ww: well-watered; ds: drought stress; NTR: number of total roots; RLD: root length density (m m⁻²); [0-120 cm], [0-50 cm] and [60-120 cm] represent the depth considered; V: variety; E: environment; *** significant at *p* = 0.001. Means with the same letters are not significantly different. The bold values indicate the highest and lowest value measured.

<figure><img src="image_53.png" /><figcaption>Figure 4</figcaption></figure>

Figure 4 - Root Length Density (RLD) of Sorghum Varieties Under Well-watered (ww) and Drought Stress (ds) Conditions at the End of the Stress Period

Notes. (A) RLD distribution at [0-50], [60-120] and [0-120] depth horizons (B) Impact of water deficit on RLD profile. Data are means +/- standard error. Significant differences are indicated by different letters.

4. Relationship Between Vegetation Indices and Sorghum's Growth Traits

Results presented in Table 7 show the relationship between LAI, biomass, and vegetation indices. Nonlinear and linear regression models were fitted using the 2018 field data set (n = 390, calibration data). Regression analysis revealed a good relationship between LAI or biomass with NDVI, CTVI, GNDVI, MSAVI2, and SR. To assess the performance of vegetation indices to estimate LAI, we compared the coefficients of determination (r²) of the relationships between NDVI, CTVI, MSAVI2, SR, and LAI that were respectively of 0.83, 0.82, 0.76 and 0.77 with highly significant p values. However, the r² for biomass estimation using the same indices were comparatively lower than those for LAI (0.6, 0.6, 0.57, and 0.47).
<table><caption>**Table 7**
Regression of Sorghum LAI and Biomass on Vegetation Indices (n = 390)</caption><thead><tr><th>Vegetation indices</th><th>Regression models</th><th>r</th><th>r<sup>2</sup></th><th>P-value</th></tr></thead><tbody><tr><td>NDVI</td><td>LAI=0.3732*e<sup>2.9648*NDVI</sup></td><td>0.91</td><td>0.83</td><td>&lt;0.001</td></tr><tr><td>CTVI</td><td>LAI=0.0069*e<sup>5.5322*CTVI</sup></td><td>0.9</td><td>0.82</td><td>&lt;0.001</td></tr><tr><td>MSAVI2</td><td>LAI=0.3392*e<sup>2.7498*MSAVI2</sup></td><td>0.9</td><td>0.82</td><td>&lt;0.001</td></tr><tr><td>SR</td><td>LAI=0.4438*SR+0.0126</td><td>0.87</td><td>0.77</td><td>&lt;0.001</td></tr><tr><td>NDVI</td><td>Biomass = 3.1153*e<sup>3.7021*NDVI</sup></td><td>0.77</td><td>0.6</td><td>&lt;0.001</td></tr><tr><td>CTVI</td><td>Biomass = 0.0206*e<sup>6.9446*CTVI</sup></td><td>0.77</td><td>0.6</td><td>&lt;0.001</td></tr><tr><td>MSAVI2</td><td>Biomass = 2.7357*e<sup>3.4587*GNDVI</sup></td><td>0.77</td><td>0.6</td><td>&lt;0.001</td></tr><tr><td>SR</td><td>Biomass = 6.0128*SR + 0.8947</td><td>0.68</td><td>0.47</td><td>&lt;0.001</td></tr></tbody></table>

NDVI, CTVI, MSAVI2 and SR: vegetation indices; r: coefficient of correlation; r²: coefficient determination; LAI: leaf area index.

Figure 5 shows the NDVI plots with the corresponding LAI. The LAI values varied from 0.3 m² m⁻² to 5.7 m² m⁻² per plant across varieties, treatments, and developmental stages during the calibration trial in 2018. A saturation of the different vegetation indices was observed above LAI values higher than 4 m² m⁻² per plant. To test the variance of calibration models, an ordinary least squares linear regression between calculated and measured LAI was done, and it revealed an r² value of 0.8 for LAI (see Figure 5). The ANOVA revealed highly significant effect of varieties, environment, and the interaction (V*E) (p<0.05) on both calculated and measured LAI and biomass with almost the same values (see Tables 8 and 9).
<figure><img src="image_54.png" /><figcaption>Figure 5 - Calibration of LAI prediction model from NDVI vegetation index</figcaption></figure>

Notes. (A) Relationship between normalized difference vegetation index (NDVI) and leaf area index (LAI); (B) Measured LAI vs. corresponding LAI values predicted using empirical equation in Figure 5A. The dashed red line in the graph is the 1:1 line.

<table><caption>Table 8
ANOVA of Leaf Area Index Measured (LAI<sub>m</sub>) and Calculated from Vegetation Indices (LAI<sub>c</sub>) and Average Performance of the Varieties Under Well-watered and Drought Stress</caption><thead><tr><th></th><th colspan="2">LAI<sub>c</sub>_NDVI</th><th colspan="2">LAI<sub>c</sub>_CTVI</th><th colspan="2">LAI<sub>m</sub></th></tr></thead><tbody><tr><td>V</td><td>ww</td><td>ds</td><td>ww</td><td>ds</td><td>ww</td><td>ds</td></tr><tr><td>V1</td><td>1.5</td><td>0.62</td><td>1.53</td><td>0.62</td><td>1.9</td><td>0.6</td></tr><tr><td>V2</td><td>1.56</td><td>0.61</td><td>1.59</td><td>0.61</td><td>1.9</td><td>0.8</td></tr><tr><td>V3</td><td>2.09</td><td>0.7</td><td>2.12</td><td>0.72</td><td>1.57</td><td>0.9</td></tr><tr><td>V4</td><td>1.6</td><td>0.58</td><td>1.63</td><td>0.57</td><td>1.4</td><td>0.45</td></tr><tr><td>V5</td><td>1.95</td><td>0.56</td><td>1.97</td><td>0.56</td><td>2.23</td><td>0.6</td></tr><tr><td>V6</td><td>1.39</td><td>0.7</td><td>1.43</td><td>0.7</td><td>1.63</td><td>0.57</td></tr><tr><td>V7</td><td>2.01</td><td>0.53</td><td>2.03</td><td>0.52</td><td>1.9</td><td>0.47</td></tr><tr><td>V8</td><td>1.93</td><td>0.68</td><td>1.96</td><td>0.69</td><td>2.63</td><td>0.8</td></tr><tr><td>V9</td><td>1.78</td><td>0.64</td><td>1.81</td><td>0.65</td><td>1.53</td><td>0.67</td></tr><tr><td>V10</td><td>1.74</td><td>0.48</td><td>1.77</td><td>0.47</td><td>1.57</td><td>0.53</td></tr><tr><td>Mean</td><td>1.75</td><td>0.61</td><td>1.78</td><td>0.61</td><td>1.83</td><td>0.64</td></tr><tr><td>V</td><td>*</td><td></td><td>*</td><td></td><td>**</td><td></td></tr><tr><td>E</td><td>***</td><td></td><td>***</td><td></td><td>***</td><td></td></tr></tbody></table>
<table><thead><tr><th></th><th>LAIc_NDVI</th><th>LAIc_CTVI</th><th>LAIM</th></tr></thead><tbody><tr><td>V*E</td><td>*</td><td>*</td><td>*</td></tr></tbody></table>

V: varieties; E: environment; ww: well-watered; ds: drought stress; CTVI and NDVI: vegetation indices; Significance codes: '**' 0.001 '**' 0.01 '*' 0.05

<table><caption>**Table 9**
ANOVA of the Biomass Per Plant Measured (BPPm) and Calculated from Vegetation Indices (BPPc) and Average Performance of the Varieties Under Well-watered and Drought Stress</caption><thead><tr><th></th><th>BPPc_MSAVI2</th><th>BPPc_SR</th><th>BPPm</th></tr></thead><tbody><tr><td>V</td><td>ww</td><td>ww</td><td>ww</td></tr><tr><td>V1</td><td>17.62</td><td>5.53</td><td>18.3</td></tr><tr><td>V2</td><td>18.27</td><td>5.38</td><td>22.55</td></tr><tr><td>V3</td><td>25.85</td><td>6.65</td><td>19.11</td></tr><tr><td>V4</td><td>18.73</td><td>4.96</td><td>17.38</td></tr><tr><td>V5</td><td>23.94</td><td>4.73</td><td>29.82</td></tr><tr><td>V6</td><td>15.77</td><td>6.62</td><td>23.87</td></tr><tr><td>V7</td><td>24.47</td><td>4.35</td><td>35.72</td></tr><tr><td>V8</td><td>23.59</td><td>6.32</td><td>27.32</td></tr><tr><td>V9</td><td>21.52</td><td>5.76</td><td>22.49</td></tr><tr><td>V10</td><td>21</td><td>3.75</td><td>18.28</td></tr><tr><td>Mean</td><td>21.08</td><td>5.42</td><td>23.48</td></tr><tr><td>V</td><td>*</td><td>*</td><td>***</td></tr><tr><td>E</td><td>***</td><td>***</td><td>***</td></tr><tr><td>V*E</td><td>*</td><td>**</td><td>***</td></tr></tbody></table>

V: varieties; E: environment; ww: well-watered; ds: drought stress; MSAVI2 and SR: vegetation indices; Significance codes: '**' 0.001 '**' 0.01 '*' 0.05
## 5. Assessment of the Impact of Water Treatment on UAV Derived Traits

The combined analysis of variance revealed highly significant effect (P ≤ 0.05) of the environment (E), variety (V), and their interaction (V\*E) on all the vegetation indices under study (see Table 10). In ww conditions, the varieties recorded on average 0.6, 1.08, 0.76, 7.37 for NDVI, CTVI, MSAVI2, and SR respectively; the stressed plants showed lower values (i.e., 0.37, 0.90, 0.44, and 3.01). In ds conditions, the variety V3 exhibited the highest indices (0.59, 0.94, 0.66 and 4.02 for NDVI, CTVI, MSAVI2, and SR); V10 had the lowest (0.20, 0.83, 0.25, and 1.74).

<table><caption>**Table 10**
Average Performance and ANOVA of the Vegetation Indices of the Varieties Under Well-watered and Drought Stress</caption><thead><tr><th></th><th colspan="2">NDVI</th><th colspan="2">CTVI</th><th colspan="2">MSAVI2</th><th colspan="2">SR</th></tr></thead><tbody><tr><td>V</td><td>ww</td><td>ds</td><td>ww</td><td>ds</td><td>ww</td><td>ds</td><td>ww</td><td>ds</td></tr><tr><td>V1</td><td>0.63</td><td>0.38</td><td>1.06</td><td>0.92</td><td>0.71</td><td>0.46</td><td>6.83</td><td>3</td></tr><tr><td>V2</td><td>0.68</td><td>0.37</td><td>1.08</td><td>0.91</td><td>0.76</td><td>0.44</td><td>6.77</td><td>3</td></tr><tr><td>V3</td><td>0.73</td><td>0.59</td><td>1.11</td><td>0.94</td><td>0.81</td><td>0.66</td><td>7.97</td><td>4.02</td></tr><tr><td>V4</td><td>0.7</td><td>0.33</td><td>1.09</td><td>0.89</td><td>0.77</td><td>0.4</td><td>7.53</td><td>2.71</td></tr><tr><td>V5</td><td>0.71</td><td>0.39</td><td>1.1</td><td>0.94</td><td>0.79</td><td>0.47</td><td>7.91</td><td>2.85</td></tr><tr><td>V6</td><td>0.62</td><td>0.36</td><td>1.05</td><td>0.89</td><td>0.7</td><td>0.42</td><td>5.69</td><td>3.24</td></tr><tr><td>V7</td><td>0.74</td><td>0.28</td><td>1.11</td><td>0.88</td><td>0.81</td><td>0.35</td><td>8.59</td><td>2.2</td></tr><tr><td>V8</td><td>0.74</td><td>0.45</td><td>1.11</td><td>0.94</td><td>0.81</td><td>0.53</td><td>8.17</td><td>4.46</td></tr><tr><td>V9</td><td>0.7</td><td>0.4</td><td>1.09</td><td>0.94</td><td>0.78</td><td>0.49</td><td>6.99</td><td>2.92</td></tr><tr><td>V10</td><td>0.67</td><td>0.2</td><td>1.08</td><td>0.83</td><td>0.75</td><td>0.25</td><td>7.33</td><td>1.74</td></tr><tr><td>Mean</td><td>0.69</td><td>0.37</td><td>1.08</td><td>0.9</td><td>0.76</td><td>0.44</td><td>7.37</td><td>3.01</td></tr><tr><td>V</td><td>**</td><td></td><td>**</td><td></td><td>***</td><td></td><td>*</td><td></td></tr><tr><td>E</td><td>***</td><td></td><td>***</td><td></td><td>***</td><td></td><td>***</td><td></td></tr><tr><td>V\*E</td><td>*</td><td></td><td>*</td><td></td><td>*</td><td></td><td>*</td><td></td></tr></tbody></table>

V: varieties; E: environment; NDVI, CTVI, MSAVI2 and SR: vegetation indices (VIs); ww: well-watered; ds: drought stress; Significant codes: '**' 0.001 '**' 0.01 '**' 0.05
Results presented in Figure 6 showed the general trend of the evolution of VIs during plants growth in both ww and ds conditions. The vegetation indices of nonstressed plants gradually increased to reach their maximum at the 60th DAS (flowering time) before dropping slightly. In the stressed plots, the vegetation indices initially recorded a slight increase but then water deficit induced a reduction that was followed by a progressive increase after rewatering.

However, the VIs values of stressed plants did not recover values of the nonstressed plants. The results highlighted in this study testify the relevance of VIs to capture the differences induced by drought and that occurred from the 30th DAS to the 55th DAS. The post flowering decrease of the VIs observed in the ww treatment was due to the saturation of the VIs as plants have grown up enough in this environment.
<figure><img src="image_55.png" /><figcaption>Figure 6</figcaption></figure>

Figure 6 - Dynamics of Estimated Vegetation Indices Under Well-watered and Drought Stress Conditions

Note. Box plots represent the estimations for all the 10 sorghum varieties at a given stage. NDVI, CTVI, MSAVI2 and SR: vegetation indices; DAS: days after sowing; ds: drought stress; ww: well-watered.

# Discussion

## 1. The Adaptive Behavior of the Sorghum Varieties Studied

The stress characterization revealed very negative values of water potential (WP = -5 bars) at the end of the stress period. This can be explained by the intensity of the stress experienced by the plants (FTSW = 0.3). To counteract the water deficit that occurs in their tissues, plants implement water
retention mechanisms with very high energy. Consequently, it is necessary
to provide equivalent energy to extract this water from the tissues (Brou
et al., 2007). The behavior of the sorghum varieties, which consists in
maintaining a high level of hydration despite the stress pressure, reflects a
dehydration tolerance mechanism, with a low water potential of the tissues
(Levitt, 1985). This limits cell growth because of loss of turgidity.

