1
Leaf photosynthetic parameters related to biomass accumulation in a 1
global rice diversity survey 2
Mingnan Qu a,#1
, Guangyong Zheng a,#
, Saber Hamdani a, Jemaa Essemine
a, Qingfeng 3
Song a, Hongru Wang
b, Chengcai Chu
b, Xavier Sirault
c and Xin-Guang Zhu
a,* 4
5
a Institute for Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 6
200032 China 7
b Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 8
Beijing 100101 China 9
c Australian Plant Phenomics Facility – The High Resolution Plant Phenomics Centre, 10
Corner Clunies Ross Street and Barry Drive, Canberra, ACT 2601, Australia. 11
12
13
*Corresponding author. Email: [email protected] 14
#These authors contribute equally to this work. 15
16
Abstract 17
Mining natural variations is a major approach to identify new options to improve crop 18
light use efficiency. So far, successes in identifying photosynthetic parameters 19
positively related to crop biomass accumulation through this approach are scarce 20
possibly due to the earlier emphasis on properties related to leaf instead of canopy 21
photosynthetic efficiency. This study aims to uncover rice natural variations to 22
identify leaf physiological parameters that are highly correlated with biomass 23
accumulation, a surrogate of canopy photosynthesis. To do this, we systematically 24
investigated 14 photosynthetic parameters (PTs) and 4 morphological traits (MTs) in 25
X.Z. conceived the experiment; M.Q., G.Z. and S.H. performed the experiments; M.Q.,
G.Z., S.H., H, J.E., Q.S., H.W., C.C., X.S. analyzed the data, M.Q and X.Z. wrote the
paper.
Funding: This work was supported by CAS Strategic Research Project [grant number
XDA08020301], Shanghai Municipal Natural Science Foundation [grant number
17YF1421800] and [grant number 14ZR1446700] and Bill & Melinda Gates
Foundation [grant number OPP1014417].
Plant Physiology Preview. Published on July 27, 2017, as DOI:10.1104/pp.17.00332
Copyright 2017 by the American Society of Plant Biologists
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2
a rice population, which consists of 204 USDA-curated minicore accessions collected 26
globally and 11 elite Chinese rice cultivars in both Beijing (BJ) and Shanghai (SH). 27
To identify key components responsible for variance of biomass accumulation, we 28
applied a stepwise feature selection approach based on linear regression models. 29
Though there are large variations in photosynthetic parameters measured in different 30
environments, we observed that photosynthetic rate under low light (Alow) was highly 31
related to biomass accumulation and also exhibited high genomic inheritability in 32
both environments, suggesting its great potential to be used as a target for the future 33
rice breeding programs. Large variations in Alow among modern rice cultivars further 34
suggest great potential of using this parameter in contemporary rice breeding for 35
improvement of biomass and hence yield potential. 36
37
Keywords: SNP-based heritability, biomass, photosynthetic traits under low light, 38
linear regression model. 39
40
Abbreviations: A: photosynthetic rates under high light; Alow: photosynthetic rates 41
under low light; Biomass: above-ground biomass; Ci: internal CO2 under high light; 42
Cilow: internal CO2 under low light; Fv/Fm: maximum PSII efficiency; gs: stomatal 43
conductance under high light; gslow: stomatal conductance under low light; GEs: 44
growth environments; h2
SNP: SNP-based heritability; Ls: stomatal limitation under 45
high light; MTs: morphological traits; PTs: Photosynthetic traits:; LfThck: leaf 46
thickness; Lslow: stomatal limitation under low light; PltHt: plant height; SPAD: SPAD 47
values; Tiller: Tiller number; WUE: water use efficiency under high light; Wlow: water 48
use efficiency under low light. 49
50
51
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52
Introduction 53
Improving photosynthetic efficiency is regarded as a major target to improve crop 54
biomass production and yield potential (reviewed by Zhu et al., 2010; Long et al., 55
2006). The canopy photosynthetic efficiency, which is determined by leaf area index, 56
canopy architecture, and leaf photosynthetic properties, plays an important role in 57
determining the biomass accumulation (Long et al., 2006; Zhu et al., 2012). 58
Historically, improvement of canopy architecture, i.e. creating cultivars with 59
semi-drawf architecture, more erect leaves, and higher leaf area index, has played an 60
important role in traditional crop breeding (Hedden 2003; Peng et al., 2008), in 61
contrast, the improvement of leaf photosynthetic properties has played a minor or no 62
role during this process. Broadly, there are two major approaches to improve 63
photosynthetic efficiency, i.e. by genetically engineering photosynthetic efficiency if 64
an engineering target is well defined and by conventional crop breeding, i.e. 65
identifying those lines with superior photosynthetic efficiency and then crossing this 66
superior photosynthetic property into desired target cultivars (Long et al., 2006; Gepts, 67
2002). In either case, the major challenge now is to define effective photosynthetic 68
traits which can lead to enhanced biomass production. We have earlier demonstrated 69
that a system approach can be used to identify new targets to improve photosynthesis 70
by combining systems modeling and an evolutionary algorithm (Zhu et al., 2008). The 71
identified targets to improve photosynthesis have been transgenically tested both 72
inside the lab and also in the field (Rosenthal et al., 2011; Simkin et al., 2015). 73
Similarly, increase the speed of recovery from photoprotection has been demonstrated 74
to be a major approach to increase canopy photosynthesis and crop yield potential 75
(Zhu et al., 2004), which has been recently validated in model crop species tobacco in 76
the field (Kromdijk et al., 2016). This success of enhancing biomass production 77
through manipulation of photosynthesis clearly demonstrates that there is huge 78
potential to improve photosynthetic efficiency for greater biomass and yield 79
production. 80
81
82
83
84
85
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Besides using a systems approach, another potentially rewarding approach to identify 86
parameters related to biomass production is through mining natural variations (Flood 87
et al., 2011; Lawson et al., 2012). The systems approach, to certain degree, increases 88
the potential range of physiological parameters that can be explored than observed in 89
existing cultivars. However, the success of this approach relies on availability of 90
highly sophisticated and accurate systems models for the process under study. The 91
advantage of mining natural variation is that we can collect biomass data and many 92
physiological parameters for a large number of germplasms simultaneously, which 93
facilitates the identification of parameters before availability of highly mechanistic 94
models for the involved processes. 95
96
So far, however, large scale systematic studies of natural variations of photosynthetic 97
parameters in major crops are scarce. Driever et al., (2014) reported natural variations 98
of photosynthetic parameters in 64 elite wheat cultivars and found that though there 99
are significant variation in photosynthetic capacity, biomass and yield, no correlation 100
exists between grain yield and photosynthetic capacity. They suggested that during 101
the breeding process, some traits might have been unintentionally selected out and 102
hence photosynthetic efficiency should be a major target to utilize during the future 103
wheat breeding (Driever et al., 2014; Carmo-Silva et al., 2017). Similar experiments 104
have been conducted in rice which reached similar conclusions, i.e. leaf 105
photosynthetic rate measured under saturating light levels do not show positive 106
correlation with biomass accumulation (Jahn et al., 2011). On the first sight, this is 107
rather contradictory to current theory of photosynthesis. However, if we consider the 108
canopy that the overall crop light use efficiency, where biomass accumulation can be 109
used as a surrogate, is determined by the total canopy photosynthesis, instead of leaf 110
photosynthesis. Indeed, our earlier modeling work showed that light-limited 111
photosynthesis can contribute up to 70% of the total canopy photosynthetic CO2 112
uptake rates, even at a moderate leaf area index of 4.8 (Song et al., 2013). The 113
proportion of light-limited photosynthesis will be even high under either high leaf 114
area index or under future elevated CO2 conditions (Zhu et al., 2012; Song et al., 115
2013). Large scale surveys of rice grain yield, harvest index and biomass 116
accumulation for rice cultivars released since 1966 have shown clearly that the grain 117
yield of cultivars released after 1980 were highly correlated to biomass accumulation, 118
suggesting improved canopy photosynthesis during the recent rice breeding (Peng et 119
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5
al., 2001; Hubbart et al., 2007). The potential factors contributing to the canopy 120
photosynthesis in rice remain unknown. 121
122
In this study, we aim to identity leaf photosynthetic parameters that are highly 123
correlated biomass accumulation, a surrogate of canopy photosynthesis. To do this, 124
we surveyed a large number of leaf photosynthetic parameters and crop architectural 125
parameters at two different locations in China, i.e. Shanghai and Beijing. In this study, 126
to enable comprehensive survey of parameters relevant to canopy photosynthesis, we 127
measured photosynthetic parameters not only under high light but also under limiting 128
light conditions, with the intension to examine whether photosynthetic rates under 129
low light are positively correlated with biomass accumulation. Finally, to minimize 130
the potential complexity of source sink interaction during the grain filling stage, we 131
used biomass accumulation before flowering to avoid the complexity of source sink 132
interaction (Chang et al., 2017). To maximize the genetic diversity utilized in this 133
study, we used both a 215 global rice diversity population consisting of 204 minicore 134
accessions and 11 elite Chinese rice cultivars. Our result revealed that photosynthetic 135
rate under low light (Alow) is highly correlated with biomass accumulation in this 136
diverse rice germplasm population under both Beijing and Shanghai environments. 137
Genetic analysis further shows that Alow is under strong genetic control and hence are 138
amenable for breeding or genetic manipulations. The large variations of Alow in 139
modern rice variations and high genetic inheritance suggest that Alow can be used as a 140
promising target in rice marker assisted breeding. 141
142
143
Results 144
145
Variability of the parameters in the global rice diversity panel 146
As shown from Table 1, natural variations for both 14 photosynthetic traits (PTs) and 4 147
morphological traits (MTs) in Beijing (BJ) and Shanghai (SH) conditions showed 148
different levels of heterogeneity. The percentage genetic variation (PGV) is used to 149
represent the levels of natural variation of traits. The PGV is calculated by the 150
differences between extreme values over mean value in the population (see M&M for 151
more details). The values of PGV in BJ ranged from 3.6 to 197.6 for PTs, and ranged 152
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from 77.2 to 227.9 for MTs; while the values of PGV in SH ranged from 21.5 to 280.1 153
for PTs and ranged from 85.9 to 170.0 for MTs. The trait with minimum natural 154
variation is Fv/Fm and its PGV under BJ/SH environmental conditions is only 12%. For 155
gas exchange related parameters under full light, the PGV values across the two 156
conditions decreased as follows: stomatal conductance under normal light (gs) > 157
stomatal limitation (Ls) > water use efficiency (WUE) > photosynthetic CO2 uptake 158
rate (A) > internal CO2 concentration under normal light (Ci), while the PGV values of 159
these parameters under low light decreased as follows: water use efficiency under low 160
light (Wlow) > stomatal limitation under low light (Lslow) > photosynthetic CO2 uptake 161
rate under low light (Alow) > stomatal conductance under low light (gslow) > internal CO2 162
concentration under low light (Cilow) (Table 1). The PGV of both dark respiration (Adark) 163
and stomatal conductance under dark (gsdark) were at least 120%, which was two times 164
higher than the PGV of the SPAD value. For morphological traits (Table 1), PGV 165
values showed drastic differences between experiments in BJ/SH environmental 166
conditions. The ranking of PGVs for biomass, tiller number, and leaf thickness 167
gradually decreased under BJ/SH environmental conditions. Most of the MTs showed 168
higher variations in PGV values under the BJ environment than those under SH, except 169
for the PGV of plant height (Table 1). 170
171
Estimation of SNP-based heritability (h2
SNP) on a functional trait provides the 172
information about whether this particular trait is under strong genetic control and hence 173
can be used as a potential parameter during crop breeding. SNP-based heritabilities 174
(h2
SNP) of PTs were in range of < 0.001 to 0.72 in BJ/SH environmental conditions 175
(Table 1). Among these PTs, only 4 PTs exhibited significant h2
SNP under BJ/SH 176
environmental conditions, including WUE, Alow, Adark, and SPAD (Table 1). For the MTs, 177
biomass accumulation and tiller number showed high h2
SNP under BJ/SH 178
environmental conditions (Table 1). 179
180
We further employed two-way ANOVA to analyze genotype × environment interaction 181
with regards to PTs and MTs. Results show that all PTs and MTs were significantly 182
different between BJ and SH environments. On one hand, we found strong 183
environmental effects on most of the collected PTs and MTs (Table 2); in contrast, 12 184
out of 18 collected traits were significantly affected by genotype factor, except Ls, gslow, 185
Cilow, Lslow, gsdark and leaf thickness (Table 2). Only Cilow was not significantly affected 186
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by environment and genotype interactions (Table 2). 187
188
Correlation between biomass and other biological parameters 189
Pearson correlation coefficient was determined to evaluate the relatedness of biomass 190
with different PTs and MTs under BJ/SH environmental conditions (Fig. 1; Table S1; 191
Fig. S2; Fig. S3). As shown in Fig. 1 and Table S1, strong correlations were observed 192
between PTs under both normal light and low light conditions when datasets 193
measured under BJ/SH environmental conditions were combined. Fig. 2 shows the 194
correlation between MTs and PTs in BJ/SH environmental conditions (Fig. 2; Table 195
S2). Results reveal that A, gs, Ci, Alow,Wlow, Cilow, Fv/Fm, and SPAD exerted positive 196
correlation with plant height, tiller number and biomass. On the other hand, WUE, Ls, 197
Lslow, and Adark exerted negative correlation with plant height, tiller number, leaf 198
thickness, and biomass. As expected, there is huge variation in the measured PTs 199
between BJ and SH environmental conditions, suggesting the strong environment 200
impacts on most of the PTs (Fig 3), as shown earlier by the strong environment effect 201
on these parameters (Table 2). Certain photosynthetic parameters, including A, gs, 202
Alow and SPAD, showed high correlation index (R2) between both BJ and SH sites 203
(Fig. 3). 204
205
Linear regression model and stepwise feature selection 206
To identify the key parameters that dominate biomass variation under BJ/SH 207
environmental conditions, we employed a linear regression model (LRM) with a 208
stepwise optimization method based on the Akaike information criterion (AIC).We 209
first evaluated the prediction accuracy of the derived LRMs under BJ, SH and 210
combined datasets (Fig. 4). Our approach used a training dataset consisting of 90% of 211
the original data and a test dataset of the remaining 10% of the data (see Materials and 212
Methods for details). As shown in Fig. 4, the models predicted the values of biomass 213
under BJ, SH and combined environments (P < 0.001) with R2 between the predicted 214
and measured biomass ranging from 0.32 to 0.76 in training dataset (Fig. 4, A, C and 215
E). Furthermore, the model predicted the test dataset with R2 ranging from 0.37 to 216
0.72 across the three models (Fig. 4, B, D and F). These results suggest that the 217
derived LRMs can predict the biomass accumulation with a high level of confidence. 218
219
The PTs identified in these three LRMs were used for further analysis. The PTs 220
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8
identified to highly correlated with biomass accumulation from different datasets were 221
shown in Fig. 5 and Table S3. The fitting equations under BJ/SH environmental 222
conditions were listed as followings: The obtained equations under BJ/SH 223
environmental conditions are: Biomass(BJ) = 0.096 gs + 0.115 Alow+ 0.311 × Plant 224
height + 0.355 × Tiller number; Biomass(SH) = 0.053 Ls - 0.081 WUE + 0.152 Alow + 225
0.077 Lslow + 0.127 × Leaf thickness + 0.341 × Plant height + 0.671 × Tiller number; 226
Biomass (Combine) = 0.072 gs + 0.169 Ci - 0.089 Ls + 0.107 Alow + 0.192 Wlow - 0.145 227
Lslow + 0.139 SPAD + 0.448 × Plant height + 0.556 × Tiller number. Two PTs (gs and 228
Alow) were identified in models for BJ; 4 PTs (WUE, Ls, Lslow and Alow) were 229
identified for SH and 7 PTs (gs, Wlow, SPAD, Ci, Ls, Lslow, and Alow) were identified 230
for the combined environments. These large variations in the identified PTs 231
responsible for biomass accumulation in different locations reflect the strong 232
environment impacts on many photosynthetic parameters. Surprisingly, even under 233
such great impacts of environments on photosynthetic parameters, Alow was 234
consistently identified to be closely associated with biomass accumulation in BJ/SH 235
environmental conditions (Fig. 5). gs was also shown to be a major variable associated 236
with biomass accumulation in both the BJ and the combined datasets. The Ls and Lslow 237
