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\begin{document}

\title{Intraspecific variation in growth response to drought stress across
geographic locations and genetic groups in \emph{Coffea}
\emph{canephora} Pierre ex A. Froehner}



\author[1]{Catherine Kiwuka}%
\author[2]{Jan Vos}%
\author[2]{Bob Douma}%
\author[3]{Pascal Musoli}%
\author[4]{John Mulumba}%
\author[5]{Valerie Poncet}%
\author[2]{Niels Anten}%
\affil[1]{National Agricultural Research Organisation}%
\affil[2]{Wageningen University \& Research}%
\affil[3]{National Coffee Research Institute, National Agricultural Research Organization}%
\affil[4]{National Agricultural Research Organization}%
\affil[5]{IRD}%


\vspace{-1em}



  
  \date{May 07, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Uganda lies within the drier end of the natural distribution range of
\emph{Coffea} \emph{canephora} and contains unexplored genetic material
that could be drought-adapted and useful for developing
climate-resilient varieties. Using experimental treatments, (i) ample
and (ii) restricted-water, response of 148 genotypes were studied
comprising wild, feral and cultivated C. canephora. Biomass allocation,
standing leaf area and leaf area growth data were collected. Linear
mixed effect models and PCA were used to analyse effect of drought on
genotypes from different: (i) cultivation status, (ii) genetic groups
and (iii) locations. We assessed the relationship between drought
tolerance for relative growth rate in leaf area (RGRA), total number of
leaves (TNL), total leaf area (TL) and total leaf dry weight (TLDW) of
genotypes at final harvest. Restricted-water reduced RGRA across genetic
groups (3.2 -- 32.5\%) and locations (7.1 -- 36.7\%) but not cultivation
status. For TNL, TL and TLDW, genotypes that performed well in
ample-water performed worse under restricted-water, indicating
growth-tolerance trade-off. Drought tolerance in RGRA and TNL were
negatively correlated with wetness index suggesting some degree of
adaptation to local climate. Findings indicate a growth-tolerance
trade-off within this tropical tree species and drought tolerance of
Uganda's \emph{C.} \emph{canephora} is somewhat associated with local
climate.%
\end{abstract}%



\sloppy


Manuscript type: Research Article

Intraspecific variation in growth response to drought stress across
geographic locations and genetic groups in \emph{Coffeacanephora} Pierre
ex A. Froehner

Catherine Kiwuka\textsuperscript{a, b, *}, Jan Vos\textsuperscript{a},
Jacob C. Douma\textsuperscript{a}, Pascal Musoli\textsuperscript{c},
John W. Mulumba\textsuperscript{b}, Val\selectlanguage{ngerman}érie Poncet\textsuperscript{d},
Niels P.R. Anten\textsuperscript{a}

\textsuperscript{a}Centre for Crop Systems Analysis, Wageningen
University, Wageningen, The Netherlands

\textsuperscript{b}Plant Genetic Resources Centre, National Agricultural
Research Organization, P.O. Box 40, Entebbe, Uganda

\textsuperscript{c}National Coffee Research Institute, National
Agricultural Research Organization, P.O. Box

185, Mukono, Uganda

\textsuperscript{d}UMR DIADE, Univ. Montpellier, IRD, Montpellier,
France

*For correspondence. E-mail: kiwukakathyrn@gmail.com, Postal address:
P.O. Box 40, Entebbe, Uganda

\section*{Abstract}

{\label{abstract}}

Uganda lies within the drier end of the natural distribution range
of\emph{Coffea canephora} and contains unexplored genetic material that
could be drought-adapted and useful for developing climate-resilient
varieties. Using experimental treatments, (i) ample and (ii)
restricted-water, response of 148 genotypes were studied comprising
wild, feral and cultivated \emph{C.} \emph{canephora.} Biomass
allocation, standing leaf area and leaf area growth data were collected.
Linear mixed effect models and PCA were used to analyse effect of
drought on genotypes from different: (i) cultivation status, (ii)
genetic groups and (iii) locations. We assessed the relationship between
drought tolerance for relative growth rate in leaf area
(RGR\textsubscript{A}), total number of leaves (T\textsubscript{NL}),
total leaf area (T\textsubscript{L}) and total leaf dry weight
(T\textsubscript{LDW}) of genotypes at final harvest. Restricted-water
reduced RGR\textsubscript{A} across genetic groups (3.2 -- 32.5\%) and
locations (7.1 -- 36.7\%) but not cultivation status. For
T\textsubscript{NL}, T\textsubscript{L} and T\textsubscript{LDW},
genotypes that performed well in ample-water performed worse under
restricted-water, indicating growth-tolerance trade-off. Drought
tolerance in RGR\textsubscript{A} and T\textsubscript{NL} were
negatively correlated with wetness index suggesting some degree of
adaptation to local climate. Findings indicate a growth-tolerance
trade-off within this tropical tree species and drought tolerance of
Uganda's \emph{C. canephora} is somewhat associated with local climate.

\textbf{Keywords:} Intraspecific variation, drought stress, growth
tolerance trade-off, local adaptation \emph{Coffea canephora.}

INTRODUCTION

Water availability is a major factor limiting global coffee production
largely because of the drought sensitivity of \emph{Coffea} species and
because a large fraction of the production is sustained by small-holder
farmers who usually lack resources to establish irrigation facilities
(DaMatta and Cochicho Ramalho, 2006; Wintgens, 2009; Craparo \emph{et
al.} , 2015). Problems of water limitation in coffee production are
expected to be aggravated by climate change. This is because across the
coffee production belt, a temperature increase of 2.1° C has been
predicted by 2050 (Parry \emph{et al.} , 2007; IPCC, 2014) and this
warming can directly result in increased vapour pressure deficits,
higher potential evapotranspiration and hence drought stress in plants.
Indirectly, the increase in global average temperatures is expected to
result in shifts in the annual precipitation with more frequent
occurrences of severe droughts (Schiermeier, 2008). The changes in
temperature and precipitation together may have strong negative effects
on coffee production (Bunn \emph{et al.} , 2015), although Verhage et
al. (Verhage, Anten and Sentelhas, 2017) reported that the
CO\textsubscript{2} fertilization effect arising from elevated
CO\textsubscript{2} concentrations could offset the negative effects of
climate change on average coffee yields by a small net increase. The
global distribution and production of coffee is therefore likely to be
significantly affected by climate change (DaMatta and Cochicho Ramalho,
2006; Davis \emph{et al.} , 2012; Jassogne \emph{et al.} , 2013). There
is a need for finding or developing drought-tolerant genotypes, and one
way of working towards this is to explore the natural diversity in wild
coffee populations.

\emph{C. canephora} Pierre ex A. Froehner is a tree native to African
tropical lowland forests stretching from Guinea in West Africa through
the Congo River Basin to Uganda in East Africa (Berthaud, 1986; Coste,
1992; Montagnon, Leroy and Yapo, 1992; Davis \emph{et al.} , 2006).
Generally, these tropical forests are characterized by abundant rainfall
(precipitation \textgreater{} 2000 mm y\textsuperscript{-1}) with a
short or no dry season, high atmospheric humidity and stable average
temperatures between 24 °C and 26 °C (Coste, 1992; DaMatta and Cochicho
Ramalho, 2006; Damatta \emph{et al.} , 2018). However, even in these
moist tropical forests, there occur periodic water shortages due to dry
spells (Engelbrecht \emph{et al.} , 2006). Furthermore, the natural
geographical distribution of \emph{C. canephora} extends into the
somewhat drier areas (Masih \emph{et al.} , 2014), \emph{e.g.} in
Uganda. Tree growth (e.g. biomass or leaf area increment, referred to as
performance hereafter) is commonly observed to decrease with drought
intensity (Grime and Hunt, 1975; Chapin, 1980; Garnier and Poorter,
2007). Across tree species (at interspecific level), there tends to be a
negative correlation between growth under well-watered conditions and
drought tolerance which is defined as the extent to which plants can
maintain these growth rates under water-stressed conditions (\emph{i.e}
., drought tolerance in growth, the ratio of growth under stressed and
unstressed conditions) (Chapin, 1980; Garnier and Poorter, 2007;
Ouédraogo \emph{et al.} , 2013). Growth and survival under dry
conditions tend to be associated with traits such as low specific leaf
area (leaf area/mass ratio), fewer or smaller stomates, small stem
vessel diameter, high fractions of dry mass in roots, low leaf area to
root mass ratio and low leaf area to sapwood ratio which tend to reduce
growth rates under well-watered conditions (Lambers, Chapin and Pons,
2008).

While multispecies comparisons are useful to understand ecological
strategies and community composition, questions regarding natural
selection and applications for breeding require additional intraspecific
comparisons across wild accessions of a species. When an environmental
stress gradient such as water availability acts as a selective force,
one may expect tolerance of a genotype to this stress factor to be
related to the climate in the site of origin (Alberto \emph{et al.} ,
2013). Analysing such patterns is important as it may provide insights
into natural selection but may also provide basic information to assess
the adaptive potential to climate change and, for crops, identify
drought-tolerant genotypes (Alberto \emph{et al.} , 2013;
Rungwattana\emph{et al.} , 2018). However, very few studies have
compared wild accessions from different climates for tropical trees such
as coffee. Rungwattana et al. (2018) compared wild accessions of rubber
(\emph{Hevea brasiliensis} ) from different locations across a rainfall
gradient in the Amazon forest and found no correlation between any of
the traits investigated and either temperature or rainfall at the site
of origin. In \emph{C. canephora'} s congener, \emph{C. arabica} ,
comparisons between nine accessions from different Ethiopian forests
showed that accessions from drier areas were more plastic in leaf gas
exchange traits in response to changes in water availability than those
from wetter areas (Beining, 2007) but another study with a similar set
of accessions found no correlations between water availability as an
experimental factor and leaf gas exchange traits (Kufa and Burkhardt,
2011).

Uganda has been reported to have substantial \emph{C} .
\emph{canephora}diversity (Musoli \emph{et al.} , 2009; Ngugi and Aluka,
2019; Kiwuka, 2020; Kiwuka \emph{et al.} , 2021) which could be explored
to identify functional diversity in regards to drought stress. But to
our knowledge, intraspecific comparisons of drought-related traits in
\emph{C} .\emph{canephora} have been limited to cultivated material
\emph{e.g.} in (DaMatta \emph{et al.} , 2003; Pinheiro and Var, 2004;
Dias \emph{et al.} , 2007; Silva \emph{et al.} , 2013; King'oro, 2014;
Menezes-Silva\emph{et al.} , 2015). While the aforementioned studies
give important insights into the morphological and physiological drivers
of drought tolerance, exploration of the variation in drought tolerance
across wild populations and potential correlations with climate need to
be done. Furthermore, none of the studies on tropical trees has explored
the extent to which drought tolerance is associated with genetic
diversity, a link that would provide helpful information to interpret
drought adaptation. Finally, as far as we know, drought tolerance in
coffee has also not been explored along a cultivation status trajectory,
i.e. comparing wild, feral (second generation or higher of formerly
cultivated material and abandoned for over 50 years) and cultivated
genotypes. It is therefore unknown whether the cultivation of \emph{C.
canephora} has been selected for or against drought tolerance.

This study was set out to determine: (i) the effect of drought on
vegetative growth (biomass and leaf area increment) of \emph{C.
canephora} genotypes, collected across a climatic gradient in Uganda and
categorised by (a) cultivation status, (b) genetic groups as
characterised by Kiwuka et al., (2021), (c) and location, indicating the
different climatic envelopes (for the years 1950 -2000), (ii) the
relationship between performance under restricted and ample-water
conditions, (iii) the relationship between drought tolerance of
genotypes and wetness index (WI) at their native location. WI, the ratio
of mean annual precipitation to mean annual potential evapotranspiration
(PET) is a reasonable proxy for local climate wetness, whereby high WI
indicates wetter climates and vice versa (note that we do not use the
original but confusing term, aridity index, from Zomer et al., (2008)).
We hypothesized that, since Uganda's wild \emph{C. canephora}populations
occur in different climatic envelopes, genotypes from dry (lower WI)
locations characterised by high temperatures, low precipitation, and
high PET and will have comparatively higher growth and performance under
restricted-water conditions than genotypes from locations with low to
moderate temperatures, high precipitation, higher WI and low PET (wet
location). Additionally, we expect a trade-off between drought tolerance
and performance, whereby the mechanisms that underlie drought tolerance
in material from dry locations are associated with slow growth and the
inability to exploit favourable conditions (McGill \emph{et al.} , 2006;
Lambers, Chapin and Pons, 2008; Sade, Gebremedhin and Moshelion, 2012;
Amissah \emph{et al.} , 2018).

MATERIALS AND METHODS

\emph{2.1. Plant material}

A total of 228 genotypes of \emph{C. canephora} Pierre ex Froehner were
collected from the wild and the National coffee germplasm collection
fields in 2014 (Kiwuka \emph{et al.} , 2021). Each genotype was
categorized according to three main sets of determinants (factors): (1)
cultivation status, (2) genetic group and (3) location.

Cultivation status was defined based on the level of management of the
material and included three levels: (i) wild-plant material collected
from tropical natural forests and free from direct human management,
(ii) feral- material collected from formerly cultivated and currently
abandoned (abandoned for at least 50 years) coffee fields. Caution was
taken not to collect from trees that were older than 15 years, as a way
of ensuring that feral materials are sampled from trees that were
belonging to at least the second generation of the abandoned coffee
fields and (iii) cultivated; a subset represented by material collected
from assembled \emph{C. canephora} germplasm fields at the National
Agricultural Research Organisation (NARO) institutes located at Kawanda
and Kituza. The sampled cultivated material represented the range of
traditional and commercial \emph{C. canephora} diversity in Uganda's
Robusta coffee cultivation and breeding system.

The second main category was genetic groups. Ugandan \emph{C.
canephora}diversity (Genetic group (O)) has been reported to be distinct
from other known genetic groups at the species level (Musoli \emph{et
al.} , 2009; Merot-L'anthoene \emph{et al.} , 2019; Kiwuka \emph{et al.}
, 2021). Ugandan \emph{C. canephora} diversity uniquely differentiates
into two main subgroups namely: (i) The Southern Central SC) and (ii)
the North Western (NW) groups, the latter of which further
differentiates into four groups corresponding to four forest locations
(Itwara, Kibale, Budongo and Zoka). (see Appendix Table A.1.) (Kiwuka
\emph{et al.} , 2021) . The third category was geographic location.
Uganda is categorized into 16 homogeneous climatological zones based on
precipitation patterns (Basalirwa, 1995) and the country's \emph{C.
canephora} diversity occurs in five of these 16 distinct climatic zones
(see Table 1 and Appendix Fig. A.1. and A.2.). The study materials were
collected from nine locations in the five distinct climatic zones (Table
1). Each location was defined based on its geographical position and
administrative boundaries: (i) Budongo; (ii) Itwara; (iii) Kalangala;
(iv) Kibale; (v) Mabira; (vi) Malabigambo and (vii) Zoka (Table 1;
Appendix Fig. A.1. and A.2.). Material from Kituza and Kawanda were not
included in this category because plants grown there were collected from
other places. Regarding the environmental gradient across locations,
NEMA, (2009) showed that Zoka is at the driest and Kalangala at the
wettest end of the range.

Table 1. Description of collection location of \emph{Coffea canephora}
study material\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Location (Code)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Geo- reference\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Cultivation status\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Cultivation status\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
No. of genotypes\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Climatic zones\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
PET (mm y-1) WI\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
PET (mm y-1) WI\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Annua l mean temper ature (\selectlanguage{ngerman}°C)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Annua l precipi tation (mm)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Budongo (BD)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
01\selectlanguage{ngerman}°43\selectlanguage{english}'27\selectlanguage{english}"N 31deg32\selectlanguage{english}'45\selectlanguage{english}"E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
wild\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
wild\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
K\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1740\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.76\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
23\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1322\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Itwara (IT)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
00\selectlanguage{ngerman}°47\selectlanguage{english}'29\selectlanguage{english}"N 30deg28\selectlanguage{english}'19\selectlanguage{english}"E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
wild\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
wild\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
L\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1604\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.89\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1422\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Kalangala (KL)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
00\selectlanguage{ngerman}°26\selectlanguage{english}'S 32deg15\selectlanguage{english}'E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
wild \& feral\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
A1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1560\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.25\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1942\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Kawanda (KW)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\selectlanguage{ngerman}°24\selectlanguage{english}'30.42\selectlanguage{english}"N 32deg32\selectlanguage{english}'09\selectlanguage{english}"E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
cultivated\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
B\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1624\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.76\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1238\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Kibale (KB)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
00\selectlanguage{ngerman}°30\selectlanguage{english}'N 30deg24\selectlanguage{english}' E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
wild\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
L\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1637\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.77\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1267\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Kituza (KT)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\selectlanguage{ngerman}°15\selectlanguage{english}'26.81\selectlanguage{english}"N 32deg47\selectlanguage{english}'27.7\selectlanguage{english}"E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
cultivated\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
28\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
B\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1573\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.93\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1464\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Mabira (MB)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\selectlanguage{ngerman}°23\selectlanguage{english}'54\selectlanguage{english}"N 33deg0\selectlanguage{english}'59\selectlanguage{english}"E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
wild\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
B\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1652\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.82\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1356\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Malabigambo (ML)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
00\selectlanguage{ngerman}°57\selectlanguage{english}'7\selectlanguage{english}'\selectlanguage{english}'S wild 31deg38\selectlanguage{english}'25\selectlanguage{english}'\selectlanguage{english}'E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
00\selectlanguage{ngerman}°57\selectlanguage{english}'7\selectlanguage{english}'\selectlanguage{english}'S wild 31deg38\selectlanguage{english}'25\selectlanguage{english}'\selectlanguage{english}'E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
00\selectlanguage{ngerman}°57\selectlanguage{english}'7\selectlanguage{english}'\selectlanguage{english}'S wild 31deg38\selectlanguage{english}'25\selectlanguage{english}'\selectlanguage{english}'E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
A1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1604\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.88\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1414\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Zoka (ZK)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
03\selectlanguage{ngerman}°01'03.0''N wild 31°39'21.0''E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
03\selectlanguage{ngerman}°01'03.0''N wild 31°39'21.0''E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
03\selectlanguage{ngerman}°01'03.0''N wild 31°39'21.0''E\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
J\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1869\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.68\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
24\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1267\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{Climatic zones as classified by} Basalirwa (1995) \emph{A1, B, K,
L, J (see Appendix Fig. A.1.); PET values were estimated following}
Hargreaves and Samani (1985) \emph{who use mean monthly temperature,
mean monthly temperature range and mean monthly extra-terrestrial
radiation} (Zomer \emph{et al.}, 2008) \emph{while WI (wetness index;
(Aridity index in} (Zomer \emph{et al.}, (2008) \emph{) defined as a
ratio of mean annual precipitation to mean annual potential
evapo-transpiration (PET). Note that genotypes from Kawanda and Kituza
are not in the analysis of location effects.}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{Sampling strategy}A hierarchical sampling strategy was employed to
collect samples (stem cuttings) from the different locations. Wild
material was collected from seven tropical natural forests: (i) Budongo
forest; (ii) Itwara Central Forest Reserve; (iii) Kalangala (Lutoboka
central forest reserve); (iv) Kibale forest national park; (v) Mabira
forest reserve; (vi) Malabigambo forest and (vii) Zoka forest. In each
location (forest) except Kalangala, samples were collected from five
sub-sites that were separated by distances of at least 5 km. From each
sub-site, five healthy \emph{C. canephora} trees were identified from
which we collected stem cuttings. Since \emph{C. canephora} is an
allogamous species, each sampled plant was considered to be genetically
unique and therefore, each sampled tree was regarded as a distinct
genotype in this study. The assumption that each sampled tree is a
unique genotype was confirmed by genetic analysis in Kiwuka et al.,
(2021). Contrary to other locations, the Kalangala site comprised
remnants of natural forest systems and secondary forests regenerated
from formerly cultivated coffee fields, and therefore, the coffee
populations in Kalangala were considered wild or feral depending on
where they were collected from. Samples that were collected from natural
forest fragments were regarded as wild while samples from collected
abandoned cultivation fields were considered as feral. The cultivated
samples were collected from two germplasm field collections of the
Ugandan National Agricultural Research Organization (NARO): National
Coffee Research Institute Kituza and from the National Agricultural
Research Laboratories at Kawanda. The cultivated genotypes were selected
on the basis of their historical and passport data with the aim of
representing the total range of traditional and commercially cultivated
\emph{C. canephora} diversity, including the two predominant forms found
in Uganda: Erecta, or upright forms, and Nganda, or spreading forms
(Thomas, 1935) and the six elite clones, namely: KW13, KW14, KW15, KW16,
KW18 and KW19 (details can be found in (Kiwuka \emph{et al.} , 2021)).

\emph{Stem cutting establishment}

All the collected stem cuttings were rooted in a screen house at the
National Agricultural Research Laboratories (NARL), Kawanda at 0\selectlanguage{ngerman}º 25' N,
32 º 32' E, 1195 m a.s.l., starting on 30\textsuperscript{th} May 2015.
The establishment of the material from stem cutting followed a tested
protocol by the National Coffee Research Institute (NaCORI,
unpublished). The collected stem cuttings were cut into 7 cm inter-nodal
wood cuttings with one pair of leaves. A total of 7,419 inter-nodal
cuttings, for all the collected genotypes (230) were planted in
poly-pots and placed in transparent plastic cages for root
establishment. The number of cuttings per genotype ranged from 7-99 the
median being 33. The poly-pots had a diameter of 5 cm and a height of 7
cm and were filled with a mixture of topsoil, sand and manure in a ratio
of 3:2:2 by volume. Before planting, each stem cutting was dipped in
rooting hormone (Seradix '2\selectlanguage{english}', 0.8\% w.w, IBA, Twiga Chemicals
Industries, Nairobi, Kenya) to boost their rooting potential. After
seven months, the young plants that had grown from the cuttings were
hardened off and, transferred into 10 L pots. The potting medium
comprised of black loamy forest soil, lake sand and decomposed cattle
manure in the ratio of 3:1:1, with a volumetric water content of 30 \%
(\selectlanguage{ngerman}± 0.22) at field capacity and 6 \% (± 0.16) at permanent wilting point
respectively (See details of the chemical and physical properties of the
potting medium in Appendix Data File A.1.). Ten grams of an inorganic
compound fertilizer comprising: 25\% nitrogen, 5\% phosphorous, 5\%
potassium and 5\% of sulphur of the total weight of the elements in the
fertilizer was added per pot. Pots were optimally irrigated for six
months before starting the experimental treatments.

\emph{Experimental design}

Out of the 230 collected genotypes, 148 produced sufficient number (\selectlanguage{english}[?]5)
of properly rooted plantlets to start the experiment with. From October
10\textsuperscript{th} to 15\textsuperscript{th} 2016, 16 months after
re-planting the stem cuttings, 1184 rooted plants were arranged into a
split-plot design; with two watering regimes (ample vs restricted-water)
as the main factor and the different \emph{C. canephora} genotypes as
the sub-factors. Plants were grown in a `rain out' screen house (40 m by
6.5 m) that was blocked into four sections, based on the variation in
radiation that was visually assessed (148 remaining genotypes x 4 blocks
(with each split into two) x 2 water regimes (ample and restricted).

To establish ample vs restricted-water availability treatments, we
assessed the potting medium's properties, \emph{e.g.} water content at
field capacity, permanent wilting point and the daily evapotranspiration
rates within the screen house by weighing over time a selection of 10
pots. Soil water loss was also estimated from monitoring soil moisture
content in pots using a soil moisture sensor (Trime-Pico 64/32, HD2 IMKO
Micromodultechnik, Ettlingen, Germany). The ample-water treatment was
set at 25 v\% which was about 80\% of soil moisture content at field
capacity, while the drought-stressed regime (restricted-water) was
sustained at 10 v\% soil moisture at the permanent wilting point.

Plants in the ample-water treatment received on average 1000 ml of water
per watering interval, which was, on average, once a week. Plants in the
restricted-water treatment were subjected to gradually increasing
severity of drought stress and the basic regime was that on average,
plants received 300 ml per week for the first month, 300 ml per
fortnight for the following month, a onetime 300 ml water gift in the
third month and finally a month without water. To minimize the potential
plant-size drought bias i.e., the fact that larger plants consume more
water and are therefore exposed on average to drier conditions, the
following procedure was used: in the initial experimental phase, a
sub-set of plants (54 plants; selected to represent the architectural
{[}number of leaves, number of primary branches, number of suckers and
leaf area{]}, variation across the experiment) were monitored to
determine their soil water content (both gravimetrically and with the
soil moisture probe) every week and their corresponding number of
leaves, number of primary branches, the number of suckers and leaf area
were non-destructively estimated. Leaf area of fully expanded leaves was
estimated from leaf length and width using the linear model (area per
leaf = leaf length x leaf width x k (k=correction factor = 0.66)) of
Schmildt et al., (2015). These data yielded a correlation between leaf
area and water loss and the relation was used as a guide to determine
the frequency of watering for every plant based on its leaf area. This
procedure ensured that size-dependent effects on the actual soil
moisture experienced by plants were minimized. At the end of the
experiment, it appeared that the amount of water supplied (W (ml)) could
be linearly related to the leaf area (L (cm\textsuperscript{2})) to each
plant was described by the formula: \emph{W} = 1479 + 0.178 \emph{L} ),
p = 0.000 and R\textsuperscript{2} = 0.27.

