# Sorghum genetic variability for adaptation to intercropping with cowpea in Sudano-Sahelian conditions

Louis-Marie Raboin, Nofou Ouedraogo, Mariam Kabore, Yacouba Zabre, Aminata Ganeme, Laurent Bonnal, Mohamed Habibou Assouma, Eric Gozé, Julie Dusserre

► To cite this version:

Louis-Marie Raboin, Nofou Ouedraogo, Mariam Kabore, Yacouba Zabre, Aminata Ganeme, et al. Sorghum genetic variability for adaptation to intercropping with cowpea in Sudano-Sahelian conditions. Field Crops Research, 2025, 330, pp.109984. 10.1016/j.fcr.2025.109984. hal-05077006

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# Sorghum genetic variability for adaptation to intercropping with cowpea in Sudano-Sahelian conditions

Louis-Marie Raboin<sup>a,b,c,*</sup> <sup>10</sup>, Nofou Ouedraogo<sup>c</sup>, Mariam Kabore<sup>d</sup>, Yacouba Zabre<sup>d</sup>, Aminata Ganeme<sup>d</sup>, Laurent Bonnal<sup>e,f</sup>, Mohamed Habibou Assouma<sup>e,f,g</sup>, Eric Gozé<sup>a,b</sup>, Julie Dusserre<sup>a,b</sup>

## ARTICLE INFO

### Keywords:

Row intercropping

Substitutive design

Sorghum bicolor

Vigna unguiculata

Genetic variability

Low input

## ABSTRACT

**Context:** Intercropping is a crop diversification strategy that enhances the sustainability of agricultural production systems by delivering a range of ecosystem services. It minimizes the risk of crop failure and increases land-use efficiency. However, limited knowledge exists about the varieties and traits that can enhance the performance of cereal/legume intercropping systems.

**Objective:** This study aims to explore the effect of sorghum genetic variability on intercropping performance and identify the genetic traits of sorghum that most influence the outcomes of intercropping.

**Method:** Thirty sorghum varieties, representing a broad range of diversity, were grown both in pure stands and as intercrops, with a single cowpea variety used as a tester. The experiment was conducted in 2020 and 2021 in Burkina Faso, using a split-plot design with three replications each year. Genotype rankings were established for both cropping systems, and genotype × cropping system interactions were evaluated.

**Results:** The total grain yield of the intercrop (sorghum + cowpea) was not significantly different from that of sorghum grown as a pure crop (2000 kg ha⁻¹ and 2051 kg ha⁻¹, respectively). However, the intercrop produced more grain protein (290 kg ha⁻¹) than the pure crop (217 kg ha⁻¹). Significant genotypic variability was observed for most traits, with few instances of significant genotype × cropping system interactions. No significant effect of sorghum genotypes on cowpea grain yield was found, nor was there a negative correlation between cowpea and sorghum grain yields in the intercrop. In 2021, competition was observed through a negative correlation between cowpea grain yield and sorghum biomass, as well as a positive correlation with sorghum harvest index. However, this competition was not for light, but for water.

**Conclusions:** The choice of sorghum varieties for intercropping in our conditions depends mainly on the farmer's objectives regarding grain quality and production, avoiding varieties with high biomass and low harvest index. Further research should focus on the effect of cowpea genetic variability on the performance of sorghum-cowpea intercropping.

## 1. Introduction

Higher diversity increases the production of natural ecosystems (Tilman et al., 1996) as well as agroecosystems (Tamburini et al., 2020; Beillouin et al., 2021) through improved utilization of resources

(nutrients, water, light), improved soil quality, and regulation of pests and diseases. Biodiversity in agroecosystems can be enhanced by intercropping, which involves growing two or more crops in the same space at the same time (Lithourgidis et al., 2011). Intercropping is a common feature of traditional farming systems in many tropical parts of

<footnote><sup>a</sup> CIRAD, UPR AIDA, TA B-115 / 02, Avenue Agropolis, Montpellier 34398 CEDEX 5, France</footnote>

<footnote><sup>b</sup> AIDA, CIRAD, Univ Montpellier, CIRAD, Montpellier 34398 CEDEX 5, France</footnote>

<footnote><sup>c</sup> INERA, Institut de l'Environnement et de Recherche Agricole, 04 BP 8645, Ouagadougou, Burkina Faso</footnote>

<footnote><sup>d</sup> Université Joseph Ki-Zerbo, Unité de Formation et de Recherche en Sciences de la Vie et de la Terre (UFR/SVT), 09BP 848, Ouagadougou, Burkina Faso</footnote>

<footnote><sup>e</sup> SELMET, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote><sup>f</sup> CIRAD, UMR SELMET, Montpellier F-34398, France</footnote>

<footnote><sup>g</sup> CIRDES, Centre International de Recherche Développement sur l'Elevage en zone Subhumide, Bobo-Dioulasso 454, Burkina Faso</footnote>

<footnote>* Corresponding author at: CIRAD, UPR AIDA, TA B-115 / 02, Avenue Agropolis, Montpellier 34398 CEDEX 5, France
*E-mail address: louis-marie.raboin@cirad.fr (L.-M. Raboin).</footnote>
the world designed to meet farmers' multiple objectives simultaneously (Jodha, 1980). Intercropping annual crops allows for improved efficiency of land use as measured through Land Equivalent Ratio (LER) in various meta-analyses: LER = 1.22 (Yu et al., 2015), LER= 1.30 (Martin-Guay et al., 2018). However, it does not achieve transgressive overyielding as it generates a slight loss in grain or calorie yield compared with the most productive sole crop (Li et al., 2023). Nevertheless, intercropping enhances a range of ecosystem services such as soil fertility (Li et al., 2021), soil erosion control (Sharma et al., 2017), carbon sequestration (Cong et al., 2015), pest and disease control (Trenbath, 1993), and weed control (Gu et al., 2021). Nitrogen and phosphorus use efficiency are enhanced in intercropping, especially in cereal and grain legume associations (Jensen et al., 2020; Tang et al., 2021). Cereal grain legume intercropping also positively affects yield stability (Raseduzzaman and Jensen, 2017). Overall, Intercropping is a very interesting lever for achieving more sustainable and resilient agriculture while providing a diversified diet and high-quality forages.

In semi-arid regions of Sub-Saharan Africa (SSA), cowpea [Vigna unguiculata (L.) Walp.] is often intercropped with sorghum [Sorghum bicolor (L.) Moench]. Both crops are drought tolerant and well adapted to their environment as they both have their center of origin in SSA. They represent a crucial asset for farm resilience in those very constrained areas (Timko et al., 2007; Hadebe et al., 2017). Sorghum cultivation is regularly expanding in SSA, reaching 65% of the world's total acreage dedicated to sorghum and 43% of total world production in 2018 (Pingali et al., 2020). Sorghum is the second most important cereal in SSA after maize. Cowpea is a very important nutritious and protein-rich grain legume in SSA, accounting for 96% of world production in 2020 (Mekonnen et al., 2022). Cowpea is grown mainly as an intercrop along with sorghum, millet or maize in dry savannas, usually using late-maturing prostrate varieties (Boukar et al., 2015). In Nigeria, the largest cowpea producer in the world, 80% of cowpea is grown in association with sorghum or other crops (Odo, 1991). In Burkina Faso, where the present study has been conducted, a traditional form of intercropping by sowing cowpea and sorghum in the same seed hole is a widely used strategy (98% of farms in three villages of Center North region) to avoid loss risks and to optimize land use (Ganeme et al., 2021). Indeed, intercropping using various variety combinations could be a way of buffering the high rainfall variability encountered in sub-sahelian regions (Sanou et al., 2016). This strategy effectively reduces runoff and soil erosion, even in regions with gentle slopes like the Central Plateau of Burkina Faso (Zougmore et al., 2010). It can also help control *Striga hermonthica* infestations, one of the most important biotic constraints for sorghum production (Carsky et al., 1994). A review stated that in SSA, land productivity of sorghum-cowpea intercrops, as measured by LER, was enhanced by 26% compared with sole crops (Namatsheve et al., 2020).

Selecting varieties within intraspecific diversity is a secondary lever for improving complementarity in resource use through interspecific intercropping. However, little is known about the varieties and traits that could improve cereal/legume intercropping systems performance. There is a lack of knowledge of the mechanisms underlying the interactions between genotype and cropping system (i.e., sole cropping vs. intercropping), as reviewed by Demie et al. (2022). Information on the varietal traits that are important for guiding the choice of associations is rarely available. Indeed, the selection process is mainly carried out in pure crops and under non-limiting growth conditions (Louarn et al., 2010). In a longer timescale, plant breeding should focus on increasing the resource-use efficiency of intercrops, considering multiple traits that would benefit mixed cropping and not simply those traits known to raise the yield of monocrops (Brooker et al., 2015). The performance rankings of genotypes in pure cultivation may differ from those in mixed cultivation (Annicchiarico et al., 2019; Litrico and Violle, 2015). However, few studies have studied the effect of genotypes on the general performance of intercropping systems such as in the pea-barley (Hauggaard-Nielsen and Jensen, 2001) or the wheat-faba bean

intercropping systems (Paul et al., 2024). For sorghum, in particular, Demie et al. (2022) counted only four that examined the effect of sorghum genetic variability on the performance of sorghum-legume intercropping systems, with only two specifically focusing on sorghum-cowpea intercropping (de Queiroz et al., 1988; Odo, 1991). All involved a relatively limited number of sorghum genotypes, ranging from 2 to 8, except for an additional experiment by Galwey et al. (1986) that evaluated the genotypic variation of 23 sorghum cultivars in response to intercropping with cowpea.

Further research is needed to investigate the contribution of genotype choice and genotype × cropping system interaction (pure stand or intercropping) to the performance of sorghum-cowpea intercropping. This should help define the extent to which it would be relevant to make specific research efforts to select sorghum varieties for intercropping with cowpeas. It should also help us identify which traits should be given priority. We present here an experiment evaluating thirty sorghum varieties with a broad genetic background grown both in pure stand and intercropped with a single cowpea variety used as a tester. This experiment was conducted over two cropping seasons (2020 and 2021) near Ouagadougou in Burkina Faso. Our objective was (1) to assess the effect of sorghum genotypes and of intercropping on a large set of traits, (2) to assess the importance of genotype × cropping system interaction and identify genotypes whose performance is significantly improved by intercropping, (3) to identify traits that influence most the outcome of intercropping.