were identified in both SH and combined datasets (Fig. 5). Based on these obtained 238
LRMs, the PTs expected to be increased to improve biomass accumulation are gs, Alow, 239
Wlow and SPAD, while those need to be decreased are WUE, Ls, and Lslow (Table S3). 240
241
Ranking of Elite Cultivars within minicore Collection 242
To further evaluate the scope to manipulate Alow for improved biomass production, we 243
examined the distribution of Alow among the minicore panel and the distribution of Alow 244
in 11 current elite rice lines (Fig. 6). Under the BJ environment, Alow exhibits a normal 245
distribution in the minicore population (Fig. 6A) and also there is a huge variation of 246
Alow among the 11 elite rice lines (Fig. 6B). The distribution pattern and ranking of Alow 247
in SH environment are shown in Fig S1. We further evaluated the potential 248
improvements of Alow by calculating the percentage difference (PD) between Alow of 249
the elite lines and the highest Alow observed in the minicore population. There are 250
potentially 76.77% and 85.49% improvement in DHX-Z and ZH11 respectively if 251
their corresponding Alow can reach the maximal Alow in minicore, i.e. that for P4140. 252
253
Discussion 254
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9
Natural variation in photosynthetic traits (PTs) is a largely unexploited resource which 255
can be used to identify new targets to breed or engineer higher photosynthetic 256
efficiency (Flood et al., 2011; Driever et al., 2014). Comparing the relatively 257
long-term perspective of engineering photosynthesis for greater yield (Long et al., 258
2015), mining natural variations of photosynthesis using natural population can lead to 259
reasonably short-term (<5 years) crop improvements (Parry et al., 2011). In this study, 260
we explored natural variations in photosynthetic parameters in rice that might are 261
related to biomass accumulation, a surrogate of canopy photosynthesis. Using linear 262
regression models (LRMs) constructed under different environments, we identified 263
photosynthetic rates under low light (Alow) as a major photosynthetic parameter with 264
high correlation with biomass accumulation under two drastically different 265
environments. Here we briefly discuss the major findings of this study and their 266
implications for rice breeding. 267
268
Natural variations and heritability of all photosynthetic parameters 269
Since 1960s, researchers started working on improving photosynthesis through 270
introgression, e.g. in soybean (Ojima, 1974). However, the progress was rather limited 271
because on one hand it remained unclear what photosynthetic traits (PTs) should be the 272
targets, and on the other hand, there were no effective molecular marks related to PTs 273
defined well enough to be used in breeding programs (Flood et al., 2011). The aim of 274
this study is to identify highly heritable PTs relevant to biomass production under 275
different environments. Since screening PTs is labor-intensive and time-consuming, 276
instead of using the global rice core collection of 1794 accessions, we used a minicore 277
diversity panel consisting of 204 global rice accessions, which is sufficiently diverse to 278
effectively represent the original core collection (Agrama et al., 2009) and also is 279
manageable especially for detailed photosynthesis phenotyping. 280
281
As expected, our data suggest that there are substantial variations among 282
photosynthetic parameters under BJ/SH environmental conditions (Table 1), 283
suggesting there is genetic diversity in PTs in rice that can be potentially exploited. 284
Furthermore, our heritability analysis shows that h2
SNP in many PTs, including SPAD, 285
stomatal limitation (Ls) and WUE under BJ/SH environmental conditions, were 286
around 0.6~0.7, which are closed to some earlier reports(Schuster et al., 1992; 287
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10
McKown et al., 2014; Geber and Dawson, 1997; Table 1), suggesting that these 288
parameters are under strong genetic control in different species. It is worth 289
emphasizing that, in this study, our estimate of heritability h2
SNP utilizes not only 290
casual genes as in the traditional variance method (reviewed by Zaitlen and Kraft, 291
2012), but also considers other single nucleotide polymorphism markers (h2
SNP) 292
(Yang et al., 2011). The observed high levels of heterogeneity and relatively high 293
h2
SNP for many PTs suggest that these traits can be used as potential candidates in 294
marker assisted breeding for rice (Ackerly et al., 2000). 295
296
Alow is a photosynthetic trait which is highly correlated with biomass accumulation 297
under different environments 298
In this study, a stepwise feature selection approach was applied to the data collected 299
under either BJ or SH environments. With this method, we identified two PTs, i.e., gs 300
and Alow, in both the BJ and its combined datasets (Fig. 5). Both gs and Alow exhibited 301
high correlation with biomass (Fig. 2; Fig. S2; Fig. S3). The values of both 302
parameters show strong correlation between BJ and SH environments (Fig. 3). gs 303
showed a high h2
SNP and substantial natural variation among rice cultivars under 304
BJ/SH environmental conditions (Table 1, 2), suggesting that gs is a good parameter to 305
be used in rice breeding. In fact, gs screening based on thermal imaging (Takai et al., 306
2010b) has already been used in some breeding programs, e.g. the wheat yield potential 307
breeding in the physiology breeding program of CYMMIT (Rajaram et al., 1994). It is 308
worth noting that, in addition to gs itself being potentially important parameter for 309
breeding, faster response of gs to fluctuating light can be an adaptive trait for rice 310
under severe drought conditions (Qu et al., 2016). 311
312
Remarkably, the Alow instead of A under normal light was identified to be a 313
photosynthetic parameter highly correlated with biomass accumulation under BJ, SH 314
and combined datasets (Fig. 5; Table S3). Alow is also under strong genetic control, as 315
shown by its high h2
SNP (Table 1). Therefore Alow is a promising target for future rice 316
breeding improvements. This finding is remarkable since although it has been long 317
recognized that canopy-, instead of leaf-, photosynthesis is a major determinant of 318
biomass accumulation, so far, direct experimental evidence supporting the importance 319
of photosynthetic efficiency under low light is lacking. The strong correlation between 320
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11
Alow and biomass accumulation reported here strongly support the notation that 321
photosynthetic CO2 uptake of the lower layer leaves, which usually experiences low 322
light levels, contribute substantially to the overall canopy photosynthesis and hence 323
biomass production. This finding increases the repertoire of parameters known so far 324
that can potentially improve canopy photosynthesis, which includes faster speed of 325
recovery from photoprotective status (Zhu et al., 2004), rapid recovery of stomata 326
conductance under fluctuating light (Lawson and Blatt, 2014; Qu et al., 2016), and 327
Rubisco with optimized kinetic properties (Zhu et al., 2004). Large scale genetic 328
screening of these different parameters and gene identification in this global minicore 329
are underway in our laboratory. 330
331
Potential value of the identified traits in current rice breeding 332
Natural distribution of Alow across the minicore panel yielded a normal distribution 333
(Fig. 6 B); furthermore, there are substantial variation of Alow in the modern elite rice 334
cultivars (Table 3; Fig. 6), suggesting large space to improve Alow to enhanced 335
biomass production in contemporary elite rice cultivars. By comparing the value of 336
Alow in the modern elite cultivars with the extreme values observed in the minicore 337
diversity panel under the BJ environment, we identified candidate donors that can be 338
used as genetic resources for Alow. For example, 76.77% and 85.49% improvement of 339
Alow can be achieved in DHX-Z and ZH11 (elite cultivar), respectively (Table 4), if 340
the causal genes (or QTLs) controlling Alow in P4140 can be transferred into these two 341
cultivars. 342
343
Conclusion 344
345
By mining natural variations of photosynthesis related traits in a natural rice diversity 346
panel, here we found that, among many photosynthetic parameters, photosynthetic 347
rates under low light (Alow) is highly correlated with biomass accumulation under 348
different environments. Furthermore, Alow shows high level of variability among 349
contemporary elite rice lines and it has a high inheritability. All these suggest that 350
Alow is a promising target for the future rice breeding programs. 351
352
Materials and Methods 353
354
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12
Plant material 355
356
The accessions from the USDA collected minicore rice diversity panel are from 76 357
countries covering 15 geographic regions, which consists of six groups, indica (35.4%), 358
