Thus, for a given target environment *j*, a positive $I_{PO}$ ($I_{PO}>0$) of a given genotype or ideotype *i* for grain (or for straw biomass) indicates a good aptitude of this genotype or ideotype *i* for grain (or for straw biomass) production for this target environment *j* (and conversely if the $I_{PO}$ is negative). A positive (or negative) $I_{PO}$ for both grain and straw biomass will therefore indicate a good (or poor) dual-production ability (grain and straw biomass) for the genotype or ideotype *i* for the given target environment *j*.

# Results

## 1. Genotypic Parameters of Ideotypes for Dual Purpose

Simulation results for the identification of optimal genotypic parameters for the new ideotypes in the three target environments are presented in Table 4. To obtain a high and stable dual production, regardless of the target environment, the following traits are key: the stay-green of the leaves must be increased with a decreasing coefficient of leaf mortality value; the structural mass (stem reserve); and maximum stem length with increasing structural mass of the internodes and the maximum internode length and the grain yield potential with increasing structural mass of the panicle. An increase in leaf greenness, stem reserve, and structural mass of panicles is therefore necessary at Bambey, Sinthiou Malem, and Nioro du Rip to obtain high and stable yields.

Genotypic parameters optimized for the three ideotypes ranged from zero to 0.0001 for the sensitivity coefficient of leaf mortality (CoeffLeafDeath), between 0.078 g.mm⁻¹ and 0.092 g. mm⁻¹ for the structural mass of the internodes (CoeffInternodeMass), between 222.5 mm and 275.6 mm for the maximum internode length (InternodeLengthMax), and between 1300 mm and 1775 mm for the maximum root length (RootFrontMax). The structural mass of the panicle (CoeffPanicleMass) was 0.3 for all three ideotypes. And the coefficient of leaf mortality remained close to zero for all the ideotypes designed.

Importantly, to achieve high and stable double yields at Bambey—the environment receiving the least rainfall—it is necessary to maintain the stay-green character and maximum root length to minimize the effects of water
stress. It is also important to note that the structural mass of the panicle has remained constant regardless of the environment and that the more unfavorable the environment, the thicker but shorter the stems are.

<table><caption>**Table 4**

Optimized Genotypic Parameters of the Ideotype Designed for Each of the Target Environments using the SAMARA Model</caption><thead><tr><th>Parameter</th><th>Root Front Max</th><th>Coeff Leaf Death</th><th>Coeff Internode Mass</th><th>Internode Length Max</th><th>Coeff Panicle Mass</th></tr></thead><tbody><tr><td>Nieleni</td><td>1500</td><td>0,0054</td><td>0,068</td><td>196,0</td><td>0,25</td></tr><tr><td>Id_Sinthiou_Malem</td><td>1300</td><td>0,0001</td><td>0,078</td><td>275,6</td><td>0,3</td></tr><tr><td>Id_Nioro_du_Rip</td><td>1402</td><td>0,0001</td><td>0,08</td><td>245,0</td><td>0,3</td></tr><tr><td>Id_Bambey</td><td>1775</td><td>0</td><td>0,092</td><td>222,5</td><td>0,3</td></tr></tbody></table>

Note. Id = Ideotype

## 2. Agronomic Performance of Sorghum Ideotypes and Suitability for Dual Purpose

The average grain and straw biomass yields of the ideotypes and the reference genotype (Nieleni) are presented in Table 5. In general, ideotype yields are higher at Sinthiou Malem and Nioro du Rip compared to Bambey. Simulated grain yields were 4345 Kg.ha⁻¹, 4282 Kg.ha⁻¹, and 3339 Kg.ha⁻¹ for the ideotype in Nioro du Rip, Sinthiou Malem, and Bambey, respectively compared to 2996 Kg.ha⁻¹, 3173 Kg.ha⁻¹, and 2292 Kg.ha⁻¹ for the reference genotype. Thus, the optimized ideotype can potentially produce 29%, 45%, and 46% more grains respectively than the reference genotype in these different environments. For straw biomass, the simulated yields for the ideotype and the reference genotype were of the same order of magnitude in each target environment. It varied, depending on the environment, between 19725 Kg.ha⁻¹ and 28670 Kg.ha⁻¹ for the ideotype and between 19893 Kg.ha⁻¹ and 28343 Kg.ha⁻¹ for the reference genotype. The optimization of the ideotypes was mainly in the direction of grain improvement.
Figure 1 presents its indices of aptitude for grain and straw biomass production in the three target environments for each genotype. It can be noted that in Sinthiou Malem, the optimization was favorable for the double production (much improvement for grain and a little for biomass compared to Nieleni); in Nioro du Rip, it played on grain only (but very strongly); and in Bambey, it also strongly focused on grain yield but to the detriment of the biomass.

<table><caption>**Table 5**
Average Grain and Biomass Yields of the Reference Genotype and Designed ideotype in each Target Environment</caption><thead><tr><td>Environment</td><td>Genotype</td><td>Straw biomass yield (Kg.ha<sup>-1</sup>)</td><td>Grain yield (Kg.ha<sup>-1</sup>)</td></tr></thead><tbody><tr><td>Bambey</td><td>Ideotype</td><td>19725 ± 3894</td><td>3339 ± 1090</td></tr><tr><td>Bambey</td><td>Nieleni</td><td>19893 ± 4369</td><td>2292 ± 1479</td></tr><tr><td>Nioro_du_Rip</td><td>Ideotype</td><td>24858 ± 3391</td><td>4345 ± 526</td></tr><tr><td>Nioro_du_Rip</td><td>Nieleni</td><td>24824 ± 3866</td><td>2996 ± 727</td></tr><tr><td>Sinthiou_Malem</td><td>Ideotype</td><td>28670 ± 3242</td><td>4282 ± 288</td></tr><tr><td>Sinthiou_Malem</td><td>Nieleni</td><td>28343 ± 3725</td><td>3173 ± 425</td></tr></tbody></table>

<figure><img src="image_66.png" /><figcaption>Figure 1 – Comparisons of the Dual-Purpose Abilities of the Three Ideotypes and the Reference Genotype Nieleni in the Three Target Environments Based on the IPO Indices</figcaption></figure>
Discussion

Crop growth modeling can guide breeding line selection by studying in silico the performance of trait combinations and by identifying the best combinations and the optimal candidate genotypes for a target environment. Our results at Bambey, Sinthiou Malem, and Nioro du Rip using the SAMARA model show that a substantial increase in dual production, especially for grain, is possible for a given genotype and environment by optimizing its genotypic parameters through a combination of optimization rules and simulations of the crop model based on the historical data of this environment. The SAMARA model confirms that traits such as maximum root length, stay-green, internode length, stem structural mass, and panicle structural mass should be considered in the identification of dual-purpose ideotypes for the environments targeted. Additionally, the range of optimal values for each of these traits was determined for each environment.

1. Importance of the Genotypic Parameters of Optimized Ideotypes

This study showed that dual-purpose sorghum ideotypes should have a
deeper maximum root length, especially in environments subject to water
stress at the end of the growth cycle and/or with deep, sandy-dominated
soils such as those of Bambey. In this study, the maximum root lengths of
the designed ideotypes ranged from 1300 mm to 1775 mm. These values are
greater than those reported by Chopart (1980) who showed, while studying
the sorghum root system in Senegal, that the maximum root length varied
between 1100 mm and 1300 mm. Such difference could be explained by the
type of variety used, as dual-purpose varieties require more resources from
the substrate, especially water, but also by the shortening of the rainy season
observed in the past few years. This rainfall decrease that is accentuated by
the climatic changes requires plants to develop more robust root systems
(deep) for a better adaptation to dry environments. Chopart (1980) also
emphasized that the new genotypes that are more efficient in terms of yield
potential should not have root systems that are less well-developed than
the existing varieties. Similarly, Sambakhé (2018), in defining grain sorghum
ideotypes in Senegal, showed the importance of long roots for stabilizing
yields in different target environments—Bambey in particular. In our environments affected by a low level of soil fertility and irregular rainfalls, the speed of progression of the root front and the degree of soil colonization by the roots are two important traits that also should be considered (Chopart, 1980).

It also appeared that dual-purpose sorghum ideotypes should have a high number of green leaves that are maintained as long as possible. This trait of interest depends on the total number of leaves—which is conditioned by the speed of leaf production (the phyllochrone)—and on the capacity of the plant to maintain its leaves green (stay-green character), especially during grain filling. Thus, the yield improvements observed in the ideotypes we have designed could be due to the improvement in the efficiency of light interception and conversion (because of the maintenance of the leaves in a green state), which makes it possible to maintain significant assimilate production for a longer period of time and consequently to cope more easily with water stress at the end of the season (Kamal et al., 2019), given that transpiration is well controlled. Consequently, to have sorghum with a good double production (grain and straw biomass) at Bambey, maintenance of a significant number of leaves in the stay-green state is essential (coefficient of leaf mortality equal to 0). The importance of keeping sorghum leaves photosynthetically active during the grain filling period has been reported as an adaptation factor to water-limiting environments (Borrell et al., 1999, 2000; Haussmann et al., 2002; Jordan et al., 2003). Additionally, all ideotypes developed had very high panicle structural mass (high potential kernel count) of 0.30 (see Table 4), which predicts a high yield potential as reported by Hassan and Mohammed (2015), who found that large panicles were associated with a high potential for grain number and grain yield. Hence, a high structural mass is advantageous in arid environments (Bambey) or in environments where fungal diseases are prevalent (Nioro du Rip). In addition, a long stem internode length should be favored, particularly in environments with good soil fertility (predominantly clay) and good rainfall distribution, such as those in Sinthiou Malem. Indeed, a positive association between plant height and grain yield has been reported for sorghum (Jordan et al., 2003), probably because of a greater efficiency in the use of radiation (George-Jaeggli et al., 2004). In addition, if the demand for grain assimilates exceeds the production through photosynthesis, an accelerated
translocation of assimilates from stems and leaves occurs (Triboi & Triboi-Blondel, 2002).

## 2. Involvement for Adaptation in our Target Environments for Breeders and Agronomists

This study shows that the double production of sorghum (grain and fodder) could be significantly increased in these target environments if new ideotypes, superior to Nieleni (especially for grain production), are developed and implemented. Rötter et al. (2011) suggested that to cope with adaptation in the current environments, only new genotypes aimed at increasing yield potential and drought tolerance, combined with adapted agronomic practices, such as the modification of sowing dates, adequate management of nitrogen fertilizers, and plant protection, would allow yield levels to be maintained and losses avoided.

The use of new breeding methods—such as ideotype definition through modeling to create more appropriate genotypes—should allow better adaptation in the target environments (Dawson et al., 2015). This ideotyping requires an *in silico* approach to design ideotypes for specific uses in a given context. Thus, this study for the design of varietal ideotypes for the three environments targeted based on traits of interest for sorghum double production, historical meteorological data, and research recommendations (technical itinerary) is only one typical example that applies for our specific environments. This simulation approach can be pursued by varying other input variables (e.g., elements of the technical itinerary, soil, or climate) for the definition of varietal and crop ideotypes (technical combinations that affect the traits of the ideotypes). Thus, this study highlights an innovative approach that appears judicious to accelerate the collaboration between crop modelers, agronomists (ecophysiologists), and breeders. More comprehensive breeding programs that would combine the definition of ideotype by simulation and current selection methods could undoubtedly shorten the time required to create new sorghum genotypes better adapted to our different environments.
### 3. Limits of the Study

Limitations due to some uncertainties are noted, either in the structure of the model or in the approach used. Indeed, the target environments are considered as limited only by physical pedoclimatic conditions (climate, soil water reserve). We do not know the response of our ideotypes to other constraints, such as limited nitrogen (N) contents. In SAMARA, a module dedicated to nitrogen has not yet been integrated. Thus, we have been unable to consider the N limitation in the design of the ideotypes. N uptake and redistribution of N during the post-anthesis phase could be an important constraint to consider in order to achieve a higher sorghum yield potential. Indeed, as noted in this study, maintaining the green leaf biomass during grain filling can increase the grain yield of sorghum grown under water limiting conditions at the end of the cycle (Kamal et al., 2019). However, the effect of this stay-green trait can be limited by nitrogen deficiency (Borrell et al., 1999; Muchow & Sinclair, 1994; Peng et al., 1995).

In this study, we optimized our ideotypes using the genotype Nieleni as a reference. It is a caudatum type and a very efficient genotype in both grain and biomass production. Optimization could be done with a guinea-type genotype as a reference or guinea and caudatum genotypes simultaneously because the guinea types, although with low harvest indexes, are robust, generally tall, and better adapted to the constraints of the target environments compared to the caudatum types, which are rather sensitive (Ndiaye et al., 2019).

In this study, too, we have optimized our ideotypes based on the available history of meteorological data for the target environments, whereas it might be more judicious to use future climate data from climate scenarios for the design of ideotypes given the threats related to climate change in the target environments we considered. Future research also may use data from a shorter and particular climatic period, such as that from the dry period of 19701990 that affected the Sahel or from the recent period of gradual return of rainfall of 19902010 (Salack et al., 2011).

Finally, another limitation of the study lies in the technical itinerary used in the ideotype design. Indeed, this itinerary being the one recommended by the research, we did not take into account some yield reduction factors that
producers face (e.g., constraint of sowing date, limited inputs, etc.). Thus, it
might be interesting to conduct the study with an itinerary that considers
constraints farmers face when designing ideotypes for target environments
and contexts to increase efficiency.

All of these limitations reveal the importance of interdisciplinarity (crop
modeling, agronomy, ecophysiology, and breeding) in the design of sorghum
ideotypes that perfectly fit a defined use in a given context, and they show
the importance of crop modeling as a powerful integrative tool for these
explorations. This study is a proof of concept on the potentialities of crop
modeling for ideotype explorations. Further simulations are required to
cover the diversity of situations encountered in farmers' fields.

Conclusion

This study aimed to use a crop model to design high-performance and
stable dual-production (grain and straw biomass) sorghum ideotypes for
the target environments of Bambey, Sinthiou Malem, and Nioro du Rip. The
results revealed that under our current soil and climate conditions, sorghum
ideotypes for dual purpose (grain and fodder) should have a thick stem, large
panicles, and a high capacity for deep soil colonization by roots, especially in
water-stressed environments such as in Bambey. This study also emphasized
the importance of stay-green, especially in the least-watered areas such as
Bambey, where the coefficient of leaf mortality is 0. Additionally, this study
allowed us to quantify the optimal range of the traits considered for each
environment targeted, which can greatly facilitate varietal choice.

Thus, our designed ideotypes can be a decision support tool for increasing
productivity in these target environments. The conclusions of the study are
potentially interesting for sorghum breeding programs, as they are part of
the first step in the design of genotypes adapted to a target environment,
namely the definition of the ideotypic traits to be favored. Thus, we
recommend that the optimal range of values for these traits are taken into
account when identifying appropriate genotypes in sorghum breeding
programs for the target environments considered.
References

ANSD (Agence Nationale de la Statistique et de la Démographie). (2018). Bulletin Mensuel des Statistiques Economiques de 2018. Division des Statistiques Economiques, Ministère de l'Economie, des Finances et du Plan, Sénégal, 109 p.

Balme, M., Galle, S., & Lebel, T. (2005). Démarrage de la saison des pluies au Sahel: variabilité aux échelles hydrologique et agronomique, analysée à partir des données EPSAT-Niger. *Sécheresse, 16*(1), 15-22.

Borrell, A. K., Bidinger, F. R., & Sunitha, K. (1999). Stay-green trait associated with yield in recombinant inbred sorghum lines varying in rate of leaf senescence. *International Sorghum and Millets Newsletter*, 40, 31-34.

Borrell, A. K., Hammer, G. L., & Henzell, R. G. (2000). Does maintaining green leaf area in sorghum improve yield under drought? II. Dry matter production and yield. *Crop Science*, 40(4), 1037-1048.

Chantereau, J., Nicou, R. (1994). Sorghum. The tropical Agriculturist Seres. CTA Wageningen, Netherlands; Macmillan, London Pmid: 24186186.

Chopart, J. -L. (1980). *Etude au champ des systèmes racinaires des principales cultures pluviales du Sénégal (arachide-milsorghoriz pluvial)* [Thèse de Doctorat d'Université, Institut National Polytechnique de Toulouse].

Dawson, I. K., Russell, J., Powell, W., Steffenson, B., Thomas, W. T. B., & Waugh, R. (2015). Barley: a translational model for adaptation to climate change. *New Phytologist*. 206(3), 913-931.

Dingkuhn, M., Pasco, R., Soulie, J. C. (2013). SAMARA V2.1: Model description and guidelines for calibration, 28 pp.

Donald, C. M. (1968). The breeding of crop ideotypes. *Euphytica*, 17, 385-403.

FAO, (2009). How to feed the World in 2050. FAO, Rome.

George-Jaeggli, B., Broad, I., Hammer, G. L., & Jordan, D. (2004). Is increased radiation use efficiency in sorghum related to increased height? In T. Fischer, N. Turner, J. Angus, L. McIntyre, M. Robertson, A. Borrell, & D.
Lloyd (Eds.), *New Directions for a Diverse Planet: Proceedings for the 4th International Crop Science Congress*.

Gerstenmier, A., Choho, T. (2015). Fourniture d'intrants agricoles. Conference Feeding Africa: An action plan for African agricultural transformation, 21-23 October 2015, Dakar Senegal.

Hammer, G. L., Kropff, M. J., Sinclair, T. R., & Porter, J. R. (2002). Future contributions of crop modelling—from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. *European Journal of Agronomy*, 18(1-2), 15-31.

Hammer, G., Cooper, M., Tardieu, F., Welch, S., Walsh, B., van Eeuwijk, F., Chapman, S., & Podlich, D. (2006). Models for navigating biological complexity in breeding improved crop plants. *Trends in Plant Science*, 11(12), 587-593.

Hammer, G. L., van Oosterom, E., McLean, G., Chapman, S. C., Broad, I. J., Harland, P., & Muchow, R. C. (2010). Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. *Journal of Experimental Botany*, 61(8), 2185-2202.

Hansen, J. W. (2002). Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges. *Agric. Syst.* 74, 309-330.

Hassan, S. A., & Mohammed, M. I. (2015). Breeding for dual purpose attributes in sorghum: Identification of materials and associations among fodder and grain yield and related traits. *Journal of Plant Breeding and Crop Science*, 7(4), 94-100.

Haussmann, B., Mahalakshmi, V., Reddy, B., Seetharama, N., Hash, C., & Geiger, H. (2002). QTL mapping of stay-green in two sorghum recombinant inbred populations. *Theoretical and Applied Genetics*, 106, 133-142. https://doi.org/10.1007/s00122-002-1012-3

ICRISAT: Sorghum [Internet]. 2009. Patancheru (AP): International Crops Research Institute for the Semi-Arid Tropics. https://www.icrisat.org/what-we-do/crops/sorghum/sorghum.htm

Jordan, D., Tao, Y., Godwin, I., Henzell, R., Cooper, M., & McIntyre, C. L.
(2003). Prediction of hybrid performance in grain sorghum using RFLP markers. *Theoretical and Applied Genetics*, 106, 559-567.

Kamal, N. M., Gorafi, Y. S. A., Abdelrahman, M., Abdellatef, E., & Tsujimoto, H. (2019). Stay-green trait: a prospective approach for yield potential, and drought and heat stress adaptation in globally important cereals. *International Journal of Molecular Sciences*, 20(23), 5837. https://doi.org/10.3390/ijms20235837

Kouressy, M., Traoré, S., Vaksmann, M., Grum, M., Maikano, I., Soumaré, M., Traoré, P. S., Bazile, D., Dingkuhn, M., & Sidibé, A. (2008). Adaptation des sorghos du Mali à la variabilité climatique. *Cahiers Agricultures*, 17(2), 95-100.

Muchow, R. C., & Sinclair, T. R. (1994). Nitrogen response of leaf photosynthesis and canopy radiation use efficiency in field-grown maize and sorghum. *Crop Science*, 34(3), 721-727.

Ndiaye, M. (2019). Modélisation d'idéotypes variétaux de sorgho sur la base de la caractérisation du fonctionnement et potentiel de sorgho multi-usages au Sénégal [Thèse de Doctorat d'Université, Université Cheikh Anta Diop de Dakar].

Ndiaye, M., Adam, M., Ganyo, K. K., Guissé, A., Cissé, N., & Muller, B. (2019). Genotype-environment interaction: trade-offs between the agronomic performance and stability of dual-purpose sorghum (*Sorghum bicolor* L. Moench) genotypes in Senegal. *Agronomy*, 9(12), 867. https://doi.org/10.3390/agronomy9120867

Peng, S., Cassman, K. G., & Kropff, M. J. (1995). Relationship between leaf photosynthesis and nitrogen content of field-grown rice in tropics. *Crop Science*, 35(6), 1627-1630.

Passioura, J. B. (1996). Simulation models: science, snake oil, education, or engineering. *Agronomy Journal*, 88(5), 690-694.

Quilot-Turion, B., Ould-Sidi, M.-M., Kadrani, A., Hilgert, N., Génard, M., & Lescourret, F. (2012). Optimization of parameters of the “Virtual Fruit” model to design peach genotype for sustainable production systems. *European Journal of Agronomy*, 42, 34-48.
R Development Core Team. (2015). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.

Reynolds, M., Bonnett, D., Chapman, S. C., Furbank, R. T., Manès, Y., Mather, D. E., & Parry, M. A. J. (2011). Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies. *Journal of Experimental Botany*, 62(2), 439–452.

Rötter, R. P., Carter, T. R., Olesen, J. E., & Porter, J. R. (2011). Crop-climate models need an overhaul. *Nature Climate Change*, 1, 175–177.

Salack, S., Muller, B., Gaye, A. T. (2011). Rain-based factors of high agricultural impacts over Senegal. Part I: integration of local to sub-regional trends and variability. *Theoretical and Applied Climatology* 106, 1–22.

Sambakhé, D., (2018). Recherche de carte d'idéotypes de sorgho d'après un modèle de culture: optimisation conditionnelle à l'aide d'un métamodèle de krigeage. [Thèse de Doctorat d'Université, Université de Montpellier 1].

Semenov, M. A., & Halford, N. G. (2009). Identifying target traits and molecular mechanisms for wheat breeding under a changing climate. *Journal of Experimental Botany*, 60(10), 2791–2804.

Semenov, M. A., & Shewry, P. R. (2011). Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. *Scientific Reports*, 1, 66. https://doi.org/10.1038/srep00066

Sinclair, T. R., & Seligman, N. G. (1996). Crop modelling: from infancy to maturity. *Agronomy Journal*, 88(5), 698–704.

Tardieu, F. (2003). Virtual plants: modelling as a tool for genomics of tolerance to water deficit. *Trends in Plant Science*, 8(1), 9–14.

