thesis drought stress

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Home > Books > Plant Stress Physiology - Perspectives in Agriculture

Salt and Water Stress Responses in Plants

Submitted: 29 August 2021 Reviewed: 04 October 2021 Published: 22 November 2021

DOI: 10.5772/intechopen.101072

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Plant Stress Physiology - Perspectives in Agriculture

Edited by Mirza Hasanuzzaman and Kamran Nahar

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Climate change-driven ecological disturbances have a great impact on freshwater availability which hampers agricultural production. Currently, drought and salinity are the two major abiotic stress factors responsible for the reduction of crop yields worldwide. Increasing soil salt concentration decreases plant water uptake leading to an apparent water limitation and later to the accumulation of toxic ions in various plant organs which negatively affect plant growth. Plants are autotrophic organisms that function with simple inorganic molecules, but the underlying pathways of defense mechanisms are much more complex and harder to unravel. However, the most promising strategy to achieve sustainable agriculture and to meet the future global food demand, is the enhancement of crop stress tolerance through traditional breeding techniques and genetic engineering. Therefore, it is very important to better understand the tolerance mechanisms of the plants, including signaling pathways, biochemical and physiological responses. Although, these mechanisms are based on a well-defined set of basic responses, they can vary among different plant species.

• abiotic stress
• response mechanisms

Author Information

Mirela irina cordea.

• University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Cluj-Napoca, Romania

Orsolya Borsai *

*Address all correspondence to: [email protected]

1. Introduction

Salinity and drought are the two major constraints that affect plant growth and crop production alongside other stress conditions such as extreme temperature, heavy metals, flooding etc. thus reducing agricultural productivity worldwide. Both the cellular and molecular responses of plants to these environmental stresses have already been investigated, however understanding these mechanisms by which plants can perceive stress signals and transmit them to cellular machinery to activate adaptive responses is a very important chain-link of plant physiology. Besides, extending knowledge about stress signal transduction becomes vital for breeding programs and genetic engineering to improve stress tolerance in crops.

Due to climate change, it is predicted that drought and salinity will became more severe in the upcoming years which could lead to a significant reduction of plant growth and yield of several economically important species. It has been estimated that worldwide food demand will increase by 70% until the end of 2050 [ 1 ] due to a population growth of 2.3 billion people. In this context, developing crop plants with high yield and better tolerance to harsh environmental conditions becomes an urgent need to meet future food demand for next generations.

In general, plant responses to salinity and drought may vary in morphological, physiological and biochemical aspects and processes. Most of the effects induced by salinity and drought are negative, however to some extent they can have positive effects as well [ 2 ]. It has been reported that salinity at certain concentrations enhanced plant fecundity due to an increase in reproduction, but it has also been observed that this enhancement was highly dependent on genotype and plant developmental stage [ 3 ]. Soil water salinity can also have a positive effect on fine particles helping them to bind together into aggregates, thus improving soil aeration, root penetration and root growth [ 4 ]. Nevertheless, salinity cannot be increased in favor of soil structure without considering the potential impacts on plant health.

Salt-stress resistance represents the ability of a plant to prevent, reduce or overcome the possible damaging effects caused directly or indirectly by the presence of excessive soluble salts (accumulation of toxic ions) in its root zone. A 50% reduction in yield can be considered a measure of salt stress.

Drought stress occurs after a relatively long period with no rains, inducing moisture stress in the soil detrimental to crop growth, especially in rainfed agriculture. The severity of drought is strongly related to the timing (growth stage of the plants) and intensity (duration of no rain period). Other factors such as soil characteristics and agricultural practices can interfere with crop yields.

Previous reports suggest that a positive transgenerational impact on seedling vigor of Brassica napus has been observed due to drought stress [ 5 ]. This phenomenon was explained as a result of the heterotic effects, altered reservoir of seed storage metabolites, and inter-generational stress memory formed by stress-induced changes in the epigenome of the seedling. Compared to salt stress, drought stress has more severe effects on plants and economy [ 6 ] but plant responses are closely related and their defense mechanisms even overlap.

The ability of a crop variety to perform better over other varieties under drought conditions is known as drought resistance which is linked to achieved yields and potential yields achievable in a given environment in the absence of drought conditions. Drought resistance is highly environment specific and yield stability might be influenced by crop management practices, and/or physiological mechanisms and might not necessarily be associated with the drought resistance ability of a genotype. In a drought resistant variety, plant growth and development are well-matched to specific drought environment(s) [ 7 ].

When sensing salinity or drought stresses, plants have the capability to combine a range of responses in order to avoid stress injuries and complete their life cycle. By the activation of various defense mechanisms plants can store reserves in their organs and use them later for yield production or, they can tolerate stress conditions without tissue dehydration [ 8 ]. Plant-associated organisms play an important role in improving the adaptation strategies of plants to environmental stresses. In this context, microorganisms, for example, can rescue plants from the deleterious effects of drought and salinity through their activity, such as nutrient solubilization, IST and production of phytohormones (IAA, Cytokinin, ABA or GA), EPS and ACC deaminase. The inoculation of plants with arbuscular mycorrhizal fungus can also increase plants’ tolerance to short term salinity exposures [ 9 , 10 ].

With all these fundamentals being provided to understand the underlying defense mechanisms of plants against stress conditions, further studies are still needed to reveal key mechanisms which govern salinity and drought tolerance responses in plants and which can lead us towards better direction in crop improvement, in order to obtain potential candidates for future saline agriculture.

2. Mechanism of salt stress and plant response

Stress factors, such as osmotic, ion toxicity, nutrient imbalance or soil pH alter the expression of several morphological, physiological and biochemical characteristics of plants. As the stress increases, plant growth is further restricted. Under severe stress conditions plants may die prematurely after germination or transplanting or can survive longer shriveling [ 11 , 12 ].

Seed germination is often hindered and/or delayed when environmental stresses occur. Seedlings often fail to survive since in this stage of growth plants are the most vulnerable [ 13 ]. Plant growth is stunted affecting most of the vegetative characters, such as leaf number, size, shoot number, plant height etc. [ 14 , 15 ]. Regarding the reproductive traits of the plants, salt stress can often induce an early flowering and abortion of flower buds [ 16 , 17 ]. Furthermore, a significant overall reduction in yield can be observed in most of the plant species subjected to salt stress. Achieved yields are usually much lower than potential yields under normal growing conditions [ 18 , 19 , 20 ].

Plant growth in saline soils is usually affected because of the osmotic effect in the soil solution. High salt concentration increases the potential forces that hold water in the soil and makes it more difficult for plant roots to extract soil moisture. During dry periods, salt in soil solutions may be so concentrated as to kill plants by sucking water from them (exosmosis) [ 21 ]. Moreover, salt in the soil solution forces a plant to exert more energy to absorb water and to exclude salt from metabolically active sites. As salinity increases, plant growth is further restricted. A saline soil should be kept wet to dilute the salt concentration so as to cause the least salt hindrance to the growing plants. Also, plant growth in sodic/alkaline soils is affected due to high ESP throughout the profile, very low infiltration and hydraulic conductivity rates [ 22 ]. The exchangeable complex of alkaline soils is largely occupied by sodium ions which cause dispersion of soil due to the breakdown of aggregates forming a dense surface crust which greatly hinders seedling emergence due to low permeability of the soil to water and air. Poor drainage in such soils is due to a high water table which further restricts plant’s ability to absorb water and nutrients in required amounts [ 23 ]. High pH results in reduced availability of some essential plant nutrients [ 24 ]. Accumulation of certain elements in plant parts at toxic levels may result in plant injury or reduced growth and even death in extreme cases. The most common toxic elements are sodium, molybdenum and boron. Selenium may also occur in toxic concentrations. Plant growth in degraded alkaline or solodic soils is largely due to poor drainage.

Crop species and varieties greatly vary regarding their response to salt stress ( Figures 1 and 2 ). Many naturally occurring plants in salt-affected soils (halophytes) have certain specific structures and adaptation strategies, for example salt glands and salt hairs on their leaves [ 25 , 26 ]. Detailed studies on salt glands in salt-tolerant plants, such as the halophyte kallar grass, Leptochloa fusca , showed the presence of enlarged cells protruding above the epidermis of both abaxial and adaxial surfaces of leaves and also on the exposed side of the leaf sheath [ 27 ]. These glands are associated with salt deposition (Na > K > Ca > Mg) on leaf surfaces. Acanthus ilicifolius and other crop species have salt glands on the adaxial leaf surface and studies have shown each gland to be surrounded by six collecting cells (salt-collecting cells) [ 28 ]. One of the most salt-tolerant plants, the halophytic wild rice, Porteresia coarctata has unicellular salt hairs on the adaxial surface of the leaves. Analysis of its leaf washing showed that Na and Cl were predominantly excreted, followed by K, Mg and Ca [ 29 ]. In other species such as Puccinellia tenuifolia the phenomenon of salt excretion has also been observed [ 30 ]. Moreover, some crop species have sunken stomata associated with the occurrence of high density of trichomes arising from the epidermis, as an adaptive mechanism to minimize water loss under stressful habitats [ 31 ].

The effect of salinity on salt-sensitive plants.

The effect of salinity on salt-tolerant plants.

Plants subjected to salt stress face the problem of reduced availability of water and response to changes in the processes related to maintenance of a favorable water balance [ 32 , 33 ]. According to previous reports, the increase in salinity resulted in a decrease in transpiration in mustard [ 34 ], quinoa [ 35 ], wheat and pearl millet [ 36 , 37 ], whereas leaf diffusive resistance (LDR) and leaf temperature increased. Higher LDR coupled with low transpiration might contribute to moisture conservation in plants under salt stress conditions [ 38 ].

Excessive salt in the root zone not only reduces the availability of water to plants, but their excessive absorption of salt increases the risk of ion toxicity and interference in the uptake of other essential nutrients [ 39 ]. Several reports indicate that increasing salinity and sodicity (Na content) decreases K ion concentration [ 40 , 41 , 42 ]. The antagonistic effect of both cations is well established. Tolerant varieties show a tendency to take up less Na while maintaining their K status.

Furthermore, plants growing at sublethal levels of salt stress may often appear greener due to increase in chlorophyll [ 43 , 44 ]. Accumulation of certain amino acids, sugars and other osmotically active organic substances in response to salt stress are indications of altered nitrogen and carbohydrate metabolism. In this regard, it has been observed, for example, that two-week-old wheat plants doubled their amino acid content after 24 hours when subjected to electrolyte concentration (EC) of 22. Amino acids are very important components of plants, exhibiting various roles. Under abiotic stress conditions they can act as osmolytes, regulate the ion transport in the plant or regulate the stomatal opening and closure [ 45 ]. Besides, they can contribute to diverse enzyme synthesis improving plant abiotic stress tolerance through gene expression [ 46 ]. Among amino acids, glutamine (Glu), phenylalanine (Phe) and proline (Pro) proved to have significant roles in response to salt stress condition such as signaling precursors (Glu), building blocks of plant structure (Phe) and beneficial solutes (Pro). In this regard, previous research results show a considerable increase in glutamine, phenylamine and especially in proline content as a response to salt stress improving plant tolerance or indicating its sensitivity [ 39 ]. In general, the highest proline accumulation occurs in lamina followed by leaf sheath, stems or shoots and roots as observed in several plant species such as Phaseolus sp ., Portulaca sp. , Triticum sp. , Solanum lycopersicum etc. ( Table 1 ) [ 57 , 58 , 59 , 60 , 61 ]. Moderately tolerant barley varieties accumulated more proline than sensitive ones [ 62 ].

Prominent amino acids and their changes in responses to salt stress.

In wheat, water-soluble proteins increased in leaves in response to salinity [ 63 ]. Another example, such as rhodes grass, Chloris gayana , could be given for the increase of trichloroacetic acid and NaOH soluble proteins in response to salinity [ 64 ]. Enzymes are also influenced by change in plant water status as well as ionic imbalance [ 65 , 66 ]. Decrease in (a) amylase activity with increase in salinity was observed in wheat and chickpea leaves after short term exposure to salt stress while activity of invertase and other enzymes of carbohydrate metabolism significantly increased [ 67 , 68 ]. Nitrate reductase activity may also decrease with increase in stress level in many species [ 69 , 70 ]. Tolerant varieties of pearl millet showed a tendency to maintain their nitrate-reductase activity [ 71 ]. Polyphenol oxidase activity has been reported to be higher in sensitive varieties of wheat, barley and rice [ 72 , 73 , 74 ].

Due to their occasional or constants exposure to harsh, unfavorable environmental conditions, plants developed a series of detoxification mechanisms to be able to maintain their growth and alleviate potential damages caused by ‘reactive oxygen species’ (ROS) - at cellular level [ 75 ].

Oxidative damage in plants often occurs as a secondary effect of different harmful environmental conditions such as drought, salinity, cold, heat, or heavy metals in the soil. Under these conditions, the level of ROS can largely increase overwhelming plant defense systems, and thus inducing multiple deleterious effects at the cellular level. These effects are the result of the oxidation of membrane lipids, amino acid residues in proteins and the bases in DNA. In general, plants respond to an increase in ROS by activating enzymatic or non-enzymatic antioxidant processes to overcome ROS accumulation. Among them, malondialdehyde (MDA), a lipid peroxidation product is considered a reliable oxidative stress marker not only in plants but in animals also, which is generated by the oxidation of membrane lipids [ 76 ]. Several scientific reports show an increase of MDA levels in response to abiotic stresses in various plant species: rice, Calendula , Miscanthus , basil, Solanum and many others [ 77 , 78 , 79 , 80 , 81 ].

Moreover, phenolic compounds are known to have multiple roles in plants; some of them being part of the structural component of cell walls, while others are involved in growth regulation and developmental processes or the activation of defense mechanisms against biotic and abiotic stresses. Several reports also describe the mediatizing effects of antioxidant properties of many phenolic compounds on plant responses to salinity and drought showing an increase in their content under high salinity and water deficit conditions [ 82 , 83 ].

Flavonoids, the most complex subclass of phenolic compounds are also involved in a wide-range of environmental interactions. The biosynthesis of flavonoids in plants is upregulated not only by UV-radiation but also in response to diverse biotic and abiotic stresses, from the depletion of mineral nutrients to salinity, cold or drought [ 84 ]. Previous studies suggest that flavonoid contents increase in plants when subjected to abiotic stress conditions and the accumulation of these compounds is tightly coupled with the intensity of the applied stress [ 85 , 86 , 87 ].

Ascorbic acid (Vitamin C) is one of the most powerful, water-soluble antioxidants as a scavenger ROS produced by most eukaryotic organisms. It occurs in all plant tissues, but mostly in the chloroplast, in mature leaves where these are fully developed and the chlorophyll levels are also the highest. It is considered the most important ROS detoxifying compound due to its ability to donate electrons in a number of enzymatic and non-enzymatic reactions [ 88 ].

Beside the above-mentioned compounds, α-tocopherols (vitamin E) are another family of antioxidants that can be found in all parts of the plants. They are the most biologically active and predominant antioxidants in the chloroplast membranes, and are mainly responsible for its protection against oxidative damages [ 89 ].

Antioxidant enzymes such as superoxide dismutase (SOD), several peroxidases (POD), catalase (CAT) and glutathione reductase (GR) play a crucial role as ROS scavengers in defense mechanisms against abiotic stresses. They are responsible for the maintenance of the proper redox equilibrium in plant cells [ 90 ]. Enzymatic activities have been studied in different plant species including both crop species and ornamental plants [ 91 , 92 , 93 ]. The results revealed that water stress, in general, led to a continuous increase of several antioxidant enzyme activities. In maize, for example, significant enhancements in the activities of several antioxidant enzymes (superoxide dismutase-SOD, catalase-CAT, ascorbate peroxidase-APX, and glutathione reductase-GR) occurred after 12 h of treatment showing an increase of 21%, 52% and 33% and 38% as compared to the control. It was also noticed that after 24 h of water stress treatment, the activities of the antioxidant enzymes showed a tendency to decrease when compared to the 12 h treatment [ 94 ].

3. Mechanism of drought resistance

Over the centuries plants have been exposed to different environmental conditions and applied diverse adaptation strategies to be able to cope with these challenges. Water deficit in plants occurs when the transpiration rate exceeds water uptake. Such water deficit is usual in most plants as a component of some developmental processes [ 95 ], but cellular water deficit can cause harmful changes in cell volume and membrane shape, disruption of water potential, decreased turgor pressure, or disruption of membranes. A total loss of free water will result in dehydration and plant loss. Plant responses to water deficit ( Figure 3 ) primarily depend on the species and genotype, but also on the length and quantity of water loss, and the age and developmental stage of the plants. Among the complex plant mechanisms and regulatory networks for drought, osmotic adjustment plays an important role in water deficit avoidance, by lowering the water potential of the cells to support water uptake and maintain turgor. At molecular level, the accumulation of mRNA during water deficit may indicate gene induction, but in order to obtain a fully functional gene product, other additional mechanisms such as translational regulation and posttranslational modification may be required. In general, plants respond to water deficit by employing some basic mechanisms to avoid water loss, protect the cellular machinery and repair damage [ 96 , 97 ].

Schematic representation of water stress effects and plant adaptation.

Susceptibility to drought can occur during the early vegetative seedling stage, during the period of panicle development prior to flowering, or/and during the post flowering stage of grain development [ 97 ]. Susceptibility during post-flowering stage is characterized by reduced seed size and grain yield, pre-mature plant and leaf senescence and increased stalk lodging [ 98 ]. Terminal post flowering drought results in an abbreviated period of grain development and therefore reduces seed size [ 97 , 99 ]. Genotypes with a high rate and reduced duration of grain filling may be more tolerant under terminal post flowering conditions [ 100 ].

Drought escape : is a strategy applied by plants in early maturing crops/crop varieties to complete the critical stages of crop growth before severe deficit occurs, focusing more on flowering and reproduction instead of developing new shoots and increasing leaf area [ 101 ]. Early growth vigor may enable a variety to establish a good plant stand rather quickly while the moisture supply is suitable. Thus, crops or crop varieties applying this strategy can escape the adverse effects of drought and perform relatively better. Many indeterminate crops respond to reirrigation by resuming their growth and still perform better [ 102 ].

Avoidance : Drought avoidance is an alternate mechanism by which plants can maintain positive tissue water relations even under limited soil moisture conditions. Mechanisms of drought avoidance typically involve water conservation at the whole plant level. Avoidance is accomplished by decreasing water loss from the shoot or by more efficiently extracting moisture from the soil [ 103 ]. Many crop varieties/crops with deep as well as dense root system may be able to maintain minimal water uptake from soil to avoid internal stress, at least during the initial stages [ 104 ]. High varietal resistance to water loss has also been observed in a few cases, for example, in wheat, rice the amount of epicuticular wax deposition is reportedly associated with water loss [ 105 , 106 ]. Previous findings suggest that different species such as Catharanthus roseus, Sorghum sp. and Oryza sativa reduced transpiration rate by as much as 44 to 82% due to water stress [ 107 , 108 , 109 ].

Tolerance: Drought tolerance is defined in a number of ways, namely, the performance per se, the stability of performance under drought and last but not least specific physiological or morphological traits that are believed to be associated with the expression of drought tolerance. The mechanisms responsible for drought tolerance are functioning at tissue or cellular level [ 99 ]. When the tissue desiccates, these mechanisms are activated to stabilize and protect the cellular and metabolic integrity of the plant. Crop varieties may differ in their ability to thrive under drought conditions. This has been demonstrated through various test regarding physio-morphological and biochemical traits including desiccation survival, heat tolerance, osmolytes, ion homeostasis etc. [ 110 , 111 , 112 , 113 , 114 , 115 ].

Recovery : Drought stress conditions may vary in duration, but when rainfall does commence the ability of a genotype (or crop variety) to recover quickly and resume active growth is an important character. In rice, recovery capacity from drought is strongly related with characters such as vegetative growth vigor, high tillering ability, shallow root system and rather long growth duration [ 116 ]. Similar characters have been observed in different annual and perennial species, in wheat, sugarcane etc. [ 117 , 118 , 119 ]

3.1 Assessment of drought resistance and plant traits associated with drought resistance

Drought resistance of an annual crop plant can at present be assessed for agronomic purposes only on the basis of yield [ 120 ]. Few of the many screening tests proposed have been adopted by breeders.

Several plant traits, such as dehydration avoidance and dehydration tolerance have been found to be positively associated with yield under stress across genotypes of wheat and barley [ 121 ]. Leaf rolling, root system, pubescence of aerial organs, reflectance of incoming solar radiation, increased heat dissipation through decreased boundary layer resistance at the organ level (narrow leaves, awns), etc., are the main traits that contribute to dehydration avoidance. In nature, a better balance is associated with a higher proportion of energy dissipated as latent heat and hence a lower canopy temperature. Dehydration tolerance related to cellular and subcellular processes can be readily assessed by measurements of membrane stability with the electrolyte leakage test [ 122 ]. It is difficult, however, to relate this type of test to plant production. Nevertheless, visual scores on morphological traits, such as leaf rolling, root habit, etc., and/or observations recorded through other methods, if any, in relation to the above-mentioned characters should invariably be used as an indirect measurement of drought resistance for practicing selection in a breeding programme.

In sorghum, the ‘stay-green’ character is reportedly associated with post-flowering drought tolerance. Stay-green is characterized as resistance to premature leaf and stalk death induced by post-flowering drought. Resistance to premature leaf and stalk death is thought to increase the potential period of grain development and thereby stabilizing the expression of seed weight [ 123 ]. Sorghum lines with high levels of stay-green have been identified and are being used in some breeding programs [ 124 , 125 , 126 ].

3.2 Genetics of plant traits associated with drought resistance

A variety of adaptive plant characteristics related to environmental stress have been investigated and were shown to exhibit genetic variation. The variability of traits extends to the physiological, morphological and chemical characteristics of the plants. These three groups of traits are the most representative and useful markers for stress tolerance identification. Drought stress can cause many changes in the physiological traits, affecting the capability of plants to maintain high level of leaf-water potential under water deficit conditions, the osmotic adjustment and last but not least the capability of plants to recover after short or long-term rehydration. The regulation of photosynthesis, by stomatal closure and the stability of cellular membranes and its maintenance are crucial for plants to tolerate stress conditions. Osmolytes, such as Pro, glycine betaine and soluble sugars also play an important role in osmotic adjustment under various stress conditions, where accumulation may greatly vary among species. Morphological or phenotypic characters are considered important in the adaptation of plant to stress conditions, their responses being reflected and becoming quantifiable through root growth and density, leaf number size and canopy area, leaf orientation, stem or shoot length and number, flower development (number and fertility, seedling survival or any other trait specific for every species (leaf succulence, pubescence etc.) [ 127 , 128 , 129 , 130 , 131 , 132 , 133 ].

‘Stay-green’ or the capacity of green color retaining for longer time of the leaves after flowering is a desirable attribute for crop production. Sorghum genetic studies of ‘stay-green’ have generally indicated a complex pattern of inheritance. It has been reported that both dominant and recessive expression were strongly influenced by the environment. Previous reports reveled the inheritance of stay-green in a set of recombinant inbred lines of sorghum [ 134 ]. Due to a quantitative trait loci (QTL) mapping in sorghum for the extension of photosynthetic period 13 regions of the genome were identified and associated with the stay-green phenotype of post-flowering drought adaptation [ 135 ]. Two QTLs were successfully identified as the ones influencing yield and ‘stay-green’ capacity under post-flowering drought conditions. The same loci were also linked to yield under successful irrigation conditions indicating the pleiotropic nature of these tolerance loci on yield under favorable environmental conditions [ 136 ]. Similarly, the QTL mapping results suggested many other loci that were linked to the rate and duration of yield development [ 137 ]. The findings also revealed that high yield and short grain development were associated with instability of yield performance under water paucity [ 138 ].

It may be noted that associations between markers and QTL were somewhat variable across testing environments. This highlights the importance of multi-environment testing when evaluating drought tolerance.

Similar studies have been carried out in maze, where 15 green-leaf-area related QTLs were detected thus identifying the most important genomic region responsible for maintaining green leaf area at the final developmental stage of maize [ 139 ].

However, the current screening and breeding techniques allow to explore the genetic basis for various plants and identify diverse traits which help the plants to perform under stress conditions, high yield performance, good quality and stress resistance remains the eternal flame for crop breeders. These desirable crop production traits and their transmission from one genotype to another will remain attractive and unexplored [ 140 ].

In this regard, selection for drought and salt resistance will therefore continue to be primarily based on yield assessment under stress conditions [ 141 ].

4. Selection and breeding for salt and drought resistant varieties

Seed germination capacity and seedling survival: Seed germination and seedling development, are the very early stages of plant development which are critical. Therefore, plants that can cope with salt stress conditions in these stages of their life cycle should be the prime requisite in the selection process for salt tolerance. Various crops and genotypes that even fail to establish themselves under defined stress conditions cannot be expected to do any better at a later stage of their growth.

Yield: Varieties highly tolerant to salinity are those that exhibit minimum reduction in relative economic yield with per unit increase in stress. The slope of regression of yield against stress gives a fairly reliable estimate of salt tolerance of a crop/genotype. This is by far the best index for identification and screening of salt-tolerant genotypes.

A number of other plant attributes, namely Na and K content in shoots/leaves, Na/K ratio, pH of the cell sap, proline content and enzyme response may also have some potential use. The only limitation to their practical use so far however, is, that the differential genotypic response observed in various crops cannot always be explained on the basis of these data. For this reason, the use of physiological characters is highly recommended to obtain more reliable information and select potential candidates for future saline agriculture.

Root architecture – which plays an important role in drought avoidance of crops. Transcriptomic differences between deep and shallow rooting systems strongly influences the ATP synthesis. Such traits can significantly improve abiotic stress resistance in crops by introducing or manipulating a single gene;

ABA-synthesis which can improve drought resistance even at seedling stage in different crops;

Direct-deep-seeding tolerance of different species which could significantly contribute to water saving and drought resistance, for example in rice production;

Yield capacity under stress conditions;

Exploitation or domestication of wild relatives (halophytes) of crop plants. Interspecific hybridization has an important role in the improvement of crop plant performance under abiotic stress conditions.

In the evaluation process for plant tolerance to salt and drought stress, it is important to take into consideration all the three groups of traits (physiological, morphological and chemical characters) and evaluate plant responses as a whole. Due to great genetic variation of the plants, in some cases it is not enough to solely analyze the physiological, chemical or morphological profile since they are interconnected.

