1 Introduction

Africa south of the Sahara (sub-Saharan Africa, SSA) is extremely sensitive to food crises. In this region, agri-food production per capita has remained stagnant over the past decades. The high growth rate in SSA could, also, result in limited access to sufficient food from staple food crops (Giller, 2020; Van Ittersum et al., 2016). In such a fragile agri-food system, producing locally and sustainably is becoming central to building a sustainable African agri-food production systems and meeting food and nutritional security. Such a strategy however is challenged by multiple biotic constraints and abiotic stresses, which results in massive crop yield losses (Setimela et al., 2018). Several previous studies have reported that biotic constraints (i.e., diseases, pests, and parasitic weeds) caused about 30% crop yield loss worldwide (Savary et al., 2019). Striga spp. parasitism is considered as one of the most devastating agriculture problems across SSA countries. Over 50% of the arable land under cereals in these countries is infested by Striga spp. (Gressel et al., 2004; Rodenburg et al., 2016). Striga hermonthica (Benth.), S. asiatica (L.) Kuntze, S. forbessii (Benth.) and S. aspera (Willd.) Benth, were reported to parasitize and cause important yield losses on maize (Zea mays L.) (Nyakurwa et al., 2018), sorghum (Sorghum bicolor L.) (Gurney et al., 2002), finger millet (Eleusine coracana L.) (Nyongesa et al., 2018), pearl millet (Pennisetum glaucum L.) (Wilson et al., 2004), and rice (Oryza sativa L.) (Fig. 1), resulting in an increased food insecurity (Adewale et al., 2020; Rodenburg et al., 2016). With climate change and the ever-increasing new contaminated areas and infestation level, the negative effect of these parasitic weeds on crop production will become more pronounced. The long-term impact of these parasitic weeds is especially high because of their persistent seeds in soils and strong dispersal ability to uninfested fields.

Fig. 1
figure 1

Striga spp. on cereal host crops in SSA. a S. asiatica on sorghum, Masvingo-Zimbabwe, b S. asiatica on maize, Manicaland, Zimbabwe (Credit (a, b): Dr. Stanford Mabasa), c, d, e S. hermonthica on maize, Ibadan-Nigeria (Credit: Dr. Badu-Apraku, IITA), f S. hermonthica, Nyanza-Kenya (Credited: Mr. Geoffrey Wanjala, Cereal Growers Association-Kenya)

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Numerous studies indicated that under highly infested fields (Fig. 1) Striga parasitism can destroy the crop resulting in 100% yield loss (Nyakurwa et al., 2018; Pfunye et al., 2021; Yacoubou et al., 2021). Almost half century ago (1940s), Striga research began and since, many research national and international institutions gave more interest in conducting research activities on all aspects related to Striga management including agronomy, metabolomics, physiology, genomics and crop improvement. This resulted in generating a total number of 2 527 research article reported in the Web of Science and Scopus out of which, 320 were related to all aspects of genetics, breeding, and resistance mechanisms (Figs. 2 and 3).

Fig. 2
figure 2

Cumulative number of Striga weed studies over half-a-century time conducted by African institutions and collaborators on specific cereal growing environments. Out of 2 527 total published papers, 320 were highly specific towards breeding for Striga resistance in cereals host crop species

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Fig. 3
figure 3

A authors keywords maps: Network visualization of authors’ keywords co-occurrence and thematic evolution in the field of research on plant resistance against Striga spp. in SSA (Map produced by VOSviewer); B Country collaboration network conducting research on Striga resistance in cereal crops in SSA

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Economically, maize grain yield losses caused by Striga are estimated to exceed 2.1 million tonnes representing US$ 7 billion (Gasura et al., 2021; Nyakurwa et al., 2018). In sorghum and millet, annual yield losses due to Striga parasitism were estimated to 8.6 million tons (Ejeta, 2007; Mallu et al., 2021). In rice, 300 thousand to half a million tons of grain yield is lost due to Striga infestation which is equivalent to US$ 117–200 million annually (Gressel et al., 2004; Rodenburg et al., 2016; Mwangangi et al., 2021). In Eastern and Northern Zimbabwe, Striga spp. affects 75.7% of farmers’ cereal fields costing about US$100 million annually (Mandumbu et al., 2019). In Nigeria’s Northern Guinea Savanna, and Kenya, sorghum production in Striga-infested fields decreased by more than 50% (Runo, 2019). In the whole West Africa, Striga infestation has a considerable economic impact on both maize and rice production, threatening the food security and livelihoods of millions of resource-poor farmers and consumers (Rodenburg et al., 2016). Failing to set up a long-term sustainable strategy for Striga management could lead to yield losses equivalent to US$ 30 million a year for each concerned country (Rodenburg et al., 2016).

The level of infestation as well as the parasitism impact on the crop are exacerbated by low soil fertility, and limited or erratic rainfall, which are respectively resulting from agricultural intensification and climate change (Berner et al., 1996). Remote sensing and geographic information systems (GIS) enabled identifying current and future Striga-risk and resources allocation priority areas, and scale out containment measures (Mudereri et al., 2020). The agronomic and economic impact of parasitic weeds as well as their distribution in SSA underlines the importance of designing different components of an integrated control strategy that could help overcoming this threat for food security in SSA.

