Annual resolution sediment layers, known as varves, can provide continuous and high-resolution chronologies of sedimentary sequences. In addition, varve counting is not burdened with the high laboratory costs of geochronological analyses. Despite a more than 100-year history of use, many existing varve counting techniques are time consuming and difficult to reproduce. We present countMYvarves, a varve counting toolbox which uses sliding-window autocorrelation to count the number of repeated patterns in core scans or outcrop photos. The toolbox is used to build an annually-resolved record of sedimentation rates, which are depth-integrated to provide ages. We validate the model with repeated manual counts of a high sedimentation rate lake with biogenic varves (Herd Lake, USA) and a low sedimentation rate glacial lake (Lago Argentino, Argentina). In both cases, countMYvarves is consistent with manual counts and provides additional sedimentation rate data. The toolbox performs multiple simultaneous varve counts, enabling uncertainty to be quantified and propagated into the resulting age-depth model. The toolbox also includes modules to automatically exclude non-varved portions of sediment and interpolate over missing or disrupted sediment. CountMYvarves is open source, runs through a graphical user interface, and is available online for download for use on Windows, macOS or Linux at https://doi.org/10.5281/zenodo.4031811.

Barnard and Steinerberger [‘Three convolution inequalities on the real line with connections to additive combinatorics’, Preprint, 2019, arXiv:1903.08731] established the autocorrelation inequality

$$\begin{eqnarray}\min _{0\leq t\leq 1}\int _{\mathbb{R}}f(x)f(x+t)\,dx\leq 0.411||f||_{L^{1}}^{2}\quad \text{for}~f\in L^{1}(\mathbf{R}),\end{eqnarray}$$

$0.411$

$0.37$

$f$

$$\begin{eqnarray}\max _{x_{1}\in \mathbb{R}}\min _{0\leq t\leq 1}\left[f(x_{1}-t)+f(x_{1}+t)\right]\leq \min _{x_{2}\in \mathbb{R}}\max _{0\leq t\leq 1}\left[f(x_{2}-t)+f(x_{2}+t)\right].\end{eqnarray}$$

$f$

$||f||_{L^{1}}$

$d,n\in \mathbb{Z}^{+}$

$f\in L^{1}$

$A$

$d\times n$

$a_{i}$

$1\leq i\leq n$

$C$

$A$

$f$

$$\begin{eqnarray}\min _{\mathbf{t}\in [0,1]^{d}}\int _{\mathbb{R}}\mathop{\prod }_{i=1}^{n}~f(x+\mathbf{t}\cdot a_{i})\,dx\leq C||f||_{L^{1}}^{n}.\end{eqnarray}$$

We investigated the efficiency of the autoregressive repeatability model (AR) for genetic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle and compared the results with those from the conventional repeatability model (REP). The data set comprised records taken during the first four calving orders, corresponding to a total of 416, 766, 872 and 766 thousand records for interval between calving to first service, days open, calving interval and daughter pregnancy rate, respectively. Both models included fixed (month and age classes associated to each calving order) and random (herd-year-season, animal and permanent environmental) effects. For AR model, a first-order autoregressive (co)variance structure was fitted for the herd-year-season and permanent environmental effects. The AR outperformed the REP model, with lower Akaike Information Criteria, lower Mean Square Error and Akaike Weights close to unity. Rank correlations between estimated breeding values (EBV) with AR and REP models ranged from 0.95 to 0.97 for all studied reproductive traits, when the total bulls were considered. When considering only the top-100 selected bulls, the rank correlation ranged from 0.72 to 0.88. These results indicate that the re-ranking observed at the top level will provide more opportunities for selecting the best bulls. The EBV reliabilities provided by AR model was larger for all traits, but the magnitudes of the annual genetic progress were similar between two models. Overall, the proposed AR model was suitable for genetic evaluations of longitudinal reproductive traits in dairy cattle, outperforming the REP model.