Tardieu, F., & Tuberosa, R. (2010). Dissection and modelling of abiotic stress tolerance in plants. *Current Opinion in Plant Biology*. 13(2), 206–212.

Triboi, E., & Triboi-Blondel, A.-M. (2002). Productivity and grain or seed composition: a new approach to an old problem. *European Journal of Agronomy*, 16(3), 163–186.

Vanlauwe, B., Bationo, A., Chianu, J., Giller, K. E., Merckx, R., Mokwunye, U.,
Ohiokpehai, O., Pypers, P., Tabo, R., Shepherd, K. D., Smaling, E. M. A., Woomer, P. L., Sanginga, N. (2010). Integrated soil fertility management: Operational definition and consequences for implementation and dissemination. *Outlook Agriculture*, 39, 17-24.

White, J. W., Hoogenboom, G., Kimball, B. A., & Wall, G. W. (2011). Methodologies for simulating impacts of climate change on crop production. *Field Crops Research*, 124(3), 357-368.
PART III

GENETIC DIVERSITY AND
IMPROVEMENT OF DRYLAND
CROPS
<figure><img src="image_67.png" /></figure>
13. Biodiversity as a Cornerstone
of Agrosystems' Sustainability in
West Africa

Anne-Céline Thuillet, IRD, France

Delphine Renard, CEFE/CNRS, France

Philippe Cubry, IRD, France

Adeline Barnaud, IRD, France

Cécile Berthouly-Salazar, IRD, France

Highlights box

* Agrobiodiversity is an asset for resilience and sustainability at different scales (species and intra-specific).

* Crop wild relatives and landraces extend the gene pool, providing a functional reservoir of genetic diversity.

* Approaches and methodologies in population genetics can help identify interesting diversity.

* Tapping genetic diversity toward the creation of large populations for breeding schemes is essential in breeding strategies.

* Agrobiodiversity management and use are central under erratic environments.
Abstract

Agrobiodiversity, encompassing crop diversity and the biodiversity that supports agricultural system functioning, plays a major role in agriculture in the context of climate change. This is all the more true in regions like West Africa, where population growth is strong and economic resources are limited. Global models predict average yield loss for major crops of 11% in West Africa by the 2050s. Moreover, climate niches of African crops are expected to shift significantly, reinforcing the need to adapt agricultural diversity and practices. A better use of agrobiodiversity, relying on improved understanding and knowledge of its characteristics, would support greater adaptation and resilience of African agrosystems and food security. Agrobiodiversity, from gene to landscape, is key to achieving food security in the face of the erratic effects of climate change. This chapter highlights novel strategies for agrobiodiversity management and use, while building sustainable and resilient agrosystems for the future of West Africa.

Keywords: Agrobiodiversity, agrosystems, crop wild relatives, resilience, climate change, food security

Introduction

Agriculture in West Africa, which relies primarily on rainfall, is highly
vulnerable to climatic events that are particularly extreme and erratic under
the influence of the African monsoon. The Sahel and West Africa are
considered today as "climate change hotspots" (Intergovernmental Panel
on Climate Change, p 1209, 2014). Data indicate long-term changes in the
seasonality of rainfall (Nicholson et al., 2018) and increased variability in
precipitation (Cornforth et al., 2019). Deep changes are expected in climate
suitability, leading to geographic or temporal redistribution of cultivated
species (Egbebiyi et al., 2020; Pironon et al., 2019).

Considering the high spatiotemporal variability of West Africa, it is essential
to focus not only on agrosystems' productivity but also on agrosystems'
resilience and sustainability. The concept of resilience, embedding
ecological and social-ecological definitions, refers to the capacity of
agrosystems to buffer, resist, adapt, or transform to continue feeding the population (Berkes, 2008; Holling, 1973; Walker et al., 2004).

Decades of research in empirical and theoretical ecology have shown that biodiversity, in a broad sense, drives systems resilience. Regarding agrosystems, biodiversity is key to establishing resilient and sustainable food production and improving livelihood (Félix et al., 2018; Lin, 2011). Crop intraspecific diversification or change in the crop portfolio is often the first adjustment chosen by smallholder farmers facing climate change impacts (Fahad & Wang, 2018; Kassie et al., 2013; Shinbrot et al., 2019). However, the nature of crop diversity changes is essential: In a recent review, Labeyrie et al. (2021) showed that farmers' decisions in response to ongoing climatic events may not be adapted to long-term climate predictions. More research is needed to make the best possible use of biodiversity in agrosystems.

Until now, the valorization of diversity was based on a vision in which the environment was relatively controlled and stable, which made it possible to respond to each constraint by optimizing a few genes within varieties for a single objective. The plurality of values (socio-agro-ecological) and the plurality of environments due to their uncertainties, force researchers and farmers to think of biodiversity as an integrated response of agrosystems and not as a reservoir of solutions. To do this, it is important to consider diversity not as a static element of ecosystems but to understand its dynamics from genes to species, functions and ecosystems, and spatial and temporal scales.

This chapter explores why and how biodiversity in agrosystems (i.e., agrobiodiversity) can participate in resilience and sustainability in West Africa. First, researchers present the role of agrobiodiversity at the agrosystems' scale and explain how it can help improve yield and stability. Second, researchers underline the importance of understanding the dynamics of diversity at its finest scale to make a better use of agrobiodiversity to face environmental constraints. Finally, researchers highlight that agrobiodiversity management and use is central to building sustainable and resilient agrosystems for the future of West Africa.
# The Role of Agrobiodiversity in Agrosystems

## 1. How Does It Work?

Agrobiodiversity includes domesticated plants and animals, wild fauna, flora, and microbiota, as well as people's knowledge that supports food production and agriculture (Qualset et al., 1995). It is a pillar of agroecology, ecological engineering, or “nature-based solution,” frameworks that all seek to promote and pilot ecological processes to produce food in a sustainable and resilient way.

The impact of biodiversity on ecosystem services was first studied in natural ecosystems (Gross et al., 2014; Isbell et al., 2015, Tilman & Downing, 1994). Different mechanisms have been identified and have been proven to function in agroecosystems: the insurance effect, the selection effect, and the complementary effect. The insurance effect principle states that in a diversified system, one species—or variety at the species level—may fail, but multiple failures are less likely (Yachi & Loreau, 1999). Different plants exhibit different responses to temperature, precipitation, pests, and diseases. Fluctuations in yield among plants can compensate for each other and stabilize the total biomass production through time. The selection effect principle outlines that the more species or varieties there are in an ecosystem, the greater the chance that the best performing crop will be included. Because one species will find the environmental conditions to be favorable, total biomass production stays stable from year to year. However, an important condition for the selection effect to happen is that the best-performing species or variety in the given year must be dominant. The complementary effect entails direct ecological interactions between plants. Functional differences between species (Hector, 1998; Loreau & Hector, 2001) can lead to reduced interspecific competition, facilitative interactions, and exploitation of different resources (i.e., soil nutrients). Plant height differences, for example, can buffer the impact of extreme climate events (Philpott et al., 2008) or create microclimates that can help control diseases and pests (Zhu et al., 2000).
## 2. Agrobiodiversity to Improve Yields

Increasing agrobiodiversity through the addition of crops varieties, species, or functional groups (e.g., cereals versus legumes) can be achieved at multiple spatial and temporal scales. Within the same fields, practices include rotations of crops within 1 year (with no temporal overlap) or year after year; inter cropping (i.e., cultivation of more than one crop species); and cultivar mixtures (i.e., cultivation of more than one cultivar of the same species; Smith & McSorley, 2000). Agrobiodiversity also entails management practices promoting noncrop species—including habitat within or around fields (e.g., hedgerows)—or soil biodiversity (i.e., inoculation of beneficial organisms, organic amendments, etc.).

Recently, research on the benefits of diversification practices bloomed worldwide (Beillouin et al., 2019), showing that promoting agrobiodiversity can create multiple benefits simultaneously. In one of the largest-scale field trials across Malawi. Snapp et al. (2010) showed that rotations involving maize and long-lived legumes, at moderate levels of nitrogen fertilizer, had similar yet more stable yields, compared with monoculture maize. In addition, these rotations provided substantial nutritional benefits (i.e., increase in protein yield), greater fertilizer-use efficiency (e.g., >100% more grain yield with the same amount of nitrogen input), and acceptability to farmers. Himmelstein et al. (2017) found that intercropping practices across Africa benefitted both yields (e.g., increase of 23% on average) and growers' incomes compared with monocropping systems. However, authors noted that the management of leguminous intercrop systems could still be optimized to reach their full potential. Until now, cultivar mixtures have been used mainly for disease and weed management (Beillouin et al., 2019). However, meta-analyses have demonstrated that mixtures significantly improved yield compared with monoculture (Reiss & Drinkwater, 2018; Smithson & Lenné 1996). They also found that yield benefits were much greater in tropical areas and where plants grow in soil stressful conditions (i.e., low soil organic content and in acidic soils).
### 3. Agrobiodiversity to Face Climate Variability

The potential for agrobiodiversity to buffer agricultural impacts of climate shocks is less researched. Among the few empirical studies assessing yield stability, results supported the positive effect of the diversity of crop species (Gaudin et al., 2015; Smith et al., 2008) on the temporal stability of yield and income (Auffhammer & Carleton, 2018; Di Falco & Chavas, 2008). To date, only two meta-analyses synthesized results on the stabilizing effect of diversity. Research showed that yield stability of cereal-grain legume intercropping systems in tropical regions is greater than both cereal and legume cultivated as sole crops (Raseduzzaman & Jensen, 2017; Reiss & Drinkwater, 2018) Facilitation mechanisms leading to greater productivity and stability within fields may be stronger under more stressful conditions (Maestre et al., 2009; Prieto et al., 2015).

Renard & Tilman (2019) investigated whether crops' diversity increases year-to-year stability of crops production in the context of climate change at the national scale over 5 decades (1961-2011) and 91 nations. Results showed that greater crop species and functional group diversity increased the stability of food supply and resilience to interannual variability of precipitation at the national level. Although agrobiodiversity can contribute to sustainable agriculture in diverse ways, how to pilot it for greater resilience deserves more research, notably in West Africa with its strong climate constraints.

## From Landscapes to Genes

### 1. A New Perspective on Selection Schemes

At the scale of varieties, the same principles may apply. The insurance strategy guides farmers' choices in regions with high variability: They prefer to have varieties that produce a lower yield under a multitude of conditions than varieties that perform extremely well only under very specific conditions (Van Oosterom et al., 1996; Weltzien et al., 1998). At the agrosystems level, more research is needed to identify combinations of species and traits that can optimize yield stability through an insurance or
a complementary effect. Achieving production stability under all climatic conditions could also rely on the valorization of intraspecific diversity. As Haussmann et al. (2012) stated, the scale of varieties should consider two levels: individual buffering and population buffering. Individual buffering is the variance of the individual's phenotype, which is expressed as a function of environmental conditions. This buffering may be accentuated by the presence of different alleles for the locus of interest. Population buffering is the variance across all individuals in the population and therefore of the intra-population diversity (see Figure 1).
<figure><img src="image_68.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 - Population buffering is the variance across all individuals in population & therefore of intra-population diversity

The idea of highlighting variances within individuals and populations to improve agrosystems' performance is recognized in ecology for natural ecosystems (Violle et al., 2012) and has been further developed for agrosystems by rethinking selection schemes (Litrico & Violle, 2015). The authors break functional traits into two types: traits of agronomic interest,
often related to productivity (i.e., grain number and weight) and interaction
traits (i.e., rooting depth, vegetative architecture, and phenology). Thus,
Litrico and Violle (2015) proposed that the variance be considered according
to the two types of traits. Agronomic traits should have a very small variance
of the trait at the individual level and the population level, so that all
individuals tend toward the same optimum. Conversely, interaction traits
should minimize the individual variance and maximize the population
variance, so that the population covers a wide range of conditions. Thus, it is
rather a question of thinking of an “ideomix” than an ideotype.

Variance, at the individual and population scales, is governed by genes
involved in the trait under consideration. Flowering earliness, for instance, is
highly regarded as adaptive to drought. It allows escape from the midseason
rainfall breaks which are characteristic of Sahelian Africa. Many studies seek
to identify the major genes involved in earliness. Fewer studies focus on the
variance within the cycle, but a variance of a few weeks may allow some
plants to survive while others do not. In pearl millet, PhyC (PHYTOCRHOME
C) has been identified as a major gene involved in precocity (Saïdou et al.,
2014; Vigouroux et al., 2011). Nevertheless, a gap of more than 15 days may
exist between the first plant to flower and the last. Such gaps are even
observed within full-sib families. Faye et al. (2022) confirmed the importance
of the PgPhyC gene with the T allele of the SNP (Single Nucleotide
Polymorphism) chr2_11155563 almost fixed in early varieties and the
alternative allele in late varieties. In addition, they identified a second gene,
FRS12 (FAR1-RELATED SEQUENCE 12) for which four SNPs were identified,
some with more subtle frequency differences between the two varieties. In
addition to these two genes, which seem to have major effects, a number of
other genes have also been detected and present a great interest in breeding
to target individual or population buffering in breeding programs.

## 2. Sources of Interesting Genetic Diversity

The described example on flowering earliness highlights the necessity to
identify reservoirs of genetic diversity, the importance of understanding
genes' involvement in traits, interactions, and the relationship between
genes and environment. This study presents functional reservoirs of
diversity and describes why this diversity could be interesting in an
integrated way of valorization. Neglected crops will not be included here but
rather detailed in a companion paper.

## 2.1 Wild Relatives as a Source of “Ready-to-Use” Genes for Crop Adaptation

Since the first domestication of wild plants some 12,000 years ago, cultivated
crops have shared a complex and long history with humans, the
environment, and their wild relatives. Despite the reduction of habitats
because of evolving environmental conditions and human spread, Crop Wild
Relatives (CWR) remain interesting reservoirs of diversity and may conceal
useful alleles in view of crop improvement and adaptation (Guarino & Lobell,
2011). Wild relatives still exchanging genes with the cultivated pool refuels
the cultivated diversity. In yam, for example, constant inclusion of wild
individuals in the cultivated pool in Benin is a factor of enrichment of the
cultivated diversity (Scarcelli et al., 2006). A genomic study on pearl millet
provided evidence that wild-to-crop gene flow increased cultivated genetic
diversity leading to adaptive introgression (Burgarella et al., 2018). Such cases
may be an effective strategy for informing new breeding directions and
for uncovering ready-to-use wild diversity relevant for crop adaptation to
current environmental changes (Burgarella et al., 2019).

Populations of African CWR are also found outside the geographical range
of their cultivated counterparts, and therefore they experience different
environmental conditions. For instance, the wild rice Oryza barthii shows a
much wider distribution than the domesticated species it gave birth to, the
cultivated African rice O. glaberrima (Lu et al., 2010). The wild ancestor of
pearl millet is found in more extreme northern latitudes than the cultivated
populations (i.e., in drier environments, see Figure 2). In these populations,
Berthouly-Salazar et al. (2016) identified genetic variation linked to abiotic
and biotic stress responses, and Ousséini et al. (2017) revealed a gene
implicated in the number of flowers, both covarying with the gradient of
rainfall. This pristine natural variation linked to local adaptation can be an
asset for crop improvement.
<figure><img src="image_69.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – Wild Ancestor of Pearl Millet is Found in More Extreme Northern Latitudes than the Cultivated Populations

## 2.2 Landraces, Genetic Load, Local Adaptation, and Cryptic Variation

Despite the well-known bottleneck effect that makes the wild compartment of landraces a complementary source of diversity, a lesser-known effect of the domestication process is the existence of a deleterious load within cultivated populations (Moyers et al., 2018). This possible “cost of domestication” refers to the increase in frequency of deleterious alleles segregating in the domesticated species as compared to the wild compartment. The existence of this genetic load implies an extended linkage and reduced genetic diversity. Intensive breeding programs would also strongly increase the number of deleterious mutations in domesticated species (Frantz et al., 2020). The study of these deleterious alleles, and of their frequency in different populations, may help orientate breeding strategies and diversity valorization, as their existence may limit the efficacy of selection and impact breeding efficacy.
From their domestication, each crop settled in new locations and colonized new environments through farmers or seed exchanges. These landraces now significantly contribute to the important genetic resources at the disposal of farmers and breeders. Two specific kinds of genetic diversity (i.e., allelic variations) may be of particular interest for crop adaptation and sustainable use in breeding while ensuring diversity conservation.

First, the variability of environmental conditions across a species range drives differential adaptation of populations, resulting in local adaptation. Local adaptation is the difference in relative fitness between a native genotype in a given environment and a nonnative genotype in the same environment (Kawecki & Ebert, 2004). A meta-analysis of 74 studies revealed that local adaptation is common among species and that a local population has on average 45% greater fitness than a foreign population (Hereford, 2009). This statement might not stand when population environments are drastically different or when selective factors are different at opposite ends of the gradient (Hereford, 2009; Popovic & Lowry, 2020). Local adaptations are critical for species survival in diversified and variable environments and thus may enable crop populations, if incorporated in their genetic background, to withstand impacts of climate changes (Razgour et al., 2019; Vargas et al., 2017).

Cryptic variations are a second source that can be just as important in coping with environmental changes, especially when environmental changes are highly contrasted and rapid (Bitter et al., 2019). Cryptic genetic variation has been reported in wild progenitors of crop species and landraces (Lauter & Doebley, 2002; Von Wettberg et al., 2018). Cryptic genetic variation is defined as standing genetic variation that does not contribute to the normal range of phenotypes observed in a population but that can bring forth phenotypic variation after environmental change (Gibson & Dworkin, 2004). Cryptic variation allows large phenotypic shifts outside the normal boundaries of phenotypic variation of a population, (i.e., when a variation in environmental conditions substantially changes the optimal phenotype). These shifts require important genetic stepping-stones that would be possible to reach only if cryptic variation precedes the change in environmental conditions. (Zheng et al., 2019). Given the current pace of environmental changes, exploring and conserving cryptic diversity should be of primary focus.
### 3. Population Genomics Can Identify Interesting Diversity: Approaches and Methodologies

The potential of this available diversity to improve agriculture in the face of climate change is huge. However, its use is far from being obvious to valorize. Population genetics is the study of allele frequency changes in a group of individuals exchanging genes (i.e., a population). Its concepts and tools are used to study microevolution, population dynamics, and conservation genetics. While broadly applied in natural conditions, population genetics approaches are also relevant in agriculture. When accessing the genetic composition of a population, several parameters can be studied: genotype frequencies, allelic frequencies, gene flows, heritability, genetic correlations, heterozygosity, and other indicators that allow an understanding of the genetic dynamics of the population under study. Using such approaches may help identify the historical effects of evolutionary forces that have shaped current diversity and reveal interesting alleles directly related to the environmental conditions to which they respond. This knowledge and understanding are essential to make an integrated use of agrobiodiversity and to ensure that it is a factor of resilience in agrosystems.

#### 3.1 Detecting Footprints of Selection

The identification of selection, phenotype-genotype links, and candidate genes is possible through the study of allelic frequencies at the whole genome scale. In this paragraph, a synthetic and theoretical way the genomic footprints left by selection in the case of a strong and recent selection by an environmental pressure is presented. The cases of polygenic selection or soft selection are more complex and harder to detect, although surely more frequent and often linked to local adaptation and rapid evolution (Hermisson & Pennings, 2017).

In case an ideal unique population at equilibrium and a recent mutation under strong positive selection is considered, the haplotype(s) carrying a beneficial mutation will increase rapidly in frequency in the population. This selection of a small number of haplotypes will lead to a reduction in diversity around the adaptive allele because polymorphisms physically linked to it will
be swept along (referred to as hitchhiking effect). This binding strength will depend on the rate of recombination and the time it takes for it to act. In other words, the lower the recombination rate, the greater the size of the haplotype will be, with a loss of diversity that will expand far away from the causative allele. Similarly, the older the selection pressure is and the more time the recombination has had to break the linkage, the smaller the region affected by the loss of diversity. Because it is this difference in diversity relative to the rest of the genome that can be used to identify the presence of adaptive alleles, it is easy to understand that it will be easier to identify adaptive alleles that have been selected recently or in regions/genomes with low recombination rates.

The loss of diversity around the adaptive allele can be measured either by classical diversity tests, such as Tajima's D, and across the spectrum of allelic frequencies, also called site frequencies spectrum (SFS). In case of neutrality, this spectrum follows a distribution of the form y = 1/x. In case of selection, the intermediate classes are very little represented or even absent, and the extreme frequencies (i.e., very rare or very frequent alleles), increase in frequency. A third pattern that appears around the adaptive allele, in addition to the reduction of diversity and the SFS change, concerns the linkage disequilibrium (LD). Within a selected haplotype, the LD is high among polymorphisms located on the same side of the adaptive allele but low between SNPs located on either side of the allele. The w-statistic has been proposed to measure this pattern.

Analyses can be performed at the metapopulation level by comparing populations. If the adaptive allele is selected in a specific population, the difference in frequencies of the adaptive allele and those physically linked to it compared to their frequencies in the other populations will be very strong. This phenomenon creates a pattern along the genome where genomic zones involved in adaptation will exhibit more differentiation between populations than the rest of the genome. This differentiation is often measured by the fixation index (FST parameter), estimated from allelic frequencies. The study of FST values, along the chromosomes between two populations, allows the identification of particularly high values of FST, which highlight candidate regions of the genome for selection, including genes involved in adaptation in one of the compared populations.

Approaches based on diversity indices, SFS or LD, are highly dependent on
the number and density of SNPs identified. Consequently, these methods are adapted to Whole Genome or capture sequencing technologies, allowing regions spanning a minimum of several dozens to several hundreds of kilobases. The GBS (Elshire et al., 2011), RADSeq (Hohenlohe et al., 2010), or small area capture approaches (Mariac et al., 2018) are more adapted for approaches based on population differentiation. When these types of methods based on FST/genetic differentiation are targeted, the choice of populations (i.e., spatial sampling protocol) is crucial (De Mita et al., 2013). Table 1 presents a synthesis of the different methods to detect selection and associated software.

