## 5. Gene Editing

CRISPR/Cas9, Cas12a, and Cas13 gene editing systems are emerging technologies that hold opportunities to increase genetic gain in breeding. Integrating CRISPR-based gene editing with genomic-assisted selection may help to purge deleterious alleles while favoring rapid genetic gain. This system yields potential interest to deal with negative epistasis traits (Sackman & Rokyta, 2018). Important traits such as tannin content, which may be negatively associated with grain mold resistance (GMR) and grain quality, or grain yield with plant height could involve negative epistasis. The condensed tannin in the pigmented testa of sorghum seed is controlled by the presence of loss-of-function alleles at both *Tannin1* (*Tan1*) and *Tannin2* (*Tan2*) genes (Wu et al., 2012, 2019), and indirectly involved in GMR (Ackerman et al., 2021; Nida et al., 2021), which can be directly edited in tannin-free elite varieties. Other loci such as YELLO SEED1 (Y1) and Y3 genes have also shown to contribute to GMR (Nida et al., 2019) and thus can be edited to develop resistant varieties.

## Conclusion

Important progress has been achieved in assembling and genetically characterizing the WA sorghum germplasm. High genetic diversity exists within and across breeding programs, with multiple ancestral populations shared across countries. To take advantage of the diverse sorghum germplasm, interdisciplinary researchers must work in synergy to understand sorghum adaptation to specific and across diverse environments. Genomics-assisted breeding through population genomics, GWAS in multiparental populations, MAS, GS, and gene-editing can offer the ability to directly trace the untapped diversity and introduce natural variation into locally preferred cultivars. However, a rigorous scientific approach, such as the GoHy method, must be employed to avoid wasting time, resources, or distracting breeding activities as a result of adopting new technologies without proper setting of development goals and rigorous hypotheses. The rigorous definition of BPPs and GoHy will not only allow testing hypotheses for functional causal variants for adaptive traits but also increase genetic gain to establish food security in West Africa. As
phenotyping and genotyping platforms continue to be more and more
sophisticated, it is urgent to enable continuous data sharing and
management with a centralized database.

Acknowledgements

Portions of this manuscript have been adapted from the Kansas State
University PhD dissertations of J.M.F. and F.M. This study is made possible
by the support of the American People provided to the Feed the Future
Innovation Lab for Collaborative Research on Sorghum and Millet through
the United States Agency for International Development (USAID) under
Cooperative Agreement No. AID-OAA-A-13-00047. The contents are the sole
responsibility of the authors and do not necessarily reflect the views of
USAID or the United States Government.

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17. Modern Approaches for
Sorghum Breeding in Mali

Niaba Teme, IER-CRRA-Sotuba, Mali

Mamoutou Kouressy, IER-CRRA-Sotuba, Mali

Salifou Sissoko, IER-CRRA-Sotuba, Mali

Dramane Sako, IER-CRRA-Sotuba, Mali

Diarah Guindo, IER-CRRA-Sotuba, Mali & CIRAD, France

Yacouba Dembélé, IER-CRRA-Sotuba, Mali

Mohamed Tekete, IER-CRRA-Sotuba, Mali

Mamoutou Korotimi Thera, IER-CRRA-Sotuba, Mali

Ankounidjou Yebedié, IER-CRRA-Sotuba, Mali

Mohamed Doumbia, IER-CRRA-Sotuba, Mali

Aliou Sissoko, IER-CRRA-Sotuba, Mali

Sekouba Sanogo, IER-CRRA-Sotuba, Mali

Mahamady Kané, Nouvelle France Genetics, USA

Aiché Traoré dite Diop, IER-CRRA-Sotuba, Mali

Mahamadou Diakité, IER-CRRA-Sotuba, Mali

Flakoro Coulibaly, IER-CRRA-Sotuba, Mali

Denis Lespinasse, Syngenta, France

Frédéric Cossic, Syngenta, France

Michel Ragot, Syngenta, France & Nouvelle France Genetics, USA

Michel Vaksmann, CIRAD, France & UMR-AGAP, France

Baptiste Guitton, CIRAD, France & International Plant Selection, France

Christian Mestres, CIRAD, France

Fabrice Davrieux, CIRAD-Réunion, France

Geneviève Fliedel¹, CIRAD, France

Jean-François Rami, CIRAD, France
Abstract

Sorghum is an important and versatile food crop in the Sudan savannah environments of West Africa. Sorghum production doubled in the last 20 years and its yield remained stable and low in Mali. Molecular markers were used to dissect quantitative variation to increase efficiency of breeding programs. From 2009-2014, marker-assisted recurrent selection (MARS), an integrated process to identify favorable alleles and to monitor their pyramiding throughout the breeding course, was implemented. Two large bi-parental populations derived from complementary recent elite inbreds, Tiandougou, Keninkeni, and Lata-3, were developed to improve grain yield productivity and stability while maintaining traditional food processing quality. Populations were phenotyped multi-locally. Quantitative Trait Loci (QTL) analyses identified, in each genomic region, the most favorable morphologic, yield components, grain quality, and flowering time parental alleles. Marker-assisted recurrent selection approach is detailed for one of the populations. Several genotypic ideotypes corresponding to ideal recombination patterns between the parents were defined. Molecular marker-guided recurrent crossing allowed favorable QTL accumulation for a large range of traits. Three marker-assisted recurrent selection cycles were accomplished. Four stable and superior elite lines were identified, leading to their registration in 2018 in national and ECOWAS (Economic Community of West African States) catalogs.

Keywords: sorghum, marker assisted selection, photoperiod, Sahel

Introduction

Sorghum (*Sorghum bicolor*) is one of the most important staple crops in West Africa, especially in the Guinea and Sudan savannah environments. Sorghum production in West Africa doubled from 1980 to 2008 because of the increase in cultivated areas although the yield remained stable and low. Past breeding efforts on sorghum in West Africa, starting in the sixties, were essentially on increasing the harvest index by developing photoperiod insensitive varieties and reducing plant height and tiller number. However, this approach showed limited positive impact mainly because making the sorghum photoperiod
insensitive removed a key component of adaptation to climatic conditions and cultural practices of the target environments (Kouressy et al., 2008a). Furthermore, new varieties showed poor grain quality because of unsuitable biochemical and physical properties or sensitivity to grain weathering. Recent approaches aim to maintain rusticity traits and grain quality of local cultivars (Vaksmann et al., 2008) with improved yield potential to support intensification.

This breeding strategy is particularly adapted to the Malian southern zone (>800 mm of annual rainfall) where better soil fertility, due to cotton cultivation, allows new sorghum varieties to express their yield potential. However, in the center zone of Mali (≥500 mm) where sorghum crop faces more severe drought constraints, early maturing varieties with a low level of photoperiod sensitivity are the main focus of breeders.

Sorghum has a wide range of use in food and feed industries, as well as in alcohol and renewable energy production. In Africa, particularly in Mali, sorghum is mainly used in traditional food and beverage (processing of main meals and traditional beer). Improved varieties are not widely adopted by end users mainly because of the poor seed quality. In Mali, one of the main traditional dishes prepared from sorghum flour is Tô, a thick porridge. The characteristics of a good Tô are firmness and retention of its firmness, water, color, and taste while stored at night. Biochemical and physical characteristics of grain and their interactions determine its quality for end users (Fliedel, 1995).

Molecular markers are useful tools to dissect highly polygenic traits into QTL, providing a way to improve breeding efficiency of polygenic traits. During the past 20 years, QTL studies have often relied on using segregating populations suitable for repeated or multi-environment characterization. However, such populations have the drawback of generally being disconnected from breeding materials because of the time needed to develop them. For this reason, QTL detected in such experimental populations are often not transferable and not applicable in breeding populations. On the contrary, QTL analyses that are integrated to the breeding process and specifically designed for target crosses should provide applicable tools to improve breeding efficiency.

Marker-assisted recurrent selection (MARS) has been proposed as an
integrated process of breeding and detection of QTL of interest. In such approaches, molecular markers are used to identify key QTL involved in target traits and environments and to monitor and pyramid favorable alleles in subsequent generations through progenies crossing. The ideal genotype that can be obtained from a bi-parental cross can be a mosaic of chromosomal segments carrying favorable alleles from both parents.

MARS schemes involve several successive generations of crossing of superior individuals, based on their molecular marker genotypes, and the use of multi-trait selection indices. This approach could enable the achievement of an ideal genotype, which is unlikely to be realized with a classical breeding process within a population of realistic size (Peleman & van der Voort, 2003). The added value of using molecular markers is to track recombination towards the breeder's ideotypes and allow simultaneous detection of QTL for all traits that need consideration. QTL analysis can also be assessed in different environments to provide a basis for Genotype by Environment (GxE) interpretation.

For now, only a few examples have been published on the application of the MARS approach (Beyene et al., 2016; Eathington et al., 2007; Johnson, 2003; Rahman et al., 2020), and to our knowledge, its use on African sorghum has not been reported. However, the MARS approach has been successfully applied by the private sector, especially in maize (Ribaut & Ragot, 2007).

Integration of molecular markers requires significant modification in the organization of a breeding program, which becomes a more complex decision-making process involving more data analysis in a shorter timeframe. For this reason, the successful application of this approach must rely on a tight interaction between breeders, genotyping services, and geneticists in charge of data analysis and crossing schemes development.

Between 2008 and 2014, as part of the Generation Challenge Programme (GCP), a project called "Improving sorghum productivity in semi-arid environments of Mali through integrated MARS" was implemented within the national breeding program of Institut d'Economie Rurale (IER) with the collaboration of Syngenta and the Centre de coopération Internationale en Recherche Agronomique pour le Développement (CIRAD). This chapter summarizes the results obtained from that project and reflects on lessons learned from implementing marker-assisted and ideotype-driven selection
in a national breeding program. Prospects for accelerating sorghum breeding
under climate uncertainties and with better precision are also highlighted.

Population Development

1. Parents' Choice

The breeding product targeted an improved grain yield (>2 t/ha), photoperiod sensitivity that provides adaptation to local production environment, and acceptable grain quality for traditional food preparation. Three varieties were used as parents for the development of new breeding populations based on their complementary attributes. The first two varieties, Tiandougou and Keninkeni, were elite varieties from the IER breeding program, while Lata-3 was an improved variety from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). Tiandougou is a Caudatum type with a thick pericarp, large seed size, and chalky grains, while Keninkeni and Lata-3 are Guinea botanical types with a thin and glassy pericarp (vitreous endosperm) and medium seed size. The three parents are medium height (< 200 cm), well adapted to the Sudano-sahelian conditions, and photoperiod sensitive. From sowing to grain's physiological maturity, Tiandougou is slightly later (127 days) than Keninkeni and Lata-3 (120 days). Two populations, Tiandougou by Keninkeni (P114) and Tiandougou by Lata-3 (P118), were selected to pursue the recurrent cycle selection.

2. Crossing and Population Development

Hand emasculation was performed among varieties during the 2008 rainy
season to generate F1 seeds. During the crosses, male and female individual
plants used in each cross were self-pollinated. The different crosses were
harvested and sown during the first off-season (2008-2009) to produce F2
seeds. Parental conformity and genetic variability tests were performed in
an off-season F2 production nursery on each F1 plant and parental seed lot
using molecular markers. The systematic control of F1 plants allowed the
identification and avoidance of several issues like self-pollinations or non-
conformity to the expected cross. Thanks to this quality control, unique F₁ plants identified as conforming to the parents were selected to develop the F₂ generation, which ensured perfect homogeneity of the populations. The F₃ seeds were then produced during the same off-season (See Figure 1). The two populations, each made up of more than 400 F₃ families, were evaluated in the field in the 2009 cropping season. Rows of 10 F₃ individuals per family were planted in 2009 and a single F₃ plant was randomly taken in each row. Ten F₄ plants per family were then sown in the 2009-2010 off-season, selfed, and bulked, leading to a total of 400 F₄:₅ bulk family seeds, which were used for multi-location phenotyping experiments in the 2010 cropping season.

<figure><img src="image_85.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 - Generation Advancement of Photoperiod-sensitive Sorghum Lines

## Field Phenotyping

Multi-location phenotyping experiments were conducted during the cropping season, in 2010 for P114 and in 2011 for P118. Three experimental research stations were identified (See Figure 2) as having reliable and homogeneous field conditions to allow large populations phenotyping: Cinzana station (13°15′N, 5°58′E W, 265 m; Sudano-sahelian savannah), Sotuba station (12°39′N, 7°56′W, 381 m; Sudano-sahelian savannah), and Farako station (11°13′N, 5°29′W, 375 m; Sudano-Guinean savannah). The three stations cover the main sorghum production areas in Mali. All three sites
have a mono-modal pattern of rainfall in summer (May to November), accounting on average for 690 mm at Cinzana, 890 mm at Sotuba, and 1,060 mm at Farako. Average maximal/minimal monthly temperatures were 35.6 °C/19.3 °C at Cinzana, 34.7 °C/20.6 °C at Sotuba, and 33.7 °C/21.0 °C at Farako.

In order to increase the number of environmental conditions, phenotyping was conducted at two different sowing dates in each location. Each population was thus phenotyped in six environments. In Sotuba, a late sowing in October was conducted to complete a photoperiod-sensitivity study of the progenies. Each population was thus observed in long and intermediate day length (usual cropping season, sowing 19 June and 15 July 2011) and short-day length (off-season, sowing 3 October 2011). Maximum astronomical day length is 12.78 h at Cinzana, 12.75 h at Sotuba, and 12.66 h at Farako.

<figure><img src="image_86.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 - Location of Phenotyping Sites in Mali and Isohyets Over the 1981-2010 Period

Experimental layouts for each population followed an augmented design. For P114, there were 447 entries with 2 rows of 4m per entry (spacing within rows: 0.4 m; spacing between rows: 0.75 m), 20 hills per plot, and 2 plants per hill. Each trial included a single replication of 401 progenies plus the two
parental lines, used as checks (23 entries of each parent) regularly included
in the design to control field heterogeneity. For P118 there were 464 entries
with 29 blocks of 16 entries (14 progeny and 2 parents used as checks) with
the same spacing and thinning as the P114. Traits recorded in the different
locations were grain yield, yield components, flowering date, plant height,
and panicle morphology. Some other traits, such as lodging, disease, and bird
damage were recorded when relevant.

Grain quality was phenotyped in both populations using grain samples
from a single environment that was selected after harvest based on the
overall quality of the harvested grain (Guindo, 2016). The grain samples
collected after harvest were analyzed in the agroclimatology laboratory of
IER. Dehulling yield, the ratio of dehulled grain weight to total grain was
measured. A mini-Tô was prepared from dehulled grain in the Food
Technology Laboratory (LTA) of IER on which Tô consistency and Tô color
were measured. In addition, a sample of harvested grain was sent to CIRAD,
France where Near Infrared Spectroscopy (NIRS) analyses were performed
on whole grain to obtain predictions for the total content of grain proteins,
lipids, and amylose (Guindo et al., 2016). Endosperm texture and pericarp
thickness were also measured by visual examination of grains. Finally, image
analysis was conducted on a sample of grains to determine grain shape and
color.