## 2. Materials and methods

### 2.1. Site and experimental conditions

The experiments were conducted in Gampela, 20 km east of Ouagadougou, in Burkina Faso (12°24' 54.427'' N, 1°23' 23.173''O). The area's climate is Sudan-Sahelian. It is characterized by a rainy season from June to October and a dry season from November to May. Annual rainfall in Ouagadoougou ranged from 652 to 895 mm over the period 2015–2024 (https://www.infoclimat.fr/climatologie/annee/2019/ouagadougou/valeurs/65503.html). The average monthly temperature varies between 25 and 33.3°C. The experimental station recorded similar cumulative rainfall in 2020 and 2021 of 855 and 818 mm respectively (Fig. 1). However, the distribution of rainfall differed between the two years, with rains beginning earlier in 2021 but also ending sooner. Gampela's soils are of the leached tropical ferruginous type, derived from granitic parent rock (BUNASOL, 1988). The soils are predominantly silty-sandy in texture, with 6.86% clay, 19.5% silt, and 73.6% sand. The soil is acidic ($pH_{water}$= 5.31) with low soil organic matter content (0.63%) and low physicochemical fertility.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Monthly rainfall recorded at the Gampela site in 2020 and 2021 and monthly rainfall data for the decade 2015–2024 in Ouagadougou.</figcaption></figure>
## 2.2. Sorghum genotypes

Thirty varieties (Table 1) were selected for this experiment within a larger sorghum collection (131 accessions in 2018 and 185 accessions in 2019) that was characterised for 23 agro-morphological traits. This selection was carried out with the goal of preserving the maximum possible diversity across most traits while identifying the varieties best adapted to our conditions. The thirty selected genotypes are represented in the PCA conducted in 2019 (Figure S1). Among these, ten were landraces, and twenty were improved varieties.

## 2.3. Experimental design

The experiment was conducted to compare 30 sorghum genotypes in two cropping systems: pure stand cultivation and intercropping with cowpea. The experiment was repeated in two cropping seasons (2020 and 2021). One cowpea variety, Komcalle, was used in the intercropping modality. It is an Improved variety of INERA (Institut de l'Environnement et des Recherches Agricoles) that presents an upright growth habit, an early 60-day cycle, and an excellent tolerance to drought. This variety is registered and already available to farmers. Inter-row and intra-row spacing was set at 0.8 m and 0.4 m between seed holes, respectively, for both component crops. In the intercropping modality, every second row of sorghum was replaced by a row of cowpeas, following a substitutive design (Figure S2).

Each year, a split plot design with three replications was used. The sorghum genotypes were allocated to the main plots and the cropping systems (intercropping of cowpea and sorghum and sole sorghum cultivation) were assigned to the subplots. This design was used to have the intercropping treatment of a given sorghum variety in direct proximity to its pure-stand reference for a better estimation of the effects of

<caption>**Table 1**</caption>

List of the thirty sorghum varieties used in this study, including their codes (as shown in the figures), country of origin, type (improved varieties or landraces), and genetic race.

<table><thead><tr><th>name</th><th>code name</th><th>country</th><th>type</th><th>Race<sup>a</sup></th></tr></thead><tbody><tr><td>014-SB-EPDU-1004</td><td>014SB</td><td>Mali</td><td>improved</td><td>GC</td></tr><tr><td>69-20</td><td>69-20</td><td>Mali</td><td>landrace</td><td>Gma</td></tr><tr><td>Baninpelga</td><td>Baninp</td><td>Burkina</td><td>landrace</td><td>G</td></tr><tr><td>C2-075-16</td><td>C2-075</td><td>Mali</td><td>improved</td><td>GC</td></tr><tr><td>CIRAD 406</td><td>CIR406</td><td>Mali</td><td>improved</td><td>GC</td></tr><tr><td>CSM 660</td><td>CSM660</td><td>Mali</td><td>landrace</td><td>G</td></tr><tr><td>F2-20</td><td>F2-20</td><td>Senegal</td><td>improved</td><td>C</td></tr><tr><td>Fiib-miougou</td><td>Fiibmi</td><td>Burkina</td><td>landrace</td><td>G</td></tr><tr><td>Grinkan</td><td>Grinka</td><td>Mali</td><td>improved</td><td>GC</td></tr><tr><td>IRAT 8</td><td>IRAT8</td><td>Burkina</td><td>improved</td><td>GK</td></tr><tr><td>ISRA-S-618-2</td><td>ISR618</td><td>Senegal</td><td>improved</td><td></td></tr><tr><td>ISRA-S-622A</td><td>ISR622</td><td>Senegal</td><td>improved</td><td></td></tr><tr><td>ISX-09005-11-5-2-9-5-9-6-11</td><td>ISX9005</td><td>Mali</td><td>improved</td><td>G</td></tr><tr><td>Kapelga</td><td>Kapelg</td><td>Burkina</td><td>landrace</td><td>G</td></tr><tr><td>Lata 3</td><td>Lata3</td><td>Mali</td><td>improved</td><td>G</td></tr><tr><td>Lata//DouaG-6-8-1-1</td><td>LataD</td><td>Mali</td><td>improved</td><td>G</td></tr><tr><td>Lata//IS15401-6-30-1-1</td><td>LataI</td><td>Mali</td><td>improved</td><td>G</td></tr><tr><td>Lata//Ribd-3-9-1-1</td><td>LataR</td><td>Mali</td><td>improved</td><td>GC</td></tr><tr><td>Loumbri</td><td>Loumbr</td><td>Burkina</td><td>landrace</td><td>GDmb</td></tr><tr><td>MAGADJI 1</td><td>Magadj</td><td>Burkina</td><td>landrace</td><td>GC</td></tr><tr><td>ND07 e21(17 ×30)F2-6-v</td><td>ND07</td><td>Mali</td><td>improved</td><td></td></tr><tr><td>P114-160A</td><td>P114</td><td>Mali</td><td>improved</td><td>GC</td></tr><tr><td>PR3009B</td><td>PR3009</td><td>Mali</td><td>improved</td><td>GC</td></tr><tr><td>Pougouyalam bangdo</td><td>Pougou</td><td>Burkina</td><td>landrace</td><td>G</td></tr><tr><td>Sariaso 12</td><td>Sar12</td><td>Burkina</td><td>improved</td><td>G</td></tr><tr><td>Sariaso 15</td><td>Sar15</td><td>Burkina</td><td>improved</td><td>C</td></tr><tr><td>Sariaso 18</td><td>Sar18</td><td>Burkina</td><td>improved</td><td>G</td></tr><tr><td>Sariaso 20</td><td>Sar20</td><td>Burkina</td><td>improved</td><td>G</td></tr><tr><td>Soubatimi</td><td>Souba</td><td>Mali</td><td>improved</td><td></td></tr><tr><td>Wed Wanga</td><td>WedWan</td><td>Burkina</td><td>landrace</td><td>C</td></tr></tbody></table>

crop association on sorghum. Pure cowpea plots, using a single variety, were not included within the split-plot design. Instead, one pure stand cowpea plot per replication was placed adjacent to the experimental layout to save space. Since the same variety of cowpea was used across all intercropping treatments in a given replication, the single cowpea plot served as a reference for the cowpea partial Land Equivalent Ratio calculations of all the intercropped plots within that replication. The main sorghum genotype plots were completely randomized within each replication. Subplots in pure stand consisted of 6 rows (4.8 m) of 8 sorghum seed holes (3.2 m) representing 15.36 m² (Figure S2). Subplots in intercropping consisted of 12 rows with an alternation of one row of cowpea and one row of sorghum, giving a total of six rows of cowpea and six rows of sorghum per plot. Plot size was 30.72 m² (9.6 m *3.2 m). Plots of cowpea grown in pure stands were established outside, but near, our experimental design to serve as a reference for calculating the Land Equivalent Ratio.

## 2.4. Crop establishment and management

Each year, the soil was plowed using a tractor. Seedbeds were prepared by secondary manual tillage. Six to ten seeds for sorghum and four to five seeds for cowpea were placed in manually made seed holes, 5 cm deep, using a daba. Before sowing, the seeds were treated with a combination of insecticide and fungicide named Calthio C (25 % endosulfan + 25 % thiram), at 2.5 g kg⁻¹ of seeds. The sowing dates were July 7th, 2020, and June 26th, 2021. Crop maintenance consisted of manual daba weeding 14 and 28 days after sowing in 2020 and 20 and 32 days after sowing in 2021. NPK (14/23/14) was applied at 75 kg ha⁻¹ after the first weeding operation. Two plants were kept in each seed hole after thinning for both sorghum and cowpea. Cowpea was treated twice with Lamda Super 2,5 EC (lambda-cyhalothrin 2.5 %) at flowering and appearance of the first pods; Sorghum was not treated.

## 2.5. Measurements

### 2.5.1. Phenology and plant development

Canopeo, a software for Android mobile, was used to estimate green canopy cover (CANOPEO; Table 2) 42 days after sowing (Patrignani and Ochsner, 2015). Leaf area index (LAI), defined as the total leaf area over a specific area of land and used as an indicator of above-ground biomass, was measured non-destructively, in individual plots using a SunScan (Delta-T Devices, Cambridge, UK) placed under the sorghum canopy. LAI was measured in all plots at the flag leaf stage. The number of leaves per stem (LFNB) was also counted at the same stage. The sorghum flowering date was recorded at the 50 % flowering stage (DTH50). Before harvest, plant height was measured on ten plants per plot, considering separately the height at flag leaf insertion (PH), the length of the apparent panicle peduncle (or panicle exertion that can take negative values if the panicle has not entirely emerged from the flag leaf sheath; PEDLG) and the length of the panicle (PANLG). Basal stem diameter (DIAM) was also measured on the same ten plants.

