High resolution AMI Large Array imaging of spinning dust sources: spatially correlated 8 micron emission and evidence of a stellar wind in L675

We present 25 arcsecond resolution radio images of five Lynds Dark Nebulae (L675, L944, L1103, L1111 & L1246) at 16 GHz made with the Arcminute Microkelvin Imager (AMI) Large Array. These objects were previously observed with the AMI Small Array to have an excess of emission at microwave frequencies relative to lower frequency radio data. In L675 we find a flat spectrum compact radio counterpart to the 850 micron emission seen with SCUBA and suggest that it is cm-wave emission from a previously unknown deeply embedded young protostar. In the case of L1246 the cm-wave emission is spatially correlated with 8 micron emission seen with Spitzer. Since the MIR emission is present only in Spitzer band 4 we suggest that it arises from a population of PAH molecules, which also give rise to the cm-wave emission through spinning dust emission.Comment: accepted MNRA

Follow-up observations at 16 and 33 GHz of extragalactic sources from WMAP 3-year data: I - Spectral properties

We present follow-up observations of 97 point sources from the Wilkinson Microwave Anisotropy Probe (WMAP) 3-year data, contained within the New Extragalactic WMAP Point Source (NEWPS) catalogue between declinations of -4 and +60 degrees; the sources form a flux-density-limited sample complete to 1.1 Jy (approximately 5 sigma) at 33 GHz. Our observations were made at 16 GHz using the Arcminute Microkelvin Imager (AMI) and at 33 GHz with the Very Small Array (VSA). 94 of the sources have reliable, simultaneous -- typically a few minutes apart -- observations with both telescopes. The spectra between 13.9 and 33.75 GHz are very different from those of bright sources at low frequency: 44 per cent have rising spectra (alpha < 0.0), where flux density is proportional to frequency^-alpha, and 93 per cent have spectra with alpha < 0.5; the median spectral index is 0.04. For the brighter sources, the agreement between VSA and WMAP 33-GHz flux densities averaged over sources is very good. However, for the fainter sources, the VSA tends to measure lower values for the flux densities than WMAP. We suggest that the main cause of this effect is Eddington bias arising from variability.Comment: 12 pages, 13 figures, submitted to MNRA

AMI observations of unmatched Planck ERCSC LFI sources at 15.75 GHz

The Planck Early Release Compact Source Catalogue includes 26 sources with no obvious matches in other radio catalogues (of primarily extragalactic sources). Here we present observations made with the Arcminute Microkelvin Imager Small Array (AMI SA) at 15.75 GHz of the eight of the unmatched sources at declination > +10 degrees. Of the eight, four are detected and are associated with known objects. The other four are not detected with the AMI SA, and are thought to be spurious.Comment: 6 pages, 5 figures, 4 table

Microwave observations of spinning dust emission in NGC6946

We report new cm-wave measurements at five frequencies between 15 and 18GHz of the continuum emission from the reportedly anomalous "region 4" of the nearby galaxy NGC6946. We find that the emission in this frequency range is significantly in excess of that measured at 8.5GHz, but has a spectrum from 15-18GHz consistent with optically thin free-free emission from a compact HII region. In combination with previously published data we fit four emission models containing different continuum components using the Bayesian spectrum analysis package radiospec. These fits show that, in combination with data at other frequencies, a model with a spinning dust component is slightly preferred to those that possess better-established emission mechanisms.Comment: submitted MNRA

AMI observations of Lynds Dark Nebulae: further evidence for anomalous cm-wave emission

Observations at 14.2 to 17.9 GHz made with the AMI Small Array towards fourteen Lynds Dark Nebulae with a resolution of 2' are reported. These sources are selected from the SCUBA observations of Visser et al. (2001) as small angular diameter clouds well matched to the synthesized beam of the AMI Small Array. Comparison of the AMI observations with radio observations at lower frequencies with matched uv-plane coverage is made, in order to search for any anomalous excess emission which can be attributed to spinning dust. Possible emission from spinning dust is identified as a source within a 2' radius of the Scuba position of the Lynds dark nebula, exhibiting an excess with respect to lower frequency radio emission. We find five sources which show a possible spinning dust component in their spectra. These sources have rising spectral indices in the frequency range 14.2--17.9 GHz. Of these five one has already been reported, L1111, we report one new definite detection, L675, and three new probable detections (L944, L1103 and L1246). The relative certainty of these detections is assessed on the basis of three criteria: the extent of the emission, the coincidence of the emission with the Scuba position and the likelihood of alternative explanations for the excess. Extended microwave emission makes the likelihood of the anomalous emission arising as a consequence of a radio counterpart to a protostar or a proto-planetary disk unlikely. We use a 2' radius in order to be consistent with the IRAS identifications of dark nebulae (Parker 1988), and our third criterion is used in the case of L1103 where a high flux density at 850 microns relative to the FIR data suggests a more complicated emission spectrum.Comment: submitted MNRA

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Soil sample timing, nitrogen fertilization, and incubation length influence anaerobic potentially mineralizable nitrogen

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNan as a tool in corn (Zea mays L.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan (0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended to be greater at sites that received \u3c 183 mm of precipitation or \u3c 359 growing degree-days (GDD) between preplant and V5, or had soil C/N ratios \u3e 9.7:1; otherwise, V5 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content \u3e 9.5%, total C \u3c 24.2 g kg−1, soil organic matter (SOM) \u3c 3.9 g kg−1, or C to N ratios \u3c 11.0:1; otherwise, PMNan tended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNan at all sites regardless of sampling methods. Since PMNan is sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNan among sites and potentially use PMNan to improve corn N management

Soil hydrologic grouping guide which soil and weather properties best estimate corn nitrogen need

Nitrogen fertilizer recommendations in corn (Zea mays L.) that match the economically optimal nitrogen fertilizer rate (EONR) are imperative for profitability and minimizing environmental degradation. However, the amount of soil N available for the crop depends on soil and weather factors, making it difficult to know the EONR from year-to-year and from field-to-field. Our objective was to explore, within the framework of hydrologic soil groups and drainage classifications (HGDC), which site-specific soil and weather properties best estimated corn N needs (i.e., EONR) for two application timings (at-planting and side-dress). Included in this investigation was a validation step using an independent dataset. Forty-nine N response trials conducted across the U.S. Midwest Corn Belt over three growing seasons (2014–2016) were used for recommendation model development, and 181 independent site-years were used for validation. For HGDC models, soil organic matter (SOM), clay content, and evenness of rainfall distribution before side-dress N application were the properties generally most helpful in predicting EONR. Using the validation data, model recommendations were within 34 kg N ha–1 of EONR for 37 and 42% of the sites with a root mean square error (RMSE) of 70 and 68 kg N ha–1 for at-planting and side-dress applications, respectively. Compared to state-specific recommendations, sites needing ha–1 or no N were better estimated with HGDC models. In contrast, for sites where EONR was \u3e150 kg N ha–1, HGDC models underestimated N needs compared to state specific. These results show HGDC groupings could aid in developing tools for N fertilizer recommendations

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied