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Author contributions: K. V. Wrote manuscript with contributions from all of the authors and greatly
contributed to experimental design and data analysis; J. B. Performed data analysis including all image
processing, statistical analysis and generated figures; S. F. Contributed significantly to conducting
experiments; D. S. Contributed significantly to experiment design; I. B. Supervised and facilitated
elemental profiling and data analysis; R. B. Supervised project and contributed to writing.
Funding: This work was supported by US Department of Energy award DE-SC0014395.
*Corresponding Author ([email protected])
Full title: High-throughput profiling and analysis of plant responses over time to abiotic 1
stress 2
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Short title: High-throughput phenotyping of stress in plants 4
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Kira M. Veley1, Jeffrey C. Berry1, Sarah J. Fentress1, Daniel P. Schachtman2, Ivan 6
Baxter1, 3, Rebecca Bart1* 7
8
1Donald Danforth Plant Science Center, Saint Louis, MO 63132 9
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2Department of Agronomy and Horticulture and Center for Plant Science Innovation, 11
University of Nebraska–Lincoln, Lincoln, NE 68588 12
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3USDA-ARS, Saint Louis, MO, USA 14
15
.CC-BY-NC 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted July 26, 2017. . https://doi.org/10.1101/132787doi: bioRxiv preprint
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ABSTRACT 16
Sorghum (Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized 17
for abiotic stress tolerance. However, measuring genotype-by-environment (G x E) 18
interactions remains a progress bottleneck. Here we describe strategies for identifying 19
shape, color and ionomic indicators of plant nitrogen use efficiency. We subjected a 20
panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen 21
deprivation and measured responses using high-throughput phenotyping technology 22
followed by ionomic profiling. Responses were quantified using shape (16 measurable 23
outputs), color (hue and intensity) and ionome (18 elements). We measured the speed 24
at which specific genotypes respond to environmental conditions, both in terms of 25
biomass and color changes, and identified individual genotypes that perform most 26
favorably. With this analysis we present a novel approach to quantifying color-based 27
stress indicators over time. Additionally, ionomic profiling was conducted as an 28
independent, low cost and high throughput option for characterizing G x E, identifying 29
the elements most affected by either genotype or treatment and suggesting signaling 30
that occurs in response to the environment. This entire dataset and associated scripts 31
are made available through an open access, user-friendly, web-based interface. In 32
summary, this work provides analysis tools for visualizing and quantifying plant abiotic 33
stress responses over time. These methods can be deployed as a time-efficient method 34
of dissecting the genetic mechanisms used by sorghum to respond to the environment 35
to accelerate crop improvement. 36
37
INTRODUCTION 38
39
The selection of efficient, stress-tolerant plants is essential for tackling the 40
challenges of food security and climate change, particularly in hot, semiarid regions that 41
are vulnerable to economic and environmental pressures (Lobell et al., 2008; Foley et 42
al., 2011; DeLucia et al., 2014; Hadebe et al., 2016). Many crop species, having 43
undergone both natural and human selection, harbor abundant, untapped genetic 44
diversity. This genetic diversity will be a valuable resource for selecting and breeding 45
crops to maximize yield under adverse environmental conditions (Leakey, 2009). 46
.CC-BY-NC 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted July 26, 2017. . https://doi.org/10.1101/132787doi: bioRxiv preprint
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Sorghum (Sorghum bicolor (L.) Moench) originated in northern Africa and was 47
domesticated 8,000 – 10,000 years ago. Thousands of genotypes displaying a wide 48
range of phenotypes have been collected and described (Deu et al., 2006; Paterson et 49
al., 2009; Lasky et al., 2015). Sorghum bicolor, the primary species in cultivation today, 50
has many desirable qualities including the ability to thrive in arid soils with minimal 51
inputs, and many end-uses (Morris et al., 2013; Vermerris and Saballos, 2013). For 52
example, grain varieties are typically used for food and animal feed production, sweet 53
sorghum genotypes accumulate non-structural, soluble sugar for use as syrup or fuel 54
production, and bioenergy sorghum produces large quantities of structural, 55
lignocellulosic biomass that may be valuable for fuel production (Murray, 2013; Rooney, 56
2014). Sorghum genotypes can be differentiated and categorized by type according to 57
these end-uses. 58
Rising interest in sorghum over the last forty years has led to efforts to preserve 59
and curate its diversity. To maximize utility, these germplasm collections must now be 60
characterized for performance across diverse environments (Furbank and Tester, 2011; 61
Fiorani and Schurr, 2013; Araus and Cairns, 2014). Deficits in our understanding of 62
genotype-by-environment interactions (G x E = P, where G = genotype, E = 63
environment and P = phenotype) are limiting current breeding efforts (Zamir, 2013). 64
Controlled-environment studies are quantitatively robust but are often viewed with 65
skepticism regarding their translatability to field settings. Further, they can often 66
accommodate only a limited number of genotypes at a time. In contrast, field level 67
studies allow for large numbers of genotypes to be evaluated simultaneously. However, 68
these studies provide limited resolution to resolve the effect of environment on 69
phenotype and often require multi-year replication. This conundrum has motivated 70
enthusiasm for both controlled environment and field level high throughput phenotyping 71
platforms. However, the use of large-scale phenotyping and statistical modeling to 72
predict field-based outcomes is challenging (Deans et al., 2015; Lipka et al., 2015; Zivy 73
et al., 2015). 74
Here, we sought to define a set of measurable, environmentally-dependent, 75
phenotypic outputs to aid crop improvement. We utilized automated phenotyping 76
techniques under controlled-environmental conditions to characterize G x E interactions 77
.CC-BY-NC 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted July 26, 2017. . https://doi.org/10.1101/132787doi: bioRxiv preprint
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on a diverse panel of sorghum genotypes in response to abiotic stress. Specifically, we 78
describe and quantify statistically robust differences among the genotypes to nutrient-79
poor conditions using three phenotypic characteristics: biomass, color, and ion 80
accumulation. Using image analysis to characterize leaf color and biomass over time in 81
conjunction with ionomics, we report measurable, genetically-encoded, phenotypic traits 82
that are affected by nitrogen treatment. This work presents a foundation for 83
understanding the range of sorghum early-responses to abiotic stress and provides 84
tools for analyzing other available datasets. 85
86
RESULTS 87
88
Phenotypic effects of nitrogen treatment on a sorghum diversity panel 89
90
Next to water, nutrient supply (most notably nitrogen availability) is often cited as 91
the most important environmental factor constraining plant productivity (Chapin et al., 92
1987; Liu et al., 2015). The initial goal of our experimental design was to enable the 93
early detection and quantification of stress responses in plants. Figure 1 illustrates the 94
overall experimental design we used to test the phenotypic effects of nitrogen treatment 95
on sorghum over the course of a three-week-long experiment using high-throughput 96
phenotyping. Three nitrogen treatments were designed to analyze the effects of source 97
(i.e. ammonium vs. nitrate) and quantity of nitrogen on plant development over time 98
(Figure 1A, methods). For this study, sorghum was chosen for its genetic diversity and 99
wide range of abilities to thrive under semi-arid, nutrient-limited conditions. In order to 100
test the role that genotype plays in response to nitrogen treatment, a panel of 30 101
sorghum lines was assembled (Table S1). This panel includes sorghum accessions 102
from all five cultivated races (bicolor, caudatum, durra, guinea and kafir), representing a 103
variety of geographic origins and morphologies (Kimber et al., 2013; Brenton et al., 104
2016). The genotypes also display a range of photoperiod sensitivities and are 105
categorized into three general production types: grain, sweet, and bioenergy. This 106
diversity was intended to generate a range of responses that could be measured and 107
attributed to either genotype, stress treatment, or both. 108
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With the use of automated phenotyping, all plants were photographed daily and 109
images were processed using the open source PlantCV analysis software package 110
((Fahlgren et al., 2015), http://plantcv.danforthcenter.org). Within each RGB image, the 111
plant material was isolated, allowing phenotypic attributes to be analyzed (Figure 1B). 112
Scripts used to make the figures within this manuscript, along with the raw data, are 113
available here: http://plantcv.danforthcenter.org/pages/data-114
sets/sorghum_abiotic_stress.html. In total, 16 different shape characteristics were 115
quantified (Figure S1). Principal component analysis (PCA) of all the quantified 116
attributes revealed that shape characteristics could be used to separate all three 117
treatments (Figure 2A). Our results indicated that “area” was the plant shape feature 118
that displayed the largest treatment effect. We consider area measurements from plant 119
images as a proxy for biomass measurements as these traits have been shown to be 120
correlated for a number of plant species, including sorghum (Fahlgren et al., 2015; 121
Neilson et al., 2015). Additionally, the effect of low nitrogen on plant color is well 122
established and RGB image-based methods have been described to estimate 123
chlorophyll content of leaves (Hu et al., 2010; Shibghatallah et al., 2013; Wang et al., 124
2014; Cendrero-Mateo et al., 2016; Junker and Ensminger, 2016; Mishra et al., 2016). 125
In contrast to shape, PCA of color (hue and intensity) attributes at the end of the 126
experiment only separated the high nitrogen treatment group away from the two lower 127
nitrogen treatment groups (Figure 2B). These data indicate that the different nitrate 128
concentrations in the two lower nitrogen treatment groups significantly affects shape but 129
not color. To further explore the effect that our experimental treatments had on the 130
measured shape characteristics and color for each individual genotype, an interactive 131
version of the generated data is available here: 132
(http://plantcv.danforthcenter.org/pages/data-sets/sorghum_abiotic_stress.html). 133
Many factors contribute to the ability of plants to utilize nutrients and presumably, 134
much of this is genetically explained. Correspondingly, genotype was a highly significant 135
variable (p-value = 0.003 when measuring area) within this dataset. To investigate how 136
much nitrogen treatment response is explained by major genotypic groupings, we 137
calculated the contribution of type, photoperiod, or race on treatment effect. Of these, 138
photoperiod was the only grouping that significantly contributed to area (Figure S2). 139
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140
Size and growth rate during nitrogen stress conditions 141
142
Nitrogen stress tolerance is a plant’s ability to thrive in low nitrogen conditions. 143
To identify sorghum varieties tolerant to growth in nutrient limited conditions, we 144
considered plant size at the end of the experiment within the most severe nitrogen 145
deprivation treatment group for all genotypes (Figure 3A). In this experiment, San Chi 146
San, PI_510757, PI_195754, BTx623 and PI_508366 were larger than average as 147
compared to all other genotypes under low nitrogen conditions. In contrast, Della, 148
PI_297155 and PI_152730 were smaller than average. Next we aimed to leverage the 149
temporal resolution available from high throughput phenotyping platforms. For these 150
experiments we considered average growth rate across the experiment (Figure 3B). 151
Overall, end plant size correlated well with overall growth rates. For example, by both 152
measures, Della displayed particularly weak growth characteristics under low nitrogen 153
conditions while BTx623 performed well. However, the correlation was imperfect. San 154
Chi San displayed the largest end size but was statistically average in terms of growth 155
rate across the experiment. Discrepancies between end-biomass and growth rate (e.g. 156
large plants with average or low observed growth rates) may indicate differences in 157
germination rates (e.g. being larger at the beginning of the phenotyping experiment). 158
Taken together, these data suggest that PI_195754, BTx623 and PI_508366 are the 159
best performing genotypes tested under low nitrogen conditions. 160
In contrast to nitrogen stress tolerance, nitrogen use efficiency is often defined as 161
a plant’s ability to translate available nitrogen into biomass. China 17 and San Chi San 162
are considered nitrogen-use-efficient genotypes, while BTx623 and CK60B have 163
previously been reported as less efficient (Maranville and Madhavan, 2002; Gelli et al., 164
2014, 2017). To further explore nitrogen use efficiency phenotypes within our 165
experiment, we factored timing of growth response differences into our analysis. For 166
each day, we analyzed biomass for each genotype within the 100% control group (100 167
NH4+/100 NO3
-) and compared that to the biomass within the 10% treatment group (10 168
