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Quantifying redox-driven changes in iron chemistry in irrigated semiarid to arid soils and their relevance for the availability of nutrients and contaminants is critical for global food security. Data across three growing seasons and two different soil types in semiarid to arid climates indicate site-independent peaks of reactive iron in soil aligned with peaks in irrigation events. The reactive iron formed during irrigation was short-lived, and the concentration was back at baseline during harvest. The significant (p< 0.01) increase of reactive iron ranging from 1589.0 ± 172.3 to 1898.0 ± 201.1 μg g–1over the growing season triggered by reducing conditions due to transient water infiltration resulted in the mobilization of organic soil carbon and affected the mobility and plant availability of nitrogen, uranium, and arsenic. Porewater samples collected during irrigation events demonstrated increasing iron concentrations over time and positively correlated (p< 0.05) with arsenic and uranium levels. Geogenic arsenic mobilization into soil porewater during peak irrigation events contained significantly (p< 0.01) higher (∼90%) reduced inorganic arsenic species. Crop tissue analysis indicated that roots contained the highest concentrations of trace elements, followed by shoots and grains. Coupled (bio)geochemical redox cycles of iron, nutrients, and contaminants seem to play a critical but so far less recognized role for crop production in irrigated agroecosystems of semiarid to arid systems.

Undergraduate hands‐on research can foster innovation and critical thinking among young scholars to delve into real‐world challenges. Specifically, exploring the critical nexus between water usage and agricultural yield, can foster academic growth and holds the key to addressing global food security in an era of increasing environmental constraints, where students can unlock insights crucial to enhancing crop yield and sustainability. Investigating the intricate relationship between water management and crop productivity through undergraduate research is exemplified in this article. Undergraduate students acquired hands‐on research experience by collecting, processing, and analyzing destructive (crop biomass samples) and non‐destructive (plant height, nodes, and leaf chlorophyll content) cropping system data on soybeans under irrigated and dryland production systems, where they worked closely with the farmer. Identifying the current research problem and study site selection, scientific decision‐making during the field study, developing critical thinking while ensuring research communication skills, and quality assurance and quality control through technology during data collection and analysis were learning outcomes. The research highlights the observed distinct performance between irrigated and non‐irrigated soybeans using non‐destructive plant health and growth indicators with plant biomass, following appropriate quality control and assurance steps. Statistically, irrigated soybeans outperformed non‐irrigated soybeans in terms of average plant height at maturity (irrigated: 97.0±1.7 cm vs. non‐irrigated: 37.4±0.6 cm; p<0.01) and number of nodes on the mainstem (irrigated: 19.5±1.2 vs. non‐irrigated: 12.6±0.8; p<0.01). Findings from this study can help ensure quality control and assurance in future cropping system projects. Through the agroecosystem study, we exhibit the importance and role of undergraduate research opportunities in developing the next generation of problem solvers.

Redox driven mobilization and plant uptake of contaminants under transiently saturated soil conditions need to be clarified to ensure food and water quality across different irrigation systems. We postulate that solid-phase iron reduction in anoxic microsites present in the rhizosphere of unsaturated soil is a key driver for mobilization and bioavailability of contaminants under nonflooded irrigation. To clarify this, two major crops, corn and soybean differing in iron uptake strategies, were grown in irrigated synthetic soil under semiarid conditions with gravimetric moisture content ∼12.5 ± 2.4%. 2-line ferrihydrite, which was coprecipitated with uranium and arsenic, served as the only iron source in soil. Irrespective of crop type, reduced iron was detected in pore water and postexperiment rhizosphere soil confirming ferrihydrite reduction. These results support the presence of localized anoxic microsites in the otherwise aerobic porous bulk soil causing reduction of ferrihydrite and concomitant increase in plant uptake of comobilized contaminants. Our findings indicate that reactive iron minerals undergo reductive dissolution inside anoxic microsites of primarily unsaturated soil, which may have implications on the mobility of trace element contaminants such as arsenic and uranium in irrigated unsaturated soils, accounting for 55% of the irrigated area in the US.

Driven by biofuel policies, which aim to reduce greenhouse gas (GHG) emissions and increase domestic energy supply, global production and consumption of bioethanol have doubled between 2007 and 2016, with rapid growth in corn-based bioethanol in the U.S. and sugar cane-based bioethanol in Brazil. Advances in crop yields, energy use efficiency in fertilizer production, biomass-to-ethanol conversion rates, and energy efficiency in ethanol production have improved the energy balance and GHG emission reduction potential of bioethanol. In the current study, the water, energy, and carbon footprints of bioethanol from corn in the U.S. and sugar cane in Brazil were assessed. The results show that U.S. corn bioethanol has a smaller water footprint (541 L water/L bioethanol) than Brazilian sugar cane bioethanol (1115 L water/L bioethanol). Brazilian sugar cane bioethanol has, however, a better energy balance (17.7 MJ/L bioethanol) and smaller carbon footprint (38.5 g CO2e/MJ) than U.S. bioethanol, which has an energy balance of 11.2 MJ/L bioethanol and carbon footprint of 44.9 g CO2e/MJ. The results show regional differences in the three footprints and highlight the need to take these differences into consideration to understand the implications of biofuel production for local water resources, net energy production, and climate change mitigation.

Soil moisture is an important state variable in terrestrial water cycles; however, only few studies are available on regional soil moisture spatial variability (SMSV), which yielded inconsistent findings about regional controls on SMSV. Here, long‐term soil moisture data were obtained from the Automated Weather Data Network and Soil Climate Analysis Network in three regions with different climate regimes across the continental U.S. Comprehensive data sets were compiled to examine regional controls on SMSV using the method of Empirical Orthogonal Function. One dominant spatial structure (EOF1) of soil moisture was found in the study regions, which explained over 75%, 67%, and 86% of the spatial variance in soil moisture in Nebraska, Utah, and the Southeast U.S., respectively. Despite the significant spatial variability in precipitation and potential evapotranspiration in all the study regions, the results showed that meteorological forcings had limited effects on regional SMSV in those regions with different climatic conditions, which differed from the traditional notion that SMSV is mainly controlled by meteorological forcings at the scale from 50 to 400 km. Instead, local factors related to soil (e.g., sand and clay fractions) were found to have significant correlations with EOF1, although the effects of other local factors (e.g., topography and vegetation) were generally negligible. This study provides strong field evidence that soil can exert much stronger impacts on regional SMSV than previously thought, which can override the effects of meteorological forcings. Future studies are still needed to elaborate on the relative roles of climate and soil in affecting regional SMSV. EOF method is used to analyze controls on regional soil moisture spatial variability in three regions over the continental U.S.Meteorological forcing is found to have limited effects on regional soil moisture spatial variabilityLocal factors related to soil play prevailing roles in determining regional soil moisture spatial distribution

Natural disasters impact agriculture. Farmers incur large losses due to crop damage. Climate/weather-driven natural events or disasters are happening often and are causing billions of dollars in losses. Crop insurance provides economic stability to the agricultural industry to make up for losses. A crop insurance claim is an extensive process and it takes time to process claims. In this paper, we propose a proof-of-concept of the novel crop damage estimation method, eCropwhich is a part of our proposed agriculture cyber-physical system. eCrop is a grid-based method. We also present a novel crop damage detection method. It is the core of eCrop. It is a Convolutional Siamese Neural Network (CSNN) based model. A meta-learning approach has been taken to train the model. An accuracy of 92.86%has been achieved. Our eCropmethod can be adapted to agricultural insurance claim processing to automatically estimate the crop damage. It is scalable to any size of the cropland and any type of crop.

