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CROP ECOLOGY, MANAGEMENT & QUALITY

Identification of Soybean Cultivars That Yield Well at Low Plant Populations

^a Texas Cooperative Extension Service, Texas A&M University, 300 School Street, Rm. 101, Madisonville, TX 77864
^b Dep. of Agronomy, Louisiana Agric. Exp. Stn., LSU Agric. Ctr., Baton Rouge, LA 70803

* Corresponding author (jboard{at}agctr.lsu.edu)

Little information is known concerning cultivar differences for optimal plant population (minimal plant population for best yield) in soybean [Glycine max (L.) Merr.]. Development of cultivars or genotypes having low optimal plant population would reduce seeding costs, avoid some diseases, and minimize lodging. The objectives of this study were to determine cultivar variability for optimal plant population, determine quantitative relationships between yield and other parameters as affected by plant population–cultivar treatment combinations, and to develop a regression model for identifying cultivars that have low optimal populations. The study was planted near Baton Rouge, LA (30° N Lat) on a commerce silt loam soil (fine-silty, mixed, nonacid, thermic, Aeric, Fluvaquent) in a randomized complete block experimental design in a split plot arrangement with four replications and two years (1997 and 1998). Main plots were14 cultivars and split plots were low (95 000 plants ha^-1) and normal (250 000 plants ha^-1) plant populations. Cultivars that optimized yield at low plant population were ‘NKRA452’ and ‘A6911’. Yields were optimized for cultivar–plant population treatment combinations achieving a total vegetative dry matter at R5 of 500 g m^-2 or greater. Partitioning of dry matter into branches was the most important parameter accounting for cultivar yield differences within low plant populations (r² = 0.56), and with the addition of a few other parameters a regression model was developed (r² = 0.83) that could rapidly and easily identify cultivars with low optimal plant population. In conclusion, genotypic differences in low optimal plant population exist and are influenced by dry matter partitioning into branches.

Abbreviations: LAI, leaf area index • maturity group, MG • total vegetative dry matter, TVDM

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