NH4+/10 NO3
-). Comparing these two populations allowed us to determine when, during 169
the course of our experiment, those figures became significantly different (Figure 4A). 170
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This analysis separated the genotypes into two broad categories: “early” responding 171
accessions and “late” responding accessions. Early- and late-responding lines were not 172
found to be significantly different in terms of size before treatment administration (Figure 173
4B, top panel). Therefore, we hypothesized that either 1) lines would be late-responding 174
because they were proficient at using any level of available nitrogen or 2) because they 175
grew slowly regardless of quantity of nitrogen supplied. We found that the early-176
responding lines were larger, on average, than the late-responding lines within the 177
100/100 treatment group (Figure 4B, bottom panel) suggesting that these lines are more 178
competent at using available nitrogen. A subset of these genotypes are displayed in 179
Figure 4C to illustrate our observations. The genotype Atlas is an example of a very 180
early responding line, and it was one of the largest plants in the 100/100 treatment 181
group, but also one of the worst-performing lines in the 10/10 treatment group (Figures 182
3A, 3B, 4C). In contrast, China 17 performed relatively well under nitrogen-limited 183
conditions (10/10), but when nitrogen was abundant (100/100) the biomass 184
accumulation was relatively poor (Figure 3A, 3B, 4C). A similar phenotype was 185
observed for PI_510757. In addition to varying the amount of nitrogen available, we also 186
tested whether any lines harbor a preference for nitrogen source. Nitrogen is typically 187
available in two ionic forms within the soil, ammonium and nitrate, both of which are 188
actively taken up into plant roots by transporters located in the plasma membrane 189
(Crawford and Forde, 2002; Kiba and Krapp, 2016). Expression of these gene products 190
and others have been shown to be responsive to nitrogen availability in sorghum (Vidal 191
et al., 2014). For example, San Chi San and China 17 are known to have higher levels 192
of expression of nitrate transporters when compared to nitrogen-use-inefficient lines 193
(Gelli et al., 2014). Notably, Atlas translated an increased ammonium level into larger 194
plant size. In contrast, San Chi San showed no change in average plant size between 195
the two lower nitrogen treatments (Figure 4C). Among the 30 tested genotypes, 16 196
displayed little difference between the 50/10 and 10/10 groups in terms of plant size 197
toward the end of the experiment (Figure S3). This highlights the importance of 198
considering both quantity and source when investigating nitrogen responses. 199
200
Combined size and color analysis over time 201
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202
In addition to affecting shape attributes, nitrogen starvation generally results in 203
reduced chlorophyll content and increased chlorophyll catabolism. Other groups have 204
used image analysis to estimate chlorophyll content and nitrogen use in rice (Wang et 205
al., 2014). The RGB images contain plant hue channel information, and this was found 206
to be a separable characteristic within the nitrogen deprivation treatment groups (Figure 207
2B). We assessed color-based responses to nitrogen treatment in the early- and late-208
responding genotypes as defined in Figure 4A (Figure 5). To facilitate this analysis we 209
used the generated histograms of images of the individual plants from each day of the 210
experiment and averaged those from the early and late categories within each treatment 211
group (Figure 5A, day 13). We found that the histograms of the plant images contained 212
two primary peaks: yellow and green. For both early- and late-responding lines, the 213
yellow peak was larger than the green for the plants in the 10/10 treatment group as 214
compared to the 100/100 treatment group. Early-responding lines within the 100/100 215
treatment group displayed the largest green-channel values. Late responding lines 216
grown under nitrogen-limiting conditions displayed the largest yellow channel values. In 217
order to further visualize color-based treatment effects, we subtracted the 10/10 218
histograms from the 100/100 histograms and plotted this difference (Figure S4). This 219
revealed that although the late responding lines were more yellow, the magnitude 220
difference from the treatment was similar for early and late lines in the yellow channel. 221
In contrast, the early-responding lines tended to have a larger green channel difference 222
between the 10/10 and the 100/100 treatment groups, with early-responding lines 223
showing a larger difference in the green channel. 224
To assess color-based treatment effects over time, we took the area under the 225
histograms (e. g. Figure 5A) for all time points and plotted them against plant age 226
(Figure 5B). Given the peaks within the histograms mentioned above, we focused on 227
these regions and defined yellow (degrees 0 - 60) and green (degrees 61 - 120) to 228
facilitate quantitative analysis. As expected, plants within the 10/10 treatment group 229
were generally more yellow (and consequently less green) over the course of the stress 230
treatment. We detect a peak difference between yellow and green occurring on day 13, 231
then the effect diminishes. A similar peak and overall pattern is seen in the 100/100 232
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treatment group, with plants greening after day 13. Focusing on either the green or the 233
yellow hue, there was no discernable difference in color over time between late- and 234
early-responding lines within the 10/10 treatment group (left panel, dotted versus 235
dashed lines, p > 0.05). However, within the 100/100 treatment group, early-responding 236
lines were consistently greener, while late-responding genotypes became increasingly 237
yellow until day 13. Late- and early-responding lines behaved differently under the 238
100/100 nitrogen treatment conditions, becoming significantly different quickly (day 10, 239
p < 0.05) and remaining so for the duration of the experiment, with the most significant 240
difference occurring on day 13 (p < 1 x 10-15). 241
Combining the above plant size- and color-based data, we conclude that the 242
‘early responding phenotype’ indicates that these plants are able to take better 243
advantage of available nutrients. Importantly, both size and color phenotypes indicate 244
that the early responding genotypes do not display the fitness advantage in low nitrogen 245
conditions. Together, these data demonstrate that color-based image analysis is 246
consistent with and complimentary to the more-established biomass measures of fitness 247
and performance. 248
249
Ionomic profiling as a heritable, independent, measurable readout of abiotic stress 250
251
In addition to the image-based analysis used above to reveal measurable size- 252
