Journal of Experimental Botany, Vol. 65, No. 8, pp. 1981–1995, 2014
doi:10.1093/jxb/eru061 Advance Access publication 17 March, 2014
Opinion Paper
Improvement of crop yield in dry environments: benchmarks,
levels of organisation and the role of nitrogen
V. O. Sadras1,* and R. A. Richards2
1 2 South Australian Research and Development Institute, Waite Campus, Adelaide, SA 5001, Australia
CSIRO Plant Industry, PO Box 1600, Canberra, ACT, Australia
* To whom correspondence should be addressed. E-mail: [email protected]
Received 16 August 2013; Revised 16 January 2014; Accepted 22 January 2014
Abstract
Crop yield in dry environments can be improved with complementary approaches including selecting for yield in the
target environments, selecting for yield potential, and using indirect, trait- or genomic-based methods. This paper
(i) outlines the achievements of direct selection for yield in improving drought adaptation, (ii) discusses the limitations of indirect approaches in the context of levels of organization, and (iii) emphasizes trade-offs and synergies
between nitrogen nutrition and drought adaptation. Selection for yield in the water- and nitrogen-scarce environments
of Australia improved wheat yield per unit transpiration at a rate of 0.12 kg ha–1 mm–1 yr–1; for indirect methods to be
justified, they must return superior rates of improvement, achieve the same rate at lower cost or provide other costeffective benefits, such as expanding the genetic basis for selection. Slow improvement of crop adaptation to water
stress using indirect methods is partially related to issues of scale. Traits are thus classified into three broad groups:
those that generally scale up from low levels of organization to the crop level (e.g. herbicide resistance), those that
do not (e.g. grain yield), and traits that might scale up provided they are considered in a integrated manner with scientifically sound scaling assumptions, appropriate growing conditions, and screening techniques (e.g. stay green).
Predicting the scalability of traits may help to set priorities in the investment of research efforts. Primary productivity
in arid and semi-arid environments is simultaneously limited by water and nitrogen, but few attempts are made to
target adaptation to water and nitrogen stress simultaneously. Case studies in wheat and soybean highlight biological
links between improved nitrogen nutrition and drought adaptation.
Key words: Drought, nitrogen, photosynthesis, plasticity, wheat, yield.
Introduction
Crop yield in dry environments can be improved with several, non-mutually exclusive approaches including selecting
for yield in the target environments, selecting for yield potential, and using indirect, trait- or genomic-based methods
(Richards, 1995; Ceccarelli et al., 2007; Cattivelli et al., 2008;
Fleury et al., 2010). In this paper, our first aim is to outline
the achievements of direct selection for yield in improving
crop yield under water deficit as a benchmark for indirect
approaches. The perspective of phenotypic plasticity (Box 1)
was used to extend the analysis of the putative trade-off
between yield potential and yield of stressed crops (Ceccarelli
and Grando, 1991a, b; Araus et al., 2002).
There is a contrast between traits that scale up from
molecular to crop level (e.g. herbicide resistance) and
traits that do not, including many aspects of crop morphology, photosynthesis, transpiration, growth, and yield
(Raper and Barber, 1970; Jarvis and McNaughton, 1986;
Pettigrew et al., 1989; Gibson et al., 1992; Jarvis, 1995;
Sinclair et al., 2004; Denison, 2009, 2012; Pedró et al.,
2012; Roth et al., 2013). This difference partially accounts
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1982 | Sadras and Richards
Box 1. Phenotypic plasticity
Phenotypic plasticity is “environmentally contingent trait expression” (DeWitt and Scheiner, 2004). DeWitt and Langerhans
(2004) place this concept in context by highlighting that plasticity is one of four strategies evolved in response to environmental variation. Strategies include specialization involving the production of one phenotype that is well adapted to a
given environment with fitness trade-offs in other environments; generalization whereby a general-purpose phenotype is
produced with moderate fitness in most environments; bed-hedging involving the production of either several phenotypes
(e.g. among units in highly modular plants) or single phenotypes probabilistically, and phenotypic plasticity where environmental factors trigger the production of alternative phenotypes. Plasticity is costly, as it requires material and energy
expenses to maintain the sensory and regulatory systems for sensing of and responding to environmental cues, and is not
necessarily adaptive because environmental cues guiding plastic responses can be unreliable (De Witt et al., 1998).
Phenotypic plasticity can be quantified as the slope of reaction norms, and using variance ratios and other measures of
data dispersion such as range. Reaction norms (Woltereck, 1909) are mathematical functions relating the phenotype and
the environment where the slopes can be taken to quantify plasticity (Box 1 Figure, A). This approach has two main advantages: it accounts for non-linear functions, hence capturing plasticities that change with environmental range (for example
Fig. 12 in Woltereck, 1909) and allows for standard errors of plasticity estimates (Box 1 Figure, B). The main limitation of
this method is that, for traits such as yield driven by multiple environmental factors, reaction norms based on any single
factor are bound to be highly scattered (Cade and Noon, 2003). This problem can be circumvented using the environmental
mean of the trait to capture the whole environmental influence (Fig. 1C). The environmental mean approach has statistical limitations and has been superseded by increasingly refined statistical models accounting for the interaction between
genotype and environment (Gauch et al., 2011) but provides a point of contact with plasticity in developmental biology
and evolution, and remains useful in crop physiology (Calò et al., 1975; Lacaze et al., 2009; Sadras et al., 2009; Sadras
and Trentacoste, 2011; Trentacoste et al., 2011; Mitchell et al., 2012). Plasticity can also be estimated as the ‘ratio of the
variance of the individual in the trait to the overall phenotypic variance of the population’ (Dingemanse et al., 2010). This
approach requires no assumptions on the shape of reaction norms but provides a single value irrespective of variations in
plasticity with environmental range. Norms of reaction and variance ratios generally return comparable rankings of plasticities (Box 1 Figure, B). Heritability is also a ratio of variances seeking to account for the genetic component of the total
phenotypic variance; hence, heritability is negatively related to, and can be used as a measure of, plasticity (Donovan et al.,
2011; Sadras and Slafer, 2012). Other methods to quantify data dispersion, such as normalized trait range, have been used
to quantify plasticity of relevant crop traits such as phenology, yield components, crop photosynthesis, and nitrogen-use
efficiency (Sadras and Slafer, 2012; D’Andrea et al., 2013).
