Soil Moisture Depletion Modelling Using a TDR Multi-Sensor System, GIS, Soil Analyzes, Precision Agriculture and Remote Sensing on Maize for Improved Irrigation-Fertilization Decisions

The effects of three drip irrigation (IR1: Farmer’s, IR2:Full (100%ETc), IR3:Deficit (80%ETc) irrigation), and two fertilization (Ft1, Ft2) treatments were studied on maize yield and biomass by applying new agro-technologies (TDR—sensors for soil moisture (SM) measurements, Precision Agriculture, Remote Sensing—NDVI (Sentinel-2 satellite sensor), soil-hydraulic analyses and Geostatistical models, SM-rootzone modelling-2D-GIS mapping). A daily soil moisture depletion (SMDp) model was developed. The two-way-ANOVA statistical analysis results revealed that irrigation (IR3 = best) and fertilization treatments (Ft1 = best) significantly affect yield and biomass. Deficit irrigation and proper fertilization based on new agro-technologies for improved management decisions can result in substantial improvement on yield (+116.10%) and biomass (+119.71%) with less net water use (−7.49%) and reduced drainage water losses (−41.02%).

Rainfed and Supplemental Irrigation Modelling 2D GIS Moisture Rootzone Mapping on Yield and Seed Oil of Cotton (Gossypium hirsutum) Using Precision Agriculture and Remote Sensing

The effects of two irrigation (IR1: rainfed; IR2: rainfed + supplemental drip irrigation), and two fertilization (Ft1, Ft2) treatments were studied on cotton yield and seed oil by applying a number of new agro-technologies such as: TDR sensors; soil moisture (SM); precision agriculture; remote-sensing NDVI (Sentinel-2 satellite sensor); soil-hydraulic analyses; geostatistical models; SM-rootzone, and modelling 2D GIS mapping. A daily soil-water-crop-atmosphere (SWCA) balance model was developed. The two-way ANOVA statistical analysis results revealed that irrigation (IR2 = best) and fertilization treatments (Ft1 = best) significantly affected yield and oil content. Supplemental irrigation, if applied during critical growth stages, could result in substantial improvement on yield (+234.12%) and oil content (+126.44%).

Human Rights Violations in Space: Assessing the External Validity of Machine-Geocoded versus Human-Geocoded Data

Political event data are widely used in studies of political violence. Recent years have seen notable advances in the automated coding of political event data from international news sources. Yet, the validity of machine-coded event data remains disputed, especially in the context of event geolocation. We analyze the frequencies of human- and machine-geocoded event data agreement in relation to an independent (ground truth) source. The events are human rights violations in Colombia. We perform our evaluation for a key, 8-year period of the Colombian conflict and in three 2-year subperiods as well as for a selected set of (non)journalistically remote municipalities. As a complement to this analysis, we estimate spatial probit models based on the three datasets. These models assume Gaussian Markov Random Field error processes; they are constructed using a stochastic partial differential equation and estimated with integrated nested Laplacian approximation. The estimated models tell us whether the three datasets produce comparable predictions, underreport events in relation to the same covariates, and have similar patterns of prediction error. Together the two analyses show that, for this subnational conflict, the machine- and human-geocoded datasets are comparable in terms of external validity but, according to the geostatistical models, produce prediction errors that differ in important respects.

Incorporating Auxiliary Data of Different Spatial Scales for Spatial Prediction of Soil Nitrogen Using Robust Residual Cokriging (RRCoK)

Auxiliary data has usually been incorporated into geostatistics for high-accuracy spatial prediction. Due to the different spatial scales, category and point auxiliary data have rarely been incorporated into prediction models together. Moreover, traditionally used geostatistical models are usually sensitive to outliers. This study first quantified the land-use type (LUT) effect on soil total nitrogen (TN) in Hanchuan County, China. Next, the relationship between soil TN and the auxiliary soil organic matter (SOM) was explored. Then, robust residual cokriging (RRCoK) with LUTs was proposed for the spatial prediction of soil TN. Finally, its spatial prediction accuracy was compared with that of ordinary kriging (OK), robust cokriging (RCoK), and robust residual kriging (RRK). Results show that: (i) both LUT and SOM are closely related to soil TN; (ii) by incorporating SOM, the relative improvement accuracy of RCoK over OK was 29.41%; (iii) by incorporating LUTs, the relative improvement accuracy of RRK over OK was 33.33%; (iv) RRCoK obtained the highest spatial prediction accuracy (RI = 43.14%). It is concluded that the recommended method, RRCoK, can effectively incorporate category and point auxiliary data together for the high-accuracy spatial prediction of soil properties.