### 2.5.2. Grain yield and yield components

To eliminate border effects, net plots were delineated within each elementary plot (Figure S2). Thus, in pure cultivation, each net plot consisted of four rows of six sorghum hills (7.68 m²). In intercropping, they comprised eight rows of six hills, with a total of four rows of sorghum and four rows of cowpea (15.36 m²). Within each net plot, the total number of sorghum stems was counted before harvest, allowing for the estimation of the number of stems per hill (STNBHILL) and the number of stems per m² (STNBM2). Then, all sorghum panicles were harvested and counted, allowing for the estimation of the number of panicles per hill (PANNBHILL), the number of panicles per stem (PANNBST), and the number of panicles per m² (PANNBM2). Panicles or pods were harvested at full maturity and sun-dried for seven days to achieve a constant weight before threshing, winnowing, and weighing

<footnote><sup>a</sup> G: guinea, Gma: guinea margaritiferum, C: caudatum, K: kaffir, Dmb: durra membranaceum</footnote>
<table><caption>**Table 2**

Description of the thirty five traits measured in this study.</caption><thead><tr><th>trait code</th><th>trait description</th><th>unit</th><th>level</th></tr></thead><tbody><tr><td colspan="4"><strong>Sorghum vegetative development</strong></td></tr><tr><td>DTH50</td><td>number of days to 50 % heading</td><td></td><td>plant</td></tr><tr><td>CANOPEO</td><td>green canopy soil cover 40 days after sowing</td><td>%</td><td>plot</td></tr><tr><td>LFNB</td><td>number of leaves</td><td></td><td>plant</td></tr><tr><td>PH</td><td>plant height</td><td>cm</td><td>plant</td></tr><tr><td>PEDLG</td><td>peduncle length (panicle exertion)</td><td>cm</td><td>plant</td></tr><tr><td>PANLG</td><td>panicle length</td><td>cm</td><td>plant</td></tr><tr><td>DIAM</td><td>stem diameter</td><td>mm</td><td>plant</td></tr><tr><td>STNBHILL</td><td>number of stems per hill</td><td></td><td>plant</td></tr><tr><td>BIOMHILL</td><td>straw biomass per hill</td><td>g</td><td>plant</td></tr><tr><td>LAI</td><td>Leaf Area Index</td><td></td><td>plot</td></tr><tr><td>STNBM2</td><td>number of stems per m<sup>2</sup></td><td></td><td>plot</td></tr><tr><td>SY</td><td>straw yield</td><td>kg.ha<sup>-1</sup></td><td>plot</td></tr><tr><td colspan="4"><strong>Sorghum yield and yield components</strong></td></tr><tr><td>GY<sub>sorg</sub></td><td>sorghum grain yield</td><td>kg.ha<sup>-1</sup></td><td>plot</td></tr><tr><td>PANNBM2</td><td>number of panicles per m<sup>2</sup></td><td></td><td>plot</td></tr><tr><td>PANNBHILL</td><td>number of panicles per hill</td><td></td><td>plant</td></tr><tr><td>PANNBST</td><td>number of panicles per stem</td><td></td><td>plant</td></tr><tr><td>GWPAN</td><td>grain weight per panicle</td><td>g</td><td>plant</td></tr><tr><td>GNBPAN</td><td>number of grains per panicle</td><td>g</td><td>plant</td></tr><tr><td>HGW<sub>sorg</sub></td><td>hundred grain weight for sorghum</td><td>g</td><td>plant</td></tr><tr><td>HI</td><td>harvest index</td><td></td><td>plant</td></tr><tr><td colspan="4"><strong>Sorghum grain and fodder quality parameters</strong></td></tr><tr><td>VITRO</td><td>sorghum grain vitrosity</td><td>1-9<br>scale</td><td>plant</td></tr><tr><td>CP</td><td>crude protein</td><td>% DM</td><td>plant</td></tr><tr><td>CF</td><td>crude fiber</td><td>% DM</td><td>plant</td></tr><tr><td>NDF</td><td>neutral detergent fiber</td><td>% DM</td><td>plant</td></tr><tr><td>ADF</td><td>acid detergent fiber</td><td>% DM</td><td>plant</td></tr><tr><td>ADL</td><td>acid detergent lignin</td><td>% DM</td><td>plant</td></tr><tr><td>IVOMD</td><td>in vitro organic matter enzymatic digestibility</td><td>% OM</td><td>plant</td></tr><tr><td>IVDMD</td><td>in vitro dry matter enzymatic digestibility</td><td>% DM</td><td>plant</td></tr><tr><td>ASH</td><td>mineral matter</td><td>% DM</td><td>plant</td></tr><tr><td colspan="4"><strong>Intercropping performance</strong></td></tr><tr><td>GY<sub>tot</sub></td><td>total grain yield (sorghum + cowpea)</td><td>kg.ha<sup>-1</sup></td><td>plot</td></tr><tr><td>LER<sub>sorg</sub></td><td>Land Equivalent Ratio of sorghum component</td><td></td><td>plot</td></tr><tr><td>LER<sub>cowp</sub></td><td>Land Equivalent Ratio of cowpea component</td><td></td><td>plot</td></tr><tr><td>LER<sub>tot</sub></td><td>Land Equivalent Ratio total (sorghum and cowpea)</td><td></td><td>plot</td></tr><tr><td>GY<sub>cowp</sub></td><td>cowpea grain yield</td><td>kg.ha<sup>-1</sup></td><td>plot</td></tr><tr><td>HGW<sub>cowp</sub></td><td>hundred grain weight for cowpea</td><td>g</td><td>plant</td></tr></tbody></table>

the grains in order to determine sorghum grain yield (GY<sub>sorg</sub>), cowpea grain yield (GY<sub>cowp</sub>), total grain yield (GY<sub>tot</sub>) and grain weight per panicle (GWPAN). Samples of two hundred grains of sorghum and cowpea have been counted and weighed in order to determine the hundred-grain weight of sorghum (HGW<sub>sorg</sub>) and the hundred-grain weight of cowpea (HGW<sub>cowp</sub>) as well as the number of grains per panicle (GNBPAN). Sorghum stems were harvested and weighed to get the total fresh weight per net plot. Fresh samples representative of the overall plot biomass were taken, weighed, and then oven-dried for 72 h at 60°C to determine dry weight and thus sorghum straw yield (SY) and straw biomass per hill (BIOMHILL). The sorghum harvest index could thus be calculated (HI).

### 2.5.3. Intercropping performance

Land productivity of varieties was evaluated using Land Equivalent Ratio (LER), defined as the area needed in sole crop to have the same yield as in intercropping (Mead and Willey, 1980). LER was calculated according to the following equation:

$$LER_{sorghum} = \text{sorghum yield in intercropping/sorghum yield in sole crop (LER}_{\text{sorg}})$$

Given the 50 % proportion of sorghum in our intercropping arrangement, a partial sorghum LER exceeding 0.5 would signify an increase in sorghum yield under intercropping conditions.

$$LER_{\text{cowpea}} = \text{cowpea yield in intercropping/cowpea yield in sole crop (LER}_{\text{cowp}})$$

$$LER_{\text{total}} = LER_{\text{sorghum}} + LER_{\text{cowpea}} (LER_{\text{tot}})$$

### 2.5.4. Grain and fodder quality

The mineral matter (ASH), crude protein (CP), crude fiber (CF), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), in vitro dry matter enzymatic digestibility (IVDMD) and in vitro organic matter enzymatic digestibility (IVOMD) of sorghum fodder biomass were estimated using near-infrared spectroscopy (NIRS). NIRS spectra were collected from dried samples (oven-dried at 60 °C for 72 h), ground to 1 mm, using a model 1093 Cyclotech sample mill (FOSS, Höganäs, Sweden). A spectrometer (Tango model, Bruker Optik GmbH, Ettingen, Germany) was used to capture spectra between 11,536 and 3952 cm<sup>-1</sup> with an 8 cm<sup>-1</sup> step. All the samples were scanned in triplicate and the spectra were averaged. NIRS models were established using 841 sorghum stem and whole plant samples for sorghum biomass chemical compositions. All models were calibrated against wet chemistry reference methods. Mineral matter was determined by ashing samples in a muffle furnace for 4 h at 550 °C. Total nitrogen was measured using the Kjeldahl procedure, and crude protein was calculated as total nitrogen × 6.25 (EN ISO 5883-2, 2009). Crude fiber was determined following the EN ISO 6865 (2001) method. NDF was analysed following the EN ISO 16472 (2006) method and ADF and ADL following the EN ISO 13906 (2008) both method using an Ankom fiber analyzer (Ankom® Tech. Co., Fairport, NY, USA). The NDF, ADF and ADL were corrected for ash measured on ADL residue. The in vitro dry matter enzymatic digestibility (IVDMD) and in vitro organic matter enzymatic digestibility (IVOMD) were determined according to the pepsin-cellulase method (Aufrère et al., 2007). In addition, grain endosperm vitrosity (VITRO) was assessed by visually estimating the proportion of the vitreous (hard) fraction of the endosperm relative to the floury (soft) fraction in a longitudinally sectioned grain. The qualitative assessment ranged from 1 to 9 (very vitreous to very floury).

## 2.6. Statistical analysis

### 2.6.1. Analysis of variance

Analysis of variance was performed using the SAS/STAT statistical analysis package (version 9.4, SAS Institute Inc., Cary, NC, USA). As the experiment was carried out in a split-plot design with the variety in main plots and the cropping system in subplots, the linear model used includes a random main plot effect, resulting in a mixed-effects linear model for the two years together:

$$Y_{ijkl} = m + a_i + b_j + c_k + d_{kl} + M_{ikl} + (ab)_{ij} + (ac)_{ik} + (bc)_{jk} + (abc)_{ijk} + E_{ijkl}$$

where $a_i$ is the effect of genotype i, $b_j$ is the effect of cropping system j, $c_k$ is the effect of year k, $d_{kl}$ is the effect of block l of year k, $M_{ikl}$ is the random effect of the main plot carrying genotype i in the block k during year l, $(ab)_{ij}$ is then interaction effect between genotype i and cropping system j, and likewise for the other interactions and $E_{ijkl}$ is the random error term.

When needed, data from 2020 and 2021 were analyzed separately. For any one year, model (1) then simplifies to:

$$Y_{ijl} = m + a_i + b_j + d_l + M_{il} + (ab)_{ij} + E_{ijkl}$$

### 2.6.2. Heritability estimations at the experiment scale

Broad sense heritability was estimated separately for pure stand and intercropped sorghum. For this estimation genotype effect has to be random.