<table><caption>**Table 1**
Synthesis of Methods to Detect Selection</caption><thead><tr><th>Sequencing methodology leads to</th><th>Number of populations needed</th><th>Types of footprint</th><th>Softwares</th><th>References</th></tr></thead><tbody><tr><td rowspan="3">genomic regions > 50,000 kb</td><td rowspan="3">1</td><td>Diversity indices<br>(Tajima's D)</td><td>ANGSD</td><td>Korneliussen et al., 2014</td></tr><tr><td>SFS</td><td>SweepFinder, SweeD</td><td>Nielsen et al., 2005; Pavlidis et al., 2013</td></tr><tr><td>LD</td><td>OmegaPlus (2012)</td><td>Alachiotis et al., 2012</td></tr><tr><td>genomic regions < 1000 kb</td><td>>2</td><td>Genetic differentiation</td><td>PCAdapt, BayeScan</td><td>Luu et al., 2017; Foll & Gaggiotti, 2008</td></tr></tbody></table>

## 3.2 Association Methods

Genomic scans looking for adaptive alleles can be complemented by approaches revealing significant correlations between allelic frequencies and phenotypic traits (genotype-phenotype association [GPA]) or environmental variables (genotype-environment association [GEA]). These methods are tailored to help understand the genetic basis of phenotypic
variation (GPA) or adaptation to the environment (GEA). The association
between phenotypic/environment and genomic variability can be examined
through Genome Wide Association Studies (GWAS) and helps decipher
metabolic pathways for a better understanding of functional processes.
These methods are necessarily sensitive to the number of points to estimate
the relationship (>100) and the kinship within the sampling. A review of tests
and study of their limitations for GEA and GPA can be found in Rellstab et al.
(2015) and Tam et al. (2019), respectively.

Studying adaptation to drought or other stresses on classical inbred lines or
more complex ones such as RILS or NAMs is a good start but it is not enough.
The diversity represented in these synthetic panels is reduced, and creating
these resources is time consuming and costly—they generally require several
generations of inbreeding of a rather large number of individuals. Whenever
possible, CWRs and landraces should be included in adaptation studies
because they are functional reservoirs of a genetic diversity that are
essential for the resilience of varieties. Then, if researchers want to extract
information from them, they must use population genomics methods that
are adapted to heterogeneous populations and that allow the detection of
local adaptations (Hoban et al., 2016).

3.3 Correlation Between Genetic Diversity and Environmental
Variability

Another emerging set of methods seeks to infer the current populations'
vulnerabilities to future climatic conditions, including increased drought
events. In contrast with well-known niche-modelling approaches that use
the extent of species distribution to draw suitable areas at the species level,
these approaches use the available genetic diversity to give a finer-scale
view of the effect of future climates on local populations (i.e., landraces).
These approaches largely rely on newly developed Machine Learning
approaches to link the variations of genetic diversity and the climate that
should have modelled it. If these approaches were initially developed and
applied to natural populations of plants or animals (Bay et al., 2018;
Fitzpatrick & Keller, 2015), there is a growing interest in applying them to
cultivated plants (Capblancq et al., 2020; Rhoné et al., 2020). A detailed
example is reported in Box 1.
Altogether, population genetics theory and its application to the study of agrobiodiversity's history and contemporary evolution in the face of current environmental constraints is a powerful tool to better understand populations' dynamics and identify how to make a reasonable and integrated use of their genetic diversity.

Box 1: A study of genomic vulnerability

A recent study has focused on estimating the local adaptation of traditional landraces of pearl millet to environmental variations in West Africa (Rhoné et al, 2020). Using a fine-scale climatic dataset and climatic variables calculated to match the growing season of the crop (Sultan et al, 2019), researchers have investigated how genetic diversity has been shaped by variation in climate conditions. Using a machine learning algorithm that monitors shifts in allele frequencies toward environmental gradients, this first step of the study determined that the impact of climate on genetic structure is important. Using the relationships established between climate variables and allelic variations, a second step of the study consisted in predicting the allelic composition that will be required to fit future

climate conditions estimated under a climate change model. By calculating a genetic distance between the current allelic composition of a landrace and the future one at the same location under climate change, researchers computed a so-called genomic vulnerability (GV, aka genetic offset). This GV informs on the magnitude of evolution needed to cope with future climate for a given landrace. For pearl millet, researchers observed a particular pattern of GV that was subsequently linked to a variation in flowering time using a GWAS approach.

To validate that the GV can efficiently predict a biological impact of climate change, researchers used a common garden experiment. Using a field in Niger as a common environment for
several landraces coming from all West Africa, researchers computed for these test landraces a GV between their original location and the common garden. They evaluated performance of the landraces by monitoring the growth and several key phenotypic traits linked to the yield. They then tested if a greater GV ended up with a lower yield. The obtained negative correlation between GV and yield estimators was highly significant, with a Pearson's r ranging from -0.31 to -0.42 depending on the considered parameter, validating the predictive power of the GV approach for the first time in a crop plant.

A last work performed under this study was to evaluate if material exchange throughout West Africa could locally reduce GV, using a newly developed measure of the remaining GV after a migration event. This investigation led to the outcome that within-country migrations would not be efficient in significantly reducing the GV but larger, regional-scale migrations should help reduce the risk of yield loss due to adverse climatic conditions.

# Management and Uses of Agrobiodiversity

## 1. Conservation Strategies

The importance of agrobiodiversity, defended in this chapter, brings another perspective on the conservation of genetic resources. While it is recognized that in situ conservation—in addition to ex-situ collections of diversity—is crucial to maintain evolutionary forces under work in populations, the question of the genetic erosion arises in debates on climate change. The pace imposed by this environmental perturbation begs the question: Can populations adapt quickly enough while not losing all important genetic variation if the selection pressure is too strong? In the face of climatic change, it becomes important to maintain evolutionary forces at work and
to ensure that these evolutionary forces will be able to maintain a dynamic
state that continues creating diversity. Evolution models must therefore be
developed to thoroughly understand how diversity will behave in the face
of climate change and to make recommendations on practices that would
promote a dynamic in favor of maintaining diversity, and against undesired
genetic erosion. In that, the role of so-called de-facto conservation, through
agricultural practices oriented toward more inclusion of agrobiodiversity is
primordial, local actors are guarantors of the genetic richness present on
their territory.

## 2. Genetic Resources Access

Despite the key role that agrobiodiversity is expected to play to build a
sustainable agriculture resilient to climate change, a paradoxical situation
prevails today. On the one hand, new genomics, phenotyping, and
information technologies promote a more rapid development of knowledge
and an increased and accelerated exploitation of genetic resources in the
public and private sectors. On the other hand, international regulations
tend to make access to and exchange of genetic resources more complex,
although underpinned by the laudable goal of equity. These instruments
(the International Treaty on Plant Genetic Resources [ITPGRFA] and the
Nagoya Protocol [NP] of the Convention on Biological Diversity [CBD]) aim
to guarantee access to genetic resources and the fair and equitable sharing
of the benefits stemming from their use. Better uses of this diversity will
require improved mechanisms for exchanging information and resources
rapidly and effectively, considering the diversity in value systems at work
among stakeholders who often have different visions and objectives for the
exchange of genetic resources (Jankowski et al., 2020).

These challenges will require coordinated transdisciplinary projects across
research disciplines (e.g., agronomy, anthropology, sociology, economics,
breeding and genetics) involving actors beyond research. Access to this
biodiversity must be preserved in itself for the functions and services
rendered within agro-ecosystems, which does not prohibit the use of the
genetic resources offered (i.e., genetic improvement). It must be done in a
sustainable manner and in a logic of common goodness at different scales
and for multiple actors. To meet the current challenges of crop adaptation
and improvement under dry environment, the authors advocate for a broad
understanding of innovation, which requires the involvement of new
diversity and new actors in the knowledge production process.

Perspectives

The identification of genes or genomic regions involved in adaptation or in the past evolution of populations significantly enriched the knowledge of the variability existing in populations and its potential usefulness, in particular through the study of the links with the environment. It also appears that a classical view, where one crop improvement need can be addressed by one appropriate allele, does not respond properly to the specific constraints imposed by the local context of West Africa or to the perspective of the ongoing rapidly changing climate. Moreover, such approaches would fail in establishing long-term sustainable and resilient agrosystems. On the contrary, the maintenance of numerous alleles in a diversity of crops seems more likely to ensure agrosystems' stability at the level of crops populations. Similarly, maintaining a diversity of crop species within and outside fields holds the potential to promote stability of food production and livelihoods.

CWR and landraces are thus potentially valuable sources of alleles of
interest, especially in the context of climatic unpredictability and of needed
resilient strategies. A classical view regarding biodiversity valorization relies
on a triptych conservation-characterization-use in prebreeding. However,
this approach is far from simple. The characterization and use of genetic
resources requires tremendous investments and precedent knowledge
regarding the genetic complexity of adaptive traits. As a result, biodiversity
valorization in breeding programs has been recognized as essential for a long
time but scarcely led to concrete successful examples, regarding the huge
potential offered by the total conserved diversity. In addition, it remains a
tit-for-tat strategy that may encounter limits when it comes to adapting
populations to important and erratic changes in the environment.
## Conclusion

Agrobiodiversity should be an integrated input at several scales—landscapes, species, individual genotypes, and genes—to contribute to more stable food systems in the face of erratic effects of climate and global changes. Allowing a broader and more efficient use of agrobiodiversity, (i.e., the study of the finest level of diversity in its dynamic state, as population genetics and genomics approaches), is essential for a better understanding and knowledge of the characters of interest to significantly improve agrosystems' functioning. Through their role, breeders and farmers are the guarantors of this agrobiodiversity. They can greatly influence its conservation by integrating the concept of diversity into their strategies. If improvement is still considered a factor of erosion in intensive agricultural systems, an inverted paradigm is possible through an approach based on an objective of stability and resilience of agrosystems.

## References

* Auffhammer, M., & Carleton, T. A. (2018). Regional crop diversity and weather shocks in India. *Asian Development Review*, 35(2), 113-130.

* Bay, R. A., Harrigan, R. J., Le Underwood, V., Lisle Gibbs, H., Smith, T. B., & Ruegg, K. (2018). 359(6371), 83-86.

* Beillouin, D., Ben-Ari, T., & Makowski, D. (2019). Evidence map of crop diversification strategies at the global scale. *Environmental Research Letters*, 14(12), Article 123001.

* Berthouly-Salazar, C., Thuillet, A.-C., Rhoné, B., Mariac, C., Ousseini, I. S., Couderc, M., Tenaillon, M. I., & Vigouroux, Y. (2016). Genome scan reveals selection acting on genes linked to stress response in wild pearl millet. *Molecular ecology*, 25(21), 5500-5512.

* Berkes, F., Colding, J., & Folke, C. (Eds.). (2008). *Navigating social-ecological systems: building resilience for complexity and change*. Cambridge University Press.

* Bitter, M. C., Kapsenberg, L., Gattuso, J.-P., & Pfister, C. A. (2019). Standing
genetic variation fuels rapid adaptation to ocean acidification. *Nature communications*, 10(1), Article 5821.

Burgarella, C., Barnaud, A., Kane, N. A., Jankowski, F., Scarcelli, N., Billot, C., Vigouroux, Y., & Berthouly-Salazar, C. (2019). Adaptive introgression: an untapped evolutionary mechanism for crop adaptation. *Frontiers in Plant Science*, 10, 4.

Burgarella, C., Cubry, P., Kane, N. A., Varshney, R. K., Mariac, C., Liu, X., Shi, C., Thudi, M., Couderc, M., Xu, X., Chitkineni, A., Scarcelli, N., Barnaud, A., Rhoné, B., Dupuy, C., François, O., Berthouly-Salazar, C., & Vigouroux, Y. (2018). A western Sahara centre of domestication inferred from pearl millet genomes. *Nature ecology & evolution*, 2(9), 1377-1380.

Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M., & Keller, S. R. (2020). Genomic prediction of (mal)adaptation across current and future climatic landscapes. *Annual Review of Ecology, Evolution, and Systematics*, 51(1), 245-269.

Cornforth, R., Parker, D. J., Diop-Kane, M., Fink, A. H., Lafore, J. P., Laing, A., Afiesimama, E., Caughey, J., Diongue-Niang, A., Hamza, I., Harou, A., Kassimou, A., Lamb., P., Lampety, B., Mumba, Z., Nnodu., I., Omotosho, J., Palmer., S., Parrish, P., ... Tompkins, A. (2019). The first Forecasters' Handbook for West Africa. *Bulletin of the American Meteorological Society*, 100(11), 2343-2351.

De Mita, S., Thuillet, A.-C., Gay, L., Ahmadi, N., Manel, S., Ronfort, J., & Vigouroux, Y. (2013). Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations. *Molecular ecology*, 22(5), 1383-1399.

Di Falco, S., & Chavas, J.-P. (2008). Rainfall shocks, resilience, and the effects of crop biodiversity on agroecosystem productivity. *Land Economics*, 84(1), 83-96.

Egbeiyi, T. S., Crespo, O., Lennard, C., Zaroug, M., Nikulin, G., Harris, I., Price, J., Forstenhäusler, N., & Warren, R. (2020). Investigating the potential impact of 1.5, 2 and 3 °C global warming levels on crop suitability and planting season over West Africa. *PeerJ*, 8, Article e8851.
Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., & Mitchell, S. E. (2011). A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. *PLOS ONE*, 6(5), Article e19379.

Fahad, S., & Wang, J. (2018). Farmers’ risk perception, vulnerability, and adaptation to climate change in rural Pakistan. *Land Use Policy*, 79, 301–309.

Faye, A., Barnaud, A., Kane, N. A., Cubry, P., Mariac, C., Burgarella, C., Rhoné., B., Faye, A., Olodo, K., Cisse, A., Couderc, M., Dequincey, A., Zekraouï, L., Moussa, D., Tidjani, M., Vigouroux, Y., & Berthouly-Salazar, C. (2022). Genomic footprints of selection in early- and late-flowering pearl millet landraces. [Manuscript submitted for publication].

Félix, G. F., Diedhiou, I., Le Garff, M., Timmermann, C., Clermont-Dauphin, C., Cournac, L., Groot, J. C. J., & Tittonell, P. (2018). Use and management of biodiversity by smallholder farmers in semi-arid West Africa. *Global Food Security*, 18, 76–85.

Fitzpatrick, M. C., & Keller, S. R. (2015). Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. *Ecology Letters*, 18(1), 1–16.

Frantz, L. A. F., Bradley, D. G., Larson, G., & Orlando, L. (2020). Animal domestication in the era of ancient genomics. *Nature Reviews Genetics*, 21(8), 449–460.

Gaudin, A. C. M., Tolhurst, T. N., Ker, A. P., Janovicek, K., Tortora, C., Martin, R. C., & Deen, W. (2015). Increasing crop diversity mitigates weather variations and improves yield stability. *PLOS ONE*, 10(2), Article e0113261.

Gibson, G., & Dworkin, I. (2004). Uncovering cryptic genetic variation. *Nature Reviews Genetics*, 5(9), 681–690.

Gross, K., Cardinale, B. J., Fox, J. W., Gonzalez, A., Loreau, M., Wayne Polley, H., Reich, P. B., & van Ruijven, J. (2014). Species richness and the temporal stability of biomass production: a new analysis of recent biodiversity experiments. *The American Naturalist*, 183(1), 1–12.
Guarino, L., & Lobell, D. B. (2011). A walk on the wild side. *Nature Climate Change*, 1(8), 374-375.

Haussmann, B. I. G., Fred Rattunde, H., Weltzien-Rattunde, E., Traoré, P. S. C, vom Brocke, K., & Parzies, H. K. (2012). Breeding strategies for adaptation of pearl millet and sorghum to climate variability and change in West Africa. *Journal of Agronomy and Crop Science*, 198(5), 327-339.

Hector, A. (1998). The effect of diversity on productivity: detecting the role of species complementarity. *Oikos*, 82(3), 597-599.

Hereford, J. (2009). A quantitative survey of local adaptation and fitness trade-offs. *The American Naturalist*, 173(5), 579-588.

Hermisson, J., & Pennings, P. S. (2017). Soft sweeps and beyond: understanding the patterns and probabilities of selection footprints under rapid adaptation. *Methods in Ecology and Evolution*, 8(6), 700-716.

Himmelstein, J., Ares, A., Gallagher, D., & Myers, J. (2017). A meta-analysis of intercropping in Africa: impacts on crop yield, farmer income, and integrated pest management effects. *International Journal of Agricultural Sustainability*, 15(1), 1-10.

Hoban, S., Kelley, J. L., Lotterhos, K., Antolin, M. F., Bradburd, G., Lowry, D. B., Poss, M. L., Reed, L. K., Storfer, A., & Whitlock, M. C. (2016). Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. *The American Naturalist*, 188(4), 379-397.

Hohenlohe, P. A., Bassham, S., Etter, P. D., Stiffler, N., Johnson, E. A., & Cresko, W. A. (2010). Population genomics of parallel adaptation in three spine stickleback using sequenced RAD tags. *PLOS Genetics*, 6(2), Article e1000862.

Holling, C. S. (1973). Resilience and stability of ecological systems. *Annual Review of Ecology and Systematics*, 4(1973), 1-23.

Intergovernmental Panel on Climate Change. (2014). Climate Change 2014 – impacts, adaptation, and vulnerability: Part B: regional aspects. Working Group II contribution to the IPCC Fifth Assessment Report. Cambridge University Press.
Isbell, F., Craven, D., Connolly, J., Loreau, M., Schmid, B., Beierkuhnlein, C., Martijn Bezemer, T., Bonin, C., Bruelheide, H., de Luca, E., Ebeling, A., Griffin, J. N., Guo, Q., Hautier, Y., Hector, A., Jentsch, A., Kreyling, J., Lanta, V., ... & Eisenhauer, N. (2015). Biodiversity increases the resistance of ecosystem productivity to climate extremes. *Nature*, 526(7574), 574-577.

Jankowski, F., Louafi, S., Kane, N. A., Diol, M., Camara, A. D., Pham, J.-L., Berthouly, C., & Barnaud, A. (2020). From texts to enacting practices: defining fair and equitable research principles for plant genetic resources in West Africa. *Agriculture and Human Values*, 37(4), 1083-1094.

Kassie, B. T., Hengsdijk, H., Rötter, R. P., Kahiluoto, H., Asseng, S., & van Ittersum, M. K. (2013). Adapting to climate variability and change: experiences from cereal-based farming in the Central Rift and Kobo Valleys, Ethiopia. *Journal of Environmental Management*, 52(5), 1115-1131.

Kawecki, T. J., & Ebert, D. (2004). Conceptual issues in local adaptation. *Ecology Letters*, 7(12), 1225-1241.

Labeyrie, V., Renard, D., Aumeeruddy-Thomas, Y., Benyei, P., Caillon, S., Calvet-Mir, L., Carrière, S. M., Demongeot, M., Descamps, E., Braga Junqueira, A., Li, X., Locqueville, J., Mattalia, G., Miñarro, S., Morel, A., Porcuna-Ferrer, A., Schlingmann, A., Vieira da Cunha Avila, J., & Reyes-García, V. (2021). The role of crop diversity in climate change adaptation: insights from local observations to inform decision making in agriculture. *Current Opinion in Environmental Sustainability*, 51, 15-23.

Lauter, N., & Doebley, J. (2002). Genetic variation for phenotypically invariant traits detected in teosinte: implications for the evolution of novel forms. *Genetics*, 160(1), 333-342.

Lin, B. B. (2011). Resilience in agriculture through crop diversification: adaptive management for environmental change. *BioScience*, 61(3), 183-193.

Litrico, I., & Violle, C. (2015). Diversity in plant breeding: a new conceptual framework. *Trends in Plant Science*, 20(10), 604-613.

Loreau, M., and Hector, A. (2001). Partitioning selection and complementarity in biodiversity experiments. *Nature*, 412(6842), 72-76. https://doi.org/10.1038/35083573
Lu, B.-R., Jackson, M., & Vaughn, D. (2010). *Wild rice taxonomy*. Rice Knowledge Bank.

Maestre, F. T., Callaway, R. M., Valladares, F., & Lortie, C. J. (2009). Refining the stress-gradient hypothesis for competition and facilitation in plant communities. *Journal of Ecology*, 97(2), 199-205.

Mariac, C., Vigouroux, Y., Duponchelle, F., García-Dávila, C., Núñez, J., Desmarais, E., & Renno, J. F. (2018). Metabarcoding by capture using a single COI probe (MCSP) to identify and quantify fish species in ichthyoplankton swarms. *PLOS ONE*, 13(9), Article e0202976.

Moyers, B. T., Morrell, P. L., & McKay, J. K. (2018). Genetic costs of domestication and improvement. *Journal of Heredity*, 109(2), 103-116.

Nicholson, S. E., Funk, C., & Fink, A. H. (2018). Rainfall over the African continent from the 19th through the 21st century. *Global and Planetary Change*, 165, 114-127.

Ousseini, I. S., Bakasso, Y., Kane, N. A., Couderc, M., Zekraoui, L., Mariac, C., Manicacci, D., Rhoné, B., Barnaud, A., Berthouly-Salazar, C., Assoumane, A., Moussa, D., Moussa, T., & Vigouroux, Y. (2017). Myosin XI is associated with fitness and adaptation to aridity in wild pearl millet. *Heredity*, 119(2), 88-94.

Philpott, S. M., Lin, B. B., Jha, S., & Brines, S. J. (2008). A multi-scale assessment of hurricane impacts on agricultural landscapes based on land use and topographic features. *Agriculture, Ecosystems & Environment*, 128(1-2), 12-20.

Pironon, S., Etherington, T. R., Borrell, J. S., Kühn, N., Macias-Fauria, M., Ondo, I., Tovar, C., Wilkin, P., & Willis, K. J. (2019). Potential adaptive strategies for 29 sub-Saharan crops under future climate change. *Nature Climate Change*, 9(10), 758-763.

Popovic, D., & Lowry, D. B. (2020). Contrasting environmental factors drive local adaptation at opposite ends of an environmental gradient in the yellow monkeyflower (Mimulus guttatus). *American Journal of Botany*, 107(2), 298-307.

Prieto, I., Violle, C., Barre, P., Durand, J.-L., Ghesquiere, M., & Litrico, I. (2015).
Complementary effects of species and genetic diversity on productivity and stability of sown grasslands. *Nature Plants*, 1(4), 1-5.

Qualset, C., Mcguire, P., & Warburton, M. (1995). In California: 'Agrobiodiversity' key to agricultural productivity. *California Agriculture*, 49(6), 45-49.

Raseduzzaman, M., & Jensen, E. S. (2017). Does intercropping enhance yield stability in arable crop production? A meta-analysis. *European Journal of Agronomy*, 91, 25-33.

Razgour, O., Forester, B., Taggart, J. B., Bekaert, M., Juste, J., Ibáñez, C., Puechmaille, S., Novella-Fernandez, R., Alberdi, A., & Manel, S. (2019). Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. *Proceedings of the National Academy of Sciences*, 116(21), 10,418-10,423.

Reiss, E. R., & Drinkwater, L. E. (2018). Cultivar mixtures: a meta-analysis of the effect of intraspecific diversity on crop yield. *Ecological Applications*, 28(1), 62-77.

Rellstab, C., Gugerli, F., Eckert, A. J., Hancock, A. M., & Holderegger, R. (2015). A practical guide to environmental association analysis in landscape genomics. *Molecular Ecology*, 24(17), 4348-4370.

Renard, D., & Tilman, D. (2019). National food production stabilized by crop diversity. *Nature*, 571(7764), 257-260.

Rhoné, B., Defrance, D., Berthouly-Salazar, C., Mariac, C., Cubry, P., Couderc, M., Dequincey, A., Assoumanne, A., Ardo Kane, N., Sultan, B., Barnaud, A., & Vigouroux, Y. (2020). Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration. *Nature Communications*, 11(1), 1-9.

Saïdou, A.-A., Clotault, J., Couderc, M., Mariac, C., Devos, K. M., Thuillet, A.-C., Amoukou, I. A., & Vigouroux, Y. (2014). Association mapping, patterns of linkage disequilibrium and selection in the vicinity of the PHYTOCHROME C gene in pearl millet. *Theoretical and Applied Genetics*, 127(1), 19-32.

Scarcelli, N., Tostain, S., Vigouroux, Y., Agbangla, C., Daïnou, O., & Pham, J.-L. (2006). Farmers' use of wild relative and sexual reproduction in a
vegetatively propagated crop. The case of yam in Benin. *Molecular Ecology*, 15(9), 2421-2431.

Shinbrot, X. A., Jones, K. W., Rivera-Castañeda, A., López-Báez, W., & Ojima, D. S. (2019). Smallholder farmer adoption of climate-related adaptation strategies: The importance of vulnerability context, livelihood assets, and climate perceptions. *Environmental Management*, 63(5), 583-595.
https://doi.org/10.1007/s00267-019-01152-z

Smith, H. A., & McSorley, R. (2000). Intercropping and pest management: a review of major concepts. *American Entomologist*, 46(3), 154-161.

Smith, R. G., Gross, K. L., & Robertson, G. P. (2008). Effects of crop diversity on agroecosystem function: crop yield response. *Ecosystems*, 11(3), 355-366.

Smithson, J. B. & Lenné, J. M. (1996). Varietal mixtures: a viable strategy for sustainable productivity in subsistence agriculture. *Annals of Applied Biology*, 128(1), 127-158.

Snapp, S. S., Blackie, M. J., Gilbert, R. A., Bezner-Kerr, R., & Kanyama-Phiri, G. Y. (2010). Biodiversity can support a greener revolution in Africa. *Proceedings of the National Academy of Sciences*, 107(48), 20,840-20,845.

Sultan, B., Defrance, D., & Iizumi, T. (2019). Evidence of crop production losses in West Africa due to historical global warming in two crop models. *Scientific Reports*, 9(1), 1-15.

Tam, V., Patel, N., Turcotte, M., Bossé, Y., Paré, G., & Meyre, D. (2019). Benefits and limitations of genome-wide association studies. *Nature Reviews Genetics*, 20(8), 467-484.

Tilman, D., Downing, J. A., & Wedin, D. A. (1994). Does diversity beget stability? *Nature*, 371, 114. https://doi.org/10.1038/371114a0

Vargas, C. A., Lagos, N. A., Lardies, M. A., Duarte, C., Manríquez, P. H., Aguilera, V. M., Broitman, B., Widdicombe, S., & Dupont, S. (2017). Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. *Nature Ecology & Evolution*, 1(4), 1-7.

Van Oosterom, E. J., Whitaker, M. L., & Weltzien, E. (1996). Integrating
genotype by environment analysis, characterization of drought patterns,
and village surveys to identify adaptive plant traits for pearl millet. In M.
Cooper and G.L. Hammer (Eds.), *Plant Adaptation and Crop Improvement*
(pp. 383-402). CAB International.

Vigouroux, Y., Mariac, C., De Mita, S., Pham, J.-L., Gérard, B., Kapran, I., Sagnard, F., Deu, M., Chantereau, J., Ali, A., Ndjeunga, J., Luong, V., Thuillet, A.-C., Saïdou, A.-A., & Bezançon, G. (2011). Selection for earlier flowering crop associated with climatic variations in the Sahel. *PLOS ONE*, 6(5), Article e19563.

Violle, C., Enquist, B. J., McGill, B. J., Jiang, L. C. H., Albert, C. H., Hulshof, C., Jung, V., & Messier, J. (2012). The return of the variance: intraspecific variability in community ecology. *Trends in Ecology & Evolution*, 27(4), 244-252.

von Wettberg, E. J., Chang, P. L., Başdemir, F., Carrasquila-Garcia, N., Korbu, L. B., Moenga, S. M., Bedada, G., Greenlon, A., Moriuchi, K. S., Singh, V., Cordeiro, M. A., Noujdina., N. V., Dinegde, K. N., Shah Sani, S. G. A., Getahun, T., Vance, L., Bergmann, E., Lindsay, D., Mamo, B. E., Warschefsky, E. J., ... & Cook, D. R. (2018). Ecology and genomics of an important crop wild relative as a prelude to agricultural innovation. *Nature Communications*, 9(1), 1-13.

Walker, B., Holling, C. S., Carpenter, S. R., & Kinzig, A. (2004). Resilience, adaptability and transformability in social-ecological systems. *Ecology and Society*, 9(2).

Weltzien E., Whitaker, M. L., Rattunde, H. F. W., Dhamotharan, M., & Anders, M. M. (1998). Participatory approaches in pearl millet breeding. In J. R Witcombe, D. S. Virk and J. Farrington (Eds.), *Seeds of Choice: Making The Most of New Varieties For Small Farmers* (pp. 143-170). Oxford and IBH Publishing Co.

Yachi, S., & Loreau, M. (1999). Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis. *Proceedings of the National Academy of Sciences*, 96, 1463-1468.

Zheng, J., Payne, J. L., & Wagner, A. (2019). Cryptic genetic variation
accelerates evolution by opening access to diverse adaptive peaks. *Science*, 365(6451), 347-353.

Zhu, Y., Chen, H., Fan, J., Wang, Y., Li, Y., Chen, J., Fan, J., Yang, S., Hu, L., Leung, H., Mew, T. W., Teng, P. S., Wang, Z., & Mundt, C. C. (2000). Genetic diversity and disease control in rice. *Nature*, 406(6797), 718-722.
14. Management of Cowpea
[Vigna unguiculata L. (Walp)]
Germplasm Diversity in Senegal:
A Crucial Asset for Breeding
Programs

Amy Bodian, ISRA/CERAAS, Senegal

**Awa Sarr**, Cheikh Anta Diop University of Dakar, Senegal & ISRA/CERAAS,
Senegal

Badara Guèye, IITA, Nigeria

Mouhamadou Moussa Diangar, ISRA/CNRA, Senegal

Tatiana Krasova Wade, IRD, Senegal

Demba Dramé, Cheikh Anta Diop University of Dakar & ISRA/CERAAS,
Senegal

Ndiaga Cissé, ISRA/CERAAS, Senegal

Diaga Diouf, Cheikh Anta DIOP University of Dakar, Senegal

Abstract

Cowpea is a legume of nutritional, socioeconomic, and therapeutic
importance in Senegal, as in other West African countries. However, biotic
and abiotic constraints affect its production. To mitigate those constraints
and enhance its productivity in Senegal, the management of cowpea genetic
resources is a priority. Managing these resources involves extensive
collection to cover maximum cowpea diversity, standard characterization,
and sustainable conservation of the local germplasm. A review of the
diversity management of the local cowpea germplasm and results from a
study conducted between 2015 and 2021 are presented in this chapter. Many
cowpea prospections and collections have been done since 1953, but they
only covered a few cowpea growing areas.

Moreover, many collected accessions have been lost due to a lack of
adequate conservation. This deprived cowpea breeding programs of great
germplasm diversity. Therefore, in 2015, a cowpea collection was made,
covering more production regions, including cultivated cowpea, hybrids, and
wild accessions. The molecular characterization of this collection showed
that the genetic diversity is higher in hybrids and wild accessions than in
the cultivated species. These results will be used for a better valorization
of cowpea genetic resources to overcome constraints encountered in the
production of this crop.

Keywords: cowpea, local germplasm, diversity, breeding program, Senegal

**Introduction**

African agriculture faces major challenges to crop productivity in family
farms. These challenges are in combination with climate change, which
worsens soil degradation, drought, and heatwaves. Food demands are
increasing due to the growing population and continual decrease in arable
lands due to urbanization and industrialization. This requires a sustainable
intensification of agricultural production based on resilient crops, such as
cowpea, that can generate income and balance the diet of populations
without degrading the ecosystem. Cowpea [Vigna unguiculata (L.) Walp] is
mainly a self-pollinating plant. This important staple food crop, well-adapted
to dry tropical regions and offering considerable economic, nutritional, and
agronomic benefits (Agbicodoet al., 2009) in the tropical savanna areas of
Africa, deserves special attention. Cowpea provides more than half of the
vegetable proteins in the diet of populations in many developing countries,
especially in sub-Saharan Africa (Aliyu & Wachap, 2014; Bhattarai et al., 2017;
Boukaret al., 2016). It also contains a significant amount of minerals and
vitamins. Moreover, cowpea has a short growing cycle, making it a reliable
lean food for populations, especially during mid-season (around September
in Senegal), when other crops have not reached maturity. It has economic
importance because large quantities of its products (green pods, seeds, and
haulms) can be marketed, providing a significant additional income to rural
populations (both men and women). Cowpea is also processed into many
popular food items such as couscous, thiakry, coffee, etc., representing
another source of income for women processors. The crop is a legume
adapted to drought conditions. It plays an important role as a source of
nitrogen for other crops, such as cereal crops, especially in low soil fertility
areas. Indeed, it fixes atmospheric nitrogen through its symbiotic
associations with bacteria of the Bradyrhizobium genus, thus reducing the
demand for nitrogen fertilizer and agricultural production costs (Asiwe et al.,
2009; Dugje et al., 2008).

According to Ng and Maréchal (1985), the crop has been cultivated in Senegal for centuries. Séne (1966) hypothesized that local early maturity varieties were introduced from Nigeria for recession cultivation in the Senegal River valley in the northern part of the country. In contrast, the late maturity varieties were introduced from Mali. They were cultivated in association with millet in the wetter areas of Senegal and spread out all over the country, favored by trade and migration. Despite its importance, cowpea yields are relatively low in Africa, particularly in Senegal, at less than 700 kg/ha, according to a recent report released by the “Agence Nationale de la Statistique et de la Démographie” (ANSD, 2021). The low yields may be due to several abiotic (cold, heat, drought, low soil fertility, salinity, etc.), biotic (diseases, pests, parasitic plants, etc.), and socioeconomic constraints (Obilana, 1987; Singh & Sharma, 1996). Therefore, breeding programs in Senegal have been focused on developing high-yielding varieties adapted to different growing areas. Diverse, available, and accessible crop collections are crucial for the success of such crop improvement programs. According to the breeding objectives, new traits can be tapped from crop germplasm diversity, using advanced tools such as marker-assisted selection to speed up cowpea breeding.

Moreover, wild relatives can be an abundant source of interesting and exploitable genes for crop improvement (Feldman & Sears, 1981). However, lack of genetic diversity, information on a genetic marker, and knowledge of agronomic traits of interest constitute many limiting factors to cowpea breeding programs. This justified all cowpea prospection missions around the country that happened in the past, which were unfortunately not always sustainably managed and conserved for future use.

Herein, we review the work carried out in Senegal on the genetic diversity
management of the local cowpea germplasm and present the results of a five-year study completed in 2021.

## Cowpea Biodiversity Management in Senegal Before 2015

According to previous reports, cowpea biodiversity management started around 1950 in Podor (the northern part of Senegal) and allowed the selection of a local variety called 58-57. This variety is resistant to drought, reaches maturity 75 days after sowing, has a prostrate stand, and produces a significant number of seeds and haulms. In the same period, several prospecting missions were made in Senegal and beyond (in West Africa), which allowed the collection of 74 accessions (Séne, 1966). Between 1964 and 1967, germplasm evaluation work permitted the identification of cowpea varieties adapted to many production zones in Senegal.

In 1979, American varieties selected for drought and high temperatures tolerance were introduced in various Senegalese agro-ecological zones and compared with local varieties (Cissé et al., 1984). Most promising varieties showing the best adaptability were evaluated in larger trials before diffusion. Furthermore, they have been crossed with local varieties for transferring drought and heat tolerance and pests and disease resistance traits (Hall, 1982, 1984). Then, the breeding program prioritized crosses between the best local and introduced materials, focusing on the ones with promising drought tolerance and high yielding traits. Several cowpea varieties developed in Senegal (Ndiambour, Mougne, Bambey 21, Diongoma, Melakh, Mouride, Yacine, and Pakau) came from those crosses. In 1983, the Senegalese cowpea germplasm collection consisted of 535 accessions, including 347 early maturing lines (not photosensitive), 72 late (photosensitive), and 31 irradiated lines from different origins. The same year, new collections were carried out in the Center and the South of Senegal to complement the existing collection. A total of 85 samples were collected (Ndiaye, 1986).

In 2002 and 2014, the inventory of the national cowpea collection revealed that several accessions were lost due to conservation issues. In 2002, the number of accessions available in the “Centre National de Recherches Agronomiques” (in Bambey) of “Institut Sénégalais de
Recherches Agricoles" (ISRA) was 247. This prompted a collection mission that resulted in the acquisition of 58 cowpea accessions collected in the Louga, Thies, and Diourbel regions of Senegal (North and Center-North) (Kouakouet al., 2007) and molecular and agro-morphological characterization of the germplasm. Unfortunately, in 2014, most of those accessions were lost again. This loss concerned all accessions collected in the main cowpea growing areas in the North and Center-North regions and the Center-South, the East, and the South of the country. Moreover, before 2015 no cowpea collection or prospection carried out in Senegal included wild cowpea relatives.

# Cowpea Biodiversity Management in Senegal Between 2015 and 2021

Between 2015 and 2017, a new national cowpea collection was set up in the framework of a project funded by the West African Agricultural Productivity Program (WAAPP). The overall objective of this project was to contribute to the fight against poverty and food insecurity through better management of cowpea genetic resources. The aim was to set up, conserve and characterize a national cowpea collection for Senegal.

## 1. Germplasm Collection

Regarding the cultivated cowpea accessions, surveys and collections were carried out between September 2015 and March 2016 in the main cowpea growing regions of Senegal (Louga, Thies, Fatick, Diourbel) and additional regions such as Saint Louis (North), Sédhiou, Kolda (South) and Kédougou (Southeast). The villages covered during the collection were selected in consultation with rural development services (Division Régionale de Développement Rural, DRDR), allowing easier access to cowpea production villages. For optimal coverage, three departments were visited in each region, and four to six villages were visited in each department. An average of 10 farmers were interviewed per village, and seed samples were collected from the fields or granaries. Accessions of wild cowpea relatives were collected between November and December 2016 in six regions (Louga,
Thiès, Diourbel, Fatick, Sédhiou, Kolda). Seven hundred thirty-one cultivated accessions and 82 wild relatives were collected across all the regions surveyed (Figure 1, Table 1).

<figure><img src="image_70.png" /><figcaption>Figure 1 - Map Showing the Villages Where Cultivated and Wild Relative Accessions were Collected (Sarr et al., 2020)</figcaption></figure>

<table><caption>Table 1
Number and Type of Cowpea Accessions Collected Per Region in Senegal</caption><thead><tr><th></th><th>Louga</th><th>Diourbel</th><th>Thiès</th><th>Fatick</th><th>Saint Louis</th><th>Sédhiou</th><th>Kédougou</th><th>Kolda</th></tr></thead><tbody><tr><th>Cultivated cowpea</th><td>152</td><td>158</td><td>89</td><td>90</td><td>122</td><td>79</td><td>35</td><td>6</td></tr><tr><th>Hybrids</th><td>14</td><td>2</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>Wild accessions</th><td>0</td><td>2</td><td>22</td><td>21</td><td>0</td><td>3</td><td>0</td><td>13</td></tr><tr><th>Total</th><td>166</td><td>162</td><td>111</td><td>115</td><td>122</td><td>82</td><td>35</td><td>20</td></tr></tbody></table>

Among the 82 accessions initially considered wild relatives, 21 were actually spontaneous hybrids between wild and cultivated cowpea (weedy). The hybrids included 20 accessions from the Senegalese Midwest area (Louga, Diourbel, Fatick) called Kodj (spontaneous germination accessions) and one
from Kolda. The wild accessions belonged to 2 species: *Vigna unguiculata* var. *spontanea* and *Vigna racemosa*. All *Vigna unguiculata* var. *spontanea* (45) originated from the Midwest, and all *Vigna racemosa* (16) were from the South of Senegal (Sédhiou and Kolda). The cultivated, hybrid, and wild accessions represent the national cowpea collection and represent the local cowpea germplasm. In this chapter, *Vigna unguiculata* var. *spontanea* and *Vigna racemosa* are considered wilds, and both hybrids and wilds are considered wild relatives.

## 2. Genetic Characterization of the Collection

The collection of cultivated cowpea and wild relatives was sown in the screenhouse at the "Centre d'Etude Régional pour l'Amélioration de l'Adaptation à la Sécheresse" (CERAAS). Among the 731 cultivated accessions, only 671 germinated and were genotyped. However, all the 82 collected samples grew and were genotyped for the wild cowpea relatives. DNA has been extracted on fresh leaves of 21-day-old plants, and genotyping was done with fifteen (15) simple sequence repeat (SSR) markers (Table 2) selected from a previous screening of cowpea markers available at CERAAS. The genetic diversity of the national cowpea germplasm was then compared with that of 25 accessions from 5 countries (Nigeria, Niger, Burkina Faso, India, USA), kindly shared by the Genetic Resources Center (GRC) of the International Institute of Tropical Agriculture (IITA). The data were analyzed using R, GenAlex, STRUCTURE, and Darwin software to calculate genetic parameters, determine the genetic structure, and build dendrograms.

<table><caption>**Table 2**
SSR Primers Used in the Genetic Study and Their Hybridization Temperature (Tm)</caption><thead><tr><td>Primer code</td><td>Sequence (5'-3')</td><td>Tm (°C)</td></tr></thead><tbody><tr><td>SSR6671-MF</td><td>CACGACGTTGTAAAACGACCAAACTTTGATATCGATCCTTG</td><td>79.5</td></tr><tr><td>SSR6671-R</td><td>GTTCTCTCATGCCATGATG</td><td>59.2</td></tr><tr><td>SSR6418-MF</td><td>CACGACGTTGTAAAACGACCAAAATTCCCAAAGACATGTAA</td><td>79.4</td></tr><tr><td>SSR6418-R</td><td>CATGCAATGGCTAAAGGACA</td><td>63.6</td></tr><tr><td>SSR6777-MF</td><td>CACGACGTTGTAAAACGACGTTATCATATATGACAGTCTTTAATC</td><td>75.2</td></tr><tr><td>SSR6777-R</td><td>CATTTGATGCGGTTGATGGG</td><td>68.3</td></tr></tbody></table>
<table><thead><tr><th>Primer code</th><th>Sequence (5'-3')</th><th>Tm (°C)</th></tr></thead><tbody><tr><td>SSR6311-MF</td><td>CACGACGTTGTAAAACGACATGCCATTGTTGAGTTGCTTT</td><td>81.5</td></tr><tr><td>SSR6311R</td><td>AGGATGTTGTAGCAGGCTAATTG</td><td>63.1</td></tr><tr><td>SSR6800-MF</td><td>CACGCGTTGTAAAACGACTGACTCTTTCTCTCAAGTTA</td><td>75.5</td></tr><tr><td>SSR6800-R</td><td>GATGGGTTGTTGAAAATAAA</td><td>56.5</td></tr><tr><td>SSR6807-MF</td><td>CACGACGTTGTAAAACGACGAACTATTATACAATCATGCACGA</td><td>79.2</td></tr><tr><td>SSR6807-R</td><td>GTAGCTTACTTCAATGATTAG</td><td>50.1</td></tr><tr><td>SSR6289-MF</td><td>CACGACGTTGTAAAACGACCCCCCAAAGTTGATGAACAC</td><td>82.5</td></tr><tr><td>SSR6289-R</td><td>TTGATGGAGTTCGCATCTTCT</td><td>63.7</td></tr><tr><td>SSR6243-MF</td><td>CACGACGTTGTAAAACGACGTAGGGAGTTGGCCACGATA</td><td>82.7</td></tr><tr><td>SSR6243-R</td><td>CAACCGATGTAAAAAGTGGACA</td><td>63.5</td></tr><tr><td>SSR6304-MF</td><td>CACGACGTTGTAAAACGACCTCTCACATGCAATCCTAAATGGC</td><td>82.9</td></tr><tr><td>SSR6304-R</td><td>CTACGATAATGAGGATAACCATC</td><td>58.1</td></tr><tr><td>SSR6323-MF</td><td>CACGACGTTGTAAAACGACCAAAGGGTCATCAGGATTGG</td><td>82.6</td></tr><tr><td>SSR6323-R</td><td>TTTAAGCAGCCAAGCAGTTGT</td><td>63.7</td></tr><tr><td>MA113-MF</td><td>CACGACGTTGTAAAACGACTCGCACACAGATCCAACATT</td><td>81.9</td></tr><tr><td>MA113-R</td><td>CCTTATTTCTGGTGGGAGCA</td><td>63.9</td></tr><tr><td>SSR6241-MF</td><td>CACGACGTTGTAAAACGACGGGCAGAATGGAGATGAGAA</td><td>83.3</td></tr><tr><td>SSR6241-R</td><td>TTGATGGAGTTCGCATCTTCT</td><td>63.7</td></tr><tr><td>SSR6819-MF</td><td>CACGACGTTGTAAAACGCGCAACATGGAGGAAGATGCAAAG</td><td>84.4</td></tr><tr><td>SSR6819-R</td><td>CAAAAGAAATCATGATCTAACTTC</td><td>57.6</td></tr><tr><td>SSR6425-MF</td><td>CACGACGTTGTAAAACGACTGCTCAGTTCTGTGGTCCTG</td><td>82.1</td></tr><tr><td>SSR6425-R</td><td>TGGTTTATTCATCCAACATAGCA</td><td>62.9</td></tr><tr><td>SSR6217-MF</td><td>CACGACGTTGTAAAACGACGGGAGTGCTCCGGAAAGT</td><td>83.6</td></tr><tr><td>SSR6217-R</td><td>TTCCCTATGAACTGGGAGATCTAT</td><td>62.7</td></tr><tr><td>M13-tailed primer</td><td>CACGACGTTGTAAAACGAC</td><td></td></tr></tbody></table>
## 2.1 Genetic Diversity of cultivated cowpea accessions

The genetic similarity values of the accessions collected from different regions ranged from 0.971 (Sédhiou and Diourbel) to 0.732 (Louga and Kolda) (Table 3). These results mean that the accessions from Sedhiou and Diourbel were genetically closer, while those from Louga and Kolda were the most distant ones. In general, the genetic diversity is low in the cultivated cowpea accessions. This low genetic diversity was found in many cowpea molecular studies using different technics (Asare et al., 2010; Badiane et al., 2012; Diouf & Hilu, 2005; Fatakun et al., 2018; Kouakou et al., 2007; Sarr et al., 2020; Sarr et al., 2021). This can be explained by the cowpea's mode of reproduction, a self-pollinating plant.