5. Conclusions

Recently, several research have been carried out to depict the complex underlying mechanisms (physiological, morphological and chemical) that control abiotic stress responses in crop plants. However, the exact genes, and their activation, which control plant defense mechanisms are still unclear. Tolerance against abiotic stresses in different crop plants has been improved by the application of transgenic technology of reactive oxygen species components, but future research studies are still needed to determine and increase yield performance and quality under harsh environmental conditions. Genetic improvement of crops needs to identify further genetic variations that allow plants to increase their tolerance against the upcoming abiotic stress levels than the ones we are facing today. It has to employ new tools to analyze the genetic, physiological and molecular basis of stress tolerance and to identify genes associated with improved resistance and integrate them into practical breeding to develop “smart” crop varieties which require lower input and provide high yield.

Conflict of interest

The authors declare no conflict of interest.

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Effect of Drought Stress on Crop Production

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Drought stress conditions are imposing a foremost restraint to crop production as a result food security is becoming a most apprehension worldwide. The circumstances have intensified because of the extreme and swift variations in worldwide climatic conditions. Drought is certainly one of the utmost imperative stress situation causing vast impression on growth and development of crop, thus affecting its productivity. Drought stress enforces modifications in fundamental morphology, physiology and biochemical aspects in plant. Thus, it is important to recognize these interferences associated with drought stress for improved crop management. Remarkably, this chapter delivers a comprehensive explanation of plant reactions towards drought stresses. Crop growth, development and production are undesirably affected by drought conditions because of physiological interruptions, physical damages and biochemical modifications in plants. Drought stresses have multidimensional impressions and consequently complicated in mechanistic action. An improved knowledge of plant reactions to drought stress has reasonable repercussion for a better crop modification and management. Thus, a holistic approach is required to fully elucidate and understand the effect of drought stress conditions towards plants for better crop production.

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Iqbal, M.S., Singh, A.K., Ansari, M.I. (2020). Effect of Drought Stress on Crop Production. In: Rakshit, A., Singh, H., Singh, A., Singh, U., Fraceto, L. (eds) New Frontiers in Stress Management for Durable Agriculture. Springer, Singapore. https://doi.org/10.1007/978-981-15-1322-0_3

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• Published: 19 February 2020

Impact of water deficit stress in maize: Phenology and yield components

• R. P. Sah   ORCID: orcid.org/0000-0003-1724-3685 2   nAff1 ,
• M. Chakraborty 2 ,
• K. Prasad 2 ,
• M. Pandit 2 ,
• V. K. Tudu 2 ,
• M. K. Chakravarty 3 ,
• S. C. Narayan 2 ,
• M. Rana 4 &
• D. Moharana 5  

Scientific Reports volume  10 , Article number:  2944 ( 2020 ) Cite this article

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• Environmental impact
• Plant breeding

Fifteen million farmers in India engaged in Maize cultivation. India would require 45 MMT of Maize by 2022. But, only 15% of cultivated area of maize is under irrigation and water shortage has been a challenge for sustainability of maize production. Water deficit stress (WDS) during pre-flowering and grain filling stages massively affects the plant performance due to imprecise traits function. Thus, the effect of WDS on non-drought tolerant (NDT) and drought tolerant (DT) maize lines were investigated. WDS increased the flowering days, days to maturity, anthesis silk interval, decreased the leaf number, abnormal expression of secondary stress responsive traits, loss of normal root architecture which overall lead to a reduction in GY/ha. WDS at flowering and grain filling stage leads to significant yield penalty especially in NDT lines than DT lines. The yield penalty was ranged from 34.28 to 66.15% in NDT and 38.48 to 55.95% in DT lines due to WDS. Using multiple statistics, traits which improve WDS tolerance in maize were identified viz; number of leaves, number of stomata on lower surface of leaf, leaf angle at ear forming node internodal length between 3 rd and 4 th leaf from top, flag leaf length, flag leaf width, ear per plants, leaf senescence, pollen stainability, root fresh weight and root length. These traits would help in trait specific breeding in maize for WDS tolerance.

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Introduction.

Maize ( Zea mays L.) is a multipurpose crop with wide adaptability to different agro-climatic conditions. It is grown in most parts of the world, up to 3000 m above mean sea level 1 .This crop is preferred by farmers due to its grain production potential being the highest among cereals 2 , its dual-purpose use (grain and fodder) 3 , 4 ; used as a cash crop (specialty corn: green ear, baby corn, sweet corn and popcorn) 5 , and raw materials for industry.

In fact, maize is not a food crop but an industrial crop, as only 12–13% of its production is used for human consumption globally 6 . It is cultivated in an area of nearly 150 Mha in approximately 160 countries, which constitutes 36% (782 Mt) of the global grain production 7 . Of the total maize grain produced in the world, approximately 70–80% is used as feed, whereas in India, approximately 49–51% is used as poultry feed, 12% is used as animal feed, and 25% is used for human consumption 8 . The maize cob powder is used as fillers for explosives in the manufacture of plastics, glues, adhesives, resin, vinegar and artificial leather. It is often a part of diluents and carrier in insecticides and pesticides formulation, and also used for pulp, paper and hard boards manufacturing. Grain is used for commercial starch extraction, corn flour, corn oil extraction, corn flakes and corn syrup preparation.

In many countries, maize is grown in areas that receive 300–500 mm of precipitation, which is near or below the critical level for obtaining a good yield 9 , 10 . India has a wide diversity of agro-climates, where maize is produce due to its highly adaptable nature 1 . In India, maize requires 500 to 600 mm of rainfall for at least good production, but production also depends on the duration of the variety. Approximately 80% of wet-season maize areas are rain-fed, where crops are susceptible to the erratic behavior of rains 11 . The rainfall mostly occurs in the early growth stages, and the crop faces water deficit stress (WDS) from the pre-flowering to late grain-filling stages. Such problems considerably affect the phenotype, reproductive system and seed set. Hence, maize production in the wet season and in rain-fed regions is declining due to natural intermittent WDS. However, dry-season maize areas are currently expanding in India due to higher productivity than in wet-season areas and are providing good-quality fodder in either the dry or green (stay-green) form for livestock. In the last 25–30 years, WDS usually occurred during the months of August to September in the wet season and after the 1 st fortnight of February in the dry season at the experimental location (Supplementary Fig.  1 ). In both periods, enough water is usually available to allow the plant to grow to the vegetative stage. Afterwards, from pre-flowering to grain filling, plants are severely affected by a prolonged rainfall interval in the wet season and a water shortage in the dry season.

Dry-season maize crops exhibit late maturity due to a prolonged cold period at the early crop growth stage between the seedling and knee-high stage. Consequently, a longer duration requires a greater water input. Hence, loss of grain production in the dry season is connected to shortages of water used for irrigation 12 . The loss of yield varies from 30–90% depending on the crop stage and the degree and duration of WDS 13 . The stages of maize susceptible to WDS are the vegetative, silking (flowering) and ear stages (grain filling), where yield loss may be as high as 25%, 50%, and 21%, respectively 14 . Thus, based on the prevailing weather in eastern India, WDS was imposed at 2 critical stages, i.e., the flowering and grain-filling stages, in our experiment.

The loss of phenotypic expression under WDS is obvious in most cereals. In maize, phenotypic expression is also suppressed after a critical level of WDS. The effect is prominent, such as a reduction in the green-leaf duration (stay-green), plant performance 1 , ear length, seed weight 15 , plant height 16 , number of grains per ear, leaf number 1 , 17 , 18 , ears per plant, kernel rows per ear, kernels per row, and early leaf senescence, even during flowering 16 , 19 . Stay-green genotypes are often superior to non-stay-green ones, especially under water deficit conditions, and are often correlated with grain-yield traits (grain yield, ear length, and kernels per row). Increases in below ground traits such as root mass and rooting depth increase the plant’s ability to cope with drought stress 20 , 21 , 22 . Hence, such traits are also considered important for identifying potential parents for hybrid development 23 . However, phenotypic expression is not always suppressed under WDS. In some cases, traits are overexpressed in certain genotypes. This overexpression depends on the nature of the traits and individual genotypic backgrounds. Thus, both loss and gain of phenotypic expression were precisely measured and considered in the present experiment.

The performance of any maize line depends on its genetic constitution and the response of the desirable trait under stress and non-stress conditions. WDS-tolerant lines were developed, and affected traits were considered as selection criteria for parental selection. However, their performance is best for a particular WDS level, and they fail to perform well under even a small change in WDS. Practically, WDS is not constant throughout the cropping period; it changes continuously depending on the crop stage, amount of available moisture and soil type. Such conditions are prevalent in the majority of experiments. Thus, proper phenotyping for the identification of key phenological traits associated with yield improvement remains a major area of research. A comparative analysis of phenotypic expression under WDS is also not available. Thus, in the present experiment, 7 levels of WDS were maintained to simulate the environmental variability of eastern India. Under these conditions, a new source of maize lines was developed and evaluated under multiple WDS levels (prevailing under wet and dry seasons in the eastern Indian region) to determine the (i) performance of maize lines under WDS conditions, (ii) range of loss and gain of phenotypic expression (LGP) under WDS, (iii) relationship between grain yield and phenological traits and (iv) effective phenotype conferring WDS tolerance in maize. The majority of the maize in eastern India is grown under rain-fed conditions, and marginal farmers in this region are unable to practice crop management strategies that might mitigate these constraints. In such situations, breeding for WDS-tolerant maize remains the best alternative. The results obtained here will help develop strategies for trait-specific breeding to enable maize improvement under WDS conditions.

Materials and Methods

Land preparation and cultural practices.

The experiments were conducted in the experimental field and rainout shelter available at the Faculty of Agriculture, Birsa Agricultural University (BAU), Ranchi, Jharkhand, India. The field was cleaned, well plowed, and cleared of debris; it was demarcated with lines and pegs and leveled using a hoe before seeds of the genotypes were sown. A pre-emergence weed control chemical, i.e., atrazine, was used at a concentration of 1 kg a.i./ha. The other cultural practices and fertilization were followed as per recommendations.

Genetic material and evaluation

Eleven maize lines were used for the present experiment, 6 of which (BAUIM-1, BAUIM-2, BAUIM-3, BAUIM-4, BQPM-4 and BAUIM-5) were developed by BAU, Ranchi; 2 of which (CM-500 and CM-111) were developed by the Indian Institute of Maize Research (IIMR), Ludhiana; and 3 of which (HKI-1532, HKI-335 and HKI-488) were developed by Chaudhary Charan Singh Agricultural University (CCSHAU), Haryana. The 3 lines developed by CCSHAU were drought tolerant (DT). These 3 lines were pre-evaluated in 2010–2011 at BAU, Ranchi, to confirm their performance under the WDS (50 kPa) used in our experiment. For both drought tolerant (DT) and non-drought tolerant (NDT) maize lines, the effects of irrigation regimes on yield, growth parameters and physiological parameters were analyzed using a randomized complete block design with three replicates. All the seeds of the maize lines were obtained from ICAR-All India Coordinated Research Projects-Maize, Ranchi Center.

Managed stress environment and irrigation

In the wet season of 2013, 3 irrigation regimes and in dry season 2014, 4 irrigation regimes were created and maintained. For the irrigated condition, the tensiometer was maintained at −30 kPa (considered as standard). Reading above −30 kPa was considered as water deficit stress (WDS); accordingly, as per the environmental variability of the region, different stress levels were created for evaluation. The details of the water management used to maintain the WDS levels are presented in Table  1 . The irrigation water was applied as per tensiometer reading placed at root-zone depth in each irrigated or WDS level. The locations selected for the tensiometer were representative of the general conditions of field.

Data collection and statistical analysis

Phenotypic data were recorded on 38 quantitative traits and grouped in to 6 categories viz; (i) Flowering and maturity: days to 50% tasseling (DA), days to 50% silking (DS), anthesis silk interval (ASI) in days and days to 75% dry husk (DDH); (ii) Vegetative and leaf traits: plant height (PH) in cm, number of leaves per plant (NL), number of stomata on upper surface of leaf (SU), number of stomata on lower surface of leaf (SL), 3 rd leaf angle from top (LA-3), leaf angle at ear forming node (LA-C), internodal length between 3 rd and 4 th leaf from top (INL 3–4) in cm, flag leaf length (FLL) in cm and flag leaf width (FLW) in cm; (iii) Ear traits: ear height (EH) in cm, ear length (EL) in cm, ear width (EW) in cm and number of husk per ear (H/C); (iv) Root traits: number of brace root per plant (NBR), root fresh weight (RFW) in grams, root dry weight (RDW) in grams, root length (RL) in cm, root volume (RV) in cc and number of roots >10 cm (RN); (v) Yield traits and stress indices: number of ear per plant (C/P), number of kernels per rows (K/R), number of kernel rows per ear (KR/C), 1000 seed weight (SW) in grams, grain yield per hectare (GY/ha) in quintals, modified stress tolerance index (KiSTI) and yield index (YI); (vi) Secondary stress responsive traits (SSRT): relative leaf water content (RLWS) in percentage, stay green (SG) in percentage, leaf senescence (LS) score, leaf rolling (LR) score, leaf firing (LF) in score, pollen stainability in percentage (PS%), tassel blast (TB) in percentage, and bareness percentage (BP). The collected data were statistically analyzed following analysis of variance, principal component analysis (PCA) and co-heritability (h 2 ) using Indo-Stat 7.5 software (Indo-stat, Hyderabad, India). The Microsoft Excel 2016 was used for regression analysis, preparation regression curve, and t-test at 5% level of significance.

