Opinion
Physiological phenotyping of plants for crop improvement Michel Edmond Ghanem1, He´le`ne Marrou2,3, and Thomas R. Sinclair3 1
International Center for Agricultural Research in the Dry Areas (ICARDA), North-Africa Platform, Rabat, Morocco UMR System, Montpellier SupAgro, Montpellier, France 3 Department of Crop Science, North Carolina State University (NCSU), Raleigh, NC 27607, USA 2
Future progress in crop breeding requires a new emphasis in plant physiological phenotyping for specific, welldefined traits. Success in physiological phenotyping to identify parents for use in breeding efforts for improved cultivars has been achieved by employing a multi-tier screening approach with different levels of sophistication and trait resolution. Subsequently, cultivar development required an integrated mix of classical breeding approaches and one or more tiers of phenotyping to identify genotypes expressing the desired trait. The role of high throughput systems can be useful; here, we emphasize that this approach is likely to offer useful results at an initial tier of phenotyping and will need to be complemented with more directed tiers of phenotyping. Need for plant phenotyping The ability of humans to select for the best performing individuals of plant species for domestication – and thereby to ‘phenotype’ – has been one of the prerequisites for the development of human civilizations [1,2]. Although the concepts were developed by Gregor Mendel, the terms ‘gene’, ‘genotype’, and ‘phenotype’ were only introduced later by the Danish botanist Wilhelm Johannsen [3] in 1909. The terminology in relation to phenotyping is not completely clear-cut [4] and the terms ‘phenotype’ and ‘phenotyping’ are interpreted in diverse ways. A 2007 paper defined a trait subject to phenotyping as ‘any morphological, physiological or phenological feature, . . . from the cell to the whole-organism level’ [5]. We expand this definition to describe ‘phenotyping’ as the application of methodologies and protocols to measure a specific trait related to plant structure or function with traits ranging from cellular to whole-plant levels [6]. In developing higher-yielding crops, it is likely that ‘physiological phenotyping’ of specific traits will be useful, if not essential, in developing higher-yielding cultivars [7,8]. Unfortunately, there are only a few examples demonstrating the potential role of effectively applying physiological phenotyping to obtain improved crop lines [9]. The objective of this paper is to evaluate the application of physiological phenotyping Corresponding author: Sinclair, T.R. ([email protected]). Keywords: crop breeding; high throughput phenotyping platform; physiological phenotyping; trait screening. 1360-1385/ ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tplants.2014.11.006
to support future breeding programs and to examine the role of some recent proposals such as ‘high throughput platforms’ to advance phenotyping. Before examining the application of physiological phenotyping in breeding, it may be useful to consider the implications of the definition given above for phenotyping. This definition puts the focus on specific characteristics that are defined at a fairly high resolution. By narrowing the specific trait to be phenotyped, it is hoped that the number of interacting processes that influence the expression of the trait are minimized and the number of alleles influencing phenotypic expression are also minimized. That is, physiological phenotyping should target specific, well-defined processes that contribute to crop improvement [7,8,10]. Such an approach to phenotyping is clearly valuable before wasting resources recording a large number of data, which could be highly auto-correlated or not indicative of ultimate targeted trait performance [6]. A ‘shotgun’ approach to phenotyping a number of plant characteristics can result in an unfocused and ambiguous approach in the assessment of traits. A goal of physiological phenotyping is to avoid attempting quantification of emergent properties expressed at or above the whole-plant level, especially those that only become apparent at the canopy and crop level. In this regard, we identify a trait suitable for phenotyping not only as ‘a distinct variant of a phenotypic property of an organism that may be inherited, be environmentally influenced or be a combination of both’ [11], but the trait is a direct expression of a basic plant process. Such basic plant processes have been defined as components of plant productivity that can be quantified at a daily time step, and directly related both to the agronomic performance of the crop and to functional processes at plant organ or tissue level [12]. Therefore, such traits as ‘drought resistance’ and ‘yield’, which require evaluation at the crop level, are not candidates for physiological phenotyping. Of course, specific components of these emergent variables are clear targets for physiological phenotyping. The difficulty of attempting to phenotype for yield, for example, is that it is influenced by many factors and likely does lend itself to tracking and advancing specific physiological traits. Therefore, yield records based on field trials across multiple locations and multiple years are not consistent with the physiological phenotyping concept [13]. The calls for ‘phenotyping for yield’ and ‘data on yield phenotypes’ [14,15] seem inappropriate in efforts to advance specific Trends in Plant Science, March 2015, Vol. 20, No. 3
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Opinion traits that can ultimately contribute to yield under defined environments. Physiological phenotyping and breeding While there are frequent calls for the application of phenotyping of physiological traits in breeding programs [16–18], the ability to phenotype plant populations has not kept pace with progress in genotyping based on plant molecular biology and in molecular-based breeding techniques [9,19,20]. The limitation in physiological phenotyping is that the methodology is commonly detailed, sophisticated, and usually complex and expensive. Phenotyping at a refined physiological level may be effectively applied to only a limited number of genotypes of no more than about 15–25 genotypes, and almost never more than 50 genotypes [10]. Consequently, the inability to physiologically phenotype a large number of genotypes for specific trait performance has been a critical limitation for more than half a century in applying physiological information in breeding [9]. The solution is that a well-defined framework may be needed to understand the role that physiological phenotyping can play in breeding programs. The breeding process has been summarized [21] into three critical steps: (i) identification of trait(s) that promote genetic yield potential; (ii) assessment of genetic variation and its nature for the desired trait(s) leading to the identification of parental genetic resources, i.e., parent identification; and (iii) incorporation of the genes governing the traits into cultivars with other desirable characteristics. Each of these steps places specific demands on physiological phenotyping. Clear differentiation of phenotyping demands in each step helps to better organize the requirements and limitations in applying physiological phenotyping. Trait identification Before launching a major phenotyping effort for a particular putative trait, a critical step is to select the physiological trait to be phenotyped based on evidence that a particular trait might enhance crop performance [9,22]. Mechanistic understanding is key in identifying useful traits for phenotyping [6]; intuition alone is not a reliable guide in the face of the potential complexity of responses. Further, intuition does not offer insight about geographical locations and how much yield change might be expected from trait modification. There appear to be two approaches to develop evidence to support the initiation of a program for alteration of a specific trait: experimentation and simulation. The experimental approach can involve mimicking the expression of a trait or, less desirable, suppressing it using experimental manipulations to assess impact on crop yield. For example, in considering the possibility of increasing drought tolerance of symbiotic nitrogen fixation of soybean [Glycine max (L.) Merr.], field experiments were undertaken to evaluate yield change under drought conditions when nitrogen fixation was replaced by applying high amounts of nitrogen fertilizer [23,24]. Removing plant dependence on symbiotically fixed nitrogen resulted in yield increases of 15–20%, which indicated the potential benefit of genetically increasing nitrogen fixation tolerance to drought. 140