aus (18.7%), tropical japonica (18.2%), temperate japonica (15.2%), aromatic (3.0%) 359
and their admixtures (9.6%) (Agrama et al., 2010; Li et al., 2010). The population 360
accounts for 12.1% of the global rice core accessions and displayed 100% coverage in 361
genetic variation (Agrama et al., 2010). In current study, we used 204 out of 217 362
accessions in the minicore population since the remaining 13 accessions have 363
extremely long growing seasons. In addition, we used 11 Chinese elite rice cultivars 364
(WCC1, WCC2, DHX-Z, HE19, KY131, XS134, ZH11, MH63, KALS, 9311, WY-4) 365
(Hamdani et al., 2015). 366
367
Measurements of leaf gas exchange 368
The 204 minicore panel and 11 elite rice lines were transplanted under two 369
environments, i.e. Beijing (BJ) (116.3943oE, 39.9820
oN) in May 2013 and Shanghai 370
(SH) (121.4530oE, 31.0428
oN) in May 2015. Averaged atmosphere temperature under 371
BJ/SH environmental conditions, during the growth periods from transplanting to 372
booting stage until large-scale measurements started, which spanned around 60 days, 373
were around 24.8 ± 3.1 and 25.5 ± 4.4 oC, respectively. Experiments were conducted 374
in pots and detailed experimental procedures were described in Hamdani et al., (2015). 375
Briefly, plants were sowed in 12L pots filled with commercial peat soil (Pindstrup 376
Substrate no. 4, Pindstrup Horticulture Ltd, Shanghai, China). For each accession, six 377
plants were planted in two pots with three plants per pot. Two pots for the same 378
accession were arranged close together to ensure formation of canopy. Pots from 379
different accessions were separated to avoid shading from a different accession. 380
During the growth period, plants were exposed to natural sunlight and were irrigated 381
daily. Fertilizers were applied twice per month. Experiments of leaf gas exchange 382
were conducted at 60 days after emergence (DAE). For each accession, we used four 383
replicates during the measurements of gas exchange related parameters. All the 384
photosynthesis measurements were finished within 10 days. To minimize the potential 385
errors introduced by potential growth stage differences, we measured photosynthetic 386
parameters from accession 1 through 215 sequentially for the first and third replicates, 387
and then from accession 215 to 1 sequentially for the second and fourth replicates. 388
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13
Plants were acclimated in a controlled room with a temperature around 27 oC and the 389
PPFD around 600 mol m-2
s-1
for at least 60 minutes before gas exchange 390
measurements. During the measurements, two levels of photosynthetic photon flux 391
density, i.e. 1200 μmol mol-1
s-1
(normal light) and 100 μmol mol-1
s-1
(low light), 392
were used. Four portable infrared gas exchange system (Li-6400XT, Li-COR Inc., 393
Lincoln, NE, USA) were used simultaneously. An automatic program was applied to 394
measure gas exchange traits under two light levels, traits under normal light include 395
photosynthetic rates (A), stomatal conductance (gs), internal CO2 (Ci), water use 396
efficiency (WUE) and stomatal limitation (Ls); traits under low light include Alow, 397
Gslow, Cilow, Wlow and Lslow (see abbreviation section for more details). The process of 398
such program is as following: leaf was first maintained under a PPFD of 1200 mol 399
m-2
s-1
for at least 5 minutes or until gs reached a steady state, then PPFD was changed 400
to 100 mol m-2
s-1
for 25 minutes allowing gs to approach steady state as described 401
from Qu et al., (2016). During the measurements, the leaf temperature was maintained 402
at 25oC and relative humidity was maintained at ~ 75%, the reference CO2 403
concentration was set as 400 μmol mol-1
and we used the top fully expanded leaves 404
for this measurements. Data were automatically recorded and the average values 405
within the last 1 minute before light switch were used for data analyzing. 406
407
Measurements of dark respiration and maximal quantum yield 408 409
Experiments for dark respiration were conducted at 60 DAE. Respiration rates were 410
determined as net rates of CO2 efflux in darkness during night after 8:00 pm according 411
to Bunce (2007). Leaf temperatures were set to 25oC, reference CO2 concentration was 412
set to be 400 μmol mol-1
, and light level was set to be 0 μmol mol-1
s-1
. 413
414
Multi-Function Plant Efficiency Analyzer (M-PEA) chlorophyll fluorometer 415
(Hansatech, Kings Lynn, Norfolk, UK) was used to measure Fv/Fm, the maximal 416
quantum yield of photosystem II, following Hamdani et al., (2015). Fm represents the 417
maximum chlorophyll fluorescence, Fo is the minimum chlorophyll fluorescence and 418
Fv = Fm-Fo (Oxborough and Baker, 1997; Huang et al., 2016; Essemine et al., 2017). 419
420
Measurements of SPAD and leaf thickness 421
Experiments for SPAD and leaf thickness were conducted at 60 DAE. To estimate leaf 422
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14
total chlorophyll content and leaf thickness, SPAD 502 Plus Chlorophyll meter 423
(Spectrum Technologies, Inc.) (Takai et al., 2010a) and Micrometer screw (Mitutoyo 424
Co.) were used, respectively. For each leaf, the chlorophyll content was estimated as 425
the mean of 5 chlorophyll content measurements at different positions in the middle 426
section of the leaf. Four replicates from four different plants were determined for both 427
leaf chlorophyll concentration and leaf thickness. 428
429
Measurements of plant morphological traits 430
The above-ground biomass accumulation (biomass), plant height (PltHt), and tiller 431
number (Tiller) were determined at 60 DAE according to Qu et al., (2016). At least 432
four replicates were measured for each parameter above. Samples for biomass 433
determinations were kept at 120 oC for 1 hour and then under 70
oC for at least 24 hours 434
in a baking oven until constant weight is reached before weights of biomass or leaf 435
segments were measured. 436
437
Regression model between biomass and morphological and photosynthetic traits 438
We used a linear regression model (LRM) to capture correlation of biomass with PTs 439
and MTs. The model is defined as follows: 440
y=β1x1+β2x2+…βvxv+ε 441
Where y is a vector representing biomass values of each rice accession, x is a vector of 442
independent variables, β is weighted coefficients corresponding to x, and ε is an error 443
vector. The model was constructed with a stepwise manner, which can identify highly 444
relevant parameters and remove low relevant parameters based on the Akaike 445
information criterion (AIC) according to Jin et al., (2014). In practice, a training 446
dataset including 90% items of the whole dataset was randomly extracted from the 447
original dataset and the remaining 10% data were used as a test dataset (Kawamura et 448
al., 2010; Iwasaki et al., 2013). The training dataset was first defined to build the 449
regression model, and then an independent validation was conducted on the test 450
dataset to check performance of the model. 451
452
Estimation of SNP-based heritability 453
The GCTA software (Version 1.25.2) (Yang et al., 2011) was employed to estimate 454
SNP-based heritability (h2
SNP) of 23 functional traits using 2.3M filtered SNPs of the 455
minicore population(Wang et al., 2016). GCTA implements the method in two steps, 456
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15
i.e., generating a high-dimensional genetic relatedness matrix (GRM) between 457
individuals and then estimating the variance explained by all SNPs by a restricted 458
maximum likelihood (REML) analysis of the phenotypes with GRM (Yang et al., 459
2011). The significance of h2
SNP is assessed by likelihood ratio test (LRT), which is 460
the ratio of likelihood under the alternative hypothesis (H1: h2SNP≠0) to that under the 461
null (H0: h2SNP=0). The LRT and its corresponding p value were reported in the 462
GCTA output file. 463
464
Data analysis 465
In order to quantitatively evaluate the genetic variation of biological traits in the 466
combined population, percentage genetic variation (PGV) was calculated as 467
[(Xmax-Xmin) /X×100 (%), where Xmax, Xmin andX stands for maximum, minimum and 468
mean value in the population, respectively (Gu et al., 2014). The Pearson correlation 469
coefficient was calculated using R package (Corrplot) (3.2.1 version). 470
471
Supplemental data: 472
Supplemental Table S1. Correlation of photosynthetic traits and morphological traits 473
in global minicore panel and elite rice cultivars across the experiments of Beijing (BJ) 474
and Shanghai (SH) sites. 475
Supplemental Table S2. Correlation of photosynthetic traits with morphological 476
traits under Beijing (BJ) and Shanghai (SH) experiments. 477
Supplemental Table S3. Feature selection across Beijing, Shanghai and combined 478
sites. 479
Supplemental Figure S1. Trait distribution of elite cultivars and the minicore 480
accessions under the SH environment. 481
Supplemental Figure S2. Correlation of photosynthetic traits with biomass under the 482
Beijing (BJ) environment. 483