Striga weeds have a large and long-lasting seed production (400–600 thousand seeds plant−1 year−1) and viability(15–20 years life-spans) that results in a very large and inevitable impacts on crop production (Chitagu et al., 2014; Dzomeku & Murdoch, 2007; Mandumbu et al., 2019). Breeding for resistance, the development of resistant varieties, and encouraging farmers to adopt and grow resistant varieties remains the most sustainable, environment friendly, long-term and economically benign method for successful control of Striga (Gasura et al., 2021; Makaza et al., 2021). This review takes a closer look at the morpho-physiological responses of host plants and potential resistant mechanisms involved in the resistance against Striga parasitism. We review the effects of Striga infestation on host growth and development, stomatal performance, water use efficiency, hormonal effects, and photosynthesis. These aspects are considered from the perspective of host plant resistance to Striga in cereal crops.

2 Morpho-physiological response of cereal host crops to Striga infestation

2.1 Striga parasitism effect on host plant growth

In response to Striga parasitism, chlorosis, withering/early wilting, and leaf rolling, are the most common symptoms in maize, rice, sorghum and millet (Badu-Apraku & Akinwale, 2011; Nyongesa et al., 2018; Rodenburg et al., 2016). Gasura et al. (2021) highlighted that the negative responses associated with Striga infestation are strongly correlated with the infection level and the number of emerged Striga per host plant which is considered as one of the resistance, tolerance or susceptibility parameters.

Nyakurwa et al. (2018) revealed that Striga-tolerant maize genotype experienced early maturation to escape Striga parasitism stress. This influenced plant anthesis, silking, grain filling, as well as seed production (Badu-Apraku et al., 2021a). Nyakurwa et al. (2018) indicated that Striga parasitism affected the anthesis-silking intervals in maize, resulting in the loss of male-female flower synchrony. Striga infestation forces, also, host plants to develop rapidly and complete their life cycle before the extreme stress level that occurs at the end of infection process (Runo, 2019). Such early maturing affects the photosynthates allocation which results with a decreased grain yield (Khan et al., 2001). This justifies the need to develop early maturing and short cycle resistant genotypes that can escape the Striga parasitism at the end of the crop cycle.

2.2 Host-physiological response to Striga parasitism

2.2.1 Stomatal closure

Research revealed that stomatal closure is the first line of defence in plants under stress conditions. It prevents further water loss through transpiration while increasing heat dissipation in the leaves (Fig. 4) (Cissoko et al., 2011; Frost et al., 1997). Stewart and Press (1990) found that witchweeds increased the transpiration rate, which may predispose hosts to water stress and thus stomatal closure. Gurney et al. (2002) reported that abscisic acid (ABA) is the main molecule that signals stomatal closure in plants under stress and may result in hormonal imbalance. Khan et al. (2001) showed that ABA from host plant roots could trigger stomatal closure even when leaf water potential is not affected. Previous studies ascertained that elevated ABA in sorghum and rice leaves could lead to decreased net photosynthesis limiting CO2 intake and an increasing O2 levels in the leaf cells (Ejeta, 2007; Gurney et al., 2002; Nyakurwa et al., 2018; Press & Stewart, 1987; Rodenburg et al., 2008).

Fig. 4
figure 4

The conceptual framework of Striga stress plant with the traits categorized by main drivers of yield (Y = LI x RUE x HI): Y = yield; LI = light intensity; HI = harvest index. The figure has been developed using the Biorender free access

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2.2.2 Water use efficiency (WUE)

Compared to non-infected plants, Striga infected plants have lower water use efficiency (WUE) (Badu-Apraku et al., 2021b; Gebremedhin et al., 2000; Shah et al., 1987). Gebremedhin et al. (2000) showed that WUE of the SRN-39 sorghum genotype was reduced by around 24% under Striga infestation which resulted in a limited dry matter accumulation. The dynamic alterations in leaf morphology all with the allometric coefficient showed that Striga parasitism leads to an afflicted host plants' water balance (Shah et al., 1987).

The competitive parasitism effects, for water and nutrients requirement, between Striga plants and their host is usually attributed to Striga negative osmotic potential compared to the host (Watling & Press, 1997). Parker and Riches (1993) reported a clear change in the host root-shoot balance in response to Striga infestation. Such dynamic changes in morphology of Striga-infected plants is considered as an adaptive mechanism to Striga inflicted competition for water and nutrients.

2.2.3 Hormonal effect and photosynthesis under Striga parasitism

Sorghum experiments conducted by Rodenburg et al. (2005) postulated that Striga resistant sorghum genotypes showed higher cytokinins concentration compared to susceptible checks. Yang et al. (2016) reported that, in addition to their role in cell division and shoot-root morphogenesis, cytokinins are also involved in the stay-green and senescence traits with specific adjustments of the chloroplast structural changes, vacuolar collapse and loss of plasma membrane integrity (Watling & Press, 1997). It was reported that, in general, Striga-resistant plants maintain the stability and the composition of their cellular membranes under Striga parasitism (Gebremedhin et al., 2000; Watling & Press, 2000). This was confirmed by Mandumbu et al. (2019) who observed a poor cellular membrane stability in susceptible sorghum genotypes under both S. hermonthica and S. asiatica parasitism. Such behaviour was associated with a lower photosynthesis rate (Mandumbu et al., 2019). Nyakurwa et al. (2018) showed that, under Striga parasitism, stomatal closure decreased CO2 fixation. Wada et al. (2019) mentioned that Striga effects on host plant have a similar effect as drought stress, where the infection can cause stress-induced cellular alterations and reactive oxygen molecule overproduction (ROS). Gebremedhin et al. (2000) and Kanampiu et al. (2018) showed that under high Striga infestation, regressive signals such as 3′-phosphoadenosine-5′-phosphate are accumulated and transported from the chloroplast to the nucleus. Other studies showed that the photosynthetic pathways and their associated enzymes (RUBISCO, PEP carboxylase and NADP-malic enzymes) remain unaffected in leaves of Striga-infected sorghum (Gurney et al., 1995) and maize (Smith et al., 1995). Frost et al. (1997) found that susceptible sorghum genotypes showed high respiration rates, that increased reactive oxygen species (ROS) leading to cell damage. This affect the photoprotective metabolites such as carotenoids, flavonoids, glutathione, ascorbate, and tocopherols throughout the host cells and its organelles (Watling & Press, 1997).

