Browsing by Author "Ingram, Benjamin R."
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Item Open Access 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, BrianAflatoxin 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.Item Open Access Detection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from unmanned aerial vehicle (UAV): a case study in a commercial vineyard(MDPI, 2017-03-15) Poblete-Echeverría, Carlos; Olmedo, Guillermo Federico; Ingram, Benjamin R.; Bardeen, MatthewThe use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating these parameters, accurate and automated segmentation methods are required to extract relevant information from RGB images. Manual segmentation of aerial images is a laborious and time-consuming process. Traditional classification methods have shown satisfactory results in the segmentation of RGB images for diverse applications and surfaces, however, in the case of commercial vineyards, it is necessary to consider some particularities inherent to canopy size in the vertical trellis systems (VSP) such as shadow effect and different soil conditions in inter-rows (mixed information of soil and weeds). Therefore, the objective of this study was to compare the performance of four classification methods (K-means, Artificial Neural Networks (ANN), Random Forest (RForest) and Spectral Indices (SI)) to detect canopy in a vineyard trained on VSP. Six flights were carried out from post-flowering to harvest in a commercial vineyard cv. Carménère using a low-cost UAV equipped with a conventional RGB camera. The results show that the ANN and the simple SI method complemented with the Otsu method for thresholding presented the best performance for the detection of the vine canopy with high overall accuracy values for all study days. Spectral indices presented the best performance in the detection of Plant class (Vine canopy) with an overall accuracy of around 0.99. However, considering the performance pixel by pixel, the Spectral indices are not able to discriminate between Soil and Shadow class. The best performance in the classification of three classes (Plant, Soil, and Shadow) of vineyard RGB images, was obtained when the SI values were used as input data in trained methods (ANN and RForest), reaching overall accuracy values around 0.98 with high sensitivity values for the three classes.Item Open Access 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, BrianAflatoxins (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.Item Open Access A field system for measuring plant and soil carbon fluxes using stable isotope methods(Wiley, 2020-06-21) McCloskey, Christopher S.; Otten, Wilfred; Paterson, Eric; Ingram, Benjamin R.; Kirk, Guy J. D.There is a lack of field methods for measuring plant and soil processes controlling soil organic matter (SOM) turnover over diurnal, seasonal, and longer time-scales with which to develop datasets for modelling. We describe an automated field system for measuring plant and soil carbon fluxes over such time-scales using stable isotope methods, and we assess its performance. The system comprises 24 large (1-m deep, 0.8-m diameter) cylindrical lysimeters connected to gas-flux chambers and instruments. The lysimeters contain intact, naturally-structured C3 soil planted with a C4 grass. Fluxes of CO2 and their 13C isotope composition are measured 3-times daily in each lysimeter, and the isotope composition is used to partition the fluxes between plant and soil sources. We investigate the following potential sources of error in the measurement system and show they do not significantly affect the measured CO2 fluxes or isotope signatures: gas leaks; the rate of gas flow through sampling loops; instrument precision and drift; the concentration-dependence of isotope measurements; and the linearity of CO2 accumulation in the chambers and associated isotope fractionation resulting from different rates of 13CO2 and 12CO2 diffusion from the soil. For the loamy grassland soil and US prairie grass (Bouteloua dactyloides) tested, the precision of CO2 flux measurements was ± 0.04 % and that of the flux partitioning ± 0.40 %. We give examples of diurnal and seasonal patterns of plant and soil C fluxes and soil temperature and moisture. We discuss the limitations of the isotope methodology for partitioning fluxes as applied in our system. We conclude the system is suitable for measuring net ecosystem respiration fluxes and their plant and soil components with sufficient precision to resolve diurnal and seasonal patternsItem Open Access GIS and multi-criteria decision-making analysis assessment of land suitability for rapeseed farming in calcareous soils of semi-arid regions(Elsevier, 2019-04-19) Ostovari, Yaser; Honarbakhsh, Afshin; Sangoony, Hamed; Zolfaghari, Farhad; Maleki, Kimia; Ingram, Benjamin R.To reverse the negative environmental properties effect on fertile lands for agriculture, land suitability evaluation is the first step in the designing the most sustainable land use and management systems. The aim of the present study was to develop and evaluate a land suitability model for rapeseed farming using topography factors, soil data and remote sensing data in calcareous soils of semi-arid regions northwestern Iran. For this purpose, stratified random sampling was used to select a set of 92 soil samples of agricultural land use from 0 to 30 cm depth. For land suitability assessment, the opinions of 19 local experts were used to make a decision for the weight of topography, soil data and remote sensing data factors by an analytic hierarchy process (AHP) from multi-criteria analysis. The input data including climate, topography, soil and remote sensing data were included that are related to rapeseed production. The results indicate the highest specific weight belongs to the soil texture (0.341), calcium carbonate equivalent (0.171) and elevation (0.114), respectively. Land suitability evaluation based on the United Nations Food and Agriculture Organization classification system indicated that 0.81% (420.8 ha) of the studied area was for high suitable (S1), 42.33% (21940.2 ha) was for moderately suitable (S2) and 11.78% (6104 ha) was for marginally suitable (S3) class. The 39.72% (20586.4) and 0.95% (492.1 ha) of studied area were located as currently not-suitable and permanently not-suitable for rapeseed productions, respectively.Item Open Access GIS-Based assessment of groundwater quality for drinking purpose in northern part of Fars province, Marvdasht(IWA, 2019-03-08) Honarbakhsh, Afshin; Tahmoures, Mohammad; Tashayo, Behnam; Mousazadeh, Milad; Ingram, Benjamin R.; Ostovari, YaserWith increasing population and freshwater shortages worldwide, it is necessary to protect vital groundwater resources using innovative methods. The main objective of this study is to use a GIS-based approach with the Groundwater Quality Index (GWQI) to analyze groundwater quality in Marvdasht located in the semi-arid region of Iran. For this purpose, we used groundwater quality data that were collected in a five-year period (2010–2015). The most influential water quality parameters were determined by performing map removal sensitivity analysis. Mean maps of the groundwater parameters showed that total dissolved solid (TDS), electrical conductivity (EC) and total hardness (TH) were the most important parameters that exceed the maximum permissible limits for drinking water. The groundwater quality of the study area is generally desirable for drinking (GWQI = 71). The GWQI map indicated that groundwater was higher quality in northern regions of the study area. The GWQI also revealed that only 2% of the study area (11 km2) was below the low quality class. According to map removal sensitivity analysis, Mg2+, TH and Na+ were identified as the most sensitive water quality parameters. Therefore, these parameters need to be monitored regularly and with increased precision.Item Open Access 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, NareshBatches 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.Item Open Access 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.Item Open Access Selecting canopy zones and thresholding approaches to assess grapevine water status by using aerial and ground-based thermal imaging(MDPI, 2016-10-07) Sepúlveda-Reyes, Daniel; Ingram, Benjamin R.; Bardeen, Matthew; Zúñiga, Mauricio; Ortega-Farías, Samuel; Poblete-Echeverría, CarlosAerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower baseline temperature values). During the 2014–2015 growing season, an experimental study under different water conditions (slight, mild, moderate, and severe water stress) was carried out in a commercial vineyard (Vitis vinifera L., cv. Carménè). In this study thermal images were obtained from different canopy zones by using both aerial (>60 m height) and ground-based (sunlit, shadow and nadir views) thermography. Using customized code that was written specifically for this research, three different thresholding approaches were applied to each image: (i) the standard deviation technique (SDT); (ii) the energy balance technique (EBT); and (iii) the field reference temperature technique (FRT). Results obtained from three different approaches showed that the EBT had the best performance. The EBT was able to discriminate over 95% of the leaf material, while SDT and FRT were able to detect around 70% and 40% of the leaf material, respectively. In the case of canopy zone analysis, ground-based nadir images presented the best correlations with stomatal conductance (gs) and stem water potential (Ψstem), reaching determination coefficients (r2) of 0.73 and 0.82, respectively. The best relationships between thermal indices and plant-based variables were registered during the period of maximum atmospheric demand (near veraison) with significant correlations for all methods.Item Open Access 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.Item Open Access Spatial and temporal analysis of rainfall concentration using the Gini index and PCI(MDPI, 2018-01-28) Sangüesa, Claudia; Pizarro, Roberto; Ibañez, Alfredo; Pino, Juan; Rivera, Diego; Garcia-Chevesich, Pablo; Ingram, Benjamin R.This study aims to determine if there is variation in precipitation concentrations in Chile. We analyzed daily and monthly records from 89 pluviometric stations in the period 1970–2016 and distributed between 29°12′ S and 39°30′ S. This area was divided into two climatic zones: arid–semiarid and humid–subhumid. For each station, the Gini coefficient or Gini Index (GI), the precipitation concentration index (PCI), and the maximum annual precipitation intensity in a 24-h duration were calculated. These series of annual values were analyzed with the Mann–Kendall test with 5% error. Overall, it was noted that positive trends in the GI are present in both areas, although most were not found to be significant. In the case of PCI, the presence of positive trends is only present in the arid–semiarid zone; in the humid–subhumid zone, negative trends were mostly observed, although none of them were significant. Although no significant changes in all indices are evident, the particular case of the GI in the humid–subhumid zone stands out, where mostly positive trends were found (91.1%), of which 35.6% were significant. This would indicate that precipitation is more likely to be concentrated on a daily scale.Item Open Access Temporal stability of within-field variability of total soluble solids of grapevine under semi-arid conditions: a first step towards a spatial model(ADERA, 2018-02-08) Verdugo-Vásquez, Nicolas; Acevedo-Opazo, César; Valdés-Gómez, Héctor; Ingram, Benjamin R.; García de Cortázar-Atauri, Iñaki; Tisseyre, BrunoAims: This work focuses on the study of the intra- and inter-annual Temporal Stability of Within-Field Variability (TSWFV) of Total Soluble Solids (TSS) as an estimate of grape maturity. Methods and results: The experiment was carried out between 2009 and 2015 in four fields located in the Maule Valley, Chile, under irrigated conditions. Each field corresponded to a different cultivar (namely Cabernet-Sauvignon, Chardonnay, Sauvignon blanc and Carménère), and data collection ranged over two to four years depending on the field. A regular sampling grid was designed within each field, and TSS was measured at each site of the grid on different dates (from veraison to harvest). A Kendall test (W) was used to analyse the TSWFV of TSS between all dates for each cultivar and season. A Spearman’s rank correlation coefficient (rs) was used to analyse the relationships between each sampling date and the date of harvest considered as the reference. Results of the study highlighted high within-field variability in TSS. The W test showed significant intra- and inter-annual TSWFV, and rs values showed a high and significant correlation between sampling dates. Conclusion: These results are of interest for precision viticulture since, under the conditions of the experiment, the spatial patterns of the TSS maps obtained 40 days before harvest remain the same until harvest. Therefore, early target sampling of TSS may provide a good estimate of the spatial variability of grape maturity at harvest. Significance and impact of the study: The inter-annual stability of the TSS spatial patterns makes it possible to propose a simple empirical spatial model that allows estimation of TSS values for the whole field using only one reference measurement, provided that historical data are available.Item Open Access Towards an empirical model to estimate the spatial variability of grapevine phenology at the within field scale(Springer, 2019-04-12) Verdugo-Vásquez, Nicolas; Acevedo-Opazo, C.; Valdés-Gómez, Héctor; Ingram, Benjamin R.; García de Cortázar-Atauri, I.; Tisseyre, B.The aim of this study is to propose an empirical spatial model to estimate the spatial variability of grapevine phenology at the within-field scale. This spatial model allows the characterization of the spatial variability of a given variable of the fields through a single measurement performed in the field (reference site) and a combination of site-specific coefficients calculated through historical information. This approach was compared to classical approaches requiring extensive sampling and phenology models based on climatic data, which do not consider the spatial variability of the field. The study was conducted on two fields of Vitis vinifera, one of cv Cabernet Sauvignon (CS, 1.56 ha) and the other one of cv Chardonnay (CH, 1.66 ha) located in Maule Valley, Chile. Date of occurrence of grapevine phenology (budburst, flowering and veraison) were observed at the within field level following a regular sampling grid during 4 seasons for cv CS and 2 seasons for cv CH. The best results were obtained with the devised spatial model in almost all cases, with a Root Mean Square Errors (RMSE) lower than 3 days. However, if the variability of phenology is low, the traditional method of sampling could lead to better results. This study is the first step towards a modeling of the spatial variability of grapevine phenology at the within-field scale. To be fully operational in commercial vineyards, the calibration process needs simplification, for example, using low cost, inexpensive ancillary information to zone vineyards according to grapevine phenology.Item Open Access WEBSEIDF: A web-based system for the estimation of IDF curves in Central Chile(MDPI, 2018-08-04) Pizarro, Roberto; Ingram, Benjamin R.; Gonzalez-Leiva, Fernando; Valdés-Pineda, Rodrigo; Sangüesa, Claudia; Delgado, Nicolás; García-Chevesich, Pablo; Valdés, Juan B.The lack of reliable continuous rainfall records can exacerbate the negative impact of extreme storm events. The inability to describe the continuous characteristics of rainfall from storm events increases the likelihood that the design of hydraulic structures will be inadequate. To mitigate extreme storm impacts and improve water governance at the catchment scale, it is vital to improve the availability of data and the array of tools used to model and forecast hydrological processes. In this paper, we describe and discuss the implementation of a web-based system for the estimation of intensity–duration–frequency (IDF) curves (WEBSEIDF) in Chile. The web platform was constructed using records from 47 pluviographic gauges available in central Chile (30–40° S), with at least 15 years of reliable records. IDF curves can be generated for durations ranging from 15 min to 24 h. In addition, the extrapolation of rainfall intensity from pluviograph to pluviometric gauges (i.e., 24-h rainfall accumulation) can be carried out using the storm index (SI) method. IDF curves can also be generated for any spatial location within central Chile using the ordinary Kriging method. These procedures allow the generation of numerical and graphical displays of IDF curves, for any selected spatial location, and for any combination of probability distribution function (PDF), parameter estimation method, and type of IDF model. One of the major advantages of WEBSEIDF is the flexibility of its database, which can be easily modified and saved to generate IDF curves under user-defined scenarios, that is, changing climate conditions. The implementation and validation of WEBSEIDF serves as a decision support system, providing an important tool for improving the ability of the Chilean government to mitigate the impact of extreme hydrologic events in central Chile. The system is freely available for students, researchers, and other relevant professionals, to improve technical decisions of public and private institutions.