The experimental treatment period lasted four months (from plant age 20
months to 24 months; age zero is when the stem cuttings were planted to
root). Data on temperature and relative humidity in the screen-house
were recorded by sensors with data logging (Tinytag logger Plus 2 Dual
Channel Temperature/Relative Humidity, TGP-4500, Gemini data loggers
Ltd., Chichester, Chichester West Sussex, UK) on an hourly basis. The
average daily temperatures and relative humidity of the screen-house
throughout the experimental treatment period were: 23.1 o C (+- 4.3) and
83.1 \% (+- 18.0) respectively while average daily vapour pressure
deficit estimates were 0.49 (+- 0.15).

\emph{Data collection}

Data were collected at three stages: (i) at the start of the treatment
phase; (ii) during the treatment phase and at (iii) at the end of the
treatment phase (Appendix Table A.2.). At the start of the treatment
phase on 25\textsuperscript{th} May 2017 (plant age 20 months) several
non-destructive measurements were done to provide a baseline for later
size increment measurements: plant height, number of nodes, number of
leaves (fully grown and proportion/fractions from estimated full size of
developing ones), length and width of fully expanded leaves and stem
diameter at 5 cm from the base. After these measurements, the youngest
fully expanded leaf pair was marked, to establish a recognition point
for measuring new growth. The second data collection stage (at the point
when 10 \% of the plants subjected to drought treatment started to
exhibit leaf wilting (scored visually), was taken 21-24 June 2017. The
final measurement occasion, at the end of the treatment phase, was
conducted at 12-26 September 2017, with measured traits as listed in
Appendix Table A.2.

\emph{Methods to measure plant properties}

Plant height was measured using a meter ruler from the base (point of
origin from the cutting) to the last node. To estimate area per leaf and
subsequently the total leaf area, we used the same model as that used
for determining leaf area in relation to the watering regimes\emph{i.e.}
we measured length and width and then used the linear model (leaf length
x leaf width x \emph{k} (correction factor)) of (Schmildt\emph{et al.} ,
2015) on all fully unfolded leaves and obtained a correction factor (k
of 0.66) that was used on all measured leaves. Leaf area on the main
stem was measured in this way for all plants. But due to the necessity
to reduce the workload, the leaf area of primaries and suckers was
measured using the aforementioned linear model, but only for all plants
in one block. For every genotype the leaf area of primaries and suckers
in blocks two, three and four were estimated from the ratio of leaf
fresh weight to leaf area, generated from the measured plants in block
1. At the end of the experiment, for each plant, leaves were separated
into leaves from the main stem, primaries and suckers. To obtain total
leaf fresh weight (T\textsubscript{LFW}) and total leaf dry weight
(T\textsubscript{LDW}), the fresh weight of all leaves was estimated by
weighing fresh leaves while leaf dry weights were measured after oven
drying (70 degC to a constant weight).

Specific leaf area (S\textsubscript{LA}) was estimated as the ratio of
leaf area and leaf dry weight accumulated within the experimental
treatment phase. The roots of each plant were harvested and cleaned
under running water and on a wire mesh. Using the water displacement
method, fine roots (excluding the taproot with a diameter larger than 3
mm) were dipped in a measuring cylinder to estimate their root volume.
The root volume and total leaf area (T\textsubscript{L}) of each plant
were used to estimate the root volume to leaf area ratio
(R\textsubscript{L}). Four growth-related traits were used to
characterize the genotype responses to drought stress. These were
relative growth rate in leaf area (RGR\textsubscript{A}, see below for
how this was calculated), the total number of leaves
(T\textsubscript{NL}) total leaf area
(T\textsubscript{L}{[}cm\textsuperscript{2}{]}), total leaf dry weight
(T\textsubscript{LDW} {[}g{]}), specific leaf area
(S\textsubscript{LA}{[}cm\textsuperscript{2} g\textsuperscript{-1}{]})
and root volume to leaf area ratio (R\textsubscript{L}
{[}cm\textsuperscript{3}cm\textsuperscript{-2}{]}). Note that all
traits, except root volume, refer to growth during the experimental
period, excluding the biomass at the start of treatments.
RGR\textsubscript{A} was used to assess in more detail the cultivation
status, location and genotype response to restricted and ample
availability of water. Relative growth rates were used for two reasons:
(i) to reduce confounding effects of initial plant size and (ii) we
dealt with very young plants for which the assumption of them being in
exponential growth phase was reasonable. We focused on area, dry mass
and number of leaves because of practical reasons (measurable
non-destructively; base measurements of biomass were not available) and
because leaf area determines light interception capacity, photosynthesis
and subsequent growth (Poorter and Remkes, 1990; Weraduwage \emph{et
al.} , 2015) (and in coffee fast vegetative growth are typically
associated with high yields (Cilas \emph{et al.} , 2006)).

RGR\textsubscript{A} was calculated as\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

(Eqn. 1)

Where \emph{L} \textsubscript{E} and \emph{L} \textsubscript{I} is leaf
area at \emph{t} \textsubscript{E} - and \emph{t}
\textsubscript{I,}respectively.

The difference \emph{t} \textsubscript{E} -- \emph{t}
\textsubscript{I}reflects the 84 days between the start of the treatment
phase (\emph{t} \textsubscript{I}) and the day of the final harvest
(\emph{t} \textsubscript{E}).

Drought tolerance was defined as the capacity of a genotype to maintain
its growth under drought stress (restricted-water) and was computed per
genotype as the ratio of the trait mean in restricted-water to the trait
mean in ample-water across blocks.

\emph{Data analysis}

Linear mixed effect models were applied to test the effects of water
treatment on selected growth traits across (i) cultivation status, (ii)
genetic group or (iii) location. Linear mixed-effect models were used
because mixed models account for unbalanced, nested designs (such as
varying numbers of genotypes by cultivation status, genetic groups and
location) that occurred in our data (Bates \emph{et al.} , 2015) . To
estimate the impact of water shortage on plant traits across cultivation
status, genetic groups and locations, genotypes were considered a random
effect both in terms of the intercept: \emph{i.e} ., the absolute trait
value in ample-water, and the slope: \emph{i.e} ., the response to
drought (difference between the trait value in ample and
restricted-water conditions). To account for the heterogeneity of
variance in the observations, variances in the traits were dependent on
the cultivation status, genetic group or location (Zuur, Ieno and
Elphick, 2010) .

The model with cultivation status had 12 parameters: three levels of
cultivation status (CS) and two water treatments (making six
parameters), three parameters of the random effect to model differences
across genotypes: (i) a parameter to model the variation of traits in
ample-water conditions (intercept), (ii) a parameter related to the
variation in the treatment effect (slope), and (iii) a parameter that
models the correlation between the intercept and the slope, and three
parameters to account for a different residual variance per cultivation
status (see Model 1 in Appendix Data Box A.1.). The model with the
genetic group had 18 parameters: two for each genetic group (making 10)
and three parameters of the random effect to model differences across
genotypes: (i) a parameter to model the variation of traits in
ample-water conditions (intercept), (ii) a parameter related to the
variation in the effect of the treatment effect (slope), and (iii) a
parameter that models the correlation between the intercept and the
slope, and five parameters to account for a different residual variance
per genetic group (see Model 2 in Appendix Data Box A.1.). Note that
while testing the genetic group effect, all genotypes that were
misclassified and/or hybrids were not considered.

Factor location analysis tests for differences in terms of the
environment but also for genetic basis, and therefore, indirectly for
putative local adaptation. Therefore, the model with location had in
total 24 parameters, two for each location (14) and three parameters of
the random effect to model differences across genotypes: a parameter to
model the variation of traits in control (intercept), a parameter
related to the variation in the treatment effect (slope), a parameter
that models the correlation between the intercept and the slope, and
seven parameters to account for a different residual variance per
location (see Model 3 in Appendix Data Box A.1.).

Post-hoc Tukey tests were performed to determine whether: (i) drought
had significant effects on performance (RGR\textsubscript{A}) of
genotypes across cultivation status, genetic groups and location; (ii)
genotypes of different cultivation status, genetic groups or location
responded significantly differently to drought and (iii) absolute
performance of genotypes in ample-water and restricted-water conditions
differed across cultivation status, genetic groups and locations. Tukey
adjustment to p-values was done in case of multiple comparisons. The
analyses were performed in R version 3.5.0 Statistical Software using
``me'', ``emmeans'' and ``ggplot2'' packages. For all the analyses, any
effect with p \textless{} 0.05 was considered statistically significant
and non-significant at p \textgreater{} 0.05.

\subsection*{Multivariate analysis of growth-related
traits}

{\label{multivariate-analysis-of-growth-related-traits}}

To explore the multivariate dependency between the measured traits, a
principal component analysis was performed on the genotypic means. Only
genotypes were included for which there were at least two replicates
available. All variables were centred to a mean of zero and scaled to
unit variance before the analysis contained both treatments. Next, to
test whether location and cultivation significantly affected the suite
of traits, a multivariate analysis was performed using a PERMANOVA. This
PERMANOVA tests, similar to a classical multivariate ANOVA, whether the
dissimilarities between genotypes from the same location, status and
treatment are smaller than the dissimilarities between genotypes across
locations, status and treatment (Anderson, 2001). We used Euclidean
distances between the centred and scaled observations, and 999
permutations.

\emph{Drought tolerance performance trade-off}

Type (II) major axis regression was performed to determine the
relationship between the genotypic average growth trait in ample water
versus restricted water. Type II regression was used to account for both
measurement errors in the independent and the dependent variable (David
and Neville, 2002) and to test whether the slope and intercept were
different from each other. The analysis was performed in R version 3.5.0
Statistical Software using the packages ``smatr''.

\emph{Drought tolerance climate relationship}

In addition, a weighted linear regression analysis was performed to
determine the relationship between drought tolerance based on
RGR\textsubscript{A,} T\textsubscript{NL} and T\textsubscript{LDW} and
wetness index (WI). The analysis was performed in R version 3.5.0
Statistical Software. Because the number of replicates varied across
genotypes in locations, we introduced weights for replicates in the
analysis. In this weighted linear regression analysis, we excluded
genotypes from Kawanda and Kituza because the genotypes in these
collections were sourced from different origins and assembled
as\emph{ex-situ} collections at NARO institutes, and therefore, we could
not retrieve the WI of these genotypes. The probability of rejecting the
null hypothesis that there is no relationship between performance in
low-water conditions and wetness index (WI) or no relationship between
drought tolerance and wetness index (WI) was set at p-value
\textgreater{} 0.05. The weighted linear regression models were fitted
with lm () functions in R version 3.5.0 Statistical Software.

\textbf{RESULTS}

The study results are presented in hierarchical order starting with: (i)
the effect of experimental treatments on the grand mean of growth
response traits (i.e., lumping genotypes together), (ii) the main
effects of factors, i.e. cultivation status, genetic groups and location
on growth response traits, (iii) the detailed synthesis of the effect of
drought on RGR\textsubscript{A} as our proxy trait for plant
performance, (iv) the relationship between performance under ample and
restricted-water conditions, (v) the relationship between performance
under restricted-water conditions and wetness index of the locations and
(vi) the relationship between drought tolerance and wetness index of the
locations.

\emph{3.1. Overall mean effects of drought on growth response traits}

The experimental treatments significantly affected all the studied
traits (Table 2 and3). Relative growth rate in leaf area
(RGR\textsubscript{A} {[}d\textsuperscript{-1}{]}), total number of
leaves (T\textsubscript{NL}), total leaf area
(T\textsubscript{L}{[}cm\textsuperscript{2}{]}), total leaf dry weight
(T\textsubscript{LDW} {[}g{]})) and specific leaf area
(S\textsubscript{LA} {[}cm\textsuperscript{2} g\textsuperscript{-1}{]})
were on average (12 -- 38 \%) lower in the restricted-water than in the
ample-water (Table 2). The larger declines for T\textsubscript{NL},
T\textsubscript{L} than in T\textsubscript{LDW} in the restricted water
treatment is consistent with the negative effect of restricted-water on
S\textsubscript{LA}. Root volume to leaf area ratio
(R\textsubscript{L}{[}cm\textsuperscript{3} cm\textsuperscript{-2}{]})
was higher in restricted-water conditions than in ample-water conditions
(Table 2), indicating a shift in the partitioning of resources towards
root growth in drought conditions.