Vector network analyzers measure both reflection (S11) and transmission (S21) functions, but S21has not been used to estimate soil dielectric permittivity independently. The objectives of this study were to (i) derive the mathematical model for S21of a multisection transmission line and (ii) test this model and demonstrate the method. The mathematical model for S21integrates multiple transmissions across a section as a feedback subsystem. Two experiments were conducted using oven‐dried sand and air to examine the S21model. In the first experiment, a waveguide was filled with one section of sand to form a three‐section transmission line; in the second experiment, two sections of sand formed a five‐section transmission line. The S11and S21functions were measured with a vector network analyzer, and the complex dielectric permittivity values were calculated. Results of Debye model fitting and sensitivity analysis demonstrated that the complex dielectric permittivity of low‐loss sand estimated from the measured S21were less variable and followed the Debye model more closely than those from the measured S11in the frequency range from 45.0 MHz to 3.0 GHz. Therefore, the complex dielectric permittivity of low‐loss materials may be estimated more precisely using measurements of S21with the derived feedback model than with the widely used method based on S11. These general behaviors are also expected for more lossy media within a limited frequency range, but the present theory and testing methods should be used to evaluate permittivity measurements under field conditions in moist heterogeneous soils.

Inverse analysis of time domain reflectometry (TDR) waveform in the frequency domain is important in measuring complex dielectric permittivity of soils. However, for widely used probes designed as impedance mismatching and nonseparable connection between probe head and coaxial cable, none of the available models can be used for the inverse analysis. The objective of this study was to derive a model which is applicable for this specific type of probes. A two‐section (probe head and probe rods) model was derived from the full model of Feng et al. (1999) by reducing its complexity on the basis of the matching design of cable tester and coaxial cable. The model was validated by comparison of the measured spectra of properly terminated coaxial cable with the theoretical values, and the accuracy of the model was studied by the comparison of the estimated complex dielectric permittivity of ethanol by the model with those measured by the network analyzer method. This model was applied to a silt loam soil under different levels of water content and electrical conductivity (EC). The results showed that the two‐section model was applicable for this specific type of probes to measure complex dielectric permittivity at low frequency range. The lowest frequency of 30 MHz was used to estimate soil complex dielectric permittivity. The real parts of the estimated soil dielectric permittivity were close to the apparent dielectric permittivity determined by travel time analysis (TTA). The soil bulk EC calculated from the imaginary parts of the estimated soil dielectric permittivity was close to the measured values.

Deals with an experiment in Hawaii which proves that landfill caps designed as alternatives to those prescribed by law can effectively increase runoff and decrease leachate production. Overview of the Resource Conservation and Recovery Act passed in 1976; Test of vegetated caps which have been designed to serve as alternative landfill covers; Functions of a meteorological station and time-domain reflectometry probes; Details of the experiment.

Goal, Scope and Background: This glasshouse study is aimed at evaluating tropical plants for phytoremediation of petroleum hydrocarbon-contaminated saline sandy subsurface soils. Tropical plants were selected for their ability to tolerate high salinity and remove No. 2 diesel fuel in coastal topsoil prior to further investigation of the phytoremediation feasibility in deep contaminated soils. The residual petroleum-hydrocarbon contaminant at the John Rogers Tank Farm site, a former petroleum storage facility, at Hickam Air Force Base, Honolulu, Hawaii, is located in a coastal area. It lies below a layer of silt in the subsurface, in loamy sand characterized by moderate salinity and high pH. Little is known regarding the ability of tropical plants to remediate petroleum hydrocarbon-contaminated subsurface soil in Hawaiian and other Pacific Island ecosystems although suitable plants have been identified and utilized for bioremediation in surface soil or marine sediments. Methods. The experiments were conducted in long narrow pots under glasshouse conditions in two phases. A preliminary experiment was done with nine tropical plants: kiawe(Prosopis pallida), milo(Thespesia populnea), common ironwood(Casuarina equisetifolia), kou(Cordia subcordata), tropical coral tree(Erythrina variegata), false sandalwood(Myoporum sandwicense), beach naupaka(Scaevola sericea), oleander(Nerium oleander), and buffelgrass(Cenchrus ciliaris).These plants were screened for resistance to high salinity treatment (2% NaCl) and two diesel fuel levels (5 and 10 g No. 2 diesel fuel/kg soil) in separate treatments. Plants that showed good tolerance of both factors were further evaluated in a second phase for their efficacy in the phytoremediation of diesel-fuel petroleum hydrocarbons under moderate salinity treatment (1% NaCl). Results. Tropical coral tree and buffelgrass were susceptible to either 2% NaCl or diesel fuel at 10 g/kg soil, but tolerant of diesel fuel at 5 g/kg soil. Kiawe, milo, kou, common ironwood,N. oleander, beach naupaka and false sandalwood were tolerant of high salinity (2% NaCl) or high diesel fuel level (10 g/kg soil). These seven plants were also tolerant of the combined adverse effects of a moderate salinity (1% NaCl) and 10 g diesel fuel/kg soil. Three trees, kiawe, milo and kou significantly accelerated the degradation of petroleum hydrocarbons in the soil spiked with 10 g diesel fuel/kg soil under a moderate salinity treatment (1% NaCl). Conclusion: Thus the tropical woody plants, kiawe, milo and kou showed potential for use in phytoremediation of petroleum hydrocarbons in coastal tropical soils. Recommendations and Outlook: Two fast growing trees, milo and kou, appeared promising for further phytoremediation evaluation in experiments that simulate the soil profile at the field site.

