Surface-Soil Aggregation and Organic C and N Fractions Under Paired Grassland and Cropland Sites in the Southeastern USA

Grasslands are expected to deliver ecosystem services by sequestering soil organic C, improving soil health and water quality, and minimizing soil erosion. Data to support these ecosystem services from contemporary managed grasslands are relatively scant, and so on-farm measurements would help bolster assessment across more diverse environmental settings. This study was conducted to compare soil properties from paired landuse of croplands and grasslands in a diversity of Major Land Resource Areas – the Piedmont, Blue Ridge, and Blackland Prairie of the southeastern USA. Four separate pastures on three collaborating farms were sampled at depth of 0-10 cm. Four samples from a neighboring cropland field were paired with these pastures. A variety of soil chemical, physical, and biological properties were determined. Soil chemical properties were occasionally different between land management systems on one of the three farms, but few consistent differences occurred across farms. Dry-stable mean-weight diameter (MWD) was not different between paired land management systems, but water-stable MWD was dramatically reduced at all three locations with cropland compared with pasture. Soil stability index (water-stable MWD divided by dry-stable MWD) averaged 0.64 mm mm-1 under cropland and 0.91 mm mm-1 under pasture, suggesting that pastures had a highly stable soil surface that was resistant to erosion and likely contributed to high water infiltration. Soil organic C and N fractions (i.e., total, particulate, and mineralizable) were all significantly greater under pasture than under cropland, indicating that these pastures were indeed storing more C and N, and contributing to greater soil biological activity. This study provides evidence that well-managed grasslands can sequester soil organic C and N, improve soil surface stability conditions to foster water infiltration and reduced runoff, and may have important implications for habit development for soil-dwelling organisms

Phonological and lexical influences on phonological awareness in children with specific language impairment and dyslexia

Children with dyslexia and/or specific language impairment have marked deficits in phonological processing, putting them at an increased risk for reading deficits. The current study sought to examine the influence of word-level phonological and lexical characteristics on phonological awareness. Children with dyslexia and/or specific language impairment were tested using a phoneme deletion task in which stimuli differed orthogonally by sound similarity and neighborhood density. Phonological and lexical factors influenced performance differently across groups. Children with dyslexia appeared to have a more immature and aberrant pattern of phonological and lexical influence (e.g., favoring sparse and similar features). Children with SLI performed less well than children who were typically developing, but followed a similar pattern of performance (e.g., favoring dense and dissimilar features). Collectively, our results point to both quantitative and qualitative differences in lexical organization and phonological representations in children with SLI and in children with dyslexia

MAGGnet : an international network to foster mitigation of agricultural greenhouse gases

Liebig, M. A. USDA-ARS, Mandan, ND, USA.Franzluebbers, A. J. USDA-ARS, Raleigh, NC, USA.Alvarez, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro Regional Córdoba. Estación Experimental Agropecuaria Manfredi (EEA Manfredi). Córdoba, Argentina.Chiesa, T. D. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina.Lewczuk, N. Instituto Nacional de Tecnología Agropecuaria (INTA). Buenos Aires, Argentina.Piñeiro, Gervasio. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina.Posse, Graciela Noemí. Instituto Nacional de Tecnología Agropecuaria (INTA). Buenos Aires, Argentina.Yahdjian, María Laura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina.8Research networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA). With involvement from 46 alliance member countries, MAGGnet seeks to provide a platform for the inventory and analysis of agricultural GHG mitigation research throughout the world. To date, metadata from 315 experimental studies in 20 countries have been compiled using a standardized spreadsheet. Most studies were completed (74%) and conducted within a 1-3-year duration (68%). Soil carbon and nitrous oxide emissions were measured in over 80% of the studies. Among plant variables, grain yield was assessed across studies most frequently (56%), followed by stover (35%) and root (9%) biomass. MAGGnet has contributed to modeling efforts and has spurred other research groups in the GRA to collect experimental site metadata using an adapted spreadsheet. With continued growth and investment, MAGGnet will leverage limited-resource investments by any one country to produce an inclusive, globally shared meta-database focused on the science of GHG mitigation

The moisture response of soil heterotrophic respiration: Interaction with soil properties

Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data-driven analysis of soil moisture-respiration relations based on 90 soils. With the use of linear models we show how the relationship between soil heterotrophic respiration and different measures of soil moisture is consistently affected by soil properties. The empirical models derived include main effects and moisture interaction effects of soil texture, organic carbon content and bulk density. When compared to other functions currently used in different soil biogeochemical models, we observe that our results can correct biases and reconcile differences within and between such functions. Ultimately, accurate predictions of the response of soil carbon to future climate scenarios will require the integration of soil-dependent moisture-respiration functions coupled with realistic representations of soil water dynamic

Modeling perennial groundcover effects on annual maize grain crop growth with the Agricultural Production Systems sIMulator

The inclusion of perennial groundcover (PGC) in maize production offers a tenable solution to natural resources-related concerns associated with conventional maize; however, insight into system management and key information gaps is needed to guide future research. We therefore extended the Agricultural Production Systems sIMulator (APSIM) to an annual and perennial intercrop by integrating annual and perennial APSIM modules. These were parameterized for Kentucky bluegrass (KB) (Poa pratensis L.) or creeping red fescue (CF) (Festuca rubra L.) as PGC using a three-year dataset. Our objectives for this intercropping modeling study were to: i) simultaneously model a PGC and annual cash crop using APSIM software; ii) utilize APSIM to understand interactive processes in the maize-PGC system; and iii) utilize the calibrated model to explore both production and environmental benefits via scenario modeling. For objective I, the integrated model successfully predicted maize total aboveground biomass (TAB) (relative root mean square error, RRMSE of 13- 27%) and PGC above- and belowground tissue N concentration (RRMSE of 11-18%). The calibrated model effectively captured observed trends in PGC biomass accumulation and soil nitrate (NO3). For objective II, model analysis showed that competition for light was the primary maize yield penalty factor from PGC, while water and N impacted maize yield later in the maize growing season. In objective III, we concluded that effective PGC suppression produces minimal maize yield loss and significant environmental benefits; conversely, poor groundcover suppression may produce unfavorable environmental consequences and decrease maize grain yield. Effective PGC suppression is key for long-term system success