# Unlocking the Power of Gene Banks: Diversity in Base Growth Temperature Provides Opportunities for Climate-Smart Agriculture

Clara Gambart, Jelle van Wesemael, Rony Swennen, François Tardieu, Sebastien Carpentier

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Clara Gambart, Jelle van Wesemael, Rony Swennen, François Tardieu, Sebastien Carpentier. Un-locking the Power of Gene Banks: Diversity in Base Growth Temperature Provides Opportunities for Climate-Smart Agriculture. Food and Energy Security, 2025, 14 (1), pp.e70029. 10.1002/fes3.70029 . hal-05051875

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ORIGINAL ARTICLE

OPEN ACCESS

# Unlocking the Power of Gene Banks: Diversity in Base Growth Temperature Provides Opportunities for Climate-Smart Agriculture

Clara Gambart¹,² ID

Jelle Van Wesemael¹ | Rony Swennen¹,³ ID

François Tardieu⁴ ID

Sebastien Carpentier¹,² ID

¹Laboratory of Tropical Crop Improvement, Division of Crop Biotechnics, KU Leuven, Heverlee, Belgium | ²Bioversity International, Biodiversity for Food and Agriculture, Leuven, Belgium | ³International Institute of Tropical Agriculture, Banana Breeding, Namulonge-Sendusu, Kampala, Uganda | ⁴Laboratoire d'Ecophysiologie Des Plantes Sous Stress Environnementaux, French National Institute for Agriculture, Food, and Environment, Montpellier, France

**Correspondence:** Sebastien Carpentier (s.carpentier@cgiar.org)

**Received:** 27 June 2024 | **Revised:** 28 November 2024 | **Accepted:** 10 December 2024

**Funding:** This work was supported by Consortium of International Agricultural Research Centers, Belgisch Ontwikkelingsagentschap, Horizon 2020 Framework Programme (LEAP-AGRI-229).

**Keywords:** base temperature | canopy growth | climate-smart agriculture | food security | genotypic diversity | growing degree days

## ABSTRACT

Implementation of context-specific solutions, including cultivation of varieties adapted to current and future climatic conditions, have been found to be effective in establishing resilient, climate-smart agricultural systems. Gene banks play a pivotal role in this. However, a large fraction of the collections remains neither genotyped nor phenotyped. Hypothesizing that significant genotypic diversity in *Musa* temperature responses exists, this study aimed to assess the diversity in the world's largest banana gene bank in terms of base temperature (*T*<sub>base</sub>) and to evaluate its impact on plant performance in the East African highlands during a projected climate scenario. One hundred and sixteen gene bank accessions were evaluated in the BananaTainer, a tailor-made high throughput phenotyping installation. Plant growth was quantified in response to temperature and genotype-specific *T*<sub>base</sub> were modelled. Growth responses of two genotypes were validated under greenhouse conditions, and gas exchange capacity measurements were made. The model confirmed genotype-specific *T*<sub>base</sub>, with 30% of the accessions showing a *T*<sub>base</sub> below the reference of 14°C. The Mutika/Lujugira subgroup, endemic to the East African highlands, appeared to display a low *T*<sub>base</sub>, although within subgroup diversity was revealed. Greenhouse validation further showed low temperature sensitivity/tolerance to be related to the photosynthetic capacity. This study, therefore, significantly advances the debate of within species diversity in temperature growth responses, while at the same time unlocking the power of gene banks. Moreover, with this case study on banana, we provide a high throughput method to reveal the existing genotypic diversity in temperature responses, paving the way for future research to establish climate-smart varieties.

## 1 | Introduction

With human population reaching its highest level ever recorded, agriculture is more than ever challenged to increase food, feed, fuel, and fiber production, without degrading the environment or impeding the future production potential (Foley et al. 2011).

Climate-smart agriculture encourages the implementation of flexible, context-specific solutions that increase the resilience and resource use efficiency of agricultural systems (Lipper et al. 2014). Hence, instead of a “one-size-fits-all” approach, the climate-smart framework encourages the cultivation of a diverse set of varieties, all adapted to the local environmental conditions

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). Food and Energy Security published by John Wiley & Sons Ltd.</footnote>
of the agro-eczone but each characterized by their specific
mechanisms to tolerate (a)biotic stress (Lipper et al. 2014).
This strategy, along with improved pest, water, and nutrient
management have been proposed as valuable opportunities to
maintain yield and alleviate yield gaps (Anderson, Bayer, and
Edwards 2020; Zorrilla-Fontanesi et al. 2020).

