Hydrologic impacts of an alternative agricultural land use: a woody perennial polyculture

U.S. Department of the InteriorU.S. Geological SurveyOpe

Inference of the optical depth to reionization τ\tau from Planck\textit{Planck} CMB maps with convolutional neural networks

The optical depth to reionization, $\tau$, is the least constrained parameter of the cosmological $\Lambda$CDM model. To date, its most precise value is inferred from large-scale polarized CMB power spectra from the $\textit{Planck}$ High-Frequency Instrument (HFI). These maps are known to contain significant contamination by residual non-Gaussian systematic effects, which are hard to model analytically. Therefore, robust constraints on $\tau$ are currently obtained through an empirical cross-spectrum likelihood built from simulations. In this paper, we present a likelihood-free inference of $\tau$ from polarized $\textit{Planck}$ HFI maps which, for the first time, is fully based on neural networks (NNs). NNs have the advantage of not requiring an analytical description of the data and can be trained on state-of-the-art simulations, combining information from multiple channels. By using Gaussian sky simulations and $\textit{Planck}$ $\texttt{SRoll2}$ simulations, including CMB, noise, and residual instrumental systematic effects, we train, test and validate NN models considering different setups. We infer the value of $\tau$ directly from $Q$ and $U$ maps at $\sim 4^\circ$ pixel resolution, without computing power spectra. On $\textit{Planck}$ data, we obtain $\tau_{\rm NN}=0.0579\pm 0.0082$, compatible with current $EE$ cross-spectrum results but with a $\sim30\%$ larger uncertainty, which can be assigned to the inherent non-optimality of our estimator and to the retraining procedure applied to avoid biases. While this paper does not improve on current cosmological constraints on $\tau$, our analysis represents a first robust application of NN-based inference on real data and highlights its potential as a promising tool for complementary analysis of near-future CMB experiments, also in view of the ongoing challenge to achieve a detection of primordial gravitational waves.Comment: 13 pages, 10 figures, 5 tables. Comments welcom

Frontiers in alley cropping: Transformative solutions for temperate agriculture

Annual row crop systems dominate agriculture around the world and have considerable negative environmental impacts. Incremental improvements to the prevailing system have been the primary focus of efforts to reduce these negative impacts, though are likely insufficient in solving the ecological challenges of row crop agriculture. This dissertation explores alley cropping (AC) – an agroforestry practice integrating trees with crops – as a transformative land-use solution to mitigate climate change, restore ecosystem services, and improve agricultural profitability. Through an inventory of all field experiments of AC to date, I identify several major gaps in AC research. In particular, AC has held a narrow focus on systems that integrate only one timber tree species with one annual grain species. I explore broadening this focus and identify key considerations for the scalable implementation of woody polycultures and tree crops for food and fodder. To evaluate the direct benefits of such systems, I then assess the potential of diversified, food-producing AC to mitigate unintended nitrogen losses in a side-by-side field experiment with row crop agriculture. I show that transitioning to AC can rapidly tighten the nitrogen cycle even during establishment years. Finally, I evaluate the economic competitiveness of the most common temperate AC system – black walnut trees for timber with annual grain alley crops – against the widespread maize-soybean rotation. Even without monetization of environmental benefits, I demonstrate that AC can improve landowner profitability across a substantial portion of the Midwest US. By exploring the frontiers in temperate AC, this dissertation highlights a multifunctional, transformative land-use alternative for temperate agriculture

Enteric methane emission estimates for Kenyan cattle in a nighttime enclosure using a backward Lagrangian Stochastic dispersion technique

This study provides methane (CH$_{4}$) emission estimates for mature female African beef cattle in a semi-arid region in Southern Kenya using open-path laser spectroscopy together with a backward Lagrangian Stochastic (bLS) dispersion modeling technique. We deployed two open-path lasers to determine 10-min averages of line-integrated CH$_{4}$ measurements upwind and downwind of fenced enclosures (so-called bomas: a location where the cattle are gathered at night) during 14 nights in September/October 2019. The measurements were filtered for wind direction deviations and friction velocity before the model was applied. We compared the obtained emission factors (EFs) with the Intergovernmental Panel on Climate Change (IPCC) Tier 1 estimates for the Sub-Saharan African (SSA) countries, which were mostly derived from studies carried out in developed countries and adapted to the conditions in Africa. The resulting EF of 75.4 ± 15.99 kg year$^{-1}$ and the EFs calculated from other studies carried out in Africa indicate the need for the further development of region-specific EFs depending on animal breed, livestock systems, feed quantity, and composition to improve the IPCC Tier 1 estimates

It Seemed Like a Good Idea at the Time (Hindsight is 2020)

Charles Kettering reportedly quipped: “99% of success is built on failure”. Yet, those failures rarely see the light of day, as publications naturally focus on successful innovations rather than the many failures that preceded them. The academic community is poorer as a result, as we are all left to re-create the same failures independently, rather than learning from one another. In this panel, we offer an opportunity to “celebrate failure”, by presenting four separate case studies of computing education initiatives that “seemed like a good idea at the time”, but ended up being spectacular failures. The presenters will discuss their “good ideas”, the disappointing results, and (most importantly) the lessons learned! Our goal is to foster a supportive community where failure is celebrated rather than criticized. We hope to laugh and learn together from these experience reports

Preliminary modeling of light availability in a diverse agroforestry system using a spatially explicit forest simulator

Researchers theorize there is a particular spacing within and between rows that maximizes light capture given size, shape, and opacity of woody species in diverse agroforestry systems (DAS). Studies of these mixed perennial cropping systems have failed to analyze this optimum spacing quantitatively. This study attempts to address this issue through the following aims: (1) determine optimal layouts for light capture, (2) calculate percentage of light received by species at different layout densities, and (3) better understand differences in light availability at plant and plot scales. This study modeled four University of Illinois DAS research treatments ranging from one to three species within a tree row. The spatially explicit forest simulator, SORTIE-ND, was used to analyze the light availability, referred to as global light index (GLI), at treatment maturity on a 1-m2 basis across the field site. Results reveal that GLI is lowest when species spacing is decreased and canopy levels do not overlap. On a plot scale, treatments containing tree rows with multiple canopy levels of distinctly separate heights allowed for maximum GLI while tree rows with only a single species had the lowest. On a plant scale, the tallest trees received near full light as long as canopies did not overlap. Understory shrubs received little to no light when density and number of tree canopies increased. Adjusting the density and number of canopy levels in DAS has significant effects on GLI, but should be further studied using additional treatments to identify quantitative optimum.Ope

Cosmology with 6 parameters in the Stage-IV era: efficient marginalisation over nuisance parameters

The analysis of photometric large-scale structure data is often complicated by the need to account for many observational and astrophysical systematics. The elaborate models needed to describe them often introduce many ``nuisance parameters'', which can be a major inhibitor of an efficient parameter inference. In this paper we introduce an approximate method to analytically marginalise over a large number of nuisance parameters based on the Laplace approximation. We discuss the mathematics of the method, its relation to concepts such as volume effects and profile likelihood, and show that it can be further simplified for calibratable systematics by linearising the dependence of the theory on the associated parameters. We quantify the accuracy of this approach by comparing it with traditional sampling methods in the context of existing data from the Dark Energy Survey, as well as futuristic Stage-IV photometric data. The linearised version of the method is able to obtain parameter constraints that are virtually equivalent to those found by exploring the full parameter space for a large number of calibratable nuisance parameters, while reducing the computation time by a factor 3-10. Furthermore, the non-linearised approach is able to analytically marginalise over a large number of parameters, returning constraints that are virtually indistinguishable from the brute-force method in most cases, accurately reproducing both the marginalised uncertainty on cosmological parameters, and the impact of volume effects associated with this marginalisation. We provide simple recipes to diagnose when the approximations made by the method fail and one should thus resort to traditional methods. The gains in sampling efficiency associated with this method enable the joint analysis of multiple surveys, typically hindered by the large number of nuisance parameters needed to describe them.Comment: 17 pages, 6 figures, 3 table

The Simons Observatory: Galactic Science Goals and Forecasts

Observing in six frequency bands from 27 to 280 GHz over a large sky area, the Simons Observatory (SO) is poised to address many questions in Galactic astrophysics in addition to its principal cosmological goals. In this work, we provide quantitative forecasts on astrophysical parameters of interest for a range of Galactic science cases. We find that SO can: constrain the frequency spectrum of polarized dust emission at a level of $\Delta\beta_d \lesssim 0.01$ and thus test models of dust composition that predict that $\beta_d$ in polarization differs from that measured in total intensity; measure the correlation coefficient between polarized dust and synchrotron emission with a factor of two greater precision than current constraints; exclude the non-existence of exo-Oort clouds at roughly 2.9$\sigma$ if the true fraction is similar to the detection rate of giant planets; map more than 850 molecular clouds with at least 50 independent polarization measurements at 1 pc resolution; detect or place upper limits on the polarization fractions of CO(2-1) emission and anomalous microwave emission at the 0.1% level in select regions; and measure the correlation coefficient between optical starlight polarization and microwave polarized dust emission in $1^\circ$ patches for all lines of sight with $N_{\rm H} \gtrsim 2\times10^{20}$ cm$^{-2}$. The goals and forecasts outlined here provide a roadmap for other microwave polarization experiments to expand their scientific scope via Milky Way astrophysics.Comment: Submitted to AAS journals. 33 pages, 10 figure

The Simons Observatory: Beam characterization for the Small Aperture Telescopes

We use time-domain simulations of Jupiter observations to test and develop a beam reconstruction pipeline for the Simons Observatory Small Aperture Telescopes. The method relies on a map maker that estimates and subtracts correlated atmospheric noise and a beam fitting code designed to compensate for the bias caused by the map maker. We test our reconstruction performance for four different frequency bands against various algorithmic parameters, atmospheric conditions and input beams. We additionally show the reconstruction quality as function of the number of available observations and investigate how different calibration strategies affect the beam uncertainty. For all of the cases considered, we find good agreement between the fitted results and the input beam model within a ~1.5% error for a multipole range l = 30 - 700.Comment: 22 pages, 21 figures, to be submitted to Ap