Box 1 Figure. (A) Reaction norms are mathematical functions relating the phenotype and the environment; the slope of the reaction norm is a
measure of phenotypic plasticity which is specific for each combination of trait, genotype, and environmental factor. (B) Comparison of plasticity of
wheat plant height calculated with two methods: the slope (±SE) of the reaction norm and variance ratio. Each point is one of 13 wheat genotypes
grown in three environments; adapted from Sadras VO, Lawson C. 2011. (This figure is available in colour at JXB online.)
for the success of biotechnology in improving crop resistance to pests and herbicides (Konduru et al., 2008; Wilson
et al., 2013), and its small impact on yield potential and
yield under stress (Reynolds and Tuberosa, 2008; Sinclair,
2011; Roth et al., 2013). Thus, our second aim is to discuss
the limitations of indirect approaches to improve crop
stress adaptation in the context of levels of organization
and scalability (Box 2).
Direct and indirect approaches to improve drought adaptation | 1983
Box 2. Levels of organization and scalability
Narrowing the definition of Wimsatt (1994) to biological systems, compositional levels of organization are hierarchical divisions of biological entities ‘organized by part-whole relations in which wholes at one level function as parts at the next
(and all higher) levels’. Levels ‘are constituted by families of entities usually of comparable size and dynamical properties,
which characteristically interact primarily with one another’ (Wimsatt, 1994). With a focus on crop improvement, relevant
levels of organization include molecule, cell, tissue, organ, individual, population, and community; ecosystem, biome, and
biosphere are relevant to other aspects of agriculture such as biodiversity and greenhouse gas emissions (Box 2 Figure).
Notwithstanding cross-level interactions, Wimsatt’s (1994) view emphasizes that molecules interact mostly with molecules (Rietman et al., 2011) and plants with plants (Harper, 1977); this partially accounts for the compartmentalized theories associated with each level. Indeed, processes can be separated into discrete classes by their time constants (Levins,
1970). Thus, levels of organization have distinct components, sizes, rates, interactions, dynamics, theories, and methods.
Scalability is an important concept in engineering, health, and computer science (Dichter et al., 2013; Jordan, 2013;
Sanghyeon et al., 2013). An operational definition is advanced here in the context of crop improvement: a trait scales up if
it remains agronomically relevant at higher levels, and eventually at the population level where yield is defined. To predict
the scalability of a trait, we need to answer the question: ‘Can the properties of the emergent whole be reduced to the
properties of its parts?’ (Le Boutillier, 2013). A positive answer means the traits at underlying levels are scalable and they
are not scalable if the answer is negative. Quantitative models are useful to answer this question, as illustrated by attempts
to scale up to the crop level the effects of rubisco (Sinclair et al., 2004) and root traits (Vadez et al., 2013). Traits can thus be
classified into three categories, those that scale up, those that do not, and those where scaling up is conditional to scaling
assumptions, growing conditions, and screening techniques (e.g. stay green as discussed in main text).
Box 2 Figure. Nested hierarchy of biological scales relevant to agriculture. Sources of images: http://www.plantcellwalls.org.au/index.php?id=8;
http://www.sbs.utexas.edu/bio406d/images/pics/poa/tripsacum dactyloides.htm; http://www.ars.usda.gov/is/graphics/photos/k5751-1.htm; http://
www.danforthcenter.org/iapb-stl/; http://www.fredhoogervorst.com/photo/410339db/. (This figure is available in colour at JXB online.)
Throughout the history of agriculture, the increase in crop
yield has largely been driven by agronomic practices enhancing the availability of water and nitrogen, and improved
cultivars to take advantage of greater resources (Sinclair
and Rufty, 2012). In Mediterranean-type environments,
wheat yield is simultaneously constrained by the availability
of water and nitrogen (Sadras, 2005; Cossani et al., 2010).
This is a particular case of a broader phenomenon: primary
productivity is co-limited by water and nitrogen in arid
and semi-arid environments (Hooper and Johnson, 1999).
Improving crop yield under water stress and nitrogen deficit
attracts research efforts worldwide. However, few attempts
are made to target adaptation to water and nitrogen stress
simultaneously. Thus, our third aim is discussing trade-offs
and synergies between nitrogen nutrition and yield in waterlimited environments.
1984 | Sadras and Richards
Direct selection for yield as a method and a
benchmark to improve stress adaptation
Direct selection for yield can target potential (Foulkes et al.,
2009) or actual yield under the prevalent stresses in specific
environments (Hernandez-Segundo et al., 2009; Yan et al.,
2011; Gupta et al., 2013). Historically, direct selection for
yield has been successful, irrespective of crop species, breeding
methods, cropping practices, and environments (Slafer, 1994).
For example, selection for yield and agronomic adaptation
in the water- and nitrogen-scarce environments of Australia
improved wheat yield per unit transpiration at a steady rate
of 0.12 kg ha–1 mm–1 yr–1 during the last century (Sadras and
Lawson, 2013); for indirect methods to be justified, they must
return superior rates of improvement, achieve the same rate
at a lower cost or provide other cost-effective benefits, such
as expanding the genetic basis for selection. Direct selection
for yield is, therefore, a benchmark for indirect methods, and
characterization of phenotypic changes resulting from selective pressure for yield provides further reference for traitbased selection (see section ‘Trade-offs and synergies…’).
Potential yield is the ‘yield of a cultivar when grown
in environments to which it is adapted; with nutrients and
water non-limiting; and with pests, diseases, weeds, lodging,
and other stresses effectively controlled’ (Evans and Fischer,
1999). Long-term time-series show that actual yield in farmers’ fields tracks potential yield (Fig. 1A, B), hence the proposition that selection under favourable growing conditions,
approximating the definition of potential yield, could contribute to adaptation to prevalent stresses (Araus et al., 2002;
Foulkes et al., 2009). However, the robust long-term link
between potential and actual yield (Fig. 1A, B) may break
down for particular combinations of genotypes and environments if there is a trade-off between yield potential and yield
under stress, as illustrated in the cross-over of reaction norms
in Fig. 1C.