and color-based outcomes in response to nitrogen treatment, we also performed 253
ionomic analysis to gain better insight into the physiological changes that occur in 254
response to nitrogen (Figure S5). It has been established that both genetic and 255
environmental factors and their interactions play a significant role in determining the 256
plant ionome (Baxter et al., 2008; Baxter and Dilkes, 2012; Chao et al., 2012; Asaro et 257
al., 2016; Shakoor et al., 2016; Thomas et al., 2016). Thus, this analysis was used to 258
explore alterations that might not be revealed by shape or color analysis but would still 259
contribute to the effect of nutrient availability. Each element was modeled as a function 260
of both genotype and treatment, and genotype was a significant factor for most 261
elements with Mo, Cd, and Co being the most affected by genotype (Figures 6, S5) 262
indicating that concentrations of these elements may be the most directly affected by 263
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genetically encoded traits. Nitrogen deprivation had a measurable effect on every 264
element (Figure 6A, B). As was seen for color (Figure 2B), PCA of the elements 265
revealed separation of the nitrogen treatments, with the two lower nitrogen treatment 266
groups separating from the high treatment group (Figure 6B). Both micro (Se, Rb, Mo, 267
Cd) and macro (K, P) nutrients contributed strongly to the PCs separating the 100/100 268
treatment group away from the other two treatments within the PCA. Interestingly, under 269
our experimental conditions, phosphorous was one of the elements with the largest 270
nitrogen treatment effect (Figure 6A). In Arabidopsis, the presence of nitrate has been 271
shown to inhibit phosphorous uptake (Kant et al., 2011; Lin et al., 2013). Consistent with 272
this, dry weight-based concentrations of phosphorous were inversely proportional to 273
administered nitrate treatment, with the 100/100 treatment group accumulating less on 274
average than either 50/10 or 10/10 (Figure 6C, p < 1 x 10-16, Student’s t-test, Tukey-275
adjusted). The 50/10 and 10/10 treatment groups were not significantly different from 276
one another on average (p > 0.05, Student’s t-test, Tukey-adjusted), further supporting 277
the importance of nitrate concentration in determining phosphate uptake in plants. This 278
data provides evidence for nitrate-phosphorous interactions in grasses that may be 279
analogous to what has been described in Arabidopsis. Additionally, this data indicates 280
that there are likely important effects of abiotic stress on root phenotypes that warrant 281
future research. 282
283
DISCUSSION 284
285
Crops adapted to nutrient-poor conditions will be an invaluable resource for 286
realizing the goal of dedicated bioenergy crops grown without irrigation and limited 287
fertilizer on marginal lands. Robust, quantitative phenotypes are a prerequisite for 288
genetic investigations and these can be gathered using high throughput phenotyping 289
and image analysis. In order to test for and quantify G x E interactions we designed a 290
strategy that utilized tightly controlled environmental conditions in a high-throughput 291
manner in the genetically diverse, stress-tolerant crop, sorghum. We characterized 292
changes in plant size and color over time as well as elemental profile as outputs of 293
stress tolerance. Importantly, this work is intended to not only produce insights into 294
.CC-BY-NC 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted July 26, 2017. . https://doi.org/10.1101/132787doi: bioRxiv preprint
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sorghum biology and crop improvement, but also serve as a resource and an important 295
step forward for high-throughput phenotyping in plants, providing analysis tools to the 296
community as a whole. 297
One important question that remains is how plants efficiently utilize available 298
nitrogen. Previous work has shown that plants use different forms of nitrogen, yet 299
preference can be influenced greatly by genotype and the environment. Factors such as 300
soil pH, CO2 levels, temperature and the availability of other nutrients have an impact 301
on nitrogen uptake (Jackson and Reynolds, 1996; Coskun et al., 2016). Additionally, 302
root architecture is affected by nitrogen source and nutrient availability. It has been 303
shown for a number of species, including maize and barley, that ammonium causes a 304
reduction in lateral root branching that can be reversed with the addition of phosphorous 305
(Drew, 1975; Ma et al., 2013; Thomas et al., 2016; Giles et al., 2017). Compounding 306
this equation, ammonium also causes acidification of the soil, which affects the uptake 307
of other nutrients and likely alters the root microbiome, further complicating most 308
analysis. Under the tested experimental conditions, some genotypes were more 309
affected by nitrogen source in terms of end biomass than others, for example the 310
difference between San Chi San and Atlas (Figure 4C). Also to this point, we show that 311
phosphorous was one of the elements with the largest treatment effect and that the 312
measured concentrations of phosphorous were higher in the low nitrogen treatment 313
groups, both of which received the same nitrate treatment, compared to the high 314
nitrogen treatment group (Figure 6). Taken together, these data are consistent with 315
what other studies that have shown: some genotypes have a preference for nitrogen 316
source and other environmental factors influence that preference. The interdependence 317
between nitrogen uptake and phenotypic output in plants highlights the necessity of 318
high-throughput, tightly controlled studies for answering these and other fundamental 319
questions. 320
Some of the most productive crops in use today are C4 grasses like corn (Zea 321
mays), sorghum (Sorghum bicolor), and sugarcane (primarily Saccharum officinarum) 322
(Reviewed in Leakey, 2009). These crops have cellular functions and chemistries that 323
result in high rates of photosynthesis in spite of drought and nutrient-poor conditions. 324
However, within each crop group, significant genetic and phenotypic variety exists. The 325
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sorghum diversity panel presented here represents a wide, yet incomplete, range of 326
known sorghum genotypic and phenotypic diversity. Tens of thousands of sorghum 327
accessions are curated and maintained by a number of national and international 328
institutions (Kimber et al., 2013). The largest such institution, the US National Sorghum 329
Collection (GRIN database), provides agronomic characteristic information for 40–60% 330
of the collection (e. g. growth and morphology characteristics, insect and disease 331
resistance, chemical properties, production quality, photoperiod in temperate climates). 332
Thus, much work is yet to be done to fully characterize and maximize the potential of 333
this hearty, productive crop species. 334
Nitrogen use efficiency is traditionally defined by the difference in biomass or 335
