Reduced soybean photosynthetic nitrogen use efficiency associated with evolutionary genetic bottlenecks
José L. Rotundo A B and Lucas Borrás A
+ Author Affiliations
- Author Affiliations
A Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Campo Exp. Villarino S/N, S2125ZAA, Zavalla, Santa Fe, Argentina.
B Corresponding author. Email: jrotundo@unr.edu.ar
Functional Plant Biology 43(9) 862-869 https://doi.org/10.1071/FP16018
Submitted: 15 January 2016 Accepted: 3 May 2016 Published: 7 June 2016
Abstract
Soybean has a narrow genetic base thought to limit future yield genetic gains. However, there is no evidence whether this reduction in genetic diversity correlates with diversity loss for any yield trait. We tested how photosynthetic nitrogen use efficiency (leaf photosynthesis per unit nitrogen, NUEp) evolved from the wild relative Glycine soja Siebold & Zucc. to the current Glycine max (L.) Merr. Five populations resulting from different evolutionary bottlenecks were evaluated under field conditions. Populations were wild ancestors, domesticated Asian landraces, North American ancestors, and modern cultivars. Genotypic differences in photosynthesis and leaf nitrogen were evident, creating a significant 3-fold variation in phenotypic NUEp. There was a parallel reduction in molecular marker and phenotypic NUEp diversity after each evolutionary bottleneck. G. soja had three times more NUEp diversity and 25% more average NUEp compared with the elite modern cultivars. Two strategies for increasing NUEp were identified: (i) increases in light saturated photosynthesis (Pmax), and, alternatively, (ii) reductions in leaf nitrogen. A modelling approach showed that NUEp will increase yield only if based on increased Pmax. Our study quantified the genetic potential of exotic germplasm available for trait-directed breeding. Results antagonise the concept that elite germplasm is always superior for any relevant yield trait when compared with undomesticated germplasm.
Additional keywords: carbon assimilation, genetic gain, natural genetic variation, phenotypic diversity, trait based hybridisation.
References
Ainsworth EA, Yendrek CR, Skoneczka JA, Long SP (2012) Accelerating yield potential in soybean: potential targets for biotechnological improvement. Plant, Cell & Environment 35, 38–52.| Accelerating yield potential in soybean: potential targets for biotechnological improvement.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XjvVWht78%3D&md5=8f45fef0eeb6e1777150ad9929c789b5CAS |
Aramburu Merlos F, Monzon JP, Mercau JL, Taboada M, Andrade FH, Hall AJ, Jobbagy E, Cassman K, Grassini P (2015) Potential for crop production increase in Argentina through closure of existing yield gaps. Field Crops Research 184, 145–154.
| Potential for crop production increase in Argentina through closure of existing yield gaps.Crossref | GoogleScholarGoogle Scholar |
Boote KJ, Jones JW, Batchelor WD, Nafziger ED, Myers O (2003) Genetic coefficients in the CROPGRO-Soybean model: links to field performance and genomics. Agronomy Journal 95, 32–51.
| Genetic coefficients in the CROPGRO-Soybean model: links to field performance and genomics.Crossref | GoogleScholarGoogle Scholar |
Bowman AW, Azzalini A (2014) R package ‘sm’: nonparametric smoothing methods. (ver. 2.2–5.4). Available at http://www.stats.gla.ac.uk/~adrian/sm [Verified 10 April 2015]
Concibido VC, La Vallee B, McLaird P, Pineda N, Meyer J, Hummel L, Yang J, Wu K, Delannay X (2003) Introgression of a quantitative trait locus for yield from Glycine soja into commercial soybean cultivars. Theoretical and Applied Genetics 106, 575–582.