For the two years together, the mixed-effects linear model for anyone cropping system is then:

$$Y_{ikl} = m + A_i + c_k + d_{kl} + (Ac)_{ik} + E_{ikl}$$
where the capital A stands for the random genotype effect and (Ac) for the random genotype × year interaction.

When needed, data from 2020 and 2021 were analyzed separately. For any one year and one cropping system, model (3) then simplifies to:

$$Y_{il} = m + A_i + d_i + E_{il}$$

When analyzing one year at a time, at the experiment scale, the estimation variance of the genotype mean is the residual variance divided by the number of plots per genotype, then:

$$H^2 = \frac{\sigma_A^2}{\sigma_A^2 + \frac{\sigma_c^2}{n_e}}$$

where $n_e$ is the number of replicates.

When analyzing the two years together, the estimation variance of the genotype mean comprises the genotype × year interaction divided by the number of years, plus the residual variance divided by the number of plots by genotype, that is:

$$H^2 = \frac{\sigma_A^2}{\sigma_A^2 + \frac{\sigma_c^2}{n_c} + \frac{\sigma_c^2}{n_c n_e}}$$

where $n_c = 2$ is the number of years and $n_e$ is the number of replicates per year.

### 2.6.3. Analysis of correlations

Pearson's phenotypic correlation coefficients based on varietal trait means over replicates were calculated using the cor.test function in R.

## 3. Results

### 3.1. Analysis of variance

The results of the combined analysis of variance across two years for all agronomic traits measured in 30 sorghum varieties either inter-cropped with cowpea or grown in pure stand are presented in Table 3. The effect of the year was highly significant for most of the traits except for CANOPEO, LAI, PANLG, LER<sub>sorg</sub>, LER<sub>tot</sub>, and HGW<sub>cowp</sub>. The effect of the cropping system was highly significant for most of the traits except for DTH50, DIAM, VITRO, GY<sub>tot</sub>, and ASH. The effect of the sorghum genotype was highly significant for all sorghum traits. However, there was no significant effect of sorghum genotypes on intercropping performance measured through LER parameters (LER<sub>sorg</sub>, LER<sub>cowp</sub>, LER<sub>tot</sub>) and grain yield of the cowpea component (GY<sub>cowp</sub>) in the intercrop.

Interactions between genotype and cropping system were observed exclusively for LAI, PANNBST, GWPAN, GNBPAN, and HI. Interactions were observed for some traits between year and genotype as well as between year and cropping system. Given these interactions, the analysis of variance was also performed for each year and the results are presented in Table S1.

### 3.2. Effect of intercropping on sorghum traits

Intercropping with cowpea affected most traits characterizing sorghum vegetative development (Table 3). Plants were slightly shorter (PH) in intercropping with reduced panicle exertion (PEDLG). Most other development parameters measured at the plant level were higher under intercropping conditions: longer panicles (PANLG), a greater number of stems per hill (STNBHILL), higher biomass per hill (BIO-MHILL) and higher number of leaves (LFNB). In contrast, at the plot level, sorghum development parameters were lower in intercropping compared to pure stands, with lower LAI, fewer stems per m² (STNBM2), and lower straw yield (SY). No effect was detected on the number of days to 50 % heading (DTH50) nor on stem diameter (DIAM). All these effects were consistent across the years except for STNBHILL, for which

intercropping had no effect in 2021. Intercropping provided a significantly better soil coverage than pure sorghum (CANOPEO).

For sorghum yield and its components, a clear contrast was observed between traits measured at the plant level and those at the plot level. At the plot level, sorghum grain yield (GY<sub>sorg</sub>) was significantly reduced under intercropping. The number of panicles per m² (PANNBM2) was also highly reduced. Conversely, at the plant level, there was a significant increase in several traits: the number of panicles per hill (PAN-HILL), the number of panicles per stem (PANNBST), the number of grains per panicle (GNBPAN), the weight of grain per panicle (GWPAN), the weight of one hundred grain (HGW<sub>sorg</sub>) and the harvest index (HI). These effects were consistent across both years of the study.

Sorghum fodder quality parameters improved under intercropping with an increase in straw protein content (CP) and digestibility indicators (IVDMD and IVOMD) on one hand, and a reduction in fiber and lignin content (CF, NDF, ADF, ADL) on the other. Only ASH content did not change significantly in intercropping. In 2020, the intercropping effect was not significant on fiber parameters (NDF, ADF, ADL) and digestibility parameters (IVDMD and IVOMD). However, the trend was also towards a slight reduction in fiber content and a slight increase in digestibility. The heritability of fodder quality parameters measured in 2020 was very low, potentially indicating issues in the data collection process, possibly due to the extended storage time of samples before NIR measurements (Table S1).

### 3.3. Intercropping performance

Over the two cropping seasons, the total grain production (GY<sub>tot</sub> = GY<sub>sorg</sub> + GY<sub>cowp</sub>; Table 3) was not significantly different between intercropping (2000 kg·ha⁻¹) and pure stand (2051 kg·ha⁻¹). However, results varied between years (Table S1). In 2021, intercropping yielded significantly less total grain than pure stands (1581 and 1764 kg·ha⁻¹, respectively) while in 2020, the contrary was observed, although the difference was not significant (2419 and 2338 kg·ha⁻¹, respectively). When translated into energy production, using the reference energy content of sorghum (3290 kcal·kg⁻¹; USDA 2024a) and cowpea (3360 kcal·kg⁻¹; USDA 2024b), the intercropping system produced 6.61 × 10⁶ kcal·ha⁻¹, which is very close to the 6.75 × 10⁶ kcal·ha⁻¹ produced by pure sorghum cultivation. In terms of protein production, using the reference protein content of sorghum (106 g·kg⁻¹; USDA 2024a) and cowpea (235 g·kg⁻¹; USDA 2024b), the intercropping system outperformed pure sorghum cultivation by producing 34 % more protein (290 kg·ha⁻¹ vs. 217 kg·ha⁻¹). The land equivalent ratio for the sorghum component (LER<sub>sorg</sub>) was equal to 0.71. This was similar in both years. Sorghum yielded more than expected from its sowing density corresponding to half the sowing density in pure stand. The LER for cowpea was LER<sub>cow</sub>= 0.58 and the total LER was LER<sub>tot</sub>= 1.29. Land use efficiency was 29 % better in intercropping than in pure stand over all our experiments with differences between years (LER<sub>tot</sub>=1.13 in 2021 and LER<sub>tot</sub>=1.45 in 2020; Table S1).

No significant effect of sorghum genotypes was observed on LER parameters except for LER<sub>tot</sub> in 2020. Moreover, no significant effect of sorghum genotypes was detected on cowpea yield (GY<sub>cowp</sub>) or hundred grain weight (HGW<sub>cowp</sub>) whatever the cropping season (Table 3; Table S1). No significant negative correlation was observed between sorghum grain yield and cowpea grain yield in the substitutive intercropping arrangement used in this experiment, regardless of the cropping season (Fig. 2).

### 3.4. Relationship between grain yields of sorghum varieties in intercropping and pure stands

A correlation was observed between sorghum grain yield in intercropping and pure stand (R²=0.44, Table 3, Fig. 3). This correlation was weaker in 2020 (R²=0.37) compared to 2021 (R²=0.57) (Table S1). The genotype × cropping system interaction was not significant, and the best
<table><caption>**Table 3**
P-values of the variance components of the split plot design combining year, genotype and cropping system. Mean value and broad-sense heritability (h²) of each trait in intercropping and in pure stand conditions. R² and P-value of the correlation between intercropping and pure stand across all varieties.</caption><thead><tr><th>level</th><th>trait<sup>a</sup></th><th>unit</th><th>year</th><th>genotype</th><th>cropping system</th><th>geno<sup>c</sup> crop sys</th><th>year x geno</th><th>year x crop sys</th><th>year geno<sup>c</sup> crop sys</th><th>intercrop mean</th><th>pure stand mean</th><th>h<sup>2</sup> intercrop</th><th>h<sup>2</sup> pure stand</th><th>R<sup>2</sup> intercrop/ pure</th><th>P intercrop/ pure</th></tr></thead><tbody><tr><td colspan="15" style="text-align: left; font-weight: bold;">Sorghum vegetative development</td></tr><tr><td>plot</td><td>CANOPEO</td><td>%</td><td>0.0917</td><td>0.0020</td><td>&lt; .0001</td><td>0.8388</td><td>0.3912</td><td>0.2240</td><td>0.7068</td><td><strong>43.22</strong></td><td>35.05</td><td>0.26</td><td>0.47</td><td>0.50</td><td>&lt; .0001</td></tr><tr><td>plot</td><td>LAI</td><td></td><td>0.7139</td><td>&lt; .0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.8612</td><td>0.1566</td><td>0.9808</td><td><strong>2.40</strong></td><td>1.49</td><td>0.76</td><td>0.88</td><td>0.83</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>LFNB</td><td></td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0008</td><td>0.2556</td><td>0.0799</td><td>0.9221</td><td>0.1039</td><td><strong>11.06</strong></td><td>10.73</td><td>0.90</td><td>0.90</td><td>0.89</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>DTH50</td><td></td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.4791</td><td>0.1626</td><td>&lt; .0001</td><td>0.9194</td><td>0.1126</td><td><strong>78.84</strong></td><td>78.73</td><td>0.97</td><td>0.96</td><td>0.98</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>PH</td><td>cm</td><td>&lt; .0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.4457</td><td>0.0006</td><td>0.0519</td><td>0.7942</td><td>241.43</td><td><strong>249.86</strong></td><td>0.98</td><td>0.98</td><td>0.98</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>PEDLG</td><td>cm</td><td>&lt; .0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.2281</td><td>0.0037</td><td>0.9399</td><td>0.8737</td><td>8.81</td><td><strong>10.20</strong></td><td>0.95</td><td>0.96</td><td>0.95</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>PANLG</td><td>cm</td><td>0.0979</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.1225</td><td>&lt; .0001</td><td>0.9430</td><td>0.2806</td><td><strong>36.24</strong></td><td>34.55</td><td>0.98</td><td>0.97</td><td>0.98</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>DIAM</td><td>mm</td><td>0.1683</td><td>&lt; .0001</td><td>0.1992</td><td>0.4382</td><td>0.0003</td><td>0.1944</td><td>0.7592</td><td>22.74</td><td>22.49</td><td>0.90</td><td>0.88</td><td>0.90</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>STNBHILL</td><td></td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0034</td><td>0.9437</td><td>0.0078</td><td>0.1370</td><td>0.4148</td><td><strong>2.21</strong></td><td>2.13</td><td>0.55</td><td>0.66</td><td>0.80</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>BIOMHILL</td><td>g</td><td>0.0003</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.3407</td><td>0.2244</td><td>0.0277</td><td>0.8465</td><td><strong>245.07</strong></td><td>193.05</td><td>0.80</td><td>0.79</td><td>0.81</td><td>&lt; .0001</td></tr><tr><td>plot</td><td>STNBM2</td><td></td><td>&lt; .0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.1578</td><td>0.0083</td><td>0.1171</td><td>0.3318</td><td>3.40</td><td><strong>6.60</strong></td><td>0.60</td><td>0.66</td><td>0.78</td><td>&lt; .0001</td></tr><tr><td>plot</td><td>SY</td><td>kg. ha<sup>-1</sup></td><td>0.0004</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.4424</td><td>0.1654</td><td>0.4190</td><td>0.6819</td><td>3762.84</td><td><strong>5952.03</strong></td><td>0.81</td><td>0.77</td><td>0.78</td><td>&lt; .0001</td></tr><tr><td colspan="15" style="text-align: left; font-weight: bold;">Sorghum yield and yield components</td></tr><tr><td>plot</td><td>GY<sub>sorg</sub></td><td>kg. ha<sup>-1</sup></td><td>&lt; .0001</td><td>0.0002</td><td>&lt; .0001</td><td>0.2403</td><td>0.0061</td><td>0.0793</td><td>0.3237</td><td>1390.86</td><td><strong>2053.07</strong></td><td>0.44</td><td>0.01</td><td>0.44</td><td>&lt; .0001</td></tr><tr><td>plot</td><td>PANNBM2</td><td></td><td>&lt; .0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.7696</td><td>0.0265</td><td>0.0099</td><td>0.2454</td><td>3.53</td><td><strong>6.24</strong></td><td>0.76</td><td>0.67</td><td>0.83</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>PANNBHILL</td><td></td><td>&lt; .0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.4653</td><td>0.0102</td><td>0.6995</td><td>0.3741</td><td><strong>2.30</strong></td><td>2.03</td><td>0.75</td><td>0.66</td><td>0.86</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>PANNBST</td><td></td><td>0.0251</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0001</td><td>0.3833</td><td>0.1661</td><td>0.0280</td><td><strong>1.05</strong></td><td>0.95</td><td>0.84</td><td>0.68</td><td>0.80</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>GWPAN</td><td>g</td><td>&lt; .0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0014</td><td>0.0057</td><td>0.5040</td><td>0.6687</td><td><strong>39.88</strong></td><td>33.02</td><td>0.70</td><td>0.42</td><td>0.62</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>GNBPAN</td><td></td><td>&lt; .0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0005</td><td>0.0014</td><td>0.3749</td><td>0.4006</td><td><strong>1827.94</strong></td><td>1564.34</td><td>0.81</td><td>0.75</td><td>0.75</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>HGW<sub>sorg</sub></td><td>g</td><td>0.0150</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.7271</td><td>0.0002</td><td>0.2405</td><td>0.8833</td><td><strong>2.27</strong></td><td>2.18</td><td>0.95</td><td>0.93</td><td>0.96</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>HI</td><td></td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0002</td><td>0.0134</td><td>0.0347</td><td>0.0876</td><td>0.9343</td><td><strong>0.28</strong></td><td>0.26</td><td>0.81</td><td>0.79</td><td>0.70</td><td>&lt; .0001</td></tr><tr><td colspan="15" style="text-align: left; font-weight: bold;">Sorghum grain and fodder quality parameters</td></tr><tr><td>plant</td><td>VITRO</td><td>1-9 scale</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.7260</td><td>0.1227</td><td>&lt; .0001</td><td>0.4380</td><td>0.5860</td><td>5.14</td><td>5.12</td><td>0.93</td><td>0.94</td><td>0.99</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>CP</td><td>%DM</td><td>0.0001</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.2692</td><td>0.1341</td><td>0.1783</td><td>0.4830</td><td><strong>4.45</strong></td><td>3.97</td><td>0.59</td><td>0.52</td><td>0.41</td><td>0.0002</td></tr><tr><td>plant</td><td>CF</td><td>%DM</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0001</td><td>0.5338</td><td>0.5877</td><td>0.5195</td><td>0.4462</td><td>37.73</td><td><strong>38.74</strong></td><td>0.47</td><td>0.67</td><td>0.60</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>NDF</td><td>%DM</td><td>0.0001</td><td>&lt; .0001</td><td>0.0037</td><td>0.2702</td><td>0.4355</td><td>0.6749</td><td>0.2568</td><td>69.73</td><td><strong>70.87</strong></td><td>0.45</td><td>0.68</td><td>0.51</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>ADF</td><td>%DM</td><td>0.0006</td><td>&lt; .0001</td><td>0.0009</td><td>0.2763</td><td>0.5504</td><td>0.6716</td><td>0.2933</td><td>41.49</td><td><strong>42.51</strong></td><td>0.46</td><td>0.67</td><td>0.52</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>ADL</td><td>%DM</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0044</td><td>0.8258</td><td>0.6746</td><td>0.7496</td><td>0.7036</td><td>6.24</td><td><strong>6.39</strong></td><td>0.64</td><td>0.69</td><td>0.76</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>IVOMD</td><td>%DM</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0080</td><td>0.6717</td><td>0.7906</td><td>0.7815</td><td>0.3911</td><td><strong>37.59</strong></td><td>36.41</td><td>0.52</td><td>0.66</td><td>0.64</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>IVDMD</td><td>%DM</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0047</td><td>0.7689</td><td>0.7954</td><td>0.5971</td><td>0.4967</td><td><strong>40.03</strong></td><td>38.92</td><td>0.53</td><td>0.66</td><td>0.67</td><td>&lt; .0001</td></tr><tr><td>plant</td><td>ASH</td><td>%DM</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.3788</td><td>0.2796</td><td>0.1033</td><td>0.8893</td><td>0.5068</td><td>6.09</td><td>5.98</td><td>0.39</td><td>0.48</td><td>0.35</td><td>0.0006</td></tr><tr><td colspan="15" style="text-align: left; font-weight: bold;">intercropping performance</td></tr><tr><td>plot</td><td>GY<sub>tot</sub></td><td>kg. ha<sup>-1</sup></td><td>&lt; .0001</td><td>0.0012</td><td>0.2176</td><td>0.1207</td><td>0.0078</td><td>0.0020</td><td>0.2048</td><td>1999.62</td><td><strong>2051.40</strong></td><td>0.29</td><td>0.01</td><td>0.35</td><td>0.0006</td></tr><tr><td>plot</td><td>LER<sub>sorg</sub></td><td></td><td>0.5190</td><td>0.2612</td><td></td><td></td><td>0.8289</td><td></td><td></td><td>0.71</td><td></td><td>0.21</td><td></td><td></td><td></td></tr><tr><td>plot</td><td>LER<sub>cowp<sup>**</sup></sub></td><td></td><td>&lt; .0001</td><td>0.1151</td><td></td><td></td><td>0.0735</td><td></td><td></td><td>0.58</td><td></td><td>0.00</td><td></td><td></td><td></td></tr><tr><td>plot</td><td>LER<sub>tot<sup>**</sup></sub></td><td></td><td>0.5190</td><td>0.1559</td><td></td><td></td><td>0.5459</td><td></td><td></td><td>1.29</td><td></td><td>0.25</td><td></td><td></td><td></td></tr><tr><td>plot</td><td>GY<sub>cowp</sub></td><td>kg. ha<sup>-1</sup></td><td>&lt; .0001</td><td>0.1141</td><td></td><td></td><td>0.0755</td><td></td><td></td><td>605.57</td><td></td><td>0.00</td><td></td><td></td><td></td></tr><tr><td>plot</td><td>HGW<sub>cowp</sub></td><td>g</td><td>0.5190</td><td>0.3204</td><td></td><td></td><td>0.6499</td><td></td><td></td><td>15.05</td><td></td><td>0.14</td><td></td><td></td><td></td></tr></tbody></table>

<footnote>** estimated with reference to cowpea plots grown in pure stands outside, but adjacent to the split plot design.</footnote>

<footnote>* see Table 2 for trait description</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 2. Correlation between cowpea and sorghum grain yield in intercropping in 2020 (r = 0.035, p-value=0.853) and 2021 (r = -0.183, p-value=0.332).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 3. Correlation between sorghum grain yield in intercropping and in pure stand across years (r = 0.66, p-value= 6.87 10⁻⁵).</figcaption></figure>

varieties in pure stand were generally the best in intercropping as well. However, two varieties, 014-SB-EPDU-1004 and Lata//IS15401-6-30-1-1, stood out for performing exceptionally well in intercropping compared to their performance in pure stand cultivation. Other varieties (ND07 e21(17 ×30) F2-6-v, C2-075-16, or CSM 660) also deviated from the main trend, showing better performance in intercropping than predicted based on their performance in pure stand. In 2020, the genotype × cropping system interaction term was close to significance (P = 0.0587; Table S1; Figure S3), and two varieties that clearly deviated from the global trend and showed higher grain yields than expected in intercropping can be reported: 014-SB-EPDU-1004 and ND07 e21(17 ×30)F2-6-v. The classification of varieties based on grain yield was very different between 2020 and 2021 when rains stopped earlier than in 2020 (Fig. 1). Early and/or drought-tolerant varieties better maintained their yields than long-cycle and/or drought-sensitive varieties, particularly in pure cultivation. This variation may explain the poor heritability calculated for grain yield in pure stand across years (Table 3).

