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Dive into the research topics where Graig Reicks is active.

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Featured researches published by Graig Reicks.


Weed Science | 2012

Microarray and Growth Analyses Identify Differences and Similarities of Early Corn Response to Weeds, Shade, and Nitrogen Stress

Janet Moriles; Stephanie A. Hansen; David P. Horvath; Graig Reicks; David E. Clay

Abstract Weed interference with crop growth is often attributed to water, nutrient, or light competition; however, specific physiological responses to these stresses are not well described. This studys objective was to compare growth, yield, and gene expression responses of corn to nitrogen (N), low light (40% shade), and weed stresses. Corn vegetative parameters from V2 to V12 stages, yield parameters, and gene expression using transcriptome (2008) and quantitative polymerase chain reaction (qPCR) (2008/09) analyses at V8 were compared among the stresses and with nonstressed corn. N stress did not affect vegetative parameters, although grain yield was reduced by 40% compared with nonstressed plants. Shade, present until V2, reduced biomass and leaf area > 50% at V2, and recovering plants remained smaller than nonstressed plants at V12. However, grain yields of shade-stressed and nonstressed plants were similar, unless shade remained until V8. Weed stress reduced corn growth and yield in 2008 when weeds remained until V6. In 2009, weed stress until V2 reduced corn vegetative growth, but yield reductions occurred only if weed stress remained until V6 or later. Principle component analysis of differentially expressed genes indicated that shade and weed stress had more similar gene expression patterns to each other than they did to nonstressed or N-stressed tissues. However, corn grown in N-stressed conditions shared 252 differentially expressed genes with weed-stressed plants. Ontologies associated with light/photosynthesis, energy conversion, and signaling were down-regulated in response to all three stresses. Shade and weed stress clustered most tightly together, based on gene expression, but shared only three ontologies, O-METHYLTRANSFERASE activity (lignification processes), POLY(U)-BINDING activity (posttranscriptional gene regulation), and stomatal movement. Based on morphologic and genomic observations, weed stress to corn was not explained by individual effects of N or light stress. Therefore, we hypothesize that these stresses share limited signaling mechanisms. Nomenclature: Corn, Zea mays L.


Weed Science | 2012

Local conditions, not regional gradients, drive demographic variation of giant ragweed (Ambrosia trifida) and common sunflower (Helianthus annuus) across northern U.S. maize belt.

Sam E. Wortman; Adam S. Davis; Brian J. Schutte; John L. Lindquist; John Cardina; Joel Felix; Christy L. Sprague; J. Anita Dille; Analiza H. M. Ramirez; Graig Reicks

Abstract Knowledge of environmental factors influencing demography of weed species will improve understanding of current and future weed invasions. The objective of this study was to quantify regional-scale variation in vital rates of giant ragweed and common sunflower . To accomplish this objective, a common field experiment was conducted across seven sites between 2006 and 2008 throughout the north central U.S. maize belt. Demographic parameters of both weed species were measured in intra- and interspecific competitive environments, and environmental data were collected within site-years. Site was the strongest predictor of belowground vital rates (summer and winter seed survival and seedling recruitment), indicating sensitivity to local abiotic conditions. However, biotic factors influenced aboveground vital rates (seedling survival and fecundity). Partial least squares regression (PLSR) indicated that demography of both species was most strongly influenced by thermal time and precipitation. The first PLSR components, both characterized by thermal time, explained 63.2% and 77.0% of variation in the demography of giant ragweed and common sunflower, respectively; the second PLSR components, both characterized by precipitation, explained 18.3% and 8.5% of variation, respectively. The influence of temperature and precipitation is important in understanding the population dynamics and potential distribution of these species in response to climate change. Nomenclature: Giant ragweed, Ambrosia trifida L. AMBTR; common sunflower, Helianthus annuus L. HELAN; maize, Zea mays L.; soybean, Glycine max (L.) Merr.


Journal of Environmental Quality | 2015

Tillage and Corn Residue Harvesting Impact Surface and Subsurface Carbon Sequestration

David E. Clay; Graig Reicks; C. Gregg Carlson; Janet Moriles-Miller; James J. Stone

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.


The Plant Genome | 2013

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

Stephanie A. Hansen; David E. Clay; C. Gregg Carlson; Graig Reicks; Youssef Jarachi; David P. Horvath

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.