Hepatitis E in Bangladesh: Insights from a National Serosurvey

Background Hepatitis E virus, typically genotypes 1 and 2, is a major cause of avoidable morbidity and mortality in South Asia. Although case fatality risk among pregnant women can reach as high as 25%, a lack of population-level disease burden data has been cited as a primary factor in key global policy recommendations against the routine use of licensed hepatitis E vaccines, one of the only effective tools available for preventing disease and death. Methods We tested serum from a nationally-representative serosurvey in Bangladesh for anti-HEV IgG. We estimated the proportion of the population with evidence of historical HEV infection and used Bayesian geostatistical models to generate high-resolution national maps of seropositivity. We examined variability in seropositivity by individual-level, household-level, and community-level risk factors using spatial logistic regression. Results We tested serum samples from 2924 individuals from 70 communities representing all divisions of Bangladesh and estimated a national seroprevalence of hepatitis E of 20% (95% CI 17-24%). Seropositivity increased with age and male sex (Odds Ratio: 2.2 male vs. female, 95% Confidence Interval: 1.8–2.8). Community-level seroprevalence ranged from 0-78% with the seroprevalence in urban areas being higher, including Dhaka, the capital, with 3.0-fold (95% Credible Interval 2.3-3.7) higher seroprevalence than the rest of the country. Conclusion Hepatitis E infections are common throughout Bangladesh. Strengthening clinical surveillance for hepatitis E, especially in urban areas may help generate additional evidence needed to appropriately target interventions like vaccines to the populations most likely to benefit.

1395Estimates of people infected with soil-transmitted-helminthiasis and drug requirements for preventive chemotherapy in Ogun, Nigeria

Background Soil transmitted helminthiasis (STH) are among the most common human infections worldwide with over 1 billion people affected. This study produced predictive risk maps of STH and estimated the number of people infected, and the amount of drug required for preventive chemotherapy in Ogun state, Nigeria. Methods Georeferenced STH infection data obtained from community cross-sectional survey, at 33 locations between July 2016-November 2018, together with remotely sensed environmental and socio-economic data were analyzed using Bayesian geostatistical models. Result An overall prevalence of 17.2% (95 % CI: 14.9, 19.5) was recorded for STH infection. Ascaris lumbricoides infections was the most predominant, 13.6% (95% CI: 11.5, 15.7), while Hookworm and Trichuris trichiura had 4.6 % (95% CI: 3.3, 5.9) and 1.7% (95 % CI: 0.9, 2.4), respectively. The predictive maps reveal a spatial pattern of high risk in the central, western and on the border connecting Republic of Benin. The model identified soil pH, soil moisture and elevation as important predictors of the STH infection. Approximately 1.1 million persons (preschoolers, school-aged children (SAC) and adults) are infected and requires 7.8 million doses. Also, 375,374 SAC were estimated to be infected, requiring 2.7 million doses for annual PC. Conclusion Our predictive risk maps and estimated PC needs provide useful information for the elimination of STH, by identifying priority areas for delivery of interventions in Ogun State, Nigeria.

Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics

With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.

Determining initial viability of local scale managed aquifer recharge projects in alluvial deposition systems

Critical groundwater overdraft is one of the greatest water issues of our time. In California, decades of overdraft have resulted in the passage of the 2014 Sustainable Groundwater Management Act, which requires critically overdrafted groundwater basins to create groundwater sustainability plans for future groundwater management. Many managers are using managed aquifer recharge (MAR) in their overall sustainability portfolio, in an attempt to balance groundwater use. Soil maps have been used in the past to determine viability of managed aquifer recharge sites. However, soil maps do not account for the high permeability pathways that exist in the subsurface, which have the potential to provide high efficiency recharge to the water table. This paper emphasizes the utility of creating data dense fine resolution geostatistical models and generating many realizations of the subsurface, which can then be used for analysis to understand the variability in recharge potential for specific recharge sites. These geostatistical realizations were investigated using connectivity metrics to evaluate the spread of highly conductive pathways throughout the subsurface. Connectivity analyses of high conductivity pathways show confidence that the study site- three vineyards located in the floodplain between the Cosumnes River and Deer Creek in Elk Grove, CA - has the potential to provide efficient recharge to the water table. These connectivity analyses can be completed prior to running computationally expensive and time intensive groundwater models and can be used as a way to understand variance between realizations of these geostatistical models.

Hepatitis E in Bangladesh: Insights from a National Serosurvey

Background Hepatitis E virus, typically genotypes 1 and 2, is a major cause of avoidable morbidity and mortality in South Asia. Although case fatality risk among pregnant women can reach as high as 25%, a lack of population-level disease burden data has been cited as a primary factor in key global policy recommendations against the routine use of licensed hepatitis E vaccines, one of the only effective tools available for preventing disease and death. Methods We tested serum from a nationally-representative serosurvey in Bangladesh for anti-HEV IgG. We estimated the proportion of the population with evidence of historical HEV infection and used Bayesian geostatistical models to generate high resolution national maps of seropositivity. We examined variability in seropositivity by individual-level, household-level, and community-level risk factors using spatial logistic regression. Results We tested serum samples from 2924 individuals from 70 communities representing all divisions of Bangladesh and estimated a national seroprevalence of hepatitis E of 20% (95% CI 17-24%). Seropositivity increased with age and male sex (OR: 2.2, 95% CI: 1.8-2.8). Community-level seroprevalence ranged from 0-78% with the seroprevalence in urban areas being higher, including Dhaka, the capital, with 3-fold (95%CrI 2.3-3.7) higher seroprevalence than the rest of the country. Conclusion Hepatitis E infections are common throughout Bangladesh, though 90% of women reach reproductive age without any evidence of previous exposure to the virus, thus likely susceptible to infection and disease. Strengthening clinical surveillance for hepatitis E, especially in urban areas may help generate additional evidence needed to appropriately target interventions like vaccines to the populations most likely to benefit.