A Bayesian approach for estimating the uncertainty on the contribution of nitrogen fixation and calculation of nutrient balances in grain legumes.

Background: The proportion of nitrogen (N) derived from the atmosphere (Ndfa) is a fundamental component of the plant N demand in legume species. To estimate the N benefit of grain legumes for the subsequent crop in the rotation, a simplified N balance is frequently used. This balance is calculated as the difference between fixed N and removed N by grains.

Soybean seed yield, protein, and oil concentration for a modern and old genotype under varying row spacings.

Progress in soybean ( max L.) breeding has led to a reduction in optimal seeding rates due to enhanced branching capacity over time. However, less is known about the changes in canopy architecture between old and modern soybean genotypes at varying row spacing and their impact on yield and seed quality through the main stem and branches.

Detecting sorghum aphid infestation in grain sorghum using leaf spectral response.

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected.

Treed Gaussian processes for animal movement modeling.

Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement.

The long shadow of woody encroachment: An integrated approach to modeling grassland songbird habitat.

Animals must track resources over relatively fine spatial and temporal scales, particularly in disturbance-mediated systems like grasslands. Grassland birds respond to habitat heterogeneity by dispersing among sites within and between years, yet we know little about how they make post-dispersal settlement decisions. Many methods exist to quantify the resource selection of mobile taxa, but the habitat data used in these models are frequently not collected at the same location or time that individuals were present.

Predicting corn tiller development in restrictive environments can be achieved to enhance defensive management decision tools for producers.

Introduction: While globally appreciated for reliable, intensification-friendly phenotypes, modern corn ( L.) genotypes retain crop plasticity potential. For example, weather and heterogeneous field conditions can overcome phenotype uniformity and facilitate tiller expression.

Developing a Decision-making Tool for Agricultural Surface Water Decontamination Using Ultraviolet-C Light.

Ultraviolet-C (UV-C) light-assisted water treatment systems are an increasingly investigated alternative to chemical sanitizers for agricultural surface water decontamination. However, the relatively high concentration of particulate matter in surface water is a major challenge to expanding its application in the production of fresh produce. The objective of this project was to test the efficacy of two commercial UV-C devices to reduce the microbial risk of agricultural water in order to develop a web application to assist growers in decision-making related to the on-farm implementation of UV-C technologies for agricultural water treatment.

Bayesian forecasting of disease spread with little or no local data.

Rapid and targeted management actions are a prerequisite to efficiently mitigate disease outbreaks. Targeted actions, however, require accurate spatial information on disease occurrence and spread. Frequently, targeted management actions are guided by non-statistical approaches that define the affected area by a pre-determined distance surrounding a small number of disease detections.

Riding out the storm: depleted fat stores and elevated hematocrit in a small bodied endotherm exposed to severe weather.

In the mid-continental grasslands of North America, climate change is increasing the intensity and frequency of extreme weather events. Increasingly severe storms and prolonged periods of elevated temperatures can impose challenges that adversely affect an individual's condition and, ultimately, survival. However, despite mounting evidence that extreme weather events, such as heavy rain storms, can impose short-term physiological challenges, we know little regarding the putative costs of such weather events.

Corn yield components can be stabilized via tillering in sub-optimal plant densities.

Introduction: Crop plasticity is fundamental to sustainability discussions in production agriculture. Modern corn ( L.) genetics can compensate yield determinants to a small degree, but plasticity mechanisms have been masked by breeder selection and plant density management preferences.

Diagnostic sensitivity of porcine biological samples for detecting African swine fever virus infection after natural consumption in feed and liquid.

African swine fever virus (ASFV) is a global threat to swine production and sustainable pork supply. Without a commercially available vaccine, prevention of ASFV entry and spread is reliant on biosecurity and early detection of infection. Although ASFV ingestion in swill or feed by naïve pigs is a likely route of initial introduction, controlled experimental studies rarely utilize natural consumption as the infection route.

Using machine learning to model nontraditional spatial dependence in occupancy data.

Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site-level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences.

A practical guide to estimating the light extinction coefficient with nonlinear models-a case study on maize.

Background: The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates.

Modeling Aceria tosichella biotype distribution over geographic space and time.

The wheat curl mite, Aceria tosichella Keifer, one of the most destructive arthropod pests of bread wheat worldwide, inflicts significant annual reductions in grain yields. Moreover, A. tosichella is the only vector for several economically important wheat viruses in the Americas, Australia and Europe.

Matching expert range maps with species distribution model predictions.

Species' range maps based on expert opinion are a critical resource for conservation planning. Expert maps are usually accompanied by species descriptions that specify sources of internal range heterogeneity, such as habitat associations, but these are rarely considered when using expert maps for analyses. We developed a quantitative metric (expert score) to evaluate the agreement between an expert map and a habitat probability surface obtained from a species distribution model.

Half-Life of African Swine Fever Virus in Shipped Feed.

African swine fever virus is transmissible through animal consumption of contaminated feed. To determine virus survival during transoceanic shipping, we calculated the half-life of the virus in 9 feed ingredients exposed to 30-day shipment conditions. Half-lives ranged from 9.

Bias correction of bounded location error in binary data.

Binary regression models for spatial data are commonly used in disciplines such as epidemiology and ecology. Many spatially referenced binary data sets suffer from location error, which occurs when the recorded location of an observation differs from its true location. When location error occurs, values of the covariates associated with the true spatial locations of the observations cannot be obtained.

A practical guide for combining data to model species distributions.

Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases.

Infectious Dose of African Swine Fever Virus When Consumed Naturally in Liquid or Feed.

African swine fever virus (ASFV) is a contagious, rapidly spreading, transboundary animal disease and a major threat to pork production globally. Although plant-based feed has been identified as a potential route for virus introduction onto swine farms, little is known about the risks for ASFV transmission in feed. We aimed to determine the minimum and median infectious doses of the Georgia 2007 strain of ASFV through oral exposure during natural drinking and feeding behaviors.

Spatiotemporal Distribution and Environmental Drivers of Barley yellow dwarf virus and Vector Abundance in Kansas.

Several aphid species transmit barley yellow dwarf, a globally destructive disease caused by viruses that infect cereal grain crops. Data from >400 samples collected across Kansas wheat fields in 2014 and 2015 were used to develop spatiotemporal models predicting the extent to which landcover, temperature and precipitation affect spring aphid vector abundance and presence of individuals carrying Barley yellow dwarf virus (BYDV). The distribution of Rhopalosiphum padi abundance was not correlated with climate or landcover, but Sitobion avenae abundance was positively correlated with fall temperature and negatively correlated to spring temperature and precipitation.

When mechanism matters: Bayesian forecasting using models of ecological diffusion.

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time.

An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics.

Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species.

The basis function approach for modeling autocorrelation in ecological data.

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy.

When can the cause of a population decline be determined?

Inferring the factors responsible for declines in abundance is a prerequisite to preventing the extinction of wild populations. Many of the policies and programmes intended to prevent extinctions operate on the assumption that the factors driving the decline of a population can be determined. Exogenous factors that cause declines in abundance can be statistically confounded with endogenous factors such as density dependence.

A comparison of breeding population estimators using nest and brood monitoring data.

For many species, breeding population size is an important metric for assessing population status. A variety of simple methods are often used to estimate this metric for ground-nesting birds that nest in open habitats (e.g.