2.2.4 Chlorophyll content

The chlorophyll content is another parameter negatively impacted by Striga infestation in host plants (Gwatidzo et al., 2020; Makani et al., 2020). Previous studies showed also that Striga parasitism causes distress in C production in host plants, which may limit N and Mg assimilation, resulting in low chlorophyll synthesis (Gurney et al., 2002; Mwangangi et al., 2021). Low chlorophyll content in host plant leaves is associated with reduced plant biomass production due to changes in photosynthetic activities (Rodenburg et al., 2016). Such decreases of chlorophyll content are usually associated with PSII quantum efficiency decreases (Amri et al., 2021; Gurney et al., 2006). Resistance genotypes, however, were able to keep a relatively high chlorophyll content, which promote photoprotective compounds biosynthesis, scavenge ROS and dissipate extra energy via xanthophyll-mediated non-photochemical quenching (Gurney et al., 1995; Watling & Press, 1997). High chlorophyll content postulates strong correlation with high photosynthetic rates (Amri et al., 2021; Nyakurwa et al., 2018). This indicates that the resistant host plants can produce photosynthates under Striga infestation and provide enough resources for their growth in addition to the parasite needs (Gasura et al., 2021; Mbuvi et al., 2017). Several previous studies highlighted that chlorophyll content can be used as an indirect indicator of sorghum (Ronald et al., 2016), maize (Nyakurwa et al., 2018), rice (Rodenburg et al., 2015) and millet (Mallu et al., 2021) tolerance to Striga as well as Orobanche resistance in faba bean (Vicia faba L.) (Amri et al., 2021). In addition, the first signals of Striga parasitism include the photochemical reactions changes in host leaves that might be used to estimate photosynthetic performance and could appear in the chlorophyll fluorescence kinetics (Frost et al., 1997). Olivier et al. (1991) showed that Striga exhibited low C assimilation rate, which is compensated by carbon transfer from their hosts. Cechin and Press (1994) has drawn attention to the obligate transfer of carbon when nitrogen moves as organic compounds from the host plant to the parasite. It has been shown that xylem sap of Striga is rich in organic and low in inorganic nitrogen (Berner et al., 1996), which suggest that part of the carbon transfer may occur in the form of amino acids or amides. If Striga is partially dependent on its host for carbon as well as nitrogen and other mineral elements, then the maintenance of high transpiration rates might be a key factor in determining its growth.

2.2.5 Chlorophyll fluorescence

Chlorophyll fluorescence (maximum quantum efficiency Fv/Fm ratio) characteristics which include electron transport rate (ETR) via PSII, photochemical (Pq) and non-photochemical quenching (NPq) are essential host plant parameters which are significantly affected by Striga parasitism and could be considered as early indicators of resistance against this parasite (Runo & Kuria, 2018). It is a non-destructive and rapid assessing tool of photochemical quantum yield and photo-inhibition that has been recommended as indirect practical screening tools for early parasitism detection, diagnosis, screening, and selection of high resistant genotypes against parasitic weeds (Amri et al., 2021; Gurney et al., 1995). Striga parasitism resulted in a continuous reduction in ETR and Pq in sorghum and rice since it is proportional to the energy transfer to the functional CO2 assimilation reaction centres (Rodenburg et al., 2008). Both ETR and Pq were strongly correlated with CO2 assimilation (Watling & Press, 2000). Carbon budget models suggest that low CO2 fixation rates by the host canopy may account for 80% of the difference in productivity between infected and non-infected host plants (Gurney et al., 2002; Khan et al., 2001). This is in harmony with the findings of Rodenburg et al. (2016) who highlighted that the rate of light saturating CO2 fixation in infected sorghum plants was limited to only 60%.

Although Striga is photosynthetic, 14C-feeding studies and measurements of stable carbon isotopes demonstrated that the parasite is partially heterotrophic with a host-derived carbon proportion varying between 5 and 35% (Gurney et al., 1995). Striga tolerant maize, sorghum, and millet genotypes have improved CO2 assimilation rate due to structural modifications (Press & Stewart, 1987; Watling & Press, 2000). The physiological response is coupled with metabolic reactions where phosphoenolpyruvate (PEP) is carboxylated to C4 acids concentrated in the mesophyll cells. The acids are then transferred, by diffusion, to the bundle sheath, where they are decarboxylated. The CO2 concentration in the bundle sheath is sufficient to suppress the oxygenase activity of Rubisco and improve photosynthesis in tolerant genotypes (Watling & Press, 1997). For C3 plants such as rice plant photorespiration results in substantial losses of fixed carbon, particularly under Striga infestation (Taniguchi et al., 2008; Watling & Press, 2000). Taniguchi et al. (2008) tried to develop a C4 photosynthetic pathway into rice, which resulted in an increased radiation use efficiency. However, previous attempts to introduce a single-cell C4 pathway into rice led to increased photosynthesis photoinhibition, leaf chlorophyll bleaching and stunting with no evidence of CO2 concentration in chloroplasts (Taniguchi et al., 2008).