An extended analysis from the perspective of phenotypic
plasticity (Box 1) shows that trade-off is only one of many
possible relationships between yield in favourable environments and yield under stress (Fig. 1D–G). In this approach,
phenotypic plasticity of yield for each genotype is calculated
as the slope of the reaction norm or the variance ratio (Box
1), and yield in high- and low-yielding conditions is plotted
against plasticity (further details can be found in Sadras et al.,
2009, 2013; Peltonen-Sainio et al., 2011). For a collection of
field peas in Australia, yield under favourable conditions
and yield under water and heat stress were positively related
(Fig. 1D). Selection for potential yield in this combination of
genotypes and environments would, therefore, favour stress
adaptation; furthermore, this strategy would be effective in
improving stress adaptation because discrimination of genotypes was larger in high-yielding environments. For a set
of sunflower hybrids in Argentina, high yield plasticity was
associated with high yield under favourable conditions and
unrelated to yield under stress (Fig. 1E); selecting for yield
potential would be neutral for stress adaptation. For a set of
recombinant inbred wheat lines in Mexico, yield plasticity
was negatively related to yield under stress and unrelated to
yield under favourable conditions (Fig. 1F); selecting for yield
in low-yielding environments would effectively improve stress
adaptation with no penalties for yield potential. For winter
rye varieties in Finland, yield in favourable environments and
yield under stress were negatively related (Fig. 1G); selecting
for potential yield therefore compromises yield under stress,
and vice versa.
In conclusion, the parallelism between the long-term trajectories of actual and potential yield indicate that selecting
for yield in favourable environments might also improve stress
adaptation; however, this high-level correlation may hide neutral or negative associations at smaller scales. The actual association between yield potential and yield under stress needs to
be assessed for each particular combination of genotypes and
environments. Irrespective of the type of association between
yield potential and yield under stress, secondary traits could
contribute to yield under both favourable and stressful conditions. Of interest, genetic and phenotypic diversity is
being investigated in crosses between pairs of well adapted,
high-yielding parents instead of the conventional crossings
between parents with high and low adaptation (Fleury et al.,
2010; Bustos et al., 2013). Traits such as photosynthesis, putatively associated with both yield potential and adaptation to
dry environments, are also discussed in the context of levels
of organization (see section ‘Trait scalability’) and in light of
phenotypic changes in response to selection for yield in dry
environments (see section ‘Trade-offs and synergies…’).
Indirect selection to improve stress
adaptation
Indirect methods, based on secondary traits or genomics,
can contribute to stress adaptation as a complement to direct
selection for yield in target environments, which remains a
highly effective method (Fig. 2A). Genomic approaches have
been reviewed extensively (Edmeades et al., 2004; Tuberosa
and Salvi, 2006; Struik et al., 2007; Cattivelli et al., 2008;
Leung, 2008; Yano and Tuberosa, 2009; Kurowska et al.,
2011). The focus here is on trait-based selection; genomic
selection is briefly discussed.
Trait-based selection
Trait-based selection has a role in complementing direct
selection for yield in the target environment. It can be viewed
as overcoming a deficiency in breeders’ germplasm so as to
enhance adaptation and yield to a target environment in the
same way that, say, a new disease resistance gene is introduced to overcome a disease susceptibility or to improve
overall robustness. For a trait to be incorporated into breeding programmes, it must satisfy six criteria (Araus et al., 2008;
Passioura, 2012): (i) it must be genetically correlated with yield
in the target environments, where the patterns of stress need
to be quantified in terms of timing, duration, and intensity,
(ii) it should be less affected by the genotype×environment
interaction than yield, (iii) it should not be associated with
low yield in favourable conditions, (iv) it must show genetic
Direct and indirect approaches to improve drought adaptation | 1985
Fig. 1. Relationship between yield in favourable conditions and yield under stress. Long-term association between potential yield and actual yield in
farmers’ fields for wheat in (A) Argentina and (B) the UK. (C) Yield of two generic cultivars as a function of environmental mean yield; the cross-over of
the linear functions indicates a trade-off between yield potential and yield under stress. Yield under favourable (open symbols) and stressful (closed
symbols) conditions as a function of phenotypic plasticity of yield for (D) field pea in Australia, (E) sunflower in Argentina, (F) wheat in Mexico, and (G) rye
in Finland. Water deficit is the dominant source of stress in all four cases. Solid lines are least square linear regressions and b are slopes ±standard errors
(SE) with asterisks indicating P <0.05 (*), P <0.01 (**), and P <0.001 (***). Sources: (A, B) Foulkes MJ, Reynolds MP, Sylvester-Bradley R. 2009. Genetic
improvement of grain crops: yield potential. In: Sadras VO, Calderini DF, eds. Crop physiology: applications for genetic improvement and agronomy. San
Diego: Academic Press, 355–385. Copyright Elsevier, with permission; (C) The authors; (D) Sadras VO, Lake L, Leonforte A, McMurray LS, Paull JG.
2013. Screening field pea for adaptation to water and heat stress: associations between yield, crop growth rate and seed abortion. Field Crops Research
150, 63–73. Copyright Elsevier, with permission; (E, F) Sadras VO, Reynolds M, De la Vega AJ, Petrie PR, Robinson R. 2009. Phenotypic plasticity of
phenology and yield in wheat, sunflower and grapevine. Field Crops Research 110, 242–250. Copyright Elsevier, with permission; (G) Peltonen-Sainio P,
Jauhiainen L, Sadras VO. 2011. Phenotypic plasticity of yield and agronomic traits in cereals and rapeseed at high latitudes. Field Crops Research 124,
261–269. Copyright Elsevier, with permission.
variability, (v) it must be genetically stable, persistent across
generations, and relevant in different genetic backgrounds,
and (vi) it must lend itself to rapid, cost-effective, and reliable
quantification. A seventh criterion could be included that
refers to the research environment and skills that facilitate
indirect selection, including research continuity over a long
term and the expertise and technology to select for indirect
traits which might not be standard in breeding programmes.