Phenotypic variation of NDT and DT maize lines

Non-drought tolerant (NDT) lines generally perform better in a favorable environment (irrigated), whereas DT lines are intentionally developed to perform better under unfavorable (WDS) conditions. The differences in these two types of lines are mostly genetic and lead to distinct phenotypic differences when the lines are grown in target environments. Thus, in the present experiment, 6 important groups of 38 traits were phenotyped carefully. The average magnitude of phenotypic change is presented in parentheses and the traits at least ±15% effect and score of >3 due to WDS were only discussed for easy understanding.

Performance of NDT maize lines under WDS conditions

An increase in the mean value of DA (9.81%), DS (14.33%) and ASI (86.44%) was observed under WDS compared to irrigated conditions. High value increased the flowering duration and extended the DDH by 11.98%. Thus, there was a negative impact on flowering and maturity traits. Similarly, the mean value of PH (18.24%), INL3–4 (17.99%), FLW (18.00%), EH (19.08%), EL (36.07%), EW (27.68), C/P (18.87%), K/R (36.72%), KR/C (29.55%), GY/ha (51.49%), KiSTI (16.67%), NBR (51.06%), RFW (38.87%), RDW (26.01%), RL (39.79%) and RV (43.14%) decreased under WDS. This decrease in the mean value of the traits reflects a reduction in the performance of the plants in terms of vegetative growth, root and yield attributes. The mean value of SSRT under WDS compared to irrigated conditions was lower in RLWC (15.36%) and PS (52.58). In contrast, it was higher in LS (score of 2.21), LR (score of 3.93), TB (score of 3.42) and BP (21.88%), as is usually expected to for these traits. Thus, the NDT lines were significantly affected by and showed a marked difference (% difference) in trait expression under WDS (Tables  2 and 3 ).

Performance of DT maize lines under WDS conditions

In the DT maize lines, the mean values of ASI (115.80%) was high under WDS than under irrigated conditions. Overall, higher means of flowering traits led to an extension of DDH by 10.60% (Tables  2 and 3 ). Similarly, the mean values of following traits were lower under WDS viz; PH (19.22%), INL3–4 (16.10%), FLL (28.28%), FLW (31.75%), EH (26.04%), EL (30.03%), EW (32.30%), C/P (16.51%), K/R (29.91%), KR/C (29.69%), SW (17.67%), GY/ha (48.55%), NBR (46.09%), RFW (50.00%), RDW (50.74%), RL (45.17%), RV (26.67%) and RN (30.62%). These decreases in the mean value of the traits reduced the expression of phenotypes related to vegetative growth, roots and yield. The SSRT also showed a lower mean value under WDS than under irrigated conditions, including RLWC (19.90%), SG (31.82%) and PS (52.73%). However, this mean value was higher under WDS than under irrigated conditions for LR (score of 3.05), TB (score of 3.37) and BP (25.378%) (Table  3 ).

The variation in the range (minimum and maximum values) for DA, DS, CP, SG and LF was higher in DT than in NDT lines under irrigated conditions. However, under WDS conditions, ASI, DDH, H/C, C/P, K/R, RLWC and SG were lower in DT than in NDT lines, which is an ideal response under WDS (Tables  2 and 3 ).

Effect of WDS on LGP

The performance of both the NDT and DT maize lines declined under WDS compared to irrigated conditions. The mean value as well as the range of 38 traits was lower under WDS. However, 10 traits (DA, DS, ASI, DDH, H/C, LS, LR, LF, TB and BP) had a higher mean and range in both the NDT and DT lines. These observations were based on the mean of the traits. In addition, another type of estimate was obtained, i.e., the response of individual lines to WDS conditions (Supplementary Table  1 ). The response of individual lines was different for each trait, which was similar to findings described on the basis of the mean value. The mean value was actually the average performance of a group of genotypes, and expression of a trait under WDS was be reduced in one line but increased in another line.

Relationship between yield and phenological traits

Grain yield is the ultimate indicator of the economic value of a maize line. It is a complex trait and has an association with numerous other traits. Thus, graphical representation using linear regression analysis of the relationship between grain yield and selected traits is presented in Figs.  1 and 2 . The traits identified for WDS tolerance only presented in graphical format. A trait was considered relevant if it exhibited R 2  ≥ 0.40 because p-value obviously high for smaller number of sample (8 lines in NDT and 3 lines in DT).

Relationship of different traits with grain yield (GY/ha) under irrigated and water deficit stress (WDS) condition. NL: Number of leaves per plant ( A ) SL: Number of stomata on lower surface of leaf ( B ) LA-C: Leaf angle at ear forming ( C ) INL3-4: Internodal length between 3rd and 4th leaf from top ( D ) FLL: Flag leaf length ( E ) FLW: Flag leaf width ( F ) C/P: Number of ear per plant ( G ) LF: Leaf senescence ( H ) I: Evaluated under irrigated condition; WDS: Evaluated under WDS condition; DT: Drought tolerant; NDT: Non-drought tolerant.

Relationship of different traits with grain yield (GY/ha) under irrigated and water deficit stress (WDS) condition. PD: Pollen stainability ( A ) RDW: Root dry weight ( B ) RL: Root length ( C ) WDS: Evaluated under WDS condition; DT: Drought tolerant; NDT: Non-drought tolerant.

Flowering and maturity

Maize is a geitonogamous species, where synchronization of DA and DS is essential to reduce ASI and a higher abundance of pollen during stigma receptivity. After survival of the plants, flowering traits are the second most important morphological parameter considered for grain setting under WDS. A linear relationship of grain yield with flowering traits and maturity was detected. The irrigated non-drought tolerant (I-NDT) lines exhibited weak relationships between all flowering and maturity traits with GY/ha. However, in the water deficit stress non -drought tolerant (WDS-NDT) lines, DA (R 2  = 0.53) and DS (R 2  = 0.43) had a strong relationship with GY/ha. Similarly, in the I-DT lines, ASI (R 2  = 0.57) had a strong relationship with GY/ha, whereas in the WDS-DT lines, ASI (R 2  = 0.40) and DDH (R 2  = 0.76) had a strong relationship with GY/ha. Both types of lines showed distinct relationships with GY/ha when grown under WDS.

Vegetative and leaf traits

Plant height improves the crop canopy and surface area. The internodal length and height are highly correlated parameters. Each internode is borne on a leaf and generally appears after 13 leaf tassels are produced. The internal water and temperature balance in plants is regulated by stomata, whose efficiency depends on their frequency. I-NDT exhibited no strong relationships with height, leaf or internode traits. However, the WDS-NDT lines had a strong and positive relationship with GY/ha in PH (R 2  = 0.93), SU (R 2  = 0.93), SL (R 2  = 0.69), LA-3 (R 2  = 0.60), and FLL (R 2  = 0.87). In the case of I-DT, only SU (R 2  = 0.51) had a strong relationship with GY/ha. However, in the WDS-DT lines, PH (R 2  = 0.55), NL (R 2  = 0.48), SU (R 2  = 0.43), SL (R 2  = 0.79), LA-3 (R 2  = 0.59), LA-C (R 2  = 0.98), INL3–4 (R 2  = 0.99), FLL (R 2  = 0.60) and FLW (R 2  = 0.99) had a strong relationship with GY/ha (Fig.  1A–F ).

The ear of the plant contains the female inflorescence, where grains are set. Ear traits such as EH, EL, EW and H/C are the major morphological traits of ears. A medium ear height helps the ear receive a large number of pollen grains for fertilization and reduces the incidence of animal damage. I-NDT had a weak relationship with GY/ha for all ear traits; however, in WDS-NDT, this relationship with GY/ha was strong and positive for EL (R 2  = 0.49). In the I-DT lines, all 3 traits, namely, EL (R 2  = 0.86), EW (R 2  = 0.96), and H/C (R 2  = 0.6), had strong and positive relationships with GY/ha. However, in the WDS-DT lines, these relationships were observed only for EL (R 2  = 0.56) and H/C (R 2  = 0.89).

Root traits

The root structure of maize plays a major role in lodging, the uptake of nutrients and water and survival under unfavorable soil conditions. Six root traits were measured in the present experiment: NBR, RFW, RDW, RL, RV and RN. I-NDT and WDS-DT lines had a strong and negative relationship with GY/ha only in RV (I-NDT, R 2  = −0.69; WDS-DT, R 2  = −0.98), whereas in WDS-NDT, a weak relationship was observed for all the root traits. In the I-DT lines, NBR (R 2  = −0.96), RDW (R 2  = −0.44), RV (R 2  = −0.76), and RN (R 2  = −0.59) had a strong and positive relationship and negative relationship in RL (R 2  = −0.69) with GY/ha) (Fig.  2B,C ).

Yield attributing traits and stress indices

Grain yield in maize is the result of different component traits. It is indirectly calculated by the number of kernels formed in each ear, test weight and number of ears per plant. In I-NDT, a weak relationship for all four traits (C/P, K/R, KR/C, and SW) with GY/ha was observed. However, in WDS-NDT, KR/C (R 2  = 0.68) had a strong and positive relationship with GY/ha. Similarly, in I-DT, K/R (R 2  = 0.47) and SW (R 2  = −0.54) had a strong relationship with GY/ha. However, in WDS-DT, GY/ha was strongly related to most of the traits; for example, a strong and positive relationship was observed in CP (R 2  = 0.82), and a strong and negative relationship was observed in KR/C (R 2  = −0.59) and SW (R 2  = 0.69). Two stress indices (KiSTI and YI) were calculated using the yield data from maize lines under both irrigated and WDS conditions. Thus, a higher and positive R 2 was expected for all traits (Fig.  1G ).

The SSRTs were expressed well under WDS conditions. These traits provide a basis for the selection of WDS-tolerant genotypes by a breeder. Eight traits, namely, PM, RLWC, SG, LS, LR, LF, PS, TB and BP, were measured. In I-NDT, BP (R 2  = −0.42) had a strong and negative relationship with GY/ha, as expected. In WDS-NDT, only SG (R 2  = 0.67) had a strong and positive relationship with GY/ha. In the case of I-DT, the traits SG (R 2  = 0.72), LS (R 2  = 0.96), and LF (R 2  = 0.62) had a strong and positive relationship with GY/ha, but in PS (R 2  = −0.43) and TB (R 2  = −0.44), a strong and negative relationship with GY/ha was observed. In the case of WDS-DT, RLWC (R 2  = 0.99), SG (R 2  = 0.99), LF (R 2  = 0.76), PS (R 2  = 0.97), and TB (R 2  = 0.67) had a strong and positive relationship with GY/ha, and LR (R 2  = 0.99) had a strong and negative relationship with GY/ha (Figs.  1H and 2A ).

Identification of effective phenotypes conferring WDS tolerance in maize

All 38 traits were subjected to principal component analysis (PCA), co-heritability (Co-h 2 ) analysis and regression coefficient (R 2 ) determination, where GY/ha was kept as a dependent variable. The PCA, Co-h 2 and R 2 values are presented in Tables  4 and 5 for the irrigated and WDS conditions, respectively. A trait was considered effective if it exhibited high PCA (≥0.20), Co-h 2 (≥0.60) and R 2 (≥0.40) values. To investigate the relationships among trait with grain yield (GY/ha) and the factors underlying yield variation, PCA was performed for all the traits. Under irrigated condition PCA explained 46.84% (PCA1: 29.04% and PCA2: 17.80%) in NDT lines and 98.7% variation was explained in DT lines (PCA1: 54.4% and PCA2: 44.3%) for yield variance (Table  4 ). The traits ASI, DDH, NL, LA-3, LA-C, FLW, EL, H/C, KR, SG, LS, TB, RV and RN were common traits in both NDT and DT lines which had considerable (≥0.20) PCA loading. But, in DT lines some additional traits SU, SL, INL3–4, FLL, EL, EW, C/P, KR/C, SW, RLWC, LR, LF, PS, BP, RFW, RDW and RL had considerable PCA loading. However, under WDS condition PCA explained 47.44% (PCA1: 26.89% and PCA2: 20.55%) in NDT lines and 100% variation was explained in DT lines (PCA1: 56.55% and PCA2: 43.45%) for yield variance (Table  5 ). The traits DA, DS, NL, LA-C, FLW, EH, EL, KiSTI, LF, PS%, RFW and RDW were common traits in both NDT and DT lines had considerable PCA loading. But, in DT lines some additional traits ASI, SL, INL3–4, FLL, C/P, KR, SG, LS, NBR and RL had considerable PCA loading. The total variance of PCA loading was higher in DT lines under both irrigated and WDS condition. Co-h 2 with GY/ha was also estimated and all the traits showed high Co-h 2 except DA, FLL, C/P, LF in NDT and DDH, C/P in DT line under irrigated condition. But, under WDS condition all the traits had high Co-h 2 in both NDT and DT lines. Co-h2 was higher in WDS condition in comparison to irrigated condition (Table  5 ).

Rainfall pattern

Maize is an extremely water-sensitive crop and most of the maize-grown areas are rain-fed. Therefore, maize in India faces WDS, which is detrimental to maize production. It is a well-accepted crop by farmers of eastern India, but the intensity of intermittent stress determines its production. In eastern India, the rainfall variability and frequency of water shortages are high, and the crop faces WDS in both the wet and dry seasons. The weather data indicated a higher frequency of WDS from the months of August to September in the wet season and after the 1 st fortnight of February in the dry season (Supplementary Fig.  1 ). Usually, crops are at the flowering and grain-filling stages in these months if they are sown in a timely manner. The intensity of WDS under field conditions is random in each season and even in each week of crop growth. Hence, sometimes known WDS-tolerant lines may fail to perform well under actual field conditions. This failure occurs because the experimental conditions were not truly representative of farmers’ fields and WDS-tolerant lines perform best under a constant magnitude of stress. Thus, we created variation in WDS approximating the prevalent WDS conditions of eastern India.