The 11 international gene banks of the Consortium of International Agricultural Research Centres (CGIAR) and the variety of national gene banks can play a pivotal role in the climate-smart agriculture framework. Under the conditions set out in the International Treaty on Plant Genetic Resources for Food and Agriculture, they can recommend accessions for breeding or distribute and evaluate varieties that possess advantages for the local conditions. However, a large fraction of the managed germplasm is not phenotyped (Anglin et al. 2018; Van den Houwe et al. 2020). We present here the case for the banana gene bank as a proof of principle. The Alliance of Bioversity International and CIAT holds the world's largest collection of banana (Musa spp.) known as the International Transit Centre (ITC). More than 1700 accessions are currently conserved, with about 50% of the collection being genotyped through next generation sequencing. This first high throughput phenotyping case study on banana wants to encourage the characterization and evaluation of our precious national and international gene banks.

Banana is a tropical C3 crop, currently cultivated in a wide range of climates in tropical and subtropical regions of Asia (43.2% of the global production), Africa (32.5%) and America (22.9%) (FAO 2023; Varma and Bebber 2019). They are consumed far beyond their cultivation area. With an annual export of 26.4 million tonnes, banana was the most important tropical export fruit in terms of quantity and monetary value in 2021 (FAO 2023). However, 84.5% of the global annual production (equaling 143.6 million tonnes) is consumed locally (FAO 2023). The East African highlands, a densely populated area in the African Great Lakes region, is the largest banana consuming region in the world (Karamura et al. 1998). The region is characterized by a hilly landscape, ranging from 900 to 2000m above sea level (m.a.s.l.), with peaks up to 5800 m.a.s.l. (WorldClim 2022). Average temperature is relatively low compared to other tropical areas, ranging from 17°C to 25°C, and decreases with increased elevation (WorldClim 2022). With a generally accepted reference base temperature (T<sub>base</sub>) of 14°C (Ganry 1980; Ganry and Meyer 1975), only few varieties are currently known to thrive relatively well above 1500 m.a.s.l. (Kamira et al. 2016; Ocimati et al. 2014). Suboptimal mean temperatures result in higher sucker development, increased crop cycle duration, reduced bunch weights and therefore, serious yield reductions (Kamira et al. 2016; Sikyolo et al. 2013; Turner et al. 2016).

Growing degree days (GDD) calculations have been extensively
used in agriculture to predict plant phenology stages (Baraibar
et al. 2018; Neild and Seeley 1977). However, most studies use
a crop-specific T<sub>base</sub>, explaining genotype-specific developmen-
tal rates by varying GDD rather than also taking into account a
different T<sub>base</sub>. This underestimates the crop genetic potential in
regions at higher elevation.

As host of the ITC banana collection, we hypothesize that sig-
nificant genotypic diversity in temperature response exists that

will enable future research to establish climate-smart variet-
ies. Hence, this study aimed to (i) assess the *Musa* genotypic
diversity in terms of *T*<sub>base</sub> and (ii) investigate the impact of a
projected climate scenario on the distribution and plant per-
formance in the East African highlands. To do so, we designed
the BananaTainer, a high throughput, tailor-made banana ger-
mplasm phenotyping installation (Carpentier and Eyland 2020;
Van den Houwe et al. 2020). In contrast to field studies, which
are time consuming and obscured by varying environmental
conditions, our approach enables a fast pre-field screening of
genotypic responses in a controlled environment (Negin and
Moshelion 2017). We present growth responses of different plant
organs in function of temperature, models for the genotypic
T<sub>base</sub> of 116 banana genotypes, and a validation of two contrast-
ing growth responses under fluctuating greenhouse conditions.

2 | Material and Methods

## 2.1 | High Throughput Phenotyping in the BananaTainer

2.1.1 | Plant Material and Growth Conditions

In vitro plantlets were obtained from the ITC of the Alliance of Bioversity International and CIAT, hosted at KU Leuven (BE) and acclimatized to autotrophic conditions for 5 weeks in a growth room in hydroponics. Per experiment, 504 selected plants (30 plants per genotype, 42 plants per reference genotype) were transferred to the BananaTainer. In total more than 6800 individual plants were grown and analyzed.