Probing for improved stress adaptation requires comparisons of putatively superior material with the best available germplasm under realistic agronomic conditions. Two
examples illustrate how this benchmarking can confirm or
disprove the value of indirect methods. Our first example is
a successful, indirect selection method for transpiration efficiency leading to the improvement of wheat yield in the dry
environments of Australia. Transpiration efficiency (TE), i.e.
the ratio of photosynthesis to transpiration, is difficult to
measure reliably during the life of the plant. Instantaneous
estimates of TE are possible using gas exchange equipment
but these are difficult and slow [hence failing criterion (vi)
above] and may not capture the weighted integration of
the trait over the daily and seasonal cycles (Sinclair, 2012).
Measurements over an extended period of time are possible by determining total biomass and transpiration in pots,
but this approach is not practical with current technologies;
high throughput methods could be developed that combine
1986 | Sadras and Richards
Fig. 2. Phenotypic changes resulting from breeding and selection for yield of wheat in Australia. (A) Yield per unit transpiration increased between 1915
and 2007 in association with increased harvest index over the whole period, and with increased biomass over the last five decades (horizontal bars). (B)
Rates of change (±SE) of shoot biomass at anthesis and maturity, pre-flowering radiation-use efficiency, shoot N, shoot N per unit soil N, and nitrogen
nutrition index between 1958 and 2007. All three nitrogen-related traits are at maturity. (C) Rate of change of leaf greenness from top to bottom of the
canopy between 1958 and 2007; for example, the greenness of leaves in the canopy layer 0.2–0.3 m increased at 0.44 SPAD units per year. Error bars
are standard errors. (D) Extinction of foliar N concentration as a function of PAR extinction in canopies of Heron (released in 1958) and Gladius (2007).
Parameter b (±SE) quantifies the extinction of nitrogen in the profile relative to the extinction of radiation. At high N, b=0.76 for Gladius compares with
b=1.06 for Heron and, at low N, b=0.95 for Gladius compares with b=1.42 for Heron. The flatter relationships (lower b) indicate a higher concentration
of N at the same level of PAR for Gladius compared with Heron. Asterisks indicate P <0.0001 (***), P <0.001 (**), and P <0.01 (*). Sources: Sadras VO,
Lawson C, Montoro A. 2012. Photosynthetic traits of Australian wheat varieties released between 1958 and 2007. Field Crops Research 134, 19–29.
Copyright Elsevier,with permission; Sadras VO, Lawson C. 2013. Nitrogen and water-use efficiency of Australian wheat varieties released between 1958
and 2007. European Journal of Agronomy 46, 34–41. Copyright Elsevier,with permission.
gravimetric measurement of transpiration with automated
systems, and measurement of plant biomass with digital
images (Harris et al., 2010; Furbank and Tester, 2011). There
are a limited number of traits that could be important to
influence TE. Increasing the early growth of wheat when
vapour pressure deficit is low is one possible trait (Richards,
1991) and this is feasible as fast selection methods to improve
vigour are available (Richards and Lukacs, 2002). Another is
glaucousness which can be screened visually; glaucousness
increases surface reflectance thereby cooling tissue surfaces
and also changes the cuticle surface and this can increase
the ratio of photosynthesis to transpiration (Richards et al.,
1986) particularly during the period of reproductive development and grain-filling. Farquhar et al. (1982) developed a
theory to show that the isotopic composition of plant carbon
may be related to TE in C3 plants. During photosynthesis,
the rarer 13C in the air is discriminated against in favour of
the abundant 12C. The amount of discrimination depends on
the balance between photosynthetic capacity and stomatal
conductance which, in turn, determines TE. Farquhar and
Richards (1984) then went on to show that over long time
scales the degree of discrimination against 13C was related
to TE in wheat and that there was genetic variation for this
trait. This has subsequently been confirmed in many other
C3 species (Richards and Condon, 1993). There are several
attractive features of this trait in relation to breeding for TE.
The relative ranking among genotypes for 13C discrimination
is largely maintained over the life of the crop, its measurement integrates TE over the life of the tissue that is sampled, genotype×environment interaction is low, heritability
is high, and screening can be conducted relatively early in
the life of the plant under favourable conditions (Condon
and Richards, 1992). Accordingly, a backcross breeding programme was commenced to incorporate low discrimination
from the older Australian wheat Quarrion into Hartog. The
latter variety was chosen due to its high yield, wide adaptation, broad disease resistance, and a robust grain quality
package as well as the fact that its 13C discrimination was
intermediate to high. Field studies conducted on closely
related sister lines to Hartog selected for either high or low
13
C discrimination demonstrated that, in the driest environments, there was about a 15% yield advantage in lines with
Direct and indirect approaches to improve drought adaptation | 1987
low 13C discrimination whereas at higher yield levels (5 t ha–1)
the advantage reduced to 2% (Rebetzke et al., 2002). The
backcross breeding programme resulted in large numbers of
closely related sister lines to Hartog and these were evaluated together with pre-commercial breeding lines with other
backgrounds. From these, the variety Drysdale was selected
and then released to farmers in New South Wales in 2002
and, shortly after, Rees was released in Queensland. These
sister lines were identified as having the highest yield in the
respective regions together with the best overall package of
disease resistance and grain quality.
Our second example is the effort to improve maize yield
in the dry environments of the USA. Roth et al. (2013)
compared maize hybrids with putative drought tolerance
(DuPont Pioneer AQUAmax brand) and standard hybrids
in well-designed, agronomically meaningful experiments.
The superiority of AQUAmax hybrids has been attributed
to improved stomatal control, reduced transpiration, higher
photosynthesis, and stay green under drought. Comparisons
were based on sets of hybrids with similar maturity to avoid
confounded phenological effects. Experimental sources of
variation included hybrids in combination with cropping
season, nitrogen rate, and plant population density, hence
accounting for the two main management levers used in rainfed maize cropping. Mild conditions in 2011 and severe heat
and water stress in the critical period of yield determination
in 2012, combined with the other sources of variation, generated an environmental range of yield from 4–15 t ha–1. Over
this range of environments, differences between the hybrids
were undetectable for yield, yield responses to nitrogen supply, and population density, and traits supposedly involved in
the differential adaption of these hybrids, namely leaf CO2
assimilation and transpiration measured at three (2011) and
seven (2012) developmental stages. Beyond the particular
findings of these comparisons, the experiment of Roth et al.