Variation in the performance of maize lines

The phenotypic expression of all 38 traits was statistically tested using a t-test. We observed that all the traits were significantly differentially expressed between irrigated and WDS conditions (Tables  2 and 3 ). Even significant phenotypes differences were also seen between the NDT and DT maize lines under stress for maximum number of traits (Supplementary Table  1 ). This variation was because of differences in the genetic constitution of the lines, which depends on the variability in the source populations from which the lines were obtained and developed 23 , 24 . Accordingly, the maximum variation among the lines was captured to identify the potential traits associated with WDS tolerance.

In general, maize is more susceptible to WDS than other rain-fed cereal crops because of its geitonogamous nature. Many traits attain a higher percentage of expression (high mean) under WDS condition in both types of maize lines i.e., DA (by 9.81% in NDT and 10.10% in DT), DS (by 14.33% in NDT and 14.68% in DT), ASI (by 86.44% in NDT and 115.80% in NDT) and DDH (by 11.98% in NDT and 10.60% in DT), LS (by score of 2.21 in NDT and 2.39 in DT), LR (by score of 3.93 in NDT and 3.05 in DT), LF (by score of 1.82 in NDT and 2.09 in DT), TB (by score of 3.42 in NDT and 3.37 in DT) and BP (by 21.88% in NDT and 25.37% in DT). As a consequence WDS delayed flowering (DA), delayed silk extrusion (DS) and resulted in asynchrony between pollen dehiscence and female receptivity (ASI) 19 , 25 , 26 , 27 . Therefore, it promoted a longer ASI. A delay in DS encourage poor fertilization which results in a higher BP 28 and higher rate of floral abortion decreases the seed set in the ear 29 , 30 . A negative change in the leaf water potential of lines increases the internal leaf temperature to save internal water, indicating phenotypic adaptation to a higher LR. Gradually, the leaf internal temperature rises and combines with the higher air temperature to induce a higher TB (by a score up to 4.90) and LF (by a score up to 2.60) in the top leaves, under WDS 31 . Simultaneously, higher expression of LS (by a score up to 4.89) occurs due to the degradation of chlorophyll in the photosynthetic apparatus 32 , 33 , 34 . Overall, these negative changes in leaf performance cause a loss of the normal phenotype and affect yield 13 .

The remaining phenological traits displayed a lower mean value under WDS in both types of maize lines. The reduction in PH (by 18.24% in NDT and 19.22% in DT) under WDS 16 , 35 is attributed to a decline in cell enlargement and higher LS 18 . Generally, maize plants produce 13 numbers of leaves, one at each internode, after which the tassel appears. In the present experiment averages of 12 numbers of leaves were formed in maize lines under irrigated conditions. The increase in the mean of LR, LS and LF under WDS promotes lower the resource capture, lower canopy photosynthesis; as a consequence, the PH and NL were reduced to an average of 11 17 . This effect extended to leaf size FLL (by 12.23% in NDT and 28.28% in DT) and FLW (by 18.00% in NDT and 31.75% in DT), which also decreased 18 under WDS. The leaf stomata (SL and SU) played a role in internal water and temperature regulation of the plants. The SL was invariably higher in number than SU but, overall their number was reduced due to WDS on both surfaces of the leaves viz; SL by 10.58% in NDT and 8.00% in DT); SU by 11.95% in NDT and 10.09% in DT. The stomatal conductance decreased, which has a direct proportional relationship with yield 36 . In maize, the flag leaf and the 2 nd leaf from the top are much smaller than the 3 rd leaf from the top (LA-3). The amount of light interception in a crop area depends on these leaf angles. LA-3 and INL3–4 determine light penetration, and LA-C indirectly measures the ear angle. Due to the greater leaf angle of the lower leaves and the smaller leaf angle of the upper leaves, light penetration and canopy photosynthesis increase 37 . Modern maize cultivars have such phenotypes and produce a higher yield 38 . Photosynthesis, which affects yield, largely takes place in five or six leaves near and above the ear 39 . We observed a 25–30° leaf angle, which supports higher light utilization by plants and provides easy passage of pollen to the stigma.

Photosynthates and carbohydrates are translocated to grains through the ear, where H/C serves as a reservoir of carbohydrates for supply during grain filling and maintains a high water content to protect the grains from abnormal external temperature and bird damage 24 . The mean value of the ear traits such as EH (by 19.08% in NDT and 26.04% in DT), EL (by 36.07% in NDT and 30.03% in DT), EW (by 27.68% in NDT and 32.30% in DT) and H/C (by 6.16% in NDT and 0.12% in DT)] was reduced under WDS. Lower EH sometimes considered as preferred because it aids to the development of short-statured plants, which leads to less lodging, but below 1 m height may lead to animal damage. EL and EW were also lower under WDS 40 as a consequence of a smaller number of grains than under normal conditions.

The effect of WDS on expression of yield and stress indices was decrease for C/P (by 18.87% in NDT and 16.51% in DT), K/R (by 36.72% in NDT and 29.91% by DT), KR/C (by 29.55% in NDT and 29.69% in DT), SW (by 8.86% in NDT and 17.67% in DT), GY/ha (by 51.49% in NDT and 48.55% in DT), KiSTI (by 16.67% in NDT and 2.63% in DT), and YI (by 12.96% in NDT and 3.33% in DT). The reduction in C/P was attributed due to a reduction in PH and increase in LS, which restricted further development of C/P. As a result, less than one C/P was formed under WDS. The traits K/R, KR/C and SW were also decreased under WDS due to embryo abortion, delay in the appearance of DS and a shortage in the carbohydrate reserve under WDS 41 . The stress index for some genotypes was lower and some had higher values. The highest value was observed for the WDS-tolerant genotypes. These genotypes can sustain a good yield under WDS in comparison to irrigated conditions 42 . The SSRT group of traits displayed both increased and decreased in expression under WDS. Some traits, such as LS, LR, LF, TB and BP showed higher expression, and others such as RLWC (by 15.36% in NDT and 19.90% in DT), SG (by 2.28% in NDT and 31.82% in DT) and PS (by 52.58% in NDT and 52.73% in DT) had lower expression under WDS. The reduction in RLWC is attributed to a reduction in the performance of leaf traits. Similarly, SG was also reduced because of a reduction in the performance of leaf traits and the higher expression of LS and LF. Furthermore, PS was lower under WDS because of disturbance of meiosis and carbohydrate metabolism 43 , 44 .

The imposed stress suppressed the below ground traits expression such as RFW (by 38.87% in NDT and 50.00% in DT), RDW (by 26.01% in NDT and 50.74% in DT), RL (by 39.79% in NDT and 45.17% in DT), RV (by 43.14% in NDT and 26.67% in DT) and RN (by 14.16% in NDT and 30.62% in DT). The trait NBR also reduces under WDS by 51.06% in NDT and 46.09% in DT maize lines. Under slight WDS, root traits are reportedly enhanced in the search for water 45 . However, under high stress intensity, the overall root architecture development is hampered and thus results in a abnormal root pattern. A reduction in overall plant growth; an increase in flowering, days to maturity and ASI; a reduction in mean vegetative, leaf, and ear traits; abnormal expression of SSRTs; and a loss of normal root architecture lead to a reduction of overall GY/ha 24 , 46 , 47 .

Comparison between DT and NDT lines under WDS

The change due to WDS in the performance of NDT and DT lines was compared for all 38 traits (Supplementary Table  2 ). DT maize lines tend to exhibit early flowering and maturity i.e, DA (0.13%), DS (1.29%), DDH (10.09%) and a lower ASI (3.41%) under WDS than NDT lines. ASI under drought has become shorter in modern cultivars, and the selection of such individuals increases the growth of ears 48 . The DT lines that exhibited high PH (7.72%), NL (7.95%), LA-C (43.23%), FLL (149.85%), EH (44.27), EW (13.87%), RL (27.34%), SW (118.30%), GY/ha (4.29%), RLWC (21.42%), SG (61.71%), RFW (18.79%), RDW (114.67%), RL (27.34%), RN (118.96%), and suitably lower LR (22.39%) and TB (1.46%) ultimately added a higher GY/ha under WDS than NDT lines. However, higher expression of LS (8.14%), LF (15.47%) and BP (15.95%) in DT lines is not desirable, and these traits were less expressed in the NDT lines. Some traits (such as SL, FLL, FLW, EW, RFW, RDW, RN, KR/C and RLWC) exhibited greater expression in the NDT lines than in the DT lines. The variation in phenotypes between NDT and DT lines is genetic since both were evaluated under alike environmental conditions. The DT lines displayed some favorable or sustainable trait expression under WDS, which contribute yield under WDS. These findings were also reported in previous research 49 . The traits that are distinct in DT and NDT lines are leaf area, C/P, PS, SG, EL 24 , KiSTI, YI 42 , NL, PH, SU (stomatal conductance), RL, RV 50 , SW 49 , LS, ASI 28 , and LR 51 .

Overall, a greater (≥30% and a score of ≥2) reduction was observed for traits such as ASI, EL, K/R, GY/ha, NBR, RFW, RL, RV, PS, LS, LR and TB in the NDT lines. Similarly, in the DT lines, ASI, FLW, EL, EW, GY/ha, NBR, RFW, RDW, RL, RN, SG, PS, LS, LR, LF and TB exhibited a greater reduction (Tables  2 and 3 ). Thus, the above 12 traits in the NDT lines and 16 traits in the DT lines were more sensitive to WDS than other traits. Thus, it is apparent that DT lines had different expression pattern for above traits which provide higher buffering capacity under WDS.

Relationship between grain yield and phenological traits

Breeding in maize primarily concerns yield improvement under target environment. Proper growth and development of maize plants comprise numerous parameters that are estimated by different traits. To understand the behavior of the traits in our present experiments, all the traits were plotted in linear regression curves against GY/ha. The coefficient of determination (R 2 ) was large (>40%), and a high percentage of the yield variation was explained by KiSTI (87%), YI (99%), BP (−42%) and RV (−69%) in I-NDT. However, in WDS-NDT, a large R 2 was obtained, and a high percentage of variation in yield was explained by DA (53%), DS (43%), PH (93%), SU (61%), SL (69%), LA-3 (60%), FLL (−87%), EL (49%), KR/C (68%), KiSTI (85%), YI (74%) and SG (67%). Similarly, for the I-DT lines, a high percentage of yield variation was explained by ASI (57%), NL (51%), SU (−51%), EL (86%), EW (96%), H/C (66%), K/R (47%), SW (−94%), KiSTI (99%), YI (99%), SG (72%), LR (62%), LF (−43%), TB (−44%), NBR (96%), RDW (44%), RL (−69%), RV (76%), and RN (59%). However, in the WDS-DT lines, a large R 2 was obtained, and a high percentage of variation in yield was explained by all the traits except for 10 traits (DA, DS, EH, EW, K/R, NBR, RFW, RDW, RL and RN). The association between phenological traits and GY/ha had stronger relation under WDS conditions than under irrigated conditions, which shows worthiness of the measure traits. The traits RV, BP, KiSTI and YI were similar variation under irrigated conditions for the NDT and DT lines. Similarly, the traits SG, KR/C, EL, FLL, LA-3, SL, SU, PH, RV, BP, KiSTI and YI were similar variation under WDS for the NDT and DT lines. Researchers have also reported a strong relationship of yield with kernel traits 28 , flowering and ASI 26 , C/P 24 , EL and EW 52 , and SW and NL 53 . However, for many of the traits, such a relationship has not been reported, but an association of grain yield with other traits such as DA, DDH 54 , leaf traits, leaf angle, SG, BP, LR, and TB 55 has been reported. Thus, using these traits, it is possible to predict the yield of a line.

Identification of effective phenotypes conferring WDS tolerance

Many phenotypic traits lead to a higher buffering capacity in maize lines in adverse environments 13 . The association of such traits with yield-related traits enhances the potential yield of lines. Generally, we used single statistics to screen relevant traits but ignored some other relationships, such as the heritability of the traits and their relationships with yield. Thus, multiple statistical analyses were performed for precise selection of traits in the present experiment. Multivariate statistics by PCA was performed to identify traits with greater contribution towards yield variance. In NDT lines PCA explained a lower variance (Irrigated: 46.84% and WDS: 47.44%) compared to DT lines (Irrigated: 98.7% and WDS: 100%) for yield. Thus, traits expression in DT line more closely related to yield variance and under WDS the relation was stronger. Beside, some common traits (both NDT and DT) showed higher loading in PCA, there were additional traits in DT lines which had higher loading (ASI, NL, SL, LA-C, INL3-4, FLL, FLW, KR, C/P, SG, LS, LS, PS%, NBR, RL, RDW and RL) towards yield variance under WDS condition. These traits also had high Co-h 2 , which is more desirable for selection under WDS. Using a combination of PCA, Co-h 2 , and R 2 , we found traits in the DT lines that were closely related to yield in both environments. Furthermore, the phenotypic expression of DT lines was more prominent than that of NDT lines, especially under WDS. Thus, relevant and effective traits were chosen from DT lines: NL, SL, LA-C, INL3-4, FLL, FLW, C/P, LS, PS%, RDW and RL. Some traits such as kernel set, grain yield, ASI, silk emergence, ear formation, ear size (or ear growth rate), adequacy of pollen viability, ears per plant, barrenness, kernels per ear, weight per kernel, and stay-green have also been associated with WDS tolerance 24 , 27 . These valuable traits must be combined with phenotypic selection for WDS tolerance in maize to construct a proper plant ideotype rather than selecting by only yield per se. Such a use of multiple trait selection has also been previously reported 12 . Multiple selection criteria were also previously used 48 to obtain a higher yield per breeding cycle, in which different traits were chosen based on their variance, heritability and genetic correlation with yield, and recently, eigenvalues (principal components) were also used 56 , 57 obtained higher yield gains under severe moisture stress conditions in maize by using a selection index.