### 3.5. Relationship between sorghum grain yield and number of days to 50 % heading

In 2020, sorghum grain yield was positively correlated with DTH50,

although not significantly (in intercropping R=0.3192 and p-value=0.0856; in pure stand R=0.1405 and p-value=0.4590). In contrast, in 2021, under more constrained conditions, sorghum grain yield was negatively correlated with DTH50. The correlation between GYsorg and DTH50 was negative and significant in intercropping (R=-0.4020, p-value=0.02765) and slightly negative but not significant in pure stand (R=-0.0823, p-value=0.6653). Additionally, in 2021, a positive and significant correlation was observed between partial LER for sorghum (LERsorg) and the number of days to 50 % heading (DTH50) in intercropping (R²=0.3227, p-value=1.058 10⁻³, Fig. 4). This relationship was not significant in 2020 (R²=0.0451, Figure S4). The rains stopped earlier in 2021, leading to a yield reduction compared to 2020, particularly for late varieties in pure stand. The relationship presented in Fig. 4 highlights the buffering effect of intercropping compared to pure cultivation which mitigated the negative impact of the early cessation of rains on late varieties in 2021.

### 3.6. Analysis of traits with significant Genotype × Cropping system interaction (G × CS)

Only five traits presented significant G × CS interaction: GWPAN, GNBPAN, PANNBST, LAI, and HI (Table 3).

For the number of grains per panicle (GNBPAN), this interaction
<figure><img src="image_6.png" /><figcaption>Fig. 4. Correlation between partial LER for sorghum (LER<sub>sorg</sub>) and number of days to 50 % heading (DTH50) in intercropping in 2021 (r = 0.568, p-value=1.058 10<sup>-3</sup>).</figcaption></figure>

concomitates with a high correlation between the pure stand and intercropping conditions (R²=0.75, Fig. 5). All varieties but four exhibited an increase in GNBPAN when intercropped. However, a few varieties deviated from this global trend showing higher GNBPAN values in intercropping than expected: 014-SB-EPDU-1004, CSM 660, C2-075-16, and Wed Wanga. These varieties also demonstrated higher yields in intercropping than expected (Fig. 3). Similar results were observed for grain weight per panicle (GWPAN), which is strongly correlated with GNBPAN (R²=0.75 or 0.57, p-value=1.19 10<sup>-10</sup> or 1.19 10<sup>-6</sup>, in intercropping and in pure stand respectively). The same varieties that stood out for GNBPAN also did for GWPAN (Figure S5).

A significant G × CS interaction was detected for the number of panicles per stem (PANNBST). However a strong correlation exists for this trait between the intercropping and pure stand conditions (R²= 0.7958, p-value=3.64 10<sup>-11</sup>, Figure S6). Two varieties are clearly distinguished by their high PANNBST in both intercropping and pure stand: Magadji1 and Fiibmiougou. All varieties showed an increase in their average number of panicles per stem in intercropping compared to pure stand, with the two aforementioned varieties exhibiting a more pronounced increase. Both are early guinea varieties. However, none of these two varieties produced high yields in intercropping (Fig. 3).

A significant G × CS interaction was detected for the Leaf Area Index (LAI). However, a strong correlation exists for this trait between intercropping and pure stand conditions (R²= 0.827, p-value=3.51 10<sup>-12</sup>, Figure S7). Two varieties, Loumbri and Sariaso 20, distinguished themselves by their positive deviation from the overall trend between intercropping and pure stand. But, neither of these varieties produced high yields in intercropping (Fig. 3). Both of these varieties also exhibited a very low harvest index (Figure S8) and adapted to intercropping conditions by increasing vegetative growth. A significant interaction was also detected for the harvest index (HI). However, the varieties Lata3 and P114 that distinguished themselves by their positive deviation from the global relationship between intercropping and pure stand (Figure S8) did not correspond to varieties that produced high yields in intercropping.

### 3.7. Sorghum traits influencing intercropping performance through impact on cowpea yield

The effect of sorghum genotype on cowpea yield was not significant (Table 3). Moreover, there was no negative correlation between sorghum grain yield and cowpea grain yield (Fig. 2). However, significant

<figure><img src="image_7.png" /><figcaption>Fig. 5. Correlation between the number of grains per panicle (GNBPAN) in intercropping and in pure stand across years (r = 0.868, p-value=5.09 10<sup>-10</sup>).</figcaption></figure>
relationships were identified between certain sorghum traits and cowpea grain yield in intercropping. In 2021, cowpea grain yield (GY<sub>cowp</sub>) was positively correlated with sorghum Harvest Index (HI) (R<sup>2</sup>= 0.26, p-value= 3.67 10<sup>-3</sup>, Fig. 6) and negatively with sorghum straw yield (SY) (R<sup>2</sup>=0.29, p-value=2.03 10<sup>-3</sup>, Fig. 7). In 2020, no such relationships were observed between GY<sub>cowp</sub> and HI (R<sup>2</sup>=0.0035, p-value=0.7577, Figure S9) and between GY<sub>cowp</sub> and SY (R<sup>2</sup>= 0.0062, p-value=0.680, Figure S10). On the other hand, the relationship between GY<sub>cowp</sub> and LAI was virtually nonexistent in both 2021 and 2020 (R<sup>2</sup>=0.0017 and 0.0062, p-value= 0.83 and 0.680 respectively; Figures S11 and S12).

# 4. Discussion

## 4.1. Production performance of intercropping

Across two experimental years and thirty sorghum cultivars, sorghum grain yields were higher in pure cultivation (2053 kg ha<sup>-1</sup>) than in intercropping (1391 kg ha<sup>-1</sup>). However, the total grain yield of the intercropping system (sorghum + cowpea) was not significantly different from the grain yield of sorghum in pure cultivation with yields of 2000 kg ha<sup>-1</sup> and 2051 kg ha<sup>-1</sup>, respectively. In terms of energy production, the intercropping system produced 6.61 10<sup>6</sup> calories ha<sup>-1</sup>, closely matching the 6.75 10<sup>6</sup> calories ha<sup>-1</sup> achieved in pure sorghum cultivation. However, the intercropping outperformed pure cultivation in protein production, yielding 290 kg ha<sup>-1</sup>, which is 34 % more than the 217 kg ha<sup>-1</sup> produced by sorghum in pure stand. These results are in line with the meta-analysis performed by Li et al. (2023), which demonstrates that intercropping does not achieve transgressive over-yielding (achieving higher yield than the most productive crop component, i.e. sorghum in our experiment) in terms of grain or calorie yield but can achieve higher protein yield, especially in low fertility conditions like ours. Land productivity was improved in intercropping with an overall estimated total LER of 1.29, corresponding to a partial LER of 0.71 for the sorghum component and an estimated LER of 0.58 for the cowpea component. In their literature review, Namatsheve et al. (2020) have determined an average LER of 1.26 in sorghum-cowpea intercrops in sub-Saharan Africa with lower partial LER values for cowpea compared to the cereal component. Intercropping enhanced the overall nutritional quality of sorghum fodder biomass, significantly increasing protein content by 12 % and slightly improving digestibility by 2.9 %. Very few studies, such as the one by Sanfo et al. (2023), have examined the impact of intercropping on fodder biomass and nutritional value. Therefore, further research is needed to explore the trade-offs

between grain and fodder production when intercropping sorghum and cowpea varieties with varying capacities for fodder or grain yield.

## 4.2. Effect of sorghum genetic variability on intercropping performance

In this experiment, a highly significant level of variability among sorghum cultivars was observed for most traits, with few instances of significant genotype × cropping system interactions. Sorghum traits in intercropping were strongly correlated with the same traits in pure stands. Broad-sense heritabilities in intercropping were similar to those calculated in pure stands. Furthermore, there was no significant effect of sorghum genotypic variability on the grain yield of the cowpea component in the intercropping system, nor was there a negative correlation between cowpea and sorghum grain yields in intercropping. These findings align with those of Galwey et al. (1986) and do not support the need for a large-scale sorghum breeding program specifically targeting this mode of intercropping. Superior genotypes can be reliably identified under pure stand conditions. Indeed, breeding for intercropping with substitutive design is costly because it requires twice the land compared to pure cultivation for evaluating the same number of genotypes. Nevertheless, even when sole-crop and intercropping yields are positively correlated, significant deviations from the trend may offer opportunities for plant breeding (Davis and Woolley, 1993). In this study, we observed a significant genotype × cropping system interaction for the number of grains per panicle (GNBPAN) for a few genotypes, 014-SB-EPDU-1004, CSM 660, Lata//IS15401-6-30-1-1-vrac, C2-075-16 and Wed Wanga. These genotypes exhibited remarkable plasticity for this trait with respective increases of 61 %, 48 %, 40 %, 37 % and 36 % in intercropping compared to pure cultivation, far exceeding the global trend (+16 %). All these cultivars also presented good yield performance in intercropping. The observed increases in GNBPAN as well as in grain weight per panicle (GWPAN) may be attributed to improved resource acquisition (Zhu et al., 2015). However, while significant genotype × cropping system interaction was detected for leaf area interception (LAI), it did not account for the exceptional yield performance of the aforementioned cultivars. The explanation may lie underground, in the phenotypic plasticity of sorghum root systems in intercropping. Varieties with high root phenotypic plasticity may have a greater capacity to explore larger soil volumes, thereby enhancing access to nutrients and water (Yang et al., 2022).

In 2021, we could observe a negative competitive relationship between sorghum and cowpea that was not observed in 2020. This competition resulted in a negative relationship between cowpea grain yield and sorghum straw yield in intercropping, while simultaneously

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Correlation between cowpea grain yield (GY<sub>cowp</sub>) and sorghum harvest index (HI) in intercropping in 2021 (r = 0.514, p-value= 3.67 10<sup>-3</sup>).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 7. Correlation between cowpea grain yield (GY<sub>cowp</sub>) and sorghum straw yield (SY) in intercropping in 2021 (r = -0.541, p-value=2.03 10<sup>-3</sup>).</figcaption></figure>

revealing a positive relationship between cowpea grain yield and sorghum harvest index (Figs. 6 and 7). This was not attributable to competition for light, as no negative correlation between cowpea yield and sorghum LAI was observed during either growing season, unlike the findings of Galwey et al. (1986). However, these latter worked with much higher sowing densities than us. Consequently, this competition may arise from limiting factors other than light, such as water or nutrient availability. Notably, water availability varied significantly between 2020 and 2021, despite similar overall cumulative rainfall (Fig. 1). The amount of rainfall from September onward, a critical period when sorghum is flowering and cowpea is filling its grain, was twice higher in 2020 (209 mm) than in 2021 (101 mm). To minimize the risk of competition, sorghum varieties with a high vegetative biomass and a low harvest index should be avoided in intercropping systems. Another consequence of water limitation conditions in 2021 was the significant positive relationship observed between the partial LER for sorghum and the number of days to 50 % heading. Late sorghum genotypes are generally unsuitable in Sub-Sahelian conditions due to the risk of post-flowering drought stress if rains stop early (Abreha et al., 2022). However, in 2021, intercropping mitigated the effects of water limitation for late sorghum varieties through temporal separation between cowpea and sorghum. The cowpea completed its growth cycle well before the late-maturing sorghum varieties, presumably reducing competition for water with sorghum in the intercrop compared with pure stands. Temporal complementarity contributes to the advantages of intercropping (Dong et al., 2018). Therefore, intercropping may allow for the use of a wider range of sorghum varieties in terms of crop cycle compared to pure cropping systems.