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Box 1. The use of models in phenotyping The choice of an appropriate crop simulation model is a delicate question that should always be addressed cautiously. First, using a mechanistic model is recommended so that model parameters are directly related to plant traits. Properly matching plant traits with model parameters is only possible when using a transparent and physiologically based model with a level of complexity no greater than required by the trait being evaluated [25,52]. Conversely, models that explicitly represent gene or QTL interactions are not necessarily appropriate because they would bring another layer of complexity, while ex-ante modeling is suitable for identifying interesting traits and developing promising genotypes without considering whether the traits of interest are genetically linked or not. As a first step, model-assisted trait screening would involve a sensitivity analysis of the response of a putative trait for enhancing in a given range of environments crop yield or any other crop performance index of interest. However, one should be aware that a crop model is not reality and reflects the designer’s point of view on the functioning of processes in the plant. Therefore, model sensitivity can be influenced by the formalisms used to construct the model. It is essential in using a model (i) to review cautiously the model formalisms to ensure that putative plant processes of interest seem to be appropriately included in the model and (ii) to evaluate the ability of the model to simulate the crop in the target environment. This would reduce the risk of discarding interesting traits only because a model inappropriately simulates the process being evaluated. Once a model has been selected and confidence has been established in the model’s ability to simulate modifications of the trait of interest, simulations can be done over a range of growing seasons and geographical locations to quantify anticipated crop responses to trait modification [6,10,53,54]. A major advantage of the modeling approach is that it allows evaluation of the probability of yield improvement across a range of climatic years for a given location. Therefore, models are crucial tools to discard risky breeding strategies and assist breeders in the reduction of economical vulnerability as they face climate variability [55].
Comparing the yield of isogenic or near-isogenic lines can also be used in trait–value assessment [25]. Since experimental approaches may be expensive, impractical, or even impossible to assess the value of a plant trait in conventional multi-site, multi-season cultivar trials, simulations using a mechanistic crop model offer another approach to explore the potential benefit of trait modification over the landscape of interest [11] (Box 1). Correspondence between observed yield improvements and model simulations for drought traits has been recently demonstrated in maize using the Agricultural Production Systems Simulator (APSIM) model [26] and in soybean using the simple simulation model (SSM) [27]. Parent identification Once evidence has been obtained indicating alteration of a specific trait may be valuable, it is then necessary to select genetic material as parents that have the potential to express the desired trait, so that they may be used to derive breeding populations. One approach to assure lines carry the desired gene(s) is the use of one or more transgenic parent. In this way, the genotype for a specific gene is assured. However, the expression of phenotype in a specific environment or over a range of environments needs to be documented under field conditions. For example, the drought-tolerant gene ZAR1 (Zea mays ARGOS1) was effectively introduced into maize populations from a transgene
Opinion parent and, critically, it was subsequently tested in a range of field environments [28]. Therefore, the use of transgenics as parents in breeding still requires major commitments to physiological phenotyping over a range of conditions. What approaches are appropriate for physiological phenotyping in evaluating putative parental lines? To increase the probability of identifying lines with high levels of expression of a trait, a large number of plant lines will need to be studied. The existing methodology for physiological selection may be so sophisticated and time consuming that only a few lines can be screened, or so crude that the results offer poor resolution in identifying parents. A solution to overcome this dilemma is a multiple-tier screen that involves a range in sophistication of methodology [10,29]. In this approach, the first-tier screen could be a simple but less accurate screen applied as a negative screen to a large number (e.g., thousands) of genotypes. Those genotypes retained in the first-tier screen would be subjected to more sophisticated screens leading to decreasing numbers of genotypes. Such multi-tiered screens for crop improvement are not new in classical breeding efforts. ‘Tandem’ selection breeding is used regularly in some crops [10]. Molecular markers for a desired trait might offer an effective approach as a first-tier screen. While molecular markers can be very useful in ensuring the possibility of phenotypic expression, they do not offer definitive information about the level of phenotypic expression being sought in superior parental lines over a range of environments [30]. Therefore, the first-tier screen with markers needs to be followed with more directed measures towards the physiological expression of a trait. The subsequent tiers in a screen involve the use of increasingly sophisticated screens that can be applied to smaller numbers of lines. Ultimately, a screen will be needed to measure directly the desired trait for potential expression in the environmental conditions for which the trait is desired. The final observations of trait expression may need to be done under controlled conditions so the range of responses for differing environmental conditions can be fully documented. A three-tier, negative-screen system has been successfully applied to identifying candidate parental soybean lines with high tolerance for nitrogen fixation under drought conditions [31]. The initial screen was rather crude, involving measurement of ureide levels of petioles collected in the field on about 3500 lines. This initial screen was followed by a controlled water-deficit screen in the field of nitrogen accumulation on about 250 lines. Finally, direct measurement of nitrogen fixation of plants of 24 lines was done in the greenhouse on plants subjected to soil drying. Consequently, in each of the first two tiers of the screen, about 10% of the entries were retained for the next tier of screening. Eight lines were ultimately identified that expressed nitrogen fixation drought tolerance. Cultivar development Once parental lines have been identified and progeny populations are developed, the final challenge is to identify high-yielding lines that expressed the desired trait. The number of lines in the progeny populations will almost certainly grow to very large numbers. Further, these lines