Genetic Maps Construction

Genotyping of P114 was completed in 2009/2010. DNA was produced from
a bulk of 10 F4 individuals from each F3 family grown in the greenhouse at
Montpellier. At the beginning of the project, and until 2010, single nucleotide
polymorphism (SNP) markers were not commonly used in sorghum. A set of
79 simple-sequence repeat (SSR) markers was thus used at the early stages
of the project for population construction. This map was then completed
with SNP markers using the SNP information available in early 2010. A first
experiment, using Illumina technology, was carried out using an array of
384 markers. Genotyping of the population was finally completed with an
additional experiment using 100 well-distributed Kasp SNPs developed by
the Integrated Breeding Platform (https://www.integratedbreeding.net/45/
products-catalogue?id=330). A map using the most informative markers was
derived with a total number of 200 loci (124 SNP, 76 SSR) (See Figure 3) that
spanned a total genetic distance of 1,793 cM and was used for QTL detection.

Genotyping of P118 was carried out in early 2012. DNA extracts were
produced from bulks of F4 individual plants sampled from each family during
seed multiplication in Sotuba in 2010. A total of 236 Kasp SNP markers were
analyzed on 401 F3 individuals. The genetic map was constructed from 224
reliable markers, spanning a total genetic distance of 1,340 cM.
<figure><img src="image_87.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 - Genetic Map with 200 Loci (124 SNP, 76 SSR) used for QTL Detection of P114
Figure 3 Continued

<figure><img src="image_88.png" /></figure>

Figure 3 Continued – Genetic Map with 200 Loci (124 SNP, 76 SSR) used for QTL Detection of P114

Genetic Analysis

QTL analyses and modeling were conducted for all traits available across environments. For a given trait, independent QTL analyses were conducted in separate environments. QTL analyses were conducted using composite interval mapping with QTL Cartographer software (Basten et al., 2002). Several hundred QTL were globally detected for all traits in all environments to identify the most favorable parental allele in each region of the genome and to define several genotypic ideotypes to achieve breeding objectives.
Grain productivity was one of the main targeted traits, and the QTL
detection allowed for the identification of several genomic regions that were
involved in grain productivity and for the assessment of QTL effects' stability
across the six environments. In addition, the genetic analysis performed
in this context allowed the deciphering of relationships between traits and
between environments, breaking possible antagonisms between traits,
testing different hypotheses, and designing several ideotypes for various
objectives or environments.

1. Large-effect QTL

Some major QTL with large effects have been detected in both populations
for pericarp thickness, flowering date, plant anthocyanin (Guindo et al. 2019),
and plant height (See Table 1):

* A major QTL for pericarp thickness, corresponding to the Z gene (Ayyangar, 1934), was located on chromosome 2 for which both guinea parents brought the Z (thin) allele.

* A major QTL for flowering date was detected on chromosome 3. In both populations, the allele brought by a Tiandougou parent on this QTL was responsible for later flowering (8 days on average).

* A major QTL for plant anthocyanin corresponding to the P gene (Ayyangar et al., 1933) was detected on chromosome 6 for which Tiandougou brought the recessive p (tan) allele.

* Finally, a major QTL for plant height was detected on chromosome 7, identified as the dwarfing gene Dw3 (Karper, 1932). Both guinea parents, Lata3 and Keninkeni, brought the Dw (tall) allele.
<table><caption>**Table 1**
List of Major QTLs Detected in Both Populations</caption><thead><tr><th rowspan="2">Trait</th><th rowspan="2">Known Gene</th><th rowspan="2">Chrom</th><th colspan="2">Effect</th><th colspan="2">Target Allele</th></tr><tr><th>P114</th><th>P118</th><th>P114</th><th>P118</th></tr></thead><tbody><tr><td>Pericarp thickness</td><td>Z</td><td>2</td><td>KK thin</td><td>Lata thin</td><td>KK</td><td>Lata</td></tr><tr><td>Flowering date</td><td></td><td>3</td><td>Tian late</td><td>Tian late</td><td>Tian</td><td>-</td></tr><tr><td>Anthocyanin</td><td>P</td><td>6</td><td>Tian Tan</td><td>Tian tan</td><td>Tian</td><td>Tian</td></tr><tr><td>Plant Height</td><td>Dw3</td><td>7</td><td>KK tall</td><td>Lata tall</td><td>KK</td><td>Lata</td></tr></tbody></table>

Grain yield and quality are closely related to flowering date because flowering is a complex trait that controls adaptation of the plant to its environment. In West Africa, most local sorghum varieties are photoperiodic short-day plants. The photoperiod sensitivity constitutes sorghum's main adaptation factor to climate and parasitic constraints in the Sahel zone (Kouressy et al., 2008b). Grouping flowering at the end of the rainy season ensures a balance between satisfaction of water needs and avoidance of many biotic constraints. Grain of early photoperiod insensitive maturing varieties is attacked by birds and altered by mold and insect complexes, while photoperiod sensitive and late varieties deplete soil moisture before the end of grain filling and escape their seeds' alteration. The reintroduction of photoperiodism in breeding programs in Mali is recent and plays a key role in sorghum improvement success in the Sahelian zone.

A major QTL for flowering time detected on chromosome 3 was only expressed under long day conditions, showing that it was related to photoperiod sensitivity. The allele brought by the Tiandougou parent was responsible for later flowering (8 days on average). These results were investigated further using a crop modeling approach (Guitton et al., 2018). The different parameters of the Crop Environmental Resource Synthesis (CERES) model corresponded to different chromosomal regions. The major QTL was related to the critical photoperiod parameter (P2O) of the CERES model, that represents, for a given genotype, the value of photoperiod under which the duration of the vegetative stage is constant. Knowledge of genetic control of flowering time now allows breeders to develop varieties specifically adapted to different Sudano-sahelian target environments.
Specifically, the modification of the critical photoperiod allowed for modification of the vegetative duration phase while preserving photoperiod sensitivity.

Ideotype Construction

QTL results were translated into genotypic ideotypes, which represent the
target parental allele all along the genome that brings favorable
contributions for key QTL. In each marker interval, the respective
contributions of each parent were compared for all detected QTL, taking into
consideration the effects of the QTL and the breeding objectives.

There were some trade-offs to be made that involved the large QTL effects (See Table 1). First, for the major QTL involved in pericarp thickness, the Z (thin) allele was selected as the target allele for both populations. Second, the large effect QTL involved in flowering time and critical photoperiod affected grain productivity, depending on the environment and the genetic background. In P114, late flowering was associated with a QTL for grain yield in all environments, and the late allele (Tiandougou) was selected as the target allele in this population. In P118, the late-flowering allele was also associated with a QTL for grain yield but only in the Farako location; the early flowering allele was associated with a major QTL for grain yield in the Sotuba and Cinzana locations. Two ideotypes were thus derived for both environments differing only on this major flowering time QTL. Third, on the P locus involved in plant anthocyanin, the p (tan) allele was the target allele for selection. Indeed, tan plants (no anthocyanin) are preferred because they generally produce whiter flour and whiter Tô.

Next, the major QTL for plant height was detected on chromosome 7 and identified as the Dw3 gene. Although the overall objective, in terms of plant height, was medium, the tall allele was selected in this region for both populations because it was associated with a QTL for panicle length and consequently with a QTL for grain yield. To compensate for the effect of this QTL on plant height, alleles that contributed to reducing plant height on all other QTL in the rest of the genome were selected (11 and 16 QTL detected in P118 and P114, respectively).

Finally, using only P114 as an example, three different genotypic ideotypes
(See Figure 4) were defined. The overall objective was to combine grain productivity (as defined by grain yield and yield components) and grain quality (vitreous endosperm, high amylose, and good Tô properties) with a medium plant height, a thin pericarp, and non-anthocyanin plants. A grain yield (GY) ideotype was constructed that gave priority to grain yield when a conflict was observed in any region between grain yield and grain quality. Additionally, a grain quality (GQ) ideotype was constructed that gave priority to grain quality. Lastly, a third ideotype (GYQ) tried to balance the contributions of both grain yield and grain quality.

<figure><img src="image_89.png" /><figcaption>Figure 4</figcaption></figure>

Figure 4 - QTLs and Ideotypes Along the 10 Chromosomes of the Genome of P114

## QTL Pyramiding by Recurrent Selection

Cycles of three recurrent selection processes of P114, conducted from September 2011 to March 2013, were achieved (See Figure 5). At each cycle, approximately 700 plants were genotyped on the flanking markers of QTL that were included in genotypic ideotypes. Genotypic data were first analyzed to check for consistency across generations and to track pedigree errors from one cycle to the next. From the genotypic data produced at each cycle, genetic values were computed for all possible crosses between all available plants. The genetic values were computed for several QTL indices and for different ideotypes. All computations were performed by Syngenta (Toulouse, France) using proprietary software. A list of best candidate crosses was determined from these genetic values.

Each single selection cycle represented a great challenge because all
operations (i.e., leaf sampling, DNA extraction, genotyping, and data analysis) needed completion before flowering. This required a harmonization of field, laboratory, and geneticist teams' efforts.

<figure><img src="image_90.png" /><figcaption>Figure 5 – MARS Project Population Development QTL Accumulation Process</figcaption></figure>
Various numbers of crosses were produced in different selection cycles (See Table 2). Crosses made during the warm off-season (i.e., between March and June) struggled to obtain appropriate flowering time among parents. Globally, about 450 families were produced by MARS for P114, either by crossing or selfing progenies. Figure 6 shows the pedigree of the 20 C3 families produced after 3 cycles, illustrating the level of intercrossing that occurred during the whole process. At each cycle of recurrent selection, many more families were produced, but only those that contributed to the final cycle are represented here.

<table><caption>**Table 2**
Number of Progenies and SNPs Analyzed and Number of Crosses and Produced at Each Recurrent Cycle in P114</caption><thead><tr><td>Cycle</td><td>Date</td><td>Nb of plants</td><td>Nb of SNPs</td><td>Nb of Crosses</td><td>Nb of Selfing</td></tr></thead><tbody><tr><td>C1</td><td>09/2011</td><td>724 F4 (20 families)</td><td>140</td><td>130</td><td>105</td></tr><tr><td>C2</td><td>01/2012</td><td>647 C1 (23 families)</td><td>140</td><td>125</td><td>70</td></tr><tr><td>C3</td><td>03/2013</td><td>558 C2 + C1 (13 C1 + 19 C2 families)</td><td>150</td><td>20</td><td>60</td></tr></tbody></table>

<figure><img src="image_91.png" /><figcaption>Figure 6 - Pedigree of the C3 Families of P114 MARS</figcaption></figure>
The material obtained through MARS was considered as pre-breeding material enriched with favorable QTL. The QTL involved in this process represent genetic factors that were identified and evaluated in experimental conditions, during several years, and in varying locations. The set of QTL involved in the genotypic ideotypes can be considered as a "must-have". Whereas a significant part of the genetic variation was not explained by the QTL which was still segregating in the populations. For this reason, we developed many different families that accumulated positive alleles at QTL but that were globally very diverse. In addition, early MARS families consisted of non-fixed material (because they resulted from a cross) but needed to be fixed toward pure lines. These families were further field evaluated, and 21 lines that showed good and stable performance were selected.

# Variety Registration

## 1. Agronomic Performances

Procedures for new variety registration in the Economic Community of West African States (ECOWAS), changed in 2009, were followed. The objectives were to guarantee users that every variety is perfectly identifiable and thus distinct, uniform, and stable (DUS) from other registered varieties. This DUS test was conducted in 2017 only at Sotuba. Variety must have a sufficient value for cultivation and use (VCU tests). VCU tests were conducted in Mali in 2016 and 2017, and a multi-location trial network was set up in collaboration with the National Committee for Plant-Based Seeds (CNSOV) at fifteen locations. The experiment included four MARS lines (C0-P114-160A-, C2_075-16, C2_075-15, and C2_007-03) and two registered elite checks (Grinkan and Tiandougou Coura). Sites chosen were between 750-950 mm isohyets in research stations and in farmer or seed companies' fields. Trials were conducted in randomized, complete block design (RCBD) with four-repetitions. The recommended sorghum population density was used (53,332 plants per hectare), and each plot comprised of 5 rows of 5 meters long, which corresponded to a net plot of 9 m². Chemical fertilizers (73 kg N/ha, 30 kg P/ha) were applied. Analysis of variance (ANOVA) was performed for each environment, and a combined ANOVA was undertaken
for all environments. Before pooling trials, Bartlett's test was used to assess the equality of variance across locations for each variable. Three trials were eliminated because of inequality of variances. Genotype x Environment (GxE) interaction was studied using the Additive Main Effects and Multiplicative Interaction Model (AMMI) (package 'agricolae') in the R environment (R_Development_Core_Team, 2008) and by the Finlay-Wilkinson approach (Finlay & Wilkinson, 1963).

The model for the combined analysis across locations was:

$$Yijk = m + Ei + gj + (gE)ij + bik + eijk$$

* Yijk is the observation in the i^th^ environment of the j^th^ genotype, in the k^th^ block of the experimental design.

* m is the grand mean

* Ei is the effect of the i^th^ environment

* gj is the effect of the j^th^ genotype

* (gE)ij is the interaction of the j^th^ genotype with the i^th^ environment

* bik is the effect of k^th^ block in i^th^ environment

* eijk is the residual error

The GxE interaction (See Table 3) was not significant for grain yield, which means that despite very large environmental disparities, lines were similar across environments. In a previous study (Sissoko et al., 2018) comparing MARS short varieties with tall landraces, a very strong GxE interaction was detected. In their case, the strong GxE interaction was explained by the very great diversity among tested varieties. In the current variety registration trials, absence of GxE interaction meant that tested varieties were similar in morphology and maturity traits.

<table><caption>**Table 3**

**Combined Analysis of Variance for Grain Yield of 6 Sorghum Lines Tested in 9 Environments in 2011 in Mali.**</caption><thead><tr><th>Source</th><th>Df</th><th>Sum of Squares</th><th>Mean Squares</th><th>F Value</th><th>P Value</th></tr></thead><tbody><tr><td>Environment (E)</td><td>8</td><td>117861282</td><td>14732660</td><td>16.19</td><td>0.000</td></tr><tr><td>Block b(E)</td><td>27</td><td>24575733</td><td>910212</td><td>4.54</td><td>0.000</td></tr><tr><td>Genotype (G)</td><td>5</td><td>9351668</td><td>1870334</td><td>9.33</td><td>0.000</td></tr></tbody></table>
<table><thead><tr><th>Source</th><th>Df</th><th>Sum of Squares</th><th>Mean Squares</th><th>F Value</th><th>P Value</th></tr></thead><tbody><tr><td>Interaction (GxE)</td><td>40</td><td>8708531</td><td>217713</td><td>1.09</td><td>0.357</td></tr><tr><td>Residuals (e)</td><td>131</td><td>26273999</td><td>200565</td><td></td><td></td></tr></tbody></table>

The Finlay-Wilkinson approach is designed to investigate the GxE interaction (See Figure 7). The method consists of fitting for each genotype, a regression of grain yield according to environmental average yield (i.e., the mean response of all varieties in each environment). As the GxE interaction was not significant, the regression lines corresponding to each variety were almost parallel. The regression slope, approximating to 1.0, indicates an average stability, except for C2_007-03 and C2_075-15, which have a significantly lower slope of 0.84.

<figure><img src="image_92.png" /><figcaption>Figure 7 - Stability Analysis According to Finlay & Wilkinson (1963)</figcaption></figure>

Yields ranged from 1,000 kg/ha to almost 3,500 kg/ha. Table 4 presents the mean grain yield comparison of the six varieties tested. Three homogenous groups were identified. The yield of C2_075-16 was significantly higher than other varieties, including the two checks. The other three MARS varieties have a significantly higher yield than that of the Grinkan check.
<table><caption>**Table 4**
Mean Yield of Multi-location Trials Realized in Mali in 2016 for VCU.</caption><thead><tr><td></td><td>Mean Yield (kg/ha)</td><td>Homogeneous Groups</td></tr></thead><tbody><tr><td>Grinkan (check)</td><td>1809</td><td>c</td></tr><tr><td>Tiandougou Coura (check)</td><td>2039</td><td>b</td></tr><tr><td>C2_007-03</td><td>2150</td><td>b</td></tr><tr><td>C0_P114-160A-</td><td>2163</td><td>b</td></tr><tr><td>C2_075-15</td><td>2247</td><td>b</td></tr><tr><td>C2_075-16</td><td>2499</td><td>a</td></tr></tbody></table>

Note. The means followed by the same letter are not different by Fisher's least significant difference (LSD) procedure at the 5% threshold.

The technological quality of MARS lines was satisfactory. There were no significant differences among lines and controls in terms of Tô color and texture. The dehulling yield ranged from good (65%) to very good (> 75%). Additionally, MARS lines exhibited significantly lower vitreousness, only C0_P114-160A- showed a vitreousness comparable to checks. The 1,000-kernel weight of the highest-yielding variety (C2_075-16) was significantly less than those of the checks.

## 2. Climate Adaptation

Methodology for studying adaptation to climate change was presented by Sissoko et al. (2018) and Kouressy et al. (2020) using CERES approach. In this approach, a study of photoperiod sensitivity allowed the calculation of the model's phenological parameters. Then, the model's predicted areas where the varieties could be grown at minimal biotic and abiotic risks.

### 2.1 Phenological Study

A trial with three sowing dates was used for P114 derived lines/varieties and checks in the phenology study (See Figure 8) under different photoperiod conditions. Phenology was determined by the date of flag-leaf ligulation. In sorghum, this observation is more accurate and repeatable than the scoring
of the average flowering date commonly practiced in agronomic experiments (Tarumoto et al., 2003). A split-plot design in two replicates was used. The main factor was the date of planting, and the secondary factor was variety. The sowing dates were June 21, July 20, and September 15. The first two sowing dates were used to determine lines behavior in a farmer's normal sowing period (i.e., long and intermediate photoperiod), while the third date (i.e., off-season planting) was to study the short photoperiod effect. A drip irrigation system was used to ensure lines development without water deficit.

<figure><img src="image_93.png" /><figcaption>Figure 8</figcaption></figure>

Figure 8 – Flag Leaf Ligulation from Planting (SFD in days) According to the Sowing Date of Six Varieties Studied in Sotuba, Mali in 2015

## 2.2 CERES Model Coefficient Calculation

Varietal responses to photoperiod were modeled using the sorghum linear CERES model (Alagarswamy & Ritchie, 1991; White et al., 2015). This model was based on a linear adjustment between photoperiod and vegetative phase length (Chantereau et al., 2001; Sanon et al., 2014). After emergence, the shortest thermal time required to reach panicle initiation was known as the Basic Vegetative Phase (BVP). During this phase, floral induction could not
occur, no matter the photoperiod conditions. The CERES model considered
that below a critical photoperiod (P2O), the vegetative stage duration was
constant and equal to the BVP. Above the P2O, the vegetative stage duration
increased as a linear function of a photoperiod, whose slope, P2R, defined
the photoperiod-sensitivity in degree days per hour of the photoperiod
increase (°Cd/h). A modified CERES model version was used (Folliard et al.,
2004) whereby photothermal time accumulation was replaced by a critical
photoperiod threshold (i.e., varying on plant age) below which sorghum
panicle initiation occurred. The three model parameters (P2O, P2R, and BVP)
were calculated using a simple method presented by Guitton et al. (2018).

## 2.3 Delineating Optimal Cultivation Areas

The method used comprised of identifying areas where a variety could be
sown during a normal planting period to minimize biotic and abiotic risks.
The optimum cultivation area of a variety was determined by combining
information on photoperiod sensitivity, climatic variability, and a farmer's
practices (e.g., early and staggering sowing dates; Soumaré, 2004). For each
rainfall station in Mali, the onset and end of the rainy season was established
using a simplified water balance model (Traoré et al., 2011). The flowering
date was predicted using the CERES model. The difference (in days) between
the predicted flowering date and the end of the rainy season gave an
adaptation index, which was calculated for each variety based on the normal
1981-2010 Malian weather stations' data. A geographical information system
(Surfer® 14, Golden Software, LLC) was used to delineate areas for which the
adaptation index was between -20 and 0 because an adapted variety, which
flowered in 20 days preceding the end of the rainy season, was considered
(Kouressy et al., 2008a).

Spatial distribution of the adaptation index used a model of linear
interpolation by kriging. This action was repeated for each variety,
considering two sowing dates. A first sowing date was simulated immediately
after the rainy season installation and a second sowing date was delayed by
one month. This duration, period of sowing, corresponded to the general
practice observed in farmers' fields.

Adaptation zones of the four studied varieties are presented in Figure 9.
The delay in sowing caused a shift of the adaptation zone to the south.
An overlap of the two zones (in purple), due to photoperiod-sensitivity, corresponded to the optimum growing zone in which variety cultivation is possible within farmers' sowing periods.

This plant modeling approach revealed merging genetic information, environmental conditions, and sowing practices for predicting crop resilience to climate change scenarios. All optimal growing areas included the Bamako region, for which they were developed. Variety C2_007-03 was slightly more sensitive to photoperiod than the other three varieties. Therefore, its flowering dates were less affected by sowing date and the overlap of the two zones was more important.

<figure><img src="image_94.png" /><figcaption>Figure 9 - Delineation of Optimal Growing Areas of Four MARS Varieties</figcaption></figure>
Conclusion

Marker-assisted recurrent selection (MARS) has provided a way to sustainably raise sorghum productivity in future cropping intensification systems. The MARS project is an example of a successful integration of methodology, tools (quality control, genetic analyses, and decision support), services (genotyping), and capacity building into the Malian national sorghum breeding program. The implementation of this approach represented a significant change in conceiving and conducting sorghum breeding, which required qualified support staff and laborers. MARS application improved breeding efficiency because it allowed the rapid and simultaneous accumulation of many traits. Molecular approach alone is not sufficient to produce the end results. Close coordination between field teams and geneticists is necessary. To be successful, the approach must necessarily be trans-disciplinary, including crop physiology and modeling, quantitative genetics, genomic prediction, and breeding. Moreover, after QTL pyramiding, breeding continuation through classical genealogical selection allowed integration of traits not previously considered by genetic analysis. This work is a step towards creating varieties that are resilient to climate change and suitable for sustainable intensification. These two concepts are closely related, as improved climate risk management will provide the foundation for sustainable intensification. The end product of the MARS project was the registration of four elites in national and regional catalogs (Figure 10).
<table><caption>Figure 10</caption><tr><td>Fig.10a - C2_007-03 named Wilibalis</td><td>Fig.10b - C2_075-15 named Wassalen</td></tr><tr><td>Fig.10c - C2_075-16 named Falenba</td><td>Fig.10d - P114-160 named Tinsan Yerekelen</td></tr></table>

Figure 10 - Registered Varieties and Equivalent Names

Acknowledgements

This work was made possible thanks to funding from The Generation Challenge Programme (GCP) and from Syngenta Foundation for Sustainable Agriculture (SFSA). The authors would like to thank CIRAD and Syngenta for its technical contributions, the Institut d'Economie Rural (IER) for providing human and research facilities, the NGO CAAD from Koutiala, Camara Seed Compagny, Comptoir 2000sa, Dounka Fa seed company, as well as the producers of Kafara, Ouré (Bougouni), Kolonto, N'Goutjina, and M'pessoba (Koutiala) villages for their participation to the VCU tests and la Direction Nationale de l'Agriculture (DNA) for seed registration.
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ENDNOTES

<footnote>[1] †Died July 07, 2021</footnote>
18. Genetic Improvement of Pearl Millet in Senegal: Past, Present and Future Prospects

**Safiétou Tooli Fall**, Colorado State University, USA & ISRA/CNRA, Senegal &
ISRA/CERAAS, Senegal

**Ghislain Kanfany**, ISRA/CNRA, Senegal

**Oumar Diack**, ISRA/CERAAS, Senegal

**Desalegn D. Serba**, USDA, USA

**Yagouba Diao**, ISRA/CNRA, Senegal

**Ousmane Sy**, ISRA/CNRA, Senegal

**Amadou Fofana**, ISRA/CNRA, Senegal

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

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

Abstract

Pearl millet is an important cereal crop for smallholder farmers' food security in Senegal and is grown on approximately 800,000 ha of land with a total national production of 600,000 tons annually. However, its production has been affected by increasing biotic and abiotic stresses such as downy mildew disease, drought and heat, and use of unimproved varieties in farmers' fields. Genetic improvement of pearl millet started in Senegal nearly a century ago and experienced three major phases that were defined by different improvement targets. Over time, Progressive national yield improvements have been made. This chapter summarizes the breeding progress of the Senegal national breeding program in the past century. It also formulates future directions for further improvement of the productivity of pearl millet to meet the increasing national demand for food and nutrition security in the midst of climate change. This chapter posits that an ambitious pearl millet improvement strategy that integrates the modern genetic and genomic tools
and novel genetic resources for resistance to biotic and abiotic stresses with
variety development work is urgently needed to revolutionize pearl millet
production in Senegal.

Keywords: pearl millet, drought adaptation, genetic improvement, Senegal

Introduction

Pearl millet [Pennisetum glaucum (L.) R. Br. syn. Cenchrus americanus (L.)] is a diploid (2x = 2n = 14) C4 and allogamous crop that belongs to the family of Poaceae (Bono & Leclercq, 1963). Archaeological and genetic findings have inferred PM was domesticated about 4,900 years ago in the western Sahara belt, more precisely in Northern Mali (Burgarella et al., 2018; Manning et al., 2011; Oumar et al., 2008).

Pearl millet is currently the sixth most important cereal crop in the world
following maize (Zea mays), rice (Oryza sativa), wheat (Triticum aestivum),
barley (Hordium vulgare), and sorghum (Sorghum bicolor) based on harvested
area (FAOSTAT, 2019). It accounts for more than half of global millet
production and is mainly cultivated in West Africa and India for household
food security and income generation, whereas it is cultivated mostly as
forage in Brazil, the United States, Mexico, and Australia. West Africa and
India cover more than 95% of its global production (FAOSTAT, 2019). India
is the largest pearl millet producer, followed by West African countries
including Nigeria, Niger, Burkina Faso, Chad, Mali, and Senegal.

In Sub-Saharan Africa, pearl millet was historically grown by farmers for
their basic subsistence. Now, it has also become a commercial crop and the
main source of income and nutrients in the most drought and heat-stressed
production areas. It is one of the rare cereals of traditional farming systems
that is able to withstand frequent and prolonged periods of drought and
high temperature in saline soils, at both the vegetative and reproductive
phases, which would cause crop failure or greater yield reduction in other
cereals (Dussert et al., 2015; Serba et al., 2017). Compared to some other
domesticated cereals, pearl millet shows a large genetic diversity and an
exceptional potential of adaptation to a wide range of ecological conditions,
especially drought, across the semiarid areas (Sehgal et al., 2015; Serba et al.
2019).
Another major asset of pearl millet is its potential to relieve malnutrition in developing countries. Food insecurity remains the major issue to be solved in Sub-Saharan Africa, where 22% of the population is severely food insecure (ANSD, 2018). Despite improvements over the last decade, the current food insecurity situation in Senegal remains critical, and 16.5% of the population was undernourished in 2018 (ANSD, 2018). The food demand will increase dramatically in the coming years following continued population growth, with the projected West African population (920 million in mid-2014) doubling by 2050. The Senegalese population is expected to reach 40 million by 2050 (FAOSTAT, 2019). Pearl millet significantly helps mitigate malnutrition, as it is more nutritious than maize, rice, wheat, and sorghum because its grain contains higher levels of protein, vitamins, amino acids, antioxidants, fibers, and essential micronutrients (Saleh et al., 2013; Serba et al., 2017). Additionally, this crop has a high potential for transformation into value-added products. Pearl millet grain is used to prepare many traditional foods and is consumed in both rural and urban areas in many forms, such as porridges, gruels, doughs, couscous, and breads. It is also used for forage purposes.

All actors in the Senegalese pearl millet value chain recognized the need to develop strategies to substantially enhance grain yield. Therefore, the breeding program is under time pressure to act as a bridge between the market demand and the crop productivity and should rapidly adopt an efficient breeding strategy to accelerate the improvement of new varieties with better resilience to climate variability in particular drought stress. This chapter highlights the progress in pearl millet breeding in the last several decades, identifies research gaps, and formulates actionable future strategies for improvement of this nationally important cereal crop, particularly in the context of climate change.

Status and Constraints for Pearl Millet Cultivation in Senegal

Pearl millet is the main staple cereal crop in Senegal, with approximately 800,000 ha of growing area, occupying about 60% of the crop area harvested (FAOSTAT, 2019). It is mainly cultivated in the central, southern, and south-eastern parts of the country, more specifically in the Fatick, Kaffrine,
Kaolack, Diourbel, Thies, Tambacounda, and Kolda regions (Figure 1). In 2019, its production in these regions represented more than 80% of total national production (ANSD, 2020). As the populations living in these regions depend particularly on pearl millet production for their livelihoods, improved production technologies are highly sought.

