Over thousands of years, terrestrial plants have successfully adapted to a dry environment characterized by temperature and light extremes. In agricultural systems, the impact of abiotic stress is influenced by genotype, environment and management interactions (Messina et al. 2009). Plants use different mechanisms to respond to challenges in the environment which allow for continued survival. Plant responses to the environment are complex as multiple abiotic or biotic stressors often affect species simultaneously (Canci and Toker 2009; Rani et al. 2020). As a result, drought and heat tolerance mechanisms operate at various spatial and temporal scales (Tardieu et al. 2018). The extent to which drought and heat impact on plants is primarily dependent on the timing and duration of the stress (Carrão et al. 2016). These factors cumulatively make it challenging to study and improve plants for drought and heat adaptation.
Although the timing and severity of stress are dependent on the environment, the majority of research suggests that drought mostly impacts the reproductive phase in several crops, including chickpea (Lamaoui et al. 2018; Ramamoorthy et al. 2017; Daryanto et al. 2015; Rani et al. 2020). As the canopy develops and water demand increases, residual soil moisture is often depleted during the critical reproductive stage. Under drought stress, pod and flower abscission increases in response to reduced assimilate supply, pod set reduces and consequently, seed yield is impacted (Leport et al. 2006; Fang et al. 2010). High temperature, has a similar impact during the reproductive phase, affecting pod set and pod filling stages leading to a yield penalty (Devasirvatham et al. 2013). Due to the nature of abiotic stress, heat and drought stress often occur simultaneously. Unsurprisingly, many traits associated with adaptation to heat stress also confer an advantage under drought. For instance, early flowering can avoid both ends of season drought and heat.
Traits vary in their importance for drought and heat adaptation and the impact that each has on agronomic performance is context dependent. QTL have been identified for traits involved in drought and heat adaptation in chickpea (Table 1). Table 1 primarily includes traits associated with phenology, yield components and root system architecture. This highlights an opportunity to understand the genetic controls of canopy development and other key traits associated with water use efficiency.
Table 1
Summary of QTL identified for drought and heat adaptation traits
Trait
|
QTL
|
Location
|
Reference
|
Root length density (cm cm− 3)
|
QR3rld01
|
CalG04, 62.56–73.06 cM bin_4_13239546 bin_4_13378761
|
(Varshney et al. 2014)
|
|
QR3rld01
|
CaLG04 ( 3.23–5.37 cM)
|
(Jaganathan et al. 2015)
|
Total dry root weight/total plant dry weight ratio (RTR, %)
|
Ca_04586,
|
CaLG04 (2.7 Mb)
|
(Singh et al. 2016)
|
|
QR3rtr01 (16.67 %)
|
CaLG04 (5 cM)
|
(Varshney et al. 2014)
|
|
QR3rtr01 ( 10.85–13.56 %
|
CaLG04 (1.81–5.37 cM)
|
(Jaganathan et al. 2015)
|
Primary root branches (PBS)
|
QR3pbs02 (12.92 %)
|
CaLG08, bin_8_6034209 bin_8_5984553
|
(Jaganathan et al. 2015)
|
100-Seed weight (g)
|
QR4100sdw02,
|
CAM2093-ICCM0349
|
(Varshney et al. 2014)
|
|
Ca_04364 (28.61%), Ca_04607(19.25%)
|
CaLG04 (1.13 cM), CaLG04 (1.36 cM)
|
(Singh et al. 2016)
|
|
QR3100sdw03 (60.41%),
|
CaLG04, flanked CaM2093–ICCM0249
|
(Varshney et al. 2014)
|
|
QR3sdw01 ( 10.78–26.91 %)
|
CaLG04 ( 0.73–5.91cM)
|
(Jaganathan et al. 2015)
|
|
qSW-1 (11.54%), qSW-2 (19.24%)
|
LG3 (CEST129- CESSR221
|
(Gupta et al. 2015)
|
Total number of seeds per plot
|
Qts02_5 (12.0%)
|
Ca5_44667768, Ca5_46955940, Ca6_7846335, Ca6_14353624
|
(Paul et al. 2018)
|
Percentage Pod Setting
|
q%podset06_5 (11.51%), q%podset08_6 (8.44%)
|
Ca5_44667768, Ca5_46955940, Ca6_7846335, Ca6_14353624
|
(Paul et al. 2018)
|
Grain Yield per Plot
|
qgy02_5 (16.04%), qgy03_6 (4.43%)
|
Ca5_44667768, Ca5_46955940, Ca6_7846335, Ca6_14353624
|
(Paul et al. 2018)
|
Pod/plant
|
QR3pod01, QR4pod02
|
CaLG04, bin_4_13239546 bin_4_13378761, CaM0772– TS45
|
(Varshney et al. 2014)
|
Filled pods per plot
|
qfpod02_5 (11.57%), qfpod03_6 (6.56%)
|
Ca5_44667768, Ca5_46955940, Ca6_7846335, Ca6_14353624