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will need to be evaluated in a number of environments. How is it possible to apply physiological phenotyping to these large breeding populations? In fact, we have concluded that it is improbable, if not impossible, to perform detailed-trait tracking through every stage of cultivar development [10]. Therefore, it seems likely that physiological phenotyping may be applied only at critical stages in the progeny evaluation process. A realistic approach is likely to be a blend of traditional selection procedures mixed with judicious application of physiological phenotyping. This mix was accomplished in the program to develop nitrogen fixation drought tolerance germplasm [32] by doing physiological phenotyping only twice in the breeding effort. Initially, progeny lines derived from the parents expressing the desired trait were simply observed in the field and negative screens were applied based on desired agronomic characteristics. That is, lines that exhibited disease and insect vulnerability, poor vigor, and inappropriate plant maturity were removed from the population. This procedure resulted in retention of 100 progeny lines that were candidates for a yield test. At this stage, the second-tier phenotyping of nitrogen accumulation when subjected to water-deficit soil was applied to the 100 lines to identify those lines most likely expressing nitrogen fixation drought tolerance. This screen resulted in the identification of 17 candidate lines for a field yield test. Finally, the field tests in water-limited environments identified two lines with yield capability superior to commercial check lines [33]. These two lines were subjected to the third-tier phenotype screen to confirm directly that the nitrogen fixation drought tolerance phenotype existed in these high-yield lines. Therefore, success was achieved in this germplasm development by combining agronomic screening, physiological phenotyping, and yield testing. Phenotyping and novel breeding approaches Modern genetic selection methods With the development of molecular markers in recent decades, identification and utilization of molecular markers was proposed as a major benefit in crop improvement. Markers can provide a framework to identify genomic regions (e.g., quantitative trait loci, QTLs) that influence traits of interest. However, collecting accurate phenotypic data has always been a major challenge for the improvement of quantitative traits using the QTL approach [20]. Apart from a few successful examples where traits were relatively simple to phenotype and highly heritable (e.g., dwarfing and flowering genes in wheat), historic attempts to evaluate traits that have low phenotypic variance and/or low heritability have seldom given reliable results, and the ultimate performance of the selected genotype was not consistent in all environments [20,34]. New population designs [35] in combination with highdensity marker coverage may increase the power to detect small-effect QTLs and estimate their effects [36,37]. Novel breeding strategies (e.g., genomic selection, GS) have been proposed to increase and hasten genetic gain in breeding by minimizing population evaluations over years and locations [38–40]. Genomic selection aims to model genome-wide SNP variation without concern for 141
Opinion identifying particular alleles, loci, or pathways or understanding how different alleles contribute to the phenotype [37]. However, under a GS model, precision phenotyping is of even greater importance when evaluating a training population because that dataset provides the basis for developing the statistical model that is then used to predict phenotypic performance in related members of a breeding population. Altogether, this indicates that more rigorous, quantitative approaches to phenotyping in GS are probably essential [37]. One challenge to discover genes or pathways that may help plants to cope with the abiotic stress response is that much past stress research was performed in laboratories under controlled conditions and relied on screening whole plants for their ability to survive severe stress. For farmers, drought survival is of little or no interest since survival-threatening stress will necessarily result in economic devastation and the marginal yield gain as a result of survival is of little consequence [10]. Also, stresses may be imposed in these experiments by plant exposure to high stress levels that rapidly induced a major trauma that rarely, if ever, occurs in nature [41]. The effects of plant growth and gene expression in response to stress can be highly dose-responsive, indicating the existence of very sensitive machinery in the plant for assessing the stress level and fine-tuning molecular responses [42]. Therefore, a brute force screen that is not related to field behavior can be misleading and is likely not useful. High throughput phenotyping platforms Image phenotyping has been proposed for monitoring growth of major grain crops [43–45] using high throughput phenotyping platforms (HTPPs). HTPPs are envisioned to obtain quantitative plant information with hundreds or thousands of lines using minimally invasive or noninvasive technologies that are integrated into screening protocols [19,46,47]. HTPP approaches exist for application both in the field (Box 2) and in controlled environments such as greenhouses and growth chambers (Box 3). How do these systems fit in with the above proposals for multiple approaches for physiological phenotyping? Field HTPP systems might be especially useful in the first-tier screen that requires methodology to phenotype large numbers (i.e., thousands) of lines. The challenge will be to ‘tune’ the field systems to provide a reasonable indicator for the probability of expression of a specific physiological trait. One such approach now being explored is the use of thermal infrared (TIR) detectors to measure plant temperature. While temperature varies in response to many factors, one response would be an elevated temperature due to partial stomatal closure [48,49]. Partial stomatal closure under high atmospheric vapor pressure deficit has been found to result in soil water conservation and, in the appropriate environment, a yield increase [50,51]. Therefore, a temperature screen could be useful in the identification of candidates for the next tier of more direct screening for the stomata response trait. The temperature screen might also be useful at an early stage in progeny selection to eliminate those lines that under high vapor pressure deficit conditions exhibit low temperatures. 142
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Box 2. Field HTPPs HTPPs have been developed for application for field phenotyping based on remote sensing observations of plants [56–58] by either crop reflectance in the visible and near infrared (NIR) wavebands or emissions in thermal infrared (TIR) wavebands. Observations in the visible and NIR wavebands may help to distinguish the vigor and early leaf area development among genotypes. The NIR wavebands are also proposed for identifying crop stress, but such changes in reflectance only occur after stress has developed and may result from several different processes influencing the timing at which a stress response is visualized. For example, hyperspectral reflectance indices were not successful in monitoring the response of various plant species and crops to different water-stress regimes [59,60]. TIR measurements have been used to assess temperature at different scales. Leaf, plant, and canopy temperatures have been measured using portable sensors manually positioned within a short distance of the plant. At larger scales, probes and camera can be placed on tractors, drones, and satellites. In all cases, it is a challenge to get representative results due to heterogeneous positioning of leaf surfaces. This difficulty is further increased if the field of view of the TIR measurement is a mixture of plants and background soil. TIR measurements have been proposed as an extensive, quick, and non-destructive method to assess plant water status and thus to identify drought-tolerant genotypes [61,62]. The difficulty is that canopy and leaf temperatures are influenced by the dynamics of the environment and many plant properties. Instability in wind speed and cloud cover will result in temperature changes that are a result of thermal and physiological lags. There are a number of traits, other than stomatal closure, that can cause variation in leaf temperatures including canopy architecture, leaf size and shape, and stomata density. Finally, stomatal closure under drought conditions, if detected with TIR measurement, is ambiguous since it can be a sign of active resistance to drought (with plant-limiting water loss), or plants that have lost turgor. A recent technological development is the use of fluorescence of excited leaf pigments as a possible basis for field HTPPs to make quantitative estimates of photosynthetic activity, plant cover rate, and nitrogen nutrition index [56,63]. There is a major challenge in separating the fluorescent signals emitted from the photosynthetic photosystem from the emissions of other leaf pigments (flavonoids and anthocyanins). Analysis of the fluorescent signals received at different wavelengths requires complex mathematical analysis. Furthermore, the results are variable and highly dependent on environmental conditions [64–66]. Finally, for every condition there seems to be a need to calibrate the relationship with nitrogen nutrition index or chlorophyll content in order to resolve a phenotype.