Table 2. Effect of drought on mean and standard error of selected growth
response traits of \emph{C. canephora}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Trait\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Ample-water grand mean (se)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Restricted-water grand mean (se)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Relative change ( \%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
RGR\textsubscript{A} {[}d\textsuperscript{-1}{]}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.016 (0.0001)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.012 (0.0001)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-25.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
T\textsubscript{NL}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
21 (0.5)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
13 (0.3)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-38.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
T\textsubscript{L} {[}cm\textsuperscript{2}{]}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
3653 (94)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
2526 (53)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-30.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
T\textsubscript{LDW} {[}g{]}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
17 (0.5)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
13 (0.4)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-23.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
SLA {[}cm\textsuperscript{2} g\textsuperscript{-1}{]}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
251 (5)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
221 (3)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-12.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
R\textsubscript{L} {[}cm\textsuperscript{3}
cm\textsuperscript{-2}{]}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.007(0.0002)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.009 (0.0002)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
28.6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Relative growth in leaf area (RGR\textsubscript{A}
{[}d\textsuperscript{-1}{]}), Total number of leaves
(T\textsubscript{NL}), Total leaf area (T\textsubscript{L}
{[}cm\textsuperscript{2}{]})} \emph{Total leaf dry weight
(T\textsubscript{LDW} {[}g{]}), Specific Leaf area (S\textsubscript{LA}
{[}cm\textsuperscript{2} g\textsuperscript{-1}{]}), Root volume to leaf
area ratio (RL {[}cm\textsuperscript{3} cm\textsuperscript{-2}{]}), se:
standard error}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Relative growth in leaf area (RGR\textsubscript{A}
{[}d\textsuperscript{-1}{]}), Total number of leaves
(T\textsubscript{NL}), Total leaf area (T\textsubscript{L}
{[}cm\textsuperscript{2}{]})} \emph{Total leaf dry weight
(T\textsubscript{LDW} {[}g{]}), Specific Leaf area (S\textsubscript{LA}
{[}cm\textsuperscript{2} g\textsuperscript{-1}{]}), Root volume to leaf
area ratio (RL {[}cm\textsuperscript{3} cm\textsuperscript{-2}{]}), se:
standard error}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Relative growth in leaf area (RGR\textsubscript{A}
{[}d\textsuperscript{-1}{]}), Total number of leaves
(T\textsubscript{NL}), Total leaf area (T\textsubscript{L}
{[}cm\textsuperscript{2}{]})} \emph{Total leaf dry weight
(T\textsubscript{LDW} {[}g{]}), Specific Leaf area (S\textsubscript{LA}
{[}cm\textsuperscript{2} g\textsuperscript{-1}{]}), Root volume to leaf
area ratio (RL {[}cm\textsuperscript{3} cm\textsuperscript{-2}{]}), se:
standard error}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Relative growth in leaf area (RGR\textsubscript{A}
{[}d\textsuperscript{-1}{]}), Total number of leaves
(T\textsubscript{NL}), Total leaf area (T\textsubscript{L}
{[}cm\textsuperscript{2}{]})} \emph{Total leaf dry weight
(T\textsubscript{LDW} {[}g{]}), Specific Leaf area (S\textsubscript{LA}
{[}cm\textsuperscript{2} g\textsuperscript{-1}{]}), Root volume to leaf
area ratio (RL {[}cm\textsuperscript{3} cm\textsuperscript{-2}{]}), se:
standard error}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{3.2. Significance of cultivation status, genetic group and
location on growth response traits}

In the linear mixed model analysis, the effects of factors (\emph{i.e.}
, cultivation status, genetic group and location) varied across growth
response traits (Table 3). The cultivation status did not have
significant effects on RGR\textsubscript{A}, T\textsubscript{NL} and
T\textsubscript{LDW} (p \textgreater{}0.05), but did significantly
affect S\textsubscript{LA} and R\textsubscript{L} (Table 3). On average,
wild genotypes had the highest S\textsubscript{LA} (244
cm\textsuperscript{-2} g\textsuperscript{-1}) but the difference was
only significant with the feral and not with the cultivated genotypes
(Appendix Table A. 3.). For R\textsubscript{L}, cultivated genotypes had
a significantly higher average value (0.0087
cm\textsuperscript{3}cm\textsuperscript{-2}) than wild and feral
genotypes and there were no significant differences between wild and
feral R\textsubscript{L} values (Appendix Table A.3.). There were no
significant interaction effects between cultivation status and treatment
for any of the selected traits, except for T\textsubscript{L} indicating
that only for T\textsubscript{L}, the treatment effect differed across
cultivation status. Under ample-water conditions, cultivation status had
no significant effects on T\textsubscript{L} while under
restricted-water conditions wild genotypes, had a significantly lower
T\textsubscript{L}than feral and cultivated genotypes whose
T\textsubscript{L}'s were not significantly affected by water
availability. In the restricted-water treatment, wild genotypes had the
lowest average T\textsubscript{L}which was 24.4 \% lower than the
highest average T\textsubscript{L}observed in feral genotypes (Appendix
Table A.3.). These findings suggest that in terms of T\textsubscript{L},
wild genotypes might be more sensitive to low water availability than
non-wild genotypes.

Genetic groups significantly differed in their T\textsubscript{L},
T\textsubscript{LDW} and R\textsubscript{L} but not in the other three
traits (Table 3). Plants from the genetic group SC had the highest mean
T\textsubscript{L} which was 60.9 \% higher than the lowest
T\textsubscript{L} observed in genetic group Kibale (Appendix Table
A.3.). The effect of the genetic group on T\textsubscript{LDW} was
similar to T\textsubscript{L} with genetic group SC having 67.6 \%
higher mean T\textsubscript{LDW} than genetic group Kibale which had the
lowest T\textsubscript{LDW} (Appendix Table A.3.). For
R\textsubscript{L}, Zoka had the highest value which was 43.9 \% higher
than the lowest R\textsubscript{L} observed in the genetic group Kibale
(Appendix Table A.3.). Interaction effects between genetic groups and
treatment were only observed in RGR\textsubscript{A}, implying that the
magnitude of the response in this trait to the water treatment differed
across genetic groups. The RGR\textsubscript{A} of genetic groups:
Budongo, SC and Zoka were significantly reduced due to restricted-water
supply but not that of genetic groups Itwara and Kibale (Fig. 1;
Appendix Table A.3.).

Overall, location as a factor had stronger effects on growth response
traits to ample and restricted-water supply than the two other factors,
cultivation status and genetic groups

(Table 3). Location had significant main effects and interaction effects
on all traits except on T\textsubscript{NL}, T\textsubscript{LDW} and
S\textsubscript{LA} (Table 3). This implies that the growth response
values significantly differed depending on the location from which the
genotypes were collected. For example, for T\textsubscript{L}, i.e. the
response trait with the strongest location effects (Table 3), location
Malabigambo had the highest average T\textsubscript{L} which was 73.7 \%
higher than the lowest T\textsubscript{L} observed in Kibale. Drought
had no significant effects on the T\textsubscript{L} of genotypes
collected from Zoka, Itwara, Kibale, Kituza and Kawanda, while it
significantly reduced T\textsubscript{L} of genotypes collected from
Malabigambo, Kalangala and Mabira (Appendix Table A.4.). In absolute
terms, under ample-water conditions, Malabigambo had a significantly
higher T\textsubscript{L} (7263 (\selectlanguage{ngerman}± 153) cm\textsuperscript{2}) than all
other locations while Kibale's T\textsubscript{L} (1413 (± 38)
cm\textsuperscript{2}), was significantly lower than
T\textsubscript{L}'s at all locations except Zoka (Appendix Table A.4.).
Similarly, under restricted-water conditions; Malabigambo had the
highest T\textsubscript{L} (3711 (± 62) cm\textsuperscript{2}) compared
to all other locations whereas Kibale had the lowest
T\textsubscript{L}(1469 (± 42) cm\textsuperscript{2}) which was
significantly lower than T\textsubscript{L} of all other locations
except Zoka (Appendix Table A. 4.).

Table 3. Significance of effects of the factors on the growth response
traits of \emph{C. canephora} Number in the table are F- values of
linear mixed models testing the effect of factors on performance\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Factors RGR\textsubscript{A}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Factors RGR\textsubscript{A}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T\textsubscript{NL}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T\textsubscript{L}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
TLDW\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
S\textsubscript{LA}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
R\textsubscript{L}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Cultivation status (CS)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Cultivation status (CS)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Treatment\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{136.367***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{60.32***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{27.19***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{14.48***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{15.93***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{26.70***}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
CS\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.91\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.05\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.63\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.41\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{4.75*}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{3.33*}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Treatment*CS\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.74\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.71\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{3.25*}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.47\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.20\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.80\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Genetic group\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Treatment\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{117.79***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{62.61***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{35.67***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{16.51***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{12.03***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{14.47***}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Genetic group\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.29\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.32\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{6.37***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{8.77***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.66\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{7.58***}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Treatment*Genetic group\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{2.76*}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.02\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.18\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.93\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.34\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.48\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Location\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Treatment\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{111.17***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{63.26***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{46.20***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{16.68***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{8.79*}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{27.7***}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Location\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{2.39*}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.00\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{9.31***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{10.78***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{5.50***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.05\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Treatment *Location\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{3.20 *}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{2.93\textbf{*}}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{3.85***}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{2.15} \textbf{}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.08\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.03\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Relative growth rate of leaf area (RGRA {[}d-1{]}), Total number
of leaves (TNL), Total leaf area (TL {[}cm2{]}), Total leaf dry weight
(TLDW {[}g{]}), Specific leaf area (SLA {[}cm2 g-1{]}) and Root volume
to leaf area ratio (RL {[}cm3 cm-2{]}). Numbers in italics indicate
significant effects: italics with *** is significant with p
\textless{}0.001, italics significant with * p \textless{} 0.05 and bold
italics is marginally significant. Two treatment levels ((i) Restricted
and (ii) ample water levels), Cultivation status three levels ((i) wild,
(ii) feral and (iii) cultivated), Genetic groups five levels ((i)
Budongo, (ii) Itwara ,(iii)Kibale, (iv) SC, and (v) Zoka), Location 7
levels ( (i) Budongo, (ii) Itwara, (iii) Kalangala, (iv) Kibale, (v)
Mabira, (vi) Malabigambo and (vii) Zoka)\textbf{.}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Relative growth rate of leaf area (RGRA {[}d-1{]}), Total number
of leaves (TNL), Total leaf area (TL {[}cm2{]}), Total leaf dry weight
(TLDW {[}g{]}), Specific leaf area (SLA {[}cm2 g-1{]}) and Root volume
to leaf area ratio (RL {[}cm3 cm-2{]}). Numbers in italics indicate
significant effects: italics with *** is significant with p
\textless{}0.001, italics significant with * p \textless{} 0.05 and bold
italics is marginally significant. Two treatment levels ((i) Restricted
and (ii) ample water levels), Cultivation status three levels ((i) wild,
(ii) feral and (iii) cultivated), Genetic groups five levels ((i)
Budongo, (ii) Itwara ,(iii)Kibale, (iv) SC, and (v) Zoka), Location 7
levels ( (i) Budongo, (ii) Itwara, (iii) Kalangala, (iv) Kibale, (v)
Mabira, (vi) Malabigambo and (vii) Zoka)\textbf{.}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Relative growth rate of leaf area (RGRA {[}d-1{]}), Total number
of leaves (TNL), Total leaf area (TL {[}cm2{]}), Total leaf dry weight
(TLDW {[}g{]}), Specific leaf area (SLA {[}cm2 g-1{]}) and Root volume
to leaf area ratio (RL {[}cm3 cm-2{]}). Numbers in italics indicate
significant effects: italics with *** is significant with p
\textless{}0.001, italics significant with * p \textless{} 0.05 and bold
italics is marginally significant. Two treatment levels ((i) Restricted
and (ii) ample water levels), Cultivation status three levels ((i) wild,
(ii) feral and (iii) cultivated), Genetic groups five levels ((i)
Budongo, (ii) Itwara ,(iii)Kibale, (iv) SC, and (v) Zoka), Location 7
levels ( (i) Budongo, (ii) Itwara, (iii) Kalangala, (iv) Kibale, (v)
Mabira, (vi) Malabigambo and (vii) Zoka)\textbf{.}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Relative growth rate of leaf area (RGRA {[}d-1{]}), Total number
of leaves (TNL), Total leaf area (TL {[}cm2{]}), Total leaf dry weight
(TLDW {[}g{]}), Specific leaf area (SLA {[}cm2 g-1{]}) and Root volume
to leaf area ratio (RL {[}cm3 cm-2{]}). Numbers in italics indicate
significant effects: italics with *** is significant with p
\textless{}0.001, italics significant with * p \textless{} 0.05 and bold
italics is marginally significant. Two treatment levels ((i) Restricted
and (ii) ample water levels), Cultivation status three levels ((i) wild,
(ii) feral and (iii) cultivated), Genetic groups five levels ((i)
Budongo, (ii) Itwara ,(iii)Kibale, (iv) SC, and (v) Zoka), Location 7
levels ( (i) Budongo, (ii) Itwara, (iii) Kalangala, (iv) Kibale, (v)
Mabira, (vi) Malabigambo and (vii) Zoka)\textbf{.}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Relative growth rate of leaf area (RGRA {[}d-1{]}), Total number
of leaves (TNL), Total leaf area (TL {[}cm2{]}), Total leaf dry weight
(TLDW {[}g{]}), Specific leaf area (SLA {[}cm2 g-1{]}) and Root volume
to leaf area ratio (RL {[}cm3 cm-2{]}). Numbers in italics indicate
significant effects: italics with *** is significant with p
\textless{}0.001, italics significant with * p \textless{} 0.05 and bold
italics is marginally significant. Two treatment levels ((i) Restricted
and (ii) ample water levels), Cultivation status three levels ((i) wild,
(ii) feral and (iii) cultivated), Genetic groups five levels ((i)
Budongo, (ii) Itwara ,(iii)Kibale, (iv) SC, and (v) Zoka), Location 7
levels ( (i) Budongo, (ii) Itwara, (iii) Kalangala, (iv) Kibale, (v)
Mabira, (vi) Malabigambo and (vii) Zoka)\textbf{.}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Relative growth rate of leaf area (RGRA {[}d-1{]}), Total number
of leaves (TNL), Total leaf area (TL {[}cm2{]}), Total leaf dry weight
(TLDW {[}g{]}), Specific leaf area (SLA {[}cm2 g-1{]}) and Root volume
to leaf area ratio (RL {[}cm3 cm-2{]}). Numbers in italics indicate
significant effects: italics with *** is significant with p
\textless{}0.001, italics significant with * p \textless{} 0.05 and bold
italics is marginally significant. Two treatment levels ((i) Restricted
and (ii) ample water levels), Cultivation status three levels ((i) wild,
(ii) feral and (iii) cultivated), Genetic groups five levels ((i)
Budongo, (ii) Itwara ,(iii)Kibale, (iv) SC, and (v) Zoka), Location 7
levels ( (i) Budongo, (ii) Itwara, (iii) Kalangala, (iv) Kibale, (v)
Mabira, (vi) Malabigambo and (vii) Zoka)\textbf{.}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Relative growth rate of leaf area (RGRA {[}d-1{]}), Total number
of leaves (TNL), Total leaf area (TL {[}cm2{]}), Total leaf dry weight
(TLDW {[}g{]}), Specific leaf area (SLA {[}cm2 g-1{]}) and Root volume
to leaf area ratio (RL {[}cm3 cm-2{]}). Numbers in italics indicate
significant effects: italics with *** is significant with p
\textless{}0.001, italics significant with * p \textless{} 0.05 and bold
italics is marginally significant. Two treatment levels ((i) Restricted
and (ii) ample water levels), Cultivation status three levels ((i) wild,
(ii) feral and (iii) cultivated), Genetic groups five levels ((i)
Budongo, (ii) Itwara ,(iii)Kibale, (iv) SC, and (v) Zoka), Location 7
levels ( (i) Budongo, (ii) Itwara, (iii) Kalangala, (iv) Kibale, (v)
Mabira, (vi) Malabigambo and (vii) Zoka)\textbf{.}}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{3.3. Detailed effects of the experimental factors as illustrated
with RGR\textsubscript{A} (our proxy trait for performance)}