Water-limiting conditions lead to altered cell elongation, mainly due to
low water flow from the xylem to neighboring cells (Fahad et al., 2017;
Nonami, 1998). The number of leaves and the size of each leaf were reduced
in drought conditions because their expansion depends on the turgor
pressure and the amount of water assimilated. This induced a decrease in
plant biomass accompanied by a reduction in leaf appearance and plant
growth. Regarding physiological traits, drought stress causes a decrease in
the transpiration rate, photosynthetic activity, and stomata conductance
(Dwivedi et al., 2008; Fracasso et al., 2016). This adaptation mechanism also
was revealed in pearl millet (Kholova et al., 2009, 2010) and cowpea (Belko et
al., 2012). Under water deficit, the diffusion of CO2 to the carboxylation sites
is limited because of stomata closure and increased mesophyll resistance.
This inhibits the transport of electrons, leading to an imbalance between the
electron transport rate and

CO2 fixation rate (Verma et al., 2018). The photosynthetic performance
is one of the parameters providing useful and quantitative information on
plants condition and vitality (Banks, 2018; Oukarroum et al., 2007; Zegada-
Lizarazu & Monti, 2013). Tingting et al. (2010) showed that the process of
photosynthesis is sensitive to changing environmental conditions, and the
way in which plants adapt to their environment is propitious to
photosynthesis. The recovery of photosynthesis upon rewatering indicates
that the PSII systems had recovered their ability to deal with the absorbed
light and the accumulated energy. Hence, oxidative permanent damage may
not have occurred at the early growth stages, or as suggested by Oukarroum
et al. (2007), the maximum quantum yield of photochemistry was not
affected by the drought. The drop of plant growth rate caused by the lack
of water during a given period often leads to difficulties in covering the
normal development in terms of height or biomass (Hud et al., 2016). This
behavior is not always a disastrous consequence. It could be a means of
adaptation that allows the plant to maintain its development but at a slower
pace. This was the case of the variety V6, for instance, which produced well
despite a low DRI in height and biomass. These are part of physiological
functioning that confer drought tolerance to sorghum (Hadebe et al., 2017;
Harris et al., 2007; Kapanigowda et al., 2013). The agro-physiological behavior
of a plant depends on the genotype, the severity of the drought, and the
time of occurrence (Chaves et al., 2002; Jaleel et al., 2008). Early water stress
acts differently on sorghum varieties depending on the variety's stage of
development. It is well-known that when drought occurs at the vegetative
stage before panicle exertion, plants recover better. This may explain why
the agronomic performances of some long-duration varieties of the *guinea*
and *bicolor* breed (V1, V4) were weakly affected by early cycle drought. Long-
duration varieties were able to catch up and stabilize production despite
a lack of water at the early stage. According to Araus et al. (1989), this
phenomenon is due to stomata control, which is more effective in the young
growth stages. This could have been the case of variety V1, which responded
to water deficit by closing its stomata, thus allowing it to limit exchanges
with the environment until water conditions become favorable and the
growth could resume and compensate for the losses due to drought.
Contrariwise, V4 showed the lowest DRI and a slight variation of stomata
conductance and yield under drought stress. A hypothesis is that this variety
recovered well after the 2 weeks allowed for recovery measurement.
However, the drought adaptation conferred by the long cycle is not sufficient
to consider such varieties appropriate for the future. Previous studies have
shown that the duration of drought episodes at the beginning of the season
is likely to increase with the worsening of global climate change impacts
(Blanc, 2012; Vadez et al., 2012), therefore even long-duration varieties may
be affected by early drought stress if the duration is long.

Roots play an important role as a support, but also provide the plant with
the water and mineral elements it needs. Thus, their study represents a very
effective means of characterizing drought adaptation. Some authors showed
that the spatial distribution of root length density determines water and
nutrient uptake (Intergovernmental Panel on Climate Change, 2014). In the
present study, the varieties V1 and V8 turned out to be very interesting. They
yielded well under drought, and their adaptations were mainly based on a
high RLD (60 cm – 120 cm) (V8) and the increase of dead leaves (V8 and V1)
contrarily to V4, which had the lowest RLD (60 cm – 120 cm) and a low grain
yield. Additionally, some varieties have densified their root system to be able
to exploit a larger surface area of soil and to increase the absorption of water and mineral nutrients (Comas et al., 2013). The high root density at depth allows them to reach moisture in the deeper soil layers and compensate for the lack of water supply.

Although the varieties V3, V4, and V5 were less productive, they could be of high interest for height or fodder breeding programs. Varieties V1, V2, V8, and V9 could be devoted for grain yield breeding. Phenotypic evaluation of germplasm can be useful for characterization, conservation, and maintenance of genetic resources (Naoura et al., 2019). This study revealed a large agro-morphological diversity of quantitative traits. Overall, the results showed that plant response to early drought was genotype dependent (Sinclair et al., 2018) and some varieties expressed a strong ability to reduce water loss by decreasing leaf transpiration rate through stomata closure and increasing the number of dead leaves. These were among the adaptation strategies used by the studied varieties to tolerate drought stress conditions in both seasons.

## 2. Monitoring Plant Growth by UAV Based Phenotyping

Recent advances in high throughput field phenotyping have boosted the power of physiological breeding (Araus & Cairns, 2014; Fahlgren et al., 2015; Hu et al., 2018; Reynolds & Langridge, 2016). Currently, UAV technology is an alternative to the manual collection of crop data, offering information on traits and factors affecting crop development and productivity with relatively shorter time and lower cost (Du & Noguchi, 2017; Yu et al., 2016). The moderate to strong relationships (see Table 7) found between the UAV-derived plant spectral traits and the leaf area index and the biomass indicate that UAVs could be useful for phenotyping West Africa sorghum genotypes (Gano et al., 2021). The NDVI is an indicator of the combined effects of chlorophyll concentration, canopy leaf area, and yield (Erdle et al., 2011). The estimation of the NDVI can be used as a reference index for the dynamic monitoring of the biomass changes during the growth season of sorghum. NDVI estimates are influenced by many factors, such as measurement time, sensors, and environmental conditions (Crusiol et al., 2017), and there is no one absolutely accurate measurement method for NDVI estimation.
Improved precision would also contribute to further applications for field management (Foster et al., 2017). Results presented herein demonstrate the importance of using NDVI related vegetation index as indirect selection criteria by reporting genetic variation for VIs among varieties, the effect of water treatment on VIs and their interaction with varieties, and the relationships between VIs and LAI and biomass of sorghum. This attests the ability of VIs in estimating growth rate, biomass accumulation during the vegetative stage and yield set up (Babar et al., 2006).

### 3. Key Traits Involved in Drought Tolerance for Breeding

As a major challenge for agricultural production, drought tolerance is a prime target for molecular approaches to crop improvement. To obtain significant results, these approaches must be based on phenotyping protocols that are appropriate at all stages of plant development (Salekdeh et al., 2009). Because drought adaptation traits are complex and polygenic, the understanding of their physiological and genetic basis is still incomplete. This challenge comes at a time when plant biologists are witnessing an explosion in the availability of new high-throughput technologies and genomic information. However, the identification of preferred selection criteria remains unclear and still makes phenotyping laborious. According to Passioura (1977), the conceptual framework for drought phenotyping is based on the equation expressing the product yield of WU (quantity of water used), WUE (conversion of WU into dry biomass), and HI (the fraction of dry matter converted into grain). Therefore, it is important to design experiments to test these factors by distinguishing the impact of WU and WUE on production. In other words, when looking at productivity, it is important to identify the effects of growth stress that may affect assimilations transport. By considering these components of performance individually, it is possible to target traits more effectively in relation to environmental constraints. In the case of early-cycle water stress, there are several key phenotypic traits highlighted (see Table 11) to help target phenotyping. In addition, it would be useful to select high yielding genotypes that are stable across environmental conditions and years. To do so, it would be interesting to follow growth-related traits such as plant height and NLA,
DWP, Tr and Pn because these traits are susceptible to environmental changes.

<table><caption>**Table 11**

**Example of Traits Associated with Different Yield Factors Worth to Phenotype Under Conditions of Early-cycle Water Stress in Sorghum**</caption><thead><tr><td>Traits</td><td>Stage</td><td>Phenotyping technique</td><td>Interest for breeding</td></tr></thead><tbody><tr><td>Root Length Density (RLD)</td><td>Growth</td><td>Count</td><td>Yield</td></tr><tr><td>Number of adventitious roots</td><td>Growth</td><td>Count</td><td>Yield</td></tr><tr><td>Soil water stock</td><td>Growth</td><td>Metric</td><td>Yield</td></tr><tr><td>Biomass rate</td><td>Growth</td><td>Metric</td><td>Biomass</td></tr><tr><td>Plant height rate</td><td>Growth</td><td>Metric</td><td>Biomass</td></tr><tr><td>Dead leaves weight</td><td>Growth</td><td>Metric</td><td>Yield</td></tr><tr><td>Photosynthesis rate</td><td>Growth</td><td>Metric</td><td>Yield</td></tr><tr><td>Transpiration rate</td><td>Growth</td><td>Metric</td><td>Yield</td></tr><tr><td>NDVI</td><td>Growth</td><td>Metric</td><td>Biomass</td></tr><tr><td>Grain weight</td><td>Harvest</td><td>Metric</td><td>Yield</td></tr><tr><td>Number of grains</td><td>Harvest</td><td>Count</td><td>Yield</td></tr><tr><td>Stem dry weight</td><td>Harvest</td><td>Metric</td><td>Biomass</td></tr></tbody></table>

Moreover, this study demonstrated successful and rapid assessment of NDVI related VIs (CTVI, MSAVI2 and SR) using a UAV platform that showed high accuracy in assessing variation in plant development. The accuracy of the UAV platform was validated by ground truth data, and it proved a significant advantage of UAV over the handheld data acquisition platform from the stem elongation stage to late grain filling stage, especially under water-limited conditions.

Drought-prone environments are diverse and the biotic and abiotic stresses that affect yield during drought periods are numerous (Richards et al., 2002). Therefore, our objective is not to propose unique criteria for drought stress phenotyping. Rather, we suggest that each experiment be conducted with a specific, realistic goal and with WUE and yield set up as reference traits (Venuprasad et al., 2007). Such reference traits will ensure the relevance of field results that are assessed and deposited in public
databases for a standardized recording and reporting of drought-related phenotypic data.

## Conclusion

This study is justified by the challenge that researchers have set to improve adaptation to early drought stress in cereals, particularly sorghum. We have shown the impact of water deficit on sorghum growth and development. The early stress is a major factor in the evolution of biomass, height, and leaf development. Even though it occurred early, its impact leads to yield instability. This study also highlighted plant adaptation mechanisms under early water deficit based on growth; photosynthesis and transpiration reduction; senescence increase; stomata closure; and roots length density increase. We also highlighted the ability of UAV platform to phenotype drought stress in West Africa sorghum varieties. Finally, we proposed key phenotyping traits that involve the different factors that govern production for a more efficient characterization of drought adaptation. Future areas of study could include phenotyping entire sorghum collections in different growing conditions during the year to better fix the adaptation mechanisms. Based on the predictions of precariousness linked to climate change, it would be more than necessary to select varieties that are able to adapt and stabilize performance independently of the season and year.

## References

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9. Toward a Regional Field
Phenotyping Network in West
Africa

**Alain Audebert**, CIRAD, France & ISRA/CERAAS, Senegal

**Delphine Luquet**, CIRAD, France

**Vincent Vadez**, IRD, Senegal & ISRA/CERAAS, Senegal

**Daniel Foncéka**, CIRAD & ISRA/CERAAS, Senegal

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

Abstract

Phenotyping plays an important role in crop science and breeding programs. The accurate, rapid acquisition of phenotypic information on plants in different environments helps researchers explore the genomes' inheritance and expression patterns and determine the association of genomic and phenotypic information and ultimately increase crop yields. Traditional methods for acquiring crop traits (e.g., plant height, biomass, yield, etc.) rely on manual sampling, which is laborious and time-consuming. Fortunately, technological advances are resolving the high-throughput phenotyping bottleneck. To optimize the benefit of breeding programs in West Africa (WA) on the sustainability and performance of cropping systems (considering climate change and agroecological transition) developing high throughput field phenotyping methods is essential. However, the establishment of this type of activity requires infrastructure, equipment, management, and most importantly, dedicated and trained staff. Also, new technologies like Unmanned Aerial Vehicles (UAV) have recently become an important tool for fast and non-destructive high throughput phenotyping. This technology is flexible, practical, and provides easy access to data and high spatial resolution. UAVs are powerful tools for phenomics and genomics studies. As such, CERAAS and its partners are developing a field phenotyping network
in the West African sub-region—the general scheme of which is presented below.

Keywords: Breeding, Field phenotyping, High-throughput, Regional network, West Africa

Introduction

Crop production must double by 2050 to respond to predicted demands of the global population (Ray et al., 2013). To meet this demand, crop yields should be increased by 2.4% annually, but currently, the average rate of yield increase is only 1.3%, with yields stagnating in up to 40% of the land under cereal production (Fischer & Edmeades, 2010). To ensure improved agricultural productivity, developing new varieties that are well adapted to specific environments is one of the main avenues for agricultural research centers. An effective approach to achieve this objective is the design of marker-assisted breeding programs which requires scientists to better understand the connection between a plant's observable characteristics (phenotype) and its genetic makeup (genotype). By establishing the connection between genotype and phenotype, it is possible to select high-yielding stress-tolerant plants and improve agricultural production to satisfy the requirements of the growing human population (Li et al., 2014; Thorp et al., 2015, 2018; White et al., 2012).

