Nitrogen and water availability effects dynamics of soil nitrogen mineralization in a maize system

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Donovan, Louise H. Comas, Joel Schneekloth, Meagan Schipanski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4474023/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2025 Read the published version in Nutrient Cycling in Agroecosystems → Version 1 posted 9 You are reading this latest preprint version Abstract Nitrogen (N) fertilizer and water availability can independently stimulate or limit soil N dynamics through direct and indirect processes. Importantly, soil N mineralization (Nmin) is a major N source for maize but affected by N fertilization and water availability. We examined in-situ net Nmin, soil enzyme activity, and maize N uptake in a semiarid region of North America in response to two levels of water availability (100% and 70% crop evapotranspiration, ET) and three levels of N fertilization (22–275 kg ha − 1 capturing low, optimal, and excess N fertilization. Nitrogen mineralization rates peaked relatively early in the growing season leading to asynchrony between soil N supply and plant demand. Later in the season when plant N uptake was highest, Nmin rates were high under low N with full water supply, and high under high N with limited water supply, resulting in an N fertilizer and water interaction. Soil L-leucine amino peptidase (LAP) and β -1,4-N-acetyl-glucosaminidase (NAG), which can be indicators of gross Nmin, increased with N fertilizer additions but were not affected by water supply. Further research is needed to understand the mechanisms underlying this interaction as well as exploring if gross Nmin has a similar response. Maize N uptake increased with N fertilizer additions under both levels of water availability but was higher in the full water supply. In the limited water availability, increased plant N uptake with increased N fertilization did not translate to large grain yield increases highlighting the impact of water stress, especially during grain fill. Net nitrogen mineralization water limitation water deficit soil N-acquiring enzymes β-1 4-N-acetyl-glucosaminidase (NAG) L-leucine amino peptidase (LAP) nitrogen x water interaction nitrogen cycling plant nitrogen uptake Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Turnover of soil organic nitrogen (N) is an important source of N for crops, capable of supplying over 50% of the N crops need in a growing season even under high levels of inorganic N fertilizer applications (Gardner & Drinkwater, 2009 ; Yu et al., 2022 ). However, plants have to compete for inorganic N with other sinks, namely microbial uptake and sorption or chemical associations on soil surfaces (Daly et al., 2021 ). If inorganic N is not stabilized in one of these sinks it can be lost to the environment, sometimes “cascading” through different environments. Agricultural production systems are the primary source of reactive N globally, contributing to soil acidification, surface water eutrophication, and increased atmospheric concentrations of nitrous oxide (N 2 O) (Galloway et al., 2003 ; Robertson et al., 2013 ). Given the environmental and soil fertility implications, proper management of soil N is important for sustainable crop production. While extensive research has focused on improving N fertilizer management, less attention has been given to how management influences the turnover of soil organic N. Soil N mineralization (Nmin) is the complex, microbially-mediated conversion of organic N to inorganic N. Microbial extracellular enzymes cleave N containing monomers making them bioavailable for microbial use and, in some cases, plant use (Schimel & Bennett, 2004 ). Microbes can use these monomers, or substrates, to meet their carbon and N needs. Excess N in the form of ammonium (NH 4 ) is released when microbial N needs are met and through microbial predation along the soil food web (Mooshammer et al., 2014 ; Whalen et al., 2013 ). Once in the soil, NH 4 tends to be rapidly oxidized by nitrifiers to nitrite (NO 2 ) and then further oxidized to nitrate (NO 3 ) (Kuypers et al., 2018 ), with NH 4 and NO 3 being the main forms of N that plants utilize (Asibi et al., 2019 ). Soil Nmin rates have been extremely challenging to quantify because they are governed by multiple dynamic factors including the climate, litter quality, microbial access to substrates, and soil physical, chemical, and biological properties (Colman & Schimel, 2013 ; Dungait et al., 2012 ; Li et al., 2019 ; Liu et al., 2017 ; Mooshammer et al., 2014 ). Rates vary across the growing season and are primarily controlled by soil temperature and moisture (Gonçalves & Carlyle, 1994 ; Ma et al., 1999b ). Estimating Nmin rates in agroecosystems can be further complicated as management factors including crop rotation, N additions, and tillage also heavily influence Nmin rates (Carpenter-Boggs et al., 2000 ; Mahal et al., 2018 ; Silgram & Shepherd, 1999 ). Soil Nmin rates can be quantified as gross N min, which is the total production of NH 4 , or net Nmin, which is the difference between gross Nmin and gross immobilization (Hart et al., 1994 ). While gross Nmin likely better reflects the soil N available for plant uptake, it can be challenging to quantify and requires expensive stable isotope methods. Therefore, more studies rely on net Nmin assays as an indicator of soil organic N turnover. Improving synchrony of Nmin rates with plant N demand increases plant N use efficiency and reduces N losses and is therefore an important goal of N management in agroecosystems (Ma et al., 1999b ). Achieving improved synchrony requires understanding the temporal dynamics of Nmin and plant N uptake. In temperate regions, Nmin rates start to increase in the spring as the soil thaws and rates can persist into the summer before declining again in the fall and winter (Cassman et al., 2002 ; Martinez-Feria et al., 2018 ). Plant N uptake is not uniform throughout the growing season. Maize N uptake specifically follows a sigmoid shape where N uptake rates are low early in the season before dramatically increasing and peaking around R1, and thereafter slowing and eventually ceasing (Ma et al., 1999a ). These seasonal patterns of Nmin and plant N uptake are highly dependent on climate and management conditions, including water and N availability. In irrigated regions, there is potential to improve synchrony through improved N fertilizer and irrigation management (Quemada & Gabriel, 2016 ) Both N and water availability affect Nmin with potentially interacting consequences, but are rarely explored together. Different soil-crop combinations do not always respond the same way to N additions making it difficult to know how much N to add (McDaniel et al., 2020 ; Puntel et al., 2016 ). Therefore, it is common for ample N, even to the point of N saturation, to be added to fields to ensure there is sufficient N for high yields (Grandy et al., 2022 ; McSwiney et al., 2010 ). This fertilizer N is often added prior to or early in the growing season as producers are often limited in their ability to add N later in the season (Udvardi et al., 2021 ). Plant N needs are partially