Denison RF (2009) Darwinian agriculture: real, imaginary and complex trade-offs as constraints and opportunities. In ‘Crop physiology applications for genetic improvement and agronomy’. (Eds VO Sadras, DJ Miralles) pp. 213–231. (Academic Press: New York)
Dornhoff GM, Shibles RM (1970) Varietal differences in net photosynthesis of soybean leaves. Crop Science 10, 42–45.
| Varietal differences in net photosynthesis of soybean leaves.Crossref | GoogleScholarGoogle Scholar |
Evans LT (1993) ‘Crop evolution, adaptation and yield.’ (Cambridge University Press: New York)
Gu J, Yin X, Stomph TJ, Struik PC (2014) Can exploiting natural genetic variation in leaf photosynthesis contribute to increasing rice productivity? A simulation analysis. Plant, Cell & Environment 37, 22–34.
| Can exploiting natural genetic variation in leaf photosynthesis contribute to increasing rice productivity? A simulation analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhvVyjurnL&md5=92956978f0855462d119ff112cdd11d6CAS |
Gur A, Zamir D (2004) Unused natural variation can lift yield barriers in plant breeding. PLoS Biology 2, e245
| Unused natural variation can lift yield barriers in plant breeding.Crossref | GoogleScholarGoogle Scholar | 15328532PubMed |
Harrison MT, Edwards EJ, Farquhar GD, Nicotra AB, Evans JR (2009) Nitrogen in cell walls of sclerophyllous leaves accounts for little of the variation in photosynthetic nitrogen-use efficiency. Plant, Cell & Environment 32, 259–270.
| Nitrogen in cell walls of sclerophyllous leaves accounts for little of the variation in photosynthetic nitrogen-use efficiency.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXjsVKlsLw%3D&md5=6e1f505460503872df313bd4b28548e9CAS |
Hyten DL, Song QJ, Zhu YL, Choi IY, Nelson RL, Costa JM, Specht JE, Shoemaker RC, Cregan PB (2006) Impacts of genetic bottlenecks on soybean genome diversity. Proceedings of the National Academy of Sciences of the United States of America 103, 16666–16671.
| Impacts of genetic bottlenecks on soybean genome diversity.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xht1egtbnE&md5=b88d0801f88cfbdc5bf92e707d148367CAS | 17068128PubMed |
Jin J, Liu X, Wang G, Liu J, Mi L, Chen X, Herbert SJ (2011) Leaf nitrogen status as a main contributor to yield improvement of soybean cultivars. Agronomy Journal 103, 441–448.
| Leaf nitrogen status as a main contributor to yield improvement of soybean cultivars.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXlslKmtbw%3D&md5=c401d3ca3e9fe50d55453ca215b99682CAS |
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. European Journal of Agronomy 18, 235–265.
| The DSSAT cropping system model.Crossref | GoogleScholarGoogle Scholar |
Long SP, Zhu XG, Naidu SL, Ort DR (2006) Can improvement in photosynthesis increase crop yields? Plant, Cell & Environment 29, 315–330.
| Can improvement in photosynthesis increase crop yields?Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xktlyltrw%3D&md5=7311811d5bb2ce3a9945c95e02730e45CAS |
Mavromatis T, Boote KJ, Jones JW, Irmak A, Shinde D, Hoogenboom G (2001) Developing genetic coefficients for crop simulation models with data from crop performance trials. Crop Science 41, 40–51.
| Developing genetic coefficients for crop simulation models with data from crop performance trials.Crossref | GoogleScholarGoogle Scholar |
Mercau JL, Dardanelli JL, Collino DJ, Andriani JM, Irigoyen A, Satorre EH (2007) Predicting on-farm soybean yields in the pampas using CROPGRO-soybean. Field Crops Research 100, 200–209.
| Predicting on-farm soybean yields in the pampas using CROPGRO-soybean.Crossref | GoogleScholarGoogle Scholar |
Mikel MA, Diers BW, Nelson RL, Smith HH (2010) Genetic diversity and agronomic improvement of north American soybean germplasm. Crop Science 50, 1219–1228.
