Systematic optimization of yield-enhancing applications in soybeans

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dc.contributor.author Haverkamp, Bryson en_US
dc.date.accessioned 2015-04-17T19:06:02Z
dc.date.available 2015-04-17T19:06:02Z
dc.date.issued 2015-04-17
dc.identifier.uri http://hdl.handle.net/2097/18942
dc.description.abstract High soybean [Glycine max.] commodity prices in recent years have led to an increase in use of yield enhancing and protecting products. These products need to be evaluated to determine if the use of multiple inputs has a positive impact on yield and how these inputs interact with agronomic practices. The objectives of this study were to evaluate products individually and collectively in input systems, examine interactions between varieties and input systems (IS), seeding rates (SR) and IS, and row spacing (RS) and IS. Field experiments were conducted at high-yielding locations in Kansas and Minnesota in 2012 to 2014 to meet these objectives. Sixteen treatments consisting of individual inputs and inputs combined in systems were evaluated in one experiment. A second experiment evaluated the variety by IS interaction by constructing 18 treatments from a factorial combination of six glyphosate [N-(phosphonomethyl) glycine] resistant varieties and three IS’s: untreated control (UTC), SOYA (combination of possible yield-enhancing products representative of those currently being marketed), and SOYA minus foliar fungicide (SOYA – foliar F). A third experiment evaluated the SR by IS interaction by constructing 12 treatments from a factorial arrangement of six SR’s and two IS’s: UTC and SOYA. A fourth experiment evaluated the RS by IS interaction by constructing 12 treatments from a factorial arrangement of three RS’s and four IS’s: UTC, fungicide and insecticide seed treatment plus foliar fungicide (STFF), SOYA, and SOYA – foliar F. Very few interactions between IS and agronomic practices were detected in any of the experiments. Varieties had an effect on multiple growth parameters but yield differences were marginal; linear-plateau and non-linear models found that seeding rates that maximized yield in this study were similar to University recommendations; and in general, narrow rows produced the greatest yields. The use of inputs and IS’s typically increased seed mass and yield above the UTC across all experiments. However, given current costs and soybean prices, yield response to IS’s was not great enough to cover the additional costs. Overall, it appears producers would be better served by focusing on agronomic practices rather than implementing input systems. en_US
dc.description.sponsorship United Soybean Board Kansas Soybean Commission en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Soybeans en_US
dc.subject Row spacing en_US
dc.subject Inputs en_US
dc.subject Agronomic practices en_US
dc.subject NDVI en_US
dc.subject Canopy coverage en_US
dc.title Systematic optimization of yield-enhancing applications in soybeans en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Agronomy en_US
dc.description.advisor Kraig Roozeboom en_US
dc.subject.umi Agronomy (0285) en_US
dc.date.published 2015 en_US
dc.date.graduationmonth May en_US


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