3 Harnessing natural host crops resistance against Striga spp.

3.1 Genetic basis for host plant resistance and defence mechanisms

Host plant resistance remain the most sustainable, economically, and eco-friendly method to control the parasitic weeds. Striga-resistance is a polygenic trait that requires combining whole-genome information (Badu-Apraku et al., 2020a; Shaibu et al., 2021). Genotyping and phenotyping are therefore critical to correlate the resistance-associated traits with particular gene loci. This strategy was useful in many previous studies conducted on maize, sorghum and rice (Mbuvi et al., 2017; Mutinda et al., 2018). Several studies reported various resistance mechanisms associated with low striga infection and higher seed yield observed in resistant genotypes under Striga-afflicted environments (Gasura et al., 2019, 2021). these mechanisms includes the low production of germination stimulant, physical barriers (i.e. lignification of cell walls), inhibition of germ tube exo-enzymes by root exudates, phyto-alexine synthesis, post-attachment hypersensitive reactions or necrosis, incompatibility, antibiosis, i.e., reduced Striga development through unfavourable phytohormone supply by the host, insensitivity to Striga toxin like iridoid glycocides (i.e. maintenance of stomatal aperture and photosynthetic efficiency) and avoidance through root growth habit (i.e. fewer roots in the upper 15–20 cm soil depth) (Fig. 4) (Dayou et al., 2021; Mutinda et al., 2018; Yacoubou et al., 2021). Overall, genomic selection via omics and system biology, can models all markers in a framework that doesn’t only identify marker-trait associations but incorporates all markers’ effects to predict yield (Table 1).

Table 1 List of reported candidate QTLs/ genes associated with Striga resistance in maize and sorghum
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Periodic genetic gains assessment for Striga resistance for SSA proved to be a key component to evaluate the effectiveness of breeding programs (Menkir & Meseka, 2019). Badu-Apraku et al. (2013) reported that the use of early maturing maize cultivars under Striga-inflicted conditions resulted in an annual genetic gain of 1.93% with an increased grain yield from 2 537 kg ha−1 (1988–2000) to 3 122 kg ha−1 (2007–2010). Similar results were reported by Badu-Apraku et al. (2019) who showed that bi-parental maize population yielded 498 kg ha−1 (16.9%) and 522 kg ha−1 (12.6%) under Striga-infested and non-infested conditions, respectively.

3.2 Pre-attachment resistance mechanisms

Host-parasite interaction is strongly dependent on germination stimulants (strigolactones; SLs) produced in host root exudates (Fig. 4). SLs are germination triggers and elicitors of the parasite attachment and the host-parasite interface. This array of signal exchanges between Striga and its hosts leading to successful parasitism is a fascinating biological phenomenon (Rich & Ejeta, 2008). Throughout their evolution, Striga have developed an adapted perception and signalling mechanisms to detect SLs in the rhizosphere of host root systems. Different genotypes of the same plant species may produce different SLs combinations, which determine their levels of resistance or susceptibility to Striga (Cardoso et al., 2011). The stereochemistry and the structural diversity of SLs influence the host biological activities and define the pre-and-post emergence host resistance levels (Yoneyama, 2020). Qualitative, quantitative, and functional differences in SLs production by different host plants raise the question of whether the resistance (or the susceptibility) in host plants depends on the quantity and/or the structural diversity of SLs. Low orobanchol biosynthesis that resulted in low Striga seed germination was reported in SRN39 sorghum variety, (Gebremedhin et al., 2000; Gobena et al., 2017). Yoneyama et al. (2010) postulated that 5-deoxystrigol was the main SL produced by Striga-susceptible sorghum cultivars, whereas sorgomol was mainly exuded by Striga-resistant cultivars.

SLs are responsible of breaking Striga seed dormancy, stimulating its germination, the development of a haustorium and radicle elongation in a chemotropic response to the concentration gradient of root exudates (Haussmann et al., 2000; Timko et al., 2012). Haustoria form a morphological and physiological graft with the host roots. They produce phytotoxins and hydrolytic enzymes to degrade the root cell walls and disrupt the host defence system (Ejeta, 2007; Rich & Ejeta, 2007). This specific host parasite interaction will stimulate the attachment, the inter and intra-cellular penetration and vascular connection between the parasitic and host plants (Mwangangi et al., 2021). Haustoria undergo rapid cell differentiation to develop tracheal elements in the cortical cells. These elements develop into xylem vessels that achieve xylem-xylem or phloem connections with the host root system and enable the parasite to take water and nutrients (Makaza et al., 2021; Shayanowako et al., 2018; Wada et al., 2019).

Pre-attachment resistance mechanisms depend on the quality and quantity of the SLs produced. Typically, plants detect SLs via the DWARF14 hydrolase receptor (D14) (Yoneyama et al., 2020). Pre-attachment resistance arises when a host produces low amounts of strigolactones or when Striga receptors are insensitive to SLs. Germination of Striga seeds require strigolactones that attach to hydrolase receptors. In a cytochemical study conducted by Olivier et al. (1991), on cell surface interactions between sorghum roots and S. hermonthica, the authors revealed that some cellulotyic enzymes from the haustoria were accompanied by secondary roots thickening of resistant plant. Mandumbu et al. (2019) also, reported that the pre-attachment resistance could be triggered by the failure of an effective development of haustorial initiation factors. Same authors mentioned that SLs production, could stimulate witchweed seed germination without a successful attachment of the parasite to the host roots. This suggested the potential use of bioactive SL analogues was proposed as good option to control and reduce Striga seed bank in the soil (Zwanenburg & Pospíšil, 2013). An Ex ante study conducted by Jamil et al. (2012) revealed that CG14, WAB56-104 and NERICA-1 rice varieties produced low amounts of SLs which was associated with low germination stimulant (LGS) genes. Similar findings were reported by Nyakurwa et al. (2018) in maize germplasm grown under SSA environments.