(2013) is a model for the rigorous assessment of stress adaptation in relevant field conditions.
Genomic selection
Genome-wide association studies or genomic selection (GS),
which has been made possible by inexpensive high-throughput genotyping platforms, is an interesting case of an emerging indirect selection method for yield or for another trait
of economic interest (Meuwissen et al., 2001). With this
approach, there is no need for a physiological understanding of yield and nothing is known of the underlying genes.
A representative subset of a breeders population (called the
training population) is used to develop associations between
yield (or any other trait) and extensive anonymous genomic
information consisting of thousands of markers which is
then applied to a larger population which is not phenotyped.
Selections based on GS from the larger population are then
made and these are progressed to the next generation for yield
assessment. There is little doubt that GS could be successful
in favourable environments which experience little seasonal
variation, as the heritability for yield in these conditions is
high and marker-yield associations should hold up. However,
in water-limited environments, where the interaction between
genotype and environment is large (Cooper et al., 1997), the
value of GS as an indirect selection criteria for grain yield is
less certain. The breeding values generated for the markers
will also be population and environment specific.
Limitations of indirect approaches in the
context of levels of organization
How does it work, why, and so what?
The limited success of indirect selection to improve crop yield
in dry environments raises questions with respect to candidate traits and their putative adaptive value. In scales from
molecular to field (Box 2), three types of questions can be
asked. First: How does it work? This elicits proximal, physiological answers that require scaling down from the level
where the question is asked; for example, to understand crop
flowering time it may be necessary to look at the relevant photoperiod and vernalization genes and epigenetic modulation
of gene expression (Cane et al., 2013; Kamran et al., 2013;
Wang et al., 2013).
Next, we can ask why? In our example, why do plants have
a particular set of vernalization- and photoperiod-responsive
genes in the first place? This elicits evolutionary explanations
(Rhone et al., 2010; Berger et al., 2011; Igartua et al., 2013).
Owing to the link between flowering time and fitness, the photoperiod pathway that channels light-related cues is present in
all known plant species (Valverde, 2011). Vernalization- and
photoperiod-response genes contributed to the clustering
of barley accessions from the Western Mediterranean, the
Fertile Crescent, Ethiopia, and Tibet (Igartua et al., 2013). In
the study by Rhone et al. (2010), experimental wheat populations were grown in contrasting environments, from southern
to northern France, where they evolved independently for
12 generations. In northern locations where winter is long
and cold, plants flowering too early in spring are likely to be
exposed to frost damage, whereas in southern locations, lateflowering plants are exposed to heat and drought stress in
early summer. With a focus on six candidate genes potentially
associated with flowering time, 12 cycles of natural selection
revealed three genes (VERNALIZATION-1, Flowering Locus
T, and CONSTANS) that were closely associated with variation in flowering time and showed marked temporal and/
or spatial variation, compared with neutral expectations,
whereas three genes (Ppd-1, LUMINI DEPENDENS, and
GIGANTEA) were slightly associated with flowering time but
did not show strong selection signatures.
Finally, we can ask: so what? This refers to agronomic
relevance; following on with our example, a certain genetic
make-up, together with environmental and agronomic information, allows the modelling of flowering time (Zheng et al.,
2013) to inform agronomic and breeding strategies to match
the pattern of water stress of a particular site (Chenu et al.,
2013). The so what question requires scaling up to the crop
level (Box 2). This is a weak element of indirect approaches
to improve adaptation to dry environments—it is easier to
split systems into components than to aggregate them up
1988 | Sadras and Richards
to where it matters in agriculture; a molecular bottom-up
approach to understanding crop growth and yield ‘is, on its
own, likely to flounder in complexity’ (Hammer et al., 2004).
Top–down approaches that primarily target crops in realistic agronomic conditions are particularly powerful to achieve
relevance (Hammer et al., 2004; Sinclair, 2011). Reductionist
approaches explaining the behaviour of the system in terms
of its components and heuristic approaches emphasizing the
arrangement of the components and the system’s emergent
properties are not mutually exclusive (Wimsatt, 2006; Le
Boutillier, 2013).
Frameworks of stress adaptation
One of the common justifications for the lack of or
slow progress in improving crop adaptation to limited
water using indirect methods is that yield is complex and
genotype×environment interactions are often large as is evident in Fig. 1D–G. The complexity of yield is not an absolute; it needs to be seen in relation to our methods. A more
fruitful explanation, we argue, is that often our methods are
naïve in relation to the complexity of yield. This formulation of the problem makes explicit the solution: methods are
needed that fully account for the nature of yield as a population-level trait (Donald, 1981; Evans, 1993; Denison, 2012).
The issue of level of organization (Box 2) is therefore central
to the success or otherwise of indirect improvement methods.
To illustrate this point, Fig. 3 compares two frameworks of
stress adaptation. The model of Fig. 3A, displays a large level
of detail on molecular processes, defines stress tolerance at
the cellular level—with no explicit links to plant or crop – and
clumps environmental effects with little consideration of their
nature, timing, intensity, and duration. The end-point of this
model is the re-establishment of cellular homeostasis, and
the functional and structural protection of membranes and
proteins which is then equated to stress tolerance or resistance. This perspective is only relevant for traits that scale up
from molecular to crop level (Box 1), hence it fails the so what
question for many morphological, photosynthesis, growth,
and yield-related traits that do not scale up (Table 1). With
an explicit focus on crop improvement, the model in Fig. 3B
uses the algorithm of Passioura (1977) to split yield into components associated with the water economy of the plant and
candidate traits are identified with putative effects on water
use, water-use efficiency or harvest index.