<figure><img src="image_95.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 – Distribution of Pearl Millet Production Areas in Senegal During the Rainy Season of 2020

The total national production of pearl millet was 574,000 tons, which accounted for at least 33% of the total cereal production, behind only rice (44%) in 2018 (FAOSTAT, 2019). These ratios among different crops change over years depending on the financial support received for different grain crops (Figure 2A). Pearl millet has occupied the largest area harvested among the cereal crops, followed by rice, sorghum, and maize over the years (Figure 2B). However, the average grain yield remains very low and unstable, never reaching the threshold of 1000 kg ha⁻¹ (Figure 2C). By comparison, pearl millet yields in India are 1,243 kg ha⁻¹ for the same year (FAOSTAT, 2019). The adoption of hybrids has led to a significant improvement in productivity in India. The first public system for robust hybrid selection of pearl millet was established in the 1960s. This system has also been supported by the private sector and has facilitated significant improvement of productivity in the country with an average increase in grain yield of 24 kg/ha/year
between 1996 and 2012 (Yadav & Rai, 2013). In 2006, over 70 hybrid varieties covered 60% of the cultivated area of pearl millet in India and these hybrid varieties were mainly developed by the private sector and consisted of more than 80% of varieties used at that time (Karandikar et al., 2018). In contrast, no hybrid variety was cultivated in Senegal.

<figure><img src="image_96.png" /><figcaption>Figure 2a</figcaption></figure>

Figure 2a – Dynamics of Cereal Production by Crop in Senegal from 1961 to 2018- (A) Total Production of Cereals (tons)
<figure><img src="image_97.png" /><figcaption>Figure 2b</figcaption></figure>

Figure 2b – Dynamics of Cereal Production by Crop in Senegal from 1961 to 2018– (B) Area Harvested (ha)

<figure><img src="image_98.png" /><figcaption>Figure 2c</figcaption></figure>

Figure 2c – Dynamics of Cereal Production by Crop in Senegal from 1961 to 2018)– (C) Grain Yield (kg ha-1)
In addition to the absence of hybrid varieties in cultivated areas, growing unimproved varieties has been the major constraint of pearl millet grain productivity (Kanfany et al., 2020). In 2009, only a few improved varieties were cultivated in 34.5% of the national production area. The varieties Thialack 2, Souna 3, and IBMV 8402 covered only 16.5%, 14%, and 4% of the area, respectively (Walker, 2015). In addition, various biotic and abiotic stress conditions, including *Striga* weed (*Striga hermonthica*), low soil fertility, drought, and insect pests are limiting factors for yield. Moreover, lack of machinery for sowing, disease and bird damage, limited access to certified seeds of high-yielding varieties, and cultivable land all affect production of pearl millet in Senegal (Kanfany et al., 2020). Meanwhile, significant improvement has been made for rice and maize, with an increased productivity of 66% and 282%, respectively, mainly due to the use of higher-yielding varieties and pertinent crop management practices to alleviate biotic and abiotic stresses.

In recent years, the reduction in rainfall volume and the short period of rainfall during the growing seasons have negatively impacted the performance of pearl millet in Senegal (ANSD, 2020). As farmers usually plant pearl millet while waiting for or in response to the first rain of the year, the crop can face severe drought stress in early stages. It is proven that drought stress at an early stage significantly reduces the plant height, biomass, and total grain number and weight in pearl millet inbred lines (Debieu et al., 2018). Early ending of the rainy season can also lead to terminal drought episodes at the grain filling stage, especially for late-maturing varieties.

According to the current climate models used in Saharan Africa, including Senegal, more inter- and intra-annual variability in the rainfall amount and distribution is expected (Brown & Lall, 2006), increasingly threatening pearl millet productivity in the country. Several studies have predicted that changed climatic conditions in Sub-Saharan Africa will lead to significant and increasing yield losses for major crops by the 2050s if cropping systems do not adapt. The associated yield losses are forecast at 17% for millet (Schlenker & Lobell, 2010). The Groundnut Basin agro-ecological zone is the most important part of the national pearl millet production area, but it is characterized by a Sudano-Sahelian climate and will be particularly affected by this climate variability.
# Past Attempts of Genetic Improvement and Achievements

## 1. Phase 1: Exploitation of the Local Germplasm for Grain Yield Improvement

The pearl millet improvement program started in Senegal in 1931 at the National Center for Agronomic Research (CNRA) in Bambey. There are two ecotypes of pearl millet traditionally grown in Senegal based on life cycle length: the short cycle ecotype (65–90 days) called ‘Souna’ and the longer cycle (120–150 days) called ‘Sanio’. Souna morphotypes flower from 50 to 60 days after planting and are adapted to ecosystems with low and irregular water supplies (350–600 mm annually rainfall). They represent 85% of total millet production and cover the most important part of the nationwide production area in the north and central regions (Diack et al., 2017). Sanio morphotypes flower from 80 to 110 days after planting and are mainly cultivated in the southern region, receiving more important rainfall (900–1200 mm).

During the first phase of the breeding program, the selection was mainly focused on producing inbred lines using pedigree selection from the two ecotypes that were collected locally across the country (Fofana, 1987). This approach led to the development of 113 inbred lines by 1949. However, due to the cross-pollinated nature of pearl millet, these lines lost vigor (Jones & Mangelsdorf, 1925). For instance, one of the local populations gave 20% more grain yield when compared to the performance of the inbred lines selected from this population (Fofana, 1987). Bono & Leclercq, 1963 realized that this breeding method could not be directly used since it only allowed the development of interesting, inbred lines. However, when the inbred lines were crossed with local populations, the top-cross hybrids produced up to 147% yield compared to the local population. By 1959, 28 inbred lines were selected and evaluated for overall performance in three consecutive years based on uniformity, yield performance, and yield component traits. The results showed that even if the homogeneity was significantly improved, the grain yield of these lines was not better than farmers’ cultivars (Fofana, 1987).
Thus, pedigree selection breeding was judged as an unsuitable tool for pearl millet yield improvement.

In 1961, the national breeding program shifted from pedigree selection to recurrent selection using five local populations: three early (Souna) and two late (Sanio) pearl millet ecotypes (Etasse, 1965). The varieties, derived from the late populations, showed low performance in the first generation of recombination and selection was focused on the early maturing populations. The best progeny was selected from the three early populations and combined to create a synthetic variety named Souna 2. This variety showed high grain yield potential with a 107% and 121% yield increase compared to landraces in the first and second generation of recombination, respectively. However, the performance in farmer's fields was still variable, mostly lower than local varieties in farmers' fields (less than 2 t/ha). This variety was further improved by top-crossing with a local population (PC28)s using Souna 2 as the tester, resulting in the selection of eight elite lines from this population. The eight lines were combined in 1969 to form Souna 3, the first variety released by the breeding program (MAER, 2012). This synthetic variety was proposed for cultivation in the central and southeastern part of the Groundnut Basin agro-ecological zone (Table 1). This variety has a compact and cylindrical panicle, a resistance to smut and downy mildew, and a grain yield potential of 2.5 to 3.5 t ha⁻¹ It has since been one of the most cultivated varieties in Senegal.

<table><caption>**Table 1**
Varieties Released for Pearl Millet Cultivation in Senegal</caption><thead><tr><td>Variety Name</td><td>Type</td><td>Year of Creation</td><td>Year of Release</td><td>Maturity Cycle (days)</td><td>Potential Grain Yield (t ha<sup>-1</sup>)</td></tr></thead><tbody><tr><td>Souna 3</td><td>Synthetic</td><td>1969</td><td>1972</td><td>90</td><td>3.5</td></tr><tr><td>IBV 8001</td><td>Synthetic</td><td>1980</td><td>1987</td><td>80</td><td>3.4</td></tr><tr><td>IBV 8004</td><td>Synthetic</td><td>1980</td><td>1987</td><td>75</td><td>2.6</td></tr><tr><td>IBMV 8402</td><td>Synthetic</td><td>1984</td><td>1987</td><td>80</td><td>2.5</td></tr><tr><td>ISMI 9507</td><td>Synthetic</td><td>1995</td><td>2010</td><td>85</td><td>2.5</td></tr><tr><td>Gawane</td><td>Composite</td><td>2006</td><td>2010</td><td>85</td><td>2.5</td></tr><tr><td>Thialack</td><td>Composite</td><td>2008</td><td>2010</td><td>95</td><td>3</td></tr></tbody></table>
<table><thead><tr><th>Variety Name</th><th>Type</th><th>Year of Creation</th><th>Year of Release</th><th>Maturity Cycle (days)</th><th>Potential Grain Yield (t ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td>2</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Souna Sine</td><td>Composite</td><td>2018</td><td>2021</td><td>90</td><td>3</td></tr><tr><td>Souna Saloum</td><td>Composite</td><td>2018</td><td>2021</td><td>90</td><td>3</td></tr><tr><td>Souna Baol</td><td>Composite</td><td>2018</td><td>2021</td><td>90</td><td>2.5</td></tr><tr><td>Taaw</td><td>Hybrid</td><td>2017</td><td>2021</td><td>75</td><td>3.5</td></tr></tbody></table>

Note. Data compiled from ISRA's variety fact sheets

## 2. Phase 2: Genetic Improvement of Harvest Index and Resistance to Drought

To achieve self-sufficiency through the improvement of harvest index in local populations, a multi-disciplinary pearl millet program involving several scientists was launched in 1970 (Fofana, 1987). This was one of the first initiatives to strengthen the breeding program after independence in 1960. Thus, 29 populations that were collected from the five main West African countries growing pearl millet were crossed with three dwarf parents sourced from the USA [23 D25 and 239 D2B and India (I 472)] (Fofana, 1987). This program developed two dwarf synthetic varieties, GAM 73 and GAM 75 (Table 1) from I 472 x HK 1133 and Tift 23 D2 x Aniata, respectively (Bilquez, 1975). These varieties performed well under irrigated and highly fertilized conditions but could not express their potential under regular farmers' field conditions.

From the 1970s, Senegal experienced a period of severe drought that occurred throughout the entire Sahelian region, affecting crop production considerably (Nouaceur et al., 2017). In addition, the improved varieties in Senegal, as in most West African countries, were not widely diffused throughout the production area due to large environmental variations (Spillane & Gepts, 2001). The multidisciplinary team also developed early maturing (60 and 75 days) synthetic varieties, which were evaluated for overall performance from 1977 to 1979. However, these early varieties
produced lower grain yield than Souna 3, even in the environments for which
they were developed.

In 1974, the Senegalese Institute of Agricultural Research (ISRA) was
established and started a new millet breeding program with the International
Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in 1977. This
millet improvement program was funded by the United Nations
Development Program (UNDP) with the main goal of diversifying the genetic
bases to develop new varieties that combine stress tolerance and high grain
yield potential. This effort made it possible to introduce several millet
accessions from India. However, these accessions were not adapted to
Senegalese growing conditions because they were matured too
early,susceptible to diseases and insects, had small panicle size, and
produced a similar grain yield as Souna 3. Besides the fact that the size of
the panicle has been correlated with the grain yield, the length of the panicle
is an important trait for the farmers related to the local practice during
harvest. Indeed, the panicles are grouped and tied in heaps after harvest to
facilitate transport.

Therefore, these accessions were crossed with local populations to
generate new breeding materials, leading to the release of three varieties
in 1987 (IBV 8001, IBV 8004, and IBMV 8402) to the central and northern
part of the Groundnut Basin agro-ecological zone (Table 1). These varieties
were early-maturing and tolerant to downy mildew (Gupta, 1986; Gupta &
Ndoye, 1991). In order to boost the national pearl millet production, a small
hybrid breeding program was initiated in 1982. Thus, several crosses were
made between the selected inbred lines and the landraces with CMS lines
introduced from ICRISAT in India. The best performing hybrid (ICMH 8413
from a cross of 81A x IBMI 8207) produced up to 50% more grain yield than
Souna 3 (Ndoye & Gupta, 1987). However, these hybrids were not widely
grown due to their downy mildew susceptibility, short panicle size, and
dwarf stature as farmers preferred tall cultivars for complementary forage
yield.

In the early 2000s, the West African National Agricultural Research
Systems (NARS), ICRISAT, coordinated with Réseau Ouest et Centre Africain
de Recherche sur le Mil (ROCAFREMI) and International Sorghum and Millet
Collaborative Research Support Program (INTSORMIL) regional networks
to characterize millet accessions collected from West and Central Africa
(Haussmann et al., 2006). Selected accessions were evaluated in regional trials to identify specific varieties that performed well within a country and/or across different agro-ecological zones in West Africa (Wilson et al., 2008). In Senegal, ICTP-8203 and GB-8735 were identified for the Sahelian zone, ISMI-9301 and ISMI-9305 for Sudano-Sahelian zone, and SOSAT-C88 and ICMV-IS-88305 for the Sudanian zone. These varieties increased grain yield up to 22%, on average, compared to the previously released varieties.

### 3. Phase 3: Current Genetic Improvement of Pearl Millet in Senegal: A Decade of Rapid Progress

Since 2007, millet genetic improvement in the National Pearl Millet Program has been financially supported by the West Africa Agricultural Productivity Program (WAAPP), a 10-year program funded by the World Bank and several other international funding programs. These funds offered new opportunities to the national program. Significant efforts have been made by the millet program to assemble and define a national core collection, improve local populations, train young scientists. Progress has also been made on strengthening collaboration with National Agricultural Research Institutes and Advanced Research Centers for Germplasm Exchange and Evaluation, integrating new tools, such as molecular markers and high-throughput phenotyping with conventional breeding, and defining breeding product profile.

Genetic diversity of the germplasm used in a breeding program determines the potential genetic gain achievable through selection (Ambati & Singh, 2017). The national breeding program has made a particular effort to understand, conserve, exploit, and manage the available genetic resources across the country. The national pearl millet collection was updated in 2014 for better representation of the different varieties covering the entire national territory, especially the late flowering varieties (Diack et al., 2017). This new collection contained 477 accessions, including 353 early flowering (Souna), 112 late flowering (Sanio) and 12 improved varieties. These accessions showed high genetic diversity (He = 0.55). This consistently resulted in a greater genetic diversity in Senegalese landraces compared to other regions of Africa and Asia and was surely linked to the proximity of the center of domestication of the crop (Hu et al., 2015).
To facilitate the exploitation of all this diversity in the breeding program, a core collection was established based on phenotypic and genotypic data (SSR markers), using an advanced maximization strategy with a heuristic approach (Kim et al., 2007). This core collection consisted of 91 accessions (22% of total accessions), including 31 Sanio and 60 Souna, that captured as much genetic diversity as possible from the collected genetic material. The assessment of this core collection's structure revealed two genetic pools (genetic differentiation between Sanio and Souna). Each pool was further divided into three phenotypic subgroups based on the targeted phenotypic traits, such as yield and flowering time. Phenotyping of this core collection revealed five traits, including two phenological traits (heading and flowering) and three morphological traits (biomass, plant height, and tillering) that could clearly differentiate the Souna ecotype from the Sanio ecotype (Diack et al., 2020).

These identified groups and subgroups constituted potential heterotic groups that could be exploited in the millet breeding program to improve the phenotypic traits associated with these differentiations. Heterotic groups are very important for hybrid breeding because they guide the choice of parental lines, optimize the use of genetic material, and maximize the performance of derived hybrids (Meena et al., 2017). The Senegalese breeding program recently initiated studies to identify and validate heterotic groups in the core collection for hybrid breeding as part of a USAID-funded project (Genetic Enhancement of Pearl Millet). This study identified the most representative genotypes of the core collection and produced F1s from crosses between these individuals. Evaluation of these F1s and their parents for general and specific combining ability is currently in progress.

As performance of hybrid lines can be predicted based on general combining ability, trait introgressions in recurrent selection schemes are more efficient using divergent heterotic groups than genetically similar heterotic groups (Reif et al., 2007). Evaluation of hybrids, generated by crossing 17 open-pollinated varieties (OPVs) in a diallel mating design in nine environments over two years in Niger and Senegal resulted in the identification of 5 potential parental lines (Nigerien CIVT, H80-10Gr, Taram and Senegalese Thialack 2, and Souna 3). These five parental lines have a high combining ability that can be used to initiate reciprocal recurrent selection (Sattler et al., 2019).
The combining ability and heterosis for grain yield and its associated traits (i.e., plant height, flowering time, panicle length and diameter, productive tillers, thousand grains weight, and panicle and downy mildew) were assessed by crossing 17 inbred lines with Sosat C88 and Souna 3. The results indicated that the inheritance of these traits was controlled by both additive and nonadditive gene effects although the contribution of the additive genes was greater (Kanfany et al., 2018a). Some inbred lines (IBL003-B-1, IBL091-1-1, IBL095-4-1, IBL110-B-1, and IBL206-1-1) showed positive General Combining Ability (GCA) effects on grain yield and negative GCA effects on downy mildew, flowering time, and plant height; therefore, they are good parents for breeding high-yielding open-pollinated varieties and hybrids (Kanfany et al., 2018b).

Crop breeding is now undergoing a paradigm shift with the development of modern approaches. These methods, such as marker assisted selection or genomic selection, are more accurate and less costly in time and effort and are a byproduct of next-generation sequencing (NGS). In an effort to understand the genomic diversity and population structure of Senegalese germplasm, all accessions in the core collection were characterized using Genotype By Sequencing (GBS). This yielded 21,663 polymorphic (biallelic) and high-quality Single Nucleotide Polymorphism (SNP) markers. Marker-trait association identified markers linked to the adaptive genes *PgPHYC* and *PgMADS11* that were involved in the variation of flowering time in millet (Diack et al., 2017). Eighteen SNPs and genes have been associated with flowering time, plant height, number of tillers, and biomass, with some of these genes acting as candidate genes for abiotic stress adaptation. These identified markers, after validation, will be useful for marker-assisted selection in the program.

Cost and time efficiency in breeding programs depend on the ability to identify parental lines at an early stage of the breeding scheme (Ambati & Singh, 2017). To this end, studies were carried out to identify donor parents for the traits of farmers' interest. Firstly, genotypes with desirable agronomic traits and stable resistance to downy mildew (DM) were identified in hotspot areas (Zoclanclounon et al., 2019). Then, inbred lines, derived from a collection of landraces originating from West and Central African countries, were evaluated in the Bambey and Nioro Research Stations under field conditions with spreader rows of DM, using SOSAT-C88 and 7042S as
resistant and susceptible controls, respectively (Kanfany et al., 2018b). Highly significant differences were observed among 101 lines tested for DM incidence and severity as well as agro-morphological traits, including plant height, flowering time, panicle length, and productive tillers. Based on DM incidence and severity of control varieties used, the testing lines were grouped into three categories: resistant (55, including SOSAT-C88), moderately resistant (16), and susceptible (30, including 7042S). Also, using DM resistance and plant height as the most discriminant factors, hierarchical ascendant cluster analysis grouped these entries into three clusters, confirming the previous categorization.

Conversely, the released Senegalese varieties (Gawane, ISMI9507, Thialack2 varieties, IBV8004, and Souna 3) were evaluated for resistance to stem borers and maize head miner (MHM) under natural infestation in the 2014 and 2015 cropping seasons (Goudiaby et al., 2018). Gawane was identified to be tolerant to stem borers and Thialack2 and ISMI9507 were identified to be resistant to MHM (Goudiaby et al., 2018). This effort to identify new sources of resistance to several diseases under Senegalese agro-ecological conditions provides new genetic resources for improvement of pearl millet for agronomic traits and resistance to DM, MHM, and stem borer. These new resources will be used in the national breeding program to combine into new varieties resistant to biotic stresses and with desirable adaptation traits.

All these efforts allowed the breeding program to select and propose four new pearl millet varieties for release in 2021, including three OPV (SL28, SL169, and SL423) and one top-cross hybrid. The OPVs were derived from local populations, which were improved through recurrent selection using the S1 method. The top-cross hybrid named TAAW was created by crossing a local population (SL175) to a male sterile parent sourced from ICRISAT (ICMA-177090).

# 4. Productivity Improvement Over the Different Phases

The average rates of yield gains from each of the three phases of pearl millet improvement in Senegal are listed in Figure 3. In the first phase (1961-1973), low average grain yield was observed throughout the country, mainly due
to drought stress in early stages and lack of adapted varieties (ANSD, 2020). Thus, the breeding efforts at that period were focused on the development of varieties adapted to specific agroclimatic zones. The breeding effort in the second phase (1974–2007) led to the release of several new varieties, with an increased yield of 2.06 kg/ha. The highest rate of productivity was achieved in the third phase (2008–2019), with a yield gain of 2.7 kg/ha/yr. However, the genetic gain through these three phases remains unknown.

<figure><img src="image_99.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 - Productivity Improvement Rates for Pearl Millet Productivity During the Three Phases of Pearl Millet Breeding in Senegal

Assuming that this continued increase in average grain yield has been achieved through genetic gain, it promises great potential for further improvement in millet yield, especially with the help of newly developed genomic tools. However, it should be considered that this rapid increase in grain yield may be caused by the combination and fixation of favorable alleles at major effect loci that underlie traits positively correlated with grain yield. Indeed, selection has been shown to generally fix favorable alleles at major effect loci faster than those at minor effect loci (Mayo & Hancock, 1981). In this case, it would be expected that the yield improvement be less significant in the coming years since it would be mainly driven by the fixation of favorable alleles at loci with minor effects and would depend more heavily on better management practices (Mayo & Hancock, 1981; Xu et al., 2017; Yeaman, 2022). Nevertheless, the breeding history described above and the current breeding pipeline which regularly brings genetic material from
the associated programs suggest that the large effect variants have not yet segregated into the breeding germplasm (Mayo & Hancock, 1981; Muleta et al., 2019). Therefore, more significant yield improvement through genetic improvement should reasonably be targeted.

## Future Genetic Improvement Strategies

Grain yield is unanimously considered the most important trait by farmers (Kanfany et al., 2020). Drought tolerance, adaptation to low soil fertility, grain color, disease and insect resistance, and early maturity are also important traits to be considered when farmers select a pearl millet variety to grow in their fields (Kanfany et al., 2020). Therefore, these should be the target traits for developing a variety that grows in Senegalese conditions. However, the order of priority for these traits varies from location to location. Incorporating farmers' preferred basic and value-added traits into the breeding product profile would certainly increase farmer acceptance of released varieties.

The breeding goal in Senegal is to release pearl millet varieties with high yields (15% increase in grain yield over Souna 3 and Thialack II), high iron contents (with > 40 ppm Fe), and better adaptation in the two agro-ecological zones (Table 2). The combined strategies described below should enable the National Pearl Millet Improvement Program to diversify its sources of germplasm and accelerate varietal development with better selection accuracy.
<table><caption>Table 2
Breeding Targets for the Two Agro-climatic Zones in Senegal Where Pearl Millet is Mostly Produced</caption><thead><tr><th>Breeding Targets</th><th>Target Agro-ecologies</th><th>Maturity (days) &amp; Rainfall</th><th>Resistance/ tolerance Required</th><th>Basic Traits</th><th>Value Added Traits</th><th>Breeding Goals</th></tr></thead><tbody><tr><td>High yielding, early maturing pearl millet OPVs for adaptation to Sahelian zone of Senegal</td><td>Target: Central and northern part of the peanut basin agro-ecological zone</td><td>Early maturity 70-90 days (350-600 mm/ annul)</td><td>Biotic stresses:<br>Downy mildew, millet head-miner Striga hermonthica Abiotic stresses:<br>Drought, Low soil fertility</td><td>2 tons/ha, \n<br>Downy mildew resistance\n<br>\n Head length > 40 cm<sup>3</sup>>Grain Yield of >2 tons/ha, Downy mildew resistanceHead length > 40 cm</td><td>High grain iron and zinc content. Striga tolerance Drought tolerance Head miner tolerance</td><td>40 ppm Fe<sup>3</sup>> 10% increase in grain yield over improved check (Souna 3) with >40 ppm Fe</td></tr><tr><td>Medium maturity, high yielding pearl millet OPV Soudanian zone of Senegal</td><td>Target: Southern part of the peanut basin agro-ecological zone</td><td>90 days\n \n (500-900 mm/ annum)<sup>3</sup>>Medium maturity/ >90 days(500-900 mm/annum)</td><td>Biotic stresses:<br>Downy mildew, head miner, striga hermonthica Abiotic stresses:<br>Drought, low soil fertility</td><td>2 tons/ha, \n<br>downy mildew resistance\n<br>\n Head length > 50 cm<sup>3</sup>>Grain Yield of >2 tons/ha, downy mildew resistanceHead length > 50 cm</td><td>High grain iron and zinc content. Striga tolerance</td><td>40 ppm Fe<sup>3</sup>> 10% increase in grain yield over improved check (Thialack II) with >40 ppm Fe</td></tr><tr><td>Early maturity, high yielding hybrids for adaptation to better endowed environments</td><td>Target: central and northern part of the peanut basin</td><td>Early 70-90 days / (350-800 mm/ annum)</td><td>Biotic stresses:<br>Downy mildew resistance, head miner Abiotic stresses:<br>Downy mildew resistance\n<br>\n Head</td><td>2.0 tons/ha\n<br>Downy mildew resistance\n<br>\n Head</td><td>High grain iron and zinc content.</td><td>40 ppm Fe<sup>3</sup>> 15% increase in grain yield over improved check</td></tr></tbody></table>
<table><thead><tr><th>Breeding Targets</th><th>Target Agro-ecologies</th><th>Maturity (days) &amp; Rainfall</th><th>Resistance/ tolerance Required</th><th>Basic Traits</th><th>Value Added Traits</th><th>Breeding Goals</th></tr></thead><tbody><tr><td>of West Africa</td><td>agro-ecological zone</td><td></td><td>stresses: Drought, low soil fertility</td><td>length > 50 cm<sup>1/2</sup> >Grain yield of > 2.0 tons/ha Downy mildew resistanceHead length > 50 cm</td><td>Striga tolerance Drought tolerance</td><td>(Souna 3) with >40 ppm Fe</td></tr></tbody></table>
# I. Towards Implementing Gonomics-enabled Breeding

In the current breeding scheme, phenotypic selection remains the major method used in Senegal. However, this approach may not be able to properly examine the hidden genetic variation available for complex agronomic traits, such as grain yield and drought resistance, as these traits typically have low heritability and are heavily influenced by environments (Wu et al., 2012). Moreover, phenotypic selection involves many selection cycles in multi-trials in different environments to accurately evaluate progeny performance, which not only needs large amounts of seed for replicated trials, but is also laborious, time-consuming, and costly. However, such tests may not be realized due to insufficient seed amount and financial support for resource-limited breeding programs of developing countries like Senegal. Furthermore, the current and predicted variation in rainfall amount and distribution in the Sahel zone has emphasized the difficulty to control genotype x environment (GxE) interactions.

In light of the challenge of increasing genetic gain with limited resources, the National Pearl Millet Improvement Program has considered making greater use of currently available genomic resources to help breeders make more informed decisions about crosses at early breeding stages. The availability of the pearl millet reference genome sequence (~1.79 Gb) and the reduction in the cost of high-throughput data acquisition offers the opportunity to strengthen the breeding pipeline (Varshney et al., 2017). Nevertheless, the choice of modern breeding tools and methods to integrate must be evaluated with the utmost caution, considering several factors, including the genetic architecture of the targeted traits. For instance, the results of simulations performed by Muleta et al. (2019) showed that there are specific conditions under which genomics-assisted recurrent selection (GARS) should be considered by small breeding programs, according to the genetic architecture of the trait. In fact, this study showed a lower cost per unit gain using phenotypic recurrent selection (PRS) compared to GARS for oligogenic traits in small new breeding programs, while GARS gave lower cost per unit gain than PRS with larger populations and polygenic traits. Thus, understanding the genetic architecture of target traits will guide the breeding program in adopting the most effective breeding pipeline to maximize genetic gain given available resources.
Genomics-assisted breeding that integrates advanced genomic tools with conventional breeding has been one of the main strategies for developing high-yielding crop varieties that are resistant to biotic and abiotic stresses. Over the past decade, the National Pearl Millet Improvement Program has focused on understanding the genomic diversity of available germplasm, identifying genetic variants of targeted traits, and uncovering the molecular and physiological mechanisms underlying the expression of these traits. Phenotypic variants identified for downy mildew, MHM, and drought tolerance and allelic variants (SNPs) identified for flowering time provide new genetic resources for the development of molecular markers linked to favorable alleles. Therefore, more accurate selection for these traits could be achieved using marker-assisted backcrossing to shorten breeding cycles (Bernardo, 2016). In the years to come, the National Pearl Millet Improvement Program will take advantage of new financial resources from the Sorghum and Millet Innovation Lab (SMIL), Innovation Lab for Crop Improvement (ILCI), and Desira-ABEE to set up a strong marker-assisted breeding program to accelerate the introgression of these genetic variants into elite lines to improve the genetic gain.

## 2. Breeding for Drought Adaptive Traits in the Senegalese Agro-ecosystem

Drought tolerance is a polygenic trait significantly influenced by the environment (Serba & Yadav, 2016). The development of drought-tolerant varieties is a challenging task that can be achieved through the introgression of adaptive traits that allow plants to grow and produce satisfactory yields under water-limited conditions. In some cases, complex traits, such as drought, can be more efficiently improved by targeting simpler and more strongly correlated traits with them (Breseghello & Coelho, 2013). Drought adaptive traits can be categorized into three main strategies: drought escape, dehydration avoidance, and dehydration tolerance (Basu et al., 2016). Drought escape allows plants to accelerate their life cycle and reproduce before drought occurs (Turner, 1896). This strategy is commonly used by annual plants although the yield is generally decreased (Lisar et al., 2016). Dehydration avoidance is a strategy for preserving the high-water potential of plants mainly by reducing water loss through transpiration and optimizing
water uptake from the soil by the root system (Basu et al., 2016). Because the mechanisms involved in this strategy consume additional energy, plant yield is generally decreased and the plants are small (Lisar et al., 2016). Avoidance is mainly achieved through traits related to root architecture, stomatal control, and transpiration efficiency (Basu et al., 2016). Dehydration tolerance involves mechanisms to maintain functionality in a water stressed environment (Turner, 1896). Dehydration tolerance involves traits such as cell remobilization of stem water-soluble carbohydrates, membrane stability and osmotic adjustment (Lisar et al., 2016). A drought tolerance cultivar can be defined as a genotype that detects water deficiency and effectively adopts combined strategies to grow and produce a satisfactory yield. Understanding the traditional cropping systems' response to water deficiency and identifying the valuable traits to be targeted are critical steps in breeding a drought tolerant cultivar.