## 4.3. Scope of this study

This study focused on the effect of sorghum genotypic variability on the performance of intercropping with cowpeas, using a single cowpea variety as a tester. Consequently, we did not assess the influence of different cowpea varieties or the potential interactions between sorghum and cowpea varieties on intercropping performance. In barley- pea intercrops, the choice of pea variety has been shown to have a greater influence on intercrop performance than the choice of barley cultivars (Hauggaard-Nielsen and Jensen, 2001). For instance, an indeterminate pea cultivar competed more aggressively with barley, negatively affecting barley yields. A similar observation was made in sorghum-cowpea intercrops, where a late-maturing, dual-purpose cowpea variety (named Gourgou), known for its high fodder biomass production, competed strongly with sorghum, thereby impacting sorghum yields (data not shown). The cowpea variety used in our

experiment, Komcalle, is designed for grain production, and characterized by early maturity, and low biomass production. Due to its limited competitiveness with associated sorghum varieties, we were able to measure high overall sorghum partial LERs (LER<sub>sorghum</sub>= 0.71, Table 3). However, cowpea varieties exhibit variable architecture (from erect to prostrate types) and earliness characteristics (Timko and Singh, 2008). Ntare (1989) demonstrated that early-maturing, erect cowpea varieties experienced greater yield reductions but had less of a negative impact on millet yield compared to indeterminate, creeping types. Another important consideration is that we used a specific intercropping design, in which every other row of sorghum was replaced, alternating one row of sorghum with one row of cowpea. However, intercropping can be implemented in various ways, with different degrees of spatial and temporal segregation between the associated crops (Brooker et al., 2015). One common alternative is the traditional intercropping system used in Burkina Faso, where prostrate cowpea and sorghum are sown in the same seed hole (Ganeme et al., 2021). It is assumed that differences in intercropping arrangements should influence the nature and intensity of plant-plant interactions. The experiment was conducted with a low level of fertilization (75 kg.ha<sup>-1</sup> NPK) on soil with low inherent physicochemical fertility, reflecting conditions typical of those experienced by most farmers. However, the level of fertilization can also affect the balance of competition for resources (light, water, and nutrients) between species and, ultimately, the outcome of intercropping.

## 5. Conclusion

This study confirms the potential of intercropping to improve land use efficiency in low-fertility sub-Sahelian conditions. However, the improvement is qualitative rather than quantitative, as it did not result in an increase in total caloric production, but rather in higher protein yields. Intercropping can also provide other benefits not addressed in this paper, such as offering smallholders insurance against crop failure. Under our conditions, we observed very few genotype × cropping system interactions related to sorghum yield performance in intercropping. Consequently, the selection of sorghum varieties for optimizing intercropping should primarily focus on grain quality and the farmer's production objectives, while avoiding varieties with high biomass and low harvest index, as they could compete with cowpeas. Future research should focus on assessing the effects of cowpea genotype and genotype × cropping system interactions on sorghum-cowpea intercrop performance. Additionally, underground interactions between cowpea and sorghum roots should be considered. This would seem to be a relevant prerequisite before launching resource-intensive breeding programs aimed specifically at optimizing intercropping.
## Funding sources

This study was financially supported both by the CGIAR research program "Grain Legumes and Dryland Cereals" (GLDC) and by the McKnight Foundation's Global Collaboration for Resilient Food Systems (CRFS) program as part of the project "Feeding the soil and feeding the cow to feed the people: co-designing agro-sylvo-pastoral systems in sudano-sahelian Burkina Faso" (3 F).

## CRediT authorship contribution statement

**Raboin Louis-Marie:** Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Assouma Habibou:** Writing – review & editing, Methodology, Formal analysis. **Bonnal Laurent:** Writing – review & editing, Methodology, Formal analysis. **Dusserre Julie:** Writing – review & editing, Supervision, Methodology, Conceptualization. **Gozé Eric:** Writing – review & editing, Supervision, Methodology, Formal analysis. **Kabore Mariam:** Writing – review & editing, Investigation. **Ouedraogo Nofou:** Writing – review & editing, Supervision, Resources. **Ganeme Aminata:** Writing – review & editing, Investigation. **Zabre Yacouba:** Writing – review & editing, Investigation.

## Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used ChatGPT in order to improve English formulation. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The authors extend their gratitude to David Boundaogo for his invaluable assistance in establishing and overseeing the field experiments at the Gampela station.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.fcr.2025.109984.

## Data availability

Data will be made available on request.

## References

* Abreha, K.B., Enyew, M., Carlsson, A.S., Vetukuri, R.R., Feyissa, T., Motlhaodi, T., Ng'uni, D., Geleta, M., 2022. Sorghum in dryland: morphological, physiological, and molecular responses of sorghum under drought stress. Planta 255, 1–23. https://doi.org/10.1007/s00425-021-03799-7.

* Annicchiarico, P., Collins, R.P., De Ron, A.M., Firmat, C., Litrico, I., Hauggaard-Nielsen, H., 2019. Do we need specific breeding for legume-based mixtures? Adv. Agron. 157, 141–215. https://doi.org/10.1016/bs.agron.2019.04.001.

* Aufrère, J., Baumont, R., Delaby, L., Peccatte, J.R., Andrieu, J., Andrieu, J.P., Dulphy, J. P., 2007. Laboratory prediction of forage digestibility by the pepsin-cellulase method. The renewed equations. INRA Prod. Anim. 20, 129–136. https://doi.org/10.20870/productions-animales.2007.20.2.3445.

* Beillouin, D., Ben-Ari, T., Malézieux, E., Seufert, V., Makowski, D., 2021. Positive but variable effects of crop diversification on biodiversity and ecosystem services. Glob. Change Biol. 27 (19), 4697–4710. https://doi.org/10.1111/gcb.15747.

* Boukar, O., Fatokun, C.A., Roberts, P.A., Abberton, M., Huynh, B.L., Close, T.J., Kyei-Bhaen, S., Higgins, T.J.V., Ehlers, J.D., 2015. Cowpea. *Grain Legumes* 219–250.

* Brooker, R.W., Bennett, A.E., Cong, W.-F., Daniell, T.J., George, T.S., Hallett, P.D., Hawes, C., Iannetta, P.P.M., Jones, H.G., Karley, A.J., Li, L., McKenzie, B.M., Pakeman, R.J., Paterson, E., Schöb, C., Shen, J., Squire, G., Watson, C.A., Zhang, C., Zhang, F., Zhang, J., White, P.J., 2015. Improving intercropping: a synthesis of research in agronomy, plant physiology and ecology. N. Phytol. 206, 107–117. https://doi.org/10.1111/nph.13132.

* BUNASOL, 1988. Etude pédologique de la stationexpérimentale de Gampela, échelle 1/5000. N° 59 279.

* Carsky, R.J., Singh, L., Ndikawa, R., 1994. Suppression of Striga hermonthica on sorghum using a cowpea intercrop. Exp. Agric. 30, 349–358. https://doi.org/10.1017/S0014479700024467.

* Cong, W.F., Hoffland, E., Li, L., Six, J., Sun, J.H., Bao, X.G., Zhang, F.S., Van Der Werf, W., 2015. Intercropping enhances soil carbon and nitrogen. Glob. Change Biol. 21 (4), 1715–1726. https://doi.org/10.1111/gcb.12738.

* Davis, J.H.C., Woolley, J.N., 1993. Genotypic requirement for intercropping. Field Crops Res. 34 (3-4), 407–430. https://doi.org/10.1016/0378-4290(93)90124-6.

* Demie, D.T., Döring, T.F., Finckh, M.R., van der Werf, W., Enjalbert, J., Seidel, S.J., 2022. Mixture× genotype effects in cereal/legume intercropping. Front. Plant Sci. 13, 846720. https://doi.org/10.3389/fpls.2022.846720.

* Dong, N., Tang, M.M., Zhang, W.P., Bao, X.G., Wang, Y., Christie, P., Li, L., 2018. Temporal differentiation of crop growth as one of the drivers of intercropping yield advantage. Sci. Rep. 8 (1), 3110. https://doi.org/10.1038/s41598-018-21414-w.

* EN ISO 6865:2001. Animal feeding stuffs — Determination of crude fibre content — Method with intermediate filtration.

* EN ISO 16472:2006. Animal feeding stuffs — Determination of amylase-treated neutral detergent fibre content (aNDF).

* EN ISO 13906:2008. Animal feeding stuffs — Determination of acid detergent fibre (ADF) and acid detergent lignin (ADL) contents.

* EN ISO 5983-2:2009. Animal feeding stuffs — Determination of nitrogen content and calculation of crude protein content — Part 2: Block digestion and steam distillation method.

* Galwey, N.W., De Queiroz, M.A., Willey, R.W., 1986. Genotypic variation in the response of sorghum to intercropping with cowpea, and in the effect on the associated legume. Field Crops Res. 14, 263–290. https://doi.org/10.1016/0378-4290(86)90063-8.

* Ganeme, A., Douzet, J.M., Traore, S., Dusserre, J., Kaboré, R., Tirogo, H., Nabaloum, O., Ouedraogo, N.W.Z.S., Adam, M., 2021. 2021. L'association sorgho/niébé au poquet, une pratique traditionnelle en zone soudano-sahélienne à faible rendement: état des lieux et pistes d'amélioration. International. J. Innov. Appl. Stud. 31 (4), 836–848.

