grain yield increased as the seed number per pod and 100-seed weight increased but that the effect of seed size on grain yield was not significant. The number of pods, seed number per pod, and 100-seed weight have been reported as major yield- contributing traits in the cowpea (Adewale et al., 2010; Brolmann & Stoffella, 1986; Odesina, 2024). These compo- nents are also affected by plant population and can operate in complex ways to affect yield compensation. Understand- ing the trends in the variation of yield components and the relationship among the components based on genetics, phys- iology, and development may be helpful in modifying the agronomic practices to optimize yield (Odesina, 2024). Harvest index has also been reported as a major factor that influences crop yield. It is defined as the total seed weight expressed as a percentage of the total plant weight or biolog- ical yield; or the ratio of dry seed weight to the total crop dry weight; or the ratio of economically important portion of the yield (e.g., grain) to the total biological yield; or the weight of the above-ground portion at the time of harvest Core Ideas ∙ Yield compensation defines the ability of a crop to increase productivity when plants grown under low plant density produce equal or higher yields when compared with those grown under optimum density. ∙ There is limited study on yield compensation mechanism in newly developed varieties of the cowpea. ∙ At 133,333 plants ha−1, semi-erect accessions (IT99K-573-1-1 and IT08K-150-27) and erect accessions (IT93K-452-1 and IT98K-205-8) of cowpea produced high yield in the Sudan savan- nah. ∙ Irrespective of plant density, the accessions IT93K-452-1-1, IT98K-205-8, IT99K-573-1- 1, and IT08K-150-27 could be considered for cultivation in the Northern Guinea Savannah agro-ecology. (Harper & Ogden, 1970; Kozlowski & Ziólko, 1988; Okelana & Adedipe, 1982). Harvest index is also defined as the frac- tion of the total dry matter that is partitioned to the harvestable plant parts or the ratio of yield to the above-ground biomass (Donald & Hamblin, 1976; Forbes & Watson, 1992). Harvest index is considered as an important selection index in crop breeding programs because it gives insight to yield efficiency and resource allocation potentials of crop varieties (Giridhar et al., 2020). Studies have shown the importance of plant population suitable for optimum yield in improved crop varieties. Seran and Brintha (2010) and Nur Arina et al. (2021) noted that excessive plant population might result in low yield due to overcrowding and competition for limited resources, and sug- gested a reduction in the number of seedlings in order to optimize yield. In soybean [Glycine max (L.) Merr], stud- ies have been carried out to determine if yield compensation resulting from low plant population could be achieved through increasing plant density, yield components (Ball et al., 2000), or the use of improved varieties (Pepper & Walker, 1988). Studies have been carried out in major crops such as sorghum [Sorghum bicolor (L.) Moench] (Ball et al., 2000) and black gram [Vigna mungo (L.) Hepper] (Biswas et al., 2002). Not much has been reported for the cowpea. Therefore, this study was aimed to investigate the mechanism of yield compensa- tion in the cowpea, with particular emphasis on responses of yield and yield components to plant density and environment in two agro-ecologies in northern Nigeria. 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 4 of 13 NAMO ET AL. T A B L E 1 Physico-chemical properties of the top soil used for the experiment. Soil properties Minjibir Shika Sand (%) 70.00 32.00 Clay (%) 21.00 23.00 Silt (%) 9.00 45.00 pH (H2O 1:1) 5.50 6.30 Organic carbon (%) 0.30 0.60 Total N (%) 0.02 0.05 Mehlich P (ppm) 9.00 12.80 Ca (cmolkg−1) 1.00 2.16 Mg (cmolkg−1) 0.25 0.83 K (cmolkg−1) 0.30 0.42 Na (cmolkg−1) 0.07 0.06 Source: International Institute of Tropical Agriculture (IITA) Analytical Services Laboratory (ASL), Ibadan. 2 MATERIALS AND METHODS 2.1 Experimental site The experiment was carried out in 2020 at two locations, namely the experimental station of the IITA, Shika, Zaria (11˚11′ N, 7˚38′ E; 686 m above sea level) in the northern Guinea Savannah, and the research farm of the IITA, Min- jibir (12˚42′ N, 8˚39′ E; 509 m above sea level) in the Sudan Savannah in Kano State, Nigeria. 2.2 Soil sampling and analysis Soil samples were collected from the topsoil from six different spots at the experimental site at a depth of 0–20 and 20–50 cm. These were composited and analyzed in the Analysis Services Laboratory of the IITA, Ibadan, Nigeria. The results of the soil analysis are as shown in Table 1. 2.3 Meteorological data Meteorological data were collected from the weather stations situated at the IITA experimental stations at Minjibir and Shika (Table 2). 2.4 Planting materials The six cowpea accessions used in this study were sourced from the Germplasm Unit of IITA, Kano station. The agro- nomic characteristics of the accessions are as shown in Table 3. T A B L E 2 Meteorological data collected in 2020 from Minjibir and Shika research stations. Location Weather reports Month Minjibir Shika Solar radiation (MJ/m2/day) August 15.76 15.22 September 17.24 19.02 October 18.41 22.10 November 18.12 20.12 December 18.37 19.31 Minimum relative humidity (%) August 54.38 70.70 September 48.02 70.99 October 26.28 44.19 November 12.38 23.93 December 9.03 18.37 Maximum relative humidity (%) August 94.96 98.66 September 95.53 98.62 October 87.42 92.03 November 72.48 64.60 December 66.97 51.12 Minimum temperature (˚C) August 22.00 19.79 September 22.41 19.76 October 20.74 18.91 November 16.47 16.52 December 11.47 15.78 Maximum temperature (˚C) August 29.74 26.50 September 31.91 29.61 October 34.66 32.02 November 31.49 31.49 December 32.70 32.56 Rainfall (mm) August 8.57 5.56 September 5.64 26.45 October 0.52 2.85 November 0.00 0.03 December 0.00 0.00 Source: International Institute of Tropical Agriculture, Minjibir and Shika sub- station. 