\emph{RGR\textsubscript{A} across cultivation status: wild, feral and
cultivated}

The relative effect of drought on RGR\textsubscript{A} was rather
similar across cultivation status (Table 4), hence confirming the
finding in Table 4 (no significant main effect and interaction effects
for cultivation status on RGR\textsubscript{A}). In absolute terms,
under ample-water conditions, wild genotypes had the highest
RGR\textsubscript{A} which was significant, but only modestly, (5.7 \%)
higher than the lowest RGR\textsubscript{A}, which was observed among
the cultivated genotypes (Table 4). Under restricted-water treatment,
wild genotypes still had the highest RGR\textsubscript{A} which was 5 \%
higher than the lowest RGR\textsubscript{A} observed among feral
genotypes (Table 4).

Table 4. Mean values and standard error (se) of Relative growth rate in
leaf area (RGRA{[}d\textsuperscript{-1}{]}) of \emph{C. canephora}
subjected to ample and restricted water treatments\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Factor\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Ample-water Mean (se)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Restricted-water Mean (se)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Relative change (\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Cultivation status}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Cultivated\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0150 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0120 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-20.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Feral\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0156 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0116 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-25.6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Wild\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0159 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0122 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-23.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Genetic group}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Budongo\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0163 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0110 (0.0001) b\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-32.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Itwara\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0144 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0124 (0.0001) ab\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-13.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Kibale\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0124 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0120 (0.0001) ab\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-3.2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
SC\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0159 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0112 (0.0001) ab\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-29.6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Zoka\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0152 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0125 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-17.8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Location}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Budongo\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0162 (0.0001) abc\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0119 (0.0001) ab\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-26.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Itwara\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0144 (0.0002) cd\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0124 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-13.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Kalangala\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0156 (0.0001) bc\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0118 (0.0001) ab\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-24.4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Kibale\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0127 (0.0001) d\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0118 (0.0001) ab\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-7.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Mabira\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0175 (0.0002) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0129 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-26.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Malabigambo\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0169 (0.0002) ab\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0107 (0.0001) b\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-36.7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Zoka\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0151 (0.0001) c\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.0126 (0.0001) a\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-16.6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Numbers are means, standard errors in brackets and different
letters in the same column show} \emph{significant differences among
means at p \textless{} 0.05 of Relative growth rate (RGRA)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Numbers are means, standard errors in brackets and different
letters in the same column show} \emph{significant differences among
means at p \textless{} 0.05 of Relative growth rate (RGRA)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Numbers are means, standard errors in brackets and different
letters in the same column show} \emph{significant differences among
means at p \textless{} 0.05 of Relative growth rate (RGRA)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{Numbers are means, standard errors in brackets and different
letters in the same column show} \emph{significant differences among
means at p \textless{} 0.05 of Relative growth rate (RGRA)}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{RGR\textsubscript{A} across genetic groups}

Table 4 and Fig. 1 show variation in the relative effect of drought on
RGR\textsubscript{A} across genetic groups with genetic group Budongo
being the most strongly affected and genetic group Kibale being least
affected by drought (see also significant genetic group * treatment
effect Table 3). Under ample-water conditions, the absolute
RGR\textsubscript{A} did not differ significantly between genetic groups
while it did under restricted-water conditions. (Table 4; Fig. 1). Under
restricted-water conditions, genetic group Zoka had the highest
RGR\textsubscript{A} which was 12.0 \% higher than the lowest
RGR\textsubscript{A} observed for genotypes from genetic group Budongo
(Table 4). Additionally, Fig. 1 and standard errors of means (Table 4)
suggest that there was wider genotypic variation in
RGR\textsubscript{A}across genetic groups under ample-water conditions
than there was under restricted-water conditions. Fig. 1. Mean RGRA
{[}d\textsuperscript{-1}{]} as a function of treatment (ample-water (AW)
and restricted-water (RW) across genetic groups (panels) and genotypes
(coloured lines). Solid black line shows the mean estimated response per
genetic group.

\emph{RGR\textsubscript{A} across locations}

There was a large variation in the relative effect of drought on
RGR\textsubscript{A} of genotypes collected from the different locations
(Table 4 and Appendix Fig. A.3.). The effect of drought on
RGR\textsubscript{A} was significant for all locations except for Kibale
and Itwara (Table 4; Appendix Fig. A.3.; Appendix Table A.5.). The mean
percentage change in performance was highest among genotypes collected
from Malabigambo, Budongo, Mabira and Kalangala, respectively, while the
effect of restricted-water supply was smallest for genotypes collected
from Kibale, Itwara, Zoka and Kituza, respectively (Table 4 and slope of
the black lines in Appendix Fig. A. 3.). In absolute terms, under
ample-water conditions, genotypes from Mabira had a significantly higher
mean RGR\textsubscript{A}, which was 27.4 \% higher than the lowest mean
RGR\textsubscript{A} in location Kibale (Table 4 and Appendix Fig. S3).
Similarly, in restricted-water conditions, Mabira had the highest and
Kibale had the lowest RGR\textsubscript{A} but the difference was much
smaller (8.5\%) (Table 4 and Appendix Fig. A.3.). Therefore, differences
between locations tended to converge in the restricted-water treatment.

Across the studied experimental factors (cultivation status, genetic
group and location), it is worth noting that results showed a tendency
of some genotypes to have higher RGR\textsubscript{A} under
restricted-water conditions than with ample-water although this effect
was not statistically significant in any of these cases (p
\textgreater{} 0.05) (Fig. 1 and Appendix Fig. A.3.). The effect
occurred in genotypes with both high and low RGR\textsubscript{A} values
in the ample-water treatment and therefore are very unlikely an
experimental artefact, whereby the genotypes could not have been
adequately watered under ample-water conditions. Additionally, for some
genotypes, the effect could be due to variations in sample size causing
the mean in restricted-water to be higher than that under ample-water
conditions.

\emph{Multivariate analysis of growth-related traits}

The PCA analysis showed that T\textsubscript{NL}, T\textsubscript{L} and
T\textsubscript{LDW} were most loaded on the first PCA axis (explaining
46\% of the variation), while S\textsubscript{LA} was mostly loaded on
the second PCA axis (explaining 20\% of the variation). See Fig. 2. The
PCA on the individual replicates showed a similar pattern. Therefore,
S\textsubscript{LA} varied mostly independently of
T\textsubscript{L}(correlation -0.002). The PERMANOVA showed that
treatment, location and cultivation status significantly affected the
dissimilarities between genotypes (p-values respectively
\textless{}0.001, 0.03, \textless{}0.001) see Appendix Table A.6.
Treatment explained 20\% of the variation in the traits, location 10\%
and cultivation status only 1.8\%.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Fig. 2. Principal component analysis (PCA) of genotypic mean trait
values showing multivariate dependency between the measured traits:
Relative growth in leaf area (meanRGRA), Total number of leaves (mean
TL), Total leaf area (meanTNL), Total leaf dry weight (meanTLDW),
Specific leaf area (meanSLA), Root volume to leaf area ratio (meanRL).
The two first axis, PC1 and PC2, account for 46 \% and 20 \% of the
total variation respectively.

\emph{What is the relationship between performance in ample and
restricted-water conditions?}

The type II regression where the genotypic means of growth-related
traits were regressed to each other in ample-water versus restricted
water revealed that across the four traits, the genotypes that performed
well in ample water performed relatively less well in restricted water.
In all cases of the aforementioned regressions, the slope was less than
one. For RGR\textsubscript{A} there was no significant relationship
between the values in ample water and those in restricted water
suggesting that comparatively well-performing genotypes are strongly
compromised in restricted water. See Fig. 3 and Appendix Table A.7. for
a table with statistics.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 3. Relationship between growth related traits in ample water versus
restricted water. The relationships were fitted with type II regression.
When these relationships were significantly different from zero are
plotted as the solid black lines. For reference, 1:1 line is shown
(dotted line).

\emph{What is the relationship between performance and tolerance under
restricted-water conditions and the wetness index of locations?}

There was a significantly (p = 0.03, R\textsuperscript{2} = 0.06)
negative relationship between RGR\textsubscript{A} of genotypes in
restricted-water conditions and the wetness index of the climate of a
genotype's origin (Fig. 4 A), illustrating that genotypes from
relatively wet areas (high wetness index) tended to have lower
RGR\textsubscript{A} in the restricted-water treatment than those from
drier locations. Performance of a genotype in restricted-water
conditions could partially be predicted from the wetness index of its
geographic location by the following formula:
RGR\textsubscript{Arestricted-water} (d\textsuperscript{-1}) = 0.014 -
0.002 (wetness index), S.E = 0.001, R\textsuperscript{2} = 0.06, F (1,
80) = 4.85, p = 0.03). The fitted slope (-0.002) has a confidence
interval of (-0.0040, -0.0002) at 95.5 \% implying that performance
under restricted-water conditions is negatively correlated with WI.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\emph{Fig. 4. Relationship between performance of C. canephora genotypes
in restricted-water conditions and wetness index of the location in
which they were collected from (A) and the relationship between drought
tolerance as estimated from RGRA and wetness index of the location (B);
wetness index (WI), high WI values indicate moist conditions and low WI
values indicate dry conditions). Both slopes were negative and
significantly different from zero at p = 0.05}

There was also a marginally significant negative (p = 0.05) relationship
between drought tolerance of genotypes and wetness index in their
location (Fig. 4 panel B), being defined by: Tolerance = 0.99 -- 0.214
(wetness index), R\textsuperscript{2} = 0.05 and S.E. = 0.106. The
negative relationship between tolerance and wetness index of the
locations possibly indicates a climatic signature related to drought
tolerance of the genotypes. This observed relationship between drought
tolerance and wetness index suggests that on average, genotypes from the
comparatively drier areas, \emph{e.g.} Zoka, tended to be somewhat more
drought tolerant than genotypes from the wetter area Kalangala. No
difference in terms of goodness of fit was found between the linear and
the other two types of non-linear models. Other traits also tended to
show a similar trend of drought tolerance being negatively correlated
with wetness index although statistically significant relations were
observed in T\textsubscript{NL} only (Appendix Fig. A.4. and Appendix
Box A.2.).