|
(Paul et al. 2018)
|
Plant height (cm)
|
QR3pht01,
|
CaLG04
|
(Varshney et al. 2014)
|
|
QR3pht06, QR3pht08
|
CaLG04
|
(Varshney et al. 2014)
|
|
qPH-1 (13.98%), qPH-2 (12.17%),
|
LG6 (NCPGR199-NCPGR202), LG7 (CEST47-NCPGR34)
|
(Gupta et al. 2015)
|
|
QR3pht03 ( 14.36–76.54 %)
|
CaLG04
|
(Varshney et al. 2014)
|
Days to 50% flowering (DF)
|
QR3df04 (67.71%),
|
CaLG08 (1.81 cM), bin_8_6034209 bin_8_5984553, ,
|
(Jaganathan et al. 2015)
|
|
QR3df01 (26.87%), QR4df06
|
NCPGR164 Ca8_3050452, TA103II–TA122
|
(Varshney et al. 2014)
|
Early flowering
|
efl-1, Efl-1
|
ICCV 2 X JG 62 (RIL)
|
(Kumar and van Rheenen 2000)
|
Days to maturity
|
QR3dm01 (47.43%)
|
CaLG06 7.33 cM
|
(Jaganathan et al. 2015)
|
Visual Score on podding behaviour
|
qvs05_6 (11.07%)
|
Ca6_7846335, Ca6_14353624
|
(Paul et al. 2018)
|
*% is the phenotypic variation explained (PVE). |
There are two main ways to adapt a crop to drought; 1) optimise the timing of water use across development and 2) improve access to water.
A determinant of water demand in chickpea is the rate and extent of canopy development (Sivasakthi et al. 2017). This is highly dependent on the timing of water use, which among other traits, is associated with phenology (Zaman-Allah et al. 2011). Canopy architecture traits such as leaf area development, canopy size and canopy conductance influence transpiration rate and in some environments, may improve drought adaptation (Thudi et al. 2014).
To improve access to water, root architecture traits have been a major target in chickpea crop improvement over many decades (Siddique et al. 2011; Saxena et al. 1993). Significant genetic variation in root traits have been reported (Kashiwagi et al. 2006; Chen et al. 2017; Purushothaman et al. 2017; Serraj et al. 2004). Moreover, many studies report that improvements in root traits, specifically root length density and rooting depth, have an overall positive impact on adaptation to drought (Ludlow and Muchow 1990; Krishnamurthy et al. 2003; Kashiwagi et al. 2005; Kashiwagi et al. 2015; Gaur et al. 2008; Ramamoorthy et al. 2017; Saxena et al. 1993). Rooting depth can exceed 100 cm (Kashiwagi et al. 2006), and water uptake from soil layers of 90–120 cm is a feature of many drought adapted genotypes (Purushothaman et al. 2017). Serraj et al. (2004) noted that increases in root depth and root length density led to greater water use translating to higher yields. Similarly, a prolific root system was determined to positively affect seed yield under drought (Kashiwagi et al. 2006; Varshney et al. 2013a).
However, changes in root architecture may have a tradeoff in some environments. Increases in rooting depth and biomass may not necessarily lead to increases in grain yields due to the metabolic cost of increased biomass partitioning and energy loss through respiration (Vadez et al. 2008; Kashiwagi et al. 2015; Ramamoorthy et al. 2017). Additionally, it is now also recognised in chickpea that temporal changes in root growth influence the effective use of available water during the crop cycle (Vadez et al. 2008; Zaman-Allah et al. 2011; Vadez et al. 2007). There is a strong association between aboveground biomass and profligate water uptake, leading to higher water use and a yield penalty in some contexts as water availability during pod initiation is critical for yield development (Fig. 2). During the vegetative period, conservative water use can improve water availability during pod initiation, which is a critical period for determining yield. Conservative water use is a function of the root system and aboveground traits such as canopy development.
The importance of considering root traits in terms of their spatial and temporal characteristics is supported by studies that describe genotypes suitable for water-limited environments as those with root growth vigour and deeper soil root proliferation at the beginning of the reproductive stage (Kell 2011; Singh et al. 1995). Furthermore, an optimal root system for efficient uptake of soil water is unlikely to be optimal for nutrient uptake. To avoid this tradeoff, there may be genetic variation in the root system architecture that could be exploited.