Globally, flooding is one of the most damaging abiotic stresses, besides drought, that affects 17 million km 2 of land surface annually. Recent research indicates that climate change is resulting in more extreme weather events, such as flooding or soil waterlogging, that negatively affect crop production. Therefore, it is imperative to understand how flooding stress affects crops and to develop improved production practices that make cropping systems more resilient and able to cope with extreme weather events. This review paper summarizes the current state of knowledge on the impacts of flooding or soil waterlogging on crop production losses, nitrogen (N) losses, and provides potential management strategies to reduce these losses. The factors affecting the extent of flooding injury in plants as well as plant adaptations under waterlogging stress are also discussed briefly. For the purpose of this review, "flooding" refers to the situation when all or part of the plant is submerged under water, whereas "soil waterlogging" refers to the situation where soil pores are saturated with water. Soil waterlogging also promotes soil N losses through runoff, leaching, and denitrification. Potential management practices that can be used to mitigate soil waterlogging stress include the use of flood-tolerant varieties, adjusting management practices, improving drainage, and practicing adaptive nutrient management strategies. However, these might be site-or crop-specific management practices and they should be validated for their economic viability before developing future management plans that promote sustainable crop yields from waterlogged soils.Abbreviations: BMP, Best Management Practice; CDSI, controlled drainage and subirrigation; EEF, enhanced efficiency fertilizer; ET, evapotranspiration; Fv/Fm, ratio of variable fluorescence to material fluorescence; GIS, Geographic Information System; NBPT, N-(n-butyl) thiophosphoric triamide; NI, nitrification inhibitor; NUE, nitrogen use efficiency; PCU, polymer-coated urea; UI, urease inhibitors.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Soybean [Glycine max (L.) Merr.] seed composition and yield are a function of genetics (G), environment (E), and management (M) practices, but contribution of each factor to seed composition and yield are not well understood. The goal of this synthesis-analysis was to identify the main effects of G, E, and M factors on seed composition (protein and oil concentration) and yield. The entire dataset (13,574 data points) consisted of 21 studies conducted across the United States (US) between 2002 and 2017 with varying treatments and all reporting seed yield and composition. Environment (E), defined as site-year, was the dominant factor accounting for more than 70% of the variation for both seed composition and yield. Of the crop management factors: (i) delayed planting date decreased oil concentration by 0.007 to 0.06% per delayed week (R2∼0.70) and a 0.01 to 0.04 Mg ha-1 decline in seed yield per week, mainly in northern latitudes (40–45 N); (ii) crop rotation (corn-soybean) resulted in an overall positive impact for both seed composition and yield (1.60 Mg ha-1 positive yield difference relative to continuous soybean); and (iii) other management practices such as no-till, seed treatment, foliar nutrient application, and fungicide showed mixed results. Fertilizer N application in lower quantities (10–50 kg N ha-1) increased both oil and protein concentration, but seed yield was improved with rates above 100 kg N ha-1. At southern latitudes (30–35 N), trends of reduction in oil and increases in protein concentrations with later maturity groups (MG, from 3 to 7) was found. Continuing coordinated research is critical to advance our understanding of G × E × M interactions.

Keywords L-A scorbic acid determination ; spectrophotometry ; ammonium molybdate reagent ; vegetables and fruitsThe reported methods for the determination of L-ascorbic acid in fruits and vegetables include titrimetryl-3 and spectroph~tometry.*-~ The widely used titrimetric method1 using 2,6dichlorophenolindophenol is subject to interference by other reducing substances.8 Further, the method is laborious and the standard dye solution is unstable and requires standardisation before use.' The specificity of the method using N-bromosuccinimide2 is no better6 owing to its reaction with olefinic and phenolic cornpound~.~~~0 L-Ascorbic acid has also been determined as the molybdophosphate complexll and with Folin -Ciocalteu reagent.12 Ammonium molybdate has been used for the detectionl3 and determination of ascorbic acid in pharmaceutical ~reparati0ns.l~ We have found that the methods using ammonium molybdate and Folin -Ciocalteu reagents cannot be used directly for the determination of ascorbic acid in fruits and vegetables owing to interference by various reducing agents. In this paper a modification of the ammonium molybdate method to minimise these interferences is reported. Experimental Apparatuspath length 1 cm and volume 10 ml.Spectrophotometer. Bausch and Lomb Spectronic-20 spectrophotometer with a cell of ReagentsAll reagents used were of analytical-reagent grade. Ammonium molybdate solution, 5% m/V. Oxalic acid solution, 0.05 M. Sulphuric acid, 5% VlV. Metaphosphoric acidacetic acid solution.Freshly prepared and containing 0.2 mM EDTA.Dissolve, with shaking, 15 g of metaphosphoric acid pellets or freshly pulverised sticks in 40 ml of acetic acid and 200 ml of water, dilute to 500 ml with water and then filter.Freshly prepared.This reagent can be kept for 3 d in a refrigerator.Standard L-ascorbic acid solution, 0.1% m/V in the oxalic acid -EDTA solution. ProceduresPreparation of calibration graph into separate 25-ml calibrated flasks. Pipette 0.1-, 0.2-, 0.3-, 0.4-, 0.5-and 0.6-ml aliquots of the standard L-ascorbic acid solution Add sufficient oxalic acid -EDTA solution to give a