| Genetic diversity and agronomic improvement of north American soybean germplasm.Crossref | GoogleScholarGoogle Scholar |
Quick WP, Schurr U, Fichtner K, Schulze ED, Rodermel SR, Bogorad L, Stitt M (1991) The impact of decreased rubisco on photosynthesis, growth, allocation and storage in tobacco plants which have been transformed with antisense RBCS. The Plant Journal 1, 51–58.
| The impact of decreased rubisco on photosynthesis, growth, allocation and storage in tobacco plants which have been transformed with antisense RBCS.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK38Xht1Gisrs%3D&md5=cddc114480f74c18091189e94cb9fbebCAS |
Reed DH, Frankham R (2001) How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution 55, 1095–1103.
| How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD38%2FisFWitA%3D%3D&md5=f5e03e103bd392ec1295b8d1f340d3f2CAS | 11475045PubMed |
Reynolds M, Dreccer F, Trethowan R (2007) Drought-adaptive traits derived from wheat wild relatives and landraces. Journal of Experimental Botany 58, 177–186.
| Drought-adaptive traits derived from wheat wild relatives and landraces.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlOltLw%3D&md5=29e4e79c2cb3e7cddcac60c99c313d35CAS | 17185737PubMed |
Reynolds M, Bonnett D, Chapman SC, Furbank RT, Manès Y, Mather DE, Parry MAJ (2011) Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies. Journal of Experimental Botany 62, 439–452.
| Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsFyrsbzK&md5=75a1b5969988012aac4c4dae53cb5b88CAS | 20952629PubMed |
Rincker K, Nelson R, Specht J, Sleper D, Cary T, Cianzio SR, Casteel S, Conley S, Chen P, Davis V, Fox C, Graef G, Godsey C, Holshouser D, Jiang GL, Kantartzi SK, Kenworthy W, Lee C, Mian R, McHale L, Naeve S, Orf J, Poysa V, Schapaugh W, Shannon G, Uniatowski R, Wang D, Diers B (2014) Genetic Improvement of US soybean in maturity groups II, III, and IV. Crop Science 54, 1419–1432.
Sinclair TR, Horie T (1989) Leaf nitrogen, photosynthesis, and crop radiation use efficiency – a review. Crop Science 29, 90–98.
| Leaf nitrogen, photosynthesis, and crop radiation use efficiency – a review.Crossref | GoogleScholarGoogle Scholar |
Stupar RM (2010) Into the wild: the soybean genome meets undomesticated relative. Proceedings of the National Academy of Sciences of the United States of America 107, 21947–21948.
| Into the wild: the soybean genome meets undomesticated relative.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXntVGl&md5=9db14c3baea00cfeac381b0e8b75e1caCAS | 21149697PubMed |
Tanksley SD, McCouch SR (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Science 277, 1063–1066.
| Seed banks and molecular maps: unlocking genetic potential from the wild.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2sXlsFSisrw%3D&md5=97a9ee5b058e439f3a04c4769c5c490bCAS | 9262467PubMed |
Wiebold WJ, Shibles R, Green DE (1981) Selection for apparent photosynthesis and related leaf traits in early generations of soybeans. Crop Science 21, 969–973.
| Selection for apparent photosynthesis and related leaf traits in early generations of soybeans.Crossref | GoogleScholarGoogle Scholar |
Xiao J, Grandlillo S, Sang Nag A, McCouch SR, Tanksley SD, Li J, Yuan L (1996) Genes from wild rice improve yield. Nature 384, 223–224.
| Genes from wild rice improve yield.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK28XntFSrtbw%3D&md5=2e1277fda6f40a1af8717054d9196860CAS |
Zhu XG, De Sturler E, Long SP (2007) Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: a numerical simulation using an evolutionary algorithm. Plant Physiology 145, 513–526.
| Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: a numerical simulation using an evolutionary algorithm.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXht1SjsLrF&md5=cfb89fb28db8d8b78264f2b90816531cCAS | 17720759PubMed |