Supplemental Figure S3. Correlation of photosynthetic traits with biomass under the 484
Shanghai (SH) environment. 485
486
487
488
489
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16
490
Supplemental Table S1. Correlation of photosynthetic traits and morphological traits 491
in global minicore panel and elite rice cultivars across the experiments of Beijing 492
(BJ) and Shanghai (SH) sites. For trait abbreviations refer to Table 1. The colors in 493
cells ranging from red to green represent the correlation coefficient from negative 494
to positive correlation. 495
Supplemental Table S2. Correlation of photosynthetic traits with morphological 496
traits under Beijing (BJ) and Shanghai (SH) experiments. The values represent 497
Pearson correlation coefficient under BJ/SH experiments. Asterisks “*”, “**” were 498
depicted as P-value<0.05, 0.01, respectively. Refer to Table 1 for detailed 499
abbreviation of each trait. 500
Supplemental Table S3. Feature selection across Beijing, Shanghai and combined 501
sites. Traits abbreviations were as mentioned in detail in title of Table 1. SD and 502
Psignify standard deviation and P-value, respectively. 503
Supplemental Figure S1. Trait distribution of elite cultivars and the minicore 504
accessions under the SH environment. A: distribution of photosynthetic rates under 505
low light (Alow) of elite cultivars within the minicore accessions. B: Histogram 506
representing the distribution of Alow in the minicore diversity panel. The enclosed 507
numbers represent: WCC1①, WCC2②, DHX-Z③, HE19④, KY131⑤, XS134⑥, 508
ZH11⑦, MH63⑧, KALS⑨, 9311⑩, and WY-4⑪
. 509
Supplemental Figure S2. Correlation of photosynthetic traits with biomass under the 510
Beijing (BJ) environment. The values of photosynthetic traits and biomass were 511
normalized ranging from 0 to 1. The values of Pearson correlation coefficient (R) 512
were calculated. Asterisks “*”, “**” represent P value < 0.05, and 0.01, 513
respectively. The abbreviated PT at right-bottom corner of each panel is as 514
described in details in Figure 1. 515
Supplemental Figure S3. Correlation of photosynthetic traits with biomass under the 516
Shanghai (SH) environment. The values of photosynthetic traits and biomass were 517
normalized ranging from 0 to 1. The values of Pearson correlation coefficient (R) 518
were calculated. Asterisks “*”, “**” represent P value < 0.05, and 0.01, 519
respectively. The abbreviated PT at right-bottom corner of each panel is as 520
described in details in Figure 1. 521
522
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17
Acknowledgments 523
This work thanks the anonymous reviewers for constructive comments which helped us 524
improve our revision. This work was supported by CAS Strategic Research Project 525
[grant number XDA08020301], Shanghai Municipal Natural Science Foundation 526
[grant number 17YF1421800] and [grant number 14ZR1446700] and Bill & Melinda 527
Gates Foundation [grant number OPP1014417]. 528
529
Figure Legends 530
Figure 1. Correlation of photosynthetic traits (PTs) and morphological traits (MTs) in 531
the global minicore panel and elite rice lines. Data were combined from Beijing (BJ) 532
and Shanghai (SH) experiments. The abbreviations of PTs are: A: photosynthetic 533
rates under high light; Alow: photosynthetic rate under low light; Biomass: 534
above-ground biomass; Ci: internal CO2 under high light; Cilow: internal CO2 under 535
low light; Fv/Fm: maximum PSII efficiency; gs: stomatal conductance under high 536
light; gslow: stomatal conductance under low light; Ls: stomatal limitation under high 537
light; Lslow: stomatal limitation under low light; SPAD: SPAD values; WUE: water 538
use efficiency under high light; Wlow: water use efficiency under low light; PltHt: 539
plant height; Tiller: tiller number; Thick: leaf thickness; Biomass: biomass 540
accumulation. 541
Figure 2. Graphic representation of correlations of different PTs with MTs in the 542
global minicore panel and elite rice lines under Beijing (BJ) and Shanghai (SH) 543
environments. Shaded area at the center represents negative correlation. Trait 544
abbreviations are given in Table 1. 545
Figure 3. Self-correlation of each photosynthetic trait in the global minicore panel 546
and elite rice lines grown in Beijing (BJ) and Shanghai (SH) environments. The 547
values of Pearson correlation coefficient (R) were calculated. Asterisks “*”, “**” 548
represent P value < 0.05, and 0.01, respectively. The abbreviated PT at right-bottom 549
corner of each panel is as reported in details in Figure 1. 550
Figure 4. Model construction and cross-validation in the global minicore panel and 551
elite rice lines under Beijing (A and B), Shanghai (C and D) and the combined (E 552
and F) datasets. The training dataset consists of 90% of the whole dataset and the 553
remaining 10% items were used as a test dataset (see Material and Method for 554
details). Predicted values of biomass versus observed values of biomass were used 555
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18
during the cross-validation. Determination index (R2) reflects the accuracy of 556
regression between predicted and observed values. 557
Figure 5. Features selections analysis on photosynthetic traits (PTs) using Linear 558
Regression Models (LRMs). Key PTs identified by models were represented 559
constructed under Beijing (BJ), Shanghai (SH) and their combined environments 560
(Combine). The equations under BJ, SH and Combined environments are listed as 561
followings: Biomass(BJ) = 0.096 gs + 0.115 Alow+ 0.311 × Plant height + 0.355 × 562
Tiller number; Biomass(SH) = 0.053 Ls - 0.081 WUE + 0.152 Alow + 0.077 Lslow + 563
0.127 × Leaf thickness + 0.341 × Plant height + 0.671 × Tiller number; Biomass 564
(Combine) = 0.072 gs + 0.169 Ci - 0.089 Ls + 0.107 Alow + 0.192 Wlow - 0.145 Lslow + 565
0.139 SPAD + 0.448 × Plant height + 0.556 × Tiller number. 566
Figure 6. Trait distribution of elite cultivars and the minicore accessions under the 567
Beijing (BJ) environment. A: Histogram representing the distribution of 568
photosynthetic rates under low light (Alow) in the minicore diversity panel. B: 569
Phenotypic distribution of Alow of elite cultivars within the minicore accessions. 570
The enclosed numbers represent: WCC1①, WCC2②, DHX-Z③, HE19④, KY131⑤, 571
XS134⑥, ZH11⑦, MH63⑧, KALS⑨, 9311⑩, and WY-4⑪
. 572
573
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19
Tables 574
Table 1. Natural variation and SNP-based heritability of PTs in the global minicore 575 panel and elite rice lines grown in Beijing (BJ) and Shanghai (SH) environments. 576 PGV was calculated as described in Materials and Methods. Symbols "*, **, ***" 577 represent P< 0.05, 0.01 and 0.001, respectively. The abbreviations of PTs are: A: 578 photosynthetic rates under high light; Alow: photosynthetic rates under low light; 579 Biomass: above-ground biomass; Ci: internal CO2 under high light; Cilow: internal 580 CO2 under low light; Fv/Fm: maximum PSII efficiency; gs: stomatal conductance 581 under high light; gslow: stomatal conductance under low light; Ls: stomatal limitation 582 under high light; Lslow: stomatal limitation under low light; SPAD: SPAD values; 583 WUE: water use efficiency under high light; Wlow: water use efficiency under low 584 light. h
2SNP represents SNP-based heritability. The two grey selected rows mean that 585
the data is available only for BJ site. n: represents the accessions number 586 587
Traits Sites N Range Mean ± SD PGV h2
SNP±S E
A (μmol m-2
s-1
) BJ 214 13.65~28.19 21.02±3.00 69.17 0.13±0.07
SH 186 12.44~39.76 24.35±5.02 112.20 0.15±0.03
gs (mmol m-2
s-1
) BJ 214 0.18~0.99 0.41±0.12 197.56 0.34±0.14*
SH 186 0.14~1.16 0.56±0.18 182.14 0.25±0.08*
WUE (mmol mol-1
) BJ 214 28.77~67.51 44.51±0.66 87.04 0.73±0.20**
SH 187 20.50~77.71 43.44±10.03 131.70 0.61±0.11**
Ci (μmol mol-1
) BJ 215 204.4~316.4 276.53±17.75 40.50 0.71±0.20**
SH 187 245.91~347.75 308.66±21.58 32.99 <0.001
Ls BJ 215 0.18~0.47 0.28±0.05 103.57 0.72±0.20**
SH 188 0.15~0.51 0.26±007 138.46 0.48±0.21**
Alow(μmol m-2
s-1
) BJ 214 2.56~6.62 4.87±0.65 83.37 0.37±0.12*
SH 187 2.26~6.42 3.76±0.67 110.64 0.36±0.12*
Gslow (mmol m-2
s-1
) BJ 214 0.17~0.26 0.12±0.04 75.00 0.08±0.13
Cilow (μmol mol-1
) BJ 214 242.10~342.60 299.76±17.19 33.53 0.29±0.13*
SH 188 265.12~380.37 353.63±15.56 32.59 <0.001
Wlow (mmol mol-1
) BJ 214 20.91~73.25 39.18±0.83 133.59 <0.001
SH 187 8.62~72.07 22.63±9.55 280.38 <0.001
Lslow BJ 214 0.27~0.79 0.61±0.83 85.25 0.08±0.03
SH 188 0.13~0.95 0.44±0.17 186.36 0.15±0.04
Adark(μmol mol-2
s-1
) BJ 214 -0.32~-0.87 -0.47±0.09 117.02 0.40±0.16*
SH 183 -0.21~-1.25 -0.53±0.22 233.96 0.26±0.06*
Gsdark (mol mol-1
) BJ 214 0.01~0.05 0.02±0.01 200.00 0.08±0.12
Fv/Fm BJ 214 0.82~0.85 0.84±0.01 3.57 0.34±0.13*
SH 182 0.66~0.83 0.79±0.03 21.52 0.12±0.03
SPAD BJ 213 27.13~65.2 37.28±4.54 102.12 0.60±0.16*
SH 180 23.53~47.70 36.47±4.77 66.27 0.53±0.17*
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Biomass (g) BJ 214 4.60~63.40 25.8±9.36 227.9 0.35±0.22*
SH 184 6.69~64.35 22.74±1.04 97.40 0.25±0.03*
PltHt(cm) BJ 214 58.33~138.0 103.26±14.65 77.2 0.07±0.02