Greenhouse HTPP systems are probably too limited in the number of plants that can be studied to allow the firsttier screen of a wide population of germplasm. Greenhouse studies to evaluate germplasm at the second-tier of screening might be very effectively done using a HTPP system. The challenge is to develop systems that are targeted specifically to the trait of interest. Unfortunately, many current HTPP systems using imaging fail to observe plant characteristics that are targeted to meaningful, fundamental traits. Such measurements as plant height, canopy width, total leaf area, leaf number or canopy shape of a plant, which are readily measured in a HTPP system, translate to indicators of early plant vigor and leaf area development. These measurements seem more appropriate for the first-tier screen in the field. The development of transgenic populations likely favors first-tier screens done with a secure greenhouse HTPP. For various reasons, it may be impractical to do first-tier screens of transgenics in the field and the number of
Opinion Box 3. Greenhouse HTPPs Greenhouse HTPPs are often constructed as fully automated facilities in greenhouses or growth chambers with robotics, environmental control, image analysis, and remote sensing techniques to assess whole plant growth and performance. Most of the HTPPs are very expensive, although lower-cost HTPP approaches are now starting to be developed [67]. In many cases, plants are delivered to a camera system via conveyor belts, whereas in other approaches, individual plants are placed in the viewing field of the camera by manual or automatic positioning of the camera at a defined orientation with respect to the plant. Images are often acquired automatically, using a precisely defined source of illumination. Proper image acquisition and image evaluation is crucial for successful extraction of the desired plant traits. Architectural or development-related plant traits are extracted from images by calculation of the shoot outline and enclosed pixel numbers. To achieve this, plant and background have to be separated precisely, based on differences in color or brightness. Images of individual plants taken with digital color cameras have been used in controlled environments to estimate plant mass and to quantify relative growth rates [44,46,68,69]. However, the main factor in early plant growth is often associated with leaf development. Some of these studies have been used to show the sensitivity of leaf growth to water deficit among different genotypes [46,70]. Genetic determinism of leaf growth is partly shared with that of processes involved in reproductive development such as silk growth and the anthesis-silking interval in maize (Zea mays L.) [70]. Synchronization of floral transition suppressed any leaf growth differential phenotype [71]. The use of HTPPs in greenhouses raises the long-standing concerns of extrapolating such observations to phenotypic expression under field conditions [56,57]. The soil volume on which plants are grown in controlled environments is usually far less than that available to plants in the field, which can affect nutrient and water regimes and alter normal patterns of growth and development [72,73]. Enclosed aerial environments are also problematic for characterizing responses relevant to field situations [61]. Solar radiation, atmospheric turbulence, and evaporation rates typically are lower in greenhouses and growth chambers than under open-air conditions [48,73]. Even mechanical vibration can induce physiological artifacts in plant growth [74,75]. In addition, limited greenhouse space or chamber volumes often preclude allowing plants to flower and set seed, making it impossible to assess effects of stresses during reproductive development.
transgenic lines to be evaluated may be fairly modest (i.e., maybe hundreds instead of thousands). As discussed above, the challenge is to develop HTPP systems that measure characteristics that relate to the specific physiological trait of interest. The specific metric to be monitored needs to be defined and its relationship to the physiological trait of interest needs to be resolved. Since it is likely HTPP systems will be used as first- or second-tier screens, it is not necessary that they offer final evaluation of a physiological trait, but they must offer appropriate screens to identify genotypes for the next tier of evaluation. Since current HTPP systems seem incapable to offer the final tier of many specific trait selections, it is necessary to develop in conjunction with HTPP systems the subsequent tier(s) of evaluation that examines directly the physiological trait of interest. That is, the HTPP results are not likely to be the end product but only an early stage of screening that allow the number of genotypes for study to be narrowed. It is necessary to develop the full series of physiological phenotyping to contribute to both parental selection and progeny selection for improved performance.
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Concluding remarks Accurate physiological phenotyping of specific plant traits appears essential in moving crop breeding forward. However, phenotyping of non-visual traits is difficult and complex. Only by clearly specifying and differentiating the physiological phenotyping approach at every breeding step will it be possible to effectively address the challenge of integrating phenotyping in a breeding program. Several key steps have been identified in this paper in breeding efforts for improved expression of specific desirable expression. We have had success with a three step methodology: (i) obtaining evidence that a hypothesized trait will lead to crop improvement; (ii) understanding the basic process of the trait to guide the development of physiological screens; and (iii) developing multi-tier phenotypic screens that allow insight about trait expression at various stages in the breeding process. Acknowledgments This work was supported by the CGIAR Research Program on Grain Legumes and the USAID/CGIAR-US Universities Linkage Program.