4. DISCUSSION

In this study, we explored Uganda's \emph{C. canephora} genotypic
diversity in a screening experiment with 148 genotypes. We specifically
explored: (i) the effects of drought on growth categorised by
cultivation status (wild, feral and cultivated), genetic groups and the
geographic location, (ii) the relationship between performance under
restricted-water and performance under ample-water conditions and (iii)
the relationship between drought tolerance and wetness index (WI). To
our knowledge, this is the first study to explore intra-specific
variation in drought responses for a large number of genotypes
(\textgreater{} 100) in a tropical tree species.

\subsection*{4.1. Effect of drought on C. canephora in growth response
traits}

{\label{effect-of-drought-on-c.-canephora-in-growth-response-traits}}

Drought significantly reduced the RGR\textsubscript{A} (relative growth
rate of leaf area), T\textsubscript{NL} (total number of leaves),
T\textsubscript{L} (total leaf area), T\textsubscript{LDW} (total leaf
dry weight), S\textsubscript{LA} (specific leaf area) and increased the
R\textsubscript{L} (root volume to leaf area) (Table 2). The latter
finding concurs with the optimal partitioning theory which entails that
in response to stress, plants allocate proportionally more resources to
the structure capturing the most limiting resources (Brouwer, 1963;
Bloom, Chapin and Mooney, 1985). Other studies (Ryser and Eek, 2000;
Shipley and Meziane, 2002) and reviews (Hoffmann and Poorter, 2002;
Eziz\emph{et al.} , 2017) also stated that, in response to stress,
plants adjust their biomass allocation in accordance to whether the most
limiting resource is above- or belowground. In our study,
T\textsubscript{NL} and T\textsubscript{L} were the most affected traits
(Table 2) implying that genotypes responded to drought stress mainly by
minimising transpirational water loss by reducing the number of leaves
and leaf area. Differential reduction in leaf area as a response to
drought stress has also been observed by other authors (DaMatta \emph{et
al.} , 2003; Pinheiro \emph{et al.} , 2004; Dias \emph{et al.} , 2007;
King'oro, 2014). Our current findings extend these observations to a
wider range of genotypes including wild, feral and cultivated material.

\emph{4.2. Variation in response across cultivation status, genetic
groups and location}

Our findings indicate that there is a clear genotypic variation in
performance (RGR\textsubscript{A}) both under ample and restricted-water
conditions (Fig. 1 and Appendix Fig. A.3.). The variation in
RGR\textsubscript{A} was larger (more than two-fold) under ample-water
than restricted-water conditions (Table 3, Fig. 1 and Appendix Fig.
A.3.). The different phenotypic responses of genotypes in ample and
restricted-water conditions (Fig. 1 and Appendix Fig. A.3.) probably
reflects an underlying genetic polymorphism that may drive different
phenotypic responses to different environments (Stearns, 1989;
Pigliucci, 2005; Forsman, 2015). The observed genotypic variation in our
study in both growth and drought tolerance can be utilised for
optimizing breeding programs initiatives to develop drought-tolerant
varieties with adequate yield capacity (Table 3; Fig. 1 and Appendix
Fig. A.3.). Results did not show significant variations in
RGR\textsubscript{A} between genotypes of different cultivation status
(wild, feral or cultivated). This probably indicates that Uganda's
breeding efforts to date have not addressed drought tolerance. Breeding
efforts have been focusing on other factors \emph{e.g.} yield and
resistance to pests and diseases, in particular generating wilt
disease-resistant coffee varieties (Musoli \emph{et al.} , 2008).
Breeding efforts in \emph{C. canephora} are relatively limited,
partially due to the perennial nature of the crop (with an economic
lifespan of about 20 years), which suggests that most of the cultivated
material is still very similar to the wild genotypes (Thomas, 1935;
Montagnon, Eskes and Leroy, 1998; Ngugi and Aluka, 2019). Indeed, Kiwuka
et al., (2021) found that Uganda's cultivated genotypes were genetically
similar to wild populations from Malabigambo, Mabira and Kalangala
forests.

Across experimental factors we studied, location exhibited the widest
range of reductions in RGR\textsubscript{A} from 7.1\% to 36.7\% in
Kibale and Malabigambo respectively (Table 4). The genetic
distinctiveness of Uganda's wild \emph{C. canephora} populations across
locations as shown in Kiwuka et al., (2021) (Appendix Table A.1.) and
their differential phenotypic response to drought (Table 4 and 5;
Appendix Fig. A.3.) indicate that Uganda's \emph{C. canephora} diversity
could be locally adapted to the climatic conditions within the
locations. The significant interaction effect between genetic group and
water treatment (Table 3) also provides evidence that the localisation
of the genetic groups (\emph{i.e.} Zoka, Itwara, Kibale and Budongo
genetic groups from the NW) could be associated with genetic effects and
putatively to adaptive potential. The possibility of genotypes being
locally adapted is also indicated by an overlap between the genetic
group (Fig. 1) and location (Appendix Fig. A.3.) effects on
RGR\textsubscript{A}. However, the strong effect of location on response
to drought could also be reflecting local differences in other factors
such as soil types that may influence selection for the difference in
growth-related traits.

\emph{4.3. Slow growth as a strategy to cope with drought stress and
evidence of a trade-off between growth and drought tolerance.}

Genotypes that had low RGR\textsubscript{A} T\textsubscript{NL},
T\textsubscript{L} and T\textsubscript{LDW} values in ample-water
conditions were comparatively less affected by drought, a scenario which
indicates a trade-off between growth and drought tolerance across the
study populations (Fig. 3). This finding concurs with an established
ecological paradigm that there is a trade-off between the capacity of
plants to grow fast when resources are abundant and their capacity to
tolerate resource shortages (Bazzaz and Bazzaz, 1996; Aerts and Chapin,
1999; Grime, 2006). The trade-off between growth and tolerance has been
linked to a conservative resource-use strategy in which slow growth
results in slow tissue turnover (\emph{i.e.} conservative use of
resources) and subsequently less dependency on the environment for
acquisition of new resources. On the contrary fast growth is associated
with high resource turnover rates, intensive resource acquisition, high
dependency on the environment and ultimately shorter lifespan (Chapin,
1980; Chapin III, Autumn and Pugnaire, 1993; Grime \emph{et al.} , 1997;
Reich \emph{et al.} , 2003; F. J. Sterck, Poorter and Schieving, 2006;
Sterck \emph{et al.} , 2011). Ecologically, slow growth has been
reported as an adaptive strategy for plants in resource limiting
conditions. Poorter, (1989) studied the ecological consequences of the
interspecific variation in relative growth rate (RGR) of plants and
concluded that differences in potential RGR between species were
habitat-related whereby fast-growing species were found in resource-rich
habitats while slow growers could be found in any adverse environmental
condition. In response to drought, a growth-tolerance trade-off could be
expected because several traits and mechanisms that confer tolerance in
dry conditions (\emph{e.g.} low specific leaf area, low stomatal size or
number) reduce water loss but also reduce rates of net photosynthesis
per unit area, which, in turn, results into slower growth under
favourable water availability (Lambers, Chapin and Pons, 2008;
Sterck\emph{et al.} , 2011).

Although the growth-tolerance trade-off has been widely studied and
established across species (interspecific), including tropical forest
trees (Poorter and Jong, 1999; F J Sterck, Poorter and Schieving, 2006;
Sterck \emph{et al.} , 2011; Amissah \emph{et al.} , 2018) much fewer
studies (Pallardy and Kozlowski, 1981; Silva \emph{et al.} , 2013;
Menezes-Silva \emph{et al.} , 2015) have been conducted to explore the
intraspecific variation of tropical trees to drought and the
manifestation of the growth-tolerance trade-off. Pallardy and Kozlowski,
(1981) revealed a probable growth-tolerance trade-off
among\emph{Populus} clones: fast-growing clones had a larger initial
rate of decline in leaf water potential with transpirational flux
density but reduced the rate of decline more than slow-growing clones as
the transpirational flux density increased. Similarly, Menezes-Silva et
al., (2015) and Silva et al., (2013) studied eight clones of
cultivated\emph{C. canephora} (variety Conilon) and found that wood
density, a trait that partially influences the plant's water-conducting
capacity, was higher in drought-tolerant clones, and was associated with
greater resistance to cavitation. This adaptation however could limit
growth under favourable water conditions as dense wood is more costly to
produce and the associated smaller xylem have lower maximum water
conductance (Silva \emph{et al.} , 2013; Menezes-Silva \emph{et al.} ,
2015).

In our study, the relatively low RGR\textsubscript{A} and high tolerance
of genotypes from Kibale, Itwara and Zoka locations (Table 4; Appendix
Fig. A.3.) suggests that those populations employ a more conservative
resource-use strategy, while genotypes from Mabira, Malabigambo,
Kalangala and Budongo employ a more rapid resource-acquisition strategy.
Similar to our results, Silva et al.,(2013) and Menezes-Silva et
al.,(2015) also found that across a set of cultivated \emph{C.
canephora} clones, the most drought-tolerant ones tended to be slow
growers. Slow growth in stressful conditions could in the long term be
more adaptive than fast growth because fast growth results in larger and
more resource-demanding plants that could eventually die off if the
resource demand is not met. Here, we showed the existence of a
growth-tolerance trade-off across a large set of wild accessions of a
perennial crop species, suggesting that intraspecific variation in
tolerance may be related to selection in natural environments. Evidence
of a growth-tolerance trade-off in our study is further corroborated in
our related experiment by Kiwuka (2020) where we studied fewer (15)
genotypes with more response traits and found that slow-growing
genotypes were more drought tolerant and less plastic for most of the
response traits.

In interpreting our findings, it should be noted that our experiment was
a pot experiment and pots have limited volume. Firstly, this could cause
a so-called pot-binding effect (Poorter \emph{et al.} , 2012;
Sinclair\emph{et al.} , 2017); pots holding insufficient water to
support transpiration and therefore growth. This could be more severe
for fast-growing plants than for slow-growing ones. However, in our set
up we accounted for this effect as we determined the relationship
between water consumption and plant size and adjusted the amount of
water gift in restricted water treatment to correct for larger plants
consuming more water (see section 2.4). Therefore, we are confident that
larger plants did not suffer greater drought stress than smaller ones in
the water-restricted treatment and that the pot-binding effect was
minimised as seen in Appendix Plate. A.1 and Appendix Plate. A.2.
Secondly, in the field, rooting depth can be a drought adaptive trait as
it allows access to deeper moister soil layers. This effect evidently
could not be mimicked in pots. Association of rooting depth with growth
potential could be either positive (fast-growth facilitating deeper
roots) or negative (larger deeper root systems imposing greater
metabolic costs and therefore, slowing growth). Altogether, it is
important to determine whether the drought-tolerance trade-off found in
our study also occurs in the field. If the observed growth-tolerance
trade-off occurs under field conditions, it would pose a dilemma for
breeding on what to select for if one cannot have both. For instance,
selecting fast growth could result in low drought tolerance which poses
a challenge especially for small scale coffee farmers who may not have
irrigation facilities to deal with drought spells. Therefore, to sustain
\emph{C. canephora}production in drought-prone environments, breeders
should break the negative correlation between poor performance and
tolerance (Table 4; Fig. 3). This proposition agrees with Damatta et
al., (2018) who suggested that breeding for drought tolerance in coffee
should aim at developing tolerant genotypes with ''acceptable yields''.
Despite the adaptive advantage of slow growth (conservative resource-use
strategy), its positive association with low performance is also a
challenge as farmers are interested in good yields. Selection for either
slow or fast-growing genotypes should therefore be done in consideration
of whether the intended production is in stressful or optimal
conditions.

