Do cover crops increase or decrease nitrous oxide emissions? A meta-analysis.

There are many environmental benefits to incorporating cover crops into crop rotations, such as their potential to decrease soil erosion, reduce nitrate (NO3) leaching, and increase soil organic matter. Some of these benefits impact other agroecosystem processes, such as greenhouse gas emissions. In particular, there is not a consensus in the literature regarding the effect of cover crops on nitrous oxide (N2O) emissions. Compared to site-specific studies, meta-analysis can provide a more general investigation into these effects. Twenty-six peer-reviewed articles including 106 observations of cover crop effects on N2O emissions from the soil surface were analyzed according to their response ratio, the natural log of the N2O flux with a cover crop divided by the N2O flux without a cover crop (LRR). Forty percent of the observations had negative LRRs, indicating a cover crop treatment which decreased N2O, while 60% had positive LRRs indicating a cover crop treatment which increased N2O. There was a significant interaction between N rate and the type of cover crop where legumes had higher LRRs at lower N rates than nonlegume species. When cover crop residues were incorporated into the soil, LRRs were significantly higher than those where residue was not incorporated. Geographies with higher total precipitation and variability in precipitation tended to produce higher LRRs. Finally, data points measured during cover crop decomposition had large positive LRRs and were larger than those measured when the cover crop was alive. In contrast, those data points measuring for a full year had LRRs close to zero, indicating that there was a balance between periods when cover crops increased N2O and periods when cover crops decreased emissions. Therefore, N2O measurements over the entire year may be needed to determine the net effect of cover crops on N2O. The data included in this meta-analysis indicate some overarching crop management practices that reduce direct N2O emissions from the soil surface, such as no soil incorporation of residues and use of non-legume cover crop species. However, our results demonstrate that cover crops do not always reduce direct N2O emissions from the soil surface in the short term and that more work is needed to understand the full global warming potential of cover crop management

Effects of an acute bout of resistance exercise on cognitive performance in preadolescents

Over the past two decades a positive effect on cognitive performance has consistently been identified following an acute bout of aerobic exercise. (Etnier, Salazar, Landers, Petruzzello, Han, & Nowell, 1997). A limited number of studies have identified a similar positive effect following acute aerobic exercise in preadolescent samples (Ellemberg & St-Louise-Deschenes, 2010; Hillman, Pontifex, Raine, Castelli, Hall, & Kramer, 2009; Pesce, Crova, Cereatti, Casella, & Belluci, 2009; Tomporowski, 2003). Resistance exercise within adult samples has also been associated with increases in cognitive performance (Chang & Etnier, 2008, 2009; Chang, Ku, Tomporowski, Chen, & Huang, 2012). There is currently no existing research examining the effects an acute bout of resistance exercise has on the cognitive performance of a preadolescent sample. A possible reason for this lack of research is the misconception that resistance exercise can have detrimental effects on the developing bodies of preadolescents. These safety concerns have been deemed unnecessary as recent statements from both the American Academy of Pediatrics (AAP) and the American College of Sports Medicine (ACSM) have determined resistance exercise in preadolescence is safe and even beneficial to the bones, joints, and muscles of developing bodies. The purpose of this research was to examine the effects an acute bout of resistance exercise has on cognitive performance by a preadolescent sample. Participants were randomly assigned to one of two different treatment conditions (exercise or control). Participants in each condition completed a number of cognitive tasks testing executive function and completed a 20 minute bout of resistance exercise. Those in the exercise condition completed the cognitive tasks immediately after the resistance exercise. Those in the control condition completed the cognitive tasks immediately before the resistance exercise. Analyses revealed that for errors within the Stroop W condition, a measure of processing speed and inhibition, there was a significant difference between groups such that the exercise group had fewer errors at the post-test than the control group. There were no significant differences for task switching, problem solving, working memory, and visual attention between groups. The results for this sample thus suggest that resistance exercise may have a clinically meaningful effect on aspects of processing speed and inhibition

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3%lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost

Descripción y distribución de Hydnotrya cerebriformis (Discinaceae: Pezizales) en México

El género Hydnotrya comprende alrededor de 13 especies de Ascomicetos hipogeos, secuestrados ectomicorrízicos distribuidos exclusivamente en bosquestemplados del hemisferio norte. En este trabajo se describe e ilustra por primera vez una especie de este género, Hydnotrya cerebriformis, para México. Esta especie se distribuye en el Eje Neovolcánico Transmexicano (Estado de México, Puebla y Tlaxcala) y Nuevo León a altitudes entre los 3,100 y 4,000 m, donde se asocia a bosques de Pinus hartwegii, P. montezumae y Abiesreligiosa, principalmente. ABSTRACT The genus Hydnotrya is composed of around 13 ectomycorrhizal, hypogeous, sequestrated Ascomycetes distributed exclusively in temperate forests in the northern hemisphere. This work describes and illustrates for the first time a species of this genus, Hydnotrya cerebriformis, from Mexico. The species is distributed in the Transmexican Volcanic Belt (in the Estado de México, Puebla, and Tlaxcala) and the state of Nuevo León at altitudes between 3,100 and 4,000 m, and associated mainly with Pinus hartwegii, P. montezumae, and Abies religiosa forests