Pre-attachment resistance is, also, determined by Striga seeds’ germination percentage, radicle length, furthest germination distance (FGD) and the number of attachments on the host root plant. These parameters depend on the germination-stimulant production level, germination inhibition, and low haustorial initiation (Gasura et al., 2019; Mallu et al., 2021; Mandumbu et al., 2019; Shayanowako et al., 2018). Other mechanisms of pre-attachment resistance have been stated by Mbuvi et al. (2017) who revealed that pre-attachment resistance could be due to the lgs1 mutation. Mallu et al. (2021) reported single nucleotide polymorphisms (SNPs) in genes associated with lgs1 as well as in other loci based on sorghum GWAS study.

3.3 Post-attachment resistance

Post attachment Striga-resistance mechanisms occur after Striga connection to the host root system. Several traits associated with such resistance mechanism include post-attachment parasite necrosis, delayed Striga emergence, number of Striga attachment/shoots per host plant, number of emerged Striga plants, Striga severity index, damage rating, Striga biomass, Striga number progress curves, severity curves, host plant biomass and grain yield (Cissoko et al., 2011; Gasura et al., 2019; Mbuvi et al., 2017; Mutinda et al., 2018). Once connected to the host roots, Striga plants start producing enzymes and RNA-based effectors to destroy root host tissues (Mutinda et al., 2018). In response, the host develops physiological and/or metabolic barriers, that hinders the Striga haustorium attachment to host xylem (Gasura et al., 2019; Mbuvi et al., 2017; Mutinda et al., 2018). Other studies reported, also, the production of the resistant compounds in rice, maize, and sorghum roots, that inhibit the haustorium connection and stipulates post-attachment host resistance response (Gobena et al., 2017; Jamil et al., 2011). Post attachment resistance can also occur when Striga penetrate the host roots but cannot reach the not the endodermis to establish a vascular link (Fishman & Shirasu, 2021). Endodermis lignification was considered as a marker for pattern (PTI) and effector(ETI) triggered immunity (Cissoko et al., 2011; Fishman & Shirasu, 2021). Yoneyama et al. (2010) reported that the root physical barrier and the cortex lignification is related to callose and suberin compounds accumulation as well as protein cross-linking in the host cell walls.

4 Breeding for resistance to Striga in cereal crops

Conventional breeding approach combined with new advanced and molecular techniques were initiated in many national and international breeding programs to develop resistant germplasm against Striga spp. (Table 2; Fig. 5) (Badu-Apraku & Fakorede, 2017). Good understanding of specific mechanisms associated with resistance would facilitate the development of improved Striga resistance germplasm (Amusan et al., 2008). Many studies have been conducted on Striga resistance associated genetics of (Badu-Apraku et al., 2020a, b, c; Frost et al., 1997; Gasura et al., 2021). Badu-Apraku et al. (2010) mentioned that Striga resistance mechanisms are quantitative inherited traits controlled by an additive gene effect. these resistance traits/genes can be associated with low number of emerged Striga plants, limited host damage, and better grain yield. In a previous study, Kulkarni and Shinde (1985) reported that Striga resistance in sorghum was more associated with additive gene effects and epistasis. This allows effective identification and transmission of additive genes from examined parents and exploitation of heterosis as highlighted in maize (Akinwale et al., 2014; Gasura et al., 2021) and sorghum (Mrema et al., 2020). Badu-Apraku et al. (2010) reported an additive gene effect that control the number of emerged Striga in maize. In absence of complete resistance to striga, the genes pyramiding remains as the most promising breeding strategy for improving resistance against Striga in cereal crops (Ejeta et al., 2000; Menkir, 2006).

Table 2 List of maize, sorghum, pearl millet, finger millet and rice genotypes identified with resistance/tolerance to Striga spp
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Fig. 5
figure 5

Breeding for Striga spp. resistance universe. i) parasitic weed research: Striga spp. genetic study, identification and validation of target functional genes, and exploration of inter and intra populations’ variability. ii) Host plant research: different breeding strategies/approaches for resistance to Striga spp. in host cereal crops

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The multi-location screening has been recommended to develop germplasm with good and stable resistance level especially under a substantial genotype x environment interaction (GEI) effects and variability of Striga populations’ virulence.

4.1 Conventional breeding approach

The objective of conventional breeding is to increase individual selection chances from generated populations (Badu-Apraku et al., 2021b). the developed germplasm is then subjected to rigorous multi-environments’ selection process in order to identify well adapted Striga resistant genotypes with different associated genes. Different approaches such as single seed descent, recurrent selection, half-sib, full-sib and S1 family selection methods all with hybrid breeding have proven successful in the development of Striga resistant germplasm (Afolayan et al., 2019; Gasura et al., 2021; Ronald et al., 2016; Shayanowako et al., 2018; Yacoubou et al., 2021).

