A Gene‐Based Model to Simulate Soybean Development and Yield Responses to Environment

Wiley - Tập 46 Số 1 - Trang 456-466 - 2006
Carlos D. Messina1, James W. Jones1, Kenneth J. Boote2, C. Eduardo Vallejos3
1Agric.& Biol. Eng. Dep. Univ. of Florida Gainesville FL 32611
2Dep. of Agronomy Univ. of Florida Gainesville FL 32611
3Horticultural Sci. Dep. Univ. of Florida Gainesville FL 32611

Tóm tắt

Realizing the potential of agricultural genomics into practical applications requires quantitative predictions for complex traits and different genotypes and environmental conditions. The objective of this study was to develop and test a procedure for quantitative prediction of phenotypes as a function of environment and specific genetic loci in soybean [Glycine max (L.) Merrill]. We combined the ecophysiological model CROPGRO‐Soybean with linear models that predict cultivar‐specific parameters as functions of E loci. The procedure involved three steps: (i) a field experiment was conducted in Florida in 2001 to obtain phenotypic data for a set of near‐isogenic lines (NILs) with known genotypes at six E loci; (ii) we used these data to estimate cultivar‐specific parameters for CROPGRO‐Soybean, minimizing root mean square error (RMSE) between observed and simulated values; (iii) these parameters were then expressed as linear functions of the (known) E loci. CROPGRO‐Soybean predicted various phenological stages for the same NILs grown in 2002 in Florida with a RMSE of about 5 d using the E loci–derived parameters. A second evaluation of the approach used phenotypic data from cultivar trials conducted in Illinois. Cultivars were genotyped at the E loci using microsatellites. The model predicted time to maturity in the Illinois variety trials with RMSE around 7.5 d; it also explained 75% of the time‐to‐maturity variance and 54% of the yield variance. Our results suggest that gene‐based approaches can effectively use agricultural genomics data for cultivar performance prediction. This technology may have multiple uses in plant breeding.

Từ khóa


Tài liệu tham khảo

10.2135/cropsci2003.1300

10.2135/cropsci1964.0011183X000400050021x

10.2135/cropsci1971.0011183X001100020022x

Boote K.J., 2003, Genetic coefficients in the CROPGRO-Soybean model: Links to field performance and genomics, Agron. J., 95, 32

Boote K.J., 1998, Agricultural systems modeling and simulation, 651

10.1016/S0308-521X(01)00053-1

Buzzel R.I., 1971, Inheritance of a soybean flowering response to fluorescent-daylength condictions, Can. J. Genet. Cytol., 13, 703, 10.1139/g71-100

Buzzel R.I., 1980, Inheritance of insensitivity to daylength, Soybean Genet. Newsl., 7, 26

10.2134/agronj2003.9900

10.2135/cropsci2001.413721x

10.2135/cropsci1996.0011183X003600030013x

10.2135/cropsci2001.413698x

Cooper M. S.C.Chapman D.W.Podlich andG.L.Hammer.2002.The GP problem: Quantifying gene-to-phenotype relationships [Online]. Available atwww.bioinfo.de/isb/2002/02/0013/(verified 4 Sept. 2005). In Silica Biol. 2:0013

10.2135/cropsci1999.3951464x

10.1016/S0958-1669(02)00297-5

Elizondo D.A., 1994, Neural network models for predicting flowering and physiological maturity of soybean, Trans. ASAE, 37, 981, 10.13031/2013.28168

Fehr W.R., 1977, Stages of soybean development. Spec. Rep. 80. Iowa Agric. Home Econ. Exp. Stn.

Grant D. andR.C.Shoemaker.2005.SoyBase the USDA-ARS soybean genome database [Online]. Available atsoybase.org(verified 4 Sept. 2005).

10.2134/agronj1994.00021962008600010007x

10.2135/cropsci1993.0011183X003300010025x

10.1104/pp.103.034827

Hoogenboom G., 2003, Improving physiological assumptions of simulation models by using gene-based approaches, Agron. J., 95, 82

10.2134/agronj1997.00021962008900040013x

10.1016/j.fcr.2004.07.014

10.1038/nature01015

Hunt L.A., 1998, Understanding options for agricultural production, 41

10.2134/agronj1993.00021962008500050025x

10.1016/S1161-0301(02)00107-7

10.1016/S1161-0301(02)00108-9

10.1073/pnas.92.10.4656

10.1007/BF00223665

10.1007/BF00211040

10.2135/cropsci1996.0011183X003600050042x

10.2135/cropsci2001.41140x

10.2135/cropsci2002.7600

10.1093/oxfordjournals.jhered.a110349

10.4141/cjps87-012

Messina C.D., 2003, Gene-based systems approach to simulate soybean growth and development and application to ideotype design in target environments

Midwestern Regional Climate Center.2005.Weather data [Online]. Available atmcc.sws.uiuc.edu/(verified 25 Aug. 2005).

10.1139/g03-079

Murray M.G., 1980, Rapid isolation of high molecular weight plant DNA, Nucleic Acids Res., 19, 4321, 10.1093/nar/8.19.4321

10.2135/cropsci1999.3961652x

10.1007/978-1-4419-0318-1

10.2135/cropsci1996.0011183X003600060033x

10.1104/pp.013839

10.2135/cropsci1989.0011183X002900060006x

10.1126/science.285.5426.380

Stewart D.W., 2003, Modeling genetic effects on the photothermal response of soybean phenological development, Agron. J., 95, 65, 10.2134/agronj2003.6500

10.2135/cropsci1999.3961571x

10.1006/anbo.1998.0755

10.1023/A:1011998116037

10.2135/cropsci2003.3190

University of Illinois.2005.Soybeans in Illinois.Variety testing data [Online]. Available atvt.cropsci.uiuc.edu/soybean.html(Verified 25 Aug. 2005).

10.1093/aob/74.1.87

Upadhyay A.P., 1994, [Glycine max (L.) Merrill] Variations in the durations of the photoperiod-sensitive and photoperiod-insensitive phases of development to flowering among eight maturity isolines of soya-bean, Ann. Bot. (London), 74, 97, 10.1093/aob/74.1.97

Vallejos C.E., 1992, A molecular marker-based linkage map of Phaseolus vulgaris L, Genetics, 131, 733, 10.1093/genetics/131.3.733

10.1111/j.1469-8137.1990.tb00524.x

10.2134/agronj1996.00021962008800030009x

White J.W., 2003, Gene-based approaches to crop simulation: Past experiences and future opportunities, Agron. J., 95, 52

10.2134/agronj2003.9000a

Zhu Y.L., 2003, Single-nucleotide polymorphisms in soybean, Genetics, 163, 1123, 10.1093/genetics/163.3.1123

Thông tin
Thông tin xuất bản

Wiley

Tập 46 Số 1

456-466

Thông tin tác giả