grain production between plants grown in resource sufficient versus resource limited 336
conditions at the end of the growing season. Stated differently, this measure asks the 337
question: How efficient is a plant at translating a provided resource (nitrogen) into plant 338
biomass. Equally important is the ability to efficiently use a limited resource. Factors that 339
play into these distinct definitions of resource use efficiency include ability to survive 340
periods of extreme stress and rapid utilization of resources as they become available. In 341
this manuscript, we make progress toward deconstructing the building blocks that make 342
up nitrogen response phenotypes. These analyses reveal diverse quantitative indicators 343
of abiotic stress and genotypic differences in stress mitigation that can be used to 344
further crop improvement. Having made progress toward deconstructing these building 345
blocks, we are now in a position to discover the underlying genetic explanations for 346
genotypic variability in resource use efficiency and tolerance to resource limited growth 347
conditions. This work forms a foundation for future research to overlay additional abiotic 348
and biotic stress conditions to achieve a holistic view of sorghum G x E phenotypes. 349
The overall goal of this research is to support such efforts and expedite the process of 350
meaningful crop improvement. 351
352
CONCLUSION 353
Plant stress tolerance is important for food security and sorghum has potential as a 354
high-yielding, stress-tolerant crop. ‘Resource use efficiency’ is often measured in one of 355
two ways: 1) a comparison between yield production under resource-sufficient and 356
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resource-limited conditions, 2) the ability to survive within resource limited 357
environments. Here we describe and apply high-throughput phenotyping methods and 358
element profiling to sorghum grown under variable nutrient levels. We quantify nitrogen 359
use efficiency in genetically diverse sorghum accessions based on color fluctuations 360
and growth rate over time and elemental profile. Through this analysis we report a time-361
efficient, robust approach to identifying resource use efficient and abiotic stress tolerant 362
plants. 363
364
MATERIALS AND METHODS 365
366
Plant growth conditions 367
Round pots (10 cm diameter) fitted with drainage trays were pre-filled with Profile® Field 368
& Fairway™ calcined clay mixture (Hummert International, Earth City, Missouri) the goal 369
being to minimize soil contaminates (microbes, nutrients, etc.) and maximize drainage. 370
Before the beginning of the experiment, the thirty genotypes of Sorghum bicolor (L.) 371
Moench (Table S1) were planted, bottom-watered once daily using distilled water 372
(reverse osmosis), then allowed to germinate for 6 days in a Conviron growth chamber 373
(day/night temperature: 32ºC/22ºC, day/night humidity: 40%/50% (night), day length: 374
16hr, light source: Philips T5 High Output fluorescent bulbs (4100 K (Cool white)) and 375
halogen incandescent bulbs (2900K (Warm white)), light intensity: 400 µmol/m2/s). On 376
day 6, plants were barcoded (including genotype identification, treatment group, and a 377
unique pot identification number), randomized, then loaded onto the Bellwether 378
Phenotyping Platform (Conviron, day/night temperature: 32ºC/22ºC, day/night humidity: 379
40%/50% (night), day length: 16hr, light source: metal halide and high pressure sodium, 380
light intensity: 400 µmol/m2/s). Plants continued to be watered using distilled water by 381
the system for another 2 days, with experimental treatments (described below) and 382
imaging beginning on day 8. 383
384
Nitrogen treatments: 385
100/100 (100% Ammonium/100% Nitrate): 6.5 mM KNO3, 4.0 mM Ca(NO3)2·4H2O, 1.0 386
mM NH4H2PO4, 2.0 mM MgSO4·7H2O, micronutrients, pH ~4.6 387
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388
50/10 (50% Ammonium/10% Nitrate): 0.65 mM KNO3, 4.95 mM KCl, 0.4 mM 389
Ca(NO3)2·4H2O, 3.6 mM CaCl2·2H2O, 0.5 mM NH4H2PO4, 0.5 mM KH2PO4, 2.0 mM 390
MgSO4·7H2O, micronutrients, pH ~4.8 391
392
10/10 (10% Ammonium/10% Nitrate): 0.65 mM KNO3, 4.95 mM KCl, 0.4 mM 393
Ca(NO3)2·4H2O, 3.6 mM CaCl2·2H2O, 0.1 mM NH4H2PO4, 0.9 mM KH2PO4, 2.0 mM 394
MgSO4·7H2O, micronutrients, pH ~5.0 395
396
The same micronutrients were used for all above treatments: 4.6 µM H3BO3, 0.5 µM 397
MnCl2·4H2O, 0.2 µM ZnSO4·7H2O, 0.1 µM (NH4)6Mo7O24·4H2O, 0.2 µM MnSO4·H2O, 398
71.4 µM Fe-EDTA 399
400
Image Processing 401
Images were analyzed by using an open-source platform named PlantCV ((Fahlgren et 402
al., 2015), http://plantcv.danforthcenter.org). This package primarily contains wrapper 403
functions around the commonly used open-source image analysis software called 404
OpenCV (version 2.4.5). To get useful information from a given image, the plant must 405
be segmented out of the picture using various mask generation methods to remove the 406
background so all that remains is plant material (see Figure 1). A pipeline was 407
developed to complete this task for the side-view and top-view cameras separately and 408
they were simply repeated for every respective image in a high-throughput computation 409
cluster. For this dataset of approximately 90,000 images with the computation split over 410
40 cores, computation time was roughly four hours. Upon completion, data files are 411
created that contain parameterizations of various shape features and color information 412
from several color-spaces for every image analyzed. 413
414
Outlier Detection and Removal Criteria 415
Each treatment group began with 9 reps per genotype for the 100/100 and 50/10 416
treatment groups and 6 reps per genotype for the 10/10 treatment group. Outliers were 417
detected and removed by implementing Cook’s distance on a linear model (Cook, 1977) 418
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that only included the interaction effect of treatment, genotype and time. That is, for 419
each observation (every image, for every plant, every day), an influence measure is 420
obtained as the difference of the model with and without the observation. After getting a 421
measure for all observations in the dataset, outliers were defined as having an influence 422
greater than four times that of the mean influence and were subsequently removed from 423
the remaining analysis. In total 5.8% of the data, 1598 images, was removed using this 424
method. 425
426
PCA 427
Three types of PCA’s are generated: one for the shape features, color features, and 428
ionomics. All shape parameterizations that are generated from PlantCV are included in 429
the dimensional reduction. Principle components of color, as defined by the hue channel 430
in two degree increments, is examined using all one hundred eighty bins in the 431
dimensional reduction. Ionomics PCA was generated using every element that passed 432
internal standards of quality. 433
434
GLMM-ANOVA 435
Using area as the response variable, a general linear mixed model was created to 436