In the last two decades, genome sequencing of crops has proceeded at a rapid pace, but the translation of these data into the identification of desirable traits has been constrained by the lack of knowledge of the associated phenotypes (Furbank & Tester, 2011; Zaman-Allah et al., 2015). Besides, marker assisted breeding programs often require working with large mapping populations (more than 250 lines in most cases). Depending on the experimental design used or the number of treatments applied, phenotyping may require thousands of individual plots to be measured. To relieve this bottleneck and to fully benefit from the available genomic information, reliable, automatic, multifunctional, and high-throughput phenotyping platforms should be developed to offer plant scientists new insight into all the aspects of living plants. In recent years, high-throughput phenotyping platforms (HTPPs) have been developed (Araus & Cairns, 2014; Yang et al.,
2013). However, most of these are fully automated facilities in greenhouses or growth chambers, equipped with a precise environmental control. Although HTPPs enable the capture of detailed, non-invasive information throughout the plant life cycle, the results from controlled environments are distinct from the actual situations that plants will experience in the field, making it difficult to extrapolate data from controlled environments to the field. As a regional center of excellence that focuses on the genetic improvement of dryland crops (e.g., sorghum, pearl millet, fonio, groundnut, cowpea and sesame), CERAAS/ISRA proposes to engage the breeding community in the development of a field phenotyping network of testing sites. The goal is to provide a rich ground for interactions across disciplines in order to develop improved varieties and management packages adapted to specific stress scenarios and sensitive to socio-cultural contexts, and hence address the multiple complex challenges facing plant breeding in West Africa. Family farming is essential, and it must meet an increasing food demand in diverse and changing agro-climatic environments and in connection with changing agricultural sectors (i.e., food, non-food agricultural products, seeds). This chapter's objective is to propose a roadmap to associate different technologies—varying from manual measurements to image analysis and UAV onboard sensors—in a field phenotyping network in the Sahelian sub-region.

# 1. Institutional Positioning

CERAAS, Centre d'Études Régional pour l'Amélioration de l'Adaptation à la Sécheresse, is a research center of ISRA (Institut Sénégalais de Recherche Agronomique) specialized in drought adaptation studies. In 2018, CERAAS was elevated from a National Center of Specialization to an ECOWAS Regional Centre of Excellence (RCE) on dryland cereals (e.g., millet, sorghum and fonio) and associated crops (e.g., peanut, cowpea and sesame). The RCE is a joint entity between CERAAS, two other centers of ISRA (the Bureau d'Analyse Macroéconomique - BAME and the Centre National de Recherche Agronomique - CNRA), the Institut de Technologie Alimentaire (ITA) and the Ecole Nationale Supérieure d'Agronomie (ENSA-University of Thies). The primary mandate of RCE is to lead research on suitable drylands-cereal technologies and varieties in the West and Central African region.
With its historical partner, CIRAD (Centre de Coopération Internationale pour la Recherche en Agronomie pour le Développement), CERAAS is running a partnership network dedicated to innovation in crop improvement in Western Africa (dP-IAVAO which stands for dispositif en partenariat-Innovation et Amélioration Variétale en Afrique de l'Ouest, in French). The proposal of the dP-IAVAO is to rethink varietal improvement that has so far focused mainly on specialized agricultural and social systems. The aim is to recognize the plurality of agricultural systems and corresponding varietal demands, and to mobilize more disciplines and advanced tools towards more agile breeding programs that respond to the diversity and complexity of these agricultural systems.

Interdisciplinarity is key to this proposal, and it needs to be effectively integrated into innovative breeding programs. The scientific vision of the dP-IAVAO is thus structured around four research domains involving multiple disciplines and constitutes a platform fostering dialogue and interactions between its components:

- *Understand the crop improvement system.* This first domain aims at better describing the different components of the agri-food system in which crop improvement operates.

- *Genetic analysis and crop improvement.* This domain, which constitutes the historical heart of crop improvement, aims to mobilize modern genetic tools to explore the diversity and complexity of agricultural systems.

- *Variety adoption and innovation management.* The effective implementation of redefined breeding objectives and the creation of corresponding varieties provides the subject of an iterative analysis on the adoption and dissemination of this new diversity within traditional diversity.

- *Partnership modalities and research practices.* The context in which life sciences research operates has become significantly more complex over the past 30 years: international regulations on access to and sharing of genetic resources have evolved considerably, and at the same time, research products are increasingly subject to intellectual property constraints related, for example, to the development of the seed sector and biotechnology.

The dP-IAVAO was also part of an evolving West African partnership context with a new research structure led by CORAF and supported by
WAAPP/PPAAO, which aims at the emergence of Regional Centers of Excellence (RCE). The dP-IAVAO, which is based on existing research facilities and projects, is improving coordination at the regional level, but also guiding the design of new flagship projects and the development of infrastructures and technical capacity. Education and training are also an essential component of the dP-IAVAO as it contributes to the training of a new generation of plant breeders.

The geographical boundaries of the dP-IAVAO include the Sahelian and Sudanese (200 - 1200 mm) areas of West Africa where the cultivation of dry cereals such as millet, sorghum, fonio, and their associated crops such as cowpea, groundnut, and sesame is predominant. In this area, several breeding programs working on these species, notably from Senegal, Mali, Burkina Faso, and Niger, are involved in projects focused on innovative approaches for crop improvement (participatory selection, marker-assisted selection, etc.). However, other countries located in these areas, (Chad, Guinea, Cameroon, Côte d'Ivoire, Ghana, Togo, and Benin) and with research activities on one or more of the crops listed above, are also associated with the dP to develop collaborations on specific topics, in particular field phenotyping and training activities. The central theme of the dP-IAVAO is therefore the integration of a multidisciplinary approach in the design and effective implementation of innovative varietal breeding programs in response to the complexity of the agro-ecological systems encountered in West Africa. In terms of design, the breeding program is considered an integration place enabling the production of genetic materials responding to the demand.

## 2. High-throughput Phenotyping Methods

The crop phenotype is an expression of the genotype (G) and the environment (E) in which it grows. Crop phenotyping generally includes agro-morphological traits that provide indications on the dynamics of yield establishment and its genotypic and environmental variability (e.g., plant height, counting LAI, lodging, crop canopy cover), canopy spectral texture (i.e., spectral features), physiological traits (e.g., chlorophyll, biomass, pigment content, photosynthesis), abiotic/biotic stress indicators (e.g., stomatal conductance, canopy temperature difference, leaf water potential,
senescence index), nutrients (e.g., nitrogen concentration, protein content), and yield. Field phenotyping is a critical component of plant breeding programs, as the ultimate expression of the genetic factors, environmental factors, and their interaction on critical production traits, such as yield potential and tolerance to abiotic/biotic stresses (Araus & Cairns, 2014; Neilson et al., 2015). Field phenotyping is increasingly recognized as a key approach capable of delivering the required throughput and an accurate description of trait expression in real-world cropping systems. However, for a higher precision, the performances of breeding materials must be evaluated through a wide range of environmental conditions (González-Dugo et al., 2015; González-Recio et al., 2014; Rahaman et al., 2015).

Traditionally, phenotyping was done manually, and it was time consuming and expensive. To move towards high throughput methods, new phenotyping approaches relied largely on imaging technologies. Different methodological approaches that use spectral reflectance, canopy temperature by thermal imaging, and visible light imaging have been proposed to evaluate phenotypic traits in the field (Araus & Cairns, 2014). The agro-morphological traits of a crop can be estimated using an RGB camera (visible) by building the digital surface model (DSM) or the digital elevation model (DEM) and by conducting image classification analysis, which can be used to estimate the plant height, the proportion of lodging area, seedling emergence, etc. (Bendig et al., 2015; Hunt et al., 2005, 2010; Li et al., 2015). The absorption and reflectance characteristics of crops can be used to calculate vegetation indices related to agro-morpho-physiological traits (Nigon et al., 2015; Øvergaard et al., 2010; Swain et al., 2010). For instance, the temperature of the canopy is closely related to the cooling capacity of the plant, which depends on the transpiration of the crop, itself reflecting the plant water potential, stomatal conductance, etc., under stress or non-stress conditions. Crop yield can be predicted using proxies based on the combination of hyperspectral and thermal infrared data (Berni et al., 2009; González-Dugo et al., 2015).

Currently, advanced field-phenotyping platforms use high throughput ground wheeled or aerial vehicles deploying multiple types of sensors to measure plant traits on a timescale of a few seconds per plot. Although phenotyping with ground vehicles such as phenomobiles is extremely precise, the process can be time-consuming if there are too many plots
to phenotype (Cobb et al., 2013; Zhang and Kovacs, 2012). Therefore, this approach would be unsuitable for an African cross-regional network due to the cost, heavy maintenance, transport, and lack of maneuverability.

Satellite imaging technologies have become extremely useful when collecting data for various agricultural applications (Li et al., 2014; Sankaran et al., 2015). However, the major limitations of using the currently available satellite sensors are the high cost, the lack of spatial resolution for the identification of desirable traits, the risk of cloud cover, and long revisit periods (Gevaert et al., 2015; Han-Ya et al., 2010). For breeding programs, the resolution of the satellite sensor is still not enough to compare individual lines or plants. In contrast, UAVs have demonstrated capabilities for large-scale monitoring of crop condition due to the high spatial and spectral resolutions of the sensors.

In recent years, the use of UAVs has increased considerably in agricultural research programs due to the drop in prices, the miniaturization, the higher resolution of sensors, the ease of use (piloting, programming of flight plans), the improvement of computing (image analysis) and its strong potential for applications in breeding programs (Ballesteros et al., 2014; Berni et al., 2009; Candiago et al., 2015; Chapman et al., 2014; Gómez-Candón et al., 2013; Hunt et al., 2005; Liebisch et al., 2015; Zhang & Kovacs, 2012). This technology provides a low-cost approach to meet the critical requirements of spatial, spectral, and temporal resolutions. To assess the precision and efficiency for field-based phenotyping in small plots by different remote sensing techniques, three remote sensing approaches including UAV, proximal sensing, and satellite-based imagery, were compared. This study demonstrated that the UAV-based remote sensing was the most suitable approach for acquiring canopy temperature and normalized difference vegetation index (NDVI) in breeding populations (Tattaris et al., 2016). Therefore, UAVs are becoming critical in the high-throughput phenotyping of many plots and field trials in a near real-time and dynamic manner. UAVs can be used to execute autonomous tasks through the use of radio remote control equipment and an auto-control system, which can be divided into several types according to the flight mode (Sankaran et al., 2015). Field phenotyping platforms that use UAV tools have a higher potential thanks to the development and use of adapted sensors such as digital cameras, multispectral cameras, hyperspectral sensors, and infrared thermal imagers,
which are commonly deployed UAV-RSP sensors. With advances in miniaturization, new sensors based on light detection and ranging (LiIDAR) are upcoming. The main applications of these sensors for breeding programs include the use of visible imaging for canopy surface modeling, crop height, and biomass estimation (Diaz-Varela et al., 2014; Mathews & Jensen, 2013; Zarco-Tejada et al., 2014); the use of visible-near-infrared spectroscopy to identify physiological status (Nigon et al., 2015; Overgaard et al., 2010; Sugiura et al., 2005; Swain et al., 2010); thermal imaging to detect water stress (González-Dugo et al., 2013, 2014); LIDAR cloud point to measure plant's fine-scale geometric parameters with high precision (Wallace et al., 2012); and microwave imaging to estimate soil moisture and canopy structure parameters by combining different spectral bands (Acevo-Herrera et al., 2010; Han-Ya et al., 2010).

## 3. Network Construction

At the national level, the field phenotyping network relies on three levels. The first level is centered on fine phenotyping. The second level is focused on varietal evaluation in different environments, while the third level consists of evaluations in farmers' fields. In addition to the breeding aspect, the network is also of significant interest for all research and development activities in agronomy. The diagram presented in Figure 1 lists the possible research actions at the different levels.

<figure><img src="image_56.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 - Diagram of Activities Within the Field Phenotyping Network
Level 1: The fine phenotyping is done at a hub that is fully equipped and highly specialized to meet the needs of high throughput (i.e., the ability to conduct experiments with a large number of germplasms tested and to carry out precise measurements in a short time step). These representative high throughput phenotyping locations must also present all the guarantees to manage experiments with different agronomic treatments and should be selected based on environmental characterization, build on existing experimental sites that are functional, and back up the most advanced NARS (e.g., ISRA in Senegal, IER in Mali). This may require the development or rehabilitation of infrastructures like weather stations, drip irrigation systems, fencing, and modern data capture tools. There might be a scope for working in collaboration with the private sector at certain sites (e.g., Adventa or Corteva). The necessary equipment to carry out high-throughput phenotyping measurements should be available at these sites. Occasionally and depending on the resources, this equipment can be deployed to other sites (levels 2 and 3). These fine high-throughput phenotyping sites should also have all the advantages to conduct studies that aim to understand better crop adaptation mechanisms using functional-structural plant models. The development of infrastructures needs to be paired with a massive long-term training of students, technicians, and scientists/breeders ("multi-purpose breeders," capable of doing both breeding and reliable phenotyping) in order to develop a sense of ownership. Hence, three sites were chosen as potential high throughput phenotyping hubs across the subregion. Each hub is representative of the diversity of climate and soil conditions in the region (Figure 2): Bambey (ISRA research station, Senegal; 14.710825° N; -16.483797° E), Sotouba (IER research station, Bamako, Mali; 12.659558° N; -7.924943° E) and Farako-ba (INERA research Station, Bobo Dioulasso, Burkina Faso; 11.094251° N; -4.332719° E).
<figure><img src="image_57.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – Regional Isohyets Mapping & Possible Location of High Throughput Phenotyping Hubs in Bambey, Bamako, Bobo Dioulasso

Level 2: This level is based on the national agricultural research sites, which generally form a good network throughout the country. The main objective of this network is to evaluate the performances of the breeding lines in different environments, including hot-spots for specific stresses and then to study genotype-environment interactions. Studies carried out at this level are of great importance to define the target population environment (TPE) at the national and regional levels. Robust crop simulation models exist that can be used to: (i) characterize climatic scenarios in all target regions (main TPE) and (ii) guide the choice of trait-management packages to optimize productivity locally, even in the context of different socio-cultural contexts. This work requires good quality agronomic data from the network of Level 2 sites. The proposed work would engage breeders in generating input data and knowledge for crop models in order to guide breeding decisions. Breeding programs would then have greater insight on breeding targets (i.e., an area with known and prevalent stress type, required traits and management options, as well as appropriate breeding approaches). Besides, enriching the analysis of TPE with a socio-cultural analysis of potential cropping system variants within the TPE is essential to better design technical solutions, in particular to target cultivar/management improvement, to local constraints.
Level 3: Farmers' fields are used to test promising breeding lines that are foreseen for release in this production system. At this level, only yield and yield components will be measured. This level is characterized by uncontrolled crop management. Therefore, to have a good estimation of varieties' performances, the number of farmer fields should be as large as possible to integrate the diversity of cropping conditions.

## 4. Data Management

Much of the power of this field phenotyping network is in the way the data it generates are connected across sites and years. An efficient data management system needs to be established to ensure that evaluation and breeding data are integrated and shared across sites, network levels, and breeding programs. CERAAS, as a partner of IBP (Integrated Breeding Platform), benefits from logistic support for the use of the BMS (Breeding Management System). BMS is a complete software suite designed to boost the efficiency of plant breeding programs. This database combines crop information management, data analysis and decision-support tools to conduct most routine breeding activities, including the integration of different levels of molecular marker use. In other words, BMS represents an ideal tool to integrate data across the regional field phenotyping network.