Trait scalability
Traits with a high heritability and under simple genetic control
often scale up to the crop level. Cereal traits in this category,
with a potential role in improving yield in dry environments,
include the presence of awns, glaucousness, osmotic adjustment, root axial resistance, plant height, and coleoptile
length (Johnson et al., 1983; Richards and Passioura, 1989;
Morgan, 2000). Also included are traits that protect root systems from soil chemical toxicities or pathogens and where
genes or gene regions have been identified such as acid soil
tolerance and cereal cyst nematode resistance. In these cases
Fig. 3. Two frameworks of stress adaptation with emphasis on (A) molecular to cellular scales and (B) plant scale where yield (YLD) is partitioned in water
use (WU), water-use efficiency (WUE) and harvest index (HI). Sources: (A) Vinocur B, Altman A. 2005. Recent advances in engineering plant tolerance
to abiotic stress: achievements and limitations. Current Opinion in Biotechnology 16, 123–132. Copyright Elsevier, with permission; (B) Reynolds M,
Tuberosa R. 2008. Translational research impacting on crop productivity in drought-prone environments. Current Opinion in Plant Biology 11, 171–179.
Copyright Elsevier, with permission. (This figure is available in colour at JXB online.)
Direct and indirect approaches to improve drought adaptation | 1989
Table 1. Examples of mismatch between traits at different levels of organization
No.
Levels
Mismatch
Source
1
Plant to crop
Raper and Barber (1970)
2
Plant to crop
3
Plant to crop
4
Molecular to crop
5
Leaf to canopy
The angle of soybean primary root lateral branches in the
soil was different for single plants and crops. For isolated
plants, the primary root branches from the taproot at
progressively greater angles of inclination and continued
downward through the soil with relatively constant
slopes. For crops, primary branches extended outward
at a constant slope toward the centre of the rows then
angled sharply downward when they entered the zone of
influence of neighbouring plants.
Shoot zenith angle (i.e. relative to vertical) of Paspalum
dilatatum shifted from 64° in isolated plants to 40° at 37
plants m–2.
Grain yield of wheat crops (200–400 plants m–2) was
unrelated to the yield of isolated plants (<20 plants m–2).
Crop yield was unrelated to 114 out of 118 plant traits.
Based on biochemical and physiological principles applied
to soybean, a hypothetical increase of 50% in mRNA
for the subunits of Rubisco results in the synthesis of
37% more Rubisco which increases light-saturated leaf
photosynthesis by 33%, which increases photosynthesis
of isolated plants by 30%, which increases crop biomass
by 18%, which increases yield by 6% if nitrogen is freely
available and reduces yield by 6% if plant transformation
does not include enhanced capacity for nitrogen
accumulation.
Chlorophyll-deficient, near isogenic soybeans Clark9 and
Clark11 (<6 g chlorophyll kg–1 leaf DM) had 14–28% higher
canopy CO2-exchange rates than wild-type Clark (>10 g
chlorophyll kg–1 leaf DM). Better distribution of light in the
canopy contributed to the higher canopy photosynthesis
of mutants. Epistatic effects may also have contributed to
the high canopy photosynthesis of the near-lethal chlorophyll mutant Clark11
molecular markers have been used to incorporate these traits
into improved varieties (Ogbonnaya et al., 2001; Raman
et al., 2005). Traits that are more complex genetically but with
a moderate heritability which may have a small cumulative
effect but operate over a long period could also scale up, as
illustrated for carbon isotope discrimination in the previous
section.
Traits such as flowering time in wheat and barley, staygreen in sorghum, and anthesis-silking interval in maize may
or may not scale up depending on the screening protocols.
Flowering time is particularly important for adaptation
in dry environments; it would scale up from plants in controlled environments to field, provided specific photoperiod
and temperature conditions are used to unmask the genes for
development that are present (Rhone et al., 2010; Cane et al.,
2013).
Crop yield is often associated with the duration of green
leaf area (Gregersen et al., 2013), hence the interest in the
persistence of physiologically active canopies under stress.
Stay-green integrates a number of lower level traits and is
mostly expressed where water stress during grain fill increases
the rate of nitrogen remobilization and leaf senescence; hence
Gibson (1992)
Pedró . (2012)
Sinclair (2004)
Pettigrew (1989); Hesketh
(1981)
its expression strongly depends on how the trait interacts with
other traits, and with environmental and management factors
(Jordan et al., 2012). Five combinations of traits and environments would contribute to stay-green in sorghum (Borrell
et al., 2000; Sinclair et al., 2005; van Oosterom et al., 2010;
van Oosterom et al., 2011; Jordan et al., 2012): (i) traits that
contribute to water saving and less severe stress during grain
fill such as early maturity, small leaves, and few tillers, high
stomatal sensitivity to drying soil, and high vapour pressure
deficit; (ii) traits that contribute to enhanced water uptake,
such as deep roots in the right combination of soil and rainfall; (iii) life-history (i.e. high perenniality) or metabolic traits
(e.g. higher carbon and nitrogen allocation to roots during
grain filling), (iv) traits that favour high source:sink ratio (e.g.
few grains); (v) developmental traits that change the seasonal
pattern of water use for the same maturity type. For plant
breeding, combinations (i) and (ii), and maybe (iii) are of
interest, but not usually (iv) or (v) as these may involve tradeoffs with harvest index and yield. The expression of the trait
and its impact on yield are therefore strongly dependent on
context: ‘if you can control and understand the context then
there is capacity to scale up from plant to crop, if you don’t
1990 | Sadras and Richards
take them into account you can’t’ (David Jordan, personal
communication, 2013).
Shorter anthesis-silking interval in maize may favour yield
under stress, and this trait varies with genotype, plant population density, and genotype×density interaction (Uribelarrea
et al., 2002). At the plant level, anthesis time is a qualitative
trait (i.e. the plant is at anthesis, or it is not) but the trait is
quantitative at the population level (i.e. % of plants at anthesis); plant-level observations in well-designed experiments
could be scaled up with specific models (Borrás et al., 2007,
2009).