identify significance sources of variance adjusting for all other sources, otherwise known 437
as type III sum of squares. Designating genotype as G, treatment as E, and time as T, 438
there are six fixed effects: G, E, GxE, GxT, ExT, GxExT. The mixed effect is a random 439
slope and intercept of the repeated measures over time. Wald Chi-Square statistic was 440
implemented and is a leave-one-out model fitting procedure which allows for adjustment 441
of all other sources. 442
443
Heatmaps 444
Every cell is a comparison of treatments using a 1-way ANOVA wherein the p-value is 445
obtained from a F-statistic generated from the sum of squares of the treatment source 446
of variation. After getting all the raw p-values, a Benjamini-Hochberg FDR multiple 447
comparisons correction is done to aid in eliminating false positives. The p-value 448
distribution was very left skewed so a log-transform is used to normalize them. 449
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Agglomerative, hierarchical clustering was used on the corrected p-values. Each 450
genotype had an associated vector of p-values and a Canberra distance is calculated 451
for all pairwise vectors which are then grouped by Ward’s minimum variance method. 452
453
Color Processing 454
PlantCV returns several color-space histograms for every image that is run through the 455
pipeline (RGB, HSV, LAB, and NIR). Every channel from each color-space is a vector 456
representing values (or bins) from 0 to 255 which are black to full color respectively. All 457
image channel histograms were normalized by dividing each of the bins by the total 458
number of pixels in the image mask ultimately returning the percentage of pixels in the 459
mask that take on the value of that bin. The hue channel is a 360 degree 460
parameterization of the visible light spectrum and contains the number of pixels found at 461
each degree. The colors of most importance are between 0 and 120 degrees which 462
correspond to the gradient of reds to oranges to yellows to greens. Colors beyond this 463
range, like cyan and magenta, have values of all zeros and are not shown. Means and 464
95% confidence intervals as calculated on a per degree basis over the replicates. Area 465
under the curve calculations were done using the trapezoidal rule within the two ranges 466
of 0 to 60 degrees and 61 to 120 degrees which are designated as yellow and green 467
peaks respectively. 468
469
Ionomics Profiling and Analysis 470
The most recent mature leaf was sampled from each plant on day 26 of each 471
experiment, placed in a coin envelope and dried in a 45ºC oven for a minimum of 48 472
hours. Large samples were crushed by hand and subsampled to 75mg. Subsamples or 473
whole leaves of smaller samples were weighed into borosilicate glass test tubes and 474
digested in 2.5 mL nitric acid (AR select, Macron) containing 20ppb indium as a sample 475
preparation internal standard. Digestion was carried out by soaking overnight at room 476
temperature and then heating to 95ºC for 4hrs. After cooling, samples were diluted to 10 477
mL using ultra-pure water (UPW, Millipore Milli-Q). Samples were diluted an additional 478
5x with UPW containing yttrium as an instrument internal standard using an ESI 479
prepFAST autodilution system (Elemental Scientific). A Perkin Elmer NexION 350D with 480
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helium mode enabled for improved removal of spectral interferences was used to 481
measure concentrations of B, Na, Mg, Al, P, S K, Ca, Mn, Fe, Co, Ni, Cu, Zn, As, Se, 482
Rb, Mo, and Cd. Instrument reported concentrations are corrected for the yttrium and 483
indium internal standards and a matrix matched control (pooled leaf digestate) as 484
described (Ziegler et al., 2013). The control was run every 10 samples to correct for 485
element-specific instrument drift. Concentrations were converted to parts-per-million 486
(mg analyte/kg sample) by dividing instrument reported concentrations by the sample 487
weight. 488
489
Outliers were identified by analyzing the variance of the replicate measurements for 490
each line in a treatment group and excluding a measurement from further analysis if the 491
median absolute deviation (MAD) was greater than 6.2 (Davies and Gather, 1993). A 492
fully random effect model is created for every element and partial correlations are 493
calculated for treatment, genotype and the interaction using type-III sum of squares. 494
495
ACKNOWLEDGEMENTS 496
We acknowledge Mindy Darnell and Leonardo Chavez from The Bellwether Foundation 497
Phenotyping core facility at the Danforth center as well as Diana Fasanello and Molly 498
Kuhs for their assistance in running the experiments. We would also like to thank Dr. 499
Greg Ziegler for his help with the ionomics analysis and Dr. Stephen Kresovich for his 500
many helpful discussions and for supplying the seed for the sorghum diversity panel. 501
502
FIGURE LEGENDS 503
504
Figure 1. Experimental Overview. A) Watering regime used for nitrogen deprivation. 505
The x-axis shows the age of the plants throughout the experiment and the y-axis 506
indicates the estimated volume of water plus nutrients (ml), calculated based on the 507
weight change of the pot before and after watering. Each dot represents the average 508
amount of water delivered each day with vertical lines indicating error (99% confidence 509
interval). Watering regime was increased due to plant age (shades of blue). The 510
experimental treatments are listed above the plots. Volume of water and source of 511
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nitrogen are indicated and was scaled based on the 100% (100/100) treatment group (1 512
mM ammonium / 14.5 mM nitrate for 100% treatment group). B) Image analysis 513
example (genotype NTJ2 from 100/100 treatment group on day 16 is shown). Top row: 514
Example original RGB image taken from phenotyping system and plant isolation mask 515
generated using PlantCV. Bottom row: two examples of attributes analyzed (area and 516
color). Scale bar = 15 cm. 517
Figure 2. Determining plant attributes affected by experimental treatments. A) Left: 518
Principle Component Analysis (PCA) plots of shape attributes for plants subjected to 519
nitrogen deprivation at the end of the experiment (plant age 26 days). 95% confidence 520
ellipses are calculated for each of the treatment groups and the dots indicate the center 521
of mass. The shape attributes included in the PCA are as follows: area, hull area, 522
solidity, perimeter, width, height, longest axis, center of mass x-axis, center of mass y-523
axis, hull vertices, ellipse center x-axis, ellipse center y-axis, ellipse major axis, ellipse 524
minor axis, ellipse angle and ellipse eccentricity. Right: Bar graph indicating 525
measurability of shape attributes, showing the proportion of variance explained by 526
treatment (i. e. treatment effect, y-axis). B) PCA plots showing analysis of color values 527
within the mask for plants subjected to nitrogen deprivation at the end of the experiment 528