## 5. Field Phenotyping Methodologies Developed in the Field Phenotyping Network

Based on the capacities of each partner/country and their financial possibilities, the development of the field phenotyping network is not synchronous among participating West African countries. The fine phenotyping hubs (level 1) and the evaluation sites (level 2) are generally located in national agricultural research stations. These sites have been well characterized previously and soil maps are usually available. They are equipped with a functional weather station, agricultural equipment, and have local manpower familiar with the requirements of research activities. For some sites, a renewal of the equipment is in progress. The field phenotyping network is expected to double in the next few years with the integration
of a near infrared spectroscopy (NIRS) network that will enable high-speed phenotyping in the field of biochemistry.

Currently, Senegal is the most advanced in setting up the network since level 1 (hub) and level 2 sites are operational. For Mali, the phenotyping equipment is missing. The purchase of the UAV, pilot training, as well as the use of the image processing pipeline, are all under consideration. Burkina Faso is lagging behind mostly due to funding reasons. Its level 1 site meets all criteria, but it still needs to be equipped with an irrigation system and UAV equipment, while in the level 2 sites, some weather stations are missing.

Across the different projects that allowed the start of West African field phenotyping network, UAVs technical solution has been chosen as an optimal solution to deploy across sites and trials. In Senegal the equipment consists of a FeHexaCopterV2 hexaCopter UAV system (Flying Eye Ltd., Sophia Antipolis, France). This UAV can carry three cameras fixed on a two-axis gimbal to point vertically downward. The first camera was an RGB ILCE-6000 digital camera (Sony Corporation, New York, NY, USA) with a 6000×4000 pixels sensor equipped with a 60 millimeter (mm) focal length lens. To minimize the blurring effect and noise in the images, the camera was set on speed priority (1/1250sec) and fixed on 100 ISO. The second camera is the AIRPHEN multispectral camera (Hiphen, Avignon, France) equipped with a 4.2 mm focal length lens and acquiring 1280×960 pixels images. The AIRPHEN includes six individual sensors equipped with filters centered on 450, 530, 560, 675, 730 and 850 nm, with a spectral resolution of 10 nm. The third camera is an infrared thermographic camera Tau 2 (Flir system, Oregon, USA) with 19 mm focal length.

The flight plan is defined with MikroKopter-Tools software (MikroKopter, Germany) to cover the entire area to be mapped and ensure an 80% frontal and lateral overlap along the track. Flight speed and image triggering depend on the flight altitude.
<figure><img src="image_58.png" /><figcaption>Figure 3 – View of an UAV Used for Image Acquisition with Its Onboard Sensors</figcaption></figure>

A semi-automatic image-processing pipeline developed by ISRA/CERAAS and CIRAD generates radiometrically calibrated and geometrically corrected multiband orthoimages using Agisoft PhotoScan digital photogrammetric software (PhotoScan Professional 1.4, Agisoft LLC, Russia) (See Part 2-Chapter 1). Real reflectance is computed using a radiometric reference target positioned to the ground and imaged at every UAV flight. This reference target is previously spectrally characterized in controlled conditions. Geometric correction is done using ground control points (GCPs) that have already been georeferenced. Orthorectification is then performed using GCPs to increase the accuracy of the generated orthoimages; vegetation indices can be extracted from the orthoimages. Using this UAV image processing pipeline, a good correlation between field measurements and proxies from the UAV images was observed in a study conducted during the 2019 cold dry season in Senegal which involved a sorghum panel (African population) of 202 entries (Gano et al., 2021) (Figure 4 and Figure 5). Similar correlation levels were observed on pearl millet and peanut populations (Diop et al., 2021) and calibration work is currently underway for cowpea.
<figure><img src="image_59.png" /><figcaption>Figure 4</figcaption></figure>

Figure 4 – Relationship Between Measured LAI and Biomass and Predicted LAI and Biomass Using the NDVI Extracted from UAV Images
## 6. Training

The training activities are already underway in different countries of the phenotyping network. A significant number of students have carried out their internships in the field with the network researchers. Two postdocs worked on the UAV phenotyping platform in order to develop the image analysis pipeline. Two PhD students are finalizing their thesis, and since the onset of the project a total of 15 MSc students have been supervised. A training session on eco-physiological and biochemical phenotyping as well as crop modeling were organized within the network or by the network's partners for PhD students, postdocs, and young scientists. It is expected that such training will be repeated over time in the framework of various projects. A sub-regional pilot contingent is already being built with two graduated pilots in Senegal.

## Conclusion

The field phenotyping network initiated in 2016 is now in its second phase of construction. The activities in Senegal are ahead of schedule, but there is little doubt that other countries participating in the network will catch up. The drone-borne imaging phenotyping methodology is now functional for sorghum and millet culture but still under construction for peanut. The existing capacities meet the requirements of field high-throughput phenotyping and for training. The field phenotyping network is being rapidly strengthened by a biochemical phenotyping network with the deployment of NIRS equipment. Accordingly, West African crop improvement programs will become autonomous for the acquisition and analysis of phenotypic data in the fields of biochemical and morpho-physiological traits, thus meeting the requirements to better support breeding programs in their goal of contributing to the food security of future generations.
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10. High-throughput Root Phenotyping: Opportunities and Challenges for the Adaptation of Arid and Semi-arid Crops to Future Climates

**Alexandre Grondin**, IRD, Senegal & ISRA/CERAAS, Senegal

**Rahul Bhosale**, University of Nottingham, UK

**Jonathan A. Atkinson**, University of Nottingham, UK

**Awa Faye**, ISRA/CERAAS, Senegal

**Dylan H. Jones**, University of Nottingham, UK

**Ezenwoko Benson**, University of Nottingham, UK

**James Burridge**, IRD, France

**Bassirou Sine**, ISRA/CERAAS, Senegal

**Vincent Vadez**, IRD, Senegal & ISRA/CERAAS, Senegal

**Tony Pridmore**, University of Nottingham, UK

**Darren M. Wells**, University of Nottingham, UK

**Laurent Laplaze**, IRD, France

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

**Malcolm J. Bennett**, University of Nottingham, UK

Abstract

Emerging constraints associated with climate change are posing an increasing threat to crop yields. In the arid and semiarid regions of Africa, crop failure, due to extreme events such as drought, is predicted to cause major food security issues. Root traits can improve crop resilience to drought and poor soil conditions by increasing the crop's ability to acquire water and soil nutrients. However, selection based on root traits in
agronomically meaningful contexts poses practical challenges. In fact, phenotyping represents a major bottleneck for root traits breeding, with trade-offs between precision and throughput. In this chapter, we will describe advances in low- and high-throughput root observation and sampling methods that can be easily deployed to measure root traits in field conditions. We will further describe a high-throughput root phenotyping pipeline that has recently been implemented to phenotype root architectural and anatomical traits in pearl millet grown in West African Sahelian conditions. These new methods can facilitate the selection of next generation crops with improved root systems that are more adapted to future climates.

Keywords: root, field, drought, pearl millet, shovelomics, laser ablation tomography

Introduction

Changing climatic conditions are challenging crop production worldwide. Therefore, it is important to develop crop varieties that are more resilient to extreme weather conditions (e.g., temperature, floods, or drought). Such efforts are particularly required for crops grown in low income Sahelian areas, where guaranteeing crop yields is of major importance for food security. Crops with improved root systems that better capture water and nutrients have recently demonstrated utility in adapting to future climates (Lynch, 2018, 2019; Rogers & Benfey, 2015; Uga et al., 2013).

Several root architectural and anatomical traits (see Figure 1) have been described as beneficial for improving drought tolerance. In maize, plants developing fewer crown roots with steeper growth angles developed more roots at depth (more carbon is invested in growth of individual roots) and showed increased drought tolerance (Gao & Lynch, 2016; Lynch, 2018). In crops growing in arid and semiarid environments—such as sorghum, pearl millet or chickpea—deep rooting may improve access to water and grain production, especially when drought occurs at reproductive and grain-filling stages (Lynch, 2013; Thorup-Kristensen et al., 2020; Vadez et al., 2013). Reduced xylem diameter may also improve transpiration efficiency (biomass produced/water used) and grain production under drought in dryland
cereals (Vadez, 2014; Vadez et al., 2014; Zhang et al., 2018). Breeding programs specifically targeting reduction in xylem diameter in wheat led to new varieties having more parsimonious use of water that yielded 11% more grains under drought conditions (Richards & Passioura, 1989).

<figure><img src="image_60.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 – Scheme of the Root Architecture and Anatomy of a Pearl Millet Plant at Vegetative Stage (1)

Note. The main architectural and anatomical root traits measured in high-throughput root phenotyping field studies are represented in the left and right boxes, respectively.

However, roots are complex organs constantly developing and adapting to the spatio-temporal heterogeneity in soil properties and resources (Maqbool et al., 2022). Although single traits have shown to improve drought tolerance in specific contexts, it is likely that more significant progress will be accomplished when considering root trait synergisms and integrated phenotypes (Lynch, 2022). Recently phenotyping multiple root traits, along with yield and yield-component related traits in field conditions, revealed
that clusters of root phenotypes allowing greater soil exploration, restriction of water uptake, and greater soil penetrability could be considered breeding ideotypes for drought tolerance in maize (Klein et al., 2020).

Identification of markers controlling these root traits through genetic approaches rely on robust high-throughput root phenotyping techniques. For pre-breeding approaches, ideally these techniques should be deployed in agronomical conditions (Lynch, 2022). Still, the high dependence of root development on the environment does not guarantee the successful use of these markers in breeding programs (Lynch et al., 2021). For that matter, identification of easy to measure root phenotypes that could represent a proxy for integrated phenotypes, and development of easy to deploy phenotyping techniques to measure them, may facilitate the integration of root traits in breeding schemes.

A large number of root phenotyping techniques in laboratory, greenhouse, or field conditions have been developed recently (for reviews see Atkinson et al., 2019; Tracy et al., 2020; Wasaya et al., 2018; Watt et al., 2020). Here, we specifically describe recent advances in low- and high-throughput techniques that can be easily deployed in field conditions for root observation, imaging, sampling, and further trait characterization. Some of these techniques can be used to identify the genetic determinants controlling root traits that are beneficial for crop adaptation to drought, as illustrated in a case study describing high-throughput phenotyping of pearl millet root architectural and anatomical traits in field trials in Senegal. These techniques may also be of interest for breeders seeking direct selection of desirable root traits in their breeding program.

# Root Phenotyping in Agronomic Conditions

## 1. Architectural Traits

**Trenches** – The trench method predicts root length density from root intersections counted on a vertical soil surface (trench). This method requires a calibration step where the relationship between root intersection density (RID) and root length density (RLD) is modeled (Chopart & Siband, 1999; Tennant, 1975). The model is relatively robust within one species grown
in one soil type; new calibration steps would be required when phenotyping
in other soil composition and organization (Faye et al., 2019b). Once the
model is created, the software Racine 2.2 calculates RLD and generates
2D maps along a soil profile (Faye et al., 2019a). These maps provide
agronomically meaningful information to estimate water or nutrient
acquisition in different soil horizons.

This method is not high-throughput but allows easy and low-cost analysis
of root system response to management practices or environmental factors.
Models for RID conversion to RLD exist for maize, sorghum, sugarcane,
rice, and pearl millet (Chopart & Siband, 1999; Chopart et al., 2008a, 2008b;
Dusserre et al., 2009; Faye et al., 2019b). In pearl millet, the model was used to
study root drought-responses of a drought-sensitive dual-purpose variety
and a less drought-sensitive inbred line in typical Sahelian soils (Faye et
al., 2019b). This approach revealed a reallocation of carbon for root growth,
combined with a reduction of RLD in topsoil layers under drought, which is
hypothesized to be beneficial for water capture (Faye et al., 2019b).

*Minirhizotron imaging* – Field minirhizotron imaging is a nondestructive method for dynamic visualization of root growth *in situ* (Vamerali et al., 2012). It involves installing a transparent glass or plastic tube inside the rooting zone of a plant and inserting a camera inside the tube to visualize the roots coming into contact with the tube. Tubes can be of different lengths and installed within the soil horizontally, vertically, or at an angle (usually between 30° and 60°) without excessive soil disturbance and with good soil-to-tube contact (Vamerali et al., 2012). Angled tubes are useful to obtain information about root vertical distribution; horizontal tubes installed at different depths provide information about root depth distribution (Johnson et al., 2001). Recently, an ingenious minirhizotron imaging facility, where plants were sown directly on top of 5.5 m tubes inserted into the soil with an angle close to the horizontal (15.8° and 23.5°), allowed a larger observation area per unit of soil depth covered (Svane et al., 2019b). Root images are usually performed using commercially available digital cameras (Arnaud et al., 2019; Ohashi et al., 2015), although improved root detection in soil was recently obtained using visible to near-infrared images (Svane et al., 2019a). Images are used to extract root traits, such as root intensity by counting root intersections on a grid superimposed on the image (Chen et al., 2019). Open-source or commercially available software such as RootFly (Zeng et al., 2008)
or RootSnap (Ohashi et al., 2015) can also be used to extract root length, root diameter, and root area from the images.

Minirhizotron imaging could be considered high-throughput because imaging can take from 90 seconds to 5 minutes depending on the imaging system and image resolution (Arnaud et al., 2019), although to date it has not been used in such fashion in field experiments. In semifield experiments (i.e., reconstructed field rather than open field), minirhizotron were used to study root depth in spring barley genotypes (Svane et al., 2019b) and the relationship between root depth and nitrate uptake in winter wheat (Chen et al., 2019; Wacker et al., 2022). The later studies demonstrated that deep root traits can predict deep nitrate uptake, which could be exploited to measure deep root growth and function using ¹⁵N tracer.