driven by water availability (Farooq et al., 2009 ), and wet-dry cycles are common due to variable precipitation patterns and limitations in irrigation water availability (Rudnick et al., 2017b ). Therefore, N fertilizer applied pre-season can end up being insufficient to meet crop demands in wetter and more productive years, or, more commonly, due to water limitations in semi-arid regions that reduce plant growth and N uptake, can result in excess N added to fields. Both fertilizer N and water can also affect Nmin with studies showing direct and indirect (and positive or negative) effects. Therefore, improving N synchrony in water-limited systems requires a better understanding of Nxwater effects Nmin rates and plant N uptake dynamics over the growing season. Fertilizer N additions can directly affect Nmin by meeting the microbial community’s N demands and changing microbial activity. The stoichiometric decomposition theory predicts that if the microbial community is limited by N then adding N fertilizer will alleviate microbial N limitation, thus, increasing microbial activity and Nmin (Chen et al., 2014b ). On the other hand, the N mining theory suggests that if the microbial community is N limited then adding N fertilizer will suppress microbial activity and Nmin. This suppression occurs by meeting microbial N demands thereby eliminating the need to decompose soil organic matter in order to acquire N (Moorhead & Sinsabaugh, 2006 ). However, microbial communities in agricultural systems can vary in their relative degree of C- or N-limitation under different fertilizer and irrigation regimes (Lundquist et al., 1999 ; Ye et al., 2022 ), leading to inconsistent support for either theory. Water is another important resource that directly affects the soil microbial community, and, thus, Nmin. As soils dry, microbes experience water stress as their water potential declines leading to loss of cell turgor and eventually loss of cellular and metabolic function or death, contributing to reduced microbial activity and Nmin rates (Schimel, 2018 ). Conversely, at the other end of the moisture spectrum, saturated soils have low oxygen levels, which also leads to decreased microbial activity and mineralization rates. Optimal soil moisture (close to field capacity) thus, is likely to support higher rates of N min than either water limitation or excess (Barakat et al., 2016 ). Additionally, water and N availability indirectly affect Nmin by altering soil organic matter quantity, accessibility, and quality. Water and N availability affect both above and belowground biomass production (Flynn et al., 2021 ; Ordóñez et al., 2021 ; Poffenbarger et al., 2017 ), which in turn affects soil organic matter accumulation (Núñez & Schipanski, 2023 ; Sherrod et al., 2003 ). Adequate moisture and N availability also increase litter quality (lower C:N ratio) (Brown et al., 2014 ; He & Dijkstra, 2014 ; Ning et al., 2018 ). Increased soil organic matter quantity and quality should increase Nmin if the microbial community is not limited by other factors (Liu et al., 2017 ; Mooshammer et al., 2014 ). Soil water indirectly affects Nmin as the soil goes through dry and wet cycles. As the soil dries, diffusion of substrates and extracellular enzymes that break down these substrates can be disrupted as water films are disconnected. These diffusive limitations along with osmotic regulation are two main reasons Nmin is reduced under drier soils (Borken & Matzner, 2009 ). However, Nmin is carried out by a broad range of soil organisms, allowing it to be drought tolerant in some instances (Homyak et al., 2017 ). Moreover, rewetting after a dry period can cause a “flash” of Nmin known as the “Birch Effect” (Borken & Matzner, 2009 ). After rewetting, both bacterial necromass and osmolytes becomes available. These N rich substrates can stimulate an increase in Nmin (Schimel, 2018 ). Given the importance of soil Nmin for crop nutrition (Yan et al., 2020 ), the overuse of N fertilizers (Battye et al., 2017 ; Zhang et al., 2015a), and the increased likelihood of water limitations in the Great Plains Region (Deines et al., 2020 ; Derner et al., 2015 ), we conducted a field experiment to better understand how different N and water availabilities alter soil N dynamics and plant responses. We hypothesized: (i) Nmin rates would increase early in season and be maintained until physiological maturity approaches, (ii) N and water additions would increase Nmin rates, even later in the season when plant N uptake is high, and (iii) N and water additions would increase maize N uptake and end of season grain production. Methods Site Description The experiment was conducted at the USDA-ARS Great Plains Research Center in Akron, Colorado (40°09 N, 103°09 W, 1,383 m). The dominant soil type is a Weld silt loam (fine, smectitic, mesic Aridic Argiustoll). The field site is within a semiarid climate with average monthly temperatures ranging from 23 ͦ C in the summer to – 2.5 ͦ C in the winter, and average annual precipitation of 420 mm occurring primarily from April to September (Nielsen et al., 2015 ). A field trial was established in 2021 and data was collected during the 2021 and 2022 growing seasons to investigate the effects of nitrogen and water availability on soil inorganic nitrogen dynamics in no-till continuous maize ( Zea mays) agroecosystems. On May 6, 2021, a 104-day maturity maize hybrid (DKC 54–64) was planted at 79,000 seeds ha − 1 and on May 10, 2022, the same maize hybrid was planted at 84,000 seeds per ha − 1 . In 2019 and 2020, the field was managed as a fully irrigated maize system with limited N applications to reduce residual soil N levels for the start of this study. Prior to this, the field had been managed using no-till practices since prior to 1990 for multiple dryland or irrigated crops. We utilized a split-split plot experimental design with a total of 36 plots. There were 3 blocks with 2 irrigation treatments randomly assigned within each block and 6 nitrogen fertilizer treatments randomly assigned within each of those irrigation treatments. Irrigation treatments consisted of full water (100% ET), and limited water (70% ET), which is near average seasonal precipitation for the region (dryland conditions). The fertilizer treatments started at 22 kg N ha − 1 and increased in increments of 50–51 kg ha − 1 with the highest rate being 275 kg N ha − 1 capturing low to excessive N fertilizer. For the current study we included only 3 N treatments, the N1 (Low N, 22 kg ha − 1 ), N5 (optimal N, 224 kg ha − 1 ), and N6 (excess N, 275 kg ha − 1 ) treatments. Nitrogen fertilizer was applied as banded urea ammonium nitrate (UAN) and liquid ammonium phosphate with 22 kg N ha − 1 and 44kg P ha − 1 added at planting as starter and the remaining N side dressed at V6 to V7. The plots were the same from one year to the next, so treatments were repeated in the same locations for both growing seasons. Soil Sampling Soil samples were taken from both water treatments in the low, optimal, and excess N (N1, N5, & N6) plots approximately every two weeks from germination to approximately R3 and then again at physiological maturity as we expected the soil N pool to