Recurrent selection was used to combine features in germplasm and develop resistant maize genotypes with low Striga infestation and damages (Badu-Apraku & Akinwale, 2011; Menkir & Kling, 2007). This increase the frequency of interesting alleles and improve the quantitatively inherited traits that may involve many gene loci with smaller effects. For open pollinated crops such as maize, the development of gene-pools with many potential resistant sources and various genetic makeup could be a good option for gene pyramiding and development of resistant germplasm. Open pollinated populations like Zea diplo SYNW-1 and TZL Comp SYNW-1 (Table 2) have significantly benefited from the high outcrossing level to transfer associated Striga resistance genes from Zea diploperennis and tropical maize (Adesina & Akinwale, 2014; Gethi & Smith, 2004). It promotes the accumulation of resistant genes for polygenic durable Striga resistance genotypes. Genotypes that combine early maturity and high yield would be good sources and potential recurrent parents for breeding to Striga resistance (Adewale et al., 2020). Kountche et al. (2013) reported a significant genetic variation in Striga resistance among 200 C5 full-sib pearl millet families (C5-FS) tested in different environments with 34% heritability.

Other studies reported that the development of genotypes with Striga resistant composite populations may be easier through the half sibling selection technique (Ejeta, 2007; Haussmann et al., 2000). (Afolayan et al., 2019) suggested the backcross approach as a good approach to develop maize and sorghum genotypes carrying polygenic resistance against Striga. Afolayan et al. (2019) used the backcross (up to BC3F1) to stabilize S. hermonthica resistance in sorghum. A sufficient number of backcrosses should be made to recover the desirable Striga resistant traits of recurrent parent (RP). Other previous studies showed a successful transfer S. hermonthica resistance QTLs from local cultivated maize populations and wild relatives by backcross breeding to new adapted maize germplasm (Mengesha et al., 2017; Shayanowako et al., 2018). The backcross breeding technique could be considered as the most effective approach if the donor has a high frequency of favourable genes associated with the Striga resistance.

Diallel analysis, also, clearly revealed the presence of quantitative genetic variation with a majority of additive effect for Striga seeds stimulation in sorghum (Haussmann et al., 2000). Potential resistance sources against Striga have been reported in maize, sorghum, rice and millet (Table 2) (Akinwale et al., 2014; Gasura et al., 2021). Crop wild relatives offers a diverse genetic pool for breeding (Shayanowako et al., 2018). Wild relatives such as Tripsacum dactyloides, Z. diploperennis, landraces, and synthetics are identified as the potential sources for Striga resistance genes (Tables 1 and 2) (Amusan et al., 2008; Gethi & Smith, 2004). Many genes associated with resistance to S. hermonthica were successfully introgressed from Z. diploperennis into cultivated maize, resulting in the development of resistant inbred lines and synthetics (Rich & Ejeta, 2008).

Difference in resistance levels observed in maize inbred lines/hybrids against Striga spp. could be related to different resistance mechanisms (Gethi & Smith, 2004). Gasura et al. (2019) reported that maize inbred lines (from LE and TZL pools) showed higher resistance level to S. hermonthica and S. asiatica compared to Diplo pool. Hybrid variety heterosis was also reported to be effective in reducing the impact of S. asiatica on crop productivity (Gasura et al., 2021).

4.2 Marker assisted breeding (MAB)

The use of molecular markers for genetic analysis and regulation of critical biophysical traits associated with striga resistance is becoming increasingly important in understanding the Striga-host interaction and the development of resistance genotypes (Fig. 5). This is determined by the type and number of genes associated with the desirable traits (Badu-Apraku et al., 2020b, c). Over the past two decades, considerable progress has been made where several studies have developed and reported molecular markers associated with Striga resistance in cereals (Table 1) (Grenier et al., 2007; Pfunye et al., 2021). Molecular markers and marker assisted selection (MAS) has substantially accelerated breeding progress for Striga resistance (Grenier et al., 2007; Yohannes et al., 2015). Through marker-assisted selection combined with advanced backcrossing (AB-QTLs), five Striga resistant QTLs were transferred from resistance donor N13 to Striga susceptible “Hugurtay” (Yohannes et al., 2015). The introgressed QTLs boosted Striga resistance and yield advantage of BC3F3 backcross progenies (Yohannes et al., 2015). In addition, QTL analysis can be integrated with transcriptomics for the identification of Striga resistant candidate genes (Fig. 5). Swarbrick et al. (2009) discovered three Striga resistance QTL associated with Striga resistance in rice through Koshihikari-Kasalath backcrossed inbred lines (BILs) study. Two of these QTLs were derived from the Kasalath allele while the other one was derived from the Koshihikari allele. The greatest QTL (Kasalath allele) was found on chromosome 4 and explained 16% of the variation in the mapping population (Swarbrick et al., 2009). Menkir and Meseka (2019) reported that the QTL of Striga resistance identified in rice has been confirmed and narrowed down and could be a tractable target for MAS.

Furthermore, genome wide association (GWAS) and marker-assisted selection studies confirmed efficacy ofthe identification and introgression of Striga resistance genes in highly adapted germplasm in maize and sorghum (Gasura et al., 2021; Grenier et al., 2007). For instance, Pfunye et al. (2021) identified markers in chromosomes 5, 6, and 7 associated with total S. asiatica emerged plants, which could be validated and used in future breeding programs. The three SNP markers found for total Striga emerged plants suggest the possibility of successful selection process at genomic level (Pfunye et al., 2021). Adewale et al. (2020) and Badu-Apraku et al. (2020a) identified a set of 24 SNP markers for various traits including S. hermonthica damage rating in maize on chromosome 1, 3, 7, 8, 9, and 10. An S9_154,978,426 locus on chromosomes 9 and 10 in maize found at 2.61 Mb close to ZmCCD1 and amt5 genes, respectively, could be associated with low strigolactone production in maize roots (Table 1) (Adewale et al., 2020). Other studies have also reported three markers associated with S. hermonthica resistance in maize physically located close to the putative genes GRMZM2G164743 (bin 10.05), GRMZM2G060216 (bin 3.06) and GRMZM2G103085 (bin 5.07) (Table 1) (Adewale et al., 2020; Gobena et al., 2017). Many candidate genes and QTLs identified were reported in Table 1 (Badu-Apraku et al., 2020b, c; Kavuluko et al., 2021; Satish et al., 2012; Stanley et al., 2021).