A trait was considered effective if it exhibited high PCA (≥0.20), Co-h 2 (≥0.60) and R 2 (≥0.40) values. To investigate the relationships among trait with grain yield (GY/ha) and the factors underlying yield variation, PCA was performed for all the traits. Under irrigated condition PCA explained 46.84% (PCA1: 29.04% and PCA2: 17.80%) in NDT lines and 98.7% variation was explained in DT lines (PCA1: 54.4% and PCA2: 44.3%) for yield variance (Table  4 ). The traits ASI, DDH, NL, LA-3, LA-C, FLW, EL, H/C, KR, SG, LS, TB, RV and RN were common traits in both NDT and DT lines which had considerable (≥0.20) PCA loading. But, in DT lines some additional traits SU, SL, INL3-4, FLL, EL, EW, C/P, KR/C, SW, RLWC, LR, LF, PS, BP, RFW, RDW and RL had considerable PCA loading. However, under WDS condition PCA explained 47.44% (PCA1: 26.89% and PCA2: 20.55%) in NDT lines and 100% variation was explained in DT lines (PCA1: 56.55% and PCA2: 43.45%) for yield variance (Table  5 ). The traits DA, DS, NL, LA-C, FLW, EH, EL, KiSTI, LF, PS%, RFW and RDW were common traits in both NDT and DT lines had considerable PCA loading. But, in DT lines some additional traits ASI, SL, INL3-4, FLL, C/P, KR, SG, LS, NBR and RL had considerable PCA loading. The total variance of PCA loading was higher in DT lines under both irrigated and WDS condition. Co-h 2 with GY/ha was also estimated and all the traits showed high Co-h 2 except DA, FLL, C/P, LF in NDT and DDH, C/P in DT line under irrigated condition. But, under WDS condition all the traits had high Co-h 2 in both NDT and DT lines. Co-h2 was higher in WDS condition in comparison to irrigated condition. Considering the PCA, Co-h 2 , and R 2 together, the most effective traits under irrigated conditions were KiSTI, YI, NBR, and RV for the NDT lines and ASI, SU, EL, EW, H/C, KR, SW, SG, LR, LF, TB, RDW, RL, and RV for the DT lines (Table  4 ).

An exposure of WDS at flowering and grain filling stage brought severe negative effects on phenological and yield traits attributes of the maize lines. Concurrently the performance of NDT and DT maize lines differed for several traits under WDS. The mean value of traits was below the desirable limit but certain genotypes showed higher mean under WDS due to their different genetic background and buffering capacity. WDS at flowering and grain filling stage leads to significant yield penalty especially in NDT lines than DT lines. The traits viz; NL, SL, LA-C, INL3-4, FLL, FLW, C/P, LS, PS%, RDW and RL were identifies specific to improve WDS tolerance in maize. In such context, the WDS tolerance traits should be in plant ideotype while selecting a line in breeding. The maize lines showed highly desirable phenotypic expression under WDS for any traits are important to conserve to identify novel gene.

Data availability

All data analyzed during this study are included in this published article in Supplementary Table 1.

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Acknowledgements

The present work is part of Ph.D. thesis of first author. The authors are thankful to Birsa Agricultural University, Ranchi for providing the facilities, assistance and financial support for conducting the present research. The first author is also thankful to Indian Council of Agricultural Research, New Delhi for allowing the study leave to first author for completion of thesis work.

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Present address: ICAR-National Rice Research Institute, Cuttack, Odisha, India

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Department of Genetics and Plant Breeding, Birsa Agricultural University, Kanke, Ranchi, Jharkhand, India

R. P. Sah, M. Chakraborty, K. Prasad, M. Pandit, V. K. Tudu & S. C. Narayan

Department of Entomology, Birsa Agricultural University, Kanke, Ranchi, Jharkhand, India

M. K. Chakravarty

Indian Grassland and Fodder Research Institute, Jhansi, Uttar Pradesh, India

ICAR-National Rice Research Institute, Cuttack, Odisha, India

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R.P. Sah conducted the experiment & written the manuscript. M. Chakraborty, K. Prasad & M.K. Chakravarty conceptualized the experiment, data management and supervised the experiments. M. Pandit helped data recording and analysis, M. Rana, and D. Moharana helped in secondary data analysis and prepared figures & tables. V.K. Tudu & S.C. Narayan helped in preparation of manuscript, review, editing & formatting of the manuscript.

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Sah, R.P., Chakraborty, M., Prasad, K. et al. Impact of water deficit stress in maize: Phenology and yield components. Sci Rep 10 , 2944 (2020). https://doi.org/10.1038/s41598-020-59689-7

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HHMI Gilliam Fellowship to Andrea Sama and Jazz Dickinson Supports New EDI Initiative

Fellowship funding paves the way for a May 10 keynote address on issues of health equity in the Latinx community

April 18, 2024

By Mario Aguilera

Andrea Sama

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UC San Diego School of Biological Sciences Graduate Student Andrea Sama and Assistant Professor Jazz Dickinson have been awarded a Howard Hughes Medical Institute (HHMI) Gilliam Fellowship. HHMI Gilliam Fellowships are annually awarded to graduate student-advisor pairs in recognition of outstanding research and a commitment to build a more inclusive scientific ecosystem. Sama and Dickinson were selected among 50 advisor-student pairs, each of which receives an annual award totaling $53,000 for up to three years. Members of the latest cohort come from 37 institutions across the United States. According to HHMI, the Gilliam Fellows Program invests in graduate students and advisors who embody leadership in science and are committed to advancing equity and inclusion in science. “The Gilliam Fellowship not only supports incredibly talented graduate students who are poised to become future leaders in science,” says Joshua Hall, senior program officer for the Gilliam Fellows Program, “but it also engages thesis advisors and institutions in the work of creating training environments in which all students can thrive.” In Dickinson’s lab, Sama, a third-year graduate student, investigates the effect of environmental stress on plant metabolism. Climate change and drought have increased stressors on plants and threaten crop yield by disrupting normal plant growth and development. Sama’s research addresses questions surrounding metabolites, the compounds produced during metabolism, and their role in plant stress and development. Her research uses high-resolution imaging techniques to analyze the localization of metabolites in maize roots. Being able to characterize such biological dynamics will provide insights into the mechanisms that plants use to handle stress as scientists seek to develop a new generation of stress-tolerant crops. Funding from the Gilliam Fellowship will go toward a new “Fireside Chat with Faculty” series launched by the UC San Diego chapter of the Society for the Advancement of Chicanos/Hispanics and Native Americans in Science (SACNAS). The series invites Hispanic, Latin, Native American and other underrepresented minorities to discuss their career arc. Speakers in the series highlight the resources that enabled them to overcome the unique challenges they experienced in science, technology, engineering and math (STEM). The series has featured speakers from the School of Biological Sciences such as Assistant Professors Fabian Rivera-Chávez, Alex Chaim, and most recently, incoming faculty member Dr. Nabora Reyes de Barboza.

Jazz Dickinson

“Our goal for this series is to demystify a scientific career, provide representation to students interested in science and build a strong community among students and faculty at UC San Diego,” said Sama, who serves as SACNAS treasurer for the UC San Diego chapter. Dickinson will use HHMI Gilliam Fellowship funds, along with support from the School of Biological Sciences’ Equity, Diversity and Inclusion committee, which funded the previous Fireside Chats, to support the upcoming Keynote Fireside Chat with Faculty event. Columbia University Associate Professor Carmela Alcántara will discuss how discrimination and stress in the Latinx community affect sleep and mental and physical health. The event will be held from 11 a.m. to 12 p.m. on May 10, 2024, at the Fred Kavli Auditorium in UC San Diego’s Tata Hall. A lunch and social gathering will follow the keynote to encourage further discussion and community building. According to Sama, the Fireside Chat series has already received feedback from students who had indicated a lack confidence in approaching professors. Following the series talks, they are more confident and feel more represented in STEM. Among her other outreach efforts, Sama has: organized a community-supported agriculture program to provide free local produce to UC San Diego graduate students with financial need to help alleviate food insecurity on campus; coordinated a graduate housing assistance grant application and award process to alleviate housing insecurity among graduate students; and helped develop a long-term basic needs stipend for graduate students with high financial need. Sama has also served as a mentor for undergraduate students in the Biology Undergraduate and Master’s Mentorship Program as well as the Enlace Summer Research Program. HHMI created the Gilliam Fellowships for Advanced Study, now called the Gilliam Fellows Program, in 2004 in honor of the late James H. Gilliam, Jr., a charter trustee of HHMI. The Gilliam community now totals 451 outstanding scientists since the program’s launch in 2004. The program recognizes that advisors play an integral role in helping their students realize their high potential. For this reason, Gilliam advisers participate in a year-long mentorship development course led by facilitator-scholars from HHMI’s Scientific Mentorship Initiative. “By offering professional development to mentors and supporting the growing Gilliam community, HHMI is working to create lasting change across campuses and throughout the wider scientific community,” says Blanton Tolbert, vice president of HHMI’s Center for the Advancement of Science Leadership and Culture (CASLC).

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Drought Stress Priming Improved the Drought Tolerance of Soybean

Mariz sintaha.

1 School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China

2 Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, China

Chun-Kuen Man

Wai-shing yung, shaowei duan, hon-ming lam, associated data.

All data generated in this study are available within this manuscript and the companion Supplementary Materials .

The capability of a plant to protect itself from stress-related damages is termed “adaptability” and the phenomenon of showing better performance in subsequent stress is termed “stress memory”. While drought is one of the most serious disasters to result from climate change, the current understanding of drought stress priming in soybean is still inadequate for effective crop improvement. To fill this gap, in this study, the drought memory response was evaluated in cultivated soybean ( Glycine max ). To determine if a priming stress prior to a drought stress would be beneficial to the survival of soybean, plants were divided into three treatment groups: the unprimed group receiving one cycle of stress (1S), the primed group receiving two cycles of stress (2S), and the unstressed control group not subjected to any stress (US). When compared with the unprimed plants, priming led to a reduction of drought stress index (DSI) by 3, resulting in more than 14% increase in surviving leaves, more than 13% increase in leaf water content, slight increase in shoot water content and a slower rate of loss of water from the detached leaves. Primed plants had less than 60% the transpiration rate and stomatal conductance compared to the unprimed plants, accompanied by a slight drop in photosynthesis rate, and about a 30% increase in water usage efficiency (WUE). Priming also increased the root-to-shoot ratio, potentially improving water uptake. Selected genes encoding late embryogenesis abundant (LEA) proteins and MYB, NAC and PP2C domain-containing transcription factors were shown to be highly induced in primed plants compared to the unprimed group. In conclusion, priming significantly improved the drought stress response in soybean during recurrent drought, partially through the maintenance of water status and stronger expression of stress related genes. In sum, we have identified key physiological parameters for soybean which may be used as indicators for future genetic study to identify the genetic element controlling the drought stress priming.

1. Introduction

Soybean is not only an economically important oil seed crop, but it also has many other desirable attributes. As a low-cost meat alternative in a vegan diet [ 1 ] and a potential scaffolding material to produce more affordable lab-grown meat [ 2 ], it could help to alleviating the meat protein crisis in the world today. Soy proteins have anti-cholesterol and anti-carcinogenic effects as well as protective effects against diabetes, kidney disease, and menopausal hot flashes [ 3 , 4 ], while soy fiber has health benefits such as acting as a laxative [ 5 ]. Soybean hull can be used commercially to produce bioethanol, butanol, enzymes (cellulases, xylanases, etc.), phytohormones (gibberellin), and prebiotics, etc. [ 6 ].

However, cultivated soybean is susceptible to drought [ 7 ], a major abiotic stress. Decreased precipitation and/or increased evaporation due to climate change will increase the severity of drought stress across the world in the second half of the 21st century [ 8 ]. According to a simulation, it is predicted that the drought-driven yield loss will increase by around 16% by the end of the 21st century without any drought adaptation by the plant [ 9 ]. Fortunately, there are various mechanisms by which soybean plants adapt to drought stress [ 10 ].

Various plants are known to perform better in subsequent stress as a result of their previous exposure to the same stress. This phenomenon of achieving better performance in the subsequent stress cycle is called stress memory [ 11 ]. This phenomenon has been reported in various plants showing drought memory responses, including Arabidopsis thaliana [ 12 , 13 , 14 ], Zea mays [ 15 ], wheat, [ 16 ] , rice [ 17 ], Cakile maritima [ 18 ], Aptenia cordifolia [ 19 , 20 ], and Silene dioica [ 21 ]. Plants with drought memory response exhibited different physiological changes, including reduced stomatal conductance, lowered photosynthesis, as well as improved relative water content (RWC), chlorophyll content, photosystem II (PSII) efficiency, and better performance against oxidative damage, than naïve plants [ 15 , 17 , 18 , 19 , 20 ]. It was found that some “trainable memory genes” in Arabidopsis can be “trained” to be expressed by the plant in the first stress cycle and are then expressed at a higher efficiency when exposed to subsequent similar stresses. These trainable memory genes were found to have some “memory marks” such as higher histone 3 lysine 4 trimethylations (H3K4me3) and higher Ser5-phosphorylated RNA polymerase II accumulation in the promoter region, which are responsible for their higher expression during subsequent stresses [ 13 , 22 ].