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50 Sinclair, T.R. (2012) Is transpiration efficiency a viable plant trait in breeding for crop improvement? Funct. Plant Biol. 39, 359–365 51 Vadez, V. et al. (2014) Transpiration efficiency: new insights into an old story. J. Exp. Bot. 65, 6141–6153 52 Sinclair, T.R. and Seligman, S. (2000) Criteria for publishing papers on crop modeling. Field Crops Res. 68, 165–172 53 Sinclair, T. and Muchow, R.C. (2001) System analysis of plant traits to increase grain yield on limited water supplies. Agron. J. 92, 263–270 54 Sinclair, T.R. et al. (2014) Soybean production potential in Africa. Glob. Food Secur. 3, 31–40 55 Sinclair, T.R. et al. (2010) Assessment across the United States of the benefits of altered soybean drought traits. Agron. J. 102, 475–482 56 White, J.W. et al. (2012) Field-based phenomics for plant genetics research. Field Crop. Res. 133, 101–112 57 Araus, J.L. and Cairns, J.E. (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19, 52–61 58 Montes, J.M. et al. (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci. 12, 433–436 59 Babar, M.A. et al. (2006) Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci. 46, 1046–1057 60 Seelig, H-D. et al. (2008) Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants. Remote Sens. Environ. 112, 445–455 61 Jones, H.G. et al. (2009) Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct. Plant Biol. 36, 978–989 62 Blum, A. et al. (1982) Infrared thermal sensing of plant canopies as a screening technique for dehydration avoidance in wheat. Field Crop. Res. 5, 137–146 63 Mistele, B. and Schmidhalter, U. (2008) Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. Eur. J. Agron. 29, 184–190 64 Sims, D.A. and Gamon, J.A. (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81, 337–354 65 Chaerle, L. et al. (2007) Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. J. Exp. Bot. 58, 773–784 66 Van Leeuwen, W.J.D. et al. (2006) Multi-sensor NDVI data continuity: uncertainties and implications for vegetation monitoring applications. Remote Sens. Environ. 100, 67–81 67 Pereyra-Irujo, G.A. et al. (2012) GlyPh: a low-cost platform for phenotyping plant growth and water use. Funct. Plant Biol. 39, 905–913 68 Tackenberg, O. (2007) A new method for non-destructive measurement of biomass, growth rates, vertical biomass distribution and dry matter content based on digital image analysis. Ann. Bot. (Lond.) 99, 777–783 69 Wiese, A. et al. (2007) Spatio-temporal leaf growth patterns of Arabidopsis thaliana and evidence for sugar control of the diel leaf growth cycle. New Phytol. 174, 752–761 70 Welcker, C. et al. (2007) Are source and sink strengths genetically linked in maize plants subjected to water deficit? A QTL study of the responses of leaf growth and of Anthesis-Silking Interval to water deficit. J. Exp. Bot. 58, 339–349 71 Cookson, S.J. et al. (2007) Day length affects the dynamics of leaf expansion and cellular development in Arabidopsis thaliana partially through floral transition timing. Ann. Bot. (Lond.) 99, 703–711 72 Parent, B. et al. (2010) Rice leaf growth and water potential are resilient to evaporative demand and soil water deficit once the effects of root system are neutralized. Plant. Cell Environ. 33, 1256–1267 73 Berger, B. et al. (2010) High-throughput shoot imaging to study drought responses. J. Exp. Bot. 61, 3519–3528 74 Biddington, N.L. (1986) The effects of mechanically-induced stress in plants? A review. Plant Growth Regul. 4, 103–123 75 Chehab, E.W. et al. (2009) Thigmomorphogenesis: a complex plant response to mechano-stimulation. J. Exp. Bot. 60, 43–56
Physiological phenotyping of plants for crop improvement Michel Edmond Ghanem1, He´le`ne Marrou2,3, and Thomas R. Sinclair3 1
International Center for Agricultural Research in the Dry Areas (ICARDA), North-Africa Platform, Rabat, Morocco UMR System, Montpellier SupAgro, Montpellier, France 3 Department of Crop Science, North Carolina State University (NCSU), Raleigh, NC 27607, USA 2
Future progress in crop breeding requires a new emphasis in plant physiological phenotyping for specific, welldefined traits. Success in physiological phenotyping to identify parents for use in breeding efforts for improved cultivars has been achieved by employing a multi-tier screening approach with different levels of sophistication and trait resolution. Subsequently, cultivar development required an integrated mix of classical breeding approaches and one or more tiers of phenotyping to identify genotypes expressing the desired trait. The role of high throughput systems can be useful; here, we emphasize that this approach is likely to offer useful results at an initial tier of phenotyping and will need to be complemented with more directed tiers of phenotyping. Need for plant phenotyping The ability of humans to select for the best performing individuals of plant species for domestication – and thereby to ‘phenotype’ – has been one of the prerequisites for the development of human civilizations [1,2]. Although the concepts were developed by Gregor Mendel, the terms ‘gene’, ‘genotype’, and ‘phenotype’ were only introduced later by the Danish botanist Wilhelm Johannsen [3] in 1909. The terminology in relation to phenotyping is not completely clear-cut [4] and the terms ‘phenotype’ and ‘phenotyping’ are interpreted in diverse ways. A 2007 paper defined a trait subject to phenotyping as ‘any morphological, physiological or phenological feature, . . . from the cell to the whole-organism level’ [5]. We expand this definition to describe ‘phenotyping’ as the application of methodologies and protocols to measure a specific trait related to plant structure or function with traits ranging from cellular to whole-plant levels [6]. In developing higher-yielding crops, it is likely that ‘physiological phenotyping’ of specific traits will be useful, if not essential, in developing higher-yielding cultivars [7,8]. Unfortunately, there are only a few examples demonstrating the potential role of effectively applying physiological phenotyping to obtain improved crop lines [9]. The objective of this paper is to evaluate the application of physiological phenotyping Corresponding author: Sinclair, T.R. ([email protected]). Keywords: crop breeding; high throughput phenotyping platform; physiological phenotyping; trait screening. 1360-1385/ ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tplants.2014.11.006
to support future breeding programs and to examine the role of some recent proposals such as ‘high throughput platforms’ to advance phenotyping. Before examining the application of physiological phenotyping in breeding, it may be useful to consider the implications of the definition given above for phenotyping. This definition puts the focus on specific characteristics that are defined at a fairly high resolution. By narrowing the specific trait to be phenotyped, it is hoped that the number of interacting processes that influence the expression of the trait are minimized and the number of alleles influencing phenotypic expression are also minimized. That is, physiological phenotyping should target specific, well-defined processes that contribute to crop improvement [7,8,10]. Such an approach to phenotyping is clearly valuable before wasting resources recording a large number of data, which could be highly auto-correlated or not indicative of ultimate targeted trait performance [6]. A ‘shotgun’ approach to phenotyping a number of plant characteristics can result in an unfocused and ambiguous approach in the assessment of traits. A goal of physiological phenotyping is to avoid attempting quantification of emergent properties expressed at or above the whole-plant level, especially those that only become apparent at the canopy and crop level. In this regard, we identify a trait suitable for phenotyping not only as ‘a distinct variant of a phenotypic property of an organism that may be inherited, be environmentally influenced or be a combination of both’ [11], but the trait is a direct expression of a basic plant process. Such basic plant processes have been defined as components of plant productivity that can be quantified at a daily time step, and directly related both to the agronomic performance of the crop and to functional processes at plant organ or tissue level [12]. Therefore, such traits as ‘drought resistance’ and ‘yield’, which require evaluation at the crop level, are not candidates for physiological phenotyping. Of course, specific components of these emergent variables are clear targets for physiological phenotyping. The difficulty of attempting to phenotype for yield, for example, is that it is influenced by many factors and likely does lend itself to tracking and advancing specific physiological traits. Therefore, yield records based on field trials across multiple locations and multiple years are not consistent with the physiological phenotyping concept [13]. The calls for ‘phenotyping for yield’ and ‘data on yield phenotypes’ [14,15] seem inappropriate in efforts to advance specific Trends in Plant Science, March 2015, Vol. 20, No. 3
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Opinion traits that can ultimately contribute to yield under defined environments. Physiological phenotyping and breeding While there are frequent calls for the application of phenotyping of physiological traits in breeding programs [16–18], the ability to phenotype plant populations has not kept pace with progress in genotyping based on plant molecular biology and in molecular-based breeding techniques [9,19,20]. The limitation in physiological phenotyping is that the methodology is commonly detailed, sophisticated, and usually complex and expensive. Phenotyping at a refined physiological level may be effectively applied to only a limited number of genotypes of no more than about 15–25 genotypes, and almost never more than 50 genotypes [10]. Consequently, the inability to physiologically phenotype a large number of genotypes for specific trait performance has been a critical limitation for more than half a century in applying physiological information in breeding [9]. The solution is that a well-defined framework may be needed to understand the role that physiological phenotyping can play in breeding programs. The breeding process has been summarized [21] into three critical steps: (i) identification of trait(s) that promote genetic yield potential; (ii) assessment of genetic variation and its nature for the desired trait(s) leading to the identification of parental genetic resources, i.e., parent identification; and (iii) incorporation of the genes governing the traits into cultivars with other desirable characteristics. Each of these steps places specific demands on physiological phenotyping. Clear differentiation of phenotyping demands in each step helps to better organize the requirements and limitations in applying physiological phenotyping. Trait identification Before launching a major phenotyping effort for a particular putative trait, a critical step is to select the physiological trait to be phenotyped based on evidence that a particular trait might enhance crop performance [9,22]. Mechanistic understanding is key in identifying useful traits for phenotyping [6]; intuition alone is not a reliable guide in the face of the potential complexity of responses. Further, intuition does not offer insight about geographical locations and how much yield change might be expected from trait modification. There appear to be two approaches to develop evidence to support the initiation of a program for alteration of a specific trait: experimentation and simulation. The experimental approach can involve mimicking the expression of a trait or, less desirable, suppressing it using experimental manipulations to assess impact on crop yield. For example, in considering the possibility of increasing drought tolerance of symbiotic nitrogen fixation of soybean [Glycine max (L.) Merr.], field experiments were undertaken to evaluate yield change under drought conditions when nitrogen fixation was replaced by applying high amounts of nitrogen fertilizer [23,24]. Removing plant dependence on symbiotically fixed nitrogen resulted in yield increases of 15–20%, which indicated the potential benefit of genetically increasing nitrogen fixation tolerance to drought. 140
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Box 1. The use of models in phenotyping The choice of an appropriate crop simulation model is a delicate question that should always be addressed cautiously. First, using a mechanistic model is recommended so that model parameters are directly related to plant traits. Properly matching plant traits with model parameters is only possible when using a transparent and physiologically based model with a level of complexity no greater than required by the trait being evaluated [25,52]. Conversely, models that explicitly represent gene or QTL interactions are not necessarily appropriate because they would bring another layer of complexity, while ex-ante modeling is suitable for identifying interesting traits and developing promising genotypes without considering whether the traits of interest are genetically linked or not. As a first step, model-assisted trait screening would involve a sensitivity analysis of the response of a putative trait for enhancing in a given range of environments crop yield or any other crop performance index of interest. However, one should be aware that a crop model is not reality and reflects the designer’s point of view on the functioning of processes in the plant. Therefore, model sensitivity can be influenced by the formalisms used to construct the model. It is essential in using a model (i) to review cautiously the model formalisms to ensure that putative plant processes of interest seem to be appropriately included in the model and (ii) to evaluate the ability of the model to simulate the crop in the target environment. This would reduce the risk of discarding interesting traits only because a model inappropriately simulates the process being evaluated. Once a model has been selected and confidence has been established in the model’s ability to simulate modifications of the trait of interest, simulations can be done over a range of growing seasons and geographical locations to quantify anticipated crop responses to trait modification [6,10,53,54]. A major advantage of the modeling approach is that it allows evaluation of the probability of yield improvement across a range of climatic years for a given location. Therefore, models are crucial tools to discard risky breeding strategies and assist breeders in the reduction of economical vulnerability as they face climate variability [55].
Comparing the yield of isogenic or near-isogenic lines can also be used in trait–value assessment [25]. Since experimental approaches may be expensive, impractical, or even impossible to assess the value of a plant trait in conventional multi-site, multi-season cultivar trials, simulations using a mechanistic crop model offer another approach to explore the potential benefit of trait modification over the landscape of interest [11] (Box 1). Correspondence between observed yield improvements and model simulations for drought traits has been recently demonstrated in maize using the Agricultural Production Systems Simulator (APSIM) model [26] and in soybean using the simple simulation model (SSM) [27]. Parent identification Once evidence has been obtained indicating alteration of a specific trait may be valuable, it is then necessary to select genetic material as parents that have the potential to express the desired trait, so that they may be used to derive breeding populations. One approach to assure lines carry the desired gene(s) is the use of one or more transgenic parent. In this way, the genotype for a specific gene is assured. However, the expression of phenotype in a specific environment or over a range of environments needs to be documented under field conditions. For example, the drought-tolerant gene ZAR1 (Zea mays ARGOS1) was effectively introduced into maize populations from a transgene
Opinion parent and, critically, it was subsequently tested in a range of field environments [28]. Therefore, the use of transgenics as parents in breeding still requires major commitments to physiological phenotyping over a range of conditions. What approaches are appropriate for physiological phenotyping in evaluating putative parental lines? To increase the probability of identifying lines with high levels of expression of a trait, a large number of plant lines will need to be studied. The existing methodology for physiological selection may be so sophisticated and time consuming that only a few lines can be screened, or so crude that the results offer poor resolution in identifying parents. A solution to overcome this dilemma is a multiple-tier screen that involves a range in sophistication of methodology [10,29]. In this approach, the first-tier screen could be a simple but less accurate screen applied as a negative screen to a large number (e.g., thousands) of genotypes. Those genotypes retained in the first-tier screen would be subjected to more sophisticated screens leading to decreasing numbers of genotypes. Such multi-tiered screens for crop improvement are not new in classical breeding efforts. ‘Tandem’ selection breeding is used regularly in some crops [10]. Molecular markers for a desired trait might offer an effective approach as a first-tier screen. While molecular markers can be very useful in ensuring the possibility of phenotypic expression, they do not offer definitive information about the level of phenotypic expression being sought in superior parental lines over a range of environments [30]. Therefore, the first-tier screen with markers needs to be followed with more directed measures towards the physiological expression of a trait. The subsequent tiers in a screen involve the use of increasingly sophisticated screens that can be applied to smaller numbers of lines. Ultimately, a screen will be needed to measure directly the desired trait for potential expression in the environmental conditions for which the trait is desired. The final observations of trait expression may need to be done under controlled conditions so the range of responses for differing environmental conditions can be fully documented. A three-tier, negative-screen system has been successfully applied to identifying candidate parental soybean lines with high tolerance for nitrogen fixation under drought conditions [31]. The initial screen was rather crude, involving measurement of ureide levels of petioles collected in the field on about 3500 lines. This initial screen was followed by a controlled water-deficit screen in the field of nitrogen accumulation on about 250 lines. Finally, direct measurement of nitrogen fixation of plants of 24 lines was done in the greenhouse on plants subjected to soil drying. Consequently, in each of the first two tiers of the screen, about 10% of the entries were retained for the next tier of screening. Eight lines were ultimately identified that expressed nitrogen fixation drought tolerance. Cultivar development Once parental lines have been identified and progeny populations are developed, the final challenge is to identify high-yielding lines that expressed the desired trait. The number of lines in the progeny populations will almost certainly grow to very large numbers. Further, these lines
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will need to be evaluated in a number of environments. How is it possible to apply physiological phenotyping to these large breeding populations? In fact, we have concluded that it is improbable, if not impossible, to perform detailed-trait tracking through every stage of cultivar development [10]. Therefore, it seems likely that physiological phenotyping may be applied only at critical stages in the progeny evaluation process. A realistic approach is likely to be a blend of traditional selection procedures mixed with judicious application of physiological phenotyping. This mix was accomplished in the program to develop nitrogen fixation drought tolerance germplasm [32] by doing physiological phenotyping only twice in the breeding effort. Initially, progeny lines derived from the parents expressing the desired trait were simply observed in the field and negative screens were applied based on desired agronomic characteristics. That is, lines that exhibited disease and insect vulnerability, poor vigor, and inappropriate plant maturity were removed from the population. This procedure resulted in retention of 100 progeny lines that were candidates for a yield test. At this stage, the second-tier phenotyping of nitrogen accumulation when subjected to water-deficit soil was applied to the 100 lines to identify those lines most likely expressing nitrogen fixation drought tolerance. This screen resulted in the identification of 17 candidate lines for a field yield test. Finally, the field tests in water-limited environments identified two lines with yield capability superior to commercial check lines [33]. These two lines were subjected to the third-tier phenotype screen to confirm directly that the nitrogen fixation drought tolerance phenotype existed in these high-yield lines. Therefore, success was achieved in this germplasm development by combining agronomic screening, physiological phenotyping, and yield testing. Phenotyping and novel breeding approaches Modern genetic selection methods With the development of molecular markers in recent decades, identification and utilization of molecular markers was proposed as a major benefit in crop improvement. Markers can provide a framework to identify genomic regions (e.g., quantitative trait loci, QTLs) that influence traits of interest. However, collecting accurate phenotypic data has always been a major challenge for the improvement of quantitative traits using the QTL approach [20]. Apart from a few successful examples where traits were relatively simple to phenotype and highly heritable (e.g., dwarfing and flowering genes in wheat), historic attempts to evaluate traits that have low phenotypic variance and/or low heritability have seldom given reliable results, and the ultimate performance of the selected genotype was not consistent in all environments [20,34]. New population designs [35] in combination with highdensity marker coverage may increase the power to detect small-effect QTLs and estimate their effects [36,37]. Novel breeding strategies (e.g., genomic selection, GS) have been proposed to increase and hasten genetic gain in breeding by minimizing population evaluations over years and locations [38–40]. Genomic selection aims to model genome-wide SNP variation without concern for 141
Opinion identifying particular alleles, loci, or pathways or understanding how different alleles contribute to the phenotype [37]. However, under a GS model, precision phenotyping is of even greater importance when evaluating a training population because that dataset provides the basis for developing the statistical model that is then used to predict phenotypic performance in related members of a breeding population. Altogether, this indicates that more rigorous, quantitative approaches to phenotyping in GS are probably essential [37]. One challenge to discover genes or pathways that may help plants to cope with the abiotic stress response is that much past stress research was performed in laboratories under controlled conditions and relied on screening whole plants for their ability to survive severe stress. For farmers, drought survival is of little or no interest since survival-threatening stress will necessarily result in economic devastation and the marginal yield gain as a result of survival is of little consequence [10]. Also, stresses may be imposed in these experiments by plant exposure to high stress levels that rapidly induced a major trauma that rarely, if ever, occurs in nature [41]. The effects of plant growth and gene expression in response to stress can be highly dose-responsive, indicating the existence of very sensitive machinery in the plant for assessing the stress level and fine-tuning molecular responses [42]. Therefore, a brute force screen that is not related to field behavior can be misleading and is likely not useful. High throughput phenotyping platforms Image phenotyping has been proposed for monitoring growth of major grain crops [43–45] using high throughput phenotyping platforms (HTPPs). HTPPs are envisioned to obtain quantitative plant information with hundreds or thousands of lines using minimally invasive or noninvasive technologies that are integrated into screening protocols [19,46,47]. HTPP approaches exist for application both in the field (Box 2) and in controlled environments such as greenhouses and growth chambers (Box 3). How do these systems fit in with the above proposals for multiple approaches for physiological phenotyping? Field HTPP systems might be especially useful in the first-tier screen that requires methodology to phenotype large numbers (i.e., thousands) of lines. The challenge will be to ‘tune’ the field systems to provide a reasonable indicator for the probability of expression of a specific physiological trait. One such approach now being explored is the use of thermal infrared (TIR) detectors to measure plant temperature. While temperature varies in response to many factors, one response would be an elevated temperature due to partial stomatal closure [48,49]. Partial stomatal closure under high atmospheric vapor pressure deficit has been found to result in soil water conservation and, in the appropriate environment, a yield increase [50,51]. Therefore, a temperature screen could be useful in the identification of candidates for the next tier of more direct screening for the stomata response trait. The temperature screen might also be useful at an early stage in progeny selection to eliminate those lines that under high vapor pressure deficit conditions exhibit low temperatures. 142
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Box 2. Field HTPPs HTPPs have been developed for application for field phenotyping based on remote sensing observations of plants [56–58] by either crop reflectance in the visible and near infrared (NIR) wavebands or emissions in thermal infrared (TIR) wavebands. Observations in the visible and NIR wavebands may help to distinguish the vigor and early leaf area development among genotypes. The NIR wavebands are also proposed for identifying crop stress, but such changes in reflectance only occur after stress has developed and may result from several different processes influencing the timing at which a stress response is visualized. For example, hyperspectral reflectance indices were not successful in monitoring the response of various plant species and crops to different water-stress regimes [59,60]. TIR measurements have been used to assess temperature at different scales. Leaf, plant, and canopy temperatures have been measured using portable sensors manually positioned within a short distance of the plant. At larger scales, probes and camera can be placed on tractors, drones, and satellites. In all cases, it is a challenge to get representative results due to heterogeneous positioning of leaf surfaces. This difficulty is further increased if the field of view of the TIR measurement is a mixture of plants and background soil. TIR measurements have been proposed as an extensive, quick, and non-destructive method to assess plant water status and thus to identify drought-tolerant genotypes [61,62]. The difficulty is that canopy and leaf temperatures are influenced by the dynamics of the environment and many plant properties. Instability in wind speed and cloud cover will result in temperature changes that are a result of thermal and physiological lags. There are a number of traits, other than stomatal closure, that can cause variation in leaf temperatures including canopy architecture, leaf size and shape, and stomata density. Finally, stomatal closure under drought conditions, if detected with TIR measurement, is ambiguous since it can be a sign of active resistance to drought (with plant-limiting water loss), or plants that have lost turgor. A recent technological development is the use of fluorescence of excited leaf pigments as a possible basis for field HTPPs to make quantitative estimates of photosynthetic activity, plant cover rate, and nitrogen nutrition index [56,63]. There is a major challenge in separating the fluorescent signals emitted from the photosynthetic photosystem from the emissions of other leaf pigments (flavonoids and anthocyanins). Analysis of the fluorescent signals received at different wavelengths requires complex mathematical analysis. Furthermore, the results are variable and highly dependent on environmental conditions [64–66]. Finally, for every condition there seems to be a need to calibrate the relationship with nitrogen nutrition index or chlorophyll content in order to resolve a phenotype.
Greenhouse HTPP systems are probably too limited in the number of plants that can be studied to allow the firsttier screen of a wide population of germplasm. Greenhouse studies to evaluate germplasm at the second-tier of screening might be very effectively done using a HTPP system. The challenge is to develop systems that are targeted specifically to the trait of interest. Unfortunately, many current HTPP systems using imaging fail to observe plant characteristics that are targeted to meaningful, fundamental traits. Such measurements as plant height, canopy width, total leaf area, leaf number or canopy shape of a plant, which are readily measured in a HTPP system, translate to indicators of early plant vigor and leaf area development. These measurements seem more appropriate for the first-tier screen in the field. The development of transgenic populations likely favors first-tier screens done with a secure greenhouse HTPP. For various reasons, it may be impractical to do first-tier screens of transgenics in the field and the number of
Opinion Box 3. Greenhouse HTPPs Greenhouse HTPPs are often constructed as fully automated facilities in greenhouses or growth chambers with robotics, environmental control, image analysis, and remote sensing techniques to assess whole plant growth and performance. Most of the HTPPs are very expensive, although lower-cost HTPP approaches are now starting to be developed [67]. In many cases, plants are delivered to a camera system via conveyor belts, whereas in other approaches, individual plants are placed in the viewing field of the camera by manual or automatic positioning of the camera at a defined orientation with respect to the plant. Images are often acquired automatically, using a precisely defined source of illumination. Proper image acquisition and image evaluation is crucial for successful extraction of the desired plant traits. Architectural or development-related plant traits are extracted from images by calculation of the shoot outline and enclosed pixel numbers. To achieve this, plant and background have to be separated precisely, based on differences in color or brightness. Images of individual plants taken with digital color cameras have been used in controlled environments to estimate plant mass and to quantify relative growth rates [44,46,68,69]. However, the main factor in early plant growth is often associated with leaf development. Some of these studies have been used to show the sensitivity of leaf growth to water deficit among different genotypes [46,70]. Genetic determinism of leaf growth is partly shared with that of processes involved in reproductive development such as silk growth and the anthesis-silking interval in maize (Zea mays L.) [70]. Synchronization of floral transition suppressed any leaf growth differential phenotype [71]. The use of HTPPs in greenhouses raises the long-standing concerns of extrapolating such observations to phenotypic expression under field conditions [56,57]. The soil volume on which plants are grown in controlled environments is usually far less than that available to plants in the field, which can affect nutrient and water regimes and alter normal patterns of growth and development [72,73]. Enclosed aerial environments are also problematic for characterizing responses relevant to field situations [61]. Solar radiation, atmospheric turbulence, and evaporation rates typically are lower in greenhouses and growth chambers than under open-air conditions [48,73]. Even mechanical vibration can induce physiological artifacts in plant growth [74,75]. In addition, limited greenhouse space or chamber volumes often preclude allowing plants to flower and set seed, making it impossible to assess effects of stresses during reproductive development.
transgenic lines to be evaluated may be fairly modest (i.e., maybe hundreds instead of thousands). As discussed above, the challenge is to develop HTPP systems that measure characteristics that relate to the specific physiological trait of interest. The specific metric to be monitored needs to be defined and its relationship to the physiological trait of interest needs to be resolved. Since it is likely HTPP systems will be used as first- or second-tier screens, it is not necessary that they offer final evaluation of a physiological trait, but they must offer appropriate screens to identify genotypes for the next tier of evaluation. Since current HTPP systems seem incapable to offer the final tier of many specific trait selections, it is necessary to develop in conjunction with HTPP systems the subsequent tier(s) of evaluation that examines directly the physiological trait of interest. That is, the HTPP results are not likely to be the end product but only an early stage of screening that allow the number of genotypes for study to be narrowed. It is necessary to develop the full series of physiological phenotyping to contribute to both parental selection and progeny selection for improved performance.
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Concluding remarks Accurate physiological phenotyping of specific plant traits appears essential in moving crop breeding forward. However, phenotyping of non-visual traits is difficult and complex. Only by clearly specifying and differentiating the physiological phenotyping approach at every breeding step will it be possible to effectively address the challenge of integrating phenotyping in a breeding program. Several key steps have been identified in this paper in breeding efforts for improved expression of specific desirable expression. We have had success with a three step methodology: (i) obtaining evidence that a hypothesized trait will lead to crop improvement; (ii) understanding the basic process of the trait to guide the development of physiological screens; and (iii) developing multi-tier phenotypic screens that allow insight about trait expression at various stages in the breeding process. Acknowledgments This work was supported by the CGIAR Research Program on Grain Legumes and the USAID/CGIAR-US Universities Linkage Program.
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