**Soil coring** – Soil coring involves the insertion of a core sampler to a defined depth at the proximity of a plant, followed by soil core breaks and soil collection at different depths for root washing, scanning, and RLD calculation (Böhm, 1979; Schroth & Kolbe, 1994; Shashidhar et al., 2012). The core sampler is usually made of steel pipes (10 cm to 45 cm in diameter and 60 cm to 2 m in length), with a sharpened end that enters the soil and a hinged opening to aid soil core removal (Henry et al., 2015; Wasson et al., 2014). The core size, number of cores taken, and location of core sampling varies between studies (Bengough et al., 2000). This technique is intrinsically prone to error and the significance of the results much depends on the location and the number of samples (Rossi & Nuutinen, 2004). Indeed, significant variations among soil coring locations within a plot have been observed. It is generally accepted that RLD variance decreases as the sample number increases. Virtual RLD measurements using the CRootBox model estimated that between 10 and 50 core samples per plot would be required to reach a 10% standard error in wheat and maize, respectively (Morandage et al., 2019; Schnepf et al., 2018). More recently, similar approaches comparing virtual soil coring using the OpenSimRoot model and actual field soil coring clarified the location (5 cm from the plant base between rows) and the sampling number (five) to best estimate RLD in maize and common bean (Burridge et al., 2020; Postma et al., 2017). Nonetheless, sampling at three locations around the plant within a plot, within four different plots, showed differences in RLD along the soil profile in rice (Grondin et al., 2018), while
a single sample per plot, within four plots, differentiated lines for RLD and maximum depth in wheat (Wasson et al., 2014).

Although soil coring cannot be considered high-throughput because of its labor-intensive characteristics, it was deployed to identify Quantitative Trait Loci (QTL) controlling root mass at different depths in a BC₂F₃-population, derived from a cross between Moroberekan and Swarna rice cultivars. Some of these QTL collocated with QTL controlling yield component-related traits (Dixit et al., 2015). However, the validity of these QTL remains to be demonstrated. To increase the throughput of soil coring for QTL identification, a new methodology was developed where coring was facilitated using a tractor-mounted hydraulic soil corer (Wasson et al., 2016). Root washing and scanning were replaced by automated counts of root impacts on both sides of soil-core breaks using UV illumination and fluorescence spectroscopy, allowing for RLD calculation directly in the field (Wasson et al., 2016).

*Shovelomics* – Shovelomics consists of manually excavating the crown root system using a shovel. The soil around the root system is then gently cleaned to manually measure root angle; to count crown roots number; or to take images to automatically extract numerous architectural traits, such as root angle, root width, lateral root length, and branching frequency using image analysis software such as Digital Imaging of Root Traits (DIRT) or Root Estimator for Shovelomics Traits (REST; Bucksch et al., 2014; Colombi et al., 2015). This method was developed on maize and used to successfully quantify root architectural traits in other crops, including common bean, cowpea, and wheat (Burridge et al., 2016; Das et al., 2015; Trachsel et al., 2011; York et al., 2018).

When combined with digital imaging, shovelomics is high-throughput and has been used to identify QTL controlling root length density or width in cowpea (Burridge et al., 2017). In maize, the deployment of this method on a panel of 400 sequenced genotypes, grown in different environments under irrigated and drought-stress conditions, allowed for better understanding of the interactions between the architectural traits associated with drought tolerance and the genetic determinants controlling architectural plasticity (Klein et al., 2020; Schneider et al., 2020b).
## 2. Anatomical Traits

**Hand & microtome sectioning** – Root anatomy is traditionally studied using histological approaches consisting in transverse sections of fixed or fresh tissue that can be observed under an optical microscope (usually 50x to 100x magnification) equipped with a digital camera for image capture (Ruzin, 1999). Fresh materials are often conserved in ethanol or methanol (usually 50% v/v or above), and sections are obtained by hand or by using a vibrating microtome. Hand sectioning requires a high level of skill obtaining fine sections without damaging samples, especially in the case of thin roots (Lhotáková et al., 2008). Microtome sectioning requires a prior time-consuming step of paraffin wax or resin embedding, although simpler root embedding protocols in agarose without fixation have been successfully developed (Zelko et al., 2012). Recently, an updated protocol increasing throughput of sectioning using a vibrating microtome on fresh, nonfixed root samples conserved in ethanol and embedded in agarose has been developed (Atkinson & Wells, 2017). The main upgrade in the method consists of using custom designed, 3D-printed molds to embed five to 15 roots in a block that can be sectioned in a single cut. It should be noted, however, that fixation or embedding can cause deformations in the tissue structure due to softening or shrinking, which requires taking multiple images per sample to increase the likelihood of quality images.

Anatomical features can be quantified from the acquired image by using simple software image analyses such as ImageJ (Schneider et al., 2012), or more automated software such as RootScan (Burton et al., 2012), PHIV-RootCell (Lartaud et al., 2015), or RootAnalyzer (Chopin et al., 2015). Although these methods are not considered high-throughput, sectioning was used to study the anatomical features in a panel of sequenced 274 rice genotypes under both irrigated and drought-stress conditions to identify genetic determinants controlling six anatomical traits (root diameter; cortex diameter; stele diameter; metaxylem diameter; and number and stele diameter in proportion of root diameter) and their plasticity (Kadam et al., 2017).

**Laser ablation tomography (LAT)** – LAT is a novel imaging technology used for rapid and simultaneous sectioning and imaging of roots at spatial scales, (e.g., from 0.1 mm to 1 cm) with micron-level resolution (Hall & Lanba, 2019;
Strock et al., 2019). The fresh root segments are often preserved in ethanol (50% v/v or above), dehydrated using graded ethanol series (e.g., 75%, 85%, 95% and 100%), and then dried in a critical point dryer to preserve their cellular structure. Methanol can also be used for preservation before transfer to 100% ethanol prior to critical drying point. LAT also is frequently conducted on fresh samples without the critical drying point step.

The LAT imaging system consists of three components: an ultrafast pulsed UV laser source, a motorized linear stage to which the root sample is mounted, and a DSLR camera or machine vision camera with infinity-corrected long working distance objectives. The beam from the laser source is deflected through a galvanometer along a rapidly scanned line, which is used as a cutting sheet to ablate the root sample by moving it into the beam path using a high-precision motorized linear stage. Autofluorescence from the ablation of the sample by UV wavelengths is simultaneously captured using the camera system. A 3D stack of the cellular structure of the sample can be generated by feeding the sample into the ablation beam at fixed increments using the motorized sample stage.

Anatomical parameters can be qualified from LAT image stacks using semiautomated software such as RootScan or MiPAR (Burton et al., 2012; Schneider et al., 2020a; Strock et al., 2019) or highly automated, next generation machine-learning and deep-learning imaging software (Hall & Lanba, 2019). This method is high-throughput (each root sample takes between 2 minutes and 5 minutes to process). LAT has been recently used to measure root anatomical traits such as root cortical aerenchyma area, cortical cell file number, and cortical cell size in 641 maize genotypes grown in the field under irrigated and water-deficit conditions in different environments (Schneider et al., 2020a). The heritability of these traits was compatible with genetic association analyses and allowed the identification of QTLs and genes possibly involved in the control of root anatomical plasticity in response to drought (Schneider et al., 2020a). LAT has also been used to visualize root colonization by arbuscular mycorrhizal fungi (Strock et al., 2019) or more recently lignification in maize (Schneider et al., 2021b).
*Case Study: High-throughput Pearl Millet Root Architectural and Anatomical Phenotyping in West African Soils*

To better identify the root architectural and anatomical traits that are beneficial for drought tolerance in pearl millet and the genetic determinants that control these traits, an experiment was set up where 160 genotypes from the Pearl Millet inbred Germplasm Association Panel (PMiGAP) were grown in field conditions in the Centre National de la Recherche Agronomique (CNRA) of the Institut Sénégalais des Recherches Agricoles (ISRA) in Bambey, Senegal (14.42°N, 16.28°W). The PMiGAP panel includes inbred lines from Africa and Asia that are fully sequenced, allowing the identification of 29,542,173 Single-Nucleotide Polymorphisms (SNPs) that can be used for association genetics (Varshney et al., 2017). In our study, plants were grown during the dry season (March to June, which allowed for full irrigation control) under two treatments: irrigated, where plants received 30 mm of water twice a week; and drought stressed, where irrigation was stopped from 21 days to 42 days after sowing (DAS) to allow field dry down. The experiment was set up in an alpha-lattice design with four replications in each treatment, one plot being comprised of three rows of 10 plants with 90 cm between rows and 30 cm between plants within the row. At 42 DAS, three plants per plot with their main tiller tagged were harvested for root phenotyping (shovelomics and anatomics using LAT; see Figure 2) while the remaining plants were kept intact for morphological, yield, and yield components measurements at maturity.
<figure><img src="image_61.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – High-throughput Root Field Phenotyping Process
### 3. Shovelomics

**Root sampling and washing** – At 42 DAS, the crown root system over a depth of 15 cm to 20 cm was sampled on three plants from the middle row using a shovel (see Figure 3a). The root system was initially washed in buckets filled with water and the shoot part was separated from the root system at around 10 cm above the main stem (see Figure 3b). The number of tillers per plant was counted and the shoots were placed in bags for measurement of aerial dry weight. The root system was further separated in half by cutting the main stem lengthwise. Half of the root system was used for imaging while the other was used for root sampling and anatomical measurements.

**Root imaging** – One representative crown root was excised from the half root system used for imaging and both were installed in the imaging chamber. The chamber consists of a black board in front of which the roots were hung, protected from direct sunlight by a white cloth. All hanging elements were of dark color so as not to be recognized as root material. Only a scale marker and the tag identifier were white in color. A digital camera was installed on a tripod approximately 1 m away from the black board and images were taken at a resolution of 12 megapixels. The shutter speed of the camera can be adjusted in case of root movement (due to wind, for instance) as well as the focal length in case of large changes in light intensity across the day. Low and homogenous light reflectance from the black board is important for further correct root segmentation by image analyses software.
<figure><img src="image_62.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 – *Pearl Millet Root Phenotyping in Field Conditions*

Notes. (A) Root sampling using the shovelomics method (photo by J. Burridge). (B) Image of the shallow root system from a single plant after washing. The root system was cut in half and one crown root was excised (photo by A. Grondin). (C) Image of the anatomy of a root emerging from the fourth whorl using laser ablation tomography (photo by D. Wells).

**Architectural traits extraction** – The automated DIRT software was used to measure the architectural traits according to Bucksch et al. (2014) and Das et al. (2015). Briefly, before being submitted to DIRT, the size of the images was reduced by 90% and bulk analyses of more than 100 images were performed at once. Although DIRT output provides 78 traits in total (among which 28 traits are specifically for monocotyledons roots), data analysis focused on the following traits:

* crown roots: root angle (top and bottom), root width (top, bottom, and accumulated over 10% to 90% depth), projected root area and averaged root density.

* excised roots: average lateral root length, distance to the first lateral root from the root apex, lateral branching frequency.
The presence of tillers makes more difficult the homogeneous root cutting
into half and root hanging in a perfectly vertical position. Therefore, for
tillering plants, root angle measurements using DIRT may be prone to errors.
Alternatively, manual root angle measurements using the ObjectJ plugin
in ImageJ can be performed (methods can be found at
https://plantscience.psu.edu/research/labs/roots/methods/computer/
rsaj).

*Materials and equipment needed for shovelomics:*

* Shovels for root sampling

* Buckets for root washing

* Paper bags for shoot harvesting and drying

* Table and cutter for stem dissection

* Black board, nails, and clamps to hang the roots

* White cloth to cover the black board

* Digital camera (Nikon D5600—other models are available) and tripod

* Hard drives for images storing

* Computer with internet access for DIRT analyses

4. Anatomics

Root sampling and storage – On the root half that was conserved for anatomical sampling (see section Shovelomics/Root sampling and washing), roots emerging from whorl four (of the layer of crown roots 1 and 8 in Figure 1) were identified. Two root segments of around 2 cm from the stem base were sampled and placed in a labeled 2 ml tube containing 50% ethanol (v/ v) for conservation (roots can be conserved in higher ethanol concentration for several years) and transported to the analysis facility. Samples were then taken through an ethanol dehydration series from 50% to 100% (v/v) in 10% increments. Roots were left to rest at each dehydration stage for a minimum of 60 minutes. Following ethanol dehydration, samples were dried using a critical point dryer. This process removes all liquid from the sample while keeping the cellular structure intact, allowing samples to be stored indefinitely in sealed containers with silica gel desiccant.

*Laser ablation tomography – LAT was conducted on a single sample at a*
time. Each dried sample is fixed in position on the sample stage using a
magnetic clamp. Anatomical sections were then captured using the LAT at
30 µm intervals from the middle 1 cm of each root sample. In most cases,
between 10 images and 20 images were captured per sample (see Figure 3c).
However, when the section of sample being imaged was damaged, either
from initial harvesting in the field or during the drying process, further
images were captured. Anatomical traits were extracted using a deep-
learning approach trained on ground truth sections that were hand
annotated using CellSeT software (Pound et al., 2012).

Materials and equipment needed for anatomicsis:

* Razor blades for root sampling on the fourth whorl

* 2 ml plastic tubes labeled with ethanol resistant stickers for storing root samples

* Ethanol 50 % v/v for conserving root samples

• Boxes for storing the 2 ml tubes

* Absolute ethanol for sample dehydration in series

* Critical point dryer (Leica EM CPD300—other models are available)

* Sealed containers and silicone desiccant for dry sample storage

* Laser ablation tomograph (LatScan 2.0, L4IS)

* Data storage device and computer for image analysis

Prospects/Opportunities

Recent developments in image-based root phenotyping techniques now
allow quantification of a number of root traits in field conditions with a
throughput that is compatible with genetic approaches (Atkinson et al., 2019;
Tracy et al., 2020). In fact, an increasing number of studies using these
techniques (especially shovelomics and LAT) in genetic association
approaches to identify QTLs controlling root traits have been published
recently. Characterization of root diversity in large panels opens
opportunities for breeding as it accelerates the identification of heterotic
parents that can be used in conventional breeding approaches to validate
and introgress these QTLs in popular varieties. In common bean for instance,
germplasm identified with increased root length density in shallow soil were
crossed with traditional varieties with deeper root system (Burridge et al.,
2016; Burridge et al., 2019). This approach allowed the release of new varieties with shallower root systems that are more tolerant to low P stress in Mozambique (Lynch, 2022; Ndoye et al., 2022).

Large-scale phenotyping leads to the identification of haplotypes at the QTL regions of interest and ultimately of the genes underlying these QTLs. Candidate genes can be found through transcriptomics for the identification of differentially expressed genes between contrasted haplotypes and validated through reverse genetics. In maize, such approaches allowed the identification of a CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) controlling root angle (Schneider et al., 2021a). The maize cipk mutant showed a 10° steeper root angle in some nodal specific positions, 18% greater shoot biomass, and 29% greater shoot nitrogen accumulation under suboptimal nitrogen availability as compared to the wild type. Haplotypes can also be used for functional analyses aimed at characterizing the physiological significance of the considered root trait. The use of contrasted maize genotypes for root multiseriate cortical sclerenchyma (lignin accumulation in walls of outer cortex cells) showed that roots with larger sclerenchyma had increased penetration ability in compacted soils (Schneider et al., 2021b).