In addition, Gowda et al. (2021) have discovered through GWAS study, some candidate genes such as GRMZM2G018508, GRMZM2G075368, GRMZM2G015520, GRMZM2G074871 and GRMZM2G127230 highly associated with emerged Striga plants in maize. Same authors reported that these genes were involved in signal and stress-related transcriptomic factors pathways (Table 1). Badu-Apraku et al. (2020b, c) also reported candidate genes coding for transcription factor families involved in plant defence signaling in maize in response to Striga parasitism like WRKY, bHLH, AP2-EREBPs, MYB, and bZIP. These genes were involved in the ABA synthesis under Striga infection (Gowda et al., 2021). GRMZM2G074871 gene was involved in the metabolism of a wide range of exogenous and endogenous compounds, biosynthesis of pigments, volatiles, antioxidants, allelochemicals and defence compounds (Table 2). Metabolomic compounds associated with Striga resistance have been reported in rice (Mutuku et al., 2019; Swarbrick et al., 2009). The up-regulation of S. hermonthica defence genes, such as pathogenesis-related proteins, pleiotropic drug resistance ABC-transporter genes were involved in phenylpropanoid metabolism and WRKY transcription factors (Swarbrick et al., 2009) (Tables 1 and 2).

In review of MAB for Striga resistance in sorghum, Grenier et al. (2007) reported that two nuclear genes HR1 and HR2, which were associated with two different markers on the genetic linkage map, controlled Striga resistance in sorghum. Low germination stimulant (lgs) gene was discovered to code for a sulfotransferase enzyme, and when silenced, the sorghum root exudates changed from 5-deoxystrigol to orobanchol compounds (Gobena et al., 2017; Mbuvi et al., 2017). Other studies reported fourteen QTLs controlling Striga resistance in maize and were strongly associated with grain yield, ears per plant, Striga severity (Stanley et al., 2021). Badu-Apraku et al. (2020b, c) reported, also, 12 different QTLs associated with Striga resistance traits in maize, and accounting for 19.4, 34.9 and 14.2% of observed phenotypic variation for grain yield, ears per plant, and Striga severity, respectively.

Plant breeders via molecular markers, are able to offer a faster and easy introgression of desired genes into improved lines as it becomes easier to manipulate and transfer novel Striga resistance genes throughout the breeding process. Contrarily, phenotyping large pools of germplasm for Striga resistance is costly, making it difficult to collect enough data for high-resolution maker-trait-association and QTL finding. However, by minimizing yield losses owing to Striga infestation, the identification of SNP markers can cement conventional breeding procedures and revitalize cereal production in resource-poor African farmers in SSA (Mengesha et al., 2017; Stanley et al., 2021; Zebire et al., 2020). This information could help in stacking several genes associated with different resistance traits into a single genotype for better and durable resistance. This was the case of sorghum genotype developed from the cross between SRN39 and Framida which was reported to provide good resistance level to S. hermonthica (Adewale et al., 2020; Grenier et al., 2007; Kavuluko et al., 2021).

The genotypic prediction has been also widely applied in the breeding for Striga resistance. It is used to predict the best-performing lines speed up the breeding cycle by capturing all marker effects and avoiding the drawbacks of marker-assisted selection (Crossa et al., 2017; Ould Estaghvirou et al., 2013). Results of random cross-validation with genomic best linear unbiased predictor (GBLUP) indicate that genotypic prediction can increase the prediction accuracy for complex traits (Gowda et al., 2021)through the use of molecular markers for diversity analysis and heterotic grouping (Haussmann et al., 2000). Diverse studies conducted in different locations across SSA using a variety of markers confirmed the presence of relationships between geographic and genetic distances among the parasite populations which should be taken into consideration throughout the selection process and breeding pipeline (Badu-Apraku et al., 2020a; Gasura et al., 2019; McDonald & Stukenbrock, 2016; Mwangangi et al., 2021; Unachukwu et al., 2017).

5 Omics and system biology in cereal Striga resistance

The use of omics approaches such as genomics, transcriptomics, proteomics, metabolomics, and bioinformatics in conjunction with whole genome sequencing significantly accelerated the genetic gain for Striga resistance in target cereal crops (Bellis et al., 2020). Next Generation Sequencing (NGS), gene editing systems, gene silencing, and overexpression approaches has provided a limited amount of genetic information for the discovery of Striga stress response pathways in plants (Deshpande et al., 2013; Yoshida et al., 2019). Omics approaches create a platform networks of interaction between genes, proteins, and metabolites that are involved in stress response mechanisms. The gene expression investigations in parasitic plants were carried out using a targeted gene method, which was mostly based on previous plant stress research. This enabled the discovery of a small number of genes associated with Striga resistance defence (Fig. 5). The differential expression of these genes was elucidated using microarrays and RNA sequencing techniques (Samejima & Sugimoto, 2018).