Riverbank filtration (RBF) offers several advantages over the direct use of surface water. A number of dissolved and suspended contaminants of surface water—including pathogens and microscopic particles—are removed during passage of surface water through the river sediment–aquifer system. Many dissolved chemicals undergo biogeochemical reactions and dilution, leading to reductions in concentrations of parent species. This research examined the potential of RBF systems to attenuate chemical shock loads that may result from chemical spills or spring flooding in agricultural watersheds. Scenarios simulated both horizontal and vertical wells, with riverbed and bank hydraulic properties varying as a function of river stage. The solute transport equation considered a range of reaction parameters. Sensitivity analysis showed that the hydraulic conductivity of the riverbed or bank materials had a pronounced effect on filtrate quality. For materials with low hydraulic conductivity, the effect on filtrate quality would be minimal, and the lag time between the contaminant peak concentrations in the surface water and the pumped water would be significant. However, further biogeochemical modeling is needed to predict the fate of contaminants during their transit to the pumping wells. Use of backup vertical wells or selected laterals of a collector well could effectively mitigate the risks.

Riverbank filtration (RBF) is a process in which pumping wells located along riverbanks induce a portion of the river water to flow toward the wells. During RBF, which has many similarities to slow‐sand filtration, river water contaminants are attenuated from a combination of processes such as filtration, microbial degradation, sorption to sediments and aquifer sand, and dilution with background groundwater. RBF systems have been operating in Europe since the 1870s. In the United States, there has been renewed interest among large‐ to medium‐sized utilities to use it as a mechanism of water production to reduce treatment costs and to meet regulations on pathogens, disinfection byproducts, and other contaminants under the Surface Water Treatment Rule. Although filtrate water quality from RBF systems can vary based on river conditions, it is possible that appropriately designed systems can serve as pretreatment for drinking water, and at the same time, the utility can receive log‐removal credits for pathogens and particles. In addition, RBF can be used as a pretreatment for membrane filtration. In the United States, a knowledge gap exists on the benefits and limitations of using RBF. This paper addresses those gaps, outlining the benefits and limitations of the process. It also illustrates that RBF can be a viable alternative to surface water at suitable sites.

ABSTRACTIn response to the Hazardous and Solid Waste Amendments, many federal facilities are carefully examining hazardous waste minimization issues. A hazardous waste minimization assessment was conducted recently at five military installations. Sources and types of waste somewhat varied among the installations. Major waste sources included motor pools and vehicle maintenance facilities; industrial maintenance and small arms shops; aviation maintenance facilities; paint shops; photography, arts and crafts shops; and hospitals, clinics, and laboratories. Typical waste streams included used engine oil, cleaning and degreasing solvents, paint thinners, antifreeze and coolants, batteries, inks, and pathological wastes. Source reduction, recycling, and treatment were considered as the three major modes of waste minimization. Through life-cycle cost analysis, feasible modes of waste minimization are presented.

A radial‐arm collector well was at more risk than vertical wells, but MCLs were not exceeded in any well. A 175‐mi (282‐km) stretch of the Illinois River was studied to assess the potential effects of flooding on the quality of pumped groundwater. Water pumped from four municipal water supply wells (pumping rates, 140‐500 gpm [0.009‐0.03 m3/s]) was analyzed. Three of these wells were vertical wells located 175‐220 ft (53‐67 m) from the river's edge at normal pool stages. The fourth well was a radial‐arm collector well with laterals extending beneath the river bed. Although concentrations of atrazine in the Illinois River during peak flows of 1995 and 1996 were as high as 6 and 12 μg/L, respectively, the concentration of atrazine in groundwater pumped at the three vertical‐well sites remained below the 0.1‐μg/L detection limit. In 1996 a small breakthrough of atrazine was observed in the collector well water. The high concentration of nitrate in river water appeared to have slightly increased the nitrate in pumped groundwater at two locations. Dilution and other factors may have affected the transport of river water contaminants in the alluvial aquifer between the river and the pumping wells.

AbstractThe chemical composition of the essential oils from the leaves of Araucaria heterophylla(also known as Norfolk Island pine), Casuarina cunninghamian, Eucalyptus citriodora, Psidium cattlenium var. lucidum(also known as strawberry guava), and litters of the first two species were studied using two gas chromatography techniques, one equipped with a mass spectrometer detector (GC/MS) and the other with a fame ionization detector (GC/FID). Six volatile compounds were identified in the leaf oil of A. heterophylla.A substantial increase in the amount of α-pinene, α-terpinene and a decrease in the amount of β-caryophyllene was noticed in the litter oil of A. heterophylla.Three volatile compounds were identifed in C. cunninghamian.Another monoterpene, α-terpinene, was observed in the litter oil of C. cunninghamian.Eleven compounds were identified in the leaf oil of P. cattlenium, of which β-caryophyllene (59.0%), α-pinene (13.2%) and myrcene (11.3%) were the major components. Thirteen volatile oil compounds were identified in the E. citriodoraleaf oil, of which citronellal (42.8%), citronellol (17.9%) and α-terpinene (11.2%) were the major components.

Artificial neural networks (ANNs) have been extensively used for forecasting problems involving water quantity and quality. In most cases, the geometry and model parameters of the ANN are set using a trial‐and‐error approach to achieve better network generalization ability, whereby the available data are divided arbitrarily into training, testing, and validation subsets. It has been shown that using the arbitrary sample selection method to assign samples into the training subset commonly results in the inclusion of samples from densely clustered regions and omission of samples from sparsely represented regions. This paper presents a systematic approach using the self‐organizing map (SOM) clustering technique that identifies which samples and determines how many samples should be included in each of the three subsets required by ANN for optimum predictive performance efficiency. In addition, this paper presents the microgenetic algorithms (μGA) that optimize ANN's geometry and model parameters in terms of the correlation coefficient (R). In the sensitivity analysis, μGA model parameters are found to be least sensitive to the optimum Rvalue, while ANN's predictive performance is significantly affected by (1) the poor selection of its geometry and model parameters and (2) the arbitrary selection of samples for the three subsets of data used. It is demonstrated that the μGA‐ANN model using the SOM technique for data division outperforms the μGA‐ANN model using arbitrary data division. For the training subset, the model using the SOM technique identifies samples that are representative of the region, requiring only 20% of the total samples, whereas the arbitrary sample selection method requires 50–90%. Because resampling on a regional scale is expensive and time consuming, substantial cost and time could be saved if resampling could be done only on the 20% representative drinking water wells.