A B S T R A C TThe effects of supplemental nitrogen (N) on soybean [Glycine max (L.) Merr.] seed yield have been the focus of much research over the past four decades. However, most experiments were region-specific and focused on the effect of a single N-related management choice, thus resulting in a limited inference space. Here, we composited data from individual experiments conducted across the US that examined the effect of N fertilization on soybean yield. The combined database included 207 environments (experiment × year combinations) for a total of 5991 N-treated soybean yields. We used hierarchical modeling and conditional inference tree analysis on the combined dataset to establish the relationship and contribution of several N management choices on soybean yield. The N treatment variables were: N-application (single or split), N-method (soil incorporated, foliar, etc.), Ntiming (pre-plant, at a reproductive stage, etc.), and N-rate (from a 0 N control to as much as 560 kg ha). Of the total yield variability, 68% was associated with the effect of environment, whereas only a small fraction of that variability (< 1%) was attributable to each N variable. Averaged over all experiments, a single N application and the split N application were 60 and 110 kg ha −1 greater yielding than the zero N control treatment, respectively. A split N application with more than one method (e.g., soil incorporated and foliar) resulted in 120 kg ha −1 greater yield than zero N plots. Split N application between planting and reproductive stages (Rn) resulted in greater yield than zero N and single application during a Rn; however, the effect was not significantly different than N application at other growth stages. Increasing the N rate increased the environment average soybean yield; however, 93% of the environment-specific N-rate responses were not significant which suggested a minimal effect of N across the examined region. A large yield variability was observed among environments E-mail address: mourtzinis@wisc.edu (S. Mourtzinis).Abbreviations: BNF, biological nitrogen fixation; C, check (no nitrogen was applied); MM, major management practices; N, nitrogen; N-rate, nitrogen rate; N-application, number of nitrogen applications; N-method, method of nitrogen application; N-timing, timing of nitrogen application (growth stage/s); P, all nitrogen was applied at planting only; PR, split nitrogen application at planting and reproductive growth stages; pP, all nitrogen was applied at pre-planting only; Rn, reproductive growth stage; R, all nitrogen was applied at a reproductive growth stage only; RR, split nitrogen application at two reproductive growth stages; V, all nitrogen was applied at a vegetative growth stage only; Vn, vegetative growth stage MARKwithin the same N rates, which was attributed to growing environment differences (e.g., in-season weather conditions, soil type etc.) and non-N related management (e.g., irrigation). Conditional inference tree analysis identified N-timing and N-rate to be conditional to irriga...

Core Ideas Excessive soil moisture resulting from extreme precipitation events during early spring can often cause decreases in corn grain yields in the midwestern United States. Each day of waterlogging resulted in an average corn grain yield loss of 0.42 Mg ha−1 and 0.72 Mg ha−1 in 2013 and 2014, respectively. Pre‐plant N fertilizer applications of non‐coated urea; polymer coated urea, and non‐coated urea+nitrification inhibitor resulted in 19% higher yields compared to the non‐treated control in 2014. Effects of rescue N fertilizer were seen on soybean yields in the succeeding year after corn, while rescue N affected corn yields only in 2014. Climatic conditions including rainfall and air temperature had a significant role in crop response to waterlogging and N fertilizer treatments. In the midwestern United States, excessive soil moisture resulting from extreme precipitation events during early spring can often cause decreases in corn (Zea mays L.) grain yields and escalate N loss. A field trial was conducted from 2013 to 2015 in Northeast Missouri to determine the effects of soil waterlogging duration, pre‐plant N and rescue N fertilizer applications on corn and succeeding soybean [Glycine max (L.) Merr] production. Plots were either non‐flooded or flooded for durations of 1, 3, or 7 d when corn was at V6 growth stage. Pre‐plant N fertilizer treatments included non‐treated control (CO), urea (NCU), urea plus nitrapyrin (NCU+NI), and polymer coated urea (PCU) applied at 168 kg N ha−1. A rescue N fertilizer application of 0 or 84 kg N ha−1 of urea plus N‐(n‐butyl) thiophosphoric triamide (NBPT) (NCU+UI) was applied at V10 growth stage. Each day of waterlogging resulted in an average corn grain yield loss of 0.42 and 0.72 Mg ha−1 in 2013 and 2014, respectively. Pre‐plant N fertilizer applications of NCU, PCU, and NCU+NI resulted in 19% higher yields compared to CO in 2014. Effects of rescue N fertilizer were seen on soybean yields in the succeeding year after corn, while rescue N positively affected corn yields only in 2014. These results indicated that rescue N fertilizer applications are not effective if drought conditions occur after its application in corn. Climatic conditions including rainfall and air temperature had a significant role in crop response to waterlogging and N fertilizer treatments.

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Core Ideas Soil solution sampling is essential to better understand water and solute movement in soils. A review of different types of soil solution samplers is provided in this paper, including: drainage lysimeter or soil column, pan lysimeter, resin bags or membranes, wick lysimeters, suction cup, and suction plate. Recent developments, modifications, and recommendation criteria are provided for selecting appropriate soil solution extraction samplers. A number of contaminants including agrochemicals (fertilizers, pesticides), heavy metals, trace elements, and pathogenic microbes along with pharmaceuticals and hormones used in animal production move through the soil and are responsible for degradation of groundwater quality. Therefore, it is essential to sample soil solution for better understanding of movement and environmental fate of various contaminants in soils. We review different soil solution extraction samplers. The soil solution samplers discussed here are: drainage lysimeter or soil column, pan lysimeter, resin bags or membranes, wick lysimeters, suction cup, and suction plate. We have reviewed 304 journal articles representing a wide array of scientific disciplines. A brief history of soil solution monitoring and terminology used for describing various soil solution samplers is also provided. This review classifies literature on the basis of type of soil solution extraction samplers, soil type, land use–land cover (LULC), and analytes measured. Recommendation criteria are provided for selecting appropriate soil solution extraction samplers based on spatial and temporal variation, cost, soil type, amount of disturbance caused during installation of soil solution samplers, and monitoring of leachates involving different cations, anions, carbon, pH, EC, colloids, pesticides, and microbes. Use of advanced techniques with lysimeters for monitoring soil moisture content, soil water potential and flux is also discussed in this review.

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