Journal of Environmental Science and Health Part B-pesticides Food Contaminants and Agricultural Wastes | 2008

Manure placement depth impacts on crop yields and N retained in soil

M. Reiman; David E. Clay; C. G. Carlson; Graig Reicks; D. W. Clay; D. E. Humburg

The objective of this study was to determine the impact of manure placement depth on crop yield and N retention in soil. Experimental treatments were deep manure injection (45 cm), shallow manure injection (15 cm), and conventional fertilizer-based management with at least three replications per site. Water infiltration, and changes in soil N and P amounts were measured for up to 30 months and crop yield monitored for three seasons following initial treatment. Deep and shallow manure injections differed in soil inorganic N distributions. For example, in the manure slot the spring following application, NO3-N in the surface 60 cm was higher (p < .01) when injected 15 cm (21.4 μ g/g) into the soil than 45 cm (11.7 μ g/g), whereas NH4-N had opposite results with shallow injection having less (p = 0.045) NH4-N (102 μ g/g) than deep (133 μ g/g) injection. In the fall one year after the manure was applied, NO3-N and NH4-N were lower (p = 0.001) in the shallow injection than the deep injection. The net impact of manure placement on total N was that deep injection had 31, 59, and 44 more kg N ha− 1 than the shallow injection treatment 12, 18, and 30 months after application, respectively. Deep manure injection did not impact soybean (Glycine max L.) yield, however corn (Zea mays L.) yield increased if N was limiting. The higher corn yield in the deep injected treatment was attributed to increased N use efficiency. Higher inorganic N amounts in the deep injection treatment were attributed to reduced N losses through ammonia volatilization, leaching, or denitrification. Results suggest that deep manure placement in glacial till soil may be considered a technique to increase energy, N use efficiency, and maintain surface and ground water quality. However, this technique may not work in glacial outwash soils due to the inability to inject into a rocky subsurface.


Weed Science | 2014

Common Sunflower Seedling Emergence across the U.S. Midwest

Adam S. Davis; Anita Dille; John L. Lindquist; Analiza H. M. Ramirez; Christy L. Sprague; Graig Reicks; Frank Forcella

Abstract Predictions of weed emergence can be used by practitioners to schedule POST weed management operations. Common sunflower seed from Kansas was used at six Midwestern U.S. sites to examine the variability that 16 climates had on common sunflower emergence. Nonlinear mixed effects models, using a flexible sigmoidal Weibull function that included thermal time, hydrothermal time, and a modified hydrothermal time (with accumulation starting from January 1 of each year), were developed to describe the emergence data. An iterative method was used to select an optimal base temperature (Tb) and base and ceiling soil matric potentials (&psgr;b and &psgr;c) that resulted in a best-fit regional model. The most parsimonious model, based on Akaikes information criterion (AIC), resulted when Tb  =  4.4 C, and &psgr;b  =  −20000 kPa. Deviations among model fits for individual site years indicated a negative relationship (r  =  −0.75; P < 0.001) between the duration of seedling emergence and growing degree days (Tb  =  10 C) from October (fall planting) to March. Thus, seeds exposed to warmer conditions from fall burial to spring emergence had longer emergence periods. Nomenclature: Common sunflower, Helianthus annuus L.


Agronomy Journal | 2009

Corn Response to Competition: Growth Alteration vs. Yield Limiting Factors

David E. Clay; David P. Horvath; J. Pullis; C. G. Carlson; Stephanie A. Hansen; Graig Reicks


Agronomy Journal | 2009

Improving the Fermentation Characteristics of Corn through Agronomic and Processing Practices

Graig Reicks; H. J. Woodard; Anthony Bly


Agronomy Journal | 2012

Winter Wheat Crop Reflectance and Nitrogen Sufficiency Index Values are Influenced by Nitrogen and Water Stress

David E. Clay; Tulsi P. Kharel; Cheryl Reese; Dwayne L. Beck; C. Gregg Carlson; Graig Reicks


Agronomy Journal | 2010

Nitrogen rate, landscape position, and harvesting of corn stover impacts on energy gains and sustainability of corn production systems in South Dakota.

F. Mamani-Pati; David E. Clay; C. G. Carlson; Graig Reicks; K. Kim

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David E. Clay

San Diego State University

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C. G. Carlson

South Dakota State University

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Jiyul Chang

South Dakota State University

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C. Gregg Carlson

South Dakota State University

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David P. Horvath

Agricultural Research Service

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Stephanie A. Hansen

South Dakota State University

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Tulsi P. Kharel

South Dakota State University

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Adam S. Davis

Agricultural Research Service

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Cheryl Reese

South Dakota State University

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