be less dynamic during the later reproductive stages. An additional soil sampling event took place when the final set of Nmin incubation tubes were removed to measure the residual soil inorganic N pool. At each sampling, four soil samples were taken per plot with a 1.75 cm diameter hand probe to a depth of 15 cm. Two samples were taken approximately 6–8 cm away from each side of one of the two middle rows of the plot. Soil samples were composited in the field and stored in a cooler with ice packs before being transported to the lab for analysis. Once back in the lab duplicate subsamples were taken for gravimetric water content, two more subsamples were taken for mineral N extractions (T0 for N mineralization estimates), and two more were taken for enzyme activity assays. N mineralization methods To measure net N mineralization, undisturbed soil cores were incubated in-situ using PVC tubes (DiStefano & Gholz, 1986 ) with ion exchange resin (IER) lysimeters (Susfalk & Johnson, 2002 ) attached at the bottom (Fig. 1 ). Briefly, 6 cm outer diameter PVC tubes were cut into 25 cm long pieces for incubating soil cores, and 7.5 cm long sections for the resin lysimeters. Each lysimeter contained two sandbags with an IER bag in the middle. The sandbags and IER bags were held in place with cheese cloth and secured with zip ties. To prevent the sand and IER bags from falling out of the bottom of the lysimeter, a 1 cm tall section of 5 cm outer diameter PVC wrapped in cheese cloth was inserted and glued to the bottom of the lysimeter PVC. The sandbags had 25 g of sand each and the resin bags had 10 g of IER beads. Due to supply chain issues, in 2021 Lewatit® NM 60 (Thermo Fisher Scientific) IER was used in 2021, and AmberLite® MB20 (Simga-Aldrich) IER was used in 2022. The bags were triple rinsed with DI water and stored at 4 ͦ C until being brought to the field in a cooler with ice for installation. A lathe machine was used to reduce the thickness of the bottom of the incubation PVC tube and the top of the PVC lysimeter. Reducing the thickness of the PVC allowed the two pieces to slide over each other and be joined together with a screw. Two incubation + lysimeter PVC tubes were installed in low, optimal, and excess N plots in both water treatments on the same day that soil sampling occurred. The PVC tubes straddled one of the middle rows of maize in the plots and were approximately 6–8 cm off the row. Cylinders were installed using a drop hammer to hammer the PVC 15 cm into the soil. The cylinders containing the top 15 cm of soil were carefully removed to avoid spilling and soil and the resin lysimeter bottoms were attached. A longer piece of PVC was then hammered into the hole to remove soil between 15–22.5 cm so that when the soil core plus lysimeter were returned to the hole for the incubation period the top surface of the soil inside the core was flush with the soil surface. The open-top soil cores and resin bags incubated in the field for approximately 2 weeks, except for the final incubation period which lasted about 1 month. After the incubation the tubes were removed from the field and transported back to the lab in a cooler with ice. Once back in the lab resin bags were removed and frozen until analysis. The soil was removed from each PVC tube, homogenized, and two subsamples were taken from each PVC tube: one for gravimetric water content, and one for mineral N extractions. Gravimetric water content was measured for both the initial soil samples and incubated soils by oven-drying a 12 g subsample of soil at 105 ͦ C for 48 hours and then reweighing it. Mineral N extractions were performed on the initial soil samples and incubated soils immediately after they got to the lab by shaking approximately 12 g of field-moist soil in 100 mL of 2M KCl for 1 hour on a reciprocal shaker. After shaking, the samples were filtered through Whatman filter paper No. 1, and stored at – 20 ͦ C until analysis. Extractions were also performed on the previously frozen resin bags following the same procedure (using resin bag instead of soil). Chemical analysis for inorganic N Mineral N KCl extracts (both initial and incubated soil as well as IER) were analyzed colorimetrically on a microplate reader (Cytation 5, BioTek Instruments, Winooski, VT) to determine nitrate (NO 3 -N) and ammonium (NH 4 -N) concentrations. The Griess method using vanadium (III) chloride (VCl 3 ) was used to determine NO 3 -N concentrations in the extracts (Doane & Horwáth, 2003 ), and NH 4 -N was determined via the Berthelot method using salicylate-hypochlorite and citrate (Sims et al., 1995 ). All inorganic N values are reported as the sum of NO 3 -N + NH 4 -N. Net nitrogen mineralization was calculated using Eq. 1: Equation 1: \(Net{N}_{{min}}=(\left(I{N}_{t}+I{N}_{r}\right)-I{N}_{0})/D\) Where Net N min refers to net nitrogen mineralization (mg N day − 1 ), IN t refers to inorganic N from the incubated soil samples, IN r refers to inorganic N from the IER, IN 0 refers to inorganic N from the initial soil sample, and D refers to the number of days of the incubation. Soil inorganic N was converted to kg ha − 1 based on the extractable N, the weight of the dry soil sample, the depth of the soil sample, and the bulk density of the soil. Bulk density data from a previous experiment at this field was used for this calculation. Briefly, 2 replicate 5.1 cm soil cores were taken from each block to a depth of 0–15 cm. The soil cores were oven dried at 105 ͦ C for 48 hours and the dry weight and volume was used to determine bulk density. Inorganic N from the IER was converted to kg ha −1 using the same calculations as for the soil core and the assumption that the N in the IER was leached from that same volume of soil. Soil Enzyme Activity Potential soil enzyme activity was measured for two enzymes related to nitrogen acquisition; namely NAG ( β -1,4-N-acetyl-glucosaminidase), which degrades chitin, and LAP (L-leucine amino peptidase), which degrades proteins. We analyzed two lab replicates at field moisture conditions the day after sampling following the protocol outlined in (Saiya-Cork et al., 2002 ). In 2021, soil slurries were made by homogenizing 1 g of each sample in approximately 60 ml of 50 mM, pH 8.1, sodium acetate buffer. In 2022, the same protocol was used, but a tris buffer was used instead because we determined it was more appropriate for our alkaline soil conditions. After homogenizing, the slurry was pipetted into black, 96-well microplates and mixed with substrate. Slurries were also mixed with buffer only or with standards (10 mM 4 methylumbelliferone, or 7-amino-4methyl coumarin) as negative quenching controls. Samples were incubated for 4 h at 25 ͦ C in the dark, and the fluorescence was read on a microplate reader (Cytation 5, BioTek Instruments, Vermont, USA) at 365 nm excitation and 450 nm emission wavelengths. Grain Yield and Plant Nitrogen Accumulation Along with the soil nitrogen measurements, aboveground plant N uptake rate, end of season aboveground nitrogen accumulation, and end of season grain yield were measured. Plant N uptake rate was determined by collecting 5 whole plants from all low, optimal, and excess N plots. In 2021 samples were collected at V3, V5, V11, R1, and R6. In 2022 samples were collected at V5, V12, R1, and R6. The samples from R6 were used to determine end of season total N uptake. Unfortunately, no data was available for V11 in 2021 and V5 in 2022. The V11 samples became moldy prior to being dried and analyzed making them unusable. The V5 samples were misplaced and never found before analysis occurred. Plants were separated into leaves, stalks, grain, and cobs, dried at 60 ͦ C and weighed. Plant samples were then ground, homogenized, and a subsample was analyzed for total N concentration using a dry combustion elemental analysis (LECO Tru-SPEC, St. Joseph, MI, USA). Plant N uptake at each growth stage was calculated by multiplying the oven dry biomass of each plant component by its corresponding N concentration and summing all the aboveground parts. Plant N uptake rate was estimated by finding the change in plant N content between growth stages divided by the number of days between growth stages. Due to missing samples from both growing seasons, we combined the two years to estimate plant N uptake at different growth stages. Grain yield was determined at R6 for each year. The grain was separated from the ears, weighed, and adjusted to 15.5% moisture. Plant N uptake rates, total N uptake at the end of the season, and grain yields were converted to a per hectare basis using the harvested area. Statistical Analysis A linear mixed effects model was used for all analyses to determine how the response variables (extractable soil inorganic N, net Nmin, enzyme activity, plant N uptake (total and rate), grain yield) were affected by irrigation treatment, nitrogen fertilizer rate, year, sampling event (where applicable), and their interactions. The split plot design and repeated measures analyses were integrated into the mixed model structure through the inclusion of block and its interactions with irrigation and N rate as random factors. The predictors were treated as categorical variables with fixed effects. Response variables were checked for normality, equal variance, and homogeneity. Inorganic soil N and enzyme activity were log-transformed to meet model assumptions. An ANOVA with the "Kenward-Roger" degrees of freedom adjustment was used to assess the significance of the fixed effects and their interactions within the context of this mixed-effects model. Post hoc means comparisons (Tukey) were conducted to assess treatment differences when treatment effects were significant within the ANOVAs. Analyses were performed in R version 4.2.2 using the lme4, lmeTest, and emmeans packages (R Core Team, 2022 ). When looking at soil N dynamics we wanted to understand the response variable dynamics over the entire season as well as our treatment effects, which were not implemented until V6 or V7. Therefore, we have 2 separate analyses, one which includes the entire growing season, and the other with only the last 3 sampling events (3 incubations and 4 soil samples) thatoccurred after our treatments were officially initiated, and cover before, during, and after peak plant N uptake. Both analyses used the same statistical approach as described above. Results Plant N uptake rates Plant N uptake rates varied across the season and increased with N fertilizer rate, especially later in the season, resulting in a Growth stage x N fertilizer interaction (Fig. 2A, Table 1 , p = < 0.001). For all N fertilizer treatment groups, plant N uptake rates increased substantially from vegetative stage 3 to vegetative stage 5 (V3 and V5) and peaked at reproductive stage 1 (R1) after which plant N uptake rates declined. Plant N uptake rates increased from V5 to R1 by 29%, 349%, and 530% for the low, optimal, and excess N rates respectively. Plant N uptake rates also varied by irrigation treatment later in the growing season resulting in a Growing season x Irrigation interaction (Fig. 2A, Table 1 , p = < 0.001). Plant N uptake rates were 32% higher in the limited water than in the full water treatment at V12. However, the full water treatment had greater plant N uptake rates during reproduction than the limited water treatment (79% and 49% higher at R1 and R6, respectively). Seasonal dynamics of Inorganic N and Net N min Extractable inorganic N (EIN) varied across the season from germination through physiological maturity (PM) and tended to be higher in the limited water treatments, especially later in the growing season of 2022. This resulted in a three-way Irrigation x Sampling Event x Year interaction (Fig. 3 , Table 2 , p = .031). The Sampling Event x Year interaction was consistent whether analyzing the full season of EIN data or just the sampling events after treatment initiation (tassel (VT) – PM), (Table 2 , p < .001). The EIN nearly doubled from the 2021 growing season to the 2022 growing season, and EIN tended to be lower at physiological maturity. When averaged over both growing years and treatments, EIN decreased by ~ 22.5% at each sampling event from V10 – PM (Fig. 3 ). For the entire growing season, net Nmin rates varied within and across the two growing seasons and changed with N fertilizer rate, resulting in a N fertilizer x Sampling Event x Year interaction (Fig. 2B, Table 2 , p = 0.005). Net Nmin rates ranged from strong net immobilization to strong net mineralization with the highest rates of mineralization typically occurring earlier in the growing season before the main application of N fertilizer and when plant N demand was lower (Fig. 2). When considering only VT – PM, net Nmin also varied across and within each growing season and exhibited a strong Sampling Event x Year interaction (Table 1 , p < 0.001). When averaged across both growing seasons and treatments, the net Nmin rates declined from close to 0.3 kg N ha − 1 day − 1 at VT to essentially 0 (-0.004 kg N ha − 1 day − 1 ) at physiological maturity (Fig. 2B). N x Water effects on Inorganic N and N min Over the course of the entire season EIN exhibited an N fertilizer x irrigation interaction where EIN values were nearly identical at the low N treatment for both water treatments but were much higher in the limited water for the optimal and excess N rates (Table 2 , p = 0.018). We found a similar trend when looking at VT – PM. The EIN was 27%, 141%, and 80% greater in the limited water treatment compared to the full water treatment for the low, optimal, and excess N rates respectively (Fig. 4A, Table 2 , p = 0.068). Over the entire season net Nmin was not affected by irrigation, N fertilizer, or an N fertilizer x irrigation interaction (Table 2 ), but when focusing on VT – PM we found an N fertilizer x irrigation interaction (Fig. 4B, Table 2 , p = 0.034). For the full water treatment, net Nmin was highest in the low N treatment (0.524 kg N ha − 1 day − 1 ) and declined by 67% and 85% as the N fertilizer rate increased to optimal N and excess N, respectively. The limited water treatment had a different trend with virtually no Nmin occurring in the low N and optimal N, but 0.34 kg N ha − 1 day − 1 for the excess N treatment (Fig. 4B). N x Water Effects and seasonal dynamics of Soil Enzyme Activity Both LAP and NAG enzyme activity increased with N rate regardless of irrigation