\emph{4.4. The link between drought tolerance and local climate}

Our results indicated a weak but statistically significant climatic
signal in relation to drought tolerance (Fig. 4). There appears to be a
trend where genotypes from wetter locations

(higher wetness index, WI) tended to be less drought tolerant than those
from drier ones (lower WI) (Fig. 4 and Appendix Fig. A. 4.). Our
findings therefore seem to agree with our expectation \emph{i.e.} that
genotypes from drier areas would be more drought tolerant than genotypes
from wetter areas, though the low R\textsuperscript{2} of the
relationship indicates that the observed signal is not very strong.
These results concur with Choat et al., (2007) who observed that
differences in water availability across sites could drive intraspecific
variation among \emph{Cordia} species. Studies (Bongarten and Teskey,
1986; Peuke \emph{et al.} , 2002; Baquedano, Valladares and Castillo,
2008) documented that the ecotypes of \emph{Pinus taeda} , \emph{Fagus
sylvatica} , \emph{Quercus coccifera} , had adaptive features which were
probably driven by the local climate. In our results, WI explained
approximately 5\% of the variation in drought tolerance in
RGR\textsubscript{A} across genotypes and further analysis preferably
over a wider climate range as well as WI data obtained from higher
resolution weather data are needed to verify the consistency of this
trend. Next, other factors may affect drought tolerance such as soil
hydraulic properties and local topology. Finally, drought tolerance as
determined in our study experiment may not fully reflect drought stress
in the field (see next section).

\emph{4.5. Considerations regarding the experimental set-up}

This paper presented results from a large screening experiment where 148
genotypes comprising 61 \% wild, 7 \% feral and 32\% cultivated, were
subjected to modest drought (restricted-water) and ample-water regimes
(see Appendix Table A. 3.). As such, for the feasibility of the
experiment, we included maximally four replicates per genotype per
treatment because this was the maximum manageable number, allowing for
the identification of the largest differences within the material.
Damage and mortality of some plants caused variation in the real number
of replicates across genotypes (Appendix Table A.8.). Consequently, the
mixed-effects model that we applied could not estimate genotype effects
very precisely but, rather, it put the genotype effects closer to the
mean effect (an effect called shrinkage). It is therefore important to
note that in our analyses, individual genotypes acted mostly as a
replication at the genotypic level to test cultivation status, genetic
group and location effects on the responses.

Despite the close relationship between vegetative growth and yield
capacity of coffee plants (Cilas \emph{et al.} , 2006), one should note
that our study focused on responses of comparatively juvenile plants and
we did not include effects of ontogenetic changes on responses yet
certain ontogenetic changes may affect performance in later life stages.
For example, as mentioned above in the discussion about growth-tolerance
trade-offs, relatively fast growth in young plants under dry conditions,
could be maladaptive later in life as it can result in larger more water
demanding phenotype. To assess how drought affects trees over a larger
time of their life, more mature trees (of about five years) need to be
considered

\emph{4.6. Conclusion and implications}

Considering climate change and its adverse effects on coffee production,
this study showed that Uganda has potentially adapted \emph{C.
canephora} genetic diversity which could be used to develop
drought-tolerant genotypes. Breeders, however, need to work towards
weakening or even breaking the trade-off between drought tolerance and
performance. As noted by Borrell et al., (2020) the conservation of
extant genetic diversity, particularly in a period of rapid
environmental change is critical to support future crop improvement. In
this regard, the Zoka population is of special interest among the
whole\emph{C. canephora} natural distribution in Africa, being within
the drier end of the climatic gradient and exhibiting relatively high
drought tolerance. Zoka is a small unique forest (the only tropical
rainforest occurring in dry northern Uganda), but its small size (12.6
km\textsuperscript{2}) makes the population particularly vulnerable to
habitat destruction. At a national level, there is a need to foster
the\emph{in-situ} conservation and management of Uganda's \emph{C.
canephora} wild populations. Strategic \emph{in-situ} conservation of
these wild populations will allow for their evolution and adaptation to
environmental stresses and consequently the continued use of the
material to offer resilience to cultivated \emph{C. canephora} material
amidst the escalating effects of climate change. National conservation
strategies should involve the restriction of \emph{C.
canephora}cultivation near any wild population to deter genetic drift
and to allow continuous adaptation of the natural populations.

DATA ACCESSIBILITY STATEMENT

Data associated with this manuscript is included and will be archived in
the Dryad data repository.

COMPETING INTERESTS STATEMENT

None declared

AUTHOR CONTRIBUTIONS

\textbf{Catherine Kiwuka} : Conceptualization, methodology, formal
analysis, investigation, data curation, writing---original draft
preparation, project administration, funding acquisition. \textbf{Jan
Vos} : Conceptualization, methodology, data curation, formal analysis,
visualization, writing---review and editing, supervision. \textbf{Jacob
C. Douma} : Data curation, formal analysis, visualization,
writing---review and editing. \textbf{Pascal Musoli} :
Conceptualization, methodology resources, writing---review and editing,
supervision.\textbf{John W. Mulumba} : Conceptualization, methodology,
writing---review and editing, supervision. \selectlanguage{ngerman}\textbf{Valérie Poncet} :
Conceptualization, methodology, formal analysis, writing---review and
editing, supervision. \textbf{Niels P.R. Anten} : conceptualization,
methodology, formal analysis, visualization, resources, writing---review
and editing, supervision.

FUNDING

The activities of this study were mainly funded by the World
Bank-supported project entitled ''Agricultural Technology and
Agribusiness Advisory Services'' (ATAAS) through National Agricultural
Research Organization (NARO). Additionally, we thank the Uganda Coffee
Development Authority (UCDA) for additional financial assistance to
support the experimental phase of the study.

ACKNOWLEDGMENTS

This study was conducted under the auspices of the Centre for Crop
Systems Analysis (CSA) of the Production Ecology and Resource
Conservation Graduate School of Wageningen University and Research and
the National Agricultural Research Organization (NARO) of Uganda. We
thank the National Forestry Authority (NFA; NFA/N/9.2/14) and the Uganda
Wildlife Authority (UWA; EDO/35/01) for permitting us to collect the
study samples. We are grateful to the director of research of the
National Agricultural Research Laboratories (NARL), Kawanda; Dr
Tushemereirwe Wilberforce and the administration team for logistical
support. Special thanks to the devoted research assistance team
comprising: M. Lumu, H. Were, S. Katende, W. O. Maganyi, R. Sekasamba,
D. Nasaazi, J. Makya, F. Okoda, E. Oluput, I. Makya, D. Kasoozi, R.
Kakooto and S. Kiconco Stella. We appreciate the Agropolis Foundation -
CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for
partially supporting the molecular characterisation of the studied
genotypes. We also acknowledge support from Dr Alexandre de Kochko and
Dr Aaron P. Davis.

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APPENDICES

Appendix A

Appendix Table A.1. Number of genotypes included in the study per
genetic group per location\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Location} & \textbf{Genetic groups} & \textbf{Genetic groups} &
\textbf{Genetic groups} & \textbf{Genetic groups} & \textbf{Genetic
groups}\tabularnewline
& \textbf{SC} & \textbf{NW} & \textbf{NW} & \textbf{NW} &
\textbf{NW}\tabularnewline
& & Itwara & Kibale & Budongo & Zoka\tabularnewline
Budongo & & & & 11 &\tabularnewline
Itwara & & 10 & & &\tabularnewline
Kalangala & 18 & & & &\tabularnewline
Kawanda & 14 & & & &\tabularnewline
Kibale & & & 7 & &\tabularnewline
Kituza & 24 & & & &\tabularnewline
Mabira & 13 & & & &\tabularnewline
Malabigambo & 7 & & & &\tabularnewline
Zoka & & & & & 23\tabularnewline
\emph{This genetic grouping is as per Kiwuka et al.} (Kiwuka \emph{et
al.}, 2021)\emph{. SC (Southern Central), NW (North Western; which
differentiates into four other distinct groups namely: Itwara, Kibale,
Budongo and Zoka). Note: genotypes that were misclassified and/or
hybrids were not considered} & \emph{This genetic grouping is as per
Kiwuka et al.} (Kiwuka \emph{et al.}, 2021)\emph{. SC (Southern
Central), NW (North Western; which differentiates into four other
distinct groups namely: Itwara, Kibale, Budongo and Zoka). Note:
genotypes that were misclassified and/or hybrids were not considered} &
\emph{This genetic grouping is as per Kiwuka et al.} (Kiwuka \emph{et
al.}, 2021)\emph{. SC (Southern Central), NW (North Western; which
differentiates into four other distinct groups namely: Itwara, Kibale,
Budongo and Zoka). Note: genotypes that were misclassified and/or
hybrids were not considered} & \emph{This genetic grouping is as per
Kiwuka et al.} (Kiwuka \emph{et al.}, 2021)\emph{. SC (Southern
Central), NW (North Western; which differentiates into four other
distinct groups namely: Itwara, Kibale, Budongo and Zoka). Note:
genotypes that were misclassified and/or hybrids were not considered} &
\emph{This genetic grouping is as per Kiwuka et al.} (Kiwuka \emph{et
al.}, 2021)\emph{. SC (Southern Central), NW (North Western; which
differentiates into four other distinct groups namely: Itwara, Kibale,
Budongo and Zoka). Note: genotypes that were misclassified and/or
hybrids were not considered} & \emph{This genetic grouping is as per
Kiwuka et al.} (Kiwuka \emph{et al.}, 2021)\emph{. SC (Southern
Central), NW (North Western; which differentiates into four other
distinct groups namely: Itwara, Kibale, Budongo and Zoka). Note:
genotypes that were misclassified and/or hybrids were not
considered}\tabularnewline
\bottomrule
\end{longtable}

Appendix Table A. 2. Traits measured to investigate response to drought
stress\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Collection phase\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Trait\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Units\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Start of the treatment (25\textsuperscript{th} May 2017)\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
plant height\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
cm\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
leaf area\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
cm\textsuperscript{2}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
no. of main stem leaves\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
stem diameter 5 cm for above the mark, 6 cm for below the mark\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
mm\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
During treatment (21\textsuperscript{th} - 24\textsuperscript{th} June
2017)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
plant height\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
cm\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
leaf area\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
cm\textsuperscript{2}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
no. of primaries\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
no. of suckers\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
no. of leaves on main stem, primaries and suckers\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
End of Treatment (12\textsuperscript{th} -26\textsuperscript{th}
September 2017)\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
plant height,\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
cm\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
leaf area\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
cm\textsuperscript{2}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
no. of primaries\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
no. of suckers\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
no. of leaves on the main stem, primaries and suckers\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
fresh weight of all leaves\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
g\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
dry weight of all leaves\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
g\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
specific leaf area\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
cm\textsuperscript{2} g\textsuperscript{-1}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
root volume\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
cm\textsuperscript{3}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Appendix Table A.4. Details of the effect of treatment and location on
total leaf area (T\textsubscript{L})\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Factor & Value & Value & Std. Error & DF & t-value &
p-value\tabularnewline
Location\textbf{:} trt & Location\textbf{:} trt & Location\textbf{:} trt
& Location\textbf{:} trt & Location\textbf{:} trt & Location\textbf{:}
trt & Location\textbf{:} trt\tabularnewline
Budongo\textbf{:} trt & Budongo\textbf{:} trt & -1025 & 360 & 708 &
-2.8487 & 0.005\tabularnewline
Itwara: trt & Itwara: trt & -842 & 957 & 708 & -0.8803 &
0.379\tabularnewline
Kalangala: trt & Kalangala: trt & -1443 & 401 & 708 & -3.5999 &
0.000\tabularnewline
Kibale: trt & Kibale: trt & 229 & 484 & 708 & 0.4744 &
0.635\tabularnewline
Mabira: trt & Mabira: trt & -1665 & 466 & 708 & -3.5709 &
0.000\tabularnewline
Malabigambo: trt & Malabigambo: trt & -3552 & 825 & 708 & -4.3070 &
0.000\tabularnewline
Zoka: trt & Zoka: trt & -500 & 318 & 708 & -1.5756 &
0.116\tabularnewline
\emph{trt denotes experimental treatment, experiment treatments had a
significant effect when p \textless{} 0.05} & \emph{trt denotes
experimental treatment, experiment treatments had a significant effect
when p \textless{} 0.05} & \emph{trt denotes experimental treatment,
experiment treatments had a significant effect when p \textless{} 0.05}
& \emph{trt denotes experimental treatment, experiment treatments had a
significant effect when p \textless{} 0.05} & \emph{trt denotes
experimental treatment, experiment treatments had a significant effect
when p \textless{} 0.05} & \emph{trt denotes experimental treatment,
experiment treatments had a significant effect when p \textless{} 0.05}
& \emph{trt denotes experimental treatment, experiment treatments had a
significant effect when p \textless{} 0.05}\tabularnewline
\bottomrule
\end{longtable}

Appendix Table A.5. Numbers of plant individuals per treatment across
cultivation status domestication\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& Treatment & Treatment & Total\tabularnewline
& Ample-water & Restricted-water &\tabularnewline
Cultivated & 121 & 178 & 299\tabularnewline
Feral & 28 & 41 & 69\tabularnewline
Wild & 225 & 326 & 551\tabularnewline
Grand total & 374 & 545 & 919\tabularnewline
\bottomrule
\end{longtable}