Maize and soybean root front velocity and maximum depth in Iowa, USA

Quantitative measurements of root traits can improve our understanding of how crops respond to soil and weather conditions, but such data are rare. Our objective was to quantify maximum root depth and root front velocity (RFV) for maize (Zea mays) and soybean (Glycine max) crops across a range of growing conditions in the Midwest USA. Two sets of root measurements were taken every 10–15 days: in the crop row (in-row) and between two crop rows (center-row) across six Iowa sites having different management practices such as planting dates and drainage systems, totaling 20 replicated experimental treatments. Temporal root data were best described by linear segmental functions. Maize RFV was 0.62 ± 0.2 cm d−1 until the 5th leaf stage when it increased to 3.12 ± 0.03 cm d−1 until maximum depth occurred at the 18th leaf stage (860 °Cd after planting). Similar to maize, soybean RFV was 1.19 ± 0.4 cm d−1 until the 3rd node when it increased to 3.31 ± 0.5 cm d−1 until maximum root depth occurred at the 13th node (813.6 °C d after planting). The maximum root depth was similar between crops (P \u3e 0.05) and ranged from 120 to 157 cm across 18 experimental treatments, and 89–90 cm in two experimental treatments. Root depth did not exceed the average water table (two weeks prior to start grain filling) and there was a significant relationship between maximum root depth and water table depth (R2 = 0.61; P = 0.001). Current models of root dynamics rely on temperature as the main control on root growth; our results provide strong support for this relationship (R2 \u3e 0.76; P \u3c 0.001), but suggest that water table depth should also be considered, particularly in conditions such as the Midwest USA where excess water routinely limits crop production. These results can assist crop model calibration and improvements as well as agronomic assessments and plant breeding efforts in this region

CANDELS Multi-wavelength Catalogs: Source Detection and Photometry in the GOODS-South Field

We present a UV-to-mid infrared multi-wavelength catalog in the CANDELS/GOODS-S field, combining the newly obtained CANDELS HST/WFC3 F105W, F125W, and F160W data with existing public data. The catalog is based on source detection in the WFC3 F160W band. The F160W mosaic includes the data from CANDELS deep and wide observations as well as previous ERS and HUDF09 programs. The mosaic reaches a 5$\sigma$ limiting depth (within an aperture of radius 0.17 arcsec) of 27.4, 28.2, and 29.7 AB for CANDELS wide, deep, and HUDF regions, respectively. The catalog contains 34930 sources with the representative 50% completeness reaching 25.9, 26.6, and 28.1 AB in the F160W band for the three regions. In addition to WFC3 bands, the catalog also includes data from UV (U-band from both CTIO/MOSAIC and VLT/VIMOS), optical (HST/ACS F435W, F606W, F775W, F814W, and F850LP), and infrared (HST/WFC3 F098M, VLT/ISAAC Ks, VLT/HAWK-I Ks, and Spitzer/IRAC 3.6, 4.5, 5.8, 8.0 $\mu$m) observations. The catalog is validated via stellar colors, comparison with other published catalogs, zeropoint offsets determined from the best-fit templates of the spectral energy distribution of spectroscopically observed objects, and the accuracy of photometric redshifts. The catalog is able to detect unreddened star-forming (passive) galaxies with stellar mass of 10^{10}M_\odot at a 50% completeness level to z$\sim$3.4 (2.8), 4.6 (3.2), and 7.0 (4.2) in the three regions. As an example of application, the catalog is used to select both star-forming and passive galaxies at z$\sim$2--4 via the Balmer break. It is also used to study the color--magnitude diagram of galaxies at 0<z<4.Comment: The full resolution article is now published in ApJS (2013, 207, 24). 22 pages, 21 figures, and 5 tables. The catalogue is available on the CANDELS website: http://candels.ucolick.org/data_access/GOODS-S.html MAST: http://archive.stsci.edu/prepds/candels and Rainbow Database: https://arcoiris.ucolick.org/Rainbow_navigator_public and https://rainbowx.fis.ucm.es/Rainbow_navigator_publi

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020–2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems’ performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N\u3csub\u3e2\u3c/sub\u3eO Emissions, and Seed Yield

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020–2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems’ performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems