Browsing by Author "Kerry, Ruth"

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    Defining and characterizing Aflatoxin contamination risk areas for corn in Georgia, USA: Adjusting for collinearity and spatial correlation
    (Elsevier, 2018-07-02) Yoo, EunHye; Kerry, Ruth; Ingram, Benjamin R.; Ortiz, Brenda; Scully, Brian
    Aflatoxin is a carcinogenic toxin to humans and animals produced by mold fungi in staple crops. Surveys of Aflatoxin are expensive, and the results are usually not available for implementing within season mitigation strategies. Identification of high and low risk areas and years is essential to reduce the number of samples analyzed for Aflatoxin concentration. Previously a risk factors approach was developed to determine county level Aflatoxin contamination risk in southern Georgia, but Aflatoxin concentrations and risk factor data were not analyzed simultaneously and all risk factors had equal weight which is unrealistic. In the current paper we propose a regression approach to overcome these problems. Spatial Poisson profile regression identified clusters of counties which have similar Aflatoxin risk and risk factor profiles, whilst explicitly taking into account multicollinearity in the risk factor data and spatial autocorrelation in the Aflatoxin data. This approach allows examination of the utility of different highly correlated variables including remotely sensed data that could give information at the sub-county level. The results identify plausible clusters compared to previous work but also give the relative importance of the risk factors associated with those clusters. The approach also helps show that some factors like well-drained soil behave differently from expectations and irrigation data is not useful.
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    Determining future aflatoxin contamination risk scenarios for corn in Southern Georgia, USA using spatio-temporal modelling and future climate simulations
    (Nature Publishing Group, 2021-06-29) Kerry, Ruth; Ingram, Benjamin R.; Garcia Cela, Esther; Magan, Naresh; Ortiz, Brenda V.; Scully, Brian
    Aflatoxins (AFs) are produced by fungi in crops and can cause liver cancer. Permitted levels are legislated and batches of grain are rejected based on average concentrations. Corn grown in Southern Georgia (GA), USA, which experiences drought during the mid-silk growth period in June, is particularly susceptible to infection by Aspergillus section Flavi species which produce AFs. Previous studies showed strong association between AFs and June weather. Risk factors were developed: June maximum temperatures > 33 °C and June rainfall < 50 mm, the 30-year normals for the region. Future climate data were estimated for each year (2000–2100) and county in southern GA using the RCP 4.5 and RCP 8.5 emissions scenarios. The number of counties with June maximum temperatures > 33 °C and rainfall < 50 mm increased and then plateaued for both emissions scenarios. The percentage of years thresholds were exceeded was greater for RCP 8.5 than RCP 4.5. The spatial distribution of high-risk counties changed over time. Results suggest corn growth distribution should be changed or adaptation strategies employed like planting resistant varieties, irrigating and planting earlier. There were significantly more counties exceeding thresholds in 2010–2040 compared to 2000–2030 suggesting that adaptation strategies should be employed as soon as possible.
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    Investigation of the potential to reduce waste through sampling and spatial analysis of grain bulks
    (Elsevier, 2021-05-25) Kerry, Ruth; Ingram, Benjamin R.; Garcia-Cela, Esther; Magan, Naresh
    Batches of grain are accepted or rejected based on average mycotoxin concentrations in a composite grain sample. Spatial analysis of mycotoxins in two grain bulks was performed to determine the spatial distribution of toxins, whether they were co-located and the proportions of grain over legislative limits. The 2D distribution of deoxynivalenol (DON) and ochratoxin A (OTA) in a truck load of wheat grain was analysed, as was the distribution of fumonisins (FB1 and FB2) in a 3D maize grain pile. The data had been previously analysed, but results here show that highly skewed data would need to be transformed to investigate spatial autocorrelation properly. In the truck of wheat grain, DON and OTA showed co-variation and, in contrast to previous studies, OTA showed spatial structure when converted to normal scores. Spatial analysis of the maize pile showed that FB1 and FB2 contamination levels were each highest near the outer face and base of the grain pile. Simulations for both grain bulks showed that, for average toxin concentrations close to legislative limits, the proportion of grain over the legislative limits can vary greatly and could be very small when toxin contamination is highly positively skewed. The implications of the results for management were considered. Post-harvest, strategically placed sensors could be used to monitor environmental conditions within the stored grain in real time and detect the first signs of spoilage allowing swift remediative action so less grain is wasted. Pre-harvest approaches for mycotoxin management are suggested as additional food waste reduction strategies.
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    Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa
    (PLOS (Public Library of Science), 2017-09-13) Hughes, Kristen; Fosgate, Geoffrey T.; Budke, Christine M.; Ward, Michael P.; Kerry, Ruth; Ingram, Benjamin R.
    The population density of wildlife reservoirs contributes to disease transmission risk for domestic animals. The objective of this study was to model the African buffalo distribution of the Kruger National Park. A secondary objective was to collect field data to evaluate models and determine environmental predictors of buffalo detection. Spatial distribution models were created using buffalo census information and archived data from previous research. Field data were collected during the dry (August 2012) and wet (January 2013) seasons using a random walk design. The fit of the prediction models were assessed descriptively and formally by calculating the root mean square error (rMSE) of deviations from field observations. Logistic regression was used to estimate the effects of environmental variables on the detection of buffalo herds and linear regression was used to identify predictors of larger herd sizes. A zero-inflated Poisson model produced distributions that were most consistent with expected buffalo behavior. Field data confirmed that environmental factors including season (P = 0.008), vegetation type (P = 0.002), and vegetation density (P = 0.010) were significant predictors of buffalo detection. Bachelor herds were more likely to be detected in dense vegetation (P = 0.005) and during the wet season (P = 0.022) compared to the larger mixed-sex herds. Static distribution models for African buffalo can produce biologically reasonable results but environmental factors have significant effects and therefore could be used to improve model performance. Accurate distribution models are critical for the evaluation of disease risk and to model disease transmission.
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    Spatial analysis of soil moisture and turfgrass health to determine zones for spatially variable irrigation management
    (MDPI, 2023-04-28) Kerry, Ruth; Ingram, Benjamin R.; Keegan, Hammond; Shumate, Samantha R.; Gunther, David; Jensen, Ryan R.; Schill, Steve; Hansen, Neil C.; Hopkins, Bryan G.
    Irrigated turfgrass is a major crop in urban areas of the drought-stricken Western United States. A considerable proportion of irrigation water is wasted through the use of conventional sprinkler systems. While smart sprinkler systems have made progress in reducing temporal mis-applications, more research is needed to determine the most appropriate variables for accurately and cost-effectively determining spatial zones for irrigation application. This research uses data from ground and drone surveys of two large sports fields. Surveys were conducted pre-, within and towards the end of the irrigation season to determine spatial irrigation zones. Principal components analysis and k-means classification were used to develop zones using several variables individually and combined. The errors associated with uniform irrigation and different configurations of spatial zones are assessed to determine comparative improvements in irrigation efficiency afforded by spatial irrigation zones. A determination is also made as to whether the spatial zones can be temporally static or need to be re-determined periodically. Results suggest that zones based on spatial soil moisture surveys and simple observations of whether the grass felt wet or dry are better than those based on NDVI, other variables and several variables in combination. In addition, due to the temporal variations observed in spatial patterns, ideally zones should be re-evaluated periodically. However, a less labor-intensive solution is to determine temporally static zones based on patterns in soil moisture averaged from several surveys. Of particular importance are the spatial patterns observed prior to the start of the irrigation season as they reflect more temporally stable variation that relates to soil texture and topography rather than irrigation management.