The architectural, anatomical, and functional characterization of specific root traits is useful for improving root structural and functional models that provide a better view of root growth, development, and functional complexity in the soil. For instance, OpenSimRoot simulates how root traits dynamically interact with the soil environment and how this interaction influences resources acquisition in the soil and plant growth (Postma et al., 2017). The integration of root models into crop and climatic models will be important to define the right root system for the right environment, and the inclusion of root traits into the definition of breeding product profiles for development of future varieties best adapted to the conditions they will be subjected to (Lynch, 2018; Windhausen et al., 2012).

Despite these recent intense prebreeding efforts, examples of deployment of root traits in breeding schemes remain scarce. In this context, continuing the efforts to develop root phenotyping in field conditions and making these techniques usable by breeders remain of major importance. Identifying shoot traits that represent proxies for the expression of root traits are of particular interest too. However, providing breeders with genetic markers for root
traits and phenotyping tools may not be enough to ensure the benefits of root traits because of their high degree of plasticity (Dwivedi et al., 2020). When a root trait is identified as beneficial in one environment, it is important to better apprehend how the expression of this trait, and its benefits, may change in other environments (Schneider & Lynch, 2020). Controlling the genotype by environment interaction affecting root traits is arguably the next biggest challenge in root research.

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## 11. Using Root-Soil Interactions in the Rhizosphere as Valuable Traits for Selection Against Drought

**Papa Mamadou Sitor Ndour**, IRD, Senegal

**Wafa Achouak**, Aix-Marseille Université BIAM/LEMIRE, France

**Thierry Heulin**, Aix-Marseille Université BIAM/LEMIRE, France

**Laurent Laplaze**, IRD, France

**Laurent Cournac**, IRD, France

### Abstract

The rhizosphere, meaning the soil volume influenced by the living roots, hosts several important ecological processes implicating the soil, the root system and active microbiota. These various interactions often impact soil carbon (C) content and nutrient dynamics, as well as soil water retention, by modifying its biological and physico-chemical properties. Well-known root adaptive traits for drought tolerance include deep rooting and increased root development, both of which ensure better exploration of the soil volume required for greater water uptake. However, the intensity of root-soil-microbiota interactions shape the size of the rhizosheath (i.e., the soil mass that remains attached to roots after plant excavation), which could modulate the water retention capacity of soil. Indeed, genotypes with larger rhizosheath respond better to drought stress than those with a smaller rhizosheath in several plant species. From a breeding perspective, intra-specific variation in rhizosheath size has recently been demonstrated in two important crops in West Africa: pearl millet and maize. Therefore, genotypes with large rhizosheath could be considered for varietal selection for adaptation to drought. Ongoing genome wide association studies (GWAS)
should confirm genetic control of rhizosheath size and map candidate genes
and investigations should be performed on the mechanisms that support this
genetically complex trait.

Keywords: Rhizosphere, Rhizosheath, Root-adhering soil, Drought, Root phenotyping

**Introduction**

West Africa faces many challenges to secure sufficient and sustainable food production. Indeed, water scarcity and soil nutrient deficiency are among the main constraints limiting crop production. Drought stress is considered one of the most harmful abiotic stresses limiting crop yields (Fang et al., 2017; Praba et al., 2009; Wang et al., 2017). Moreover, drought events are projected to be exacerbated by climate change in much of the world including West-African countries (IPCC, 2018).

Plants explore the soil for water and for various nutrients they need for growth through their roots. They can then develop their root system architecture, associate with a beneficial microbiota, and improve the biological and physico-chemical conditions of the rhizosphere to face drought stress (White et al., 2013a, 2013b). Therefore, it may be possible to cope with drought by selecting crop varieties that can better exploit/use the soil water for sustainable production. The interactions between plant roots and soil that occur in the rhizosphere, defined as the volume of soil affected by the exudates secreted by plant roots and colonized by soil microbiota (Pinton et al., 2007), are very important to explore.

An important manifestation of the interactions between plant roots, soil, and root-associated microbiota, is the formation of the rhizosheath, or the aggregation of soil particles around the root after plant excavation (Brown et al., 2017; George et al., 2014; Pang et al., 2017). The rhizosheath is also the part of the rhizosphere where the root-soil-microbiota interactions are more intense and therefore it presents optimized physico-chemical conditions for water and mineral acquisition compared to bulk soil. Many factors contribute to its formation, including root physiology (e.g., exudation, association with soil microbiota), or morphology (e.g., root architecture, length, and density of root hair) (Ndour et al., 2020; Pang et al., 2017). Because
of its role in plant growth under abiotic stress (including drought), this trait
is now gaining attention of plant biologists, especially by those exploring
the possibility of developing genetic tools for plant breeding, a now widely
discussed topic regarding various crop species. Importantly, the formation of
rhizosheath has been studied in pearl millet, the main cereal crop cultivated
in West Africa (Ndour et al., 2017).

In this chapter, we will (1) discuss the relationship between plant rhizosphere functioning and drought stress; (2) analyse the intra-specific variability of the interactions in the rhizosphere for some important crops in West Africa and; (3) discuss the opportunity to integrate these root traits in plant breeding programs.

Response of the Rhizosphere to Drought Stress

Water is a crucial resource for plant development, and drought stress is the most damaging constraint for plant productivity (Leng & Hall, 2019). Through their roots, plants acquire water and minerals directly from the soil. The root system is of great importance in the interaction between crop and soil fertility as they are the main interface between the plant and the soil (Mommer et al., 2016). Moreover, water uptake depends on the soil's physico-chemical characteristics, the physiology of plant roots and their morphological characteristics, as well as on the interactions between soil components and plant roots. Drought has several immediate consequences on plant physiology, including reduced seed germination rate, stomatal closure, reduced relative leaf water content and cell membrane stability, increased formation of reactive oxygen species, increased susceptibility to diseases, and reduced grain yield and quality (Zia et al., 2021). In the long term, plants develop various adaptation mechanisms to access water more efficiently. These include the development of several architectural and anatomical root traits such as root depth and aerenchyma formation (see Chapter 10, by Grondin et al. in this book), but also very interesting physiological traits which involve the soil matrix and its hosted microbiota (Grover et al., 2011; Lynch, 2011, 2019; Rodriguez et al., 2004).

The different interactions that take place in the plant-soil-microbe
interface define rhizosphere functioning. These interactions are mediated by
the root exudation (i.e., the deposition of organic compounds in the soil by plant roots). Root exudates are defined as the substances actively released by root in the soil from various mechanisms (Haichar et al., 2014; Nguyen, 2009; Sasse et al., 2018) and have a strong impact on the functioning of the rhizosphere (Cantó et al., 2020). Indeed, several studies have reported the influence of root exudation on the microbial composition of the rhizosphere in various crop species (Carvalhais et al., 2015; Ge et al., 2017; Haichar et al., 2008; Iannucci et al., 2021; Ma et al., 2018; Micallef et al., 2009; Neumann et al., 2014). Moreover, plant root exudation can be modulated in return by the rhizosphere microbiota (Canarini et al., 2019; Korenblum et al., 2020). Importantly, these interactions help plants to better withstand the various adverse effects of drought stress.

# 1. Plant Growth-promoting Microbiota

Microbial communities in the rhizosphere may be involved in plant response to drought. Under drought conditions, plant growth can be promoted by the direct response of soil microbiota as well as by indirect effects resulting from modification of root exudation (Vries et al., 2020). For instance, drought leads to a modification of the microbial community in the rhizosphere, including an increase in the relative abundance of *Actinobacteria* and *Firmicutes*, and a decrease of *Proteobacteria* and *Bacteroidetes* (Naylor & Coleman-Derr, 2018). In particular, a study on 30 species of angiosperms showed a significant correlation between the relative abundance of the root-colonizing *Streptomyces* and the drought tolerance of the host plant (Fitzpatrick et al., 2018). In this sense, a recent study on sorghum showed that drought stress increased the colonization of *Streptomyces* within the root and the rhizosphere, and that this colonization increases root development during drought (Xu et al., 2018). Recently, strains of *Acinetobacter calcoaceticus* and *Penicillium sp* isolated for their P-solubilization potential have been shown to mitigate the adverse effects of drought stress in foxtail millet (Kour et al., 2020). It will therefore be interesting to test on crop species cultivated in West Africa and their wild relatives, and to verify whether there is a genetic variation of rhizosphere and endosphere oligotrophic microbial communities that could help plants to better withstand drought conditions.
## 2. Arbuscular Mycorrhizal Fungi (AMF)

The association between plant roots and arbuscular mycorrhizal fungi is one of the most important plant symbiosis with soil microbiota (Cantó et al., 2020). It is widely recognised that plants associated with AMF have a greater water uptake capacity related to increased soil volume explored through the hyphal network. The positive effect of AMF on plant water uptake and plant drought tolerance was reported in both cereals and legumes. For example, inoculation of cowpea with the mycorrhizal fungus *Glomus deserticola* enabled the plant to withstand water stress and produced increased yield compared to the uninoculated control treatment (Oyewole et al., 2017). This positive effect was also observed in drought-stressed maize after inoculation with *Glomus versiforme* due to an improvement in the deleterious effects of drought-induced oxidative damage (Begum et al., 2019). In the case of pearl millet, mycorrhizal inoculation mitigated the effects of water deficit and helped the plants to maintain their biomass production (Fabbrin et al., 2015). So, these symbiotic associations are reliable opportunities and should be better explored for crop adaptation against drought in the West-African context.

## 3. Root Exudation and Rhizosheath Formation

Another striking and complex example of the physiological response of roots to drought is the formation of the rhizosheath around the root system. The rhizosheath is considered the most active compartment of the rhizosphere. The formation of the rhizosheath is an aggregation process responding to many factors including root exudation and production of bacterial exopolysaccharides (Ndour et al., 2020). Recently, it has been related to carbon exudation and dynamics in the rhizosphere at the early stages of development in pearl millet (Ndour et al., 2022). Price (1911) first hypothesized that plants developed rhizosheath formation as a mechanism to improve drought tolerance and root protection under arid conditions, a hypothesis that was recently confirmed (Alami et al., 2000; Benard et al., 2016; Pate & Dixon, 1996). Several other studies have reported a positive effect of a large rhizosheath in the growth of plants subjected to drought, such as foxtail millet and wheat (Basirat et al., 2019; T.-Y. Liu et al., 2018). A similar
result was also obtained for a drought-tolerant chickpea in which Rabbi et
al. (2018) showed greater water storage in the rhizosphere soil linked to a
larger rhizosheath and greater exudation of root mucilage. Indeed, some
of the factors affecting the rhizosheath, such as root exudation (mainly
C exudation) and the associated exopolysaccharide production by
rhizobacteria, increase organic matter content as well as soil aggregation
in the rhizosphere (Alami et al., 2000; Berge et al., 2009). A recent study
shows that moderate water stress leads to the formation of rhizosheath in
rice, and increased soil porosity and water content in the root-adhering soil
volume compared to bulk soil (Zhang et al., 2020).

Overall, various studies have shown that the rhizosheath trait increased plant fitness, particularly under drought stress conditions. It can be assumed that the combination of better water retention due to the increase in organic matter content, particularly microbial polysaccharides, and improved soil structuration, which both occur in the rhizosheath, contributes to better water storage and availability in the immediate root periphery. Thus, genetic variability of these physiological traits on crop species could be very interesting from a plant breeding perspective to improve crop management, especially in arid regions. The various interactions taking place in the rhizosphere and contributing to drought stress tolerance are summarized in figure 1.
<figure><img src="image_63.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 – Interactions Between Roots, Soil & Microbiota, which Occur in Rhizosphere to Help Plants Cope with Drought Stress

Note: EPS = Exopolysaccharide, PGPR = Plant Growth Promoting Rhizobacteria, AMF = Arbuscular Mycorrhizal Fungi
# Genetic Variation of Rhizosphere Interactions in Crops: Example of the Rhizosheath

It is widely recognized that interactions in the rhizosphere play an important role in the response of plants to abiotic stresses and in particular in plant drought tolerance (Cantó et al. 2020). However, the relevant plant traits that characterize these interactions are often difficult to define due to their complexity. Consequently, intra-specific variations in these interactions are difficult to demonstrate. Nevertheless, the size of the rhizosheath has been used to test the genetic variability of the intensity of rhizosphere interactions in various species.

## 1. The Specific Rhizosheath Weight as a Proxy to Phenotype Rhizosphere Interactions

Different methods have been used to phenotype for rhizosheath size (reviewed in Ndour et al., 2020). The specific rhizosheath weight (i.e., the relative mass of dry soil that adheres firmly to the roots after plant has been excavated and roots have been shaken) is now widely considered as a reliable proxy to evaluate the rhizosheath size (Ndour et al., 2020). Operationally, it is defined as the ratio between the mass of dry root-adhering soil and the mass of dry root (RAS/RT). This emerging parameter was used to estimate the rhizosheath size on wheat (Amellal et al., 1998; Gouzou et al., 1993), sunflower (Alami et al., 2000; Sandhya et al., 2009), durum wheat (Kaci et al. 2005), barley (George et al. 2014) and on pearl millet (Ndour et al., 2017). Another parameter used to estimate the rhizosheath size is the ratio between the RAS dry mass and the total root length (RAS/RL), the latter of which was reported to be strongly correlated with the RAS/RT ratio (Adu et al., 2017). This method of estimating rhizosheath size by calculating the RAS/RT or the RAS/RL ratio seems to be effective for high-throughput phenotyping as it was used in numerous studies that aim to highlight its genetic determinants.
## 2. Case Study of the Phenotyping of Plant Rhizosphere Using the Rhizosheath Size

The rhizosheath size (RAS/RT or RAS/RL) would be genetically determined in many cereal species. First, phenotypic variation has been demonstrated in wheat (Delhaize et al., 2012; Haling et al., 2010; James et al., 2016), in barley (George et al., 2014; Gong & McDonald, 2017) and maize (Adu et al., 2017).

Our preliminary phenotyping study evaluated the impact of pearl millet genotypic variation on rhizosheath size using the RAS/RT ratio. Briefly, we grew 86 different inbred lines of pearl millet in MW pots filled with an arenosol sampled at the Centre National de Recherches Agronomiques (CNRA) of Bambey (Senegal), and each pot watered to its water holding capacity. After 28 days of growth, the plants were harvested and the root-adhering soil mass (RAS) and the root biomass (RT) were weighted and used to calculate the RAS/RT ratio, which was used as an estimator of rhizosheath size (Ndour et al., 2017). The results showed genotypic differences in pearl millet rhizosheath size. Subsequently, we replicated this experiment using the same protocol with a larger panel comprising 181 pearl millet lines for GWAS purposes. The results confirmed the genetic control of this trait within this larger panel of pearl millet lines with a 3-fold variation of the RAS/RT ratio between the more contrasting phenotypes (Ndour et al., 2021) and a broad heritability of 0.72 (Fig. 2).
<figure><img src="image_64.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – Rhizosheath Size (RASRT Ratio) of 181 Pearl Millet Lines Grown in Greenhouse Conditions. n = 8 repetitions for each line.

3. Microbial Diversity Phenotyping in the Rhizosphere
of Pearl Millet

As mentioned above, this rhizosheath size variation can be explained by
many factors. However, in a study we conducted in a set of 9 pearl millet
inbred lines selected for their contrasting rhizosheath size (RAS/RT), we
focused on the microbiota component and compared the diversity of
bacterial communities in the rhizosphere. This study concluded that the
size of this rhizosheath was related with bacterial diversity since, based on
16S rDNA sequences data, the relative abundances of some taxa including
*Proteobacteria* and *Firmicutes* varied significantly between lines
characterized by small and large rhizosheath (Fig. 3). Moreover, the
-diversity indices (e.g., Shannon, Chao1, Richness) calculated from these
sequences are higher for large rhizosheath lines compared to those with
small rhizosheath. This finding confirmed the response of this trait to
rhizosphere microbiota (Alami et al., 2000; Bezzate et al., 2000; Fernández
Bidondo et al., 2012) and suggested its genetic control by the host plant,
probably through root exudation, and its effect on root-soil-microbiota
interaction.
<figure><img src="image_65.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 – Relative Abundance of 15 Major Bacterial Phyla in Rhizosphere of 9 Contrasted Pearl Millet Lines and in Unplanted Control Soil (T) (Ndour et al., 2017)

Another study we performed with contrasting rhizosheath pearl millet lines grown in the field showed that some enzymatic activities (e.g., chitinase, acid phosphomonoesterase, FDA hydrolysis) were higher in the root-adhering soil of the large rhizosheath lines compared to that of small rhizosheath lines (Ndour et al., 2021), suggesting that plant genetics (through root exudation variation) control not only the structure of the microbial community but also their enzymatic activities. This finding confirms the importance of root-soil-microbiota interactions, notably the importance of rhizosheath formation in C, nutrient dynamics in the rhizosphere, and consequently in plant nutrition under abiotic stress such as drought.

Opportunities for Plant Breeding for Drought Stress
Tolerance

The potential of targeting root functioning for plant improvement against abiotic stress is well documented (Cantó et al., 2020; Lynch, 2019). This is particularly relevant in arid and semi-arid regions of West Africa where plants face multiple constraints including drought stress (Dossa et al., 2017; Drabo et al., 2019). Genetic variations in these root traits could be studied for the major crops of the region in order to ensure sustainable crop production.
For the rhizosheath trait for which genetic variations have been found in various crops, QTLs have been already detected in wheat (Delhaize et al., 2012; James et al., 2016; M. Liu et al., 2017) and barley (George et al., 2014; Gong & McDonald, 2017), although the molecular mechanisms explaining these phenotypes remain unknown. Adu et al. (2017) showed that rhizosheath was larger in maize improved varieties compared to landraces; based on these results, one may hypothesize that GWAS with a larger collection could therefore be used to detect QTLs and controlling genes in this important cereal for West-African countries. This trait should be further investigated for the other common cereals in West Africa such as sorghum. For pearl millet, the QTLs controlling this rhizosheath trait remain to be detected. However, the availibility of a reference pearl millet genome which was recently sequenced (Varshney et al., 2017) should make possible the rapid determination of the candidate genes related to this trait.

## Conclusion

Drought is the main abiotic stress negatively affecting crop yield via plant growth, health, and reproduction (Fahad et al., 2017; Lamaoui et al., 2018; Leng & Hall, 2019). In response to this constraint, plants adapt their root morphology but also develop complex interactions with the soil and the root-associated microbiota. In the case of root interactions, the formation of rhizosheath improves plant drought tolerance through the combination of (1) exploration of a larger soil volume and (2) better water retention capacity due to increased organic matter content. The genetic variability of these root traits observed in pearl millet opens new opportunities for genetic selection to support crop and food production resilience in West Africa.

## Acknowledgements

This work was supported by the NewPearl grant (co-funded by Agropolis Fondation [project N° AF 1301-015, as part of the "Programme Investissement d'Avenir" ANR-10-LABX-0001-0 1] and Fondazione Cariplo [N° FC 2013-0891]). We acknowledge the CORAF through the West Africa Agricultural Productivity Programme (WAAPP) and the Ministry of higher education and
research of Senegal government for the financial support allocated to PMSN
during his doctoral studies. We also thank the French National Research
Institute for Sustainable Development (IRD) and the Make Our Planet Great
Again (MOPGA) initiative of the French government for co-funding the
postdoctoral fellowship of PMSN.

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j.micres.2020.126626
12. Designing Dual-purpose
Sorghum Ideotypes for High
Grain and Biomass Yields
Suitable for Various Target
Environments in Senegal

Malick Ndiaye, ISRA/CNRA, Senegal

Myriam Adam, CIRAD, France

Diariétou Sambakhé, ISRA/CERAAS, Senegal

Aliou Sissoko, IER-CRRA-Sotuba, Mali

Bertrand Muller, CIRAD, Madagascar

Abstract

Sorghum is a water-efficient crop and an important cereal in semiarid and arid environments where water availability limits production. Providing farmers with sorghum genotypes with specific agro-morphological characteristics of high double production (grains and biomass) would help to maintain or obtain high yields in these environments. The SAMARA model was used to define dual-purpose sorghum ideotypes for different target environments. Model calibration and evaluation were carried out with experimental data collected in Nioro du Rip in 2015 and Sinthiou Malem from 2013 to 2016 on 10 contrasting sorghum genotypes. Daily weather conditions from 1950 to 2016 were used for the simulations. Also considered were the different environmental (soil and climate) conditions of Bambey, Nioro du Rip, and Sinthiou Malem (Senegal). Simulations for the identification of optimal genotypic parameters in these target environments highlighted three ideotypes showing performances for a dual-purpose (grain and fodder) production superior to the reference genotype Nieleni. This was thanks
to their strong capacity to colonize the soil (long roots) and to maintain
green leaves (stay-green) throughout the cycle. Hence, long roots and stay-
green trait should be strongly considered while selecting or developing dual-
purpose genotypes of sorghum for the target environments, especially in
less-watered areas like Bambey.

Keywords: Sorghum, dual-purpose, SAMARA, ideotype, target environment, Senegal

**Introduction**

Food security has become a major issue given the need to increase the
global food supply by about 70% in 2050 (FAO, 2009). However, given the
increasing limits to the expansion of cultivated areas in developing countries
(land saturation linked to demographic changes), a significant increase in
crop productivity seems necessary to achieve this objective. Sorghum is
the fifth most important cereal crop in the world and is the staple food of
more than 500 million people in more than 30 countries (ICRISAT, 2009).
It is a water-efficient crop, making it an important cereal in semiarid and
arid environments where water is the main limiting factor in production.
Despite its high-yield potential in terms of grains and biomass, sorghum
production is very sensitive to environmental conditions. In Senegal, the
average yield of sorghum, the third most important rainfed cereal after millet
and maize, remains low (1020 Kg.ha⁻¹; ANSD, 2018) with an estimated total
area of more than 221,329 ha for a national production of 225,865 tons.
Indeed, its cultivation faces several constraints including low soil fertility
(Vanlauwe et al., 2010) and the lack of or low use of agricultural inputs
(i.e., fertilizers, seeds of improved varieties; Gerstenmier & Choho, 2015). In
addition, rainfall has inter- and intra-annual variabilities (Hansen, 2002).

To boost sorghum and other cereal crop production, farmers must be
provided with genotypes that are more efficient and better adapted to their
growing conditions. Thus, new sorghum genotypes with specific agro-
morphological characteristics that result in a high double production (grains
and biomass) in the target environments should be prioritized given the
emergency to increase sorghum yields. Several traits have been identified
because of their potential to develop sorghum ideotypes with high grain and
biomass yields: internode length, stem diameter, leaf number and size, and number of grains per panicle (Ndiaye, 2019).

Donald (1968) was one of the first to propose an “ideotype selection” approach to underpin varietal improvement programs, which until then had been based on two selection philosophies: “elimination of defects” and “selection for yield” (Chantereau & Nicou, 1991). He defined *varietal ideotype* as an idealized plant, or plant model (not yet in the sense of a mathematical model), that should yield a greater quantity or quality when grown as a cultivar. On the other hand, *selection for yield* is limited by the fact that the desirable combination of traits is usually achieved by chance.

Reynolds et al. (2011) emphasized the importance of mathematical modeling as a powerful tool for elucidating optimal combinations including tradeoffs between traits for the creation of varietal ideotypes. Cultivation models based on ecophysiological processes are commonly used in basic and applied research in plant science and natural resource management (Hammer et al., 2002; Passioura, 1996; Rötter et al., 2011; Sinclair & Seligman, 1996; White et al., 2011) because they provide the best-known framework for understanding complex plant processes and their interactions with climate and the environment. These models play an increasing role in guiding basic research by providing quantitative predictions and highlighting knowledge gaps (Hammer et al., 2006, 2010; Semenov & Halford, 2009; Semenov & Shewry, 2011; Tardieu, 2003). Modeling can thus provide a rational approach to design and test in silico new sorghum ideotypes optimized for given environments (Hammer et al., 2006; Quilot-Turion et al., 2012; Semenov & Halford, 2009; Semenov & Shewry, 2011; Tardieu & Tuberosa, 2010).

Therefore, this study aims to illustrate the capacity of modeling in the design of sorghum ideotypes with high yields of both grain and biomass for target environments in Senegal using the SAMARA crop model that simulates crop growth and development at a daily time step.
## 1. Model Description

The SAMARA model (Simulator of crop trait Assembly, Management Response and Adaptation) was initially implemented on the EcoTrop platform (programmed in Delphi language, Kouressy et al., 2008), a model development and simulation platform. This platform integrates a series of tools and interfaces: a library of simulation modules, a simulation engine, a user interface with graphical and numerical outputs, simulation scenarios that can be run in bulk, and a database for observed and simulated data among them. A description and commented source code of the model, the definition of all parameters (e.g., crop, plot, soil, and cropping practices), and all input and output variables are available at https://umr-agap.cirad.fr/nos-recherches/equipes-scientifiques/modele-samara. Only the principles and processes relevant to this study are described here. The version used for this study has been recoded under R version 3.2 (R Development Core Team, 2015) to facilitate the link with parameter optimization software and to make it more flexible within the developer community.

SAMARA is a deterministic crop model. Its objective is to simulate the growth of monocotyledons such as tropical cereals, rice, and sorghum. SAMARA simulates—according to radiation, temperature, water, and CO2—the growth and yield of cereals at the canopy scale at daily time steps. Input data for the model includes:

* Standard daily meteorological data (i.e., solar radiation or possibly insolation, minimum and maximum temperatures, mean wind speed, minimum and maximum relative humidity, potential evapotranspiration [Food and Agriculture Organization, FAO], and precipitation);

* Hydrodynamic properties of the soil (i.e., volumetric water content at wilting point, field capacity, and saturation); and

* Technical itineraries (especially sowing date and density).

Simple concepts described by Kouressy et al. (2008) are used to simulate biomass gain using the Big-Leaf approach, Lambert-Beer's law for the
interception of photosynthetically active radiation, and radiation use efficiency for carbon assimilation.

## 2. Model Calibration and Evaluation

### 2.1 *Experimental Data*

The modeling work (calibration and evaluation) was based on data from 11 trials conducted during the rainy seasons of 20132016 at three research stations of the Institut Sénégalais de Recherches Agricoles (ISRA), namely Sinthiou Malem (in 2013, 2014, 2015, and 2016), Bambey (in 2013), and Nioro du Rip (in 2015; see Table 1). The environmental conditions at the different sites are given in Table 2. The main characteristics of these trials (soil type, agronomic management, and standard daily climate data—minimum, maximum, and average temperature; minimum, maximum, and average relative humidity; global radiation; average wind speeds at 2 m from the ground; and rainfall height) that served as inputs for the model were described in detail by Ndiaye et al. (2019).

<table><caption>**Table 1**
List of Trials Used for the Calibration and Evaluation of the Model</caption><thead><tr><th>Trial</th><th>Zone</th><th>Code</th><th>Soil Type*</th><th>Sowing Date</th><th>Previous Crop</th><th>Calibration</th><th>Evaluation</th></tr></thead><tbody><tr><td>Sowing 1/2013</td><td>BBY</td><td>B13D1</td><td>Sandy</td><td>17/07/2013</td><td>Fallow</td><td></td><td></td></tr><tr><td>Sowing 2/2013</td><td>BBY</td><td>B13D2</td><td>Sandy</td><td>31/07/2013</td><td>Fallow</td><td></td><td></td></tr><tr><td>Sowing 1/2013</td><td>SIN</td><td>S13D1</td><td>Sandy loamy</td><td>25/07/2013</td><td>Fallow</td><td></td><td>1</td></tr><tr><td>Sowing 2/2013</td><td>SIN</td><td>S13D2</td><td>Sandy loamy</td><td>6/8/2013</td><td>Fallow</td><td></td><td>1</td></tr><tr><td>Sowing 1/2014</td><td>SIN</td><td>S14D1</td><td>Sandy</td><td>17/07/2014</td><td>Peanut</td><td>1</td><td></td></tr><tr><td>Sowing 2/2014</td><td>SIN</td><td>S14D2</td><td>Sandy</td><td>6/8/2014</td><td>Peanut</td><td>1</td><td></td></tr><tr><td>Sowing 1/2015</td><td>SIN</td><td>S15D1</td><td>Sandy</td><td>9/7/2015</td><td>Peanut</td><td></td><td></td></tr><tr><td>Sowing</td><td>SIN</td><td>S15D2</td><td>Sandy</td><td>8/8/2015</td><td>Peanut</td><td></td><td>1</td></tr></tbody></table>
<table><thead><tr><th>Trial</th><th>Zone</th><th>Code</th><th>Soil Type*</th><th>Sowing Date</th><th>Previous Crop</th><th>Calibration</th><th>Evaluation</th></tr></thead><tbody><tr><td>2/2015</td><td></td><td></td><td></td><td>2015</td><td></td><td></td><td></td></tr><tr><td>Sowing 1/2015</td><td>SIN</td><td>S16D1</td><td>Sandy loamy</td><td>25/07/2016</td><td>Fallow</td><td>1</td><td></td></tr><tr><td>Sowing 1/2015</td><td>NIO</td><td>N15D1</td><td>Sandy</td><td>16/07/2015</td><td>Cowpea</td><td></td><td></td></tr><tr><td>Sowing 2/2015</td><td>NIO</td><td>N15D2</td><td>Sandy loamy</td><td>13/08/2015</td><td>Fallow</td><td></td><td>1</td></tr></tbody></table>

Note. BBY = Bambey; SIN = Sinthiou Malem; NIO = Nioro du Rip.

*Classification according to the USDA method based on average data over the 0-30 cm horizon.

<table><caption>Table 2a
Environmental Conditions of the Different Trial Sites</caption><thead><tr><th>Code</th><th>Coordinates</th><th>Alt (m)</th><th>*SAN (%)</th><th>CS (%)</th><th>N (%)</th><th>OM (%)</th><th>Rain (mm)</th><th>R0-30 (mm)</th></tr></thead><tbody><tr><td>B13D1</td><td>14°42′N</td><td>20</td><td>94.2</td><td>6.6</td><td>0.15</td><td>3.1</td><td>644</td><td>180</td></tr><tr><td>B13D2</td><td>16°29′W</td><td></td><td>94.2</td><td>6.6</td><td>0.15</td><td>3.1</td><td>566</td><td>253</td></tr><tr><td>S13D1</td><td>13°49′ N</td><td>23</td><td>89.4</td><td>11.6</td><td>0.21</td><td>4.3</td><td>575</td><td>146</td></tr><tr><td>S13D2</td><td>13°55′ W</td><td></td><td>89.4</td><td>11.6</td><td>0.21</td><td>4.3</td><td>536</td><td>183</td></tr><tr><td>S14D1</td><td></td><td></td><td>91.2</td><td>10.2</td><td>0.17</td><td>5.7</td><td>488</td><td>158</td></tr><tr><td>S14D2</td><td></td><td></td><td>90.9</td><td>9.7</td><td>0.17</td><td>4.5</td><td>377</td><td>190</td></tr><tr><td>S15D1</td><td></td><td></td><td>93.7</td><td>6.3</td><td>0.32</td><td>3.5</td><td>505</td><td>52</td></tr><tr><td>S15D2</td><td></td><td></td><td>93.2</td><td>6.8</td><td>0.33</td><td>3.8</td><td>455</td><td>259</td></tr><tr><td>S16D1</td><td></td><td></td><td>84.1</td><td>15.9</td><td>0.55</td><td>10.6</td><td>447</td><td>230</td></tr><tr><td>N15D1</td><td>13°45′ N</td><td>45</td><td>92.4</td><td>7.6</td><td>0.31</td><td>3.5</td><td>943</td><td>196</td></tr><tr><td>N15D2</td><td>15°45′ W</td><td></td><td>87.0</td><td>13.0</td><td>0.43</td><td>6.1</td><td>747</td><td>329</td></tr></tbody></table>

<table><caption>Table 2b</caption><thead><tr><th>Code</th><th>Coordinates</th><th>Alt (m)</th><th>R30-60 (mm)</th><th>R60-90 (mm)</th><th>R90-120 (mm)</th><th>Tmin (°C)</th><th>Tmax (°C)</th><th>**Healthy</th></tr></thead><tbody><tr><td>B13D1</td><td>14°42′N</td><td>20</td><td>352</td><td>110</td><td>3</td><td>23</td><td>33.9</td><td>2</td></tr><tr><td>B13D2</td><td>16°29′W</td><td></td><td>256</td><td>56</td><td>1</td><td>22.8</td><td>33.9</td><td>3</td></tr><tr><td>S13D1</td><td>13°49′ N</td><td>23</td><td>365</td><td>59</td><td>6</td><td>21.4</td><td>35.3</td><td>5</td></tr><tr><td>S13D2</td><td>13°55′ W</td><td></td><td>306</td><td>46</td><td>1</td><td>21.2</td><td>35.4</td><td>5</td></tr></tbody></table>
<table><thead><tr><th>Code</th><th>Coordinates</th><th>Alt (m)</th><th>R30-60 (mm)</th><th>R60-90 (mm)</th><th>R90-120 (mm)</th><th>Tmin (°C)</th><th>Tmax (°C)</th><th>**Healthy</th></tr></thead><tbody><tr><td>S14D1</td><td></td><td></td><td>213</td><td>88</td><td>31</td><td>22.2</td><td>35.7</td><td>4</td></tr><tr><td>S14D2</td><td></td><td></td><td>156</td><td>31</td><td>1</td><td>22.1</td><td>35.6</td><td>5</td></tr><tr><td>S15D1</td><td></td><td></td><td>259</td><td>153</td><td>43</td><td>21.8</td><td>34.7</td><td>2</td></tr><tr><td>S15D2</td><td></td><td></td><td>155</td><td>44</td><td>2</td><td>21.2</td><td>34.9</td><td>4</td></tr><tr><td>S16D1</td><td></td><td></td><td>155</td><td>24</td><td>38</td><td>22.5</td><td>35.6</td><td>5</td></tr><tr><td>N15D1</td><td>13°45' N</td><td>45</td><td>361</td><td>261</td><td>126</td><td>20.6</td><td>33.8</td><td>3</td></tr><tr><td>N15D2</td><td>15°45' W</td><td></td><td>273</td><td>145</td><td>0</td><td>19.7</td><td>33.8</td><td>4</td></tr></tbody></table>

Notes: BBY = Bambey, SIN = Sinthiou Malem, NIO = Nioro du Rip, Alt = Altitude, SAN = Sand, CS = Clay + Silt, * = classification according to the USDA method based on average data over the 0-30 cm horizon, R₀₃₀ = total rainfall between 0 and 30 days after sowing, R₃₀₆₀ = total rainfall between 30 and 60 days after sowing, R₆₀₉₀ = total rainfall between 60 and 90 days after sowing, R₉₀₁₂₀ = total rainfall between 90 and 120 days after sowing, ** = score given to a given environment according to disease level: The favorable situation takes the score 5 (absence of disease) and the unfavorable situation the score 1 (strong presence of disease), Rain = total rainfall during the trial.

The plant material consisted of 10 genotypes from various regions of West and Central Africa that are known to perform well in their respective zones of distribution: Fadda, Nieleni, Pablo, Soumba, F2-20, 621B, SK5912, IS15401, Grinkan, and CSM63E. These genotypes have been selected to constitute a contrasting sample in terms of cycle duration, plant architecture (height, stem diameter in particular), structural characteristics (lignin, cellulose), and grain and biomass production. The characteristics of these 10 genotypes were described by Ndiaye et al. (2019). In these trials, observations on flowering and maturity dates, leaf area indices (LAI), biomass and plant height dynamics, yields (grain and biomass), and yield components were used for model calibration and evaluation. Total aboveground biomass and its distribution among stems, leaves and panicles, LAI, and leaf area numbers were monitored weekly in 2013 and 2014 and every 12 days in 2015 and 2016 for up to 10 days after flowering.
## 2.2 Calibration and Evaluation

The calibration of a model should in principle be carried out using data from trials conducted in well-controlled agronomic situations so that factors not considered by the model (e.g., diseases and weeds) do not sway data. Then the calibration of the model must be evaluated using data that are different and independent from those of the calibration and that come from trials also conducted under good agronomic conditions, if possible. Thus, the data from the three trials conducted in 2014 and 2016 at Sinthiou Malem (S14D1, S14D2, and S16D1) were used to calibrate the phenology, the dynamics of plant biomass, the height, the number of leaves appeared, and the final yields. The LAI was only calibrated on trial S16D1 because of the lack of LAI data in 2014 and because S16D1 was the best trial of all of the experiments with a very good soil fertility, an absence of disease, and low overall rainfall. Therefore, the LAI data from this trial are assumed to be the best. In addition to their good agronomic conditions, S14D1 and S14D2 had a 20-day delay between sowing dates, which is important for the calibration of the phenology. The evaluation of the model was done with trials conducted in 2013 and 2015 at Sinthiou Malem (S13D1, S13D2, and S15D2) and in 2015 at Nioro du Rip (N15D2; see Table 1). These trials had relatively good agronomic conditions (i.e., absence of diseases for S13D1 and S13D2; and less diseases for S15D2 and N15D2; overall more or less good fertility; see Table 2). The S13D1 and S13D2 assays were used for the evaluation of phenology, dynamics of plant biomass, height, number of leaves, yield, and yield components; S15D2 and N15D2 were used only for phenology, yield, and yield components. The dynamic measurements on these trials had been abandoned because of a weak development and heterogeneities observed on some plots. Parameterization was done as described by Ndiaye (2019). Details of further model parameters and their function and default values are provided in https://umr-agap.cirad.fr/nos-recherches/equipes-scientifiques/modele-samara.
## 2.3 Choice of Traits of Interest and Genotypic Parameters Used for Optimization

SAMARA, like most crop models, involves many parameters whose values need to be known or estimated so that the model can function correctly. There are 76 such parameters in SAMARA (Dingkuhn et al., 2013). Some have little influence; others are relatively well-known and not very variable, so that it is not necessary to optimize all of them. A choice must therefore be made for ideotype design based on the characteristics of interest (i.e., the output variables of the model) to be optimized and the sensitivity of these variables to the parameters that influence them. Thus, the choice of the parameters on which optimization for ideotype design must be done is determined either by expert opinion or based on field and modeling studies carried out beforehand for the identification of the model parameters to which the traits of interest are sensitive, in this case, grain and biomass yields.

Our previous study on the associations and plasticity of traits contributing to both grain and biomass yields (Ndiaye, 2019) highlighted four traits that could improve the yield potential of dual-purpose sorghum thanks to their strong correlation with grain and biomass yields. These traits are the number of green leaves, stem diameter, internode length, and number of grains per panicle (strongly correlated to grain yield). We defined the parameters of the SAMARA model that correspond to these traits of interest and whose values affect the output variables of interest (i.e., grain yield and biomass yield). For the optimization, the genotypic parameters of the model used were similar to the traits explained above. They are coefficient of sensitivity to leaf mortality (CoeffLeafDeath) for the number of green leaves, structural mass of the internode (CoeffInternodeMass) for the stem diameter, maximum length of the internode (InternodeLengthMax) for the internode length, and structural mass of the panicle (CoeffPanicleMass) for the number of grains per panicle. Additionally, the maximum root length (RootFrontMax) was considered because of its importance in the studied environments, which are often confronted with water stress. The range of values of these parameters (minimum and maximum values) based on the minimum and maximum values observed during the experiments or on the range of values of the parameters derived from the genotype calibration is given in Table 3.
<table><caption>**Table 3**
Genotypic Parameters Used for Ideotype Optimization and their Range of Variation</caption><thead><tr><td>Parameter</td><td>Definition</td><td>Unit</td><td>Range of variation</td></tr></thead><tbody><tr><td>RootFrontMax</td><td>Maximum root length</td><td>mm</td><td>200-2000*</td></tr><tr><td>CoeffLeafDeath</td><td>Coefficient of sensitivity to leaf mortality</td><td>-</td><td>0-0,03</td></tr><tr><td>CoeffInternodeMass</td><td>Structural mass of the internode</td><td>g.mm<sup>-1</sup></td><td>0.005-0,1</td></tr><tr><td>InternodeLengthMax</td><td>Maximum length of the internode</td><td>mm</td><td>80-500</td></tr><tr><td>CoeffPanicleMass</td><td>Structural mass of the panicle</td><td>fraction</td><td>0,12-0,3</td></tr></tbody></table>

Note. *Range of variation given in the SAMARA model

## 2.4 Optimization Method to Define Dual-Purpose Sorghum Ideotypes

All three experimental sites were used as target environments for the optimization of dual-use sorghum ideotypes. For each environment, 67 years of site-specific daily weather conditions were used for the simulations: official data of the national meteorological division over the period 1950-2016 cleaned by ISRA-CERAAS and comprising average rainfall, maximum and minimum temperatures, solar radiation and insolation, minimum and maximum humidities, wind speed at 2 m from the ground, and potential evapotranspiration (FAO method). Each environment is also characterized by its soil type, its technical itinerary (resulting from our experiments), and by a sowing date based on the rainfall criterion used by Sambakhé (2018), for example, a cumulative rainfall of 30 mm over 3 consecutive days. This criterion is an adaptation of the 20 mm over 3 days criterion usually used in West Africa (Balme et al., 2005) to secure sorghum emergence. Indeed, in the target environments, and in general in West Africa, increasing instability in the start dates of the rainy seasons with dry sequences often necessitates reseeding.

Given its dual-use capabilities, the genotype Nieleni was considered as our reference and used to define the initial genotypic parameters required for the SAMARA model (Ndiaye et al., 2019). The values from the calibration were used. Then we optimized for each target environment the five genotypic parameters that were described in the previous section to define the typical
ideotype of each of these environments. For this optimization, the minimum and maximum values (range of variation for optimization) were defined according to the minimum and maximum values observed in all our experiments for the Nieleni genotype (see Table 3).

For this purpose, the Rgenoud genetic optimization algorithm (Package R) was coupled to the SAMARA model (Sambakhé, 2018) to determine the optimal values of the genotypic parameters for the best performances (maximum grain and straw biomass yields) of sorghum ideotypes in a target environment. We optimized the five parameters chosen over the 67 years of climate in each target environment. This made it possible to define the ideotype, per target environment, with the highest average yield (grain and biomass) over 67 years and the lowest coefficient of variation. Thus, we obtain one ideotype per target environment with a high and stable grain and biomass yield. The P function to be optimized parameter was defined as follows:

$$P = \max \{\text{Simulated average yield of 67 years for a Minimum coefficient of variation}\} \quad (1)$$

## 2.5 Analysis of Ideotype Performance

A scatterplot was performed to compare the reference genotype Nieleni and the ideotype resulting from the model optimization across the target environments. For this, the reference genotype and the reference ideotype for each target environment over the 67 years were first simulated. Then, to compare the performance of Nieleni and the ideotype in terms of grain and straw biomass production, a potential index (IPO) for grain (or straw biomass) production for a given target environment was used

$$I_{PO} = \frac{Y_{ij} - \bar{Y}}{\bar{Y}} \quad (2)$$

where IPO = potential index of a given genotype or ideotype i for the production of grain or straw biomass for a given target environment j; Yij = grain (or biomass) yield of a given genotype or ideotype i in a given environment j over the 67 years; $\bar{Y}$ = overall average grain (or straw biomass) yield of the two genotypes over the same period across the different target environments.