treatment (Fig. 5, Table 2 , p = 0.018 & 0.008 respectively). Nitrogen fertilizer increased LAP activity by 39% and 22% in the optimal N and excess N treatments compared to the low N treatment, respectively. Soil NAG activity increased by 43% for both the optimal and excess N treatments compared to the low N treatment. Enzyme activity varied across the growing season and years (Table 2 , p < 0.001). When focusing on LAP specifically, we found that average end of season (between R1 and R3) LAP activity was positively correlated with end of season plant N uptake (cor = 0.429, p = 0.009, Fig. 6A). There was no correlation between NAG and plant N uptake (cor = 0.26, p = 0.126, Fig. 6B). N x Water Effects on Cumulative Plant Nitrogen Uptake and Yields Total plant N uptake was higher in the 2022 growing season compared to the 2021 growing season (Table 3 , p = 0.077) with average N uptake being 131 kg ha − 1 and 147 kg ha − 1 for the 2021 and 2022 growing season respectively. Across both years, plant N uptake increased independently with increases in both N fertilizer and water availability (Table 3 , p < 0.001 & 0.037). Fertilizer N increased plant N uptake by 146% and 169% when comparing the low N treatment to the optimal and excess N treatments, respectively. Full water availability increased N uptake by 26% relative to the limited water availability (Fig. 7A). The effect of N fertilizer on grain yield was dependent on irrigation treatment resulting in an N fertilizer x irrigation interaction (Fig. 7B, Table 3 , p = 0.027). Under full water, yields increased by 68% and 78% for the optimal and excess N treatments relative to the low N treatment, respectively. In the limited water treatment, grain yields increased by only 36% and 32% as N fertilizer rates increased (Fig. 7B). There was no difference between grain yields across the two growing seasons (Table 3 , p = 0.115). Discussion We conducted a field experiment to better understand how N fertilizer and irrigation affect soil Nmin dynamics and plant responses. We found that plant N uptake peaked around R1 and increased with additions of water and fertilizer. In contrast, net Nmin rates peaked relatively early in the growing season, often before treatments were initiated, and steadily declined as physiological maturity approached. This resulted in asynchrony between plant N demands and Nmin especially with increased N and water availability. We hypothesized that both N fertilizer and irrigation would independently increase net Nmin rates, but instead we found that net Nmin rates were governed by an N fertilizer x irrigation interaction. With full water availability, Nmin was highest in the low N fertilizer treatment, while under limited water availability, N min was highest under excess N fertilization. An important consideration is that we measured net N min, which can differ substantially from gross N min rates (Booth et al., 2005 ). Plant N uptake and soil N enzyme activity data suggest that gross N min dynamics may have differed from our net N min measurements. Enzyme activity, which can be an indicator of gross mineralization rates (Elrys et al., 2021 ), increased with N fertilizer and was not effected by irrigation regime. Plant N uptake, an integrated indicator of gross soil N availability from N min and N fertilizer, was higher as both N additions and water availability increased. This supported our final hypothesis that plant N accumulation and grain yields would increase with N and water availability. Increased N uptake did not translate directly to increased grain yields in the limited water treatment. Therefore, yields exhibited an N fertilizer x irrigation interaction. In the full water treatment grain yields were highest at the excess N rate, and for the limited water treatment the optimal N treatment led to the highest yield. Plant N uptake rates and Net Nmin over the entire season: We found that plant N uptake rates were higher later in the growing season, often peaking at R1, and were higher with increased N fertilizer and water. Interestingly, with low N fertilizer availability, N uptake rates showed much less variation across plant growth stages. Our findings are in line with the literature as many studies have found that maize N uptake peaks around R1 and increases with N and water availability (Guo et al., 2022 ; Ma et al., 1999a ; Osterholz et al., 2017 ). Net Nmin rates over the entire season were not affected by N fertilizer, irrigation, or an N fertilizer x irrigation interaction, and rates peaked relatively early in the growing season resulting in asynchrony between Nmin rates and plant N demand. Although this decline in Nmin after the initiation of N fertilizer could suggest that fertilizer addition suppressed Nmin, we also found a decline in the low N treatment, suggesting the decline was due to other temporal dynamics. Many studies have found Nmin rates vary within a growing season and across multiple growing seasons (Loecke et al., 2012 ; Maysson M. Mikha et al., 2006 ; Studt et al., 2021 ), and our findings of Nmin rates being higher in the earlier vegetative stages than the latter is in line with other studies (Ma et al., 1999b ; Mahal et al., 2019 ). There is often a flush of Nmin early in the season as soil temperatures rise and microbial activity increases (Cassman et al., 2002 ; Miller & Geisseler, 2018 ). This can lead to asynchrony between soil N supply and plant N needs contributing to high springtime nitrogen losses, specifically nitrate leaching (Danalatos et al., 2022 ; Martinez-Feria et al., 2018 ). Variations of Nmin rates across and within growing seasons could be due to root exudation, precipitation, temperature, and the soil inorganic pool size (Gonçalves & Carlyle, 1994 ; Ma et al., 1999b ; Mahal et al., 2019 ). Somewhat surprisingly, climate variables (soil moisture or temperature) did not explain the seasonal patterns in Nmin rates. While rewetting soil after a dry period can cause a flash of Nmin (Fierer et al., 2003 ; Xiang et al., 2008 ), high magnitude perturbations in soil moisture can negatively affect Nmin rates (Barakat et al., 2016 ). In the current study, it is possible that soil moisture was more consistent earlier in the season when temperatures and plant water use was lower. As temperatures and plant water use increased later in the season, more extreme soil drying-rewetting cycles could have also occurred, thus, decreasing Nmin rates. Post fertilizer soil N dynamics Maize N uptake fluctuates throughout the growing season with a relatively short window of high N uptake (Udvardi et al., 2021 ). The onset of rapid N accumulation starts around V5, the midpoint is around V12, and peak N uptake happens near R1 or R2 before declining again (Mahal et al., 2019 ; Osterholz et al., 2017 ). Therefore, we wanted to focus on the effects of N fertilizer and irrigation on soil N dynamics before, during, and after peak N uptake. Repeated fertilizer additions, as done here, have shown to increase total soil N over multiple seasons (Brown et al., 2014 ; Dhillon et al., 2018 ) including in the inorganic N pool (Fujita et al., 2018 ; Grandy et al., 2013 ). Our results of higher EIN in the 2022 growing season compared to the 2021 growing season are consistent with this literature. A decline in EIN as the growing season progressed, as found here, was also consistent with that observed by others as plant N uptake increased (Mahal et al., 2019 ). Finally, increased EIN with water deficits due to reduced plant N uptake is also consistent with previous studies (He & Dijkstra, 2014 ). In the current study, Net Nmin rates varied within each growing season and across the two growing seasons post fertilizer application (VT – PM). We hypothesized that high Nmin rates would be maintained later in the season when plant N uptake was high, especially with warmer soil temperatures and under full irrigation. Although Nmin rates peaked early in the growing season, there was still Nmin occurring when plant N uptake was highest. Consequently, Nmin and subsequent plant N uptake during grain fill can be beneficial as leaf N content and photosynthetic rates are maintained leading to higher yields (Osterholz et al., 2018 ; Subedi & Ma, 2005 ). The methods used in this study do not allow us to determine how much plant accumulated N came from Nmin, which would require an additional technique, such as a 15 N tracer study, to better understand the contribution of soil N turnover to plant N uptake under different nitrogen fertilizer and water rates. Based on the results from our study, we have to reject our hypothesis that N fertilizer and irrigation would both increase net N min rates. Instead, our results show an N fertilizer x irrigation interactive effect on Nmin rates. When looking at N fertilizer and water alone we see that N fertilizer additions yield different responses in the literature using varying methods. Additions of N fertilizer have increased Nmin rates relative to zero N (Biau et al., 2012 ; Ma et al., 1999b ; Maysson M. Mikha et al., 2006 ), though the highest N fertilizer rates do not always yield the highest Nmin rates (Al-kaisi et al., 2008 ; Chen et al., 2019 ; Fujita et al., 2018 ; Ouyang & Norton, 2020 ). Nitrogen fertilizer additions can also suppress Nmin (Carpenter-Boggs et al., 2000 ; Mahal et al., 2019 ), “destabilize” Nmin where N fertilizer has both the highest and lowest Nmin rates (Studt et al., 2021 ), and yield different responses based on the cropping system (Breza et al., 2023 ). When considering water alone, Nmin rates tend to be highest when there is adequate soil moisture (Barakat et al., 2016 ). At the global scale, Nmin rates increase with precipitation (Colman & Schimel, 2013 ; Elrys et al., 2021 ; Li et al., 2019 ), and irrigation tends to increase Nmin rates (Valé et al., 2007 ). While Nmin can be fairly drought tolerant due to flashes of Nmin after rewetting dry soils and the broad range of microbes carrying out Nmin (Homyak et al., 2017 ; Y. Wang et al., 2017 ), the flashes cannot always compensate for the reduction in mineralization that occurred during the dry period (Maysoon M Mikha et al., 2005 ), and high magnitude perturbations of soil moisture are adverse to Nmin (Barakat et al., 2016 ). There are few Nitrogen x Water experiments that report Nmin rates, especially in agronomic field settings. However, the N fertilizer x irrigation interaction on Nmin that we found is consistent with other Nitrogen x Water experiments (Wang et al., 2017 ). In the current study the N fertilizer x irrigation interaction observed could have been due to the differing effects N fertilizer and irrigation have on root growth and root exudation (Flynn et al., 2021 ; Ordóñez et al., 2021 ; Zhu et al., 2016 ). If root exudation and root growth, especially fine roots that turnover quickly, increased in the limited water excess N treatment relative to other limited water treatments, Nmin rates could have increased (Chen et al., 2014a ; Jilling et al., 2018 ). Given that N fertilizer x irrigation studies on Nmin are limited, further research is needed. Both soil N cycling enzyme activities assayed in this study (LAP and NAG) increased with N fertilizer and were not affected by irrigation (Fig. 5). Water deficits and retirement of irrigated lands have shown to decrease microbial biomass and enzyme activity (Flynn et al., 2021 ; Núñez et al., 2022 ). However, some soil microbial communities can also be quite drought tolerant (Fierer et al., 2003 ; Homyak et al., 2017 ), which may have been the reason there was no irrigation effect in the current study. The effect of N additions on the direction and magnitude of LAP and NAG is variable in the literature with positive (Chen et al., 2018 ; Fujita et al., 2018 ; Saiya-Cork et al., 2002 ), neutral (Jian et al., 2016 ), and time x N fertilizer interactions being reported (Grandy et al., 2013 ). Microbial biomass and composition can elicit different responses to N additions (Geisseler & Scow, 2014 ; Guo et al., 2019 ; Treseder, 2008 ), and depend on the ecosystem, N fertilizer rates, and duration of the study (Jia et al., 2020 ). In the current study, N additions increased both LAP and NAG activity which is with other studies (Chen et al., 2018 ; Zhang et al., 2015b ). Net Nmin is the difference between two opposite and simultaneous microbial processes; namely gross mineralization and gross immobilization, and the net N left over from these processes is available for plant use (Liu et al., 2017 ). Incubating soil cores in the field is a common method for measuring net Nmin and provides some advantages over lab incubation methods and other field methods (Hart et al., 1994 ). However, there are some limitations associated with our method of measuring net Nmin. First, we excluded living plant roots which influence N cycling via rhizodeposition (Meier et al., 2017 ; Zhu et al., 2016 ). Additionally, Net Nmin is not well correlated with gross mineralization especially as the duration of the incubation increases as most soil inorganic N pools have a turnover time of ~ 1 day (Booth et al., 2005 ). While gross mineralization (total production of NH 4 ) measurements can be costly, time consuming, and complicated relative to other methods, some argue that is a better measure of soil N supplying capabilities especially since plants can compete effectively against soil microbes for inorganic N and do not need to wait for “leftover net N” (Elrys et al., 2021 ; Osterholz et al., 2017 ). Nevertheless, net Nmin is still commonly used and is considered a good “index” of plant available N (Hart et al., 1994 ; Li et al., 2019 ; Schimel & Bennett, 2004 ). Soil enzyme activities are closely correlated to gross N mineralization, so increased LAP and NAG activity could suggest increased N turnover (Elrys et al., 2021 ). Our results then suggest that N fertilizer may have enhanced N turnover and, thereby, gross N min that was not reflected by our net N min measurements. If true, this would support our hypothesis that N fertilizer enhanced gross N min. Depolymerization of N containing compounds is considered a rate-limiting step for soil N cycling as depolymerization makes N bioavailable (Geisseler et al., 2010 ; Mooshammer et al., 2014 ; Schimel & Bennett, 2004 ). LAP and NAG, are extracellular enzymes excreted by soil microorganisms to depolymerize N containing substrates specifically proteins and chitin (Jian