The BananaTainer is a container (12m×2.4m×2.9m) based, tailor-made, growth chamber (Urban Crop Solutions, Waregem, BE). The three level design allowed to grow plantlets up to a height of 55cm (Figure S1). Plants were positioned according to a re-restricted randomized design, ensuring 10-14 plants per genotype per level. They were grown per six in an 80cm×60cm tray holding 4L of nutrient solution, adjusted from Swennen et al. (1986). Every 12min the solution was refreshed with complete drain reuse. Filtering was ensured by a UV and 80µm particle filter. The environment was set at 70% relative humidity (RH), 400ppm CO2, and 12h/12h (day/night) light regime. LED lights (Urban Crop Solutions, Waregem, BE) provided a spectrum relevant for banana (50:45:5 blue:red:far red) (van Wesemael et al. 2020). Plant growth was evaluated at several temperature regimes: three level regimes programmed around an average temperature of 20°C (25°C/15°C 12h/12h day/night regime, vapor pressure deficit (VPD) of 0.45kPa), three around 25°C (30°C/20°C, 0.55kPa) and three around 30°C (35°C/25°C, 0.61kPa) for 6, 5, and 4weeks, respectively. Ambient temperatures and relative humidity were recorded in the BananaTainer by six loggers, two at each level (TESTO 175-H1, DE), resulting in an enhanced resolution of the screened temperature range between 18°C and 30°C (Figure S2). The regimes around 20°C correspond to the climatic conditions during the rainy season in the East African highlands, the regimes around 25°C mimic warmer conditions and the regimes around 30°C represent predicted optimal conditions, based on the areas currently under dessert banana cultivation (Varma and Bebber 2019). One hundred and sixteen accessions, belonging to 30 different subgroups were evaluated around 20°C and 22
accessions around 30°C of which four additionally around 25°C (Table S1). This subset covers a large genetic diversity, with special attention to the Mutika/Lujugira subgroup. Cachaco (ITC0643, ABB, Bluggoe subgroup) and Mbwazirume (ITC1356, AAA, Mutika/Lujugira subgroup) were chosen as reference genotypes, given their contrasting growth and stomatal responses to abiotic factors (Eyland et al. 2021; Kissel et al. 2015). They were present in each BananaTainer experiment.

## 2.1.2 | Growth Quantification

Plant size was measured non-destructively at the start of every BananaTainer experiment. These measurements included canopy area, plant fresh weight and height of the pseudostem. At the end of each experiment, plant growth was quantified both destructively and non-destructively. While non-destructive measurements were identical to the measurements at the start, destructive measurements consisted of fresh organ mass (leaves, pseudostem with rhizome, and roots) and leaf area of the individual leaves.

Canopy and individual leaf area were determined from topview RGB images with blue background and red reference surface of 50 cm² (Figure S3). These pictures were analyzed, using a color-based foreground-background segmentation based on SLIC superpixels (EBImage R-package, Olés et al. 2023). As such, the green plant parts and the red reference surface of known area were segmented out of the image enabling leaf area extraction (Figure S3) (Equation 1),

$$ \text{Leaf area} = 50 \times \frac{\text{Number of green pixels}}{\text{Number of red pixels}}. \quad (1) $$

Given the different durations of the experiments, interpolation was used to estimate the morphological variables, related to plant development, plant growth, and mass distribution at 4 weeks and compare the effects of the different treatments. As temperatures were kept stable during each experiment, leaves developed at a constant rate (Turner and Lahav 1983). Moreover, plant mass increases proportionally with leaf area, enabling the estimation of all morphological parameters at 4 weeks. This resulted in a dataset with a total of 36 variables (crop ontologies as defined by van Wesemael et al. 2019; Table S2).

## 2.1.3 | Base Temperature ($T_{\text{base}}$) Estimation

To evaluate $T_{\text{base}}$, genotype-specific canopy growth curves were established using the beta distribution model (Equation 2, Figure S4). This model has been successfully used to model yield (Ruiz-Vera et al. 2018; Varma and Bebber 2019), vegetative (Devi and Reddy 2018; Turner and Lahav 1983) and photosynthetic responses (Adams et al. 2017) to temperature:

$$ Y = Y_{\max} \times \frac{(T - T_{\text{base}}) \times (T_{\max} - T)^{\beta}}{(T_{\text{opt}} - T_{\text{base}}) \times (T_{\max} - T_{\text{opt}})^{\beta}} \quad \text{and} \quad \beta = \frac{T_{\max} - T_{\text{opt}}}{T_{\text{opt}} - T_{\text{base}}}. \quad (2) $$

where Y represents the weekly canopy growth; $Y_{\max}$, the maximum weekly canopy growth; T, the temperature; and $T_{\text{base}}$, $T_{\text{opt}}$,

and $T_{\max}$ the base, optimal, and maximum temperature at which canopy growth occurs, respectively.