SH 188 55.0~130.75 91.11±13.78 170.04 <0.001
Tiller number BJ 214 5.67~30.67 12.93±4.36 193.3 0.25±0.12*
SH 188 6.25~25.67 23.27±2.99 85.93 0.20±0.05*
LfThck (μm) BJ 214 105.67~420.0 210.85±49.86 149.1 <0.001
SH 183 175.07~355.01 209.43±32.30 95.22 <0.001 588 589 590 591 592 593
Table 2. Analysis of variance (F-values and significance) on effects of environments, 594 genotype and environment × genotype interaction on each photosynthetic trait. 595 Stomatal conductance measured under low light (gslow) and at night (gsdark) were not 596 determined under SH experimental condition. ND: no determination. Symbols "*, **, 597 ***" were depicted as P < 0.05, 0.01 and 0.001, respectively. See Table 1 for the 598 detailed abbreviations of the PTs. 599
600
Traits Genotype Environment Interactions
A 5.494*** 165.50*** 8.931***
gs 5.607*** 358.80*** 11.100***
WUE 6.795*** 16.40*** 10.360***
Ci 3.350*** 376.50*** 2.488***
Ls 0.296 135.80*** 4.681***
Alow 3.617*** 708.80*** 6.958***
gslow 1.155 ND ND
Wlow 1.197* 333.40*** 17.340***
Cilow 1.907 109.10*** 1.127
Lslow 0.397 114.72*** 15.110***
Adark 4.338*** 55.84*** 21.120***
gsdark 0.980 ND ND
SPAD 7.68*** 10.18*** 5.438***
Fv/Fm 3.695*** 118.20*** 0.328***
Biomass 6.549*** 45.40*** 16.860***
PltHt 2.554*** 176.80*** 5.650***
Tiller number 1.696*** 2022.00*** 17.910***
LfThck 0.463 986.00*** 15.440*** 601
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602 603
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Table 3. Comparison of photosynthetic rates under low light (Alow) between elite 604 cultivars with those of the minicore accession showing highest Alow. Percentage 605 difference (PD) was expressed as: (Alow of extreme accession - Alow of elite 606 cultivar)/(Alow of extreme accession) × 100%. P4140 is an accession in the minicore 607 population which showed the highest values in Alow in the BJ environment. 608 609
610 Elite accessions Mean±SD PD
WCC1 5.39±0.45 22.88
WCC2 5.82±0.83 13.65
DHX-Z 3.74±1.03 76.77
HE19 5.93±0.29 11.68
KY131 5.66±0.88 17.02
XS134 4.87±0.51 35.91
ZH11 3.57±0.60 85.49
MH63 4.66±0.15 41.97
KALS 4.98±0.56 32.97
9311 5.12±0.51 29.21
WY-4 4.57±0.72 44.83
Target accession P4140
611 612 613 614 615
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616
617 Reference 618
Ackerly DD, Dudley SA, Sultan SE, Schmitt H, Coleman JS, Linder CR, 619
Sandquist DR, Geber MA, Evans AS, Dawson TE, et al (2000) The evolution 620
of plant ecophysiological traits: recent advances and future directions. 621
Bioscience 50: 979–995 622
Agrama HA, Yan WG, Jia M, Fjellstrom R, McClung A (2010) Genetic structure 623
associated with diversity and geographic distribution in the USDA rice world 624
collection. Nat Sci 2: 247–291 625
Agrama HA, Yan W, Lee F, Robert F, Chen MH, Jia M, McClung A (2009) 626
Genetic assessment of a mini-core subset developed from the USDA rice 627
genebank. Crop Sci 49: 1336–1346 628
Bunce JA (2007) Direct and acclimatory responses of dark respiration and 629
translocation to temperature. Ann Bot 100: 67–73. 630
Carmo-Silva E, Andralojc PJ, Scales JC, Driever SM, Mead A, Lawson T, 631
Raines CA, Parry MAJ (2017) Phenotyping of field-grown wheat in the UK 632
highlights contribution of light response of photosynthesis and flag leaf 633
longevity to grain yield. J Exp Bot 61: 235–261 634
Chang TG, Xin CP, Qu MN, Zhu XG (2017) Evaluation of protocols for measuring 635
leaf photosynthetic properties of field-grown rice. Rice Sci 24: 1–9 636
Driever SM, Lawson T, Andralojc PJ, Raines C A, Parry M A J (2014) Natural 637
variation in photosynthetic capacity, growth, and yield in 64 field-grown wheat 638
genotypes. J Exp Bot 65: 1–15 639
Essemine J, Qu M, Mi H, Zhu X-G (2016) Response of chloroplast NAD(P)H 640
dehydrogenase-mediated cyclic electron flow to a shortage or lack in 641
ferredoxin-quinone oxidoreductase-dependent pathway in rice following 642
short-term heat stress. Front Plant Sci 7: 1–15 643
Essemine J, Xiao Y, Qu M, Mi H, Zhu XG (2017) Cyclic electron flow may 644
provide some protection against PSII photoinhibition in rice (Oryza sativa L.) 645
leaves under heat stress. J Plant Physiol 211: 138–146 646
Flood PJ, Harbinson J, Aarts MGM (2011) Natural genetic variation in plant 647
photosynthesis. Trends Plant Sci 16: 327–35 648
www.plantphysiol.orgon March 30, 2020 - Published by Downloaded from Copyright © 2017 American Society of Plant Biologists. All rights reserved.
24
Geber MA, Dawson TE (1997) Genetic variation in stomatal and biochemical 649
limitations to photosynthesis in the annual plant, Polygonum arenastrum. 650
Oecologia 109: 535–546 651
Gepts P (2002) A comparison between crop domestication, classical plant breeding, 652
and genetic engineering. Crop Sci 42: 1780–1790 653
Gu J, Yin X, Stomph TJ, Struik PC (2014) Can exploiting natural genetic variation 654
in leaf photosynthesis contribute to increasing rice productivity? A simulation 655
analysis. Plant, Cell Environ 37: 22–34 656
Hamdani S, Qu M, Xin C-P, Li M, Chu C, Govindjee, Zhu XG (2015) Variations 657
between the photosynthetic properties of elite and landrace Chinese rice cultivars 658
revealed by simultaneous measurements of 820nm transmission signal and 659
chlorophyll a fluorescence induction. J Plant Physiol. doi: 660
10.1016/j.jplph.2014.12.019 661
Hedden P (2003) Constructing dwarf rice. Nature Biotechnology 21: 873–874. 662
Huang W, Yang YJ, Hu H, Cao KF, Zhang SB (2016) Sustained diurnal 663
stimulation of cyclic electron flow in two tropical tree species Erythrophleum 664
guineense and Khaya ivorensis. Front Plant Sci 7: 1–12 665
Hubbart S, Peng S, Horton P, Chen Y, Murchie EH (2007) Trends in leaf 666
photosynthesis in historical rice varieties developed in the Philippines since 1966. 667
J Exp Bot 58: 3429–3438 668
Iwasaki T, Takeda Y, Maruyama K, Yokosaki Y, Tsujino K, Tetsumoto S, 669
Kuhara H, Nakanishi K, Otani Y, Jin Y, et al (2013) Deletion of tetraspanin 670
CD9 diminishes lymphangiogenesis in vivo and in vitro. J Biol Chem 288: 671
2118–2131 672
Jahn CE, Mckay JK, Mauleon R, Stephens J, McNally KL, Bush DR, Leung H, 673
Leach JE (2011) Genetic variation in biomass traits among 20 diverse rice 674
varieties. Plant Physiol 155: 157–68 675
Jin H, Qiao F, Chen L, Lu C, Xu L, Gao X (2014) Serum metabolomic signatures 676
of lymph node metastasis of esophageal squamous cell carcinoma. J Proteome 677
Res 13: 4091–4103 678
Kawamura K, Watanabe N, Sakanoue S, Lee HJ, Inoue Y, Odagawa S (2010) 679
Testing genetic algorithm as a tool to select relevant wavebands from field 680
hyperspectral data for estimating pasture mass and quality in a mixed sown 681
pasture using partial least squares regression. Grassl Sci 56: 205–216 682
www.plantphysiol.orgon March 30, 2020 - Published by Downloaded from Copyright © 2017 American Society of Plant Biologists. All rights reserved.
25
Kromdijk J, Glowacka K, Leonelli L, Gabilly ST, Niyogi KK, Long SP (2016) 683
Improving photosynthesis and crop productivity by accelerating recovery from 684
photoprotection. Science 354: 857–861. 685
Lawson T, Blatt MR (2014) Stomatal Size , Speed , and Responsiveness Impact on 686
Photosynthesis and water use efficiency. Plant Physiol 164: 1556–1570 687
Lawson T, Kramer DM, Raines C a. (2012) Improving yield by exploiting 688
mechanisms underlying natural variation of photosynthesis. Curr Opin 689
Biotechnol 23: 215–220 690
Li X, Yan W, Agrama H, Hu B, Jia L, Jia M, Jackson A, Moldenhauer K, 691
McClung A, Wu D (2010) Genotypic and phenotypic characterization of genetic 692
differentiation and diversity in the USDA rice mini-core collection. Genetica 138: 693
1221–30 694
Long SP, Marshall-Colon A, Zhu XG (2015) Meeting the global food demand of 695
the future by engineering crop photosynthesis and yield potential. Cell 161: 56–696
66 697
Long SP, Zhu XG, Naidu SL, Ort DR (2006) Can improvement in photosynthesis 698
increase crop yields? Plant, Cell Environ 29: 315–330 699
McKown AD, Guy RD, Quamme L, Klápště J, La Mantia J, Constabel CP, 700
El-Kassaby YA, Hamelin RC, Zifkin M, Azam MS (2014) Association 701
genetics, geography and ecophysiology link stomatal patterning in Populus 702
trichocarpa with carbon gain and disease resistance trade-offs. 703
Mol Ecol 23: 5771–5790 704
Ojima M (1974) Improvement of Photosynthetic Capacity in Soybean Variety. Japan 705
Agric Res Q 8: 6–12 706
Oxborough K, Baker NR (1997) Resolving chlorophyll a fluorescence images of 707
photosynthetic efficiency into photochemical and non-photochemical 708
components - Calculation of qP and Fv’/Fm’ without measuring Fo’. Photosynth 709
Res 54: 135–142 710
Parry MAJ, Reynolds M, Salvucci ME, Raines C, Andralojc PJ, Zhu XG, Price 711
GD, Condon AG, Furbank RT (2011) Raising yield potential of wheat. II. 712
Increasing photosynthetic capacity and efficiency. J Exp Bot 62: 453–467 713
Peng S, Laza RC, Visperas RM, Sanico AL, Cassman KG KG (2001) Grain yield 714
of rice cultivars and lines developed in the Philip- pines since 1966. Crop Sci 40: 715
307–314 716
www.plantphysiol.orgon March 30, 2020 - Published by Downloaded from Copyright © 2017 American Society of Plant Biologists. All rights reserved.
26
Peng S, Khush GS, Virk P, Tang Q, Zou Y (2008) Progress in ideotype breeding to 717
increase rice yield. Field Crops Research 108: 32–38 718
Qu M, Hamdani S, Li W, Wang S, Tang J, Chen Z, Song Q, Li M, Zhao H, 719
Chang T, et al (2016) Rapid stomatal response to fluctuating light: An 720
under-explored mechanism to improve drought tolerance in rice. Funct Plant 721
Biol 43: 727–738 722
Rajaram S, van Ginkel M, Fischer RA (1995) CIMMYT’s wheat breeding 723
mega-environments (ME). In Proceedings of the 8th International Wheat 724
Genetics Symposium, Jul. 19-24. Beijing, China. 725
Rosenthal DM, Locke AM, Khozaei M, Raines CA, Long SP, Ort DR (2011) 726
Over-expressing the C3 photosynthesis cycle enzyme Sedoheptulose-1-7 727
Bisphosphatase improves photosynthetic carbon gain and yield under fully open 728
air CO2 fumigation (FACE). BMC Plant Biol 11: 123 729
Schuster WSF, Phillips SL, Sandquist DR, Ehleringer JR (1992) Heritability of 730
carbon isotope discrimination in Gutierrezia-microcephala (Asteraceae). Am J 731
Bot 79: 216–221 732
Simkin AJ, McAusland L, Headland LR, Lawson T, Raines CA (2015) Multigene 733
manipulation of photosynthetic carbon assimilation increases CO2 fixation and 734
biomass yield in tobacco. J Exp Bot 66: 4075–4090 735
Song Q, Zhang G, Zhu XG (2013) Optimal crop canopy architecture to maximise 736
canopy photosynthetic CO2 uptake under elevated CO2-A theoretical study using 737
a mechanistic model of canopy photosynthesis. Funct Plant Biol 40: 109–124 738
Takai T, Kondo M, Yano M, Yamamoto T (2010a) A quantitative trait locus for 739
chlorophyll content and its association with leaf photosynthesis in rice. Rice 3: 740
172–180 741
Takai T, Yano M, Yamamoto T (2010b) Canopy temperature on clear and cloudy 742
days can be used to estimate varietal differences in stomatal conductance in rice. 743
F Crop Res 115: 165–170 744
Wang H, Xu X, Vieira FG, Xiao Y, Li Z, Wang J, Nielsen R, Chu C (2016) The 745
power of inbreeding: NGS based GWAS of rice reveals convergent evolution 746
during rice domestication. Mol Plant. doi: 10.1016/j.molp.2016.04.018 747
Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: A tool for 748
genome-wide complex trait analysis. Am J Hum Genet 88: 76–82 749
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27
Zaitlen N, Kraft P (2012) Heritability in the genome-wide association era. Hum 750
Genet 131: 1655–1664 751
Zhu XG, Long SP, Ort DR (2010) Improving photosynthetic efficiency for greater 752
yield. Annu Rev Plant Biol 61: 235–61 753
Zhu XG, Long SP, Ort DR (2008) What is the maximum efficiency with which 754
photosynthesis can convert solar energy into biomass? Curr Opin Biotechnol 19: 755
153–159 756
Zhu XG, Ort DR, Whitmarsh J, Long SP (2004) The slow reversibility of 757
photosystem II thermal energy dissipation on transfer from high to low light may 758
cause large losses in carbon gain by crop canopies: A theoretical analysis. J Exp 759
Bot 55: 1167–1175 760
Zhu XG, Song Q, Ort DR (2012) Elements of a dynamic systems model of canopy 761
photosynthesis. Curr Opin Plant Biol 15: 237–244 762
763
764
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Figure 1. Correlation of photosynthetic traits (PTs) and morphological traits (MTs) in
the global minicore panel and elite rice lines. Data were combined from Beijing (BJ)
and Shanghai (SH) experiments. The abbreviations of PTs are: A: photosynthetic rates
under high light; Alow: photosynthetic rate under low light; Biomass: above-ground
biomass; Ci: internal CO2 under high light; Cilow: internal CO2 under low light; Fv/Fm:
maximum PSII efficiency; gs: stomatal conductance under high light; gslow: stomatal
conductance under low light; Ls: stomatal limitation under high light; Lslow: stomatal
limitation under low light; SPAD: SPAD values; WUE: water use efficiency under
high light; Wlow: water use efficiency under low light; PltHt: plant height; Tiller: tiller
number; Thick: leaf thickness; Biomass: biomass accumulation.
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Figure 2. Graphic representation of correlations of different PTs with MTs in the global
minicore panel and elite rice lines under Beijing (BJ) and Shanghai (SH)
environments. Shaded area at the center represents negative correlation. Trait
abbreviations are given in Table 1.
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Figure 3. Self-correlation of each photosynthetic trait in the global minicore panel
and elite rice lines grown in Beijing (BJ) and Shanghai (SH) environments. The
values of Pearson correlation coefficient (R) were calculated. Asterisks “*”, “**”
represent P value < 0.05, and 0.01, respectively. The abbreviated PT at right-bottom
corner of each panel is as reported in details in Figure 1.
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Figure 4. Model construction and cross-validation in the global minicore panel and
elite rice lines under Beijing (A and B), Shanghai (C and D) and the combined (E
and F) datasets. The training dataset consists of 90% of the whole dataset and the
remaining 10% items were used as a test dataset (see Material and Method for
details). Predicted values of biomass versus observed values of biomass were used
during the cross-validation. Determination index (R2) reflects the accuracy of
regression between predicted and observed values.
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Figure 5. Features selections analysis on photosynthetic traits (PTs) using Linear
Regression Models (LRMs). Key PTs identified by models were represented
constructed under Beijing (BJ), Shanghai (SH) and their combined environments
(Combine). The equations under BJ, SH and Combined environments are listed as
followings: Biomass(BJ) = 0.096 gs + 0.115 Alow+ 0.311 × Plant height + 0.355 ×
Tiller number; Biomass(SH) = 0.053 Ls - 0.081 WUE + 0.152 Alow + 0.077 Lslow +
0.127 × Leaf thickness + 0.341 × Plant height + 0.671 × Tiller number; Biomass
(Combine) = 0.072 gs + 0.169 Ci - 0.089 Ls + 0.107 Alow + 0.192 Wlow - 0.145 Lslow +
0.139 SPAD + 0.448 × Plant height + 0.556 × Tiller number.
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Figure 6. Trait distribution of elite cultivars and the minicore accessions under the
Beijing (BJ) environment. A: Histogram representing the distribution of
photosynthetic rates under low light (Alow) in the minicore diversity panel. B:
Phenotypic distribution of Alow of elite cultivars within the minicore accessions. The
enclosed numbers represent: WCC1①, WCC2②, DHX-Z③, HE19④, KY131⑤, XS134⑥, ZH11⑦, MH63⑧, KALS⑨, 9311⑩, and WY-4⑪.
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Parsed CitationsAckerly DD, Dudley SA, Sultan SE, Schmitt H, Coleman JS, Linder CR, Sandquist DR, Geber MA, Evans AS, Dawson TE, et al (2000) Theevolution of plant ecophysiological traits: recent advances and future directions. Bioscience 50: 979-995
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Agrama HA, Yan WG, Jia M, Fjellstrom R, McClung A (2010) Genetic structure associated with diversity and geographic distribution inthe USDA rice world collection. Nat Sci 2: 247-291
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Agrama HA, Yan W, Lee F, Robert F, Chen MH, Jia M, McClung A (2009) Genetic assessment of a mini-core subset developed from theUSDA rice genebank. Crop Sci 49: 1336-1346
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Bunce JA (2007) Direct and acclimatory responses of dark respiration and translocation to temperature. Ann Bot 100: 67-73.Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Carmo-Silva E, Andralojc PJ, Scales JC, Driever SM, Mead A, Lawson T, Raines CA, Parry MAJ (2017) Phenotyping of field-grown wheatin the UK highlights contribution of light response of photosynthesis and flag leaf longevity to grain yield. J Exp Bot 61: 235-261
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Chang TG, Xin CP, Qu MN, Zhu XG (2017) Evaluation of protocols for measuring leaf photosynthetic properties of field-grown rice.Rice Sci 24: 1-9
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Driever SM, Lawson T, Andralojc PJ, Raines C A, Parry M A J (2014) Natural variation in photosynthetic capacity, growth, and yield in 64field-grown wheat genotypes. J Exp Bot 65: 1-15
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Essemine J, Qu M, Mi H, Zhu X-G (2016) Response of chloroplast NAD(P)H dehydrogenase-mediated cyclic electron flow to a shortageor lack in ferredoxin-quinone oxidoreductase-dependent pathway in rice following short-term heat stress. Front Plant Sci 7: 1-15
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Essemine J, Xiao Y, Qu M, Mi H, Zhu XG (2017) Cyclic electron flow may provide some protection against PSII photoinhibition in rice(Oryza sativa L.) leaves under heat stress. J Plant Physiol 211: 138-146
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Flood PJ, Harbinson J, Aarts MGM (2011) Natural genetic variation in plant photosynthesis. Trends Plant Sci 16: 327-35Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Geber MA, Dawson TE (1997) Genetic variation in stomatal and biochemical limitations to photosynthesis in the annual plant,Polygonum arenastrum. Oecologia 109: 535-546
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Gepts P (2002) A comparison between crop domestication, classical plant breeding, and genetic engineering. Crop Sci 42: 1780-1790Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Gu J, Yin X, Stomph TJ, Struik PC (2014) Can exploiting natural genetic variation in leaf photosynthesis contribute to increasing riceproductivity? A simulation analysis. Plant, Cell Environ 37: 22-34
Pubmed: Author and TitleCrossRef: Author and Title www.plantphysiol.orgon March 30, 2020 - Published by Downloaded from
Copyright © 2017 American Society of Plant Biologists. All rights reserved.
Google Scholar: Author Only Title Only Author and Title
Hamdani S, Qu M, Xin C-P, Li M, Chu C, Govindjee, Zhu XG (2015) Variations between the photosynthetic properties of elite andlandrace Chinese rice cultivars revealed by simultaneous measurements of 820nm transmission signal and chlorophyll a fluorescenceinduction. J Plant Physiol. doi: 10.1016/j.jplph.2014.12.019
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Hedden P (2003) Constructing dwarf rice. Nature Biotechnology 21: 873-874.Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Huang W, Yang YJ, Hu H, Cao KF, Zhang SB (2016) Sustained diurnal stimulation of cyclic electron flow in two tropical tree speciesErythrophleum guineense and Khaya ivorensis. Front Plant Sci 7: 1-12
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Hubbart S, Peng S, Horton P, Chen Y, Murchie EH (2007) Trends in leaf photosynthesis in historical rice varieties developed in thePhilippines since 1966. J Exp Bot 58: 3429-3438
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Iwasaki T, Takeda Y, Maruyama K, Yokosaki Y, Tsujino K, Tetsumoto S, Kuhara H, Nakanishi K, Otani Y, Jin Y, et al (2013) Deletion oftetraspanin CD9 diminishes lymphangiogenesis in vivo and in vitro. J Biol Chem 288: 2118-2131
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Jahn CE, Mckay JK, Mauleon R, Stephens J, McNally KL, Bush DR, Leung H, Leach JE (2011) Genetic variation in biomass traits among20 diverse rice varieties. Plant Physiol 155: 157-68
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Jin H, Qiao F, Chen L, Lu C, Xu L, Gao X (2014) Serum metabolomic signatures of lymph node metastasis of esophageal squamous cellcarcinoma. J Proteome Res 13: 4091-4103
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Kawamura K, Watanabe N, Sakanoue S, Lee HJ, Inoue Y, Odagawa S (2010) Testing genetic algorithm as a tool to select relevantwavebands from field hyperspectral data for estimating pasture mass and quality in a mixed sown pasture using partial least squaresregression. Grassl Sci 56: 205-216
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Kromdijk J, Glowacka K, Leonelli L, Gabilly ST, Niyogi KK, Long SP (2016) Improving photosynthesis and crop productivity byaccelerating recovery from photoprotection. Science 354: 857-861.
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Lawson T, Blatt MR (2014) Stomatal Size , Speed , and Responsiveness Impact on Photosynthesis and water use efficiency. PlantPhysiol 164: 1556-1570
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Lawson T, Kramer DM, Raines C a. (2012) Improving yield by exploiting mechanisms underlying natural variation of photosynthesis.Curr Opin Biotechnol 23: 215-220
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Li X, Yan W, Agrama H, Hu B, Jia L, Jia M, Jackson A, Moldenhauer K, McClung A, Wu D (2010) Genotypic and phenotypiccharacterization of genetic differentiation and diversity in the USDA rice mini-core collection. Genetica 138: 1221-30
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
www.plantphysiol.orgon March 30, 2020 - Published by Downloaded from Copyright © 2017 American Society of Plant Biologists. All rights reserved.
Long SP, Marshall-Colon A, Zhu XG (2015) Meeting the global food demand of the future by engineering crop photosynthesis and yieldpotential. Cell 161: 56-66
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Long SP, Zhu XG, Naidu SL, Ort DR (2006) Can improvement in photosynthesis increase crop yields? Plant, Cell Environ 29: 315-330Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
McKown AD, Guy RD, Quamme L, Klápšte J, La Mantia J, Constabel CP, El-Kassaby YA, Hamelin RC, Zifkin M, Azam MS (2014)Association genetics, geography and ecophysiology link stomatal patterning in Populus trichocarpa with carbon gain and diseaseresistance trade-offs. Mol Ecol 23: 5771-5790
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Ojima M (1974) Improvement of Photosynthetic Capacity in Soybean Variety. Japan Agric Res Q 8: 6-12Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Oxborough K, Baker NR (1997) Resolving chlorophyll a fluorescence images of photosynthetic efficiency into photochemical and non-photochemical components - Calculation of qP and Fv'/Fm' without measuring Fo'. Photosynth Res 54: 135-142
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Parry MAJ, Reynolds M, Salvucci ME, Raines C, Andralojc PJ, Zhu XG, Price GD, Condon AG, Furbank RT (2011) Raising yield potentialof wheat. II. Increasing photosynthetic capacity and efficiency. J Exp Bot 62: 453-467
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Peng S, Laza RC, Visperas RM, Sanico AL, Cassman KG KG (2001) Grain yield of rice cultivars and lines developed in the Philip- pinessince 1966. Crop Sci 40: 307-314
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Peng S, Khush GS, Virk P, Tang Q, Zou Y (2008) Progress in ideotype breeding to increase rice yield. Field Crops Research 108: 32-38Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Qu M, Hamdani S, Li W, Wang S, Tang J, Chen Z, Song Q, Li M, Zhao H, Chang T, et al (2016) Rapid stomatal response to fluctuatinglight: An under-explored mechanism to improve drought tolerance in rice. Funct Plant Biol 43: 727-738
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Rajaram S, van Ginkel M, Fischer RA (1995) CIMMYT's wheat breeding mega-environments (ME). In Proceedings of the 8thInternational Wheat Genetics Symposium, Jul. 19-24. Beijing, China.
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Rosenthal DM, Locke AM, Khozaei M, Raines CA, Long SP, Ort DR (2011) Over-expressing the C3 photosynthesis cycle enzymeSedoheptulose-1-7 Bisphosphatase improves photosynthetic carbon gain and yield under fully open air CO2 fumigation (FACE). BMCPlant Biol 11: 123
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Schuster WSF, Phillips SL, Sandquist DR, Ehleringer JR (1992) Heritability of carbon isotope discrimination in Gutierrezia-microcephala (Asteraceae). Am J Bot 79: 216-221
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Simkin AJ, McAusland L, Headland LR, Lawson T, Raines CA (2015) Multigene manipulation of photosynthetic carbon assimilationincreases CO2 fixation and biomass yield in tobacco. J Exp Bot 66: 4075-4090
Pubmed: Author and TitleCrossRef: Author and Title
www.plantphysiol.orgon March 30, 2020 - Published by Downloaded from Copyright © 2017 American Society of Plant Biologists. All rights reserved.
Google Scholar: Author Only Title Only Author and Title
Song Q, Zhang G, Zhu XG (2013) Optimal crop canopy architecture to maximise canopy photosynthetic CO2 uptake under elevatedCO2-A theoretical study using a mechanistic model of canopy photosynthesis. Funct Plant Biol 40: 109-124
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Takai T, Kondo M, Yano M, Yamamoto T (2010a) A quantitative trait locus for chlorophyll content and its association with leafphotosynthesis in rice. Rice 3: 172-180
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Takai T, Yano M, Yamamoto T (2010b) Canopy temperature on clear and cloudy days can be used to estimate varietal differences instomatal conductance in rice. F Crop Res 115: 165-170
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Wang H, Xu X, Vieira FG, Xiao Y, Li Z, Wang J, Nielsen R, Chu C (2016) The power of inbreeding: NGS based GWAS of rice revealsconvergent evolution during rice domestication. Mol Plant. doi: 10.1016/j.molp.2016.04.018
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: A tool for genome-wide complex trait analysis. Am J Hum Genet 88: 76-82Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Zaitlen N, Kraft P (2012) Heritability in the genome-wide association era. Hum Genet 131: 1655-1664Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Zhu XG, Long SP, Ort DR (2010) Improving photosynthetic efficiency for greater yield. Annu Rev Plant Biol 61: 235-61Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Zhu XG, Long SP, Ort DR (2008) What is the maximum efficiency with which photosynthesis can convert solar energy into biomass?Curr Opin Biotechnol 19: 153-159
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Zhu XG, Ort DR, Whitmarsh J, Long SP (2004) The slow reversibility of photosystem II thermal energy dissipation on transfer from highto low light may cause large losses in carbon gain by crop canopies: A theoretical analysis. J Exp Bot 55: 1167-1175
Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
Zhu XG, Song Q, Ort DR (2012) Elements of a dynamic systems model of canopy photosynthesis. Curr Opin Plant Biol 15: 237-244Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title
www.plantphysiol.orgon March 30, 2020 - Published by Downloaded from Copyright © 2017 American Society of Plant Biologists. All rights reserved.