* Gu, C., Bastiaans, L., Anten, N.P., Makowski, D., van Der Werf, W., 2021. Annual intercropping suppresses weeds: A meta-analysis. Agric., Ecosyst. Environ. 322, 107658. https://doi.org/10.1016/j.agee.2021.107658.

* Hadebe, S.T., Modi, A.T., Mabhaudhi, T., 2017. Drought tolerance and water use of cereal crops: a focus on sorghum as a food security crop in sub-Saharan Africa. J. Agron. Crop Sci. 203 (3), 177–191. https://doi.org/10.1111/jac.12191.

* Hauggaard-Nielsen, H., Jensen, E.S., 2001. Evaluating pea and barley cultivars for complementarity in intercropping at different levels of soil N availability. Field Crops Res. 72 (3), 185–196. https://doi.org/10.1016/S0378-4290(01)00176-9.

* Jensen, E.S., Carlsson, G., Hauggaard-Nielsen, H., 2020. Intercropping of grain legumes and cereals improves the use of soil N resources and reduces the requirement for synthetic fertilizer N: a global-scale analysis. Agron. Sustain. Dev. 40, 1–9. https://doi.org/10.1007/s13593-020-0607-x.

* Jodha, N.S., 1980. Intercropping in traditional farming systems. J. Dev. Stud. 16 (4), 427–442. https://doi.org/10.1080/00220388008421770.

* Li, C., Stomph, T.J., Makowski, D., Li, H., Zhang, C., Zhang, F., van der Werf, W., 2023. The productive performance of intercropping. Proceedings of the National Academy of Sciences, 120(2), e2201886120. https://doi.org/10.1073/pnas.2201886120.

* Li, X.F., Wang, Z.G., Bao, X.G., Sun, J.H., Yang, S.C., Wang, P., Wang, C.B., Wu, J.P., Liu, X.R., Tian, X.L., Wang, Y., Li, J.P., Wang, Y., Xia, H.Y., Mei, P.P., Wang, X.F., Zhao, J.H., Yu, R.P., Zhang, W.P., Che, Z.X., Gui, L.G., Callaway, R.M., Tilman, D., Li, L., 2021. Long-term increased grain yield and soil fertility from intercropping. Nat. Sustain. 4 (11), 943–950. https://doi.org/10.1038/s41893-021-00767-7.

* Lithourgidis, A.S., Dordas, C.A., Damalas, C.A., Valachostergios, D., 2011. Annual intercrops: an alternative pathway for sustainable agriculture. Aust. J. Crop Sci. 5 (4), 396–410. https://search.informit.org/doi/10.3316/informit.281409060336481.

* Litrico, I., Violle, C., 2015. Diversity in plant breeding: a new conceptual framework. Trends Plant Sci. 20 (10), 604–613. https://doi.org/10.1016/j.tplants.2015.07.007.

* Louarn, G., Corre-Hellou, G., Fustec, J., Pelzer, E., Julier, B., Litrico, I., Hinsinger, P., Lecomte, C., 2010. Déterminants écologiques et physiologiques de la productivité et de la stabilité des associations graminées-légumineuses. Innov. Agron. 11, 79–99. https://doi.org/10.17180/41ch-qa28.

* Martin-Guay, M., Paquette, A., Dupras, J., Rivest, D., 2018. The new green revolution: sustainable intensification of agriculture by intercropping. Sci. Total Environ. 615, 767–772. https://doi.org/10.1016/j.scitotenv.2017.10.024.

* Mead, R., Willey, R.W., 1980. 'The Concept of a "Land Equivalent Ratio" and Advantages in Yields from Intercropping'. Exp. Agric. 16 (3), 217–228. https://doi.org/10.1017/S0014479700010978.

* Mekonnen, T.W., Gerrano, A.S., Mbuma, N.W., Labuschagne, M.T., 2022. Breeding of vegetable cowpea for nutrition and climate resilience in Sub-Saharan Africa: progress, opportunities, and challenges. Plants 11 (12), 1583. https://doi.org/10.3390/plants11121583.

* Namatsheve, T., Cardinael, R., Corbeels, M., Chikowo, R., 2020. Productivity and biological N 2-fixation in cereal-cowpea intercropping systems in sub-Saharan Africa.
Africa. A review. Agron. Sustain. Dev. 40, 1–12. https://doi.org/10.1007/s13593-020-00629-0.

Ntare, B.R., 1989. Evaluation of cowpea cultivars for intercropping with pearl millet in the Sahelian zone of West Africa. Field Crops Res. 20, 31–40. https://doi.org/10.1016/0378-4290(89)90021-X.

Odo, P.E., 1991. Evaluation of short and tall sorghum varieties in mixtures with cowpea in the Sudan savanna of Nigeria: land equivalent ratio, grain yield and system productivity index. Exp. Agric. 27 (4), 435–441. https://doi.org/10.1017/S0014479700019426.

Patrignani, A., Ochsner, T.E., 2015. Canopeo: a powerful new tool for measuring fractional green canopy cover. Agron. J. 107 (6), 2312–2320. https://doi.org/10.2134/agronj15.0150.

Paul, M.R., Demie, D.T., Seidel, S.J., Döring, T.F., 2024. Evaluation of multiple spring wheat cultivars in diverse intercropping systems. Eur. J. Agron. 152, 127024. https://doi.org/10.1016/j.eja.2023.127024.

Pingali, P.R., Deevi, K.C., Birthal, P.S., 2020. Enabling markets, trade and policies for enhancing sorghum uptake. Sorghum 21st Century.: Food-Fodd. -Feed-Fuel a Rapidly Chang. World 17–39. https://doi.org/10.1007/978-981-15-8249-3_2.

de Queiroz, M.A., Willey, R.W., Galwey, N.W., 1988. The effect of intercropping with cowpea on genotype × environment interaction in sorghum. Euphytica 39, 175–184. https://doi.org/10.1007/BF00039871.

Raseduzzaman, M., Jensen, E.S., 2017. Does intercropping enhance yield stability in arable crop production? A meta-analysis. Eur. J. Agron. 91, 25–33. https://doi.org/10.1016/j.eja.2017.09.009.

Sanfo, A., Zampaligre, N., Delma, B.J., Kulo, A.E., Ouedraogo, A., Rios, E.F., Dubeux, J., Boote, J.K., Adesogan, A., 2023. Sorghum [Sorghum bicolor (L.) Moench] and cowpea [Vigna unguiculata (L.) Walpers] intercropping improves grain yield, fodder biomass, and nutritive value. Front. Anim. Sci. 4, 1233570. https://doi.org/10.3389/fanim.2023.1233570.

Sanou, J., Bationo, B.A., Barry, S., Nabie, L.D., Bayala, J., Zougmore, R., 2016. Combining soil fertilization, cropping systems and improved varieties to minimize climate risks on farming productivity in northern region of Burkina faso. Agric. Food Secur 5, 1–12. https://doi.org/10.1186/s40066-016-0067-3.

Sharma, N.K., Singh, R.J., Mandal, D., Kumar, A., Alam, N.M., Keesstra, S., 2017. Increasing farmer's income and reducing soil erosion using intercropping in rainfed maize wheat rotation of Himalaya, India. Agr. Ecosyst. Environ. 247, 43–53. https://doi.org/10.1016/j.agee.2017.06.026.

Tamburini, G., Bommarco, R., Wanger, T.C., Kremen, C., Van Der Heijden, M.G., Liebman, M., Hallin, S., 2020. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6 (15), eaba1715. https://doi.org/10.1126/sciadv.aba1715.

Tang, X., Zhang, C., Yu, Y., Shen, J., van der Werf, W., Zhang, F., 2021. Intercropping legumes and cereals increases phosphorus use efficiency; a meta-analysis. Plant Soil 460, 89–104. https://doi.org/10.1007/s11104-020-04768-x.

Tilman, D., Wedin, D., Knops, J., 1996. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379 (6567), 718–720. https://doi.org/10.1038/379718a0.

Timko, M.P., Singh, B.B., 2008. Cowpea, a multifunctional legume. In: Moore, P.H., Ming, R. (Eds.), Chapter 10, Genomics of Tropical Crop Plants. Springer, pp. 227–258. https://doi.org/10.1007/978-0-387-71219-2_10.

Timko, M.P., Ehlers, J.D., Roberts, P.A., 2007. Cowpea. In: Kole, C. (Ed.), Pulses, Sugar and Tuber Crops, Genome Mapping and Molecular Breeding in Plants. Springer-Verlag, Berlin Heidelberg, pp. 49–67. https://doi.org/10.1007/978-3-540-34516-9_3_Vol. 3.

Trenbath, B.R., 1993. Intercropping for the management of pests and diseases. Field Crops Res. 34 (3-4), 381–405. https://doi.org/10.1016/0378-4290(93)90123-5.

USDA, 2024a. FoodData Central Search Results. https://fdc.nal.usda.gov/fdc-app.html#/food-details/169716/nutrients).

USDA, 2024b. FoodData Central Search Results. https://fdc.nal.usda.gov/fdc-app.html#/food-details/173758/nutrients).

Yang, H., Xu, H.S., Zhang, W.P., Li, Z.X., Fan, H.X., Lambers, H., Li, L., 2022. Overyielding is accounted for partly by plasticity and dissimilarity of crop root traits in maize/legume intercropping systems. Funct. Ecol. 36 (9), 2163–2175. https://doi.org/10.1111/1365-2435.14115.

Yu, Y., Stomph, T.J., Makowski, D., van der Werf, W., 2015. Temporal niche differentiation increases the land equivalent ratio of annual intercrops: a meta-analysis. Field Crop Res 184, 133–144. https://doi.org/10.1016/j.fcr.2015.09.010.

Zhu, J., van der Werf, W., Anten, N.P., Vos, J., Evers, J.B., 2015. The contribution of phenotypic plasticity to complementary light capture in plant mixtures. N. Phytol. 207 (4), 1213–1222. https://doi.org/10.1111/nph.13416.

Zougmore, R., Kambou, F.N., Ouattara, K., Guillobez, S., 2010. Sorghum cowpea intercropping? An effective technique against runoff and soil erosion in the Sahel (Saria, Burkina Faso). Arid Soil Res Rehabil. 14, 329–342. https://doi.org/10.1080/08903060050136441.