Traits such as root and shoot angle, with implications for
capture of water, nutrients, and radiation, are density dependent (Table 1: nos 1 and 2). To the extent that density-dependent responses vary with genotype (Khan and Bradshaw, 1976;
Harper, 1977; Brekke et al., 2011), screening for this type of
trait in individual plants is less likely to scale up to the crop
level—crop architecture is, by definition, a population-level
trait. Similarly, yield is a population-level trait, hence the general lack of correlation between plant traits and crop yield
(Table 1: no. 3).
The limits to improve yield by further increasing harvest
index in crops like wheat, combined with new technologies,
have reinvigorated interest in the enhancement of photosynthesis (Parry et al., 2011). For attempts to increase photosynthesis with bottom-up methods, issues of scales (Table 1: nos
4 and 5) and trade-offs are relevant. Physiological trade-offs
are well established, for example, between photosynthetic rate
per unit leaf area and total leaf area (Hesketh et al., 1981).
A significant trade-off, particularly important in proteinrich seed crops, involves the dual role of leaves as sources of
reduced carbon, which requires the maintenance of the photosynthetic apparatus, and reduced nitrogen, which requires
the destruction of photosynthetic structures (Sinclair and
de Wit, 1975). An evolutionary perspective is relevant in the
pursuit of enhanced photosynthesis: improvements that are
both simple (i.e. any change that has arisen repeatedly by past
natural selection), and trade-off free (i.e. increasing fitness in
all conditions) are unlikely (Denison, 2009, 2012). Although
a notable exception here, which is trade-off free in warmer
but not cooler environments, is C4 photosynthesis which is
reported to have had 66 independent origins in hot environments and evolved when atmospheric CO2 declined to very
low levels (Sage et al., 2012). The role of photosynthesis to
increase the yield of water-stressed crops also involves an
important element of scale. In drying soil, tissue expansion
is affected earlier and more severely than photosynthesis per
unit leaf area, hence the surplus of reduced carbon often
observed in water-limited plants; effectively, water deficit
uncouples growth from photosynthesis (Muller et al., 2011).
The evidence supporting this statement is strong, and the
superficial corollary is that improvement of photosynthesis
is irrelevant for the yield of crops under water stress. Here
is where explicit consideration of time scale is important:
the decoupling of growth and photosynthesis occurs during periods of stress, but on a seasonal basis the contribution of growth during times of stress is small compared with
cumulative growth when soil moisture is adequate (Fischer
and Turner, 1978; Loomis, 1983). The uncoupling of growth
and photosynthesis during water limitations, therefore, does
not preclude yield gains from enhanced photosynthesis; the
impact of higher photosynthesis on cumulative growth and
final yield depend on the seasonal dynamics of water availability (Fischer and Turner, 1978; Schwinning and Ehleringer,
2001).
The traits are therefore classified into three broad groups:
those that generally scale up (e.g. herbicide resistance), those
that do not (e.g. grain yield), and traits that might scale up
provided they are considered in a integrated manner with
scientifically sound scaling assumptions, appropriate growing conditions, and screening techniques (e.g. stay green).
Dealing with traits at a level of organization below the crop
that scale up or do not is trivial: the former is used and the latter is discarded in crop improvement. Traits for which scaling
is contingent to higher-level interactions are interesting and
challenging. Machine translation is a useful analogy for these
traits (Crystal, 1994). The early attempts to use machines
to translate texts between two languages—developed in the
1950s under the influence of post-war cryptography—used
a word-for-word approach. This approach produced nonsensical translations because it only considered the semantic
aspect of language; grammar was ignored. Likewise, translating ‘leaf photosynthesis’ into ‘crop photosynthesis’ overlooks
the grammar of the crop.
Trade-offs and synergies between
improved nitrogen nutrition and yield in
water-limited environments
There are close biological and agronomic links between the
economies of carbon, water, and nitrogen of crops; however, the efficiencies in the use of water and nitrogen can be
unrelated, related positively (synergy) or negatively (tradeoff) depending on the environmental and genetic sources of
variation, the level of organization, and time scale at which
efficiencies are defined (Cabrera-Bosquet et al., 2007; Sadras
and Rodriguez, 2010). At the crop level, there is a robust
nitrogen-driven trade-off between yield per unit evapotranspiration and yield per unit nitrogen uptake that is supported empirically (Belder et al., 2005; Kim et al., 2008) and
explained by physiological principles (Sadras and Rodriguez,
2010): under nitrogen deficit, yield per unit evapotranspiration is low because of low (i) transpiration:soil evaporation
ratio from constrained ground cover and root–soil exploration, (ii) radiation-use efficiency, and (iii) grain set and
harvest index, whereas (iv) yield per unit nitrogen uptake is
higher at low nitrogen supply. The experiment of CabreraBosquet et al. (2007) illustrates other relationships in potted
wheat. For a factorial combination of three water regimes,
two nitrogen regimes, and four genotypes, shoot biomass per
unit evapotranspiration was negatively related to photosynthetic nitrogen-use efficiency (light-saturated photosynthesis
per unit nitrogen in flag leaf), and positively related to both
nitrogen-use efficiency (biomass per unit N available) and N
uptake efficiency (nitrogen in shoot biomass per unit nitrogen
Direct and indirect approaches to improve drought adaptation | 1991
supply). The relationship between biomass per unit evapotranspiration and nitrogen utilization efficiency (biomass
per unit shoot nitrogen) was nitrogen-dependent: these traits
were unrelated at high nitrogen supply and negatively related
at low nitrogen supply.
A genotype-driven link between nitrogen nutrition and
yield under water stress has been demonstrated for wheat in
Australia. Selection for yield and agronomic adaptation in
the water- and nitrogen-scarce environments (Fischer, 2009)
has consistently improved wheat yield since the early 20th
century to the present day. Changes in traits related to the
carbon, water, and nitrogen economy of the crop help to
understand trade-offs and synergies in the capture and efficiency in the use of these resources; phenotypic changes have
been characterized in two series of studies focusing on early
breeding until the mid-1980s (Siddique et al., 1989, 1990a,
b) and the last five decades (Sadras and Lawson, 2011, 2013;
Sadras et al., 2012). In parallel with sustained yield gains,
crop water use remained largely unchanged, hence a linear
increase in yield per unit transpiration (Fig. 2A). Early breeding adjusted phenology to local conditions and harvest index
increased consistently during the whole period. Despite the
sustained increase in harvest index, this trait can contribute
further to wheat yield improvement in Australia (Sadras and
Lawson, 2011) in contrast to European varieties that are
closer to their biophysical limit (Foulkes et al., 2011).
The limits to further improvement mediated by enhanced
harvest index highlight the importance of improved biomass production; selection for yield in the UK has already
increased radiation-use efficiency, biomass, and nitrogen
uptake of wheat in recent decades (Shearman et al., 2005),
hence the interest in understanding the physiological links
between these traits. Figure 2B shows the rate of change in
shoot biomass and underlying traits for Australian wheats
released over the last five decades. There was an increase in
biomass at maturity which was fully accounted for by the
increase in biomass at anthesis. Increased biomass at anthesis was associated with enhanced radiation-use efficiency
between stem elongation and anthesis. It is noteworthy that
the enhancement of crop photosynthetic capacity was unrelated to leaf-level photosynthesis and respiration but was
related to improved crop nitrogen nutrition and shifts in the
distribution of light and nitrogen in the canopy (Fig. 2B–D).
Selection for yield (and maybe greenness?) has dramatically
transformed the phenotype of wheat: modern varieties have
an enhanced ability to absorb soil nitrogen and the enhanced
nitrogen nutrition index emphasizes the improved nutritional status of crops independently of changes in biomass
(Fig. 2B). Whereas all leaves of modern wheats are greener
than those of their older counterparts, the largest differences
are found at the bottom of the canopy (Fig. 2C). This is partially related to enhanced light penetration but for a given
light intensity, modern varieties have a higher concentration
of foliar nitrogen (Fig. 2D). The premium for higher quality
wheats, particularly in the last two decades, may also have
indirectly contributed to changed N dynamics in more recent
varieties (Sadras and Lawson, 2013).This transformation of
the wheat phenotype is striking in view of the complexities
involved in direct manipulation of crop photosynthesis
(Sinclair et al., 2004; Denison, 2009; Parry et al., 2011).
A genotype-driven link between nitrogen nutrition and
drought adaptation has also been demonstrated for soybean
in the USA (Sinclair et al., 2007; Sinclair, 2011). Owing to
soybean’s dependence on symbiotic nitrogen fixation, the
similar association between nitrogen nutrition and adaptation to drought in soybean and wheat is only superficial.
For soybean, the proposition was that maintenance of nitrogen fixation favours yield under water stress (Sinclair et al.,
2007; Sinclair, 2011). Different screening approaches were
combined, including coarser methods to screen large numbers of lines (selection for low ureide concentration in petioles, putatively reflecting sensitivity to drought) and more
refined methods for fewer lines (acetylene reduction activity).
Selected lines were compared with high-yielding commercial
cultivars under broad environmental conditions. Two lines
were identified that outperformed commercial checks under
water deficit, but trade-offs were apparent under high-yielding conditions. Line R01-416F out-yielded checks by 17% in
environments returning less than 3 t ha–1, whereas R01-581F
out-yielded checks by 10% in a narrow environmental window between 2.5 and 3.6 t ha–1; this highlights the dependence of adaptive traits to the pattern of water stress (Jordan
and Miller, 1980; Schwinning and Ehleringer, 2001).
Concluding remarks
Sustained yield improvement of wheat in low-rainfall, lownitrogen environments of Australia highlight the achievement of direct selection for yield for well over a century. In
the last 50 years, breeding for wheat yield has increased crop
photosynthesis and shoot biomass in both the stressful environments of Australia and in the high-yielding European
environments. Thus, whereas selection for yield in favourable environments may or may not improve stress adaptation
depending on the combination of genotypes and environments, traits such as radiation-use efficiency can be adaptive
across wide environmental ranges. Dissecting radiation-use
efficiency showed an impressive transformation of the phenotype including improved nitrogen uptake and changes in
the distribution of light and nitrogen in the canopy. Further
progress in drought adaptation could benefit from a dual perspective on nitrogen and water.
Direct selection for yield continues to be streamlined and
improved particularly with the help of superior statistical
tools that have improved logistics and the design of field trials
and data interpretation; costs for field trials, however, remain
high as tens of thousands and often hundreds of thousands
of plots are required. Owing to larger interaction between
genotype and environment, more plots are required in drier
than in more favourable conditions. Thus, indirect methods
will continue to attract attention as more cost-effective methods of selection are required. However, the failure of indirect
methods to contribute to yield in many cases is not because
yield is complex, it is rather that methods and perspectives
do not account for the levels of organization and time scales
1992 | Sadras and Richards
where traits are relevant or otherwise. Predicting the scalability of traits may help to set priorities in the investment of
research efforts.
Opinion papers like ours often advocate for collaborations across disciplines. Progress is being made in this direction, as illustrated by the International Wheat Consortium
(Reynolds et al., 2011). However, epistemological barriers
remain (Andersen and Wagenknecht, 2013) that partially
relate to levels of organization, and their implications for spatial and time scales, system structure and dynamics, theories
and methods. Levels of organization are not just conceptual
constructs; they ‘cut Nature at its joints’ (Wimsatt, 1976), and
scientists at the end of the relevant levels for crop improvement are indeed practising very different sciences (Levins,
1970). Finding ‘trading zones’ (Andersen and Wagenknecht,
2013) is essential for interdisciplinary research. Whereas crop
improvement is a common interest across levels, theoretical
and methodological gaps are real obstacles for effective collaborations. One of the main contributions of molecular science has been a deeper focus on evolution as the fundamental
source of biological diversity (Buss, 1987) whereas evolutionary insights are central to crop science (Donald, 1981; Evans,
1993; Denison, 2009), hence the opportunity to expand the
trading zone by asking questions about crop adaptation to
drought in an evolutionary framework.
Acknowledgements
We thank David Jordan for insights on stay-green, and Lucas Borrás
and Maria Otegui for useful comments on the anthesis-silking interval. VOS research in grain crops is funded by the Grains Research and
Development Corporation of Australia and the South Australian Grain
Industry Trust.
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