Through evolution, traditional pearl millet landraces showed evidence of different mechanisms of adaptation to climate change, resulting from natural selection as well as from farmers' selection. On the one hand, the development of the root system of pearl millet is distinguished by a fast-growing primary root that rapidly colonizes the deeper horizons of the soil at an early stage (Passot et al., 2016). This trait allows access to the water available in a deeper soil horizon. Comparison of root development parameters in 16 inbred lines originating from Indian, West African, and Central African landraces showed significant variation in primary root length and lateral root density that could be exploited in subsequent breeding efforts (Passot et al., 2016). On the other hand, West African pearl millet farmers preferably cultivate high tillering landraces with small grains (Serba & Yadav, 2016). These landraces can produce secondary tillers and fill the grain very quickly. Since yield potential is related to the number of tillers and grains, among other morphological traits, this strategy allows them to efficiently improve yield under drought conditions. Finally, evidence is beginning to accumulate that flowering time is under selection in West Africa in response to rainfall variability. Flowering time directly correlates with annual rainfall (Saïdou et al., 2009).

Comparison analysis of pearl millet accessions collected in 1976 and 2003 in Niger showed a significant shift: varieties collected in 2003 flowered slightly earlier and had a shorter spike than in 1976 (Saïdou et al, 2009). This
response to selection for flowering time was significantly associated with polymorphisms in two genes PHYC and PgMADS11 (Mariac et al., 2011; Saïdou et al., 2009). These polymorphisms for early and late flowering alleles in these two genes were found in the landraces in Niger and were also more recently revealed in the Senegalese germplasm (Diack et al., 2020; Vigouroux et al., 2011). These findings suggest that drought escape is a main mechanism of adaptation to drought in West Africa. Understanding the genetic basis of these morpho-physiological traits associated with drought tolerance, in particular the flowering period, and then identifying the favorable alleles to introgress into the elite background are promising steps for the rapid selection of a drought-tolerant cultivar.

### 3. Reinforcement of Hybrid Breeding Strategies

In view of the urgent need for a rapid increase in productivity, it is crucial to establish a strong hybrid breeding program to focus on breeding high-yielding hybrid cultivars. Combining ability studies have clearly highlighted the prevalence of high levels of heterosis and potential to breed for hybrids with high and stable yield, as well as multiple disease resistance and abiotic stress tolerance in Senegalese environments. Previous studies showed the superiority of hybrid varieties over the best landraces for agromorphological and agronomic traits in West African pearl millet germplasm (Pucher et al., 2016).

Inspired by the success of hybrids in India, a collaboration has been initiated by millet breeding programs in several West African countries (Senegal, Niger, Burkina Faso, and Mali) as part of the SMIL project for the development of dual-purpose pearl millet hybrid lines with enhanced grain nutritional quality and stover digestibility. The aim of the SMIL project was to characterize genetic diversity, validate superior germplasm accessions, and identify genetic variants for traits related to stover quality and digestibility (i.e., grain mineral content, grain and stover yields) using 100 germplasm accessions collected from the related countries (Serba et al., 2020). Adoption of dual-purpose pearl millet hybrid varieties are expected to improve nutritional security of smallholder farming families, crop-livestock integration, and income generation.
## Conclusion

Pearl millet is the main cereal grown in Senegal. The traditional farming system has played a crucial role in creating and maintaining a high diversity among landraces. The Senegalese breeding program has invested a great deal of effort for nearly a century in collecting, maintaining, and more recently understanding this genetic diversity. In parallel, several varieties were derived from the local population using the conventional breeding approach. The regional and international partnerships developed have made it possible to enrich the genetic material and support all these selection efforts, resulting in a significant improvement in yield performance, especially in the last phase ranging from 2008-2019. This last decade was particularly marked by progress towards the implementation of genomics-based breeding and the strengthening of hybrid breeding strategies. Despite their yield performance, the varieties released so far do not seem to hold all the agro-morphological characteristics that lead to their high adoption by farmers. The high genetic variation found in Senegalese landraces that possess adaptive traits to the local environment, as well as traits preferred by farmers, is a promising resource for developing high-yielding crop varieties that are resistant to specific biotic and abiotic stresses. In addition, the future breeding strategies outlined in this chapter, and the more participatory breeding approach should enable the breeding program to improve genetic gain more effectively for farmers' priority traits. Further, they will contribute to addressing the challenge of sustainably improving pearl millet productivity for food security and income generation in Senegal.

## Acknowledgements

This study is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Collaborative Research on Sorghum and Millet through the United States Agency for International Development (USAID) under Cooperative Agreement No. AID-OAA-A13-00047. The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government.
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19. Breeding for Drought Adaptation and Fresh Seed Dormancy of Groundnut in Senegal: Advances, Challenges, and Prospects

Issa Faye, ISRA/CNRA, Senegal

Aïssatou Sambou, ISRA/CERAAS, Senegal

Gualbert Seraphin Dorego, ISRA/CNRA, Senegal

Daniel Foncéka, CIRAD & ISRA/CERAAS, Senegal

Hodo-Abalo Tossim, ISRA/CERAAS, Senegal

Amadou Ka, ISRA/CNRA, Senegal

Thierno Sarr, Gaston Berger University, Senegal

Ousmane Sy, ISRA/CNRA, Senegal

Abstract

Groundnut (Arachis hypogaea L.) is a multi-purpose legume widely cultivated in the semiarid tropics. In Senegal, groundnut is historically an important crop, and it remains one of the commodities with a huge potential for exports. However, due to the country's location in the Sahelian zone in West Africa, crop production is subject to low and erratic rainfall patterns and unpredictable end of the rainy season. Therefore, groundnut breeding programs were initiated early in the 20th century to develop and release drought-tolerant cultivars with fresh seed dormancy (to avoid rapid sprouting). This review highlights the significant advances made in these research areas to reduce yield losses and groundnut quality deterioration. Challenges concerning the effects of climate change on agricultural production, which will keep increasing over the coming decades, are also
addressed herein. Finally, we provided some insights on integrating the new
breeding tools and methods such as high-throughput phenotyping, marker-
assisted and genomic selection to breed groundnut varieties that are more
adapted to the agro-ecologies in Senegal.

Keywords: groundnut, drought, stress, seed dormancy, cultivars, Senegal

Background

In Senegal, the agricultural sector accounts for more than 9.8% of the gross domestic product (ANSD, 2022), with groundnut being the most important cash crop. Groundnut crop also plays a central role in the development of the rural economy. It employs nearly 87% of the working population, positioning Senegal among the 10 largest world producers. However, groundnut productivity in Senegal (1.46 kg ha⁻¹) is still less than the world average yield, estimated at 1.69 kg ha⁻¹ (FAOSTAT, 2020).

Drought stress is the foremost limiting constraint of sub-Saharan African countries' agriculture (Burke et al., 2010; World Bank, 2015). If one were to estimate the influence of different types of deficiency on the balance of world agricultural production, lack of water would appear to be one of the most important limiting factors. This statement is particularly true in countries located in Sub-Saharan Africa. In addition, there is still a dynamic increase in these countries' populations (Guengant & May 2013) posing an additional challenge for local agriculture, which remains the main source of income for 90% of workers and provides more than 50% of export earnings. Thus, it becomes imperative to enhance crop productivity by increasing yield to provide sufficient food to that increasing population. Although investigations in water resources management to deal with water scarcity have been extensively conducted, drought stress remains the most important limiting factor for agricultural productivity.

Lack of fresh seed dormancy can lead to heavy crop sprouting and yield loss, particularly when terminal drought spells occur. In addition, it may be associated with heavy aflatoxin contamination as the causal fungus, *Aspergillus flavus*, can invade sprouting pods easily during germination compared to intact seeds (Martin et al., 1999). Therefore, as an important agronomic trait, fresh seed dormancy contributes to groundnut-based food
safety I it may prevent kernels from being contaminated by aflatoxin. In
general, cultivars belonging to the subspecies *Arachis hypogaea* subsp.
*hypogaea* (Virginia and Runner types) have inherent dormancy, while those
of the *Arachis hypogaea* subsp. *fastigiata* (Spanish and Valencia types) are
extremely prone to sprouting because they usually lack fresh seed dormancy.
From a breeding perspective, a few Spanish-type varieties with fresh seed
dormancy have been developed from intersubspecific (i.e., Spanish x
Virginia) crosses. In Sub-Saharan African countries, where most groundnut
cultivars are of the Spanish type, fresh seed dormancy is a desirable trait to
offset in situ germination that may occur due to late rains after groundnut
maturity. However, until now, only a few Spanish varieties with fresh seed
dormancy have been released. Taaru was a recent Spanish-type variety with
deep fresh seed dormancy released by the ISRA groundnut breeding
program in Senegal (CORAF, 2018).

In this chapter, we highlighted the main achievements in developing and
releasing varieties adapted to drought-prone environments, including the
potential use of high-throughput phenotyping methods and recently
available groundnut genomic and genotyping resources in modern breeding
approaches for improving groundnut varieties' tolerance to drought stress.
We also discussed state of the art practices in fresh seed dormancy
phenotyping and genetics and pointed out the main achievements of
ICRISAT and ISRA breeding programs.

An Overview of the History of Groundnut Breeding at
ISRA

In Senegal, research in groundnut improvement started in 1898 when the
Bambey research station was a pilot site to serve as a research platform for
the French colonies in the Sudanian zone in Africa. Since then, groundnut
has remained an important cash crop in Senegal. Over decades, the variety
development was conditioned by the scenario of prevailing climatic
conditions.

**Before 1970:** There was no water deficit constraint. The cv. 55-437 and 47-16 were released and widely cultivated in the northern region of Senegal (Louga). These cultivars are both 90-day maturing varieties, but the cv.
55-437 is an erect type, while cv. 47-16 is a spreading type. In the central region (Diourbel, Thiès, and Kaolack), the 120-day maturing varieties 57-422 and 48-15 were the largest cultivated varieties.

**1971–1983:** This period corresponded to the beginning of the drought periods in all the Sahel zone. During the 1970s, the drastic reduction of the rainfall and its erratic pattern, particularly in the Northern region of Senegal, resulted in the replacement of the cv 47-16 by the cv. 55-437 and was the only variety recommended in the Northern region (Louga). Breeding programs established during this period created a new cultivar: cv. 73-30 was released for its drought tolerance and fresh seed dormancy (Bockelee-Morvan, 1983). The late-maturing cv. 57-422 (120 days) was replaced in this large production area by the newly released cv. 73-33 that matures in 110 days. The cv. 57-422 was cornered to a small area around Diourbel.

**1984–1996:** This period was characterized by the release of the cv. Fleur 11, a variety with high yield potential and drought tolerance. Because of its big seed size, it has been recommended for confectionery, particularly when produced under irrigated conditions, to prevent pre-harvest aflatoxin contamination. Meanwhile, a breeding program that focuses on short duration varieties was established and resulted in the release of cv. GC-8-35 (80 days) for the replacement of cv. 55-437 in the Northern region of Senegal, where the rainy season was shortening considerably in comparison to the rest of the country. The cv. 55-422 was almost replaced by the medium duration cv. 73-33 in the Central and Southern region of the groundnut basin.

**1997–2010:** During this period, the breeding program on earliness also resulted in the release of four cultivars, 78-936, 73-9-11, 55-33, and SRV1-19 in 2009. Now, the cv. GC-8-35 is replaced by cv. 55-33 because the latter produces a higher haulm while maturing in 80 days. Its geographical area is expanding to the Central region of the groundnut basin.

**2011–Present:** In 2004, we started a new breeding program using wild species as a donor of favorable alleles. With this breeding program, chromosome segment substitution lines (CSSLs) were developed, and six were released in 2017/2018. These lines have a high yield associated with bigger seed sizes (Tossim et al., 2020), particularly those named “Tosset” and “Rafet Kaar.” These cultivars have a 100-kernel weight (60 to 65g), much
higher than the recurrent parent, cv. Fleur 11. The remaining cultivars (Raw Gadu, Yaakar, Jambar, and Kom Kom) are high-yielding and show better yield stability in the Senegalese groundnut basin than Fleur 11.

Regarding fresh seed dormancy, a specific breeding program for this trait was initiated in 1997. It resulted in developing seven lines with deep fresh seed dormancy (Ndoye, 2001). However, it was only in 2018 that the line named “Taaru” was released for cultivation in Senegal for its good yield, deep fresh seed dormancy, and high oil content.

# Groundnut Improvement for Drought Adaptation

## 1. Description of the Different Drought Scenarios and Implications in Breeding

Senegal has a decreasing rainfall gradient, ranging from less than 474 mm in the Northern groundnut basin to more than 1034 mm in the Southern region (Table 1). The upper northern groundnut basin region is drier (less than 400 mm/year), and only extra-early maturing varieties with drought tolerance traits are recommended for cultivation there. Overall, the rainfall trend shows a neat decrease since the 70s, and more frequent and severe dry spells are noted, even in Southern Senegal. Severe dry spells occurring at an early stage of the groundnut cultivation, particularly in the Northern region, might have a significant effect on productivity. There are also dry spells at later stages of the growing season, which somewhat contribute to the gap between potential crop yield and on-farm production. Therefore, both intermittent and terminal drought stress need to be considered when selecting drought tolerant varieties in Senegal.
<table><caption>**Table 1a**
Average Rainfall, Rainy Season Duration, Dry Spells Frequency and Duration at Early and Late Stages in Selected Sites Across Agroecological Zones in Senegal Over the Period 1981–2010</caption><thead><tr><th>Agroecological Zone/Site</th><th>Average Rainfall (mm)</th><th>Growing Season Duration (days)</th><th>Occurrence of Dry Spells (lasting at least 10 days) at Early Stage</th></tr></thead><tbody><tr><td>North Groundnut Bassin- Bambey</td><td>487.4</td><td>74</td><td>18/40</td></tr><tr><td>North Groundnut Bassin- Diourbel</td><td>474.4</td><td>77</td><td>21/40</td></tr><tr><td>South Groundnut Bassin- Kaolack</td><td>610</td><td>99</td><td>13/40</td></tr><tr><td>Eastern region-Sinthiou Maleme</td><td>713</td><td>96</td><td>13/40</td></tr><tr><td>Southern region- Kolda</td><td>1034.3</td><td>105</td><td>14/40</td></tr></tbody></table>

<table><caption>**Table 1b**</caption><thead><tr><th>Agroecological Zone/Site</th><th>Duration of Drought Spells at Early Stage and Mean Standard Error</th><th>Occurrence of Dry Spells (lasting at least 10 days) at Late Stage</th><th>Duration of Drought Spells at Late Stage and Mean Standard Error</th></tr></thead><tbody><tr><td>North Groundnut Bassin- Bambey</td><td>10±1 (24)</td><td>13/40</td><td>9±1 (26)</td></tr><tr><td>North Groundnut Bassin- Diourbel</td><td>10±1 (22)</td><td>15/40</td><td>9±1 (21)</td></tr><tr><td>South Groundnut Bassin- Kaolack</td><td>9±1 (23)</td><td>30/40</td><td>12±1 (20)</td></tr><tr><td>Eastern region-Sinthiou Maleme</td><td>8±1 (16)</td><td>Dec-40</td><td>8±1 (14)</td></tr><tr><td>Southern region- Kolda</td><td>8±1 (15)</td><td>16/40</td><td>9±1 (17)</td></tr></tbody></table>
Note. Values between brackets are the maximum duration of dry spells observed (Source: Service d'Agroclimatologie, ISRA/CNRA, Bambey, 2010)

To overcome the severe drought episodes in the 1970s, groundnut breeding programs in Senegal focused on developing and releasing early and drought-tolerant varieties. Three major occurrences of drought were identified and characterized: early-stage drought (after emergence or early vegetative phase), intermittent drought (at flowering and pod development stage), and terminal drought (at pod filling stage), with all types being detrimental to yield and/or its components (Clavel et al., 2007; Rao et al., 1985). Three different strategies that are related to drought tolerance have been identified in plants, including legumes (Turner et al., 2001):

Drought escapers: genotypes belonging to this class are extra-early (75-80 days) or early (90 days) maturing varieties. This drought adaptation strategy is particularly important in dry regions with frequent end-season droughts. In the northern part of Senegal, where the rainy season lasts less than 90 days, extra-early maturing varieties have been recommended for cultivation. For instance, the variety GC-8-35 was released in 1996 to replace the variety 55-437, which was previously largely cultivated in this region. Cultivar 55-437 is a 90-days maturing cultivar that was no longer getting enough rain to mature in Northern Senegal at the end of the 1990s. The breeding programs released four other extra-early maturing lines (78-936, 55-33, 73-9-11, and SRV1-19). Except for line 78-936 (an introduction), all the other ones have been developed from crosses between Chico and Senegalese elite varieties (55-437 and 73-30). The varieties 55-33 and SRV1-19 were developed from crosses between 55-437 and Chico, using a backcross and a recurrent selection scheme, respectively. These extra-early maturing varieties have higher yield performances under drought stress because they avoid drought better than CG-8-35, thanks to their physiological and membrane resistance. A breeding program is ongoing to improve these extra-early maturing lines for higher (pod and haulm) yields associated with drought tolerance traits.

Drought avoiders: genotypes that can cope with drought stress using mechanisms limiting water loss. They have different physiological mechanisms to avoid drought stress: i) a deep root system for better water uptake). This trait is critical to avoiding drought, but its limitation is a weak correlation between measurements in controlled environments and the field.
With the availability of large-capacity lysimetric tubes that were recently developed at ISRA, one could investigate better the correlation of root growth parameters with physiological traits and yield under drought stress for many entries; ii) stomatal conductance (Gs) and transpiration rate (E) – in groundnut, intervarietal differences are difficult to observe even under severe drought stress. Likely, there is not much genetic variability in groundnut.

Drought tolerant: These plants would use osmotic adjustment – this mechanism has been extensively investigated in Senegal. Still, little or low genetic variability was observed between genotypes of the same phenology. Clavel et al. (2007) found that genotypic differences are greatest two weeks after drought onset.

Integrating drought adaptation traits (especially for avoidance and tolerance) in breeding programs requires high throughput phenotyping methods to screen large numbers of lines quickly.

## 2. Boundaries to Improve Genotypes for Drought Tolerance

Breeding for drought escape by reducing the growth duration of the varieties has been an important strategy adopted by breeding programs to tackle late water deficit stress and ensure good yields under end-season drought stress. However, breeding programs have to deal also with dry periods occurring during the plant's developmental stage since drought spells are frequent in the Sahelian zone. The most widely used approach to evaluate genotypes' adaptation to drought is an empirical approach that measures yield under water-limiting conditions after withholding irrigation for a given period during the developmental and reproductive stages to mimic drought spells (Clavel et al., 2007; Nigam et al., 2005). However, inconsistent climatic conditions across studies have led to contradictory statements. Besides, drought tolerance is a complex and polygenic trait.

Attempts to integrate physiological traits and yield in variety development for drought tolerance have resulted in limited success. For instance, low-cost, rapid, and easily measurable indicators for three significant
physiological features of drought tolerance - the amount of water transpired (T), water-use efficiency (W), and harvest index (HI) - were developed to assess drought tolerance in crops (Devi et al., 2010; Devi et al., 2019; Tuberosa, 2012). However, application in groundnut breeding programs has been limited because of practical difficulties associated with measuring these traits under field conditions in large breeding populations. Songsri et al. (2008) found high correlations between drought tolerance index (DTI), pod yield, root length density (RLD), and harvest index (HI), indicating that RLD contributed greatly to pod yield and HI under drought conditions. This association is relevant to groundnut because pods develop underground and adequate moisture in the root zone is critical for peg and pod development. However, phenotyping for root traits in field conditions and in a large population is quite challenging.

Drought tolerance can be enhanced by improving soil water extraction and/or water use efficiency. Therefore, we recently developed a good capacity for phenotyping using a lysimetric system and high-throughput phenotyping platforms to optimize the measurements of physiological and root traits under drought stress conditions. Besides, the high throughput phenotyping methods are paving the way to solving the issue of accurate drought phenotyping in breeding programs as they usually evaluate large populations in a short period of time. Our preliminary results in this research area are quite promising as significant correlations were found between measurements of leaf temperature and leaf area index using handheld devices and with cameras embarked on drones.

## 3. Genetic and Genomic Resources to Breed Drought-Tolerant Genotypes

### 3.1 *Collections*

The key to success for any breeding program is the availability of a large genetic diversity for economically important traits. Characterizing and identifying the sources of variability are important steps in the pre-breeding activities of any species. In groundnut, thousands of accessions are maintained in different countries, including India (15445 accessions), the
USA (9310 accessions), and China (7837 accessions) (Barkley et al., 2016). To dispose of an easily manageable and cost-effective germplasm set, it is recommended to establish a core collection (representing 10% of the overall collection) or even a mini core collection (10% of the core collection and therefore 1% of the overall collection). Thus, from the various collections kept in different countries, core collections of 1704, 831, and 576 accessions were developed and maintained respectively in India, the USA, and China (Pandey et al., 2012). From these core collections, mini core collections of 184, 112, and 298 accessions have been developed in India, the USA, and China, respectively (Jiang et al., 2010; Upadhyaya et al., 2002). Based on phenotypic data, geographic origin, and taxonomy, ICRISAT developed, as part of the Generation Challenge Program (GCP) initiative, a composite reference set of 300 genotypes (Pandey et al., 2012). Recently, in the framework of the Peanut Innovation Lab (PIL), a USAID Feed the Future initiative, ISRA has assembled a collection of 1049 accessions from nine countries in West, East, and Southern Africa, in partnership with breeders from national research programs of these countries. These accessions were genotyped at the University of Georgia (UGA) using the Affymetrix chip (45K SNP markers). The genotypic data have been made publicly available at https://www.peanutbase.org. From that germplasm, a subset of 300 accessions representing the whole genetic diversity in the entire collection was established using both the genotypic data and breeders' knowledge. That subset is characterized in all the nine germplasm-contributing countries for various traits, including yield, drought tolerance, and fresh seed dormancy. The phenotypic data generated will be used for association mapping studies to better understand the genetics and mechanisms involved in drought tolerance in groundnut.

Because of the narrow genetic base of groundnut, compared to other
crop species, wild species related to cultivated groundnut represent valuable
sources of useful alleles essential for groundnut improvement. Major centers
holding wild *Arachis* species collections include Texas A&M University (1200
accessions), U.S. Department of Agriculture (USDA, 607 accessions), North
Carolina State University, (NCSU, 406 accessions), Brazil EMBRAPA-
CENARGEN (1220 accessions), ICRISAT in India (477 accessions) and the
Instituto de Botánica del Nordeste (IBONE, 472 accessions) in Argentina
(Pandey et al., 2012). In addition, breeding programs worldwide have
developed different types of biparental populations, including recombinant
inbred lines, advanced backcross QTL lines, and chromosome segment substitution lines (Foncéka et al., 2012; Table 2). These genetic materials could be useful sources for identifying QTLs involved in drought tolerance using high-throughput genotyping and accurate and rapid phenotyping approaches.

<table><caption>**Table 2**
List of Main Interspecific Populations Derived from Cultivated and Wild Species of Groundnut</caption><thead><tr><th>Population</th><th>Lines/Types</th><th>Segregating Traits</th><th>Research Institute/University</th></tr></thead><tbody><tr><td><b>A. duranensis</b> × <b>A. stenosperma</b></td><td>87 RILs</td><td>Late leaf spot resistance, transpiration, response to drought stress, various aspects of plant morphology</td><td>EMBRAPA</td></tr><tr><td><b>A. hypogaea</b> cv. Fleur11 × (A. ipaënsis × A. duranensis)</td><td>81 BC4F7</td><td>Seed size and pod and haulm yield, disease resistance</td><td>ISRA (CERAAS), CIRAD</td></tr><tr><td><b>A. hypogaea</b> cv. Fleur11 × (A. batizocoi×A. duranensis)</td><td>133 BC2F7</td><td>Seed size and pod and haulm yield, disease resistance</td><td>ISRA (CERAAS)</td></tr><tr><td><b>A. hypogaea</b> cv. Fleur11 × (A. valida×A. duranensis)</td><td>300 BC2F7</td><td>Seed size and pod and haulm yield, disease resistance</td><td>ISRA (CERAAS)</td></tr><tr><td><b>Tifrunner</b> × C76-16, Florida-07 × C76-16</td><td>400 RILs</td><td>Drought tolerance and reduced PAC</td><td>USDA-ARS, UGA, NCSU</td></tr><tr><td>TAG 24 × ICGV 86031</td><td>318 RILs</td><td>Drought related traits</td><td>ICRISAT</td></tr><tr><td>ICGS 44 × ICGS 76</td><td>188 RILs</td><td>Drought related traits</td><td>ICRISAT</td></tr><tr><td>ICGS 76 × CSMG 84-1</td><td>176 RILs</td><td>Drought related traits</td><td>ICRISAT</td></tr></tbody></table>

## 3.2 Genomics Resources

Genomic resources are powerful tools to harness the genetic variation in germplasm collections for the faster generation of improved varieties. The groundnut scientific community has made a considerable leap from insufficient to abundant genomic resources in this area. Multiple
publications provide details on the available resources (Ozias-Akins et al., 2017; Pandey et al., 2012; Pandey et al., 2014; Varshney et al., 2014; Vishwakarma et al., 2017). All these tools and technologies are useful for basic and applied groundnut improvement research. In addition to the various molecular markers and genetic maps, the genome sequences of the wild diploid parents of cultivated groundnut (Arachis ipaensis and Arachis duranensis) and the tetraploid variety Tifrunner (Bertioli et al., 2016; Chen et al., 2016) are valuable resources. Sequencing the reference genome is one of the most important milestones for any crop species to accelerate the process of understanding genome architecture, gene discovery and molecular breeding for complex traits such as drought. Available groundnut genome sequences paved the way for the development and deployment of highly informative Affymetrix technology Single Nucleotide Polymorphism (SNP) chips "Axiom_Arachis" containing 58k and 48k markers (Clevenger et al., 2017; Pandey et al., 2017). Genome sequencing has also enabled the development of more simple sequence repeats (SSR) and insertions/deletion (INDELs) markers (Vishwakarma et al., 2017; Zhao et al., 2017). After completing the reference genome sequences, a great challenge for researchers is understanding the functions of the entire sets of genes. The gene expression atlas of groundnut provides detailed information on gene expression in different genotypes, organs, tissues, cells, and developmental stages (Pandey & Varshney, 2018). Such resources were developed by the University of Georgia using RNA sequencing data generated from 22 tissues (Clevenger et al., 2017). ICRISAT also reported the development of another gene expression atlas (AhGEA) using RNA-Seq data of 19 tissues from five stages of an early maturing, high-yielding, drought-tolerant groundnut variety, ICGV 91114 (Pandey & Varshney, 2018). These resources could be useful for conducting high-resolution trait mapping, marker development, candidate gene discovery, and molecular breeding for drought tolerance in groundnut.

Moreover, several QTLs are identified for drought tolerance traits, but their validation and application in marker-assisted selection (MAS) are challenging (Foncéka et al., 2012; Faye et al., 2015; Gautami et al., 2012; Ravi et al., 2011). These authors suggested that marker-assisted recurrent selection or genomic selection (GS) would be more appropriate in order to combine favorable alleles from so many regions. GS-assisted breeding has been tested in groundnut using a training population (TP) of 340 advanced breeding lines
followed by multi-seasons and multi-locations phenotyping for important
agronomic traits (Janila et al., 2016a). Currently, few successful examples of
molecular breeding products are available in groundnut (Chu et al., 2011;
Janila et al., 2016b; Varshney et al., 2014). With the greater development
of genetic resources and advanced genomic tools, the potential to develop
drought-tolerant cultivars is greater than ever. New emerging breeding
technologies and methods such as genomic selection and genome editing
are promising tools. Genetic engineering and genome editing have great
potential in improving groundnut for drought tolerance as such technologies
are not limited by ploidy and cross ability barriers. Several research groups,
particularly in the United States, China, and India, have developed transgenic
groundnut lines to resist viruses, insects, fungi, as well as for drought
tolerance, and grain quality. Brasileiro et al. (2014) reviewed the progress
achieved in Arachis genetic transformation. Tolerance was reported in
drought-susceptible groundnut variety JL24 by over-expressing the gene
DREB1A (Bhatnagar-Mathur et al., 2008). This transgenic event improved
groundnut yield through a complex molecular mechanism involving a
cascade of stress signaling and growth-regulating genes. However, in most
West African countries, particularly Senegal, limited by the availability of
research funds, suitable infrastructures, and regulatory approval, the use
of genetic engineering tools remains uncertain. In such conditions, crop
modeling is more applicable, especially for traits that are difficult to
phenotype, such as physiological traits.

Groundnut Improvement for Seed Dormancy

In the Sahelian zone, late rains after groundnut's physiological maturity can cause important yield losses (20 and 50%) in varieties lacking fresh seed dormancy (Gautreau, 1984; Khalfaoui, 1991; Ndoye, 2001). In addition, the sprouting of non-dormant varieties can increase aflatoxin contamination of kernels (Martin et al., 1999). Thus, there is a need to develop Spanish-type varieties with fresh seed dormancy to improve groundnut productivity and seed quality for areas prone to late rains.
# 1. Development and Release of Dormant Varieties

ICGV 86155, ICGV 86156, ICGV 86158, ICGV 87378, and ICGV 87921 Spanish groundnut (Arachis hypogaea L. subsp. fastigiata Waldron var. vulgaris Harz) improved germplasm lines (Reg. no. GP-79 to GP-83, PI 594969 to PI 594973), bred by ICRISAT, India, were released in 1995 for their fresh seed dormancy in the Spanish background (Upadhyaya et al., 1997). Another improved Spanish peanut (Arachis hypogaea L. subsp. fastigiata var. vulgaris) germplasm bred by ICRISAT, ICGV 93470 (Reg. no. GP-102, PI 614087) was released in 1999 for its early-maturity and fresh seed dormancy (Upadhyaya et al., 2001).

In Senegal, the first Spanish dormant variety released is the variety 73-30 (Bockelee-Morvan, 1983). It has deep dormancy and could be used as a donor parent in breeding programs for early maturing and dormant groundnut varieties. A second dormant Spanish variety derived from a cross between the variety Fleur 11 and 73-30 was recently released. That new variety named "Taaru" has a bigger seed size than variety 73-30, while its oil content is higher (53%) than most of the Senegalese groundnut varieties.

Screening of various groundnut germplasm, including core collections, should allow the identification of new sources of fresh seed dormancy for further varietal improvement. In our breeding program, we recently evaluated the above-mentioned collection of 289 accessions for fresh seed dormancy. Among the 189 short-duration cycle accessions, 16 were classified as having deep dormancy (percentage of germinated seeds < 10%), 23 were classified as having light dormancy (< 30%), and the remaining 150 accessions were classified as non-dormant (percentage of germinated seeds > 30%). Out of the 100 long-duration cycle accessions, 40 accessions were classified with deep dormancy, 26 accessions with light dormancy, and 34 accessions as non-dormant (Unpublished data). Some accessions with fresh seed dormancy are listed below (Table 3). This germplasm is an important source of accessions with fresh seed dormancy.
<table><caption>**Table 3**
List of Selected Accessions with Deep Fresh Seed Dormancy Maintained in Breeding Programs in Africa</caption><thead><tr><td>Duration Cycle Length</td><td>Origin /Country /Maintainer</td><td>Accessions</td><td>Other Characteristics</td></tr></thead><tbody><tr><td>Short</td><td>Senegal/ISRA</td><td>73-30</td><td>Drought tolerance</td></tr><tr><td>Short</td><td>Senegal/ISRA</td><td>Taaru</td><td>High oleic content (53%)</td></tr><tr><td>Long</td><td>Senegal/ISRA</td><td>69-101</td><td>Leaf spot & Rosette resistance</td></tr><tr><td>Long</td><td>Senegal/ISRA</td><td>Turquie 2</td><td>Confectionery type and mid-oleic content</td></tr><tr><td>Short</td><td>Ghana/CSIR-SARI</td><td>ICGV 13110</td><td>ND</td></tr><tr><td>Short</td><td>Ghana/CSIR-SARI</td><td>ICGV 15017</td><td>ND</td></tr><tr><td>Long</td><td>Ghana/CSIR-SARI</td><td>NUMEX 05</td><td>High yield</td></tr><tr><td>Short</td><td>Uganda/NARO</td><td>SVG-GB-10010</td><td>ND</td></tr><tr><td>Short</td><td>Uganda/NARO</td><td>ICGV SM 08577</td><td>ND</td></tr><tr><td>Short</td><td>Malawi/DAR</td><td>ICGV SM 5738</td><td>ND</td></tr><tr><td>Short</td><td>Malawi/DAR</td><td>ICGV 6057</td><td>ND</td></tr><tr><td>Short</td><td>Malawi/DAR</td><td>ICGV SM07544</td><td>ND</td></tr><tr><td>Short</td><td>Mozambique/IIAM</td><td>Pan 13005</td><td>ND</td></tr><tr><td>Short</td><td>Mozambique/IIAM</td><td>ICGV-SM 03530</td><td>ND</td></tr></tbody></table>

Note. ND: not determined

## 2. Phenotyping for Fresh Seed Dormancy

Assigning a phenotypic value to every individual of the population is critical to achieving a genetic gain in any breeding program; in other words, assuming the same selection intensity and population variance, the higher the heritability, the more important the genetic gain. However, because heritability is somewhat a matter of accuracy of the phenotyping approach, biases can sometimes be misleading. Phenotyping for seed dormancy can be done either by evaluating the *in situ* number of sprouts or weight and expressing it as a percentage of the total number or mass of harvested seeds or by performing an *in vitro* seed germination test (Faye et al., 2010;
Gautreau, 1984; Khalfaoui, 1991; Ndoye, 2001; Upadhyaya & Nigam, 1999) using fresh or cured seeds. Considering the above statements related to dormancy phenotyping, it seems necessary to highlight tips that can improve phenotyping and, subsequently, the selection of dormant cultivars. Firstly, one needs to make sure the seeds are fully mature. Due to its indeterminate nature of flowering, seed maturity level may vary from one pod to another for the same individual plant. A simple method for selecting mature seeds is the observation of the black coloration of the hull's internal parenchyma (Miller & Burns, 1971). This parenchyma turns brown to black when the pod is fully mature and white to yellow if the pod is immature. Secondly, as the seed coat and cotyledons and embryos have a role in imparting dormancy in groundnut (Nautiyal et al., 1994), care should be taken when shelling pods for the *in vitro* seed germination tests. Thirdly, two-seeded pods have deeper dormancy at their basal seed than at their apical seed (Ketring & Morgan, 1971). Therefore, it seems more relevant to use only apical seeds when phenotyping for fresh seed dormancy to harmonize sampling.

### 3. Genetic Improvement for Seed Dormancy

Various genetic studies were carried out to understand the inheritance of fresh seed dormancy in peanuts (Asibuo et al., 2008; Faye et al., 2010; Gautreau, 1984; Khalfaoui, 1991; Lin & Lin, 1971; Ndoye, 2001; Upadhyaya & Nigam, 1999). Some studies revealed that fresh seed dormancy is controlled by a dominant allele of a single gene (Asibuo et al., 2008; Faye et al., 2010; Upadhyaya & Nigam, 1999). In contrary, other studies (Khalfaoui, 1991; Ndoye, 2001) reported polygenic inheritance with epistasic effects. More recently, Vishwakarma et al. (2016) found that the trait is controlled by two recessive genes. These converses may be due to the different phenotyping methods and the nature of the crosses used by authors. Some crosses were intra-subspecific (Spanish × Spanish), while others were inter-subspecific (Spanish × Virginia). In most of the studies of seed dormancy genetics in groundnut, authors concluded that broad sense heritability was moderate to high (<0.50), indicating the involvement of both significant genetic and environmental factors (Faye et al., 2010; Khalfaoui, 1991; Ndoye,
2001). In addition, significant genotype × year effects were reported (Ndoye, 2001).

4. Toward the Identification of Molecular Markers Linked to Seed Dormancy in Groundnut

Vishwakarma et al. (2016) identified two major QTLs (qfsd-1 and qfsd-2) for fresh seed dormancy in the linkage groups A05 and B06, respectively. Using a recombinant inbred line (RIL) mapping population derived from a cross between Tifrunner (a dormant Runner type) and GT-C20 (a non-dormant Spanish type), Wang et al. (2022) identified two major seed dormancy QTLs on chromosome A04 and A05 with 43.2% and 51.6% of the phenotype variation explained, respectively. Besides, a candidate gene (Arahy.KB746A) coding and ethylene-responsive transcription factor was found very close to the QTL mapped on chromosome A05. Deploying the QTL-seq approach, Kumar et al. (2020) identified other key genomic regions and candidate genes for fresh seed dormancy on the B05 and A09 pseudomolecules. They developed a validated marker (GMFSD1) that can be used in MAS programs to improve seed dormancy in groundnut.

In our breeding program in Senegal, we developed two RILs populations for fresh seed dormancy. One of the RILs populations is derived from a cross between cv. Fleur 11 and cv. 73-30, while the other is derived from a cross between ICGV 96894 and cv. 73-30. We identified eight lines with deep dormancy, 3 RILs (C1P15-18-4, C1P24-1-1, C1P18-6-5, and C1P30-9-8) of the population Fleur 11 × 73-30, and 4 RILs (C3P11-8-2; C3PNI-1-3, C3P7-1-1, and C3P6-2-5) from the cross ICGV 96894 × 73-30 (Figure 1). Further analyses are ongoing for the mapping of seed dormancy QTLs in these two populations.
<figure><img src="image_100.png" /><figcaption>**Figure 1**</figcaption></figure>

Figure 1 - Germination Test of Fresh Seeds of Recombinant Inbred Lines Along with the Parental Lines

Note. (A): C1P30-9-8, a line with deep fresh seed dormancy; (B): cv 73-30, the dormant parental line; (C): cv Fleur 11, the non-dormant parental line; (D), (E) and (F) are non-dormant recombinant inbred lines.

Conclusion

In Senegal, the groundnut breeding program was established in the early
20th century. Among its great achievements is the development and
registration of very early-maturing varieties (75-80 days), among which cv.
55-33 and GC-8-35 are widely cultivated in the Northern region of Senegal
where the rainfall is particularly low and the rainy season is very short (less
than three months). Similarly, a clear shortening of the rainy season in the
southern region of the Groundnut Basin has led to the release of Spanish
varieties maturing 90-days after planting, among which is «Taaru», a variety
with 2-3 weeks post maturity fresh seed dormancy. Other short-duration
varieties with bigger grain sizes than previous varieties were developed
(Raw Gadu, Rafet Kaar, Yaakar, Tosset, Jaambar, and Kom Kom) using wild introgressions from the synthetic (Arachis duranensis × Arachis ipaënsis). As wild-related species have been proven effective in improving groundnut productivity under harsh conditions such as drought and diseases, new crosses, including other wild-related species, have been initiated to develop advanced backcross populations. Beyond the long-term objective of releasing new varieties, those populations could be useful in deciphering physiological and genetic traits related to drought tolerance in groundnut. On another hand, breeding for short-duration varieties with fresh seed dormancy is necessary to facilitate farmers' wide adoption of these varieties. Therefore, it is critical to consider fresh seed dormancy as a key selection trait, particularly when breeding groundnut for Sahelian and Sudano-Saharan zones. With the fast development of high throughput phenotyping platforms and the availability of groundnut genomic resources, significant progress could be achieved toward higher genetic gains for all the traits contributing to yield and seed quality in groundnut under marginal environments.

## References

ANSD, Agence Nationale de la Statistique et de la Démographie (2022). Situation économique et sociale du Sénégal 2019. Rapport d'activité de l'ANSD, 310 pages.

Asibuo, A. Y., Richard, A., Osei, S. -K., Dapaah, K. H., Seth, O. -D., & Adelaide, A. (2008). Inheritance of fresh seed dormancy in groundnut. *African Journal of Biotechnology*, 7(4), 421-424.

Barkley, N. A., Upadhyaya, H. D., Liao, B., & Holbrook, C. C. (2016). Global resources of genetic diversity in peanut. In Stalker, H. T. & Wilson R. F. (Eds), *Peanuts*, AOCS Press (pp 67-109). https://doi.org/10.1016/ B978-1-63067-038-2.00003-4.

Bertioli, D. J., Cannon, S. B., Froenicke, L., Huang, G., Farmer, A. D., Cannon, E. K. S., Liu, X., Gao, D., Clevenger, J., Dash, S., Ren, L., Moretzsohn, M. C., Shirasawa, K., Huang, W., Vidigal, B., Abernathy, B., & Chu, Y. (2016). The genome sequences of Arachis duranensis and Arachis ipaensis, the
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<figure><img src="image_101.png" /></figure>
# Chapters' corresponding authors

## Part 1

**Chapter 1** - Manzamasso Hodjo (hodjo@ksu.edu) and Timothy Dalton (tdalton@ksu.edu)

**Chapter 2** - Aissatou Sambou (sambou.aissatou@yahoo.fr)

**Chapter 3** - Melinda Smale (msmale@msu.edu)

**Chapter 4** - Amidou Assima (amidou.assima@gmail.com)

**Chapter 5** - Mor Ngom (morngom49@gmail.com)

**Chapter 6** - Tebila Nakelse (tebila.nakelse@gmail.com) and Timothy Dalton (tdalton@ksu.edu)

## Part 2

**Chapter 1** - Modou Mbaye (modou.mbaye@isra.sn)

**Chapter 2** - Boubacar Gano (boubacar1.gano@ucad.edu.sn) and Alain Audebert (alain.audebert@cirad.fr)

**Chapter 3** - Alain Audebert (alain.audebert@cirad.fr)

**Chapter 4** - Alexandre Grondin (alexandre.grondin@ird.fr)

**Chapter 5** - Papa Mamadou Sitor Ndour (sitndour@yahoo.fr)

**Chapter 6** - Malick Ndiaye (agromalick@yahoo.fr)

## Part 3

**Chapter 1** - Anne-Céline Thuillet (anne-celine.thuillet@ird.fr)

**Chapter 2** - Amy Bodian (miamybo@gmail.com or amy.bodian@isra.sn)

**Chapter 3** - Adeline Barnaud (adeline.barnaud@ird.fr), Claire Billot (claire.billot@cirad.fr) and Mame Codou Gueye (mcodougueye1@gmail.com or codou.gueye@isra.sn)

**Chapter 4** - Jacques Martin Faye (jmartin.faye@gmail.com)

**Chapter 5** - Niaba Teme (niabateme@gmail.com)

**Chapter 6** - Ndjido Ardo Kane (ndjido.kane@isra.sn)

**Chapter 7** - Issa Faye (issafaye2001@yahoo.fr or issa.faye@isra.sn)
Book Abstract

This book focuses on three important elements in the development of cereal and legume crops in semi-arid West Africa. The first section illustrates the socioeconomic factors that affect the food system for these crops and contains an overview of crop production and consumption in the region. Then, important inputs that affect system productivity are presented: preferences for new seed varieties, yield response to fertilizer, counterfeit herbicides, climate information, and the way farmers develop expectations about the weather events that shape cropping outcomes. The final chapter of section one is dedicated to understanding urban consumer preferences for processed food products derived from pearl millet.

The second section addresses the state of the art of phenotyping and the modeling of crop adaptation to dryland farming systems. Chapters in section two focus on the regional network for phenotyping, high throughput phenotyping in field situations, root architectures, and crop ideotypes. In addition, empirical evidence is presented on root and soil interactions and the effectiveness of unmanned aerial vehicles (UAVs to assess sorghum physiology.

Finally, the third section investigates the genetic diversity and improvement of dryland crops. Chapters in this section focus on biodiversity and agricultural system sustainability, orphan crops such as fonio, and the utilization of a regional germplasm collection in the improvement of cowpea, groundnut, pearl millet, and sorghum.