To reduce the variability in growth response, per level, every plant was assigned to the nearest temperature and relative humidity logger (Section 2.1) and the average genotypic growth response per logger was calculated. In this way, six datapoints per experiment and genotype were obtained. If genotypes were screened in multiple experiments all datapoints were merged for growth modelling. Hence, growth models were based on 6, 12, and 18 datapoints for genotypes present in one, two, and three experiments, respectively. Model parameters were estimated by minimizing the root mean square error (RMSE) based on the Fletcher variable metric method across 100 distinct initial values (Rvmmmin R-package, Nash 2021). Among the 100 models per genotype, only models below the accuracy threshold were retained. This threshold was determined by a partial least squares discriminant analysis and decision tree modelling (Figure S5) (mixOmics R-package, Cao et al. (2021); tree R-package, Ripley 2022). The genotype-specific model with minimum RMSE was selected as final growth model.

$T_{\text{base}}$ precision is represented by the average standard deviation of the modelled $T_{\text{base}}$ of the reference genotypes (Section 3.2). As these genotypes were present in each BananaTainer experiment, multiple growth curves were developed for all experiment combinations.

## 2.1.4 | Current and Future Climate Impact Predictions

The spatial impact of current and future climates on plant performance in the current banana growing region of the East African highlands was evaluated in two different ways, using qGIS (v3.16.1-Hannover; qGIS Development Team (2018)). First, raster calculations were made to visualize the number of genotypes with $T_{\text{base}}$ larger than the average yearly temperature. Second, GDD raster calculations were made for two selected Mutika/Lujugira genotypes with contrasting $T_{\text{base}}$, Guineo (ITC0005; AAA) and N'Dundu (ITC0732; AAA), to evaluate the annual heat unit accumulation above their genotype-specific $T_{\text{base}}$ (Equation 3):

$$ \text{GDD} = \sum \left( \frac{\text{daily maximum} + \text{daily minimum} (^{\circ}\text{C})}{2} - T_{\text{base}} \right) \quad (3) $$

Global monthly current and future climate data was downloaded from WorldClim (2022) with a spatial resolution of 30 s (0.86 km² at the equator). Future climate is based on the CMIP6 future climate projections with emission scenario SSP5-8.5.

## 2.2 | Growth Response Validation in the Greenhouse

### 2.2.1 | Plant Material, Growth Conditions, and Quantification

Two Mutika/Lujugira genotypes with contrasting $T_{\text{base}}$, that is Guineo and N'Dundu, were selected for further validation. Both genotypes were evaluated during two greenhouse experiments: one in December 2020 and one in March 2021 (average
temperature of 17.1°C and 25.3°C, respectively). Frequency histograms of the experienced conditions are presented in the Figure S6.

*In vitro* plantlets, obtained from ITC, were acclimatized to autotrophic conditions for 8 weeks in the greenhouse in 10 L pots containing peat-based compost. The plants were then randomly positioned on high precision balances (max weight of 12 kg ± 1 g) of the Phenospex platform (Phenospex, Heerlen, NL). This multi-lysimeter setup registers the systems' weight every minute and couples it to a balance-specific watering unit, ensuring on a daily basis plant-specific irrigation (5 mL accuracy) until a water content of 2.3 $g g^{-1}$ (1.83 pF). To avoid evaporation, the soil was covered by plastic. Therefore, the systems' weight loss was only related to plant transpiration.

To follow up growth, pseudostem height was measured and top view images were taken weekly (Section 2.1.2) to evaluate canopy area ($LA_{topview}$). At the end of every experiment fresh weight of the leaves, pseudostem with rhizome, and roots were measured.

### 2.2.2 | Light- and CO₂ Response Curves

Light- and CO₂ response curves were developed for both genotypes (3 replicates per genotype) on the second fully expanded leaf using a LI-6800 with Multiphase Flash Fluorometer head (LI-COR, Nebraska, USA). Measurements were made in the morning to avoid afternoon stomatal closure (Eyland et al. 2021; van Wesemael et al. 2019).

Light response curves were developed after exposing the plants to 1500 µmol m⁻²s⁻¹ until a stable photosynthetic rate (A) was recorded. Afterwards PAR dropped to 0 µmol m⁻²s⁻¹ in several steps (1500–1200–900–600–500–450–400–350–300–250–200–150–50–0 µmol m⁻²s⁻¹) and a stable A was recorded at every level. The hyperbola-based and Ye function were applied to model light response for each plant using the excel routines of de Lobo et al. (2013). Models with the lowest RMSE were chosen.

CO₂ response curves were developed after exposure to 400 ppm CO₂ and 750 µmol m⁻²s⁻¹, as this PAR was considered saturating by all light response curves. After stabilization of A, CO₂ dropped to 30 ppm and afterwards increased to 1000 ppm in several steps (400–300–200–100–30–400–600–800–1000–400 ppm). CO₂

response models were developed using the Excel routines of Sharkey and colleagues (Sharkey 2016; Sharkey et al. 2007) to estimate the maximum carboxylation rate ($V_{cmax}$), maximum electron transfer rate ($J_{max}$), and maximum triose phosphate usage ($TPU_{max}$).

## 2.3 | Statistics

All data processing and statistical analyses were performed in R (V4.0.5). Treatment and genotypic differences in growth responses in the BananaTainer were tested using the Kruskal–Wallis rank sum test, followed by pairwise Wilcoxon post hoc tests ($\alpha$=0.05). Segmented regression on leaf area was performed to detect significant breakpoints in growth rate during the greenhouse experiments.

# 3 | Results

## 3.1 | Growth Temperature Responses Are Organ-Specific

Ambient temperature affected plant organs in different ways, with shoot growth (leaves, pseudostem, and rhizome) being more strongly influenced by a change in temperature regime than root growth. After 4 weeks at the 30°C BananaTainer regimes, new leaves appeared significantly faster ($p < 2.2 \times 10^{-16}$), pseudostems were significantly taller ($p < 2.2 \times 10^{-16}$) and shoot fresh weight significantly increased ($p = 2.5 \times 10^{-12}$). Root fresh weight was not significantly affected by temperature regime, resulting in differentially distributed masses among the organs. While pseudostem:plant ratios were significantly increased ($p < 2.2 \times 10^{-16}$), root:shoot ratios significantly declined ($p < 2.2 \times 10^{-16}$) (Figure 1).

## 3.2 | T<sub>base</sub> Is Genotype-Dependent, Revealing Diversity Within Subgroups

To determine the variability in $T_{base}$, genotype-specific canopy growth models of the two reference genotypes were estimated. The average standard deviation of $T_{base}$ was 1.4°C. While Cachaco's predicted $T_{base}$ was 17.0°C, it was extrapolated to 11.5°C for Mbwazirume (Figure 2).

<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Organ-specific growth responses. Pseudostem:plant (A) and root:shoot ratios (B) at the end of the experiment of the 22 genotypes screened at 20°C (blue) and 30°C (red) in the BananaTainer. Different letters indicate significant differences ($a > b$; $\alpha$ = 0.05; $n_{20°C}$ = 642, $n_{30°C}$ = 614).</figcaption></figure>
<figure><img src="image_4.png" /></figure>

**FIGURE 2** | Modelled canopy growth curves of the reference genotypes (Equation 2). Shaded area represents the SD of the 5 and 12 growth models developed for Cachaco and Mbwazirume, respectively. Black dots show the average growth response per experienced temperature based on a total of 411 Cachaco and 1058 Mbwazirume plants.

<figure><img src="image_5.png" /></figure>

**FIGURE 3** | Histogram of the predicted base temperature ($T_{\text{base}}$) of 116 genotypes. Dark blue bars represent the genotypes of the Mutika/Lujugira subgroup. Arrows refer to N'Dundu ($T_{\text{base}}$ = 8.7°C) and Guineo ($T_{\text{base}}$ = 13.6°C). Other rectangles correspond to different subgroups.

Among all screened genotypes, the average predicted $T_{\text{base}}$ was 14.3°C (±4.1°C). Thirty-five of the 116 genotypes (30%) had a predicted $T_{\text{base}}$ below the generally accepted reference $T_{\text{base}}$ of 14°C (Figure 3). $T_{\text{base}}$ of the Mutika/Lujugira genotypes, endemic to the East African highlands, spanned a large temperature range. N'Dundu had one of the lowest predicted $T_{\text{base}}$ (8.7°C), while for Guineo $T_{\text{base}}$ was predicted close to the reference (13.6°C) (Figure 3).

## 3.3 | Greenhouse Experiments Validate the Genotype Dependent $T_{\text{base}}$ and Root:Shoot Ratios

During the greenhouse experiment with an average temperature of 17.1°C, N'Dundu was the strongest growing genotype in terms of canopy, pseudostem and fresh plant growth (Figure 4). N'Dundu showed an exponential growth response determined by its break-point in linear regressions 3 weeks after the start of the experiment (Figure S7). During the last 3 weeks, growth increased more strongly than during the first 3 weeks. Additionally, comparing

with the 25.3°C greenhouse experiment, plants showed similar morphology changes, as the root:shoot ratio significantly decreased with increasing temperature ($p$ = 2.3×10⁻⁸) (Figure S8). In this fluctuating environment both root and shoot fresh weight significantly increased with temperature ($p$ = 6.3×10⁻⁸ and 3.3×10⁻⁹, respectively). On average, root fresh weight increased 2.5 times, while shoot fresh weight quadrupled.

## 3.4 | The Photosynthetic Capacity Plays a Crucial Role in Temperature Growth Responses

Light- and CO₂ response curves revealed significant increases in photosynthetic capacity ($A_{\text{max}}$) and light saturation point ($I_{\text{max}}$) with increased temperature ($p < 10^{-4}$; data not shown), as a result of significant increases in the three rate limiting processes ($V_{\text{cmax}}$, $J_{\text{max}}$, and TPUₘₐₓ) ($p < 10^{-4}$; data not shown). An increase in average temperature from 17.1°C to 25.3°C almost quadrupled $V_{\text{cmax}}$ (data not shown), while $J_{\text{max}}$ and TPUₘₐₓ values almost tripled (data not shown).
Both genotypes responded significantly different at 17.1°C (Figure 5), with N'Dundu showing a significantly higher $A_{max}$ and $I_{max}$ ($p$=0.04953) (Figure 5B,C). Among the three rate limiting processes, $J_{max}$ and $TPU_{max}$ were significantly higher in N'Dundu ($p$=0.04953) (Figure 5E–F). Additionally, transpiration efficiency (TE), defined as the increase in fresh plant weight per unit of water transpired, was significantly higher for N'Dundu at 17.1°C ($p$=0.00232) (Figure 5A).

## 3.5 | Future Climates Will Boost Plant Development Rates in the East African Highlands

Despite the relatively low $T_{base}$ of some genotypes, none of the screened genotypes are currently able to survive in the highest altitude regions of the East African highlands (Figure S9A). However, at lower located regions all screened genotypes would be able to grow as the average yearly temperature exceeds their

<figure><img src="image_6.png" /><figcaption>**FIGURE 4** | Growth response at the 17.1°C greenhouse experiment. (A) Average weekly canopy, (B) pseudostem (PS), and (C) fresh plant growth at the 17.1°C greenhouse experiment. Different letters represent significant genotypic differences ($a > b$; $\alpha$=0.05; $n$=8).</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**FIGURE 5** | Genotype-specific biochemical capacity at 17.1°C. (A) Transpiration efficiency (TE), (B) maximum photosynthetic rate ($A_{max}$), (C) light saturation point ($I_{max}$), (D) maximum carboxylation rate ($V_{cmax}$), (E) maximum electron transfer rate ($J_{max}$), and (F) maximum triose phosphate usage ($TPU_{max}$) at average day temperature of 17.1°C. Colors correspond to the genotype. Different letters indicate significant genotypic differences ($a > b$; $\alpha$=0.05; $n$=3).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**FIGURE 6** | Current and future yearly GDD accumulation of Guineo and N'Dundu in the banana-growing region of the East African highlands. Upper row (A–D) represents the graphs for Guineo, bottom row (E–H) for N'Dundu. From left to right, graphs represent the GDD accumulation in current climate (A, E), 2041–2060 (B, F), 2061–2080 (C, G), and 2081–2100 (D, H) (Source: Banana cultivating region from Ocimati et al. 2019; current and future climate from WorldClim 2022).</figcaption></figure>

genotype-specific $T_{\text{base}}$ (Figure S9A). In Kawanda (Central Uganda), Guineo would currently accumulate 2900 GDD annually (Figure 6A). N'Dundu would acquire 4700 GDD annually (Figure 6E), accumulating as much GDD as Guineo in about 60% of the time.

Climate change, and its predicted increase in temperature, will also boost their development rates (Figure 6B–D,F–H). According to the SSP5-8.5 emission scenario, mean annual temperature is expected to increase in Kawanda with 6.2°C up to 27.5°C by 2100. At this average daily temperature, Guineo and N'Dundu would accumulate 75% to 46% more GDD as compared to the current environmental conditions, respectively (Figure 6D,H). This way, they would acquire 5000 and 6860 GDD annually (Figure 6D,H).

# 4 | Discussion

## 4.1 | Long-Term Exposure to Stable Temperature Conditions Reveals Genotypic Diversity in $T_{\text{base}}$

The beta distribution model revealed with high precision genotypic diversity in terms of $T_{\text{base}}$ (Figures 2 and 3). Within species diversity in temperature growth responses is under debate (Parent and Tardieu 2012). Turner and Hunt (1984) claimed that genotype-specific $T_{\text{base}}$ exists and reported an estimated $T_{\text{base}}$ range for 26 screened banana genotypes between 6°C and 14°C. However, the latter study used linear extrapolation to determine the genotype-specific $T_{\text{base}}$. This method was later found to be less precise when used at temperatures close to the optimal temperature (Yan and Hunt 1999), thereby overestimating $T_{\text{base}}$ during extrapolation. We confirm this genotype-specific $T_{\text{base}}$

and reported that 20 of the 35 genotypes, that presented a $T_{\text{base}}$ below the reference $T_{\text{base}}$ of 14°C, belonged to the most important subgroups on the African continent (Mlali, Mshare, Mutika/Lujugira and Plantain) (Table S1). This considerable within subgroup variation in low temperature response was already observed by Turner et al. (2016) in plantains.

It is known that the Mutika/Lujugira genotypes have arisen by a cross between a *Musa acuminata* subsp. *banksii/Musa schizocarpa* hybrid and *Musa acuminata* subsp. *zebrina*, with a dominant contribution of *zebrina* (Christelová et al. 2017; Němečková et al. 2018). These hybridization events are thought to have occurred close to New Guinea and Java, from where the AA and AAA hybrids were introduced in East Africa during the first millennium (Perrier et al. 2019; Schoenbrun 1993). Based on comparative linguistic evidence, it is assumed that banana reached the area of the Great Lakes region before the end of the first millennium (Perrier et al. 2019). Mitotic instability along with other somatic mutations, epigenetics and continuous selection by farmers have led to the current wide phenotypic diversity (Kitavi et al. 2020; Perrier et al. 2019; Shepherd and Da Silva 1996; Šimoníková et al. 2020). This makes the East African highlands a secondary center of origin, and the East African highland bananas are unique to the region.

## 4.2 | Photosynthetic Capacity Contributes to the Genotype-Specific Temperature Suitability

Growth ranking of the 17.1°C greenhouse experiment (Figure 4) validated the predictions of superior growth for N'Dundu. Its photosynthetic capacity was about twice as high as that of Guineo, due to significant higher maximal triose phosphate
usage ($TPU_{max}$) and the capacity to process incident light by the photosynthetic apparatus $J_{max}$ (Figure 5). During photosynthesis, triose phosphates are exported from the chloroplast and converted to sucrose and starch, releasing inorganic phosphate which returns to the chloroplast to support further photosynthesis. $TPU_{max}$ reflects the maximal triose phosphate use rate established by starch and sucrose synthesis and hence represents the capacity to regenerate inorganic phosphate. The $TPU_{max}$ is known to be one of the major rate limiting photosynthetic processes at suboptimal temperature (Sage and Kubien 2007). If sucrose and starch synthesis are too slow, metabolites accumulate and $CO_2$ fixation is inhibited by a depletion of inorganic phosphate. Also $J_{max}$ is of utmost importance to grow under suboptimal temperature. It reflects the capacity to regenerate ribulose 1,5-bisphosphate. As observed in many other tropical species, the combination of high light intensities and relatively low temperature more severely affects photosynthetic rates due to photoinhibition, resulting in rapid reduction of starch levels in sensitive species (Bongi, Cnr, and Giugno 1987; Taylor, Jepsen, and Christeller 1972). This process of photoinhibition is expected to be of importance in subtropical and montane field conditions. Moreover, since it is very challenging to uncouple temperature and VPD and as nocturnal measurements neglect the impact of temperature on stomatal behavior and consequently gas exchange and photosynthesis, it may explain why Parent and Tardieu (2012) were not able to show significant variability in temperature response within the same species.

## 4.3 | Genotypic Diversity in Mutika/Lujugira Genotypes: A Treasure for Versatile Performance in Future Tropical Highlands

Temperature-wise, bananas are expected to thrive better in the East African highlands in future climates as compared to the current conditions (Figure 6). Given that the average first crop cycle of banana varieties requires between 3400 and 4500 GDD (Lassoudiere 2007; Turner 1995), Guineo would be able to finish its crop cycle within 12 months by 2040 in Kawanda (Figure 6B). In contrast, N'Dundu's relatively low $T_{base}$ would enable it to finish its crop cycle within a year under the current conditions if no other (a)biotic stress factors are at play (Figure 6E). Important to note is that environmental conditions resulting in the most vigorous growth of a genotype are also considered most optimal for yield. In banana, biomass is strongly linked to bunch yield. Strong positive correlations between bunch weight and leaf emergence rate (Swennen and De Langhe 1985), plant dry weight at harvest (Taulya et al. 2014), pseudostem volume at flowering (Stevens et al. 2020), pseudostem girth at flowering (Nyombi et al. 2009), and leaf area at flowering have been found (Aeberli et al. 2023).

Although the greenhouse experiment provides validation of two contrasting genotypes under more fluctuating conditions, validation in more agricultural relevant systems will be essential, especially since temperature changes are often intertwined with changes in other environmental parameters. Increased temperature in the highlands will coincide with increased evaporative demand (Muthoni et al. 2019). The majority of the Great Lakes Region currently has a bimodal rainfall pattern with a short and a long dry season (van Asten, Fermont, and Taulya 2011).

Mutika/Lujugira genotypes have been proven to be “risk takers”, meaning they have a relatively low water use efficiency and keep a high transpiration rate for a longer period during the day (Eyland et al. 2021; Kissel et al. 2015; van Wesemael et al. 2019). Increased temperature will thus correspond to increased water demand. Calberto and colleagues estimated the increased water demand associated to climate change to range from 12% to 15% (Calberto, Staver, and Siles 2015). Besides increased water demand, root:shoot ratios are predicted to decrease with increased mean annual temperature (Figure 1B, Figure S8), creating an additional imbalance between water supply and demand. As already recommended by Manners et al. (2021), adapted farm management will have to be applied to fully exploit the benefits of climate change. Currently Mutika/Lujugira genotypes grow best at elevations between 900 and 1200 m.a.s.l. (Kamira et al. 2016) and drought is already a major limiting factor in the area (van Asten, Fermont, and Taulya 2011). Hence, cultivation of Mutika/Lujugira genotypes might need to shift to higher elevations (Figure S9), where evaporative demand is limited and excessive water loss avoided. Depending on the water management, farmers would need to opt for more drought tolerant genotypes, such as Simili Radjah and Cachaco (Eyland et al. 2021; van Wesemael et al. 2019). But those more hardy ABB types are not suitable to make matooke. Therefore, breeding new East African highland bananas is also needed (Eyland et al. 2023).

## 4.4 | High Throughput Phenotyping Helps to Unlock the Power of a Gene Bank

Gene banks are important to conserve genetic material to address global food security because agricultural biodiversity can be lost in their natural habitat. Large collections for several crops have been established, but up till today these treasures remain underutilized (Anglin et al. 2018). The low proportion of characterized and evaluated conserved material has been put forward as the main reason (Anglin et al. 2018; Van den Houwe et al. 2020). High throughput phenotyping, as described in this study, helps to understand the potential of the conserved genotypic diversity in a fast and efficient way (Joseph Fernando, Selvaraj, and Ghamkhar 2023). Hand in hand with genomics and informatics technologies, these tools can reveal redundancy and sampling gaps in collections, guiding gene bank curators in new collection and conservation missions. The publicly accessible characterization and evaluation data will help researchers in their selection of the most suitable genotype for their specific purpose (Anglin et al. 2018; Langridge and Waugh 2019).

## 5 | Conclusion and Future Perspectives

This case study on banana revealed intra-crop variation in temperature growth responses, suggesting that genotypes with suitable characteristics for specific agro-ecozones can be found. This high throughput phenotyping technology provides useful information for recommending accessions for breeding or by distributing and evaluating the varieties under the conditions set out in the International Treaty on Plant Genetic Resources for Food and Agriculture. We, therefore, recommend stakeholders to continue screening gene bank collections in controlled environments, confirm the potential under agricultural relevant
situations and evaluate the genotype × environment × management interactions.

## Acknowledgements

This high throughput phenotyping study was the result of a collaboration between the engineering team of Urban Crop Solutions and the laboratory of Tropical Crop Improvement at KU Leuven. The authors would, therefore, like to thank Jean-Pierre Coene, Maarten Vandecruys, and Hendrik Siongers for their technical assistance to the hard- and software of the BananaTainer. The authors gratefully acknowledge Edwige Andre, Hendrik Siongers, Loïck Derette, Els Thiry, Hien Do, Barbara Grymonprez, Stan Blomme, Daan Van Gils, Simon Costers, and Kaat Hebbelinck for the plant propagation and their assistance during plant growth and phenotyping. C.G. and J.v.W. were supported by a PhD scholarship funded by the Belgian Development Cooperation project "More fruit for food security: developing climate-smart bananas for the African Great Lakes region". The authors thank all donors who supported this work through their contributions to the CGIAR Fund (http://www.cgiar.org/who-we-are/cgiar-fund/fund-donors-2/), and in particular to the CGIAR Research Program Roots, Tubers and Bananas (RTB-CRP) and to the ERA-Net transnational call European Research Projects LEAP Agri H2020 co-fund project on food & nutrition security & sustainable agriculture, with funding from national funding agencies for the Project "Phenotyping The Banana Biodiversity to Identify Climate Smart Varieties With Optimal Market Potential in Africa and Europe."

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

Data available on request from the authors.

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**Supporting Information**

Additional supporting information can be found online in the Supporting Information section.