C. Gregg Carlson

Collecting Representative Soil Samples for N and P Fertilizer Recommendations

Soil fertilizer recommendations in modern crop production rely on laboratory analysis of representative soil samples. Regardless on how soil samples are collected (grid points, management zones, or whole fields) the accuracy and precision of the fertilizer recommendation can be improved by considering the factors influencing nutrient variability. As producer’s crop enterprise varies, it is recommended that producers select approaches that are suited for their operation. The objectives of this guide are to discuss how management influences nutrient variability and to provide insight into designing soil sampling protocols that provide accurate and precise fertilizer recommendations.

Tillage and Corn Residue Harvesting Impact Surface and Subsurface Carbon Sequestration

Corn stover harvesting is a common practice in the western U.S. Corn Belt. This 5-yr study used isotopic source tracking to quantify the influence of two tillage systems, two corn ( L.) surface residue removal rates, and two yield zones on soil organic C (SOC) gains and losses at three soil depths. Soil samples collected in 2008 and 2012 were used to determine C enrichment during SOC mineralization, the amount of initial SOC mineralized (SOC), and plant C retained in the soil (PCR) and sequestered C (PCR - SOC). The 30% residue soil cover after planting was achieved by the no-till and residue returned treatments and was not achieved by the chisel plow, residue removed treatment. In the 0- to 15-cm soil depth, the high yield zone had lower SOC (1.49 Mg ha) than the moderate yield zone (2.18 Mg ha), whereas in the 15- to 30-cm soil depth, SOC was higher in the 60% (1.38 Mg ha) than the 0% (0.82 Mg ha) residue removal treatment. When the 0- to 15- and 15- to 30-cm soil depths were combined, (i) 0.91 and 3.62 Mg SOC ha were sequestered in the 60 and 0% residue removal treatments; (ii) 2.51 and 0.36 Mg SOC ha were sequestered in the no-till and chisel plow treatments, and (iii) 1.16 and 1.65 Mg SOC ha were sequestered in the moderate and high yield zone treatments, respectively. The surface treatments influenced C cycling in the 0- to 15- and 15- to 30-cm depths but did not influence SOC turnover in the 30- to 60-cm depth.

Landscape Features Impact on Soil Available Water, Corn Biomass, and Gene Expression during the Late Vegetative Stage

Crop yields at summit positions of rolling landscapes often are lower than backslope yields. The differences in plant response may be the result of many different factors. We examined corn (Zea mays L.) plant productivity, gene expression, soil water, and nutrient availability in two landscape positions located in historically high (backslope) and moderate (summit and shoulder) yielding zones to gain insight into plant response differences. Growth characteristics, gene expression, and soil parameters (water and N and P content) were determined at the V12 growth stage of corn. At tassel, plant biomass, N content, 13C isotope discrimination (Δ), and soil water was measured. Soil water was 35% lower in the summit and shoulder compared with the lower backslope plots. Plants at the summit had 16% less leaf area, biomass, and N and P uptake at V12 and 30% less biomass at tassel compared with plants from the lower backslope. Transcriptome analysis at V12 indicated that summit and shoulder‐grown plants had 496 downregulated and 341 upregulated genes compared with backslope‐grown plants. Gene set and subnetwork enrichment analyses indicated alterations in growth and circadian response and lowered nutrient uptake, wound recovery, pest resistance, and photosynthetic capacity in summit and shoulder‐grown plants. Reducing plant populations, to lessen demands on available soil water, and applying pesticides, to limit biotic stress, may ameliorate negative water stress responses.

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.