Advances in genomics provide new discoveries of important Striga resistance genes in different cereal crops (Rich & Ejeta, 2008). Proteomics has been, also, successful in identifying and characterizing several proteins and their regulation in cereal crops under Striga infestation (Fig. 5). Despite the low levels of host-based genome-wide differentiation, Ichihashi et al. (2015) and Lopez et al. (2019) identified a set of parasite transcripts associated specifically with maize and sorghum resistance to Striga. The findings could be in line with the genes associated with nutrient transport, defence and pathogenesis, and plant hormone response (Stewart & Press, 1990; Yoneyama, 2020). These genes are significant in host-parasite interactions with varying expression level in different hosts (Deshpande et al., 2013).

In Furthermore, Scholes and Press (2008) reported a number of candidate transcripts that showed host-specific interactions were revealed in the vegetative tissues of emerged S. hermonthica shoots. Lopez et al. (2019) revealed the identification of Striga resistance or susceptible genes involved in haustoriogenesis through transcriptomics, proteomics and RNAseq. Yoshida and Shirasu (2012) and Yoshida et al. (2019) obtained S. hermonthica transcriptome data by sequencing cDNA using the Sanger method. There is consistency with the serendipitous findings by Westwood et al. (2012) who generated S. hermonthica sequences, which gave an immense reservoir of molecular markers for understanding population dynamics, as well as insight into parasitism biology and progress toward understanding parasite virulence and host resistance mechanisms.

The genome data provides a foundation for understanding the origins of parasitism during evolution (Deshpande et al., 2013; Yoshida et al., 2019). Striga-related transcriptome research has recently accelerated but the majority of investigations are centred on deciphering Striga's transcriptome and its evolutionary traits (Deshpande et al., 2013; Ichihashi et al., 2015; Mounde et al., 2020; Westwood et al., 2012).

The next generation sequencing (NGS) have also provided a large amount of transcriptome data from S. hermonthica plants at different development stages (Gobena et al., 2017). Mutuku et al. (2015) reported a new gene (WKRY45) associated with Striga resistance in rice. Yoshida and Shirasu (2012), also, reported an RNA-seq that could be associated with genes involved in Striga resistance in finger millet. Similarly, Mutuku et al. (2015) reported the abundance of genes in in finger millet plants during active Striga parasitism and that could be associated with nutrients and chemical flow between the host and the parasite.

In addition, according to preliminary analyses of gene expression changes in susceptible and resistant sorghum genotypes, Deshpande et al. (2013) and Tamir (2021) showed that many of the identified up-regulated genes are associated with defence responses to microbial pathogens, such as cytochrome P450s and WRKY transcription factors,. Two signal anchor proteins (XP 003627937 and XP 003616487) were found to be up-and-down-regulated under Striga parasitism in GuluE and IE2396 finger millet genotypes, respectively (Deshpande et al., 2013; Erick et al., 2019).

Erick et al. (2019) showed that most of the up-regulated proteins are involved in electron transport, photosynthesis, and oxidation-reduction processes, with the exception of cysteine protease 1 (hydrolase). Same authors showed that increased electron transport and photosynthesis are two of the counteractive responses of cereals against Striga infestation. During redox processes involved in plant-parasite interactions, the changes are catalyzed by quinone oxidoreductases to initiate haustorium induction in plant parasites (Ichihashi et al., 2015; Yoshida & Shirasu, 2012). Cytochrome P450 gene is one of the defence-related genes found as being up-regulated during ethylene or jasmonate treatment or during host plant infection by Striga (Mbuvi et al., 2017; Runo, 2019). Nevertheless, there are other up-regulated proteins in individual cereal genotypes, such as pathogenesis-related protein (PRB1-2-like) and glycine-rich RNA-binding protein 1-like (GrpA), could indicate resistance to the Striga invasion (Hiraoka et al., 2009). A GrpA gene (RAB15) was, also, reported to transmit signal to receptors and induce the activation of JA sensitive defence genes in maize (Erick et al., 2019; Rich & Ejeta, 2008).

Swarbrick et al. (2009) mentioned that specific genes and enzymes were involved in the phenylpropanoid metabolism in rice under Striga parasitism. Mutuku et al. (2019) and Lopez et al. (2019) reported, also, that enzymes groups, that include transferases, oxidoreductases, and hydrolases, were over-represented in finger millet transcriptome under Striga parasitism.

The development of a high-throughput microarray-based Diversity Arrays Technology (DArT) for several crop species, including sorghum, as well as Next Generation Sequencing (NGS) based Genotyping-by-Sequencing (GbS) tools could significantly accelerate Striga diversity knowledge. The development of Striga-specific assays would allow the simultaneous screening of hundreds of molecular markers and the comparison of Striga populations (Fig. 5) at micro (field) and macro (country and region) scales (Deshpande et al., 2013).

To date, no large-scale analysis of the metabolite recruited during parasitic infection have been performed, but targeted metabolic and cytological analyses have shown that numerous secondary metabolites are involved in the resistance to Striga. The breeding for high concentrations of these compounds in roots by either classical breeding or transgenesis is a promising approach (Fig. 5).

6 Conclusion

Most identified resistance sources need further exploration of the resistance mechanisms that are involved, the associated genes, and their potential introgression into well-adapted high yielding varieties. The knowledge of genome sequences is rapid, and so is that of the physiological and molecular roles of plant genes. The genetic engineering technologies and use of gene-editing and CRISP/Cas9 could help in developing new Striga resistant genotypes and exploring further the molecular and genetic mechanisms involved in the resistance to Striga in maize, sorghum, millet and rice. The genetic variation within and between Striga populations should also be considered in designing efficient management strategies involving different control components.