Stress memory response has also been assessed in soybean. For example, salt-primed seedlings were found to have altered histone 3 lysine 4 dimethylations (H3K4me2), H3K4me3, and histone 3 lysine 9 acetylation (H3K9ac) marks throughout the genome to promote the response related to salt tolerance [ 23 ]. Soluble sugar and proline contents have been found to increase in the initial drought stress and then remain stable in the subsequent stresses to protect soybean plants from drought-induced osmotic stress [ 24 ]. The expressions of the proline synthesizing gene ( P5CS1 ) and two genes encoding NAC transcription factors (related to the abscisic acid [ABA]-mediated pathway) showed changes in the subsequent drought stresses in soybean [ 24 ]. In another study using microarray analyses with a DNA chip, 392 soybean genes were found to have at least four-fold elevation in expression while 613 genes were found to have at least four-fold reduction in expression in the repeat stress treatment compared to the initial stress. The genes that showed elevated transcript levels in the subsequent stress included transcription factors, a trehalose (a disaccharide maintaining membrane fluidity) biosynthesis enzyme, Late Embryogenesis Abundant proteins, and PP2C-family proteins. The genes with reduced transcript levels during the repeated stress included those related to photosynthesis and primary metabolic pathways [ 25 ]. The drought-priming-responsive genes, including osmotin and the WRKY , SMP , MYB , NAC , PP2C , AP2 , and LEA families, have been found to have very high induction during the second stress treatment [ 25 ]. In some cases, the memory obtained from the first drought stress treatment was transferable to the next generation. The transgenerational effect of drought stress was observed in soybean when the stress was experienced by the maternal line during the reproductive stage, resulting in reduced seed quality, rate of germination, and seed vigor in the F1 generation [ 26 ] and higher dehydrin protein levels in seeds [ 27 ]. From these studies, it is obvious that soybean has a drought memory response.

In soybean, most prior studies focused on the changes in metabolite concentrations and gene expressions after priming in soybean. However, whether these changes are reflected in the physiology is still unclear. Previously, we have identified a drought-sensitive soybean variety, C08, in a gradual soil-drying experiment [ 28 ]. We hypothesize that the drought stress tolerance of C08 might be enhanced through drought stress priming. The enhanced drought tolerance might be assessed through the physiological parameters. In this study, we attempted to investigate the physiological changes upon drought stress priming. To study the changes in the physiology of soybean plants involved in the drought stress memory response, C08 plants were treated once or twice with drought stress. Drought sensitivity, growth and photosynthetic performance were compared between the different treatment groups to determine the impact of drought stress memory on the plant physiology. The expressions of stress memory marker genes were also investigated. This is the first comprehensive physiological study of drought stress priming in soybean.

2.1. Glycine Max C08 Demonstrated Drought Stress Memory

The soybean cultivar C08 has previously been demonstrated to be a drought-sensitive variety [ 28 ]. To determine the effect of drought stress memory on these plants, the seedlings of soybean C08 were treated with drought stress once (unprimed, 1S) or twice (primed, 2S). For the 2S treatment group, a recovery period was given to the plants in between the two drought treatments. Well-watered plants were used as control (unstressed, US). The detailed treatment design can be found in Figure S1 . In two independent experiments, the drought-treated plants were suffering from different degrees of drought damage ( Figure 1 ). We quantitatively assessed the overall performance of the plants using the drought stress index (DSI), and percentage of surviving leaves. The US plants had a DSI = 1, meaning that all the plants were healthy at the end of the experiments ( Figure 2 a). On the contrary, the soybean plants under 1S treatment had a significantly higher DSI compared to the 2S treatment group ( Figure 2 a). Similarly, the soybean plants under 1S treatment had a lower percentage of surviving leaves than those under 2S treatment ( Figure 2 b). Both the DSI and percentage of surviving leaves indicated that the soybean plants receiving repeated drought treatments performed better than those receiving only a single treatment. The first drought treatment may have primed the plants for responding to the subsequent stress.

Phenotypes of the drought-sensitive soybean C08 plants after drought treatment. ( a ) Left: plants after 2 cycles of drought treatment (2S: 7 days without irrigation until the first sign of wilting, followed by 5 days of recovery and then 10 days without irrigation). Right: plants receiving only 1 cycle of drought treatment (1S: 10 days without irrigation with no priming). ( b ) Phenotype of unstressed plants (US) well irrigated throughout the experiment. ( c ) The 2S plants laid out individually on a flat surface. ( d ) The 1S plants laid out individually on a flat surface.

Performance of primed (2S) and unprimed (1S) soybean C08 plants under drought treatment compared to the untreated control (US). ( a ) Drought stress index, n = 28–30 plants. ( b ) Percentage of surviving leaves, n = 20–30 plants. ( c ) Relative water content, n = 19–29 plants. ( d ) Shoot water retention, n = 26–30 plants. Error bars indicate standard deviation. Wilcoxon rank-sum test was used to compare between the mean values of each treatment following one-way ANOVA. Different letters above the bars indicate significant differences between groups at p < 0.05. Each experiment was performed twice (First and Second Experiment), with similar results.

2.2. Priming for Drought Stress Resulted in Better Water Retention Abilities than in Unprimed Plants

A better water retention ability could improve the survivorship of a drought-stressed plant. To determine whether the plants receiving repeated drought treatments gained a better water retention ability, the relative water content (RWC) and the shoot water content were assessed.

Relative water content (RWC) is a commonly used parameter for measuring the amount of water retained by a leaf. We measured the RWC of the middle leaflet of the top trifoliate leaves. Both the 1S and 2S treatments led to a significant reduction in RWC in these plants compared to the US control ( Figure 2 c). However, consistent with the results on DSI and percentage of surviving leaves, the RWC of the 1S plants (62.77% ± 17.42) was significantly lower than that of the 2S plants (76.38% ± 7.36). A similar trend was also observed in another independent experiment, with a significant difference between the two treatment groups ( Figure 2 c).

Furthermore, we assessed the shoot water content under the three treatment conditions. The single drought (1S) treatment led to a significant drop in the shoot water content ( Figure 2 d). In contrast, the plants receiving two drought treatments (2S) were able to maintain a significantly higher shoot water content than those in the 1S group. That means the primed plants were able to maintain a better water status in the subsequent stress.

We speculated that a lower rate of water loss from the leaves was the cause of a better RWC and shoot water content. Hence, the rate of water loss from a detached leaflet of the top trifoliate leaf was examined. Although the rate of water loss from the leaves of the 2S and 1S plants was similar at 15 min after detachment ( Figure 3 ), the differences became larger as time went on. The leaves from the 1S plants lost significantly more water than those from the 2S plants. In both independent experiments, the 1S plants had around 40% water loss in terms of relative weight loss within 60 min of leaf detachment while the 2S plants lost only around 30%. This suggested that the 2S plants were better at reducing water loss than the 1S plants.

Rates of fresh weight loss over an hour from a detached leaflet of the top trifoliate leaves of primed (2S), unprimed (1S) and unstressed control (US) soybean C08 plants relative to the initial fresh weight immediately after detachment. n = 12 plants. Error bars indicate standard deviation. The experiment was performed twice (Experiments 1 and 2), with similar results.

2.3. Drought Stress Memory Reduced the Rates of Photosynthesis, Transpiration and Stomatal Conductance and Increased Water Usage Efficiency

Under drought conditions, the photosynthetic functions of plants are largely hampered due to the closure of stomata for water conservation. Thus, we investigated the effect of drought stress priming on photosynthesis. As expected, the drought treatments have reduced the rates of photosynthesis, transpiration rate and stomatal conductance of plants in both treatment groups (1S and 2S) when compared to the well-watered controls (US) ( Figure 4 a–c). Furthermore, plants in the 2S group had a significantly lower transpiration rate and stomatal conductance accompanied by a slightly lower photosynthetic rate than those in the 1S group.

Changes in the photosynthesis-related parameters of primed (2S) and unprimed (1S) soybean C08 plants under drought treatment compared to the unstressed control (US). ( a ) Rate of transpiration, n = 12 plants. ( b ) Stomatal conductance, n = 12 plants. ( c ) Rate of photosynthesis, n = 12 plants. ( d ) Water usage efficiency, n = 12 plants. Error bars indicate standard deviation. Wilcoxon rank-sum test was used to compare between the mean values of each treatment following one-way ANOVA. Different letters above the bars indicate significant differences between groups at p < 0.05.

By lowering the transpiration rate to conserve water, photosynthesis is slowed down, which in turn hampers plant growth. A high instantaneous water usage efficiency (WUEi), expressed as the ratio of the rate of photosynthesis to the rate of transpiration, would infer that the plant has maintained a high photosynthetic rate and/or a low rate of water loss through transpiration. Drought priming reduced both photosynthesis and transpiration in soybean plants but increased the WUEi. The drought treatments improved the WUEi of the plants from both 1S and 2S groups compared to those from the US group ( Figure 4 d), as the drop in the transpiration rate was more prominent than the reduction in photosynthesis. Moreover, the primed 2S soybean plants demonstrated an even better WUEi than the 1S soybean plants, suggesting the plants in the 2S treatment group were more adapted to the drought stress.

Next, we investigated whether the drop in photosynthesis was due to the disintegration of chlorophyll under the stress conditions. Although there was a slight drop in the chlorophyll content in the plants from the 1S treatment group compared to those from the US control group, the difference was only statistically significant in one of the two independent experiments ( Figure 5 ). In contrast, there was no significant difference in chlorophyll contents between the plants from the 2S treatment group and the US control group in either experiment.

Box-and-whisker plots of the chlorophyll contents of primed (2S) and unprimed (1S) soybean C08 plants under drought treatment compared to the unstressed control (US). The whiskers represent the maximum and minimum values in the sample. Wilcoxon rank-sum test following one-way ANOVA was used to compare between the mean values of each treatment. Different letters indicate significant differences between groups at p < 0.05. n = 19–30.

2.4. Drought Stress Memory Did Not Affect Growth Performance beyond the Effects of Drought Stress Itself

Water deficiency can lead to the diminished growth of plants. As expected, the plants from the 1S and 2S treatment groups had significantly lower shoot and root weights than those from the US group ( Table 1 ). However, interestingly, although the 2S plants received drought treatment twice and the 1S plants were treated only once, there was no significant difference in shoot and root lengths or shoot and root weights between the 1S plants and the 2S plants. Furthermore, the maximum root length of the 2S plants was the longest among the three treatment groups ( Table 1 ), which also led to a significant increase in the root-length-to-shoot-length ratio ( Table 1 ).

Growth parameters of the drought-treated soybean plants.

Note: 1S, unprimed; 2S, primed; and US, unstressed soybean plants. Values are expressed as mean ± standard deviation. Letters beside the values indicate significant differences between groups ( p < 0.05). Pairwise comparisons between groups were done by Wilcoxon rank-sum test following one-way ANOVA. The weights are expressed in grams and length in centimeters. # root-length-to-shoot-length ratio. The experiment (Exp.) was performed twice.

2.5. Drought Stress Memory Induced the Expressions of Selected Drought Priming-Responsive Genes

The expressions of selected transcription factor genes related to drought stress responses were analyzed using RT-qPCR. Four putative candidate genes were selected from among the drought priming-responsive genes from previous drought stress studies on soybean [ 25 ]. These genes all showed significantly higher induction in the group with priming (2S) compared to the unprimed group (1S) in two independent experiments ( Figure 6 ).

The relative expression levels of selected genes in primed (2S) and unprimed (1S) soybean C08 plants under drought treatment and the unstressed control (US) were analyzed by RT-qPCR. ( a ) Expression of Glyma.06G248900, ( b ) Glyma.05G234600, ( c ) Glyma.14G195200, and ( d ) Glyma.19G147200 was calculated by 2 −ΔΔCT method. The data are presented as the mean of three technical replicates ± SD. Tukey’s honest significance test was used to compare between the mean values of each treatment following one-way ANOVA. Different letters above the bars indicate significant differences between groups at p < 0.05. act11 and elf1b were used as the reference genes. The scale on the left y -axis refers to the data from the first experiments and that on the right y -axis refers to those from the second experiment.

3. Materials and Methods

3.1. plant materials, growth and treatment conditions.