Appendix Table A.6. Number of replicates per treatment per genotype\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Location & Genotype & Number of replicates per treatment & Number of
replicates per treatment & Total\tabularnewline
& & Ample-water & Restricted-water &\tabularnewline
Budongo & BD 1.1 & 4 & 4 & 8\tabularnewline
& BD 1.5 & 4 & 3 & 7\tabularnewline
& BD 2.1 & 2 & 4 & 6\tabularnewline
& BD 2.2 & 3 & 4 & 7\tabularnewline
& BD 2.3 & 4 & 4 & 8\tabularnewline
& BD 2.4 & 4 & 4 & 8\tabularnewline
& BD 2.5 & 4 & 3 & 7\tabularnewline
& BD 3.1 & & 2 & 2\tabularnewline
& BD 3.2 & 4 & 4 & 8\tabularnewline
& BD 3.3 & 3 & 4 & 7\tabularnewline
& BD 4.1 & 4 & 3 & 7\tabularnewline
& BD 4.2 & 2 & 4 & 6\tabularnewline
& BD 4.3 & 2 & 4 & 6\tabularnewline
& BD 4.4 & 4 & 4 & 8\tabularnewline
& BD 4.5 & 1 & 4 & 5\tabularnewline
& BD 5.5 & 4 & 4 & 8\tabularnewline
Itwara & IT 2.2 & 1 & 4 & 5\tabularnewline
& IT 2.3 & 3 & 4 & 7\tabularnewline
& IT 3.3 & 3 & 4 & 7\tabularnewline
& IT 4.2 & 4 & 4 & 8\tabularnewline
& IT 4.3 & 1 & 4 & 5\tabularnewline
& IT 4.4 & & 2 & 2\tabularnewline
& IT 4.5 & & 4 & 4\tabularnewline
& IT 5.1 & & 1 & 1\tabularnewline
& IT 5.2 & & 1 & 1\tabularnewline
& IT 5.3 & 1 & 3 & 4\tabularnewline
Kibale & KB 2.1 & 4 & 4 & 8\tabularnewline
& KB 2.2 & & 2 & 2\tabularnewline
& KB 2.4 & & 4 & 4\tabularnewline
& KB 3.1 & 1 & 3 & 4\tabularnewline
& KB 3.3 & 3 & 4 & 7\tabularnewline
& KB 3.4 & 2 & 4 & 6\tabularnewline
& KB 4.3 & 1 & 2 & 3\tabularnewline
& KB 4.4 & 1 & 3 & 4\tabularnewline
& KB 4.5 & 1 & 2 & 3\tabularnewline
Kalangala & KL 1.1 & 3 & 4 & 7\tabularnewline
& KL 1.2 & 2 & 4 & 6\tabularnewline
& KL 1.3 & 4 & 4 & 8\tabularnewline
& KL 1.4 & 4 & 4 & 8\tabularnewline
& KL 1.5 & 4 & 4 & 8\tabularnewline
& KL 2.2 & 4 & 4 & 8\tabularnewline
& KL 2.4 & 2 & 4 & 6\tabularnewline
& KL 3.2 & 4 & 3 & 7\tabularnewline
& KL 3.5 & 4 & 4 & 8\tabularnewline
& KL 4.3 & 4 & 4 & 8\tabularnewline
& KL 5.2 & 1 & 3 & 4\tabularnewline
& KL 5.3 & & 2 & 2\tabularnewline
& KL 5.4 & 3 & 4 & 7\tabularnewline
& KL 6.1 & & 4 & 4\tabularnewline
& KL 6.2 & 4 & 4 & 8\tabularnewline
& KL 6.3 & 4 & 4 & 8\tabularnewline
& KL 6.4 & 1 & 4 & 5\tabularnewline
& KL 7.2 & 1 & 4 & 5\tabularnewline
& KL 8.3 & & 2 & 2\tabularnewline
Kituza & KT 0.1 & 1 & 4 & 5\tabularnewline
& KT 0.2 & 3 & 4 & 7\tabularnewline
& KT 0.3 & 1 & 3 & 4\tabularnewline
& KT 0.4 & 2 & 3 & 5\tabularnewline
& KT 0.5 & 4 & 4 & 8\tabularnewline
& KT 0.7 & 4 & 4 & 8\tabularnewline
& KT 0.8 & 2 & 4 & 6\tabularnewline
& KT 1.0 & & 3 & 3\tabularnewline
& KT 1.2 & 3 & 4 & 7\tabularnewline
& KT 1.6 & 3 & 4 & 7\tabularnewline
& KT 1.7 & 4 & 4 & 8\tabularnewline
& KT 1.8 & 2 & 4 & 6\tabularnewline
& KT 2.0 & 1 & 4 & 5\tabularnewline
& KT 2.1 & & 4 & 4\tabularnewline
& KT 2.3 & 2 & 4 & 6\tabularnewline
& KT 2.4 & 3 & 4 & 7\tabularnewline
& KT 2.5 & 1 & 4 & 5\tabularnewline
& KT 2.6 & 4 & 4 & 8\tabularnewline
& KT 2.7 & 2 & 3 & 5\tabularnewline
& KT 2.8 & & 3 & 3\tabularnewline
& KT 2.9 & 1 & 4 & 5\tabularnewline
& KT 3.0 & 4 & 2 & 6\tabularnewline
& KT 3.1 & 4 & 4 & 8\tabularnewline
& KT 3.2 & 4 & 4 & 8\tabularnewline
& KT 3.3 & 3 & 4 & 7\tabularnewline
& KT 3.4 & 3 & 4 & 7\tabularnewline
& KT 3.5 & 4 & 4 & 8\tabularnewline
& KT 3.6 & 4 & 4 & 8\tabularnewline
Kawanda & 238/29/1 & 4 & 4 & 8\tabularnewline
& 267s/25/7 & 3 & 4 & 7\tabularnewline
& KW 0.1 & 4 & 4 & 8\tabularnewline
& KW 0.2 & 4 & 4 & 8\tabularnewline
& KW 0.3 & 1 & 4 & 5\tabularnewline
& KW 0.6 & 1 & 4 & 5\tabularnewline
& KW 0.8 & & 4 & 4\tabularnewline
& KW 0.9 & & 2 & 2\tabularnewline
& KW 1.0 & 4 & 4 & 8\tabularnewline
& KW 1.1 & 4 & 4 & 8\tabularnewline
& KW 1.2 & 4 & 4 & 8\tabularnewline
& KW 1.4 & 1 & 4 & 5\tabularnewline
& KW 1.5 & 2 & 3 & 5\tabularnewline
& KW 1.6 & 4 & 4 & 8\tabularnewline
& KW 1.7 & 4 & 4 & 8\tabularnewline
& KW 1.8 & & 4 & 4\tabularnewline
& KW 1.9 & 4 & 4 & 8\tabularnewline
& KW 2.0 & 4 & 4 & 8\tabularnewline
& KW 2.1 & 4 & 4 & 8\tabularnewline
Mabira & MB 1.4 & 4 & 3 & 7\tabularnewline
& MB 2.3 & 3 & 4 & 7\tabularnewline
& MB 2.4 & 2 & 4 & 6\tabularnewline
& MB 2.5 & 2 & 4 & 6\tabularnewline
& MB 3.1 & 4 & 4 & 8\tabularnewline
& MB 3.2 & & 4 & 4\tabularnewline
& MB 3.3 & 4 & 4 & 8\tabularnewline
& MB 3.4 & 3 & 4 & 7\tabularnewline
& MB 3.5 & 3 & 3 & 6\tabularnewline
& MB 4.1 & 3 & 4 & 7\tabularnewline
& MB 4.3 & 4 & 4 & 8\tabularnewline
& MB 4.4 & & 3 & 3\tabularnewline
& MB 4.5 & 4 & 4 & 8\tabularnewline
& MB 5.1 & 4 & 3 & 7\tabularnewline
& MB 5.2 & 4 & 4 & 8\tabularnewline
Malabigambo & ML 2.1 & 4 & 4 & 8\tabularnewline
& ML 2.3 & 4 & 4 & 8\tabularnewline
& ML 2.4 & 4 & 4 & 8\tabularnewline
& ML 5.1 & 4 & 4 & 8\tabularnewline
& ML 6.1 & 4 & 4 & 8\tabularnewline
& ML 6.2 & 4 & 4 & 8\tabularnewline
& ML 6.3 & 4 & 4 & 8\tabularnewline
Zoka & ZK 1.1 & 3 & 4 & 7\tabularnewline
& ZK 1.2 & & 4 & 4\tabularnewline
& ZK 1.3 & 3 & 4 & 7\tabularnewline
& ZK 1.4 & 4 & 4 & 8\tabularnewline
& ZK 1.5 & 3 & 4 & 7\tabularnewline
& ZK 2.1 & 4 & 4 & 8\tabularnewline
& ZK 2.2 & & 2 & 2\tabularnewline
& ZK 2.3 & 3 & 4 & 7\tabularnewline
& ZK 2.4 & 4 & 4 & 8\tabularnewline
& ZK 2.5 & 2 & 4 & 6\tabularnewline
& ZK 3.1 & & 4 & 4\tabularnewline
& ZK 3.2 & 1 & 4 & 5\tabularnewline
& ZK 3.3 & & 2 & 2\tabularnewline
& ZK 3.4 & 4 & 4 & 8\tabularnewline
& ZK 3.5 & 2 & 4 & 6\tabularnewline
& ZK 4.1 & 4 & 4 & 8\tabularnewline
& ZK 4.2 & 3 & 4 & 7\tabularnewline
& ZK 4.3 & 4 & 4 & 8\tabularnewline
& ZK 4.4 & & 4 & 4\tabularnewline
& ZK 4.5 & 4 & 4 & 8\tabularnewline
& ZK 5.1 & 2 & 3 & 5\tabularnewline
& ZK 5.2 & 3 & 4 & 7\tabularnewline
& ZK 5.3 & 1 & 4 & 5\tabularnewline
& ZK 5.4 & 3 & 4 & 7\tabularnewline
& ZK 5.5 & & 4 & 4\tabularnewline
\textbf{Grand Total} & & \textbf{374} & \textbf{545} &
\textbf{919}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\emph{Appendix Fig. A.1. Locations and the different climatic zones in
which they occur; A: Location (climatic zone); Budongo (K), Itwara (L),
Kalangala (A1), Kawanda (B), Kibale (L), Kituza (B), Mabira (B),
Malabigambo (AI) and Zoka (J). B: Red and green indicate points of
sample collection and codes of the samples respectively.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\emph{Appendix Fig. A.2. Principal component analysis (PCA) of 19
bioclimatic variables: Temperature related variables coloured
(cornflowerblue): Annual Mean Temperature (BIO1), Mean Diurnal Range
(Mean of monthly (max temp - min temp)) (BIO2), Isothermality
(BIO2/BIO7) (* 100) (BIO3), Temperature Seasonality (standard deviation
*100) (BIO4), Max Temperature of Warmest Month (BIO5), Min Temperature
of Coldest Month (BIO6), Mean Temperature of Wettest Quarter (BIO8),
Mean Temperature of Driest Quarter (BIO9), Mean Temperature of Warmest
Quarter (BIO10), Mean Temperature of Coldest Quarter (BIO11),
precipitation related coloured (burlywood4): Annual Precipitation
(BIO12), Precipitation of Wettest Month (BIO13), Precipitation of Driest
Month (BIO14), Precipitation Seasonality (Coefficient of Variation)
(BIO15), Precipitation of Wettest Quarter (BIO16), Precipitation of
Driest Quarter (BIO17), Precipitation of Warmest Quarter (BIO18),
Precipitation of Coldest Quarter (BIO19) and 3 other environmental
variables, Altitude, Aridity (Wetness Index (WI)) and Potential
Evapotranspiration (PET) coloured (darkseagreen4) at Ugandan C.
canephora wild sites. The two first axis, PC1 and PC2, account for 64.7
\% and 21.3 \% of the total variation, respectively.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\emph{Appendix Fig. A.3. Mean RGRA {[}d\textsuperscript{-1}{]} as a
function of treatment (ample-water (AW) and restricted-water (RW) across
location (panels) and genotypes (coloured lines). Solid black line shows
the mean estimated response per location.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Appendix Fig. A.4. Relationship between tolerance of growth traits
in\emph{C. canephora} genotypes and wetness index of the location in
which they were collected from (A) Drought tolerance in Total Number of
leaves (DTT\textsubscript{NL}) and wetness index (B); Drought tolerance
in Total Leaf area (DTTL) and wetness index (C) Drought tolerance in
Total Leaf Dry Weight (DTTLDW) and wetness index, high wetness index
values indicate moist conditions and low WI values indicate dry
conditions\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

Appendix Plate A1. Photos showing the root extraction process and
evidence of minimal or no pot-binding effect.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

Appendix Plate A2. Experimental shelter overview and size of the plants
at final harvest.

\selectlanguage{english}
\FloatBarrier
\end{document}

