Seeding rate in hard red spring wheat (HRSW) (Triticum aestivum L.) production impacts input cost and grain yield. Predicting the optimal seeding rate (OSR) for HRSW cultivars can aid growers and eliminate the need for costly seeding rate research. Research was conducted to determine the OSR of newer HRSW cultivars (released in 2013 or later) in diverse environments. Nine cultivars with diverse genetic and phenotypic characteristics were evaluated at four seeding rates in 11 environments throughout the northern Great Plains region in 2017-2018. Results from ANOVA indicated environment and cultivar were more important than seeding rate in determining grain yield. Though there was no environment x seeding rate interaction (P=0.37), OSR varied among cultivar within each environment. Cultivar x environment interactions were further explored with the objective of developing a decision support system (DSS) to aid growers in determining the OSR for the cultivar they select, and for the environment in which it is sown. Data from seeding rate trials conducted in ND and MN from 2013-2015 were also used. A novel method for characterizing cultivar for tillering capacity was developed and proposed as a source for information on tillering to be used in statistical modelling. A 10-fold repeated cross-validation of the seeding rate data was analyzed by 10 statistical learning algorithms to determine a model for predicting OSR of newer cultivars. Models were similar in prediction accuracy (P=0.10). The decision tree model was considered the most reliable as bias was minimized by pruning methods, and model variance was acceptable for OSR predictions (RMSE=1.24). Findings from this model were used to develop the grower DSS for determining OSR dependent on cultivar straw strength, tillering capacity, and yield of the environment. Recommendations for OSR ranged from 3.1 to 4.5 million seeds ha-1. Growers can benefit from using this DSS by sowing at OSR relative to their average yields; especially when seeding new HRSW cultivars.

Two studies were conducted from 2017 – 2019 at Stoneville, MS the first was to determine optimum seeding rates on single and twin row configuration during both late and early planting dates. The second study was to compare two row configurations with two planting populations (whole plot) but add management strategies (subplots) within each whole plot. From this data, yield optimization could be reached within each system that likely encompasses everything a producer in the Mississippi Delta could encounter during a growing season. Soybean seed from this data was analyzed for protein, oil, and fatty acid composition to determine which systems produce greater seed quality in terms of seed composition. Normal (non-symptomatic) and seed visually infected with purple seed stain (PSS) were compared to determine how infected soybean seed compared to normal soybean seed. Both seed (infected and non-symptomatic) were compared by measuring seed composition, germination, and vigor. During 2018 and 2019 at Verona, MS the same whole plot with subplot test was used but at this location under rained conditions. Soybean grown under rained conditions is a common practice in that particular region of the state (Northeast) so treatments were designed to be useful to producers in that area.

Within Arkansas, Louisiana, Mississippi, Missouri, and Texas, rice acreage is rotated with soybean due to both crops’ adaptability to the clay soils of the midsouthern USA. Two row patterns, two maturity groups, and six seeding rates were examined at Stoneville, MS, in 2009-2010, with respect to soybean growth and yield produced on silt loam soil. Optimal yield for MG IV was 333,000 seed ha-1 (297,000 plants ha-1). Twin-row soybean increased seed yield 7 to 10% more than single-row due to greater LAI, NDVI, and node and pod production. Rice field experiments quantified N loss via ammonia volatilization and determined grain yield for various N sources and preflood application timing. Cumulative ammonia volatilization loss on Tunica clay was minimal (10% of applied N). Grain yields were 6% less when fertilizer was applied 10 days before flood (dbf) as compared to 1 dbf; N sources are available to minimize ammonia volatilization loss.