(plant age 26 days). All 360 degrees of the color wheel were included, binned every 2 529
degrees. 530
Figure 3. Growth response of genotypes to nitrogen deprivation. A) Boxplot 531
showing average plant size (area) at the end of the experiment (day 26), * q-values < 532
0.01) with outliers (dots) at the end of the experiment for the 10/10 treatment group. The 533
median is indicated by a black bar within each box. B) Growth rate (average change in 534
area per day, days 10-22) for the 10/10 treatment group. The dotted lines indicate the 535
treatment group average in both panels. Genotypes that displayed greater than average 536
(blue) or less than average (magenta) growth are indicated. Error bars: 95% confidence 537
intervals for both graphs. 538
Figure 4. Timing of response to nitrogen: size changes in late and early 539
responding genotypes. A) Statistical analysis of differences in area over time (bottom, 540
plant age) for the 30 sorghum genotypes analyzed. q-values for the heat map are 541
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indicated in blue, with darkest coloring representing most significance. The Canberra 542
distance-based cluster dendrogram (right) was generated from calculated q-values. B) 543
Box plots showing average biomass (area) with outliers (colored dots) for late- (left) and 544
early- (right) responding lines from panel A at the beginning (day 8, top) and end (day 545
26, bottom) of the experiment. The median is indicated by a black bar within each box. * 546
indicates significant difference between early and late groups (p-value < 5 x 10-6). C) 547
Scatter plots representing plant area (y-axis) by treatment (x-axis) at the beginning (day 548
8), middle (day 19), and end (day 26) of the experiment for chosen late responding (left) 549
and early responding (right) genotypes (key, right). Each dot represents an individual 550
plant on a day and dotted lines connect genotypic averages. 551
Figure 5. Color changes in late and early responding genotypes to nitrogen 552
treatment. A) Average histograms illustrating percentage of identified plant image mask 553
(y-axis) represented by a particular hue degree (x-axis). Presented is the average of the 554
early- and late-responding lines on day 13 of the experiment. Yellow and green areas of 555
the hue spectrum are highlighted as such. B) Change in yellow (degrees 0 - 60) and 556
green (degrees 61 - 120) hues over time for 100/100 (left) and 10/10 (right) treatment 557
groups. Plotted is the area under the curves presented in A (y-axis) over the duration of 558
the experiment (x-axis) for early- and late-responding genotypes. Grey areas indicate 559
standard error. 560
Figure 6. Ionomic profiling of genotypes at the end of the experiment. A) The 561
percent variance explained by each partition of the total variance model (above). B) 562
Left: PCA plots (all elements) colored by treatment for individual genotypes (left) and 563
95% confidence ellipses (right). The percent variance explained by each component is 564
indicated in parentheses. Right: Loadings for each element from the first two PCs are 565
shown on the y-axis and are color filled based on the direction and strength of the 566
contribution. Positive direction is colored blue and negative direction is colored red. For 567
a given element, the color for PC1 and PC2 are related by the unit circle and saturation 568
of the color is equal to the length of the projection into each of the two directions. C) 569
Boxplots representing dry weight concentrations for all elements and all nitrogen 570
treatments. Concentrations are reported as parts-per-million (y-axis: mg analyte/kg 571
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sample) for each genotype (x-axis). The median is indicated by a black bar within each 572
box. Magenta line: mean phosphorous concentration for given treatment group. 573
574
SUPPLEMENTAL DATA: 575
Table S1 - Genotypic information for accessions included in this study. 576
Figure S1. All shape parameterizations returned from PlantCV had correlations 577
calculated to all other shapes. Correlation is on a scale from -1 to 1 indicating inversely 578
or directly correlated and is being shown in color from red to blue. Radius of the circle in 579
each cell is on a scale between 0 and 1 which corresponds to the absolute value of the 580
correlation. 581
Figure S2. Tables showing results of ANOVA indicating significance of experimental 582
variation explained by either genotype, type, photoperiod or race as found by Wald’s 583
Chi-Square tests with their associated degrees of freedom (DF). Significant p-value < 584
0.1, bold. All three nitrogen treatments are included in the calculations. 585
Figure S3. Statistical analysis of differences between 50/10 and 10/10 groups from the 586
nitrogen deprivation experiment in area over time (bottom, plant age) for the 30 587
sorghum genotypes analyzed. q-values for the heat map are indicated in blue, with 588
darkest coloring representing most significance. The Canberra distance-based cluster 589
dendrogram (right) was generated from calculated q-values. 590
Figure S4. Color changes in individual late and early responding genotypes when the 591
peak experimental effects were observed (day 13). To make the figure average 592
histograms from the indicated genotypes within the 100% and 10% treatment groups 593
were subtracted from one another. Grey areas indicate standard error. 594
Figure S5. Boxplots representing dry weight concentrations for all elements and all 595
nitrogen deprivation treatments. Concentrations are reported as parts-per-million (y-596
axis: mg analyte/kg sample) for each genotype (x-axis). 597
Supplemental files: 598
599
Processed data: 600
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sorg_nitrogen_all_shapes.csv – Processed shape data from nitrogen experiment. 601
Ionomics_RawData_Nitrogen.csv – Processed ionomics data from nitrogen experiment. 602
603
604
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A Nitrogen deprivation (NH4 / NO3)+ -
Figure 1. Experimental Overview. A) Watering regime used for nitro-gen deprivation. The x-axis shows the age of the plants throughout the experiment and the y-axis indicates the estimated volume of water plus nutrients (ml), calculated based on the weight change of the pot before and after watering. Each dot represents the average amount of water delivered each day with vertical lines indicating error (99% con�dence interval). Watering regime was increased due to plant age (shades of blue). The experimental treatments are listed above the plots. Volume of water and source of nitrogen are indicated and was scaled based on the 100% (100/100) treatment group (1 mM ammonium / 14.5 mM nitrate for 100% treatment group). B) Image analysis example (genotype NTJ2 from 100/100 treatment group on day 16 is shown). Top row: Example original RGB image taken from phenotyping system and plant isolation mask generated using PlantCV. Bottom row: two examples of attributes analyzed (area and color). Scale bar = 15 cm.