<table><caption>**Table 3**
Genetic Similarity Values Between the Cultivated Cowpea Accessions Collected from Different Regions in Senegal</caption><thead><tr><th>Thiès</th><th>Sédhiou</th><th>Kédougou</th><th>Saint Louis</th><th>Louga</th><th>Diourbel</th><th>Fatick</th><th>Kolda</th></tr></thead><tbody><tr><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0.916</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0.860</td><td>0.956</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0.851</td><td>0.907</td><td>0.826</td><td>1.000</td><td></td><td></td><td></td><td></td></tr><tr><td>0.912</td><td>0.945</td><td>0.861</td><td>0.882</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>0.866</td><td><strong>0.971</strong></td><td>0.917</td><td>0.897</td><td>0.948</td><td>1.000</td><td></td><td></td></tr><tr><td>0.954</td><td>0.912</td><td>0.830</td><td>0.891</td><td>0.922</td><td>0.906</td><td>1.000</td><td></td></tr><tr><td>0.762</td><td>0.782</td><td>0.768</td><td>0.745</td><td><strong>0.732</strong></td><td>0.736</td><td>0.770</td><td>1.000</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

The intra-regional variability was greater (79%) than the inter-regional variability (14%) (Table 4). These results agree with those of Sarret al.(2020) (75% versus 11%). The high intra-regional diversity could be linked to many different accessions in each region. In contrast, the low genetic diversity between regions could be partly explained by the distribution of the same cowpea seeds (same varieties found everywhere) in all the regions through donations, seed companies, or agricultural extension services.
<table><caption>**Table 4**
Analysis of Molecular Variance of Cultivated Cowpea Accessions</caption><thead><tr><td>Source of variation</td><td>Variations (%)</td><td>Fst</td></tr></thead><tbody><tr><td>Among Populations</td><td>14</td><td>0.141***</td></tr><tr><td>Among Individuals</td><td>79</td><td></td></tr><tr><td>Within Individual</td><td>7</td><td></td></tr><tr><td>Total</td><td>100</td><td></td></tr></tbody></table>

Fst: Genetic differentiation Index; ***p ≤ 0.001

The dendrogram showed three main genetic groups and several subgroups. The genetic structure of collected accessions was independent of geographical origin. However, cultivated cowpea accessions from floodplain recession areas were all in a single subgroup. This can be explained by the low gene flow between accessions cultivated in floodplain recession areas and the rainfed areas. They are cultivated in different growing systems and flower at different times (Figure 2).
<figure><img src="image_71.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – Dendrogram Based on Genetic Dissimilarity Showing Genetic Relationships Between Cultivated Cowpea Accessions from Eight Regions of Senegal

## 2.2 Genetic Diversity of Wild Relatives Accessions (Hybrids and Wilds)

The dendrogram showed that hybrids and wilds were divided into three groups according to their type and origin (Figure 3). The first group contained the wild accessions from southern Senegal (Kolda and Sédhiou). The second group contained all the accessions from the Midwest (Louga, Thiès, Diourbel, and Fatick). This group was subdivided into two subgroups. The first subgroup comprises the 45 wild accessions described as *Vigna unguiculata* var. *spontanea* from Thiès, Fatick, and Diourbel. The second is
the 20 accessions identified as hybrids (weedy) called “Kodj” in the local name from Louga, Fatick, and Diourbel. Usually, these hybrids germinate spontaneously in farmers’ fields. After harvest, farmers did not mix them with their seeds. They sort the seed of the hybrids and throw them away because they take a longer time to cook as their seed coat is harder than that of the cultivated. Moreover, the seed coat of the hybrids is smoother and the size smaller than that of the cultivated cowpea but larger than that of the wild species. The third group included only the one hybrid from Kolda.

<figure><img src="image_72.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 – Dendrogram Based on Genetic Dissimilarity Showing Genetic Relationships Between Cowpea Hybrid and Wild Accessions from Six Regions of Senegal

The genetic differentiation index (Fst) was high, reaching 0.169 between wild accessions from Midwest (Vigna unguiculata var. spontanea) and hybrids (weedy), 0.234 between those from Midwest and the South (Vigna racemosa), and 0.353 between hybrids and southern wild accessions (Table 5). Wilds from the South belong to the *Vigna racemosa* species. This species is different from *Vigna unguiculata var spontanea* from the Midwest. The speciation and the geographical difference could have limited the gene flow between these two species and explained the high genetic differentiation index.
<table><caption>**Table 5**
Genetic Differentiation Index (Fst) Between Hybrids and Wild Accessions</caption><thead><tr><th></th><th>Hybrids</th><th>Wild from Midwest</th><th>Wild from South</th></tr></thead><tbody><tr><th>Hybrids</th><td>0.000</td><td></td><td></td></tr><tr><th>Wild from Midwest</th><td>0.169</td><td>0.000</td><td></td></tr><tr><th>Wild from South</th><td>0.353</td><td>0.234</td><td>0.000</td></tr></tbody></table>

## 2.3 Genetic Diversity of the Overall Collection (Cultivated, Hybrids, and Wilds)

Cluster analysis of the entire Senegalese cowpea collection (i.e., cultivated, hybrids, and wild accessions) showed two genetic groups with 1.3% admixture. The first one contained the hybrids and the wild accessions (*Vigna unguiculata* var. *spontanea* and *Vigna racemosa*). The second group included the cultivated accessions. The principal coordinate analysis (PCoA) showed the two distinct genetic groups (Figure 4). The dendrogram of the cultivated cowpea, hybrids, and wild accessions' genetic dissimilarity showed three major groups. The first and second were homogeneous and included only the cultivated accessions. The third group included mainly the wild accessions and the hybrids (Figure 5). The fact that hybrids were clustered with wilds could be explained by their genetic proximity revealed by the 15 SSSR used.

<footnote>***p-value<0.001</footnote>
<figure><img src="image_73.png" /><figcaption>Figure 4a</figcaption></figure>

Figure 4a – STRUCTURE Analysis and Principal Coordinate Analysis (PCoA) of Cultivated Cowpea, Hybrids, and Wild Accessions

<figure><img src="image_74.png" /><figcaption>Figure 4b</figcaption></figure>

Figure 4b – STRUCTURE Analysis and Principal Coordinate Analysis (PCoA) of Cultivated Cowpea, Hybrids, and Wild Accessions
<figure><img src="image_75.png" /><figcaption>Figure 4c</figcaption></figure>

Figure 4c – STRUCTURE Analysis and Principal Coordinate Analysis (PCoA) of Cultivated Cowpea, Hybrids, and Wild Accessions

<figure><img src="image_76.png" /><figcaption>Figure 5</figcaption></figure>

Figure 5 – Dendrogram Showing Genetic Dissimilarity Between Local Cowpea Germplasm (Cultivated Cowpea, Hybrids, and Wild Accessions) from Senegal
All the values of the diversity indices calculated for the two populations, the mean number of alleles (Na), expected heterozygosity (He), and fixation index (F), based on the allelic frequencies for the 15 loci tested, were higher for the hybrids and wilds than for the cultivated cowpea. The genetic differentiation index value (Fst) was 0.229, with the number of migrants (Nm) equal to 2.557 (Table 6).

<table><caption>**Table 6**
Genetic Diversity Between Cultivated Cowpea and Wild Relatives</caption><thead><tr><th>Parameters</th><th>Group II</th><th>Group I</th></tr></thead><tbody><tr><td>Sample size (N)</td><td>662</td><td>83</td></tr><tr><td>Mean number of alleles (Na)</td><td>5.4</td><td>6.267</td></tr><tr><td>Expected Heterozygosity (He)</td><td>0.382</td><td>0.552</td></tr><tr><td>Fixation Index (F)</td><td>0.834</td><td>0.869</td></tr><tr><td>Genetic differentiation Index (Fst)</td><td colspan="2">0.229***</td></tr><tr><td>Number of migrants (Nm)</td><td colspan="2">2.557</td></tr></tbody></table>

## 2.4 Comparison of the Genetic Diversity of the Senegalese Cowpea Germplasm with the Reference Cowpea Collection from IITA

The average genetic diversity observed for the national cowpea germplasm was lower (0.457) than the average diversity of accessions from Nigeria (0.508). At the same time, it was comparable to those obtained for the accessions from India (0.430). These values were higher when compared with the values obtained for accessions from Niger (0.328), Burkina Faso (0.380), and the USA (0.385).

The highest genetic differentiation index was observed between Senegal and USA (Fst = 0.253, p-value = 0.002). So, the Senegalese accessions and those from the USA are the most distant. Important differentiations were also found between Senegal and India (Fst = 0.155), Senegal and Niger (Fst = 0.183), and Senegal and Burkina Faso (Fst = 0.147). The genetic differentiation index (Fst = 0.115) is moderate between Senegal and Nigeria. The ancient

<footnote>***p-value < 0.001</footnote>
introduction could explain this in Senegal of accessions from Nigeria (Sène, 1966). The lowest Fst was noted between Indian and Nigerian accessions (0,101); it could mean that they are genetically closer (Table 7). This study represents accessions from Niger, Nigeria, Burkina Faso, India, and the USA by five samples each. Therefore, the comparison made in this part is only valid for this small number of samples.

<table><caption>**Table 7**
Genetic Differentiation Index (Fst) Between the Accessions From Different Countries</caption><thead><tr><th>Senegal</th><th>USA</th><th>India</th><th>Niger</th><th>Nigeria</th><th>Burkina Faso</th><th></th></tr></thead><tbody><tr><td>0.000</td><td></td><td></td><td></td><td></td><td></td><td>Senegal</td></tr><tr><td>0.253</td><td>0.000</td><td></td><td></td><td></td><td></td><td>USA</td></tr><tr><td>0.155</td><td>0.149</td><td>0.000</td><td></td><td></td><td></td><td>India</td></tr><tr><td>0.183</td><td>0.253</td><td>0.198</td><td>0.000</td><td></td><td></td><td>Niger</td></tr><tr><td>0.115</td><td>0.171</td><td>0.101</td><td>0.133</td><td>0.000</td><td></td><td>Nigeria</td></tr><tr><td>0.147</td><td>0.209</td><td>0.188</td><td>0.119</td><td>0.117</td><td>0.000</td><td>Burkina Faso</td></tr></tbody></table>

The dendrogram showed three groups. The first and second groups were homogeneous and included only cultivated accessions from Senegal. The third was divided into two subgroups. The first subgroup enclosed all accessions from IITA, the hybrids, and wild relatives, and the second included some cultivated accessions from Senegal. The Senegalese hybrids and wild accessions have clustered well in the same subgroup regardless of geographical origin (Figure 6).
<figure><img src="image_77.png" /><figcaption>Figure 6</figcaption></figure>

Figure 6 – Dendrogram Showing Genetic Dissimilarity Between the Accessions from Senegal and Those from IITA

Conclusion and Perspectives

Thanks to the important collection and characterization efforts, ISRA has assembled a wide collection of cowpea genetic resources that is representative of the local germplasm and combines old and new collections. The old collection consisted of only cultivated cowpea, while the new one consisted of cultivated hybrids and wild accessions. Besides, this new collection has been genotyped. Cowpea wild relatives, having higher genetic diversity than cultivated cowpea with moderate differentiation, can provide important genes for tolerance or resistance to various stresses that hinder production. Similarly, the five accessions from the USA that were most distant from those from Senegal could provide new genes for crop
improvement. The good management of the local cowpea germplasm could
improve crop productivity in Senegal in the context of climate change. This
improvement will positively impact the livelihoods of both male and female
farmers, both nutritionally and economically.

Following this work of collecting and characterizing local Senegalese
germplasm, it would be interesting to evaluate the wild relatives for traits
related to drought tolerance, *Striga*, and *Macrophomina* resistance which
are major constraints to cowpea cultivation in Senegal. The most promising
accessions could then be used to make crosses with cultivated cowpea to
identify QTLs associated with the traits of interest and yield components.

Acknowledgements

We thank the West African Agricultural Productivity Program (WAAPP) for
funding the study and the *International Institute of Tropical Agriculture* (IITA)
for providing the global accession from Niger, Nigeria, Burkina Faso, India,
and the USA. We also thank AblayeNgom and Rémy Pasquet for their help in
identifying cowpea species.

References

Agbicodo, E. M., Fatokun, C. A., Muranaka S., Visser, R. G. F., & Linden Van Der, C. G. (2009). Breeding drought tolerant cowpea: Constraints, accomplishments, and future prospects. *Euphytica*, *167*, 353–370.

Agence Nationale de la Statistique et de la Démographie (ANSD). (2021).
Bulletin mensuel des statistiques économiques et financières d'Avril, p. 110.

Aliyu, B., & Wachap, E. (2014). Vegetable cowpea as a source of cheap protein and an environmentally friendly crop for urban cities. *Emerging Infectious Diseases*, *181*, 301–312.

Asare, A. T., Gowda, B. S., Galyuon, I. K. A., Aboagye, L. L., Takrama, J. F., & Timko, M. P. (2010). Assessment of the genetic diversity in cowpea (Vigna unguiculata L. Walp.) germplasm from Ghana using simple sequence
repeat markers. *Plant Genet Resour*, 8, 142-150. https://doi.org/10.1017/S1479262110000092

Asiwe, J. A. N., Balane, A., & Dacora, F. D. (2009). *Evaluation of cowpea breeding lines for nitrogen fixation at ARC-Grain Crop Institute, Potchefstroom, South Africa*. The 16th International Congress on Biological Nitrogen Fixation, Montana, USA. 14-19.

Badiane, F. A., Gowda, B. S., Cissé, N., Diouf, D., Sadio, O., & Timko, M. P. (2012). Genetic relationship of cowpea (Vigna unguiculata) varieties from Senegal based on SSR markers. *Genetics and Molecular Research*, 11, 292-304.

Bhattarai, P., Thomas, A. K., Cosacak, M. I., Papadimitriou, C., Mashkaryan, V., Zhang, Y. & Kizil, C. (2017). Modeling amyloid-β42 toxicity and neurodegeneration in adult zebrafish brain. *Journal of Visualized Experiments: JoVE*, Oct 2017(128).

Boukar, O., Fatokun, C. A., Huynh, B. L., Roberts, P. A., & Close, T. J. (2016). Genomic tools in cowpea breeding programs: Status and perspectives. *Front. Plant Sci*, 7(757). https://doi.org/10.3389/fpls.2016.00757

Cissé, N., 2015. Fiches variétales niébé et sorgho. *Fiches techniques ISRA*, 8(4).

Cissé, N., Thiaw, S., & et Sène, A. (1984). Projet CRSP/NIEBE - Essais variétaux
Dot. Ronéo - ISRA/Bambey, p. 7.

Diouf, D. & Hilu, K. W. (2005). Microsatellites and RAPD markers to study genetic relationships among cowpea breeding lines and local varieties in Senegal. *Genetic Resources and Crop Evolution*, 52,1057-1067.

Diouf, M., Diallo, S., Badiane, F. A., Diack, O., & Diouf, D. (2021). Development of new cowpea (Vigna unguiculata) mutant genotypes, analysis of their agromorphological variation, genetic diversity and population structure. *BIOCELL*. https://doi.org/10.32604/biocell.2021.013706.

Dugje, I. Y., Ekeleme, F., Kamara, A.Y., Omoigui, L.O., Tegbaru, A., Teli, I. A.,
& Onyibe, J. E. (2008). Guide to safe and effective use of pesticides for
crop production. *International Institute of Tropical Agriculture, Jan.* 2008.
https://doi.org/10.13140/2.1.2721.8566
Fatokun, C., Girma, G., Abberton, M., Gedil, M., Unachukwu, N., Oyatomi, O., Yusuf, M., Rabbi, I., & Boukar, O. (2018). Genetic diversity and population structure of a mini-core subset from the world cowpea (Vigna unguiculata (L.) Walp.) germplasm collection. *Scientific Reports*, 8(16035), https://doi.org/10.1038/s41598-018-34555-9

Feldman, M., & Sears, E. R. (1981). The wild gene resources of wheat. *Sci Amer*, 2441,102-112.

Hall, E. A. (1982). Report on Research at the University of California, Riverside in 1982 and proposal research for 1983. *Manuscript – Riverside CA – UCR, Department of Botany and Plant Sciences*, 12.

Hall, E. A. (1984). Summary Report Research at the University of California, Riverside in 1983. *Manuscript – Riverside, CA – UCR Department of Botany and Plant Sciences*, 5.

Kouakou, C. K., Roy-Macauley, H., Guèye, M. C., Otto, M. C., Rami, J. F., & et Cissé, N. (2007). Diversité génétique des variétés traditionnelles de niébé [*Vigna unguiculata* (L.) Walp.] au Sénégal: étude préliminaire. *Plant Genet Resour News*, 1(152), 33-44.

Morita, R., Kusaba, M., Iida, S., Yamaguchi, H., Nishio, T., & Nishimura, M. (2009). Molecular characterization of mutations induced by gamma irradiation in rice. *Genes & Genetic Systems*, 84, 361-370. https://doi.org/10.1266/ggs.84.361

Naito, K., Kusaba, M., Shikazono, N., Takano, T., Tanaka, A., Tanisaka, T., & Nishimura, M. (2005). Transmissible and nontransmissible mutations induced by irradiating *Arabidopsis thaliana* pollen with γ-rays and carbon ions. *Genetics*, 169, 881-889. https://doi.org/10.1534/genetics.104.033654

Ndiaye, M. (1986). Bilan de 30 ans de recherche sur le niébé au Sénégal. Rapport ISRA.

Ng, N. Q., & Marechal, R. (1985). Cowpea taxonomy, origin and germplasm. In: Cowpea research, production and utilization. Singh, S. R. and Rachie, K.O. (eds.), John Wiley and Sons, Chichester, UK, pp 11-21.

Obilana, A. T. (1987). Breeding cowpea for Siriga resistance. In L. J. Musselman (Ed.), *Parasitic weeds in agriculture* (pp. 243-253). CRC-Press, Inc.
Sarr, A., Bodian, A., Gbedevi, K. M., Ndoye Ndir, K., Oyatomi, A. O., Gueye, B., Foncéka, D., Diop, E. A. M. C., Diop, B. M., Cissé, N., & Diouf, D. (2020). Genetic diversity and population structure analyses of wild relatives and cultivated cowpea (Vigna unguiculata (L.) Walp) from Senegal using simple sequence repeat markers. *Plant Molecular Biology Reporter*. https://doi.org/10.1007/s11105-020-01232-z

Sarr, A., Bodian, A., Ouattara, B., Diangar, M., M., Sall, M. N., Diop, E. A. M. C., Diouf, D., & Cissé, N. (2021). Population structure and diversity among improved cowpea varieties from Senegal based on microsatellite markers. *Global Journal of Molecular Biology*, 3(7). https://doi.org/10.28933/gjmb-2020-12-2305

Séne, D. (1966). Inventaire des principales variétés de niébé (Vigna unguiculata Walpers) cultivées au Sénégal. L'Agronomie Tropicale, 927-933.

Singh, B. B., & Sharma, B. (1996). Restructuring cowpea for higher yield. *Indian Journal of Genetics and Plant Breeding* 56, 389-405.
15. From Shade to Light: Fonio,
an African Orphan Crop,
Towards Renewed Challenges

Adeline Barnaud, IRD, France

Claire Billot, CIRAD, France

**Michael D. Abrouk**, King Abdullah University of Science and Technology,
Saudi Arabia

**Joseph Adjebeng-Danquah**, CSIR-SARI, Ghana

**Louise Akanvou**, CNRA, Côte d'Ivoire

**Hanin Ibrahim Ahmed**, King Abdullah University of Science and
Technology, Saudi Arabia

**Charlotte O.A. Adje**, University of Abomey-Calavi, Benin

**Yacoubou Bakasso**, University Abdou Moumouni, Niger

**Mamadou Billo Barry**, IRAG, Guinea

**Enoch G. Achigan-Dako**, University of Abomey-Calavi, Benin

**Abdou R. Ibrahim Bio Yerima**, University of Abomey-Calavi, Benin

**Ndjido Ardo Kane**, ISRA/CERAAS, Senegal

**Simon G. Krattinger**, King Abdullah University of Science and Technology,
Saudi Arabia

**Happiness Oselebe**, Ebonyi State University, Nigeria

**Sani Saidou**, University of Diffa, Niger

**Emmanuel Sekloka**, University of Parakou, Benin

**Edak Aniedi Uyoh**, University of Calabar, Nigeria

**Yves Vigouroux**, IRD, France

**Mame Codou Gueye**, ISRA/CERAAS, Senegal
## Highlights box

* Fonio (*Digitaria exilis Stapf*) is a promising crop under a large range of environmental conditions

* Recent advances in genomic resource availability open up the road for more effective plant breeding

* Targeting these domestication genes would represent a first important step towards a significant improvement of fonio

* Needs for a coordinated transdisciplinary collaboration across research disciplines involving actors beyond research

* Fonio, a key crop in advancing agricultural development beyond the Green Revolution model

## Abstract

The FAO recently estimated that demand for food will increase by 70% by 2050. The challenge will not only be on increasing the food supply but also on improving its nutritional value under an accelerating rate of environmental and social changes. One solution would be the wider use of underutilized crops to diversify alimentation and develop sustainable and low-input agriculture. Are orphan crops commodities for the future, and how can they be promoted? In this regard, fonio (*Digitaria exilis Stapf*) appears to be a promising crop. It is an indigenous staple cereal from Western Africa playing a crucial role in food security. Additionally, it is a model crop in important up-to-date concepts such as resilience, agroecology, population health, climatic changes, poverty reduction, and women's empowerment. Nevertheless, fonio has received limited attention from mainstream research compared to other dryland cereals, such as pearl millet and sorghum. Increasingly available genomic resources promise to promote advanced breeding strategies in fonio. This paper presents the
past, present, and future of fonio research. We argue the need for
interdisciplinarity and multistakeholder research approaches for increasing
fonio production, conservation, and sustainable uses.

Keywords: Fonio, Neglected and Underutilized Species (NUS), Sustainable agriculture, Food and nutritional security, Breeding strategies, Resilience

Introduction

The FAO recently estimated that demand for food should be increased by 70% by 2050. The challenge will not only be on increasing the food supply but also on improving its nutritional value and alleviating poverty under environmentally sustainable production systems. Agricultural productivity is expected to decrease in the near future because of climate change, groundwater depletion, and land degradation. This decrease will particularly impact Sub-Saharan Africa (SSA), one of the most food-insecure regions globally (FAO et al., 2019). The agricultural sector in SSA is dominated by smallholder farmers (AGRA, 2016) and employs more than half of the population. SSA agriculture is characterized by almost exclusively rainfed crops (Lowder et al., 2016). In a context of multiple social and environmental constraints of varying intensity over time, increasing attention is being paid to agrobiodiversity to enhance resilience and adaptation of farming systems (Guarino & Lobell, 2011; Padulosi et al., 2011; Pironon et al., 2019). Beyond productivity, agrobiodiversity is also an asset supporting numerous ecosystem services (including associated biodiversity pest and disease control, etc.), flow regulation (including water quality, regulation of biogeochemical cycles, etc.), soil fertility, and sociocultural services (Altieri, 1999; Beilloin et al., 2021).

More than 7,000 species have been used throughout human history as
agricultural or horticultural crops. However, food safety and nutrition
worldwide now rely primarily on 12 species of cereals and 23 species of
vegetables (Altieri, 1999) with wheat, rice, and maize alone providing more
than 50% of the plant energy consumed by humans (FAOStat, 2010).
Agricultural research has mainly concentrated on these few promoted
species, but the vast majority of cultivated species have been neglected.
Suitable for a large variety of agro-ecosystems, neglected crops have
recently regained interest for their utility to unlock marginal lands for agriculture. Despite the call for increased use of underutilized crops, by the International Center for Underutilized Crops (ICUC) and the Global Facilitation Unit for Underutilized Species (GFU), to diversify alimentation and develop sustainable agriculture, those crops remain a largely untapped reservoir of agrobiodiversity. We will focus here on a neglected cereal from West Africa, fonio.

# 1. Is Fonio a Crop for the Future?

In addition to the well-known cereals such as sorghum and pearl millet, Africa exhibits several small millets, notably fonio. The term fonio encompasses two species (*Digitaria exilis* Stapf – white fonio and *Digitaria iburua* Stapf – black fonio) that are genetically differentiated (Figure 1). These cereals are indigenous staple crops from Western Africa with great potential for agriculture in marginal environments. However, black fonio covers only a slight disjunct geographical area in Benin and Nigeria, so we will focus on the widespread white fonio, *Digitaria exilis* (Figures 2 and 3a-b).

In a region stretching from Senegal to the Cameroon-Nigeria border, fonio is cultivated under a large range of environmental conditions, from the tropical seacoast in Sierra Leone to arid climate in the Sahel zone and high-altitude grasslands, such as the Jos Plateau (Figure 3a and 3b).
<figure><img src="image_78.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 - Principal Component Analysis of the Pattern of Genetic Diversity among Digitaria Species- Digitaria Exilis (white fonio) Digitaria Uburua (black fonio) & Digitaria Longiflora (wild progenitors of white fonio)

<figure><img src="image_79.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 - Digitaria Exilis, White Fonio, Plant
<figure><img src="image_80.png" /><figcaption>Figure 3a</figcaption></figure>

Figure 3a – Fonio, Digitaria exilis, an indigenous staple crop from Western Africa. A – Fonio fields growing short cycle and long cycle landraces; B – Fonio harvest; C – Fonio grains

<figure><img src="image_81.png" /><figcaption>Figure 3b</figcaption></figure>

Figure 3b – Geographic distribution of fonio in the bioclimatic regions of West Africa (CILSS, 2016)

This level of adaptation is particularly relevant in the climate uncertainty context. Because of the historical and contemporary shifts toward high-yielding grain crops, its distribution has been disjunct and reduced to relic (Blench, 2016). Fonio, which is both drought-tolerant and does not depend
on external inputs, becomes a key crop to plant. The two main producers are Guinea (530 Kt, 76% of the overall production, FAO 2019 production data) and Nigeria (83 Kt, 12%), making a significant comeback in several producing countries. Providing reliable yields on marginal lands, fonio contributes significantly to food and nutritional security, both at local and regional levels. Owing to its fast-maturing landraces, fonio is a key crop during food shortages (Vall et al., 2011). Such crop is crucial in Africa, where hunger is rising in almost all African subregions, with 20 percent of the population undernourished (Fao et al., 2019). Fonio has several nutritional properties compared to other staple cereal grains, thanks to its high levels of sulfur amino acid methionine and cystine, which enable it to provide all 20 amino acids (Jideani & Jideani, 2011). Additionally, it is regarded as a valuable source of income, especially for women and small-scale farmers, as shown by increasing urban market opportunities (FONIO, a project financed by the Common Fund for Commodities and supervised by FAO, http://fonio.cirad.fr/). The price of a kilo of fonio could be 1.5 to 2 times higher than a kilo of rice (Vodouhe et al., 2007): $120/Mt on the global market in 2018 in Guinea (FAOSTAT). Besides, there are large opportunities for value chain development (e.g., Senegal is importing almost half of its consumption with large opportunities for development in other countries, FAOSTAT). Furthermore, its cultivation requires few land works and is adapted to low fertile soils (Vodouhe et al., 2007). In addition, fonio is often grown on acidic soils with high aluminium content in monocultural systems after a fallow period (Gigou et al., 2009). Finally, fonio benefits from cultural embedding as it plays a key role in ritual systems in many African societies across West Africa, even where it is no longer the dominant crop in an agricultural system (Adoukonou-Sagbadja et al., 2006; Ballogou et al., 2014; Blench, 2016; Diop et al., 2018).

Despite the promising nature of fonio to ensure food security and income increase for farmers, it has received limited attention from mainstream research in terms of the development of genomic and breeding resources. Investing in research on fonio will help realize the full potential of this crop. This chapter provides comprehensive insight into the global status of its germplasm, diversity, promising genomic resources, and breeding challenges.
# Fonio Genomic Resources to Foster Sustainable Agriculture Transition

Increasingly available genomic resources promise to advance research, trait discovery, and breeding in fonio to complement basic agronomical achievements aimed at improving fonio cultivation.

## 1. Unlocking Genomic Resources

Fonio, an underutilized crop, does not benefit from the same scientific knowledge and genomic resources as model crop species, but this is changing rapidly. Over the last two decades, many studies on genetic diversity have increased understanding of its biology and diversity, showing that fonio is a tetraploid species (2n = 4x = 36) (Adoukonou-Sagbadja, Schubert et al., 2007) with a high degree of selfing (Barnaud et al., 2017). Later studies mainly provided information about the distribution of its phenotypic and genetic diversity on a local and regional scale (Adoukonou-Sagbadja et al., 2007; Barnaud et al., 2012; Dansi et al., 2010; Hilu et al., 1997; Ibrahim Bio Yerima et al., 2020; Koreissi-Dembélé et al., 2013; Kuta et al., 2005; Kwon-Ndung & Ochigbo, 2004; Olodo et al., 2019). The development of novel DNA sequencing technologies have provided the most significant change over the last decade, 'moving from genotyping to genome typing' (Luikart et al., 2003). While these technical advances were mainly deployed in major crops, they can now be transferred directly to underutilized crops. The decrease in costs of next-generation sequencing technologies, combined with better expertise leading to automatic analysis of increasing data, directly benefited underutilized crops. The generation of high-quality genomic resources has accelerated basic and applied research, trait discovery, and breeding of major cereal crops such as rice, maize, sorghum, and wheat. Fonio now benefits from high-quality genomic resources that improve production. In fact, the size of the genome was estimated to be 893 Mb (Abrouk et al., 2020). New recently generated resources include a chloroplast genome (Scarcelli et al., 2011), transcriptomes (Sarah et al., 2017), a chromosome-scale genome reference assembly (Abrouk et al., 2020; Wang et al., 2021), and genome resequencing of 183 wild individuals and cultivated varieties (Abrouk et al., 2020).
These genomic resources allow for a comprehensive assessment of fonio
evolution history and its pattern of genetic diversity. Detailed knowledge
of the patterns of genetic diversity is essential in the identification of the
relevant genetic resources needed to develop breeding programs. Using the
deep re-sequencing of 157 white fonio landraces (Figure 4) highlighted the
strong impact of geographic, climatic, and anthropogenic factors on shaping
the diversity of fonio (Abrouk et al., 2020). These advances in genomic
resource availability open the road for more effective plant breeding. The
discussion on the fonio breeding product profile was initiated with the value
chain actors.

<figure><img src="image_82.png" /><figcaption>Figure 4</figcaption></figure>

Figure 4 – Genomic Diversity of Fonio Landraces in West Africa

2. Challenges and Prospects of Fonio Breeding Strategies

The agronomical research on fonio enhanced cultivation practices and post-harvest technologies. Recent progress was made in fonio cultivation practices, including the optimal planting period, soil types, optimal sowing density, and fertilization rates (Gigou et al., 2009; Gueye et al., 2015; Ndor & Dauda, 2016). Advances are also noted in post-harvest technologies, which have long hampered the development of fonio cultivation, now increasing its economic potential (FONIO, http://fonio.cirad.fr/). However, no improvement program based solely on the hybridization of complementary progenies has been conducted on fonio because of the technical difficulty in manipulating extremely tiny floral organs. However, in recent years, mass selection strategy, based on homogenization, has been conducted on traditional heterogeneous populations of fonio to screen for homogeneous
and best genotypes for several qualitative (raceme color, etc.) and
quantitative (size, earliness, yield, etc.) traits (Kanlindogbè et al., 2020).
Existing research has only focused on screening local diversity, targeting
some high-yield varieties proposed to certification, such as the Senegalese
varieties CFS52 called Fofana, CVF 477 called Fonio bi, and Niata in Senegal
(Fofana et al., 2017; Kanfany et al., 2018).

Given recent advances in our understanding of genetic diversity and
availability of genomic tools (Abrouk et al., 2020; Ibrahim Bio Yerima et al.,
2021a; Sarah et al., 2017; Scarcelli et al., 2011), fonio now has the potential
to be dramatically improved for yield with selective breeding (Ibrahim Bio
Yerima et al., 2021b). Compared to other cereals, fonio still shows many
unfavorable ‘wild’ traits, such as seed shattering, small seeds, and lack of
apical dominance that could be targeted for breeding (Kanlindogbe et al.,
2020). Knowledge of fonio’s domestication and evolutionary history provides
key elements for defining a genetic improvement strategy. A detailed analysis
of the genetic diversity revealed that most ‘domestication’ genes identified
in major cereals had not experienced a strong selection during fonio
domestication (Abrouk et al., 2020). Targeting these genes would represent
the first important step towards a significant improvement of fonio. For
instance, identifying natural mutants, mutagenesis, or genome editing of the
seed-shattering DeSh1-9B locus on chromosome 9B could rapidly result in a
fonio cultivar with abolished seed shattering (Abrouk et al., 2020).

Beyond the traits associated with domestication, fonio adaptation to
various agro-climatic conditions is important to the exploration of the
underlying genetics associated with these adaptations. However, these
approaches rely on the availability of genetic resources, and as with many
other minor crops, germplasm available in national or international Genbank
are scarce. For fonio, a very limited number of accessions (<2000) are
conserved globally. At the instigation of the FAO, IRD (Institut de Recherche
pour le Développement) and IPGRI (International Plant Genetic Resources
Institute) collected more than 600 D. exilis accessions from Benin, Burkina
Faso, Mali, Guinea, Niger, and Togo between 1977 and 1988. Over the last
decade, a collective effort by the authors of this chapter, involving national
and international institutions, has been made to collect accessions of D.
exilis in Benin (54 accessions), Guinea (415 accessions), Nigeria (221
accessions), Niger (247 accessions), and Senegal (175 accessions). These
collections relied on a common survey protocol to collect landraces and associated traditional ecological knowledge. These collections are stored in national gene banks and at ARCAD (Crop biodiversity research and resource center) in Montpellier, France. National collections are also available for some producing countries (Ayenan et al., 2017). The development of regional and mini-core collections should be the next step and will be an essential resource for allele mining studies for abiotic and biotic stresses.

Breeding strategies for minor crops such as fonio should perhaps not copy the ones developed for major crops but go beyond the agronomic traits' selection and yield increase. Indeed, in the context of family farming systems, where the neglected underutilized species (NUS) are the most relevant, there are two main challenges: (1) a need to adapt the NUS to accelerating rates of environmental changes; and (2) a general need to optimize the multifunctionality of NUS. Faced with an accelerating rate of environmental change, greater diversity into cropping systems is an increasingly recognized strategy in enhancing agroecosystem functioning and ecosystem services (Hajjar et al., 2008). To reach this objective, the breeding strategy for NUS could be inspired by a new framework, derived from ecological theory, that enables diversity to be incorporated into plant-breeding programs (Litrico & Violle, 2015) and provides another conceptual framework for increased yield target moving from an ideotype to an 'ideomix.'

In smallholder family farming, farmers value fonio for its multifunctionality through its nutrition and health benefits, its tolerance to stress (including drought), its ability to grow under low nutrient input conditions, and its value in traditional foods, different uses, and cultures. Thus, breeding strategies need to go beyond the Green Revolution model of improving and raising the yields of staple crops. Involving farmers in participatory breeding schemes could help solve this problem while meeting farmers' needs for varietal adaptation to a combination function: climate change adaptation, biotic and abiotic resistance, and cultural and culinary needs. Much progress has been made in participatory plant breeding approaches (PPBA), and research must build on these developments (Ceccarelli & Grando, 2020) for a review. Such PPBA requires better consideration of farmers' perspectives, knowledge, and practices through the involvement of such actors and other stakeholders in research projects.
Strategies in Promoting Conservation and Sustainable
Use of Fonio

Setting up a global fonio valorization will need to strengthen both *in situ* and *ex situ* conservation. Indeed, while the need to access fonio's seeds/genetic resources for farming and scientific communities is growing, one of the primary losses is the diversity of landraces, the reservoir of genetic diversity and adaptation. Fonio has long since been reduced to the status of a marginal cereal because of its diminutive grains and the difficulties of its traditional processing. Thus, farmers are turning away from fonio production and, by ricochet, from biodiversity conservation. Threat on fonio genetic diversity, along with a wealth of traditional knowledge about their cultivation and use, is already reported in most of the producing countries (Leclerc et al., personal communication), in Togo (Adoukonou-Sagbadja, 2004) and Benin (Dansi et al., 2010; Sekloka et al., 2015) at an alarming rate. Hence, assessment, collection, and conservation of existing fonio diversity is crucial before they are lost forever. This approach is true not only for fonio but for most underutilized crops from West Africa. Of the 7.4 million plant accessions conserved in gene banks worldwide, minor crops and underutilized species are underrepresented (Commission on Genetic Resources for Food and Agriculture, 2010).

Furthermore, conservation efforts are unevenly distributed across the
world. In Africa, for example, national gene banks are more dedicated to local
genetic resources than conserving the over 10,000 accessions only located in
eastern Africa (Commission on Genetic Resources for Food and Agriculture,
2010). Priority action for the conservation of fonio should focus on known
collection gaps and wild relatives and on *in situ*/on-farm conservation
actions. Indeed, using existing landrace diversity of crop species and
favoring crop varietal replacement is viewed as an efficient short-term
strategy to adapt to rapid environmental changes (Lobell & Tebaldi, 2014;
Pironon et al., 2019; Rojas et al., 2019). A study on pearl millet showed how
trans-frontier assisted migration of people could help towards mitigating
crop's vulnerability to future climate change (Rhoné et al., 2020). This
strategy identifies landraces among currently cultivated, climate-adapted
landraces that will still be suitable in future conditions combining spatial
genetic structure and spatial climate data (Fitzpatrick & Keller, 2015). This
strategy could be particularly relevant for fonio but will require setting
action plans at the scale of West Africa.

Besides, conservation is of particular importance in relation to the large
interconnection and interdependence of worldwide populations and
agriculture, but conservation governance, including exchanges, should also
be considered. International treaties provide frameworks to which many
countries are now adhering and provide forums for governance changes,
including a broadening of stakeholders. As the actors interacting around the
NUS are likely to be different from those investing in major species, they are
complementary models for considering new modes of governance.

Filling the Gap Between Research and Development: A
Need for a Strong Network of Partnerships Beyond
Research

Minimal funding is invested in the underutilized crops because they are unknown by the majority (especially for fonio in West Africa), so we innovative promotional strategies for these species. Fonio will greatly benefit from a collective effort from the scientific community in researching, promoting, conserving, and sustainably using fonio. This chapter brings together an interdisciplinary research community (e.g., agronomists, plants geneticists, ethnobotanists, plant breeders) working collectively to develop knowledge and genomic resources within a collaborative framework. Future actions will not only need to bring together all the scientific expertise at a disciplinary (biology, social sciences) and an interdisciplinary level, they will require the development of a strong network of partnerships with research institutions and stakeholders - from smallholder farmers to policymakers. Action plans should concentrate on (1) establishing common approaches, such as multi-environment phenotyping and monitoring on-farm conservation, (2) employing strong leadership able to synergize the various initiatives, (3) sustaining long-term collaborative efforts, and (4) training a new generation of researchers and stakeholders dedicated to NUS.
## Conclusion

While the development of agriculture has sometimes been seen as antagonistic with the climatic challenge and the conservation of biodiversity, agriculture is increasingly seen as an important part of the solution. Moving towards sustainable agricultural models is not only one of the keys to promoting the well-being and health of human populations but can have a real and considerable impact on reducing carbon emissions and on biodiversity conservation. Indeed, better utilization of local and often neglected crops such as fonio (Digitaria exilis) in diversified cropping systems can be an important first step toward secure food provision in times of uncertainty, while also sustaining the conservation of genetic resources. This chapter provided insight into the importance of fonio and the challenges related to the uses of genetic diversity through either breeding or landraces-assisted migration. For farmers, who are at a high-level risk, to change practices (in our case, to adopt a higher level of diversity), we need to integrate different aspects of the socio-economic environment, including agricultural helping policies. Thus, there must be a comprehensive and coordinated transdisciplinary collaboration across research disciplines (e.g., agronomy, anthropology, sociology, economics, breeding and genetics) and involving actors beyond research to shift the status of fonio out of the 'minor and underutilized' crops' group. NUS, such as fonio, has the potential to 'end hunger, achieve food security and improved nutrition and promote sustainable agriculture', as articulated in the UN SDG 2 and plays a key role in advancing agricultural development beyond the Green Revolution model.

## Acknowledgements

We thank farmers and local authorities for their consent and collaboration. Collection of fonio landraces diversity over the last decade was supported through several projects funded by WAAPP/PPAAO 2A (CERA58ID06 SE), by the Agropolis Foundation (ARCAD project), by an ANR grant (AfriCrop project, ANR-13-BSV7-0017), and by the European Commission (EwaBelt, H2020-862848).
References

Abrouk, M., Ahmed, H. I., Cubry, P., Šimoníková, D., Cauet, S., Pailles, Y., Bettgenhaeuser, J., Gapa, L., Scarcelli, N., Couderc, M., Zekraoui, L., Kathiresan, N., Čížková, J., Hřibová, E., Doležel, J., Arribat, S., Bergès, H., Wieringa, J. J., Gueye, M., Krattinger, S. G. (2020). Fonio millet genome unlocks African orphan crop diversity for agriculture in a changing climate. *Nature Communications*, 11(1), 4488. https://doi.org/10.1038/s41467-020-18329-4

Adoukonou-Sagbadja, H. (2004). Collecting fonio (Digitaria exilis Kipp. Stapf, D. iburua Stapf) landraces in Togo. *Plant Genetic Resources Newsletter* (1979), 139, 63-67.

Adoukonou-Sagbadja, H., Dansi, A., Vodouhè, R., & Akpagana, K. (2006). Indigenous knowledge and traditional conservation of fonio millet (Digitaria exilis, Digitaria iburua) in Togo. *Biodiversity and Conservation*, 15(8), 2379-2395. https://doi.org/10.1007/s10531-004-2938-3

Adoukonou-Sagbadja, H., Schubert, V., Dansi, A., Jovtchev, G., Meister, A., Pistrick, K., Akpagana, K., & Friedt, W. (2007). Flow cytometric analysis reveals different nuclear DNA contents in cultivated Fonio (Digitaria spp.) and some wild relatives from West-Africa. *Plant Systematics and Evolution*, 267(1-4), 163-176. https://doi.org/10.1007/s00606-007-0552-z

Adoukonou-Sagbadja, H., Wagner, C., Dansi, A., Ahlemeyer, J., Daïnou, O., Akpagana, K., Ordon, F., & Friedt, W. (2007). Genetic diversity and population differentiation of traditional fonio millet (Digitaria spp.) landraces from different agro-ecological zones of West Africa. TAG. *Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik*, 115(7), 917-931. https://doi.org/10.1007/s00122-007-0618-x

Alliance for a Green Revolution in Africa. (2016). *Africa agriculture status report: Progress towards agricultural transformation in Africa*. AGRA. https://agra.org/wp-content/uploads/2017/09/aasr-report-2016-press0409201601.pdf

Altieri, M. (1999). The ecological role of biodiversity in agroecosystems.
Agriculture, Ecosystems & Environment, 74(1-3), 19-31. https://doi.org/
10.1016/S0167-8809(99)00028-6

Ayenan, M. A. T., Sodedji, K. A. F., Nwankwo, C. I., Olodo, K. F., & Alladassi, M. E. B. (2017). Harnessing genetic resources and progress in plant genomics for fonio (Digitaria spp.) improvement. *Genetic Resources and Crop Evolution*, 65, 373-386. https://doi.org/10.1007/s10722-017-0565-6

Ballogou, V. Y., Soumanou, M. M., Toukourou, F., & Hounhouigan, J. D. (2014). Indigenous knowledge on landraces and fonio-based food in Benin. *Ecology of Food and Nutrition*, 53(4), 390-409. https://doi.org/10.1080/03670244.2013.811388

Barnaud, A., Vignes, H., Risterucci, A.-M., Noyer, J.-L., Pham, J.-L., Blay, C., Buiron, M., Vigouroux, Y., & Billot, C. (2012). Development of nuclear microsatellite markers for the fonio, Digitaria exilis (Poaceae), an understudied West African cereal. *American Journal of Botany*, 99(3), e105-e107. https://doi.org/10.3732/ajb.1100423

Barnaud, A., Vigouroux, Y., Diallo, M. T., Saidou, S. I., Piquet, M., Barry, M. B., Bakasso, Y., Zekraoui, L., Rivallan, R., Kane, N. A., & Billot, C. (2017). High selfing rate inferred for white fonio [Digitaria exilis (Kippist.) Stapf] reproductive system opens up opportunities for breeding programs. *Genetic Resources and Crop Evolution*, 64(7), 1485-1490. https://doi.org/10.1007/s10722-017-0515-3

Beillouin, D., Ben-Ari, T., Malézieux, E., Seufert, V., & Makowski, D. (2021). Positive but variable effects of crop diversification on biodiversity and ecosystem services. *Global Change Biology*, 00, 1-14. https://doi.org/10.1111/gcb.15747

Blench, R. M. (2016). Vernacular names for African millets and other minor cereals and their significance for agricultural history. *Archaeological and Anthropological Sciences*, 8(1), 1-8. https://doi.org/10.1007/s12520-012-0104-5

Ceccarelli, S., & Grando, S. (2020). Participatory plant breeding: Who did it, who does it and where? *Experimental Agriculture*, 56(1), 1-11. https://doi.org/10.1017/S0014479719000127
Comité Permanent Inter-états de Lutte contre la Sécheresse dans le Sahel [CILSS], 2016, Landscapes of West Africa—A window on a changing world: Ouagadougou, Burkina Faso, CILSS, 219 p. at http://dx.doi.org/10.5066/ F7N014QZ

Commission on Genetic Resources for Food and Agriculture (Éd.). (2010). *The second report on the state of the world's plant genetic resources for food and agriculture*. Commission on Genetic Resources for Food and Agriculture, Food and Agriculture Organization of the United Nations.

Dansi, A., Adoukonou-Sagbadja, H., & Vodouhè, R. (2010). Diversity, conservation and related wild species of Fonio millet (Digitaria spp.) in the northwest of Benin. *Genetic Resources and Crop Evolution*, 57(6), 827-839.
https://doi.org/10.1007/s10722-009-9522-3

Diop, B. M., Gueye, M. C., Agbangba, C. E., Cisse, N., Deu, M., Diack, O., Fofana, A., Kane, N. A., Ndir, K. N., Ndoye, I., Ngom, A., Leclerc, C., Piquet, M., Vigouroux, Y., Zekraoui, L., Billot, C., & Barnaud, A. (2018). Fonio (Digitaria exilis (Kippist) Stapf): A Socially Embedded Cereal for Food and Nutrition Security in Senegal. *Ethnobiology Letters*, 9(2), 150-165.
https://doi.org/10.14237/ebl.9.2.2018.1072

FAO, IFAD, UNICEF, WFP & WHO. (2019). *The State of Food Security and Nutrition in the World 2019. Safeguarding against economic slowdowns and downturns*. FAO.

FAOSTAT. (s. d.). Consulté 23 février 2021, à l'adresse http://www.fao.org/faostat/fr/#home

Fitzpatrick, M. C., & Keller, S. R. (2015). Ecological genomics meets community-level modelling of biodiversity: Mapping the genomic landscape of current and future environmental adaptation. *Ecology Letters*, 18(1), 1-16. https://doi.org/10.1111/ele.12376

Fofana, A., Gueye, M., Kanfany, G., Tall, H., & Djiba, S. (2017). *Fiche technique du fonio*. Fiche Technique ISRA.

Gigou, J., Stilmant, D., Diallo, T. A., Cissé, N., Sanogo, M. D., Vaksmann, M., & Dupuis, B. (2009). Fonio millet (Digitaria exilis) response to n, p and k
fertilizers under varying climatic conditions in west Africa. *Experimental Agriculture*, 45(4), 401-415. https://doi.org/10.1017/S0014479709990421

Guarino, L., & Lobell, D. B. (2011). A walk on the wild side. *Nature Climate Change*, 1(8), 374-375. https://doi.org/10.1038/nclimate1272

Gueye, M., Kanfany, G., Fofana, A., Gueye, M., Noba, K., & Grove, J. (2015). Effect of planting date on growth and grain yield of fonio millet (Digitaria exilis Stapf) in the Southeast of Senegal. *International Journal of Biological and Chemical Sciences*, 9(2), 581. https://doi.org/10.4314/ijbcs.v9i2.1

Hajjar, R., Jarvis, D. I., & Gemmill-Herren, B. (2008). The utility of crop genetic diversity in maintaining ecosystem services. *Agriculture, Ecosystems & Environment*, 123(4), 261-270. https://doi.org/10.1016/j.agee.2007.08.003

Hilu, K., M'Ribu, K., Liang, H., & Mandelbaum, C. (1997). Fonio millets: Ethnobotany, genetic diversity and evolution. *South African Journal of Botany*, 63, 185-190.

Ibrahim Bio Yerima, A. R., Achigan-Dako, E. G., Aissata, M., Sekloka, E., Billot, C., Adje, C. O. A., Barnaud, A., & Bakasso, Y. (2020). Agromorphological characterization revealed three phenotypic groups in a region-wide germplasm of fonio (Digitaria exilis (Kippist) Stapf). *West Africa. Agronomy*, 10(11), 1653. https://doi.org/10.3390/agronomy10111653

Ibrahim Bio Yerima, A. R., Issoufou, K., Adje, C., Mamadou, A., Oselebe, H., Gueye, M., & Achigan-Dako, E. (2021a). Genome-wide scanning enabled SNP discovery, linkage disequilibrium patterns and population structure in a panel of fonio (Digitaria exilis [Kippist] Stapf) germplasm. *Frontiers in Sustainable Food Systems, 5*. https://doi.org/10.3389/fsufs.2021.699549

Ibrahim Bio Yerima, A. R., & Achigan-Dako, E. G. (2021b). A review of the orphan small grain cereals improvement with a comprehensive plan for genomics-assisted breeding of fonio millet in West Africa. *Plant Breeding*, 140(4), 561-574. https://doi.org/10.1111/pbr.12930

Jideani, I. A., & Jideani, V. A. (2011). Developments on the cereal grains Digitaria exilis (acha) and Digitaria iburua (iburu). *Journal of Food Science and Technology*, 48(3), 251-259. https://doi.org/10.1007/s13197-010-0208-9
Kanfany, G., Gueye, M., & Gueye, M. C. (2018). Rapport technique pour l'homologation de variétés de fonio. Rapport ISRA.

Kanlindogbè, C., Sekloka, E., Achigan-Dako, E. G., & Kwon-Ndung, E. (2020). Mass selection of fonio landraces (Digitaria exilis) grown in Benin: Pathway, homogeneity assessment and genotypes screening. *Plant Breeding*, 139(6), 1266-1280. https://doi.org/10.1111/pbr.12876

Kanlindogbe, C., Sekloka, E., & Kwon-Ndung, E. H. (2020). Genetic resources and varietal environment of grown fonio millets in West Africa: Challenges and perspectives. *Plant Breeding and Biotechnology*, 8(2), 77-88. https://doi.org/10.9787/PBB.2020.8.2.77

Koreissi-Dembélé, Y., Fanou-Fogny, N., Hulshof, P. J. M., & Brouwer, I. D. (2013). Fonio (Digitaria exilis) landraces in Mali: Nutrient and phytate content, genetic diversity and effect of processing. *Journal of Food Composition and Analysis*, 29(2), 134-143. https://doi.org/10.1016/j.jfca.2012.07.010

Kuta, D. D., Kwon-Ndung, E., Dachi, S., Bakare, O., & Ogunkanmi, L. A. (2005). Optimization of protocols for DNA extraction and RAPD analysis in West African fonio (Digitaria exilis and Digitaria iburua) germplasm characterization. *African Journal of Biotechnology*, 4(12),

Kwon-Ndung, E. H., & Ochigbo, A. A. (2004). Acha (fonio) genetic diversity, distribution and conservation in Nigeria. https://www.researchgate.net/profile/Emmanuel_Kwon-Ndung/publication/267839335_Acha_(fonio)_genetic_diversity_distribution_and_conservation_in_Nigeria/links/5483bf280cf2e5f7ceacc8ae.pdf

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

Lobell, D. B., & Tebaldi, C. (2014). Getting caught with our plants down: The risks of a global crop yield slowdown from climate trends in the next two decades. *Environmental Research Letters*, 9(7), 074003 (8pp). https://doi.org/10.1088/1748-9326/9/7/074003

Lowder, S. K., Skoet, J., & Raney, T. (2016). The number, size, and distribution
of farms, smallholder farms, and family farms worldwide. World
Development, 87, 16–29. https://doi.org/10.1016/j.worlddev.2015.10.041

Luikart, G., England, P. R., Tallmon, D., Jordan, S., & Taberlet, P. (2003). The power and promise of population genomics: From genotyping to genome typing. *Nat Rev Genet*, 4(12), 981–994. https://doi.org/10.1038/nrg1226

Ndor, E., & Dauda, S. (2016). Effect of organic and inorganic fertilizers on yield and economic return of Acha (Digitaria spp) varietes in Lafia, Nigeria. *Global Journal of Agricultural Research*, (4)3, 13–20.

Olodo, K. F., Gueye, M. C., Calatayud, C., Diop, B. M., Kane, N. A., Ngom, A., Ntui, V. O., Barreto, M.-M. S., Uyoh, E. A., Abraham, S., Vigouroux, Y., Billot, C., & Barnaud, A. (2019). EST-SSR development for Digitaria exilis and its relatives D. iburua and D. longiflora from transcriptome sequences. *Plant Genetic Resources*, 17(3), 280–284. https://doi.org/10.1017/S1479262118000400

Padulosi, S., Heywood, V., Hunter, D., & Jarvis, A. (2011). Underutilized species and climate change: Current status and outlook. In S. S. Yadav, R. J. Redden, J. L. Hatfield, H. Lotze-Campen, & A. E. Hall (Eds.), *Crop adaptation to climate change* (pp. 507–521). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470960929.ch35

Pironon, S., Etherington, T. R., Borrell, J. S., Kühn, N., Macias-Fauria, M., Ondo, I., Tovar, C., Wilkin, P., & Willis, K. J. (2019). Potential adaptive strategies for 29 sub-Saharan crops under future climate change. *Nature Climate Change*, 9(10), 758–763. https://doi.org/10.1038/s41558-019-0585-7

Rhoné, B., Defrance, D., Berthouly-Salazar, C., Mariac, C., Cubry, P., Couderc, M., Dequincey, A., Assoumanne, A., Kane, N. A., Sultan, B., Barnaud, A., & Vigouroux, Y. (2020). Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration. *Nature Communications*, 11(1), 5274. https://doi.org/10.1038/s41467-020-19066-4

Rojas, M., Lambert, F., Ramirez-Villegas, J., & Challinor, A. J. (2019). Emergence of robust precipitation changes across crop production areas in the 21st century. *Proceedings of the National Academy of Sciences of
the United States of America, 116(14), 6673-6678. https://doi.org/10.1073/pnas.1811463116

Sarah, G., Homa, F., Pointet, S., Contreras, S., Sabot, F., Nabholz, B., Santoni, S., Sauné, L., Ardisson, M., Chantret, N., Sauvage, C., Tregear, J., Jourda, C., Pot, D., Vigouroux, Y., Chair, H., Scarcelli, N., Billot, C., Yahiaoui, N., Ruiz, M. (2017). A large set of 26 new reference transcriptomes dedicated to comparative population genomics in crops and wild relatives. *Molecular Ecology Resources*, 17(3), 565-580. https://doi.org/10.1111/1755-0998.12587

Scarcelli, N., Barnaud, A., Eiserhardt, W., Treier, U. A., Seveno, M., d'Anfray, A., Vigouroux, Y., & Pintaud, J. C. (2011). A set of 100 chloroplast DNA primer pairs to study population genetics and phylogeny in monocotyledons. *Plos One*, 6(5), e19954. https://doi.org/10.1371/journal.pone.0019954

Sekloka, E., Adoukonou-Sagbadja, H., Paraïso, A. A., Yoa, B. K., Bachabi, F.-X., & Zoumarou-Wallis, N. (2015). Evolution de la diversité des cultivars de fonio pratiqués dans la commune de Boukoumbé au Nord-Ouest du Bénin. *International Journal of Biological and Chemical Sciences*, 9(5), 2446-2458. https://doi.org/10.4314/ijbcs.v9i5.16

Vall, E., Andrieu, N., Beavogui, F., & Sogodogo, D. (2011). Les cultures de soudure comme stratégie de lutte contre l'insécurité alimentaire saisonnière en Afrique de l'Ouest: Le cas du fonio (Digitaria exilis Stapf). *Cahiers Agricultures*, 20(4), 294-300.

Vodouhe, R. S., Dako, G. E. A., Dansi, A., & Adoukonou-Sagbadja, H. (2007). *Fonio: A treasure for West Africa* [Conference session]. Plant Genetic Resources and Food Security in West and Central Africa., Ibadan, Nigeria.

Wang, X., Chen, S., Ma, X., Yssel, A. E. J., Chaluvadi, S. R., Johnson, M. S., Gangashetty, P., Hamidou, F., Sanogo, M. D., Zwaenepoel, A., Wallace, J., Van de Peer, Y., Bennetzen, J. L., & Van Deynze, A. (2021). Genome sequence and genetic diversity analysis of an under-domesticated orphan crop, white fonio (Digitaria exilis). *GigaScience*, 10(3), giab013. https://doi.org/10.1093/gigascience/giab013
16. Past, Present, and Future of
West African Sorghum
Improvement: Building a
Roadmap for Climate-adaptive,
Farmer-adopted Varieties

**Jacques Martin Faye**, ISRA/CERAAS, Senegal

**Cyril Diatta**, ISRA/CNRA, Senegal

**Fanna Maïna**, INRAN, Niger

**Geoffrey P. Morris**, Colorado State University, USA

Abstract

Food security and sustainable economic prosperity are key goals for the countries of West Africa (WA). Sorghum (Sorghum bicolor) is among the most important staple crops in WA and has adapted to diverse environments and cultures over many years. However, the process of adaptation is not perfect, and the environment and stakeholder demand is changing, so further sorghum improvement is necessary. National and international sorghum breeding programs are working to improve yield, abiotic stress resilience, and biotic stress resilience. However, it has proved difficult to retain essential end-users' traits, such as grain quality, and adoption of new varieties has remained low throughout the region. The natural genetic variation within locally adapted and preferred cultivars is key to rapidly develop new climate-adaptive varieties by continuously changing allele frequency and directly selecting outstanding lines through efficient genomics-assisted breeding. It is critical to understand the genetic architecture of adaptive traits, develop genomic tools for marker-assisted and genomic selections, and establish effective data management systems
across the region. In this chapter, we describe the genetic diversity of the sorghum germplasm, as well as present an overview of sorghum improvement and perspectives for delivering rapidly and sustainably climate-adaptive breeding products in WA.

Keywords: sorghum, West Africa, breeding, adaptation, genetic diversity

## The Sorghum Crop

Sorghum (Sorghum bicolor L. Moench), a C4 grass used mainly for grain and biomass production, is a staple crop for over 500 million people in Africa and Asia (National Research Council, 1996) and grown across various climates (Harlan & de Wet, 1972). Sorghum is a diploid species with 10 pairs of chromosomes and is a predominantly a self-pollinating crop, though it will readily cross-pollinate; gene flow is also commonly seen in sorghum (Barnaud et al., 2008b). The small size of the sorghum genome (about 730 Mbp) makes it a good model system among grass species (McCormick et al., 2018; Paterson et al., 2009) for genomic studies and genomics-assisted breeding.

Cultivated sorghums belong to Sorghum bicolor subspecies. bicolor, with their domestication estimated about 8,000 years ago in East Africa (around present-day Ethiopia and Sudan), followed by their diversification before diffusing south, east, and west (Harlan & de Wet, 1972; Wendorf et al., 1992). A second center of domestication was proposed in West Africa (WA) for guinea margaritiferum (Deu et al., 1994; Folkertsma et al., 2005). Five basic botanical types and ten intermediate types have been defined based on the spikelet and panicle morphology (Harlan & de Wet, 1972). These types—bicolor, durra, guinea, caudatum, and kafir—are originally distributed with respect to geographic regions across Sub-Saharan Africa. These types are used by sorghum breeders worldwide at different levels based on their agronomic importance.

Additionally, four wild races of sorghum have been defined: virgatum, aethiopicum, arundinaceum, and verticilliflorum; these predominate in northeastern Africa, the semi-arid, tropical, and savanna Sahelian regions of West Africa, respectively (de Wet, 1978). These races are grouped into S. bicolor subspecies verticilliflorum (Harlan & de Wet, 1972). In sorghum,
wild relatives are rarely used for crop improvement (Ananda et al., 2020), perhaps due to the great diversity available in the domesticated gene pool, compared to crops such as wheat, where wild relatives are often used as donors of traits such as disease resistance (Gill et al., 2011). Still, West African wild, weedy, and admixed sorghums may be useful sources of favorable alleles for stress resilience traits if suitable alleles cannot be identified in the domesticated germplasm (Tesso et al., 2008).

Regardless of botanical type, almost all traditional varieties (landraces) of sorghum in West Africa are tall (2–4 m) and photoperiod-sensitive; that is, they require a critical minimum day-length to flower. Guineas, characterized by an open panicle and resistance to pathogen infection, are predominantly distributed in the humid savannas of West Africa. Caudatums are highly appreciated by breeders, mainly due to their high yield potential (ISRA, 2005), especially in Senegal. Durras, with a compact panicle and usually curved peduncle, are more adapted to the semi-arid Sahelian zone and usually used in breeding programs to contribute drought resilience traits.

# Sorghum in West Africa

## 1. Cropping Systems and Uses

In West Africa, sorghum is mostly cultivated in environments characterized by limited soil fertility, drought, heavy bird attacks, and phosphorus- and aluminum-limited soils (Leiser et al., 2015), where other cereals such as maize and pearl millet can hardly survive. Almost all sorghum production in the region depends on rain-fed farming systems, along with lack of agricultural inputs and vulnerability to climate change, with drought acting as the most common climate factor that limits the production (Mundia et al., 2019). The diversity of farming systems and cultural influences, such as human consumption of sorghum grain for the preparation of meals, including couscous, *lakh*, *To*, *nieling*, *mony*, and local beer, are key factors for sorghum production (Barnaud et al., 2008a, 2008b). Grain quality for food consumption and farmers' preferences remain valuable parameters that determine the types of sorghum varieties grown by smallholder farmers. It has been suggested that locally focused studies need to be undertaken
to plan for climate-adaptive varieties to meet the needs of the growing
population (Mundia et al., 2019). However, the development of broadly
adaptive and resilient varieties would contribute to sustainable food security
globally.

## 2. Genetic Diversity

The genetic diversity of West African sorghum is extremely high and is structured by botanical type, with several ancestral populations within botanical type across countries (Deu et al., 2006; Deu et al., 2008; Faye et al., 2019, 2021b; Folkertsma et al., 2005; Maina et al., 2018; Olatoye et al., 2018). However, several gaps exist in the national and international ex situ collections of WA with untapped genetic diversity and less accessibility to the global sorghum community (Upadhyaya et al., 2017). The French colonial science agency ORSTOM (Office de la recherche scientifique et technique outre-mer), predecessor to the current IRD (Institut de Recherche pour le Développement) made collections of sorghum landraces in Senegal, Nigeria, Niger, and Mali in the 1970s (Borgel & Séquier, 1977; Clément & Houdiard, 1977). These collections exist in several genebanks (IRD, ICRISAT, USDA-NPGS, etc.) and constitute sources of adaptive traits for breeding programs. In national breeding programs, most of the duplicated seed lots of these collections have been lost due to poor conservation. More recent collections have been assembled to partly complete the existing gaps and are available to the global sorghum community as ex situ collections. The existing germplasm conservation must remain a key activity to limit the loss of diversity in small and young breeding programs.

Sorghum local varieties harbor genetic variation that should be favorable for developing climate-adaptive varieties (Meyer & Purugganan, 2013). Sorghum is one of the most genetically diverse crops due to its extensive diversification, local adaptation across multiple agroclimatic zones, and continuing wild-crop gene flow that occurred after its domestication (Sagnard et al., 2011). This diversity presents great opportunities, such as the identification of climate-resilient and biotic stress resistant sources (Faye et al., 2021a); however, the diversity of WA sorghum poses challenges such as understanding local adaptation and recovering adaptive traits across various environments. Advances in sequencing and high-throughput genotyping,
such as genotyping-by-sequencing (Elshire et al., 2011; Poland et al., 2012) can generate cost-effective and high-density SNP (single nucleotide polymorphism) markers. The development of high-throughput genotyping, combined with large-scale phenotyping, helps to identify natural variants controlling adaptive traits (Faye et al., 2021a; b).

# Sorghum Improvement in West Africa

West African smallholder farmers have selected and diffused adapted sorghum for thousands of years through mass selection and seed exchange. Unfortunately, this history of sorghum improvement by smallholders remains poorly understood. Early sorghum breeding in francophone West Africa, prior to independence, was conducted by IRAT (Institut de recherches agronomiques tropicales et des cultures vivrières), a French colonial institute. The sorghum improvement program was based in Bambey, Senegal but was part of a research network across French colonial West Africa for germplasm and multi-environment trials. After independence, the national breeding programs of francophone countries of West Africa, (Institut Sénégalais de Recherches Agricoles-ISRA in Senegal, Institut National de la Recherche Agronomique du Niger-INRAN in Niger, Institut d'Economie Rurale-IER in Mali, Institut Togolais de Recherche Agronomique-ITRA in Togo, and Institut de l'Environnement et de Recherches Agricoles-INERA in Burkina Faso) were established to continue the work of IRAT. These programs work in close collaboration with international breeding programs to develop or test varieties for diverse environments. In later sections, we will focus on the history of the formalized sorghum improvement programs in Senegal and Niger.

## 1. History of Sorghum Improvement in Senegal

Sorghum breeding in Senegal reflects sorghum breeding in WA and has yielded several good varieties that were developed for specific agro-ecological regions. Table 1 depicts the main sorghum varieties that were released and disseminated to farmers for the different agro-ecological zones
of Senegal. However, considerable extension efforts must be made to disseminate these varieties in all smallholder farmers and cropping systems.

<table><caption>**Table 1**
The Sorghum Varieties Developed and Disseminated in Senegal</caption><thead><tr><th>Varieties</th><th>Code</th><th>Year of Release</th><th>Maturity Cycle (days)</th><th>Seed Type</th><th>Grain Yield (ton ha-1)</th><th>Agro-ecological Zone</th></tr></thead><tbody><tr><td>Payenne</td><td>ISRA-S-618-1</td><td>2015</td><td>85-90</td><td>White creamy Semi-vitreous</td><td>2.5-3.5</td><td>North peanut basin (300-600 mm)</td></tr><tr><td>Golobe</td><td>ISRA-S-618-2</td><td>2015</td><td>85-90</td><td>White creamy Semi-vitreous</td><td>2.5-3.5</td><td>North peanut basin (300-600 mm)</td></tr><tr><td>CE180-33</td><td>CE180-33</td><td>1983</td><td>90</td><td>White floury</td><td>2-3</td><td>Centre-north (300-600 mm)</td></tr><tr><td>CE151-262</td><td>CE151-262</td><td>1980</td><td>90</td><td>White creamy Semi-vitreous</td><td>4-5</td><td>Senegal river valley</td></tr><tr><td>CE196-7-2-1</td><td>CE196-7-2-1</td><td>1983</td><td>95-100</td><td>White creamy Semi-vitreous</td><td>3-3.5</td><td>Centre-west (Mbour area)</td></tr><tr><td>CE145-66</td><td>CE145-66</td><td>1980</td><td>100</td><td>White floury</td><td>2.5-3</td><td>Centre-north, Sine-Saloum, South-east</td></tr><tr><td>Darou</td><td>ISRA-S-622B</td><td>2011</td><td>105</td><td>White creamy Semi-vitreous</td><td>2.5-3</td><td>South peanut basin (600-800 mm)</td></tr><tr><td>Faourou</td><td>ISRA-S-621B</td><td>2011</td><td>105</td><td>White creamy Semi-vitreous</td><td>2.5-3</td><td>South peanut basin (600-800 mm)</td></tr><tr><td>F2-20</td><td>F2-20</td><td>1983</td><td>110</td><td>White creamy Semi-vitreous</td><td>3-3.5</td><td>Centre-south, South, and South-east</td></tr><tr><td>Nguinthe</td><td>ISRA-S-621A</td><td>2011</td><td>110</td><td>White creamy Semi-vitreous</td><td>2-3</td><td>South-east and North Casamance (800-1000mm)</td></tr><tr><td>Nganda</td><td>ISRA-S-622A</td><td>2011</td><td>110</td><td>White creamy Semi-vitreous</td><td>2-3</td><td>South-east and North Casamance (800-1000mm)</td></tr></tbody></table>
## 1.1 Senegalese Sorghum Cropping Systems

Sorghum is cultivated in Senegal in three different cropping systems. The most important, in terms of production, is rainfed cultivation (sowing in July) in the southern groundnut basin, eastern Senegal, and middle and upper Casamance. The two other production systems are flood recession cultivation (décrué) from November to April along the Senegal River valley (Roy, 2008) and irrigated cultivation during the rainy season (sowing in July) and cold off-season (sowing in October) in the Senegal River valley. Some varietal improvement efforts were carried out in each of the three different cropping systems. Three basic botanical types (guinea, caudatum, and durra) and their intermediate types are predominantly cultivated.

## 1.2 Beginning of Formal Sorghum Improvement Activities

There have been several phases of sorghum improvement in Senegal (Figure 1). Sorghum genetic improvement started in the 1950s, but it was not until 1960 that defined breeding targets were established for sorghum varietal improvement (Mauboussin et al., 1977). Before 1960, the objective was to focus on farmers' preferences by selecting sorghum lines phenotypically, similar to local cultivars, with better productivity and grain quality. Between 1960 and 1965, sorghum lines were selected more intensively. Breeding objectives were still focused on general grain yield improvement while obtaining varieties close to local cultivars. Short stature was not systematically sought, and trials were carried out without fertilizer inputs. A mass selection activity in the local cultivars of the flood recession and rainfall cropping systems was carried out on the Senegal River. This period also marked the beginning of the development of sorghum hybrids in Senegal. However, the hybrid program was not continued consistently, and no sorghum hybrids were released in Senegal (ISRA, 2005; FAO, 2012).
<figure><img src="image_83.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 – The Different Phases of Sorghum Breeding in Senegal

1.3 1960s–1980s: Landrace Purification and Green Revolution-style Varieties

Starting in 1965–1966, several initiatives were undertaken, including the improvement of farming practices, the introduction of founder cultivars from the world collection, the addition of a section for diversification in the Senegal River area, and, above all, the definition of precise selection targets and objectives by the agro-ecological region (Mauboussin et al., 1977). Generally, common selection objectives were defined, then specific objectives were added for each area considered. These common objectives, which broadly correspond to “Green Revolution” style varieties (Swaminathan, 2006), included (a) moderate plant height (~1.8–2 m for rainfed system, ~1.5 m for irrigated system); (b) seedling vigor; (c) semi-compact and erect panicle with good exsertion; (d) high productivity and yield stability; (e) good grain quality with vitreous endosperm, medium viscosity of grain flour, and without brown layer, tannin, and anthocyanin. Specific objectives were also defined for different cropping zones: lines with short maturity cycles less than 90 days, good dryness, and resistance to grain mold for the central zone of the peanut basin; lines with intermediate maturity cycle between 100–110 days and resistance to grain mold for the south-central zone of the peanut basin; lines with late maturity cycle between 110–120 days and resistance to grain mold for the south-eastern Senegal and northern Casamance; and hybrids and lines with early maturity cycle between 80–90 days, very productive, and short stature of about 1.80m for the Senegal River zone.
Selection activities between 1970 to 1980 focused on development of early maturity lines and hybrids for the rainfed and irrigated systems (ISRA, 2005; Luce, 1988). Some of the key varieties that were developed during this period are presented in Table 1. Among them, IRAT 204 (also known as CE 151-262) was developed in 1980 from a cross between IRAT 11 (CE 90) and 73-71 (IS 12610), an Ethiopian line. This variety was developed particularly for the Soudano-Sahelian agro-climate zone (annual rainfall of 500-700 mm) and has been used in several breeding programs in WA because of its high tolerance to biotic and abiotic stresses, such as drought, leaf disease, and insect damage (Muleta et al., 2021).

1.4 1980s–1990s: Refocusing Breeding Targets for Caudatum and Guinea Pure lines

In 1987–1988, the breeding target was reoriented because of the difficulties of the dissemination and adoption of the improved caudatum varieties relative to traditional varieties belonging mainly to the guinea type (ISRA, 2005; Luce, 1995). A study conducted in 1987 and 1988 showed that local guinea cultivars were generally less susceptible to grain mold than improved caudatum varieties, had good adaptive traits, good resistance to diseases and insects, and good grain quality, but their productivity was generally low and their height high. It was therefore proposed to conduct two breeding targets for the Center-South and South zones, with the objectives to (a) emphasizing obtaining caudatum varieties with a good resistance to grain mold and (b) obtaining guinea varieties with short stature and good yield potential while maintaining the characteristics of traditional cultivars (FAO, 2012; ISRA, 2005; Luce, 1988). The year 1988 marked the beginning of pedigree selection on the guinea sorghum type. A recurrent selection program was also initiated for the creation of populations between guinea and caudatum types. In 1993, the selection program for the irrigated system in the Senegal river was stopped.
## 1.5 2000s–2010s: Focus on Multi-purpose Caudatum Varieties for Central Senegal

From 2000 to 2018, the program focused on the development of multi-purpose, white grain, tannin-free varieties with high yield potential, short to intermediate maturity cycle, and good grain quality that could be used for both food and feed. A cross between the varieties IRAT 204 and Sorvato-1, a line with good grain quality from Togo, resulted in the development and release of six pureline caudatum type varieties (Nguinthe, Faourou, Nganda, Darou, Payenne, and Golobe), all tannin-free, white grain, and sweet stems (FAO, 2012). These varieties were successfully used in bread making to produce compound bread (20% sorghum flour and 80% wheat flour). Their vegetative part, highly prized by animals, is stored after harvest and used as fodder during the dry season. The average adoption rate of the improved varieties in Senegal has increased from low (5%) for the varieties released in the 1980s (Trouche et al., 1999) to moderate (25%) for varieties released in the 2010s (Ndiaga Cisse, personal communication, March 2018). This current adoption rate is comparable to the 27.4% average adoption rate of improved sorghum cultivars in Sub-Saharan Africa (Walker & Alwang, 2015). Two of the major factors limiting the adoption of improved varieties is the lack of financial resources and material to apply the full technology package, as well as the fact that improved varieties do not meet farmers’ grain quality preferences. The hybrid program initiated in the 1960s was also resumed in 2012. Recently, ten hybrids were developed, five for the south peanut basin zone and five for the north zone, all of which are in the registration process (Diatta et al., 2019, 2021).

## 1.6 2020s–Onward: Focus on Breeding Adaptive Varieties for Particular Agro-climates

Since 2018, emphasis has been put on defining and using breeding product profiles (BPPs) based on end-users' preferences and adaptation criteria to guide breeding program objectives.

In the Senegal River, farmers preferred traits consist of short stature, more precocity (<90 d) than the elite improved varieties, and the presence of awn on the panicle to prevent bird attacks (Bullard, 1988; Perumal &
Subramaniam, 1973). The current varieties, Golobe and Payenne, are medium-stature and early-maturing. The first breeding target is to improve the presence of awn on the panicle. The second target is to develop varieties that are insensitive to the cool-off season because high stature varieties tend to reduce their height during the cool season.

In the central and south agro-ecological zones, the main challenges are related to crop management practices; thus, the goal is to deliver varieties that are adapted to the length of the growing season (e.g., moderate photosensitive varieties), or early (< 90 d), medium (95-105 d), and late (> 110 d) maturing varieties for a given zone. Photoperiod sensitivity is an adaptive trait where cycle durations of sensitive varieties coincide with growing season lengths, allowing for local adaptation to abiotic (water limitation) and biotic (grain mold and bird attacks) constraints (Curtis, 1968; Kouressy et al., 2008). In addition, the open panicle trait and grain hardness are targeted as proxies to reduce grain mold damage in caudatum varieties in the central and southern regions. The variety Grinkan from the Mali breeding program is used to bring semi-open panicle, seedling vigor, and moderate photoperiod sensitive traits into elite varieties. In parallel, more efforts are being put into guinea sorghum types for the sorghum growing regions with annual rainfall >800 mm by combining elite caudatum varieties and local guineas to develop varieties with vitreous and hard grains.

To increase the adoption rate of sorghum varieties, current breeding activities involve gathering farmers' preferences, starting with end-users' demands, and integrating farmers in the process of varietal selection (Aissata, 2018; Sissoko et al., 2019). These demands are formulated as BPPs for the different agro-ecological zones. To develop new versions of farmers' preferred cultivars, backcross populations are being developed to introgress the *lgs-1* striga resistance allele from the donor line SRN39 (Gobena et al., 2017), staygreen alleles from the donor line B35, grain mold resistance, and high grain protein digestibility into elite varieties using marker-assisted backcross (MABC).
## 2. History of the Nigerien Sorghum Breeding Program

### 2.1 Role of *Niger* within the *French Colonial System*

Sorghum breeding activities started earlier with the French institute IRAT in the 1960s across WA through screening and evaluations of local varieties in Niger. Promising varieties were selected for yield improvement and varietal purification for broad adaptation using either mass selection or pure line selection (Chantereau et al., 2002). The French institute, Office de la Recherche Scientifique et technique Outre-mer (ORSTOM), which became the Institute of Research for Development (IRD) collected also sorghum landraces, across Niger in 1976 and 2003 with national partners (Bezançon et al., 2009). Local varieties such as Jan Jare, Bogoba, Babadia Fara, and Babadia Ja were used as screening material from Niger. The objectives were to develop high yielding varieties with medium height and adapted to semi-arid regions. The variety IRAT 204 (early maturing at 90 days) was among the promising varieties developed for WA, which represents 2.74% of the total sorghum cultivated area in Niger. Moreover, IRAT 11 (known as L30, derived from line 137-62 and Jan Jare) was selected for medium maturity (110 days) in the 1970s. However, L30 was not adopted by farmers due to its low food quality.

### 2.2 *Launching of the INRAN National Program*

INRAN was created in 1975 following IRAT programs in Niger with four regional research centers in the country based at Kollo, Maradi, Niamey, and Tahoua. The objectives were to contribute to food security and rural development in Niger under the ministry of agriculture. Sorghum breeding is among the earlier programs of other crops in Niger attempting to develop high yielding varieties tolerant to stresses. Major targeted traits were early maturity, acceptable food quality, and good nutritional value. Caudatum and durra-caudatum were the types adapted for semi-arid regions.
## 2.3 1980s–2010s: INRAN Collaborations with INTSORMIL and ICRISAT

Sorghum breeding in Niger started with germplasm exchange through international programs. Early generations and advanced generations from existing US parents were introduced. Several breeding nurseries were conducted and germplasm exchanged during the International Sorghum and Millet Collaborative Research Support Program (INTSORMIL) to establish strong and durable selection programs in Niger. Sorghum hybrid production started with germplasm from international collaborations for joint evaluations with breeding programs from Purdue University and the International Crops Research Institut for the Semi-Arid Tropics (ICRISAT). Progenies developed by ICRISAT were evaluated in Niger through the Sorghum Elite Progeny Observation Nursery (SEPON). From 1991 to 1995, two sorghum varieties were released: the variety SEPON 82 (M 90038) and SRN39 (ICSV 1007 BF) (Bantilan et al., 2004). Moreover, germplasm exchanges with international partnerships lead to the development of hybrid varieties, such as the NAD1 hybrid (ATx623 x MR732), which was released for the Sahelian agro-climatic zone in 1991 (Ministère de l'Agriculture du Niger, 2012). The NAD1 hybrid is a white seeded and medium-maturity line that yields over two tons per hectare, higher than the local check Mota Maradi (Kapran et al., 1997, 2007). However, the developed varieties were either not broadly adapted or less appreciated by end-users especially during food preparation. NAD1, SRN39, and SEPON 82 represent 0.45%, 0.07%, and 4.95% respectively of the total sorghum cultivated area in Niger in 2009 (Walker & Alwang, 2015).

## 2.4 2020s–Onward

Nowadays, the Niger breeding program aims to focus more on understanding the needs of end-users through Participatory Rural Appraisal (PRA) and market needs, while accelerating breeding cycles to deliver new varieties with better appreciation (Aissata, 2018). The current breeding targets are defined as BPPs to release adapted lines for different regions across the country. The main breeding targets involve breeding for high yielding varieties with tolerance to drought stress; resistance to striga,
downy mildew, midge, and long smut; early maturing; and high grain and forage quality.

# Genetic and Genomic Resources for West African Sorghum Breeding

The sorghum germplasm in WA, including landraces, were (and are still being) adapted to various environmental variations and farming practices for many generations; thus, selection must have favored the fixation of adaptive loci (Orr, 1998) useful for breeding (Leiser et al., 2014). The majority of elite varieties in breeding programs of WA were developed over 30 years ago. Though some of these varieties might be obsolete, several of them are still adapted to different environments across WA. For example, IRAT 204, SEPON 82, Sorvato-1, Sariaso 11, Sariaso 15, Grinkan, Mota Maradi, and SRN39 are key elite breeding lines. These key varieties have been extensively used to generate crosses across breeding programs, and it is expected that the presence of shared common haplotypes across diverse breeding populations may facilitate deciphering the genetic control of key traits and their pyramiding in improved varieties.

## 1. Genome-wide Genotyping of Genebank Collections

The thousands of West African sorghum accessions available in genebanks are potentially useful sources of trait donors for pre-breeding (Kimber et al., 2013; Upadhyaya et al., 2017). Recently, using sequencing technologies, several genebank collections have been characterized, providing useful resources for breeding programs of the region (Bellis et al., 2020; Billot et al., 2013; Faye et al., 2019; Lasky et al., 2015; Maina et al., 2018; Olatoye et al., 2018). In WA, national and international sorghum collections were genotyped using high-density SNP markers that are publicly available to the global scientific community (Faye et al., 2019, 2021b; Maina et al., 2018; Olatoye et al., 2018). Genomic analyses showed several ancestral populations structured hierarchically with respect to botanical types and agro-climatic zones across countries.
A West African sorghum association panel (WASAP) was assembled by breeders, physiologists, and geneticists in Senegal, Niger, Togo, and Mali and genotyped using GBS (Faye et al., 2021b). Based on 159,101 high-quality biallelic SNPs obtained for this panel, genetic characterization showed that high genetic diversity exists in sorghum across WA where 8 ancestral populations are found in Senegal, Niger, Mali, and Togo germplasms. The genetic characterization of the WASAP and different collections, ex-situ, allowed to better understand the genetic diversity, population structure, putative alleles for local adaptation, and farmer selections existing in WA. These genomic resources for WA sorghum germplasm would contribute to the genetic dissection of adaptive traits and development of new varieties adapted to the current and future environmental conditions.

## 2. The Potential of Genome-wide Association Studies for Trait Discovery

Cultivars harboring favorable alleles for local adaptation are potential donors of winning traits and can be used for pre-breeding through genomics-assisted breeding. Genome-wide association studies (GWAS) help to identify natural variations that control key traits in adapted cultivars and multiparental populations. GWAS have been successfully applied in sorghum panels including WA cultivars, with the identification of known and novel loci associated with photoperiodic flowering, plant height, drought tolerance, grain pigmentation, farmer preferences, and climatic variables (Bellis et al., 2020; Faye et al., 2019, 2021b; Hu et al., 2019; Lasky et al., 2015; Maina et al., 2018; Olatoye et al., 2018). The Maturity genes, Ma6, SbCN8, and SbCN12, have been mapped several times using different sorghum panels of WA, suggesting the predominant role of these genes for photoperiodic flowering differences in the WA sorghums. However, the strong population structure observed in the germplasm suggests applying regional GWAS on specific populations or on multiparental populations with balanced gene pools.

The backcross-nested association mapping (BC-NAM) populations developed by the Mali breeding program (Teme, 2018), Australia breeding program (Jordan et al., 2011), and the sorghum MAGIC population from Purdue University (Ongom & Ejeta, 2017) were shown to segregate for key adaptive agronomic traits, including high yield, drought tolerance, early
maturity, grain quality, high protein digestibility, and disease and pest
resistance. These multiparental populations constitute useful genetic
resources for GWAS and varietal improvement in WA (Teme, 2018). Also,
following a similar strategy, nested-association mapping populations (NAM)
were developed in Senegal and Niger to constitute useful genetic resources
for breeding programs.

Perspectives on New Breeding Technologies for
Sorghum in West Africa

1. Overview of Adoption of New Technology:
Separating Fads from Solutions

Technology in plant breeding and genetics is constantly evolving. Crop improvement programs must carefully consider which new technologies they adopt or not (Bernardo, 2016b; Simmonds, 1991). Some technologies may be useful for some programs and not for others, or for specific breeding targets within a program and not others. All breeding programs, from well-financed commercial breeding to small developing country NARS programs, must consider the breeding technology to use depending on the status of their breeding program and the genetic architecture of priority traits. For instance, a simulation of a Haitian sorghum breeding showed that genomic selection may, or may not, be more cost-effective than phenotypic selection depending on the underlying genetic architecture for yield variation (Muleta et al., 2019). This highlights the need to integrate effective selection methods for small NARS programs. Genomics-assisted breeding based on shared genetic and genomic resources can help to leverage limitations of field phenotyping for optimal selection in early generations across multiple environments (Cobb et al. 2018; Deshpande et al., 2016).

The development of genomic tools contributes to solving the roadblocks that limit or delay the process of achieving breeding product concepts using conventional breeding. Conventional breeding has yielded many elite varieties of important economic values. However, phenotypic selection in early generations for many adaptive traits, such as photoperiodic flowering and drought tolerance, is difficult for small and young breeding programs.
One of the reasons is that multiple field trials are generally required for optimum selection. Crop variety update is a slow process in small and young breeding programs and can take up to 10 years from first crossing to releasing new varieties to farmers and end-users. This process can take even longer when considering large-scale farmer diffusion and adoption, while a limited attention is given to most staple food crops such as sorghum in Africa. With advances in next-generation sequencing technologies and cost-effective genotyping platforms, there are opportunities to modernize small and young breeding programs to accelerate varietal development and continuously release new founder lines through genomics-assisted breeding (Poland, 2015). Figure 2 resumes the key local and elite germplasm useful for the development of climate-adaptive varieties for smallholder farmers and end-users using genomics-assisted breeding.

<figure><img src="image_84.png" /><figcaption>Figure 2 – Development of *Climate-adaptive Varieties through Genomics-enabled Breeding Network Systems*</figcaption></figure>
## 2. Rapid Generation Advancement and Targeted Use of SNPs

Rapid generation advancement has been proposed to accelerate variety product profile development in the International Rice Research Institute (IRRI) breeding program (Collard et al., 2019). The method consists of rapidly fixing genotypes to homozygosity with 3.5 to 4 generations per year and starting yield trials with $F_5$-derived lines for seed increase instead of using $F_7$ lines. In this context, quality and uniformity control of seed lots and crosses can also contribute to establishing an efficient breeding product development. In the case of sorghum in WA, a defined set of several SNP markers can be used for quality control of any pair or multiple founder breeding lines. Having highly polymorphic markers will complement the available trait-associated cost-effective Kompetitive Allele Specific PCR (KASP) markers to establish an efficient marker-assisted selection (MAS) program for forward breeding of simply inherited traits. Quality control activities of WA breeding programs can be carried out at the regional facility established at ISRA/CERAAS. However, out-sourcing genotyping in specialized laboratories would be more effective for large-scale genotyping, especially for saving breeders' time and focusing on main breeding activities.

## 3. Systems for Data Management and Sharing

Enabling data sharing and developing a centralized database that stores phenotypes, genotypes, and pedigree breeding information will contribute to establishing an efficient genomics-assisted breeding pipeline in WA. Since 2014, the Breeding Management System (BMS) of the Integrated Breeding Platform (IBP) has actively helped the management of breeding activities of several crops, including sorghum, in several NARS in WA. Such a centralized database would be particularly beneficial for GS model training to improve prediction accuracy and have access to correlated phenotypic and genotypic data from different environments. These datasets would contain high frequency of common haplotypes shared across populations and a wide range of environmental trials. The more related data and larger training populations available to train GS models, the better the prediction accuracy as previously demonstrated in the International Maize and Wheat
Improvement Center (CIMMYT) wheat breeding (Battenfield et al., 2016). Accessible breeding and training populations will be potentially useful to develop climate resilient varieties because of the extensive agro-climate variation and local environment testing that were exercised by breeders in the region through the IAVAO network.

## 4. Genomic Selection

Genomic selection approach was first established in animal breeding and is an efficient method to accelerate breeding cycling and increase genetic gain (Jannink et al., 2010). Its usage in crop breeding programs is recent and has shown promising results, especially in crops such as maize, wheat, and barley (Michel et al., 2017; Pierre et al., 2016; Zhang et al., 2017). Simulations and empirical studies of GS have demonstrated a potential to increase genetic gain while reducing the number of breeding cycles in several food crops (Battenfield et al., 2016; Bernardo, 2016a). In GS, the whole performance of individuals is assessed regardless of interaction between positive and negative alleles by estimating the individual total performance based on individual genomic-estimated breeding values (GEBV), thus more efficient when dealing with polygenic traits. Both genomic selection and MAS provide advantages to more rapidly increasing genetic gain per unit time and unit cycle, and therefore can be integrated together in the breeding pipeline for more efficient breeding.

Applications of GS are currently limited in small breeding programs, including WA sorghum breeding. Most NARS breeding programs run elite by exotic breeding rather than elite by elite breeding compared to most advanced public or private international breeding programs. However, correlated genotypes exist in diverse West African sorghum populations because the genetic diversity is structured by botanical types and subpopulations within botanical type across countries (Faye et al., 2021b) and diverse key founder lines such as IRAT204, Sorvato-1, Grinkan, SEPON 82, and Sariaso 11 are shared among breeding programs. Continuous sharing of breeding materials across breeding programs will be useful for effective GS implementation.