Seeds of Glycine max C08 (cultivar name: Union) [ 29 ] were germinated in vermiculite in a greenhouse at the Chinese University of Hong Kong (22°25′7″ N 114°12′26″ E), Shatin, Hong Kong, China. Five-day-old healthy seedlings were transplanted in modified plastic soft drink bottles filled with sandy loam soil and peat moss in a 1:1 ratio [ 28 ]. The tops of the plastic bottles were removed and flapping windows were made for inserting a soil moisture probe for monitoring the soil moisture over time. Five holes were drilled at the bottom of each bottle. The window flaps were sealed with water-resistant tape to prevent water loss through evaporation when the probe was removed. The plants were allowed to grow until the first trifoliate leaf was fully open at an average monthly temperature of 25 °C and average air humidity of 69%. During this period, the plants were sub-irrigated with the same amount of water (30 mL/plants/day) [ 28 ], and were divided into three groups: unstressed (US) control, once-stressed (1S), and twice-stressed (2S). Each group began with 30 seedlings to maximize the statistical power [ 30 ]. After the first trifoliate leaf was fully open, the first drought cycle was applied to the 2S group by withdrawing the irrigation [ 28 ]. The soil moisture content of the 2S group was measured with a soil moisture meter (TZS-W, China) every other day through the window on the bottle until the reading dropped to 8 (after 7 days). The probe reading of 8 corresponds to 35% of field capacity and the plants showed the sign of wilting at this point in a preliminary study. Then irrigation was resumed on the next day to allow the plants to recover for 5 days. After the recovery period, irrigation water was withdrawn from both 1S and 2S treatment groups, while the US treatment group was kept well-watered for a 10-day period. The experiment was repeated twice. The treatment scheme is depicted graphically in Figure S1 .

3.2. Determination of Drought Stress Index (DSI)

The DSI was determined according to the visual assessment scheme [ 31 ] with some modifications ( Figure S2 ). As the plants used in this experiment have around five nodes, a 10-point scale was used to properly differentiate the phenotypes between the plants ( Figure S2 ). The plants showing no symptoms of stress were scored 0 whereas the dead plants scored 10. Scoring was done to all plants at the end of the second drought treatment of the 2S plants.

3.3. Determination of the Growth-Related Parameters

At the end of the second stress cycle, the number of nodes, total number of leaves, and number of surviving leaves of all plants were counted. Maximum root length and shoot lengths were measured using a ruler. The fresh weight of the shoot was measured using an analytical balance. The roots were gently washed with tap water and both shoots and root were dried in an oven at 65 °C for 72 h before measuring the biomass. The percentage of surviving leaf was calculated using this formula:

3.4. Determination of the Relative Water Content (RWC)

The middle leaflet of the topmost trifoliate leaves of the treated plants were harvested for the determination of RWC. Upon collection, the leaves were immediately sealed in plastic zip bags and placed on ice to prevent water loss. After measuring the fresh weight, the leaves were soaked in tap water in the zip bags. The turgid weights of the fully saturated leaves were measured after soaking for 24 h. The leaves were then dried in an oven at 65 °C for 48 h for the measuring the leaf dry weight. RWC was calculated using this formula:

where FW, DW, and TW were the fresh weight, dry weight, and turgid weight of the leaflet, respectively.

3.5. Calculation of the Shoot Water Content

After treatment, the entire above-ground parts of the treated plants were collected. After measuring the fresh weight, the plant materials were dried in an oven at 65 °C for 72 h to determine the dry weight. The shoot water content was calculated in relation to the fresh weight with this formula [ 32 ]:

where SFW was the shoot fresh weight and SDW was the shoot dry weight. Shoot referred to all the above-ground tissues including stems and leaves.

3.6. Measurement of Photosynthesis-Related Parameters

The topmost trifoliate leaves from each treatment group were selected for the measurement. Within each treatment group, plants with similar DSI were selected for the measurement. The rates of photosynthesis, transpiration and stomatal conductance were measured using the LI-COR 6800 portable photosynthesis system between 9:00 a.m. to 12:30 p.m. local time with a photosynthetic photon flux density of 800 μmol m −2 s −1 , CO 2 concentration at 395–400 μmol mol −1 , and 40% leaf chamber air humidity at the ambient air temperature of 25 °C. The average of two measurements taken from the same position on the top trifoliate leaves of each selected plant was used. Instantaneous Water Usage Efficiency (WUEi) was calculated by dividing the ratio of photosynthesis (after converting the µmol CO 2 to mol CO 2 ) by the rate of transpiration. Measurements were taken at the end of the second drought stress (10th day).

Chlorophyll contents of the middle leaflet of the trifoliate leaves were measured using a chlorophyll concentration meter (MC 100, Apogee, Logan, UT, USA). The measurement was taken between 9:00 a.m. to 12:30 p.m. local time from all surviving plants at the end of the second drought stress.

3.7. Measurement of Rate of Water Loss (RWL) from Leaves

The plants used for measuring gaseous exchange were also used for measuring the rate of water loss. One leaflet was detached from the top trifoliate leaf and the fresh weight was measured immediately. The leaflets were then left uncovered at room temperature and humidity and their weight was taken every 15 min using an analytical balance. The percentage weight loss of each detached leaflet from its initial fresh weight was calculated for each time point.

3.8. RT-qPCR Analyses

Total RNA was extracted from the top two trifoliate leaves using TRIzol reagent. Samples were collected from 3 plants from each treatment group. After that, DNase I treatment was performed to eliminate DNA contamination prior to converting the RNA to cDNA using iScript™ cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA, cat# 1708890) according to the manufacturer’s protocol. Gene expressions were then analyzed by quantitative PCR using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, Hercules, CA, USA, cat# 172-5271). The polymerase chain reaction was carried out using a CFX384 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). Fold changes in expression were calculated by the 2 −ΔΔCt method using act11 and elf1b as reference genes [ 33 , 34 ]. For the target genes, the specific primers were designed spanning the exon-intron junction using the primer-BLAST tool (NCBI) to prevent the amplification of genomic DNA. These primer sequences can be found in Table S1 .

3.9. Statistical Analysis

Data from the 1S, 2S, and US groups were compared by one-way ANOVA by rank (Kruskal-Wallis test). Then post hoc pairwise comparisons were done by Wilcoxon rank-sum test (non-parametric) using the R programming and Graphpad Prism software version 8.0.0 (San Diego, California, USA, www.graphpad.com ). Fold changes in gene expression were compared by Tukey’s honest significance test following one-way ANOVA. The differences were considered to be significant at p < 0.05.

4. Discussion

Stress memory refers to the ability of pre-stressed plants to gain higher tolerance towards the subsequent stress, compared to naïve plants experiencing stress for the first time. Several studies have investigated drought stress memory response in soybean. For example, the changes in soluble sugar and protein concentrations [ 24 ], and gene expressions [ 25 ] in drought stress-primed soybean have been evaluated. Furthermore, potential transgenerational drought stress memory has also been delineated [ 26 , 27 ]. Here we have added to the understanding of drought stress memory by evaluating the physiological responses of soybean to drought stress priming.

In our study, primed (2S) plants showed physiological adaptation and less severe symptoms of drought than unprimed plants (1S) due to the presence of stress memory.

Roots can sense the dryness of the soil and send a signal, e.g., ABA, to the shoot as the earliest warning message to close the stomata [ 35 ]. Faster declination of stomatal conductance, rate of transpiration, and a lower rate of water loss from the detached leaf under drought stress are the traits associated with a more drought-tolerant soybean genotype [ 28 , 36 ]. In our study, the primed plants had much lower stomatal conductance and rate of transpiration, indicating more tolerance of the drought. This might have been achieved by better root-to-shoot communication, or a higher sensitivity towards ABA signal. In potato plants, drought priming induced a thicker cuticular layer [ 37 ] which is inversely proportional to water loss from the detached leaf [ 38 ]. In our study, a lower rate of water loss from the detached leaf in the primed plants might have been achieved by rapid stomatal closure or thicker cuticle.

Root-to-shoot ratio increases during drought stress in various plants [ 39 , 40 ] and it is associated with a more drought-tolerant genotype in soybean [ 41 ]. As the root-to-shoot ratio increases during each drought stress cycle, primed plants receiving two cycles of stress had a higher root-to-shoot ratio. This helped the primed group to exploit the water resources deeper in the soil. Similar to other studies on various plants [ 13 , 15 , 42 ], primed plants also showed higher water content in our study. It was achieved by the tighter control of stomata as well as a higher root-to-shoot ratio as discussed above. Higher water content reduced wilting and leaf death resulting in a lower drought stress index and a higher percentage of alive leaves in the primed group.

Lower total chlorophyll content was expected in the primed group due to exposure to drought stress twice. However, soybean plants underwent a rapid increase in plant height and overcompensated the chlorophyll content during the recovery phase between two stress cycles [ 43 ]. Thus, in the two repeats of our study, primed plants had similar/higher chlorophyll content compared to unprimed plants. Similarly, no significant difference between total chlorophyll content in the primed and unprimed group is also observed in other plants [ 20 , 21 ]. This was achieved by either overcompensation of chlorophyll content during the recovery phase or the less damaging effect of drought on photosynthetic machinery in the primed plants. The mechanism for chlorophyll overcompensation is not well understood. We speculate that the priming stress enhanced the production of chlorophyll while preventing it from disintegration. For example, we observed a strong induction of the LEA protein-encoding gene in the second stress cycle. LEA protein overexpressing transgenic lines had lower ROS, higher total chlorophyll content, and higher drought tolerance in other plants [ 44 , 45 , 46 ]. In soybean, lower ROS content and higher chlorophyll content are associated with a more tolerant genotype [ 47 ]. LEA proteins are known to scavenge ROS [ 46 ]. Thus, higher induction of LEA protein might have contributed to reducing ROS’s damaging effect on chlorophyll in the primed group.

In plants, drought-induced stomatal closure to retain water leads to a lower rate of photosynthesis. It is therefore important to have a good balance between water retention and plant growth. Soybean plants achieve high WUE during moderate drought, then WUE declines when soil is further dried [ 48 ]. In our study, priming caused the plants to retain higher WUE in drier soil, while unprimed plants failed. This resulted in lower water usage per photosynthetic carbon assimilation. Consequently, despite receiving stress twice, the primed group did not have significantly lower root and shoot weight than the unprimed group. The compensation by rapid growth during the recovery phase between two stress cycles might have also contributed to this [ 43 ].

Drought stress priming also led to strong responses at the transcriptional level. The RT-qPCR analyses showed that the selected genes, PP2C (Glyma.14G195200), MYB (Glyma.05G234600), NAC (Glyma.06G248900), and LEA (Glyma.19G147200) had higher expressions in the primed plants compared to the unprimed plants. In a previous study, microarray analyses revealed higher transcript levels of all these genes during the second drought stress compared to the first stress in soybean [ 25 ], which is consistent with our qPCR analyses. The stronger response of the gene expression could potentially be explained by the chromatin remodeling and the global change in histone modification during the priming stress [ 23 ]. ABA is known to induce pathways that mediate the conversion of PP2C chromatins from a repressive to an active state that can create epigenetic memory [ 49 ]. ABA-PP2C pathway mediates stomatal closure during drought stress [ 50 ]. PP2C (Glyma.14G195200) has been found to have a higher expression in drought-tolerant transgenic lines overexpressing the ABA biosynthesizing gene [ 51 ]. Thus, higher expression of this PP2C gene in primed plants is an indication that primed plants had higher sensitivity toward the drought stress-induced ABA and expected to have a rapid stomatal closure. A previous study demonstrated that transgenic soybean overexpressing MYB (Glyma.05G234600) showed better tolerance to drought, higher proline (osmoprotectant) contents, longer primary root, and higher induction of other genes related to drought (such as RD22B) [ 52 ]. Higher induction of this MYB gene in primed plants might have resulted in a higher primary root length. LEA genes including Glyma.19G147200 are already reported to be linked with drought memory response in soybean [ 25 ] and other plants [ 42 ]. In our study, higher LEA gene expression might have helped primed plants stabilize macromolecules during dehydration stress [ 53 ]. NAC (Glyma.06G248900) is an ortholog of A. thaliana AtRD26 that contains a cis-regulatory element for MYB , ABRE , and DREB , etc. [ 54 ]. It is highly expressed in response to polyethylene glycol (PEG), abscisic acid (ABA), and drought [ 55 ], and is one of the most highly expressed NAC s in soybean during drought stress (known as NAC073) [ 56 ]. Higher expression of this NAC gene indicates better induction of interconnected drought-responsive pathways in the primed group.

Our observation suggested that drought stress priming mainly enhanced the drought avoidance of the soybean plant, through reducing water loss by being either a water-acquirer/seeker or a water-saver without much compromise in growth.

5. Conclusions

Drought priming significantly improved the drought stress response in the drought-sensitive soybean genotype C08 through enhancing its drought avoidance mechanisms. Furthermore, drought priming also induced the expressions of drought tolerance-related genes, leading to a more effective drought stress response. The percentage of live leaves was significantly increased while the Drought Stress Index was decreased, resulting in stronger and healthier plants.

Acknowledgments

We thank F.-L. Wong for providing various suggestions throughout the project. J.Y. Chu copy-edited this manuscript. Any opinions, findings, conclusions or recommendations expressed in this publication do not reflect the views of the Government of the Hong Kong Special Administrative Region or the Innovation and Technology Commission.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212954/s1 , Figure S1: Experimental design, Figure S2: Drought stress index, Figure S3: Summery discussion graph, Table S1: Primer sequences.

Funding Statement

This research was funded by the Hong Kong Research Grant Council Area of Excellence Scheme (AoE/M-403/16) and the Lo Kwee-Seong Biomedical Research Fund to H.-M.L.

Author Contributions

Conceptualization, M.S. and H.-M.L.; methodology, M.S., W.-S.Y. and S.D.; validation, M.S.; formal Analysis, M.S.; investigation, M.S., C.-K.M., W.-S.Y. and S.D.; resources, H.-M.L.; writing—original draft preparation, M.S., M.-W.L. and H.-M.L.; writing—review and editing, M.S., W.-S.Y. and H.-M.L.; visualization, M.S.; supervision, H.-M.L.; project administration, H.-M.L.; funding acquisition, H.-M.L. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

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