et al., 2016 ). Depolymerization of proteins is especially a rate-limiting step as proteins account for 60% of the organic N in plant and microbial cells meaning LAP activity should have a greater effect on soil N cycling than NAG (Chen et al., 2018 ). Given that net Nmin and gross Nmin are not well correlated, it is not surprising that enzyme activity and net Nmin showed different responses to the treatments. Plant N uptake and Grain Yield We hypothesized that both irrigation and N fertilizer would lead to increased N uptake, and based on our results, we can confirm this hypothesis. In the current study, N uptake increased under the full water treatment and as N fertilizer rates increased (Fig. 7A). It is well documented that water limitations reduce N uptake (Djaman et al., 2013 ; Hammad et al., 2017 ; He & Dijkstra, 2014 ). Plant N uptake is reduced under water limitations due to decreased transpiration (Rudnick et al., 2017a ), and therefore mass flow (Lambers et al., 2008 ), as well as decreased soil N supply from decreased Nmin rates (Elrys et al., 2021 ; Maysoon M Mikha et al., 2005 ). Plant N uptake can still be relatively high due to drought increasing root biomass (Flynn et al., 2021 ; Hammad et al., 2017 ) and expression of root genes associate with N uptake (H. Wang et al., 2017 ). However, shoot growth potential is a driver of plant N uptake, thus reductions of aboveground growth limit N accumulation (Peng et al., 2010 ; Y. Wang et al., 2017 ). Nitrogen fertilizer has been shown to increase plant N uptake (Barbieri et al., 2008 ; Dordas & Sioulas, 2009 ; Zhu et al., 2016 ), and sufficient N fertilizer can increase shoot biomass, root biomass, and evapotranspiration in maize (Ordóñez et al., 2021 ; Rudnick et al., 2017a ) all of which can increase N uptake. In the current study N fertilizer increased the soil inorganic N pool (Figs. 3 & 4A) and soil N-acquiring enzyme activity (Fig. 5) which could have increased N availability for the plants (Chen et al., 2018 ; Geisseler et al., 2010 ). Plant N uptake is an integrated indicator of soil N availability and we found that plant N uptake was positively correlated with enzyme activity around peak N uptake (Fig. 6A) suggesting enzyme activity did in fact increase bioavailable N. We hypothesized that N fertilizer and irrigation would increase grain yields. We found this to be true, but the effect of N fertilizer on grain yield was dependent on water limitations. Under water limitation, grain yields did not differ between the optimal N fertilizer treatment and the excess N treatment. In the current study it is possible that N additions increased vegetative growth and therefore transpiration which lead to insufficient water during grain fill for the limited water treatments (Rudnick et al., 2017a ). This could explain why the limited water, optimal and excess N treatments doubled N uptake relative to the full water low N treatment, but still had lower yields. This highlights the importance of water resources in the Great Plains Region of the U.S. It is well documented that plant growth and yields decline with water limitations, but the extent of the decline depends on timing and severity of water limitations (Comas et al., 2019 ; Westgate & Boyer, 1985 ). Plant response to water and N is greater than its response to each resource in isolation, and maximum plant growth is achieved when both resources are non-limiting (Quemada & Gabriel, 2016 ). It is important to properly manage resources such as N fertilizer in order to optimize economic return and avoid environmental consequences (Zhang et al., 2015a), and here we find that N fertilizer rates need to be adjusted based on water availability and yield potential. Finding the optimal N rate is challenging (McDaniel et al., 2020 ; Puntel et al., 2016 ), especially when water availability is highly variable as it is in the Great Plains (Derner et al., 2015 ; Rudnick et al., 2017b ; Schlegel et al., 2019 ). Being able to adjust N fertilizer throughout the growing season based on water availability and plant N needs may help increase economic returns and resource use efficiency (Quemada & Gabriel, 2016 ). Conclusion In summary, we found an apparent asynchrony between Nmin rates and peak plant N demand regardless of N fertilizer and irrigation treatment. Improving synchrony between soil N supply and plant N demand is an important goal for agroecosystems as it improves yields and sustainability. When focusing on the later part of the growing season when plant N uptake is higher we found that Nmin rates were governed by an N fertilizer x irrigation interaction. Further research is necessary to understand what is underlying this interaction, as well as understanding whether gross N min patterns follow the net Nmin patterns we found. Plant N uptake was high when N fertilizer rates were high, but yields were still low when water was limiting. The effect of N fertilizer on grain yields was dependent on water availability and too much N was marginally detrimental to grain yields when water was limiting. Being able to adjust N fertilizer based on water availability and plant N needs, especially throughout the season if possible, may help improve economic returns, grain yields, and reduce N losses simultaneously. Declarations Author Contribution TD collected and analyzed the data and wrote the manuscript. All authors contributed to the study design, data interpretation, and reviewing and editing the manuscript. Acknowledgement The authors would like to thank Paul Campbell and Cody Hardy, at the USDA-ARS in Akron for their help managing field operations; Stacey Poland, Tyler Pokoski, and Tyler Untiedt and the team at the USDA-ARS for their help with data collection; Dr. Bo Stevens and Josh Wenz and the team at the USDA-ARS in Fort Collins for their help with data collection and processing; Brittani Meis, Oliver Hoffman, and the Agroecology Lab at Colorado State University for their help with sample collection and processing; and Dr. Ann Hess and the Franklin A. Graybill Statistics and Data Science Laboratory at Colorado State University for statistical consulting. This work was supported with funds from USDA-ARS NP211 project 3012-13210-001-000D. 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Bold values indicate significance at p F) Irrigation (W) 1 2 8.82 0.097 N Fertilizer (N) 2 8 42.46 < 0.001 Growth Stage (GS) 4 48 78.94 < 0.001 W x N 2 8 1.00 0.410 W x GS 4 48 10.90 < 0.001 N x GS 8 48 14.10 < 0.001 Table 2. Analysis of variance summary statistics for soil extractable inorganic nitrogen (EIN) for the entire season (Fig. 3), net nitrogen mineralization (Nmin) rates over the entire season (Fig. 2B), EIN from approximately tassel (VT) – physiological maturity (PM) (Fig. 4A), Net Nmin rates from approximately VT - PM (Fig. 4B), soil N-acquiring enzyme activity leucine amino peptidase (LAP) from approximately VT - PM (Fig. 5A) and b-1,4-N-acetyl-glucosaminidase (NAG) from approximately VT - PM (Fig. 5B). Bold values indicate significance at p F) NumDF DenDF F value Pr(>F) Irr (W) 1 2 7.89 0.107 1 2 4.88 0.158 N Fertilizer (N) 2 8 29.95 < 0.001 2 8 1.98 0.200 Sampling Event (S) 7 180 7.78 < 0.001 6 156 18.52 < 0.001 Year (Y) 1 180 22.98 < 0.001 1 156 9.39 0.003 W x N 2 8 7.00 0.018 2 8 2.43 0.150 W x S 7 180 1.57 0.146 6 156 0.98 0.440 N x S 14 180 1.67 0.065 12 156 1.43 0.157 W x Y 1 180 0.07 0.785 1 156 0.02 0.884 N x Y 2 180 1.62 0.200 2 156 4.45 0.013 S x Y 7 180 16.73 < 0.001 6 156 12.79 < 0.001 W x N x S 14 180 0.64 0.828 12 156 0.84 0.610 W x N x Y 2 180 1.48 0.229 2 156 2.58 0.079 W x S x Y 7 180 2.26 0.031 6 156 1.11 0.360 N x S x Y 14 180 1.14 0.328 12 156 2.50 0.005 W x N x S x Y 14 180 0.66 0.813 12 156 1.70 0.070 Soil extractable inorganic N (VT - PM) Net N Mineralization (VT - PM) Source of Variance NumDF DenDF F value Pr(>F) NumDF DenDF F value Pr(>F) Irr (W) 1 2 14.78 0.061 1 2 2.10 0.285 N Fertilizer (N) 2 8 14.77 0.002 2 8 1.32 0.319 Sampling Event (S) 3 84 8.90 < 0.001 2 60 4.16 0.020 Year (Y) 1 84 61.29 < 0.001 1 60 0.01 0.924 W x N 2 8 3.83 0.068 2 8 5.29 0.034 W x S 3 84 0.02 0.995 2 60 2.50 0.091 N x S 6 84 1.76 0.118 4 60 1.45 0.229 W x Y 1 84 3.24 0.075 1 60 0.43 0.514 N x Y 2 84 0.32 0.728 2 60 0.58 0.563 S x Y 3 84 15.80 < 0.001 2 60 11.31 F) NumDF DenDF F value Pr(>F) Irr (W) 1 2 0.01 0.943 1 2 0.01 0.928 N Fertilizer (N) 2 8 6.87 0.018 2 8 9.22 0.008 Sampling Event (S) 3 84 41.21 < 0.001 3 84 10.76 < 0.001 Year (Y) 1 84 182.20 < 0.001 1 84 184.28 < 0.001 W x N 2 8 1.10 0.378 2 8 1.66 0.250 W x S 3 84 5.92 0.001 3 84 4.26 0.008 N x S 6 84 0.53 0.784 6 84 0.75 0.612 W x Y 1 84 10.11 0.002 1 84 0.03 0.868 N x Y 2 84 0.20 0.821 2 84 1.91 0.155 S x Y 3 84 6.79 < 0.001 3 84 42.24 < 0.001 Table 3 . Analysis of variance summary statistics end of season aboveground plant N uptake (Fig. 7A) and end of season grain yield adjusted to 15.5% moisture content (Fig. 7B). Bold values indicate significance at p F) NumDF DenDF F value Pr(>F) Irrigation (W) 1 2 25.31 0.037 1 2 93.62 0.011 N Fertilizer (N) 2 8 76.81 < 0.001 2 8 18.91 0.001 Year (Y) 1 12 3.73 0.077 1 12 2.90 0.115 W x N 2 8 1.22 0.345 2 8 5.84 0.027 W x Y 1 12 0.65 0.437 1 12 2.97 0.111 N x Y 2 12 0.78 0.480 2 12 0.59 0.571 W x N x Y 2 12 0.19 0.830 2 12 2.07 0.169 Additional Declarations No competing interests reported. Supplementary Files SupplementarMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2025 Read the published version in Nutrient Cycling in Agroecosystems → Version 1 posted Editorial decision: Revision requested 18 Sep, 2024 Reviews received at journal 18 Sep, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviews received at journal 24 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers invited by journal 25 May, 2024 Editor assigned by journal 25 May, 2024 Submission checks completed at journal 25 May, 2024 First submitted to journal 24 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Donovan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYHACAwjF3sDA8AAuyIZbPQ9cC88BBoYEBgYJiGqitEgkEKnFnoF54+OCisPy5pKPHz5IqKir45/ffIDhQ9lhPLawFRvPOHPYcOfsNGODhDOHJSSOsSUwzjiHTwuPmTRvWxrjhts5bBKJbQckGI7xGDDzthHWYr/h5hmQljoJ+WP8H5j/EtZik7jhBg9IC7OEwTEeBmZGfFoOA/3Cc8YmecMZiF8kNx5LMzjYcy4dpxb29uaNj3kqJGw3HD/88MGHijp+ucNAxo8ya5xaGJixCR7ArX4UjIJRMApGATEAAGahTn8H+ZN+AAAAAElFTkSuQmCC","orcid":"","institution":"Colorado State University","correspondingAuthor":true,"prefix":"","firstName":"Tyler","middleName":"C.","lastName":"Donovan","suffix":""},{"id":308556966,"identity":"28b5099e-b098-4908-980c-0540513e5f79","order_by":1,"name":"Louise H. Comas","email":"","orcid":"","institution":"USDA Agricultural Research Service","correspondingAuthor":false,"prefix":"","firstName":"Louise","middleName":"H.","lastName":"Comas","suffix":""},{"id":308556967,"identity":"38aa201b-061b-4c6a-9024-3305b754ca75","order_by":2,"name":"Joel Schneekloth","email":"","orcid":"","institution":"Colorado State University Extension","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"","lastName":"Schneekloth","suffix":""},{"id":308556968,"identity":"01745c08-e1de-48f0-b52b-36effd6a76a2","order_by":3,"name":"Meagan Schipanski","email":"","orcid":"","institution":"Colorado State University","correspondingAuthor":false,"prefix":"","firstName":"Meagan","middleName":"","lastName":"Schipanski","suffix":""}],"badges":[],"createdAt":"2024-05-24 18:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4474023/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4474023/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10705-025-10406-8","type":"published","date":"2025-04-22T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57835815,"identity":"973354ad-79ee-4d09-837c-9e73600571ff","added_by":"auto","created_at":"2024-06-06 08:53:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34728,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic showing PVC set up for in-situ undisturbed soil core incubations for net nitrogen mineralization measurements. Top PVC piece contain undisturbed soil core from 0 – 15 cm. The lysimeter was attached to the top PVC piece and had two sandbags and one ion exchange resin bag which captured inorganic N as it moved through the core. The insert for the lysimeter kept the bags in place.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/97235d970c98f9e45d2422b9.png"},{"id":57835814,"identity":"93b64915-1d22-4ca3-a7d5-e43d67bbf729","added_by":"auto","created_at":"2024-06-06 08:53:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePanel A) Maize nitrogen uptake rate (kg N ha\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eday\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) by maize growth stage, nitrogen fertilizer treatment, and irrigation treatment. Maize growth stage corresponds to when plant samples were collected, and plant N content and uptake rates were determined. “V” corresponds to vegetative stage and the number corresponds to how many collard leaves are present. “R” corresponds to reproductive stage and the number corresponds to which stage with R1 meaning silking and R6 meaning physiological maturity. Panel B) Net nitrogen mineralization (Nmin) rates in the top 0 – 15 cm over the entire growing season by nitrogen fertilizer treatment and growing season. Nmin rates include NH4-N + NO3-N. Growth stage corresponds to the approximate maize growth stage when soil core incubations began with the final incubation lasting from approximately R3 – PM. In both panels error bars depict \u0026nbsp;one SE. Nitrogen fertilizer application was applied in two doses, one at planting and one at V7 which is indicated by the dotted black line.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/95645a4beded36014a111b81.png"},{"id":57836457,"identity":"6a8045b8-9009-4d60-8481-c3bf7f7674f8","added_by":"auto","created_at":"2024-06-06 09:01:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSoil extractable inorganic nitrogen (EIN) in the top 0 – 15 cm across the entire growing season by irrigation treatment and growing season. EIN includes both NH4-N + NO3-N. Growth stage corresponds to the approximate growth stage of maize when the soil sample was collected. “V” corresponds to vegetive stages with VE representing emergence, V4 – V10 representing the amount of collard leaves present, and VT representing when tassels appeared. “R” corresponds to reproductive stages with R1 representing silking and R3 representing milk stage. