David E. Clay

Spring Clipping, Fire, and Simulated Increased Atmospheric Nitrogen Deposition Effects on Tallgrass Prairie Vegetation

Abstract Defoliation aimed at introduced cool-season grasses, which uses similar resources of native grasses, could substantially reduce their competitiveness and improve the quality of the northern tallgrass prairie. The objective was to evaluate the use of early season clipping and fire in conjunction with simulated increased levels of atmospheric nitrogen deposition on foliar canopy cover of tallgrass prairie vegetation. This study was conducted from 2009 to 2012 at two locations in eastern South Dakota. Small plots arranged in a split-plot treatment design were randomized in four complete blocks on a warm-season grass interseeded and a native prairie site in east-central South Dakota. The whole plot consisted of seven treatments: annual clip, biennial clip, triennial clip, annual fire, biennial fire, triennial fire, and undefoliated control. The clip plots consisted of weekly clipping in May to simulate heavy grazing. Fire was applied in late April or early May. The subplot consisted of nitrogen applied at 0 or 15 kg N · ha−1 in early June. All treatments were initially applied in 2009. Biennial and triennial treatments were reapplied in 2011 and 2012, respectively. Canopy cover of species/major plant functional groups was estimated in late August/early September. Annual clipping was just as effective as annual fire in increasing native warm-season grass and decreasing introduced cool-season grass cover. Annual defoliation resulted in greater native warm-season grass cover, less introduced cool-season grass cover, and less native cool-season grass cover than biennial or triennial defoliation applications. Low levels of nitrogen did not affect native warm-season grass or introduced cool-season cover for any of the defoliation treatments, but it increased introduced cool-season grass cover in the undefoliated control at the native prairie site. This study supports the hypothesis that appropriately applied management results in consistent desired outcomes regardless of increased simulated atmospheric nitrogen depositions.

Developing Productivity Zones from Multiple Years of Yield Monitor Data

Producers who have been collecting yield monitor data for multiple years are asking: “How can this yield data be used to improve management?” A potential use for these data sets is incorporating them to define a type of management zone known as a productivity zone. There are at least two different approaches proposed for identifying productivity zones. The first approach is to calculate the impact of zone boundaries on fertilizer recommendations. Chang et al. (2004) reported that landscape specific yield goals, combined with grid-cell sampling, can be used to improve nitrogen (N) and phosphorus (P) fertilizer recommendations by 35 and 59%, respectively. This approach requires that extensive soil sampling be conducted to define initial soil conditions and then a model be used to calculate fertilizer recommendations for each zone. A second approach is to determine the impact of productivity zones on yield variability (Bakhsh et al., 2000; Fridgen et al., 2000; Diker et al., 2002; and Kitchen et al., 2002). This approach assumes that the best method of zone delineation minimizes yield variability. Due to the widespread availability of multiple-year yield monitor data sets and relatively simple method of collection (versus grid soil sampling), many producers would opt for the second method of productivity zone delineation.

Characterization of Soybean Yield Variability Using Crop Growth Models and 13C Discrimination

During the past several years, crop models have successfully been used to test the hypothesis that water stress is the primary factor that causes spatial yield variability in soybean [Glycine max (L.) Merr.] fields. However, there have been few attempts to validate this hypothesis through direct temporal and spatial measurements of water stress during the season. Recently, a technique has been developed to relate plant tissue 13C levels to the temporal water stress experienced by soybean plants. The purpose of this work was to compare the spatial yield loss simulated by a crop model with yield loss measured by 13C discrimination ( .) for a soybean field in South Dakota. The field was divided into 0.9-ha grids and the CROPGRO-Soybean model was calibrated to minimize error between simulated and observed yield in each grid over two seasons (1998, 2000). 13C discrimination was measured at 50 points representing 23 of the grids used in the crop modeling analysis in 2000. Simulated yield loss in grids that encompassed each 13C point in 2000 were compared to measurements of yield loss using the 13C discrimination technique. Initially, the root mean square error and r2 between simulated and measured yield loss was 259 kg ha-1 and 0.24, respectively. Upon closer inspection, it was observed that yield in 5 grids with the highest error likely were influenced by processes that are not represented in the crop model. Removing these values dramatically improved the agreement between simulated and observed yield loss, giving an RMSE of 216 kg ha-1 and r2 of 0.81. Both 13C discrimination and simulation results indicated that substantial yield loss occurred due to water stress in the summit/backslope areas of the field.