Introduction: Understanding the spatial distribution of opioid abuse at the local level may facilitate community intervention strategies. The purpose of this analysis was to apply spatial analytical methods to determine clustering of opioid-related emergency medical services (EMS) responses in the City of Calgary. Methods: Using opioid-related EMS responses in the City of Calgary between January 1st through October 31st, 2017, we estimated the dissemination area (DA) specific spatial randomness effects by incorporating the spatial autocorrelation using intrinsic Gaussian conditional autoregressive model and generalized linear mixed models (GLMM). Global spatial autocorrelation was evaluated by Morans I index. Both Getis-Ord Gi and the LISA function in Geoda were used to estimate the local spatial autocorrelation. Two models were applied: 1) Poisson regression with DA-specific non-spatial random effects; 2) Poisson regression with DA-specific G-side spatial random effects. A pseudolikelihood approach was used for model comparison. Two types of cluster analysis were used to identify the spatial clustering. Results: There were 1488 opioid-related EMS responses available for analysis. Of the responses, 74% of the individuals were males. The median age was 33 years ( IQR: 26-42 years) with 65% of individuals between 20 and 39 years, and 27% between 40 and 64 years. In 62% of EMS responses, poisoning/overdose was the chief complaint. The global Morans Index implied the presence of global spatial autocorrelation. Comparing the two models applied suggested that the spatial model provided a better fit for the adjusted opioid-related EMS response rate. Calgary Center and East were identified as hot spots by both types of cluster analysis. Conclusion: Spatial modeling has a better predictability to assess potential high risk areas and identify locations for community intervention strategies. The clusters identified in Calgarys Center and East may have implications for future response strategies.

Predictions of weed seedling populations from seedbank data should characterize the spatial distribution as well as the composition and abundance of weeds. The spatial distribution of seedbank and seedling populations of common lambsquarters and annual grasses (giant foxtail, large crabgrass, and fall panicum) were described in moldboard plow and no-tillage soybean fields from 1990 to 1993. Spearman rank correlations between seedbank and seedling densities were significant for common lambsquarters in both tillages and all years, but for annual grasses correlations were significant only in no-tillage. Semivariograms showed spatial autocorrelation in seedbank and seedling populations of common lambsquarters in all years in no-till, but less often in the moldboard plow field. Annual grass seed and seedling populations were autocorrelated in the no-till field every year except 1993, and in the moldboard plow field in 1992 and 1993 only. Cross-semivariograms showed spatial continuity between seedbank and seedling population densities in 3 of 4 yr in no-till for common lambsquarters, and in all years of no-till and 1 yr of moldboard plow for annual grasses. Grey-scale field maps of common lambsquarters seedbanks corresponded visually to maps of seedling populations and could have been used to target control efforts, but visual correspondence between annual grass seedbank and seedling maps was poor. Seedbank and seedling mapping may be useful for site-specific management, but additional information is needed to understand the variation in the relationships between these two populations over time and space.

Crop rotation has long been considered one of the simplest and most effective tools for managing weeds. In this paper, we demonstrate how crop rotations can be strategically arranged to harness a novel mechanism of weed suppression: weed-weed competition. Specifically, we consider how crop stacking, or increasing the number of consecutive plantings of a single crop within a rotation, can decrease the size of the weed seed bank, by forcing weeds to compete with each other in similar environments for longer periods of time, while still reaping the traditional benefits of crop rotation. Using an annual plant model, we investigate the theoretical effects of stacked crop rotations on weeds that have different life-history strategies and phenology. Our results show that when weeds compete within a season, stacking can reduce the weed seed bank compared to rotations without stacked crops. Although more research is needed to fully understand the effects of crop stacking on other aspects of the system, such as insect pests and diseases, our research suggests that crop stacking has the potential to improve weed suppression without additional inputs, and their associated costs and externalities. More generally, improving management by changing the temporal arrangement of disturbances is a novel, process-based approach that could likely be applied to other weed management practices, such as mowing and herbicide application, and which could involve mechanisms other than weed-weed competition. Leveraging this new application of existing ecological theory to improve weed management strategies holds great promise.

This study presents a way to parametrically model and simulate multivariate distributions under potential non-normality, autocorrelation and heteroscedasticity and illustrates its application to agricultural risk analysis. Specifically, the joint probability distribution (pdf) for West Texas irrigated cotton, corn, sorghum, and wheat production and prices is estimated and applied to evaluate the changes in the risk and returns of agricultural production in the region resulting from observed and predicted price and production trends. The estimated pdf allows for time trends on the mean and the variance and varying degrees of autocorrelation and non-normality (kurtosis and right- or left-skewness) in each of the price and production variables. It also allows for any possible price-price, production-production, or price-production correlation.