Color intensity
B
RGB image Plant isolation mask
Shape (height and area)
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Nitrogen deprivation100/100 50/10 10/10
A
BFigure 2. Determining plant attributes a�ected by experimental treatments. A) Left: Principle Component Analysis (PCA) plots of shape attributes for plants subjected to nitrogen deprivation at the end of the experiment (plant age 26 days). 95% con�dence ellipses are calculated for each of the treatment groups and the dots indicate the center of mass. The shape attributes included in the PCA are as follows: area, hull area, solidity, perimeter, width, height, longest axis, center of mass x-axis, center of mass y-axis, hull vertices, ellipse center x-axis, ellipse center y-axis, ellipse major axis, ellipse minor axis, ellipse angle and ellipse eccentricity. Right: Bar graph indicating measurability of shape attributes, show-ing the proportion of variance explained by treatment (i. e. treatment e�ect, y-axis). B) PCA plots showing analysis of color values within the mask for plants subjected to nitrogen deprivation at the end of the experiment (plant age 26 days). All 360 degrees of the color wheel were included, binned every 2 degrees.
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A Plant size (10/10 treatment group, day 26)
* *
Area
(cm
2 )
Growth rate (10/10 treatment group, day 26)
p-value > 0.05
p-value < 0.05Better than averageWorse than average
Slop
e (c
m2 / d
ay)
Figure 3. Growth response of genotypes to nitrogen deprivation. A) Boxplot showing average plant size (area) at the end of the experiment (day 26), * q-values < 0.01) with outliers (dots) at the end of the experi-ment for the 10/10 treatment group. The median is indicated by a black bar within each box. B) Growth rate (average change in area per day, days 10-22) for the 10/10 treatment group. The dotted lines indicate the treatment group average in both panels. Genotypes that displayed greater than average (blue) or less than average (magenta) growth are indicated. Error bars: 95% con�dence intervals for both graphs.
B
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A 100/100 versus 10/10 (area)
q-value> 0.05
< 5 x 10-4
5 x 10-3
0 4522.5
Plant Age (days)q-value distance
C
Genotype
Nitrogen stress
Nitrogen stress
Genotype
Nitrogen stress
day
8da
y 19
day
26
Late Responder Early Responder
lateearly
Figure 4. Timing of response to nitrogen: size changes in late and early responding genotypes. A) Statistical analysis of di�erences in area over time (bottom, plant age) for the 30 sorghum geno-types analyzed. q-values for the heat map are indicated in blue, with darkest coloring representing most signi�cance. The Canberra distance-based cluster dendrogram (right) was gener-ated from calculated q-values. B) Box plots showing average biomass (area) with outliers (colored dots) for late- (left) and early- (right) responding lines from panel A at the beginning (day 8, top) and end (day 26, bottom) of the experiment. The median is indicated by a black bar within each box. * indicates signi�cant di�erence between early and late groups (p-value < 5 x 10-6). C) Scatter plots representing plant area (y-axis) by treatment (x-axis) at the beginning (day 8), middle (day 19), and end (day 26) of the experiment for chosen late responding (left) and early respond-ing (right) genotypes (key, right). Each dot represents an individual plant on a day and dotted lines connect genotypic averages.
Late Responders
Early Responders
*
100/1
00
100/1
0050
/1050
/1010
/1010
/10
*
100 / 100
50 / 10
10 / 10
*
100/1
00
100/1
0050
/1050
/1010
/1010
/10
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100/100 10/10
Difference
Early Responders (average)Late Responders (average)100/100 10/10
Hue Channel (degrees) Hue Channel (degrees)
Perc
enta
ge o
f Mas
k Ex
plai
ned DifferenceDi�erence (10-100)
Perc
enta
ge o
f Mas
k Ex
plai
ned
A
Figure 5. Color changes in late and early responding genotypes to nitrogen treatment. A) Average histograms illustrating percentage of identi�ed plant image mask (y-axis) represented by a particular hue degree (x-axis). Presented is the average of the early- and late-responding lines on day 13 of the experiment. Yellow and green areas of the hue spectrum are highlighted as such. B) Change in yellow (degrees 0 - 60) and green (degrees 61 - 120) hues over time for 100/100 (left) and 10/10 (right) treatment groups. Plotted is the area under the curves presented in A (y-axis) over the duration of the experiment (x-axis) for early- and late-responding genotypes. Grey areas indicate standard error.
DifferenceDi�erence (10-100)100/100 10/10
Plant Age (days)
B
Area
und
er h
isto
gram
EarlyLate
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A
B
Phos
phor
us (m
g) /
kg ti
ssue
Figure 6. Ionomic pro�ling of genotypes at the end of the experiment. A) The percent variance explained by each partition of the total variance model (above). B) Left: PCA plots (all elements) colored by treatment for individual genotypes (left) and 95% con�dence ellipses (right). The percent variance explained by each component is indicated in parentheses. Right: Loadings for each element from the �rst two PCs are shown on the y-axis and are color �lled based on the direction and strength of the contribution. Positive direc-tion is colored blue and negative direction is colored red. For a given element, the color for PC1 and PC2 are related by the unit circle and saturation of the color is equal to the length of the projection into each of the two directions. C) Boxplots representing dry weight concentrations for all elements and all nitrogen treatments. Concentrations are reported as parts-per-million (y-axis: mg analyte/kg sample) for each genotype (x-axis). The median is indicated by a black bar within each box. Magenta line: mean phosphorous concentration for given treatment group.
Direction0.8
0.4
0.0
-0.4
C
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