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Globally, agriculture irrigation accounts for 70% of water use and is facing extensive and increasing water constraints. Well-designed irrigation scheduling can help determine the appropriate timing and water requirement for crop development and consequently improve water use efficiency. This research aims to assess the probability of irrigation needed for agricultural operations, considering soil moisture, evaporation, and leaf area index as indicators of crop water requirement. The decision on irrigation scheduling is taken based on a three-step methodology. First, relevant variables for each indicator are identified using a Random Forest regressor, followed by the development of a Long Short-Term Memory (LSTM) model to predict the three indicators. Second, errors in the simulation of each indicator are calculated by comparing the predicted values against the actual values, which are then used to calculate the error weights (normalized) of the three indicators for each month (to capture the seasonal variations). Third, the empirical distribution of each indicator is obtained for each month using the estimated error values, which are then adjusted based on the error weights calculated in the previous step. The probabilities of three threshold values (for each indicator) are considered, which correspond to three levels of irrigation requirement, i.e., low, medium, and high. The proposed approach provides a probabilistic framework for irrigation scheduling, which can significantly benefit farmers and policymakers in more informed decision-making related to irrigation scheduling.

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM's predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil-plant-atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m− 2 day− 1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 liters m− 2 day− 1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

During early 2019, a series of events set the stage for devastating floods in eastern Nebraska, western Iowa, and southeastern South Dakota. When the floodwaters hit, dams and levees failed, cutting off towns while destroying roads, bridges, and rail lines, further exacerbating the crisis. Lives were lost and thousands of cattle were stranded. Estimates indicate that the cost of the flooding has topped $3 billion as of August 2019, with this number expected to rise. After a warm and wet start to winter, eastern Nebraska, western Iowa, and southeastern South Dakota endured anomalously low temperatures and record-breaking snowfall. By March 2019, rivers were frozen, frost depths were 60–90 cm, and the water equivalent of the snowpack was 30–100 mm. With these conditions in place, a record-breaking surface cyclone rapidly developed in Colorado and moved eastward, producing heavy rain toward the east and blizzard conditions toward the west. In areas of eastern Nebraska, western Iowa, and southeastern South Dakota, rapid melting of the snowpack due to this rain-on-snow event quickly led to excessive runoff that overwhelmed rivers and streams. These conditions brought the region to a standstill. In this paper, we provide an analysis of the antecedent conditions in eastern Nebraska, western Iowa, and southeastern South Dakota and the development of the surface cyclone that triggered the historic flooding, along with a look into the forecast and communication of flood impacts prior to the flood. The study used multiple datasets, including in situ observations and reanalysis data. Understanding the events that led to the flooding could aid in future forecasting efforts.

Improvement of nutrient use efficiency and limiting trace elements such as arsenic and uranium bioavailability is critical for sustainable agriculture and food safety. Arsenic and uranium possess different properties and mobility in soils, which complicates the effort to reduce their uptake by plants. Here, we postulate that unsaturated soil amended with ferrihydrite nanominerals leads to improved nutrient retention and helps reduce uptake of these geogenic contaminants. Unsaturated soil is primarily oxic and can provide a stable environment for ferrihydrite nanominerals. To demonstrate the utility of ferrihydrite soil amendment, maize was grown in an unsaturated agricultural soil that is known to contain geogenic arsenic and uranium. The soil was maintained at a gravimetric moisture content of 15.1 ± 2.5%, typical of periodically irrigated soils of the US Corn Belt. Synthetic 2-line ferrihydrite was used in low doses as a soil amendment at three levels (0.00% w/w (control), 0.05% w/w and 0.10% w/w). Further, the irrigation water was fortified (~50 μg L-1 each) with elevated arsenic and uranium levels. Plant dry biomass at maturity was ~13.5% higher than that grown in soil not receiving ferrihydrite, indicating positive impact of ferrihydrite on plant growth. Arsenic and uranium concentrations in maize crops (root, shoot and grain combined) were ~ 20% lower in amended soils than that in control soils. Our findings suggest that the addition of low doses of iron nanomineral soil amendment can positively influence rhizosphere geochemical processes, enhancing nutrient plant availability and reduce trace contaminants plant uptake in sprinkler irrigated agroecosystem, which is 55% of total irrigated area in the United States.

The impact of commonly-used livestock antibiotics on soil nitrogen transformations under varying redox conditions is largely unknown. Soil column incubations were conducted using three livestock antibiotics (monensin, lincomycin and sulfamethazine) to better understand the fate of the antibiotics, their effect on nitrogen transformation, and their impact on soil microbial communities under aerobic, anoxic, and denitrifying conditions. While monensin was not recovered in the effluent, lincomycin and sulfamethazine concentrations decreased slightly during transport through the columns. Sorption, and to a limited extent degradation, are likely to be the primary processes leading to antibiotic attenuation during leaching. Antibiotics also affected microbial respiration and clearly impacted nitrogen transformation. The occurrence of the three antibiotics as a mixture, as well as the occurrence of lincomycin alone affected, by inhibiting any nitrite reduction, the denitrification process. Discontinuing antibiotics additions restored microbial denitrification. Metagenomic analysis indicated that Proteobacteria, Bacteroidetes, Actinobacteria, and Chloroflexi were the predominant phyla observed throughout the study. Results suggested that episodic occurrence of antibiotics led to a temporal change in microbial community composition in the upper portion of the columns while only transient changes occurred in the lower portion. Thus, the occurrence of high concentrations of veterinary antibiotic residues could impact nitrogen cycling in soils receiving wastewater runoff or manure applications with potential longer-term microbial community changes possible at higher antibiotic concentrations.

A four-decade dataset (1974-2013) of 107,823 nitrate samples in 25,993 wells from western and eastern parts of Nebraska was used to assess long-term trends of groundwater nitrate concentration and decadal changes in the extent of groundwater nitrate-contaminated areas (NO3-N ≥ 10 mg N/L) over the entire state. Spatial statistics and regressions were used to investigate the relationships between groundwater nitrate concentrations and several potential natural and anthropogenic factors, including soil drainage capacities, vadose zone characteristics, crop production areas, and irrigation systems. The results of this study show that there is no statistically significant trend in groundwater nitrate concentrations in western Nebraska, in contrast with the increasing trend (p < .05) to the east. The spatial extent and nitrate concentrations in contaminated groundwater in center pivot-irrigated areas was less than in gravity-irrigated areas. Areas with a thicker vadose zone and larger saturated thickness of the aquifer have relatively lower nitrate concentrations. The results of a classification and regression tree (CART) model indicate the difference in the influence of physical factors on groundwater nitrate concentrations between western and eastern Nebraska, namely that groundwater nitrate concentrations correspond with vadose zone thickness, effective hydraulic conductivity, and saturated thickness in the west, while in eastern Nebraska, concentrations are correlated with average percent sand in the topsoil (0-150 cm), well depth, and effective hydraulic conductivity.

Several areas in Hawaii have background concentrations of unexploded ordnance (UXO) as a consequence of past and current military training. The presence of UXO and the release of their che...

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.

To meet global health and sanitation goals, there is a continued need for sustainable wastewater treatment alternatives that require minimal energy and investment. Vermifiltration, a technology gaining relevance in Africa and Asia, may be an alternative to traditional wastewater treatment systems due to its cost-effectiveness, ease of application and maintenance, and sustainability. However, nitrogen removal in vermifiltration is not well understood since most of the prior research focuses on organics removal. Thus, a state of the art review is necessary to separately focus on the mechanisms associated with nitrogen removal in vermifiltration, along with the factors affecting nitrogen removal. For the first time, this review attempts to present the types of vermifilter based on their flow pattern. The review further discusses the current status of the application of vermifiltration, along with the benefits and limitations associated with the adoption of this technology. It also explores possible strategies that could be adopted to maximize the nitrogen removal potential of vermifilters as optimizing nitrogen removal is critical for improving the performance of vermifiltration based treatment systems.

Water productivity benchmarks for irrigated and rainfed agriculture will provide relevant information to manage scarce water resources and control groundwater level decline. We analyse the temporal and spatial variation of the water productivity (WP) of maize and soybean in Nebraska, with WP defined as harvested crop weight per total evapotraspiration. The results show that WP of both maize and soybean increase from west to east within Nebraska and have increased over the last 25 years, mainly due to the increase in crop yields (land productivity). We derive WP benchmarks for each crop per climate zone. Increasing actual WPs in the state to benchmark levels will increase yields by 21 % for maize and by 19 % for soybean. The WP benchmark levels for the two crops presented here will help formulating targets for closing water productivity gaps and improving the sustainability of water use in the state.

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models' performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability.

This study combines empirical predictive and economics models to estimate the cost of remediation for domestic wells exceeding suggested treatment thresholds for nitrates. A multiple logistic regression model predicted the probability of well contamination by nitrate, and a life cycle costing methodology was used to estimate costs of nitrate contamination in groundwater in two areas of Nebraska. In south-central Nebraska, 37% of wells were estimated to be at risk of exceeding a threshold of 7.5 mg/L as N, and 17% were at risk of exceeding 10 mg/L as N, the legal limit for human consumption in the United States. In an area in northeastern Nebraska, 82% of wells were at risk of exceeding the 10 mg/L as N legal threshold. Reverse osmosis Point-of-Use (POU) treatment was the option with the lowest costs for a household (3&ndash, 4 individuals), with an average of $4&ndash, $164 total regional cost per household per year depending on the threshold for treatment. Ion exchange and distillation were the next most cost-effective options. At the community level (~10,000 individuals), a reverse osmosis Point-of-Entry (POE) treatment system was the most expensive option for a community due to high initial costs and ongoing operation and maintenance costs, whereas the biological denitrification system was least expensive due to economies of scale. This study demonstrates integrated modeling methods to assess water treatment costs over time associated with groundwater nitrate contamination, including quantification of at-risk wells, and identifies suitable options for treatment systems for rural households and communities based on their cost.

The growing global food security crisis is complicated by the need for increased crop production with less arable land and limited water resources. Reuse of treated wastewater for agricultu...

In Table 2, the unit for nitrogen, phosphate, and potash should have been Mg instead of g (Figure Presented).

The complementary relationship areal evapotranspiration (CRAE) model estimates actual evapotranspiration (ET) based on potential and wet environment evapotranspiration rates. The existing C...

Water is the basis of life. And, unlike various forms of energy generation, water cannot simply be created where it is needed. Freshwater is remarkably scarce, comprising just 2.5% of the global wa...