2.5 Experimental layout, planting, and management The randomized complete block design in a split-plot arrange- ment with three replicates was used. The recommended optimum density of 133,333 plants ha−1, alongside three lower densities representing 75% (99,999 plants ha−1), 50% (66,666 plants ha−1), and 25% (33,333 plants ha−1) of the optimum level, were used in this study. The main plot con- sisted of four plant densities (33,333 plant ha−1; 66,666 plants ha−1; 99,999 plants ha−1 and 133,333 plants ha−1). The subplots consisted of six cowpea accessions, namely 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense NAMO ET AL. 5 of 13 T A B L E 3 Agronomic characteristics of cowpea accessions used in the experiment. Accession Original name National code Growth pattern Growth habit Maturity period SAMPEA-8 IT93K-452-1 NGVU-05-23 Determinate Erect Early maturing SAMPEA-14 IT99K-573-1-1 NGVU-11-30 Semi-determinate Semi-erect Medium maturing SAMPEA-11 IT89KD-288 NGVU-09-26 Indeterminate Prostrate Late maturing _ IT98K-205-8 _ Determinate Semi-erect Early maturing _ IT08K-150-27 _ Semi-determinate Erect Medium maturing _ DANILA _ Indeterminate Prostrate Late maturing Source: Seed Bank, International Institute of Tropical Agriculture, Kano Station. IT89KD-288, IT93K-452-1, IT99K-573-1-1, IT98K-205-8, IT08K-150-27, and DANILA. The field was disc-harrowed and ridged before planting. The subplots measured 3 × 4 m, consisting of four rows each with inter- and intra-row spacing of 75 and 20 cm, respec- tively. The seeds were cleaned to avoid mechanical mixtures and were treated with 250 g of Apron Star [Thiamethoxam (200 g/kg) + metalaxyl-M (200 g/kg) + difenoconazole (20 g/kg)] to protect them from soil-borne pathogens. Three seeds per hill were sown by hand on August 17 and August 21, 2020, at Minjibir and Shika locations, respectively. The seedlings were thinned down to one per hill at 2 weeks after sowing (2 WAS). A starter dose of NPK 15:15:15 was applied at 2 WAS at the rate of 14.25 kg ha−1. Cypermethrin [cyano- (3-phenoxyphenyl)methyl)3-(2,2-dichloroethenyl)-2,2- dimethycylopropane-1-carboxylate] was applied at the rate of 120 μL ha−1 to control insect pests. Ampligo (chlorantranilip- role [10%] + lambda-cyhalothrin [5%] Zc), manufactured by Syngenta Crop Protection AG, Switzerland, was applied at the rate of 100 mL ha−1, after the emergence of flowers, to control damage by the pod borer, Maruca (Maruca vitrata). 2.6 Phenotyping Field observation and data collection were commenced at 2 WAS using the two middle rows in each plot for sampling. The pods were harvested at 106 and 120 days after sowing at Shika and Minjibir locations, respectively. All the pods harvested from each plot were weighed. The weight was converted to the equivalent in kilograms per hectare before the statistical analysis. Shelling percentage is the ratio of seed weight to pod weight. It was computed as the ratio of the shelling weight to the pod weight and multiplied by 100 (Sakariyawo et al., 2017) as follows: Shelling percentage (%) = Shelling weight (kg) Pod weight (kg) × 100 A seed-counting machine (Dimo’s S-JR Automatic Seed Counter, Dimo’s Labtronics) was used to count 100 seeds from each plot. The seeds were weighed using the Camry ZE11 digital weighing balance. The biological yield was computed by weighing the biomass at harvest from each plot. The weight was extrapo- lated to the equivalent in kg ha−1 before the statistical analysis. Harvest index was computed as the ratio of grain yield to biological yield (Amanullah & Inamullah, 2016) as follows: Harvest index (%) = Grain yield ( kg ha−1 ) Biological yield ( kg ha−1 ) × 100 The pods were dried for 2 days after harvest and then threshed. The seeds were then dried in the midday sun for 4 h for 6 days. The seeds were weighed, and the weight was extrapolated to kg ha−1, using the following formula (Toungos et al., 2019): Grain yield ( kg ha−1 ) = Grain yield (kg) Plot size (m) × net row (m) × 10, 000 ( m2) Total grain yield was computed at 12% moisture content, which is the safe storage moisture content of the cowpea. 2.7 Data analysis Data collected from both locations were subjected to two- way analysis of variance tests separately and then combined, using the R Core Team (version 4.1.0; 2021). The plant density and cowpea accessions were considered as fac- tors in determining the mean square and the F-test. The means were separated using the Duncan’s new multiple- range test at 5% level of portability. Correlation coefficient was computed using the R-programming software (version 4.1.0) to determine the relationship between yield and yield components. 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 6 of 13 NAMO ET AL. T A B L E 4 Main effects of plant density and cowpea accession on pod yield and 100-seed weight at Minjibir and Shika. Treatments Pod yield (kg ha−1) 100-seed weight (g) Minjibir Shika Pooled Minjibir Shika Pooled Density (plants ha−1) 33,333 1883.98b 527.87b 1205.93b 16.22a 19.22a 18.15a 66,666 1888.52b 579.17b 1233.84b 16.32a 18.92a 17.63a 99,999 2136.11a 750.18a 1443.15a 16.81a 18.60a 17.67a 133,333 2156.39a 781.67a 1469.03a 16.97a 18.72a 17.78a Accession IT89KD-288 1834.72c 724.17a 1279.44b 17.34b 20.57b 18.96b IT93K-452-1 1870.14bc 706.94a 1288.54b 15.03c 15.87c 15.45c IT99K-573-1-1 2000.14abc 700.97a 1350.56ab 18.08b 19.61b 18.85b IT98K-205-8 2035.69abc 695.83a 1365.76ab 15.14b 16.25c 15.70c IT08K-150-27 2118.75ab 807.49a 1463.13a 20.10a 24.45a 22.28a DANILA 2238.06a 322.91b 1280.49b 14.70c 16.50c 15.60c Level of significance DEN *** *** *** ns ns ns ACS *** *** * *** *** *** EVR *** *** Interactions DEN × ACS ns ns ns ns ns ns DEN × EVR ns ns ACS × EVR ** ns ACS × DEN × EVR *** ns CV (%) 15.20 27.32 18.86 13.8 17.8 17.2 Note: Means followed by the same letter(s) within the same column are not significantly different at 5% level of probability. Abbreviations: ACS, accession; CV, coefficient of variability; DEN, density; EVR, environment; ns, not significant. ***p < 0.001, **p < 0.01, *p < 0.05. 3 RESULTS 3.1 Pod yield Table 4 shows the main effects of plant density and acces- sion on pod yield and 100-seed weight at Minjibir and Shika locations. At the Minjibir location, pod yield increased with increasing plant density, ranging from 1883.98 kg ha−1 at 33,333 plants ha−1 to 2156.39 kg ha−1 at 133,333 plants ha−1. Pod yield was highest in the accession DANILA (2238.06 ha−1) and lowest in the accession IT89KD-288 (1834.72 kg ha−1). At the Shika location, pod yield also increased with increasing plant density, from 527.87 kg ha−1 at 33,333 plants ha−1 to 781.67 kg ha−1 at 133,333 plants ha−1. Pod yield was highest in the accession IT08K-150-27 (807.49 kg ha−1) and lowest in the accession DANILA with a pod yield value of 322.91 kg ha−1 (Table 4). The result of the combined analysis showed that pod yield also increased as plant density increased, ranging from 1205.93 kg ha−1 at 33,333 plants ha−1 to 1469.03 kg ha−1 at 133,333 plants ha−1. The highest pod yield of 1463.13 kg ha−1 was observed in the T A B L E 5 Interaction effects of accession and environment on pod yield (kg ha−1). Accession Minjibir Shika IT89KD-288 1834.72c 724.17d IT93K-452-1 1870.14c 706.94d IT99K-573-1-1 2000.14bc 700.97d IT98K-205-8 2035.69abc 695.83d IT08K-150-27 2118.75ab 807.50d DANILA 2238.06a 322.92e LSD (0.05) 204.59 Note: Means followed by the same the letter(s) within the same column are not significantly different at 5% level of probability. Abbreviation: LSD, least significant difference. accession IT08K-150-27 while the lowest (1279.44 kg ha−1) was observed in the accession IT89KD-288. The pod yield was generally higher at Minjibir than at the Shika location (Table 4). The interaction of accession and environment on pod yield was significant (Table 5). The highest pod yield of 2238.06 kg 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense NAMO ET AL. 7 of 13 ha−1 was observed in the accession DANILA when it was planted at the Minjibir location but lowest in the same acces- sion DANILA (322.92 kg ha−1) when it was planted at the Shika location (Table 5). 3.2 100-seed weight At the Minjibir location, the 100-seed weight was statistically similar across the plant densities. The 100-seed weight was significantly higher in the accession IT08K-150-27 (20.10 g) than in the other accessions (Table 4). At the Shika location, the 100-seed weight was statistically similar across the plant densities. The 100-seed weight was also significantly higher (p < 0.001) in the accession IT08K-150-27 (24.45 g) than in the other accessions. Unlike the pod yield, the 100-seed weight was generally higher at Shika than at the Minjibir loca- tion (Table 4). The results of the combined analysis showed that the 100-seed weight was similar across the plant densities. The 100-seed weight was highest in the accession IT08K- 150-27 (22.28 g) and lowest in the accession IT93K-452-1 (15.45 g). 3.3 Shelling percentage Table 6 shows the main effects of plant density and acces- sion on shelling percentage and biological yield at Minjibir and Shika locations. At the Minjibir location, shelling per- centage was statistically similar across the plant densities. It was significantly higher in the accession IT08K-150-27 (35.94%) than in the other accessions. At the Shika loca- tion, the shelling percentage was also similar across the plant densities but differed significantly amongst the cowpea accessions, with the highest value (43.27%) observed in the accession DANILA and the lowest (36.74%) in the acces- sion IT99K-573-1-1. The shelling percentage was generally higher at Shika than at the Minjibir location. The results of the combined analysis showed that shelling percentage was statistically similar irrespective of the plant density or cowpea accession (Table 6). 3.4 Biological yield At the Minjibir location, the biological yield was high- est (2925.46 kg ha−1) at 133,333 plants ha−1 and lowest (1832.87 kg ha−1) at 33,333 plants ha−1. The biological yield ranged from 3162.50 kg ha−1 in the accession IT08K-150-27 to 1898.61 kg ha−1 in the accession DANILA. At the Shika location, the biological yield varied from 497.22 kg ha−1 at 33,333 plants ha−1 to 773.15 kg ha−1 at 133,333 plants ha−1. The biological yield was highest in the accession IT08K- 150-27 (852.77 kg ha−1) and lowest (272.22 kg ha−1) in the accession DANILA at the Shika location. The biological yield was generally higher at Minjibir than at the Shika location. The results of the combined analysis showed that the biologi- cal yield differed significantly with plant density and cowpea accession (Table 6). 3.5 Harvest index At the Minjibir location, a significantly lower harvest index (54%) was observed at 133,333 plants ha−1 than at the other plant densities (Table 7). The harvest index was highest in the accession DANILA and lowest in the accession IT08K-150- 27 with values of 92% and 47%, respectively (Table 7). At the Shika location, the lowest harvest index of 58% was observed at 133,333 plants ha−1 compared to the other planting densi- ties. Harvest index was highest in the accession IT89KD-288 (94%) and lowest in the accession IT08K-150-27 (61%). Results of the combined analysis showed that harvest index decreased with increasing plant density. The highest harvest index was observed in the accession DANILA (81%), while the lowest (46%) was observed in the accession IT08K-150- 27 (Table 7). Harvest index was generally higher at Shika than at the Minjibir location. 3.6 Total grain yield At the Minjibir location, the total grain yield increased with increasing plant density from 1323.43 kg ha−1 at 33,333 plant ha−1 to 1494.8 kg ha−1 at 133,333 plant ha−1. Total grain yield was highest in the accession DANILA and lowest in the accession IT89KD-288 with yield values of 1597.36 and 1270 kg ha−1, respectively (Table 7). Like Minjibir, the total grain yield at the Shika location increased as the plant den- sity increased. Total grain yield was highest in the accession IT08K-150-27 and lowest in the accession DANILA with val- ues of 512.92 and 174.4 kg ha−1, respectively. The results of the combined analysis showed that total grain yield increased with increasing plant density, ranging from 819.6 to 972.2 kg ha−1 at 33,333 and 133,333 plants ha−1, respectively. Total grain yield was generally higher at Minjibir than at the Shika location (Table 7). The interaction of plant density and accession on total grain yield at the Minjibir location was significant. The highest total grain yield of 1793.3 kg ha−1 was observed in the accession DANILA at 99,999 plants ha−1, while the lowest (1058.9 kg ha−1) was observed in the accession IT08K-150-27 at 66,666 plants ha−1 (Table 8). Also, the interaction of accession and environment on total grain yield was significant (Table 9). The highest total grain yield of 1597.36 kg ha−1 was observed when the accession DANILA was planted at the Minjibir 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 8 of 13 NAMO ET AL. T A B L E 6 Main effects of plant density and accession on shelling percentage and biological yield at Minjibir and Shika. Shelling percentage (%) Biological yield (kg ha−1) Treatments Minjibir Shika Pooled Minjibir Shika Pooled Density (plants ha−1) 33,333 32.98a 39.53a 36.30a 1832.87c 497.22b 1165.05c 66,666 33.96a 39.64a 36.93a 2235.09bc 531.48b 1383.29b 99,999 33.30a 40.08a 36.70a 2663.42ab 648.15ab 1655.79a 133,333 34.96a 41.15a 37.42a 2925.46a 773.15a 1849.31a Accession IT89KD-288 33.76b 39.15ab 36.45a 2488.19b 525.00c 1506.60b IT93K-452-1 32.17d 42.47a 37.32a 2386.11b 655.56bc 1520.83b IT99K-573-1-1 33.93b 36.74b 35.34a 2384.72b 759.72b 1572.22b IT98K-205-8 33.29bc 40.37ab 36.83a 2165.14b 609.72bc 1387.43b IT08K-150-27 35.94a 38.59ab 37.27a 3162.50a 852.77a 2007.64a DANILA 32.35 cd 43.27a 37.81a 1898.61b 272.22d 1085.42c Level of significance DEN ns ns ns *** *** *** ACS *** * ns *** *** *** EVR *** *** Interactions DEN × ACS ns ns ns ns ns ns DEN × EVR ns ns ACS × EVR ns ns ACS × DEN × EVR ns ns CV (%) 6.13 14.1 14.5 32.3 52.9 71.52 Note: Means followed by the same letter(s) within the same column are not significantly different at 5% level of probability. Abbreviations: ACS, accession; CV, coefficient of variability; DEN, density; EVR, environment; ns, not significant. ***p < 0.001, **p < 0.01, *p < 0.05. location. The lowest grain yield of 174.4 kg ha−1 was observed when the same accession, DANILA, was planted at the Shika location. 3.7 Correlation of yield components with total grain yield At Minjibir and Shika locations, the pod yield was posi- tively correlated with total grain yield. A similar trend was observed when the data were combined. Shelling percentage was negatively correlated with total grain yield at Minjibir and Shika locations as well as when the data were combined. The 100-seed weight was positively and significantly corre- lated with total grain yield at the Shika location (Table 10). Biological yield and total grain yield were positively and significantly correlated at Shika and in the combined anal- ysis. The harvest index was positively and significantly correlated with total grain yield at the Minjibir location (Table 10). 4 DISCUSSION AND CONCLUSION The differences in pod yield and 100-seed weight may be attributed to genotype and environment. Zhao et al. (2017) and Bakal et al. (2020) reported that pod yield increased with decreasing plant density; in other words, the number of plants that compete for available nutrients and other resources decreased as the plant density decreased. In this study, how- ever, pod yield increased with increasing plant density. These differences could have been due to genotypic differences, growth habit, and architectural types. Bakal et al. (2020) observed that cowpea varieties with prostrate growth habit produced pods, which were not as heavy as those produced by the erect or semi-erect types. This could be due to mutual shading, for which reason photosynthesis no longer exceeds respiration in older leaves, which then cease to be net pro- ducers of dry matter (Namo, 2005). Therefore, the assimilates available for pod development and grain-filling in prostrate varieties are reduced. The number and size of the pods have been reported to contribute to the final grain yield (Kamara 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense NAMO ET AL. 9 of 13 T A B L E 7 Main effects of plant density and accession on harvest index and grain yield at Minjibir and Shika. Harvest index (%) Grain yield (kg ha−1) Treatments Minjibir Shika Pooled Minjibir Shika Pooled Density (plants ha−1) 33,333 79.00a 71.00a 65.00a 1323.43b 315.83b 819.63b 66,666 60.00b 75.00a 56.00b 1297.13b 342.13b 819.63b 99,999 60.00b 77.00a 55.00b 1494.26a 438.52a 966.39a 133,333 54.00b 58.00b 50.00b 1494.82a 449.63a 972.22a Accession IT89KD-288 55.00bc 94.00a 50.00bc 1270.00b 424.17b 847.08a IT93K-452-1 57.00bc 62.00b 51.00b 1338.89b 375.42b 857.15a IT99K-573-1-1 61.00b 65.00b 53.00b 1380.69b 438.47ab 909.58a IT98K-205-8 68.00b 68.00b 57.00b 1426.39ab 393.75b 910.07a IT08K-150-27 47.00c 61.00b 46.00c 1401.11b 512.92a 957.01a DANILA 92.00a 70.00b 81.00a 1597.36a 174.44c 885.90a Level of significance DEN *** ns ** * *** *** ACS *** * *** * *** ns EVR *** *** Interactions DEN × ACS ns ns ns * ns ns DEN × EVR ns ns ACS × EVR ns *** ACS × DEN × EVR ns ns CV (%) 36.5 36.6 37.0 20.7 42.5 62.7 Note: Means followed by the same letter(s) within the same column are not significantly different at 5% level of probability. Abbreviations: ACS, accession; CV, coefficient of variability; DEN, density; EVR, environment; ns, not significant. ***p < 0.001, **p < 0.01, *p < 0.05. T A B L E 8 Interaction effects of accession and plant density on grain yield (kg ha−1) at Minjibir. Accession Density (plants ha−1) 33,333 66,666 99,999 133,333 IT89KD-288 1323.9cdefg 1245.0defg 1259.4defg 1251.7defg IT93K-452-1 1301.7cdefg 1186.7efg 1587.2abcd 1280.0cdefg IT99K-573-1-1 1434.4abcdefg 1115.0fg 1372.8bcdefg 1600.6abcd IT98K-205-8 1100.0 g 1513.3abcde 1377.2bcdefg 1715.0ab IT08K-150-27 1340.0bcdefg 1058.9 g 1575.6abcd 1630.0abcd DANILA 1440.6abcdefg 1663.9abc 1793.3a 1491.7abcdef LSD (0.05) 388.28 Note: Means followed by the same the letter(s) within the same column are not significantly different at 5% level of probability. et al., 2016). Medium-maturing varieties of cowpea have been reported to produce a higher number of pods and pod yield (Kamara et al., 2016). Differences in pod yield could also be attributed to the phenological differences in the accessions studied. Late-maturing accessions had a longer period of pro- duction and partitioning of photosynthates from the source to the sink (Ahmed & Abdelrhim, 2010; Kamara et al., 2016). Shelling percentage is believed to have a positive and sig- nificant effect on seed yield. A higher shelling percentage indicates that a greater proportion of pod weight is comprised of seeds, which directly translates to increased seed yield. This relationship has been reported in different crops like maize [Zea mays (L.)] and peanut [Arachis hypogaea (L.)] (Keno et al., 2024). In this study, shelling percentage was generally 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 10 of 13 NAMO ET AL. T A B L E 9 Interaction effects of accession and environment on grain yield (kg ha−1). Location Accession Minjibir Shika IT89KD-288 1270.00c 424.17de IT93K-452-1 1338.89bc 375.42e IT99K-573-1-1 1380.69bc 438.47de IT98K-205-8 1426.38b 393.75e IT08K-150-27 1401.11b 512.92d DANILA 1597.36a 174.44f LSD (0.05) 117.88 Note: Means followed by the same the letter(s) within the same column are not significantly different at 5% level of probability. T A B L E 1 0 Correlation coefficients of yield components with total grain yield at Minjibir and Shika. Traits Minjibir Shika Pooled Pod yield 0.267* 0.917** 0.990** Shelling percentage −0.462** −0.232* −0.610** 100-seed weight 0.002ns 0.405** 0.052ns Biological yield 0.199ns 0.755** 0.810** Harvest index 0.407** −0.007ns −0.029ns Abbreviation: ns, not significant. **p ≤ 0.001; *p ≤ 0.05; higher at the Minjibir than at the Shika location, and this was reflected in the total grain yield, which was also higher at the Minjibir than at the Shika location. Bakal et al. (2020) reported that an increase in plant density resulted in increased seed yield. The result of the correlation analysis showed that shelling percentage was negatively correlated with grain yield at both locations and in the combined analysis. This implies that accessions with higher shelling percentage had lower grain yields and vice versa. This is contrary to the find- ings reported for other crops, indicating that more of the resources produced by the cowpea accessions (especially the indeterminate types) used in this study could have been allocated to pod development or other non-seed compo- nents. The interaction of accession and environment on pod yield in this study showed that the cowpea acces- sions responded differently to plant density in the different environments. A proportion of the total dry matter produced by a crop is partitioned to the sink (the grains in legumes and corn); in other words, yield is a function of the total dry mat- ter produced by a crop. Therefore, the higher the biological yield, the higher the amount of photosynthates available to be translocated to the grain during the grain-filling period. Bakal et al. (2020) noted that biological yield increased with increasing plant density. Kamara et al. (2014) reported a similar finding in the soybean. In this study, biolog- ical yield was positively correlated with the total grain yield. Harvest index is believed to be influenced by many fac- tors among which is the partitioning of assimilates within a plant. The dry matter partitioning is in turn determined by the change from vegetative to the reproductive growth in most crops. In indeterminate flowering crops like soy- bean, tomato, and cotton, a proportion of dry matter produced after flowering is used to produce new leaves rather than to fill reproductive sinks (Ashley, 1972; Ismail & Khalifa, 1987). Spitters (1983) observed that yield/biomass ratio did not remain constant but varied with plant density and that harvest index decreased as plant density increased. This was evident in this study, indicating that harvest index decreased as plant density increased, especially in indeterminate acces- sions where the assimilates produced were partitioned to newly formed leaves. In this study, the planting of cowpea accessions at lower densities resulted in higher harvest index at both Minjibir and Shika locations, which is in line with the findings of Giridhar et al. (2020), who suggested a lower plant density to attain a higher harvest index in the cowpea. The highest harvest index observed in the cowpea accessions IT89KD-288 and DANILA suggests that the rate of translocation of assimi- lates from the source to the sink was more efficient in the prostrate accessions. Samarrai et al. (1983) noted that har- vest index could be considered as a promising yield-selection criterion. Siddique et al. (1987) suggested that kernel weight should be used as a selection criterion for early identification of productive mutants in wheat (Triticum aestivum L.). Grain yield is a function of several factors, while the biological yield is a function of another set of factors. Both the grain yield and the biological yield, which determine the harvest index, may vary with environment, management practices, pests, dis- eases, and ergonomic conditions (Ismail, 1993). This has been demonstrated in the present study. DeLougherty and Crook- ston (1979) identified the population density as a major factor influencing the harvest index. The increase in total grain yield with increasing plant den- sity in this study is in line with the findings of Biswas et al. (2002) in the black gram, Kamara et al. (2014) in soybean, and Bakal et al. (2020) in the peanut. Kamara et al. (2016) reported that the photosynthetic rate increased with increas- ing plant density and the intercepted photosynthetically active radiation (IPAR). Kamara et al. (2016) also noted that increas- ing plant density resulted in increasing biological efficiency and the total grain yield. The same trend was observed in this study. The interactions of accession and environment as well as accession and plant density on total grain yield indicate that the cowpea accessions responded differently to the plant 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense NAMO ET AL. 11 of 13 density at the two locations. Chen et al. (2017) and Iseki et al. (2023) also noted that cowpea accessions responded differently to plant densities at different locations. The yield differentials observed at Minjibir and Shika locations could be partly attributed to the incidence of leaf scab disease (Sphaceloma spp.) at the Shika location, which affected the photosynthetic leaf area. This study was aimed at identifying the mechanism of yield compensation in some accessions of cowpea at differ- ent environments. The results of the study have shown that plant density and environment affect yield and yield com- ponents in the cowpea. The study showed that for optimum grain yield in the cowpea, different accessions should be cul- tivated at different locations and at different plant densities. At the Minjibir location, the erect accessions (IT93K-452-1 and IT98K-205-8) and semi-erect accessions (IT99K-573-1- 1 and IT08K-150-27) could be cultivated at 133,333 plants ha−1. The prostrate accessions (IT89KD-288 and DANILA) could be cultivated at 99,999 plants ha−1. At the Shika loca- tion, the erect and semi-erect accessions performed better than the prostrate types irrespective of the plant density. Therefore, the accessions IT93K-452-1-1, IT98K-205-8, IT99K-573-1- 1, and IT08K-150-27 could be considered for cultivation at the Shika location. AU T H O R C O N T R I B U T I O N S Timothy Aku Otsanjugu Namo: Conceptualization; funding acquisition; investigation; supervision; validation; writing— original draft; writing—review and editing. Gideon Oluwas- eye Oyebode: Data curation; project administration. Ife- oluwa Simeon Odesina: Data curation; formal analysis; methodology; project administration; software; visualization; writing—review and editing. Patrick Obia Ongom: Data curation; formal analysis; methodology; software; visualiza- tion. Ousmane Boukar: Conceptualization; investigation; supervision; validation. Tersur Theophilus Akpensuen: Funding acquisition; project administration. Grace Obaiya Utoblo: Formal analysis; software. A C K N O W L E D G M E N T S The authors acknowledge the financial assistance provided for this project by the Tertiary Education Trust Fund (TET- Fund), a funding agency of the Federal Republic of Nigeria, through the University of Jos, Jos, Nigeria. The authors also acknowledge the in-kind support given by the Interna- tional Institute of Tropical Agriculture, Kano Sub-station, Nigeria. Support for writing this paper was received by the Natural Environment Research Council (NERC) under the Research program ‘AgZero+: Towards sustainable, climate- neutral farming’ (NE/W005060/1) at Rothamsted. AgZero+ is an initiative jointly supported by NERC and the Biotech- nology and Biological Sciences Research Council (BBSRC) of the United Kingdom. C O N F L I C T O F I N T E R E S T S T AT E M E N T The authors declare no conflicts of interest. O R C I D Timothy Aku Otsanjugu Namo https://orcid.org/0000- 0002-4801-0182 R E F E R E N C E S Aboki, E., & Yuguda, R. (2013). Determinants of profitability in cow- pea production in Takum Local Government Area of Taraba state, Nigeria. Journal of Agricultural Sciences, 4, 33–37. https://doi.org/ 10.1080/09766898.2013.11884699 Adewale, B. D., Okonji, C., Oyekanmi, A. A., Akintobi, D. A. C., & Aremu, C. O. (2010). Genotypic variability and stability of some grain yield components of Cowpea. African Journal of Agricultural Research, 5(9), 874–880. Ahmed, M. E. N., & Abdelrhim, A. J. (2010). Effects of plant density and cultivar on growth and yield of cowpea [Vigna unguiculata (L.) Walp]. Australian Journal of Basic & Applied Science, 4(8), 3148– 3153. Amanullah, & Inamullah (2016). Dry matter partitioning and harvest index differ in rice genotypes with variable rates of phosphorus and zinc nutrition. Rice Science, 23(2), 78–87. https://doi.org/10.1016/j. rsci.2015.09.006 Ashley, D. A. (1972). C-labelled photosynthate translocation and utiliza- tion in cotton plants. Crop Science, 12(1), 69–74. https://doi.org/10. 2135/cropsci1972.0011183İ001200010023x Bakal, H., Kenetli, A., & Arioglu, H. (2020). The effect of plant den- sity on pod yield and some agronomic characteristics of different growth-type peanut varieties (Arachis hypogaea L.) grown as a main crop. Field Crops Research, 25(1), 92–99. https://doi.org/10.17557/ tjfc.748671 Ball, R. A., Purcell, L. C., & Vories, E. D. (2000). Short-season soybean yield compensation in response to population and water regime. Crop Science, 40(4), 1070–1078. https://doi.org/10.2135/ cropsci2000.4041070x Biswas, D. K., Biswas, D. K., Hauge, M. M., Hamid, A., Ahmed, J. U., & Rahman, M. A. (2002). Influence of plant population density on growth and yield of two black gram varieties. Journal of Agronomy, 1(2), 83–85. https://doi.org/10.3923/ja.2002.83.85 Boukar, O., Fatokun, C. A., Roberts, P. A., Abberton, M., Huynh, B. L., Close, T. J., Kyei-Boahen, S., Higgins, T. J. V., & Ehlers, J. D. (2015). Cowpea. In A. M. De Ron (Ed.), Grain legumes (Vol. 10, pp. 219–250). Springer. https://doi.org/10.1007/978-1-4939-2797-57 Boukar, O., Togola, A., Chamarthi, S., Belko, N., Ishikawa, H., Suzuki, K., & Fatokun, C. (2019). Cowpea [Vigna unguiculata (L.) Walp] breeding. In J. Al-Khayri, S. Jain, & D. Johnson (Eds.), Advances in plant breeding strategies (pp. 201–243). Springer. https://doi.org/10. 1007/978-3-030-23400-3 Brolmann, J. B., & Stoffella, P. J. (1986). Differences in yield stability among cowpea (Vigna unguiculata) cultivars. Soil and Crop Science Society of Florida Proceedings, 45, 118–120. Chen, K., Camberato, J. J., & Vyn, T. J. (2017). Maize grain yield and kernel component relationships to morphophysiological traits in commercial hybrids separated by four decades. Crop Science, 57(3), 1641–1657. https://doi.org/10.2135/cropsci2016.06.0540 DeLougherty, R. L., & Crookston, R. K. (1979). Harvest index of corn as affected by population density, maturity rating and 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://orcid.org/0000-0002-4801-0182 https://orcid.org/0000-0002-4801-0182 https://orcid.org/0000-0002-4801-0182 https://doi.org/10.1080/09766898.2013.11884699 https://doi.org/10.1080/09766898.2013.11884699 https://doi.org/10.1016/j.rsci.2015.09.006 https://doi.org/10.1016/j.rsci.2015.09.006 https://doi.org/10.2135/cropsci1972.0011183x001200010023x https://doi.org/10.2135/cropsci1972.0011183x001200010023x https://doi.org/10.17557/tjfc.748671 https://doi.org/10.17557/tjfc.748671 https://doi.org/10.2135/cropsci2000.4041070x https://doi.org/10.2135/cropsci2000.4041070x https://doi.org/10.3923/ja.2002.83.85 https://doi.org/10.1007/978-1-4939-2797-57 https://doi.org/10.1007/978-3-030-23400-3 https://doi.org/10.1007/978-3-030-23400-3 https://doi.org/10.2135/cropsci2016.06.0540 12 of 13 NAMO ET AL. environment. Agronomy Journal, 71(4), 577–580. https://doi.org/10. 2134/agronj1979.00021962007100040014x Donald, C. M., & Hamblin, J. (1976). The biological yield and harvest index of cereals as agronomic and plant breeding criteria. In Advances in agronomy (Vol 28, pp. 361–405). Elsevier. https://doi.org/10.1016/ S0065-2113(08)60559-3 Edematie, V. E., Fatokun, C., Boukar, O., Adetimirin, V. O., & Kumar, L. (2021). Inheritance of pod length and other yield components in two cowpea and yardlong bean crosses. Agronomy, 11(4), 682. https://doi. org/10.3390/agronomy11040682 Food and Agriculture Organization. (2020). FAOSTAT statistical database. https://www.fao.org/faostat/en/#data/QCL Forbes, J. C., & Watson, D. (1992). Plants in agriculture. Cambridge University Press. Gerrano, A. S., Adebola, P. O., Jansen Van Rensburg, W. S., & Venter, S. L. (2015). Genetic variability and heritability estimates of nutri- tional composition in the leaves of selected cowpea genotypes [Vigna unguiculata (L.) Walp.]. HortScience, 50(10), 1435–1440. https://doi. org/10.21273/HORTSCI.50.10.1435 Gerrano, A. S., Jansen van Rensburg, W. S., & Adebola, P. O. (2017). Preliminary evaluation of seed and germination traits in cowpea (Vigna unguiculata) genotypes. South African Journal of Plant and Soil, 34(5), 399–402. https://doi.org/10.1080/02571862.2017. 1317849 Giridhar, K., Raju, P. S., Pushpalatha, G., & Patra, C. (2020). Effect of plant density on yield parameters of cowpea (Vigna unguiculata L.). International Journal of Chemical Studies, 8(4), 1732–1735. https:// doi.org/10.22271/chemi.2020.v8.i4f.10090 Harper, J. L., & Ogden, J. (1970). The reproductive strategy of higher plants: I. The concept of strategy with special reference to Senecio vul- garis L. The Journal of Ecology, 58(3), 681. https://doi.org/10.2307/ 2258529 Iseki, K., Ikazaki, K., & Batieno, B. J. (2023). Heterogeneity effects of plant density and fertilizer application on cowpea grain yield in soil types with different physicochemical characteristics. Field Crops Research, 292, 108825. https://doi.org/10.1016/j.fcr.2023.108825 Ismail, A. M. A. (1993). Irrigation, planting date and intra-row spac- ing of soybean grown under dry farming systems. Qatar University of Science Journal, 13(2), 253–263. Ismail, A. M. A., & Khalifa, F. M. (1987). Irrigation, planting date and intra-row spacing of soybean grown under dry farming systems. Qatar University of Science Bulletin, 7, 133–150. Kamara, A. Y., Ewansiha, S. U., Boahen, S., & Tofa, A. I. (2014). Agronomic response of soybean varieties to plant population in the guinea savannas of Nigeria. Agronomy Journal, 106(3), 1051–1059. https://doi.org/10.2134/agronj13.0435 Kamara, A. Y., Tofa, A. I., Kyei-Boahen, S., Solomon, R., Ajeigbe, H. A., & Kamai, N. (2016). Effect of plant density on the performance of cowpea in Nigerian savannas. Experimental Agriculture, 54(1), 120– 132. https://doi.org/10.1017/s0014479716000715 Kebede, E. (2020). Grain legumes production and productivity in Ethiopian smallholder agricultural system, contribution to livelihoods and the way forward. Cogent Food & Agriculture, 6(1), 1722353. https://doi.org/10.1080/23311932.2020.1722353 Kebede, E., & Bekeko, Z. (2020). Expounding the production and impor- tance of cowpea [Vigna unguiculata (L.) Walp.] in Ethiopia. Cogent Food & Agriculture, 6(1), 21–40. https://doi.org/10.1080/23311932. 2020.1769805 Keno, T., Mace, E., Godwin, I., Jordan, D., & Kelly, A. (2024). The use of fixed shelling percentage biases genotype selection in hybrid maize multi-environment yield trials. Field Crops Research, 315(2024), 109437. https://doi.org/10.1016/j.fcr.2024.109437 Kozak, M., & Mądry, W. (2006). Note on yield component analysis. Cereal Research Communications, 34(2–3), 933–940. https://doi.org/ 10.1556/CRC.34.2006.2-3.222 Kozlowski, J., & Ziólko, M. (1988). Gradual transition from vegetative to reproductive growth is optimal when the maximum rate of repro- ductive growth is limited. Theoretical Population Biology, 34(2), 118–129. https://doi.org/10.1016/0040-5809(88)90037-8 Liu, X. B., Liu, B., Wang, C., Jin, J., Herbert, S. J., & Hashemi, M. (2010). Responses of soybean yield and yield components to light enrichment and planting density. International Journal of Plant Production, 4(1), 1–10. Liu, X., Jin, J., Wang, G. H., & Herbert, S. J. (2008). Soybean yield physi- ology and development of high-yielding practices in Northeast China. Field Crops Research, 105(3), 157–171. https://doi.org/10.1016/j.fcr. 2007.09.003 Manggoel, W., Uguru, M. I., Ndam, O. N., & Dasbak, M. A. (2012). Genetic variability, correlation and path coefficient analysis of some yield components of ten cowpea [Vigna unguiculata (L.) Walp] acces- sions. Journal of Plant Breeding and Crop Science, 4(5), 80–86. https://doi.org/10.5897/JPBCS12.007 Molosiwa, O. O., Gwafila, C., Makore, J., & Chite, S. M. (2016). Pheno- typic variation in cowpea [Vigna unguiculata (L.) Walp] germplasm collection from Botswana. International Journal of Biodiversity Conservation, 8, 153–163. Namo, O. A. T. (2005). Screening for source-sink potential in some sweet potato [Ipomoea batatas (L.) Lam.] lines in Jos-Plateau, Nige- ria [Ph.D. Thesis, University of Jos]. Lambert Academic Publishing, Omniscriptum GmbH Co. KG. Ndiaga, C. (2001). Genotype x row spacing and environment interaction of cowpea in semi-arid zones. African Crop Science Journal, 9(2), 339–368. https://doi.org/10.4314/acsj.v9i2.27606 Nur Arina, M. M. Y., Surdiana, S., Mohd Fauzi, R., & Zulkefly, S. (2021). Radiation dynamics on crop productivity in different cropping systems. International Journal of Agronomy, 2021, Article 4570616. https://doi.org/10.1155/2021/4570616 Nwofia, G. E. (2012). Yield and yield components in vegetable cowpea on an Ultisol. African Journal of Agricultural Research, 7(28), 4097– 4103. https://doi.org/10.5897/AJAR12.402 Odesina, I. S. (2024). Mechanism of yield compensation in some accessions of cowpea grown at different plant densities and envi- ronments [Unpublished M.Sc. Dissertation]. Department of Plant Science and Biotechnology, University of Jos. https://hdl.handle.net/ 10568/173419 Okelana, M. A. O., & Adedipe, N. O. (1982). Effects of gibberellic acid, benzyl amine and 2-chloroethylphosphonic acid (CEPA) on growth and fruit abscission in the cowpea (Vigna unguiculata L.). Annals of Botany, 49, 485–491. https://doi.org/10.1093/oxfordjournals.aob. a086273 Omoigui, L., Kamara, A. Y., Ekeleme, F., & Aliyu, K. T. (2020). Guide to cowpea production in Northern Nigeria. IITA. Pepper, G. E., & Walker, J. T. (1988). Yield Compensation for stand defi- ciencies by determinate and indeterminate growth habit in soybean. Agronomy Journal, 80(1), 1–4. https://doi.org/10.2134/agronj1988. 00021962008000010001x 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://doi.org/10.2134/agronj1979.00021962007100040014x https://doi.org/10.2134/agronj1979.00021962007100040014x https://doi.org/10.1016/S0065-2113(08)60559-3 https://doi.org/10.1016/S0065-2113(08)60559-3 https://doi.org/10.3390/agronomy11040682 https://doi.org/10.3390/agronomy11040682 https://www.fao.org/faostat/en/#data/QCL https://doi.org/10.21273/HORTSCI.50.10.1435 https://doi.org/10.21273/HORTSCI.50.10.1435 https://doi.org/10.1080/02571862.2017.1317849 https://doi.org/10.1080/02571862.2017.1317849 https://doi.org/10.22271/chemi.2020.v8.i4f.10090 https://doi.org/10.22271/chemi.2020.v8.i4f.10090 https://doi.org/10.2307/2258529 https://doi.org/10.2307/2258529 https://doi.org/10.1016/j.fcr.2023.108825 https://doi.org/10.2134/agronj13.0435 https://doi.org/10.1017/s0014479716000715 https://doi.org/10.1080/23311932.2020.1722353 https://doi.org/10.1080/23311932.2020.1769805 https://doi.org/10.1080/23311932.2020.1769805 https://doi.org/10.1016/j.fcr.2024.109437 https://doi.org/10.1556/CRC.34.2006.2-3.222 https://doi.org/10.1556/CRC.34.2006.2-3.222 https://doi.org/10.1016/0040-5809(88)90037-8 https://doi.org/10.1016/j.fcr.2007.09.003 https://doi.org/10.1016/j.fcr.2007.09.003 https://doi.org/10.5897/JPBCS12.007 https://doi.org/10.4314/acsj.v9i2.27606 https://doi.org/10.1155/2021/4570616 https://doi.org/10.5897/AJAR12.402 https://hdl.handle.net/10568/173419 https://hdl.handle.net/10568/173419 https://doi.org/10.1093/oxfordjournals.aob.a086273 https://doi.org/10.1093/oxfordjournals.aob.a086273 https://doi.org/10.2134/agronj1988.00021962008000010001x https://doi.org/10.2134/agronj1988.00021962008000010001x NAMO ET AL. 13 of 13 R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R- project.org/ Sakariyawo, O. S., Soremi, P. A. S., Okeleye, K. A., & Aderibigbe, S. G. (2017). Variation in the performance of contrasting maturity class of cowpea cultivars (Vigna unguiculata L. Walp) in the derived Savanna. Agro-Science, 15(2), 41. https://doi.org/10.4314/as.v15i2.6 Samarrai, S. M., Seyam, S. M., Main, H. R., & Dafie, A. A. (1983). Growth periods, harvest index and grain yield relationships in barley. Rachis, 6, 21–24. Seran, T. H., & Brintha, I. (2010). Review on maize-based intercropping. Journal of Agronomy, 9(3), 135–145. https://doi.org/10.3923/ja.2010. 135.145 Siddique, K. A., Arian, M. A., & Jafri, K. A. (1987). Use of kernel weight as a criterion for the selection of productive mutants in wheat. Rachis, 6, 53. Spitters, C. J. T. (1983). An alternative approach to the analysis of mixed cropping experiments. 1. Estimation of competition effects. Nether- lands Journal of Agricultural Science, 31(1), 1–11. https://doi.org/10. 18174/njas.v31i1.16957 Toungos, M. D., Njodi, M., Babayola, M., & Kashim, H. (2019). Multi- locational trial on SAMMAZ Maize (Zea mays (L)) variety for yield performance in the Northern Guinea Savannah Zone, Nige- ria. International Journal of Scientific Research and Engineering Development, 2(2), 754–788. Udensi, O., Ikpeme, E. V., Edu, E. A., & Ekpe, D. E. (2011). Relationship studies in cowpea [Vigna unguiculata (L.) Walp] landraces grown under humid lowland condition. International Journal of Agricultural Research, 7(1), 33–45. https://doi.org/10.3923/ijar.2012.33.45 Yusuf, Z., Zeleke, H., Mohammed, W., Hussein, S., & Hugo, A. (2017). Correlation and path analysis of groundnut (Arachis hypogaea L.) genotypes in Ethiopia. International Journal of Plant Breeding and Crop Science, 4(2), 197–204. Zhao, C., Shao, C., Yang, Z., Wang, Y., Zhang, X., Wang, M., & McGiffen, J. M. E. (2017). Effect of planting density on pod devel- opment and yield of peanuts under the pattern of precision-planted peanuts. Legume Research, 40(5), 901–905. How to cite this article: Namo, T. A. O., Odesina, I. S., Akpensuen, T. T., Utoblo, G. O., Boukar, O., Ongom, P. O., & Oyebode, G. O. (2025). Yield and yield components responses to plant density in cowpea grown in two savannah agro-ecologies in Nigeria. Agrosystems, Geosciences & Environment, 8, e70252. https://doi.org/10.1002/agg2.70252 26396696, 2025, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/agg2.70252 by Patrick O bia O ngom - N igeria H inari N PL , W iley O nline L ibrary on [25/11/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://www.R-project.org/ https://www.R-project.org/ https://doi.org/10.4314/as.v15i2.6 https://doi.org/10.3923/ja.2010.135.145 https://doi.org/10.3923/ja.2010.135.145 https://doi.org/10.18174/njas.v31i1.16957 https://doi.org/10.18174/njas.v31i1.16957 https://doi.org/10.3923/ijar.2012.33.45 https://doi.org/10.1002/agg2.70252 Yield and yield components responses to plant density in cowpea grown in two savannah agro-ecologies in Nigeria Abstract Plain Language Summary 1 | INTRODUCTION 2 | MATERIALS AND METHODS 2.1 | Experimental site 2.2 | Soil sampling and analysis 2.3 | Meteorological data 2.4 | Planting materials 2.5 | Experimental layout, planting, and management 2.6 | Phenotyping 2.7 | Data analysis 3 | RESULTS 3.1 | Pod yield 3.2 | 100-seed weight 3.3 | Shelling percentage 3.4 | Biological yield 3.5 | Harvest index 3.6 | Total grain yield 3.7 | Correlation of yield components with total grain yield 4 | DISCUSSION AND CONCLUSION AUTHOR CONTRIBUTIONS ACKNOWLEDGMENTS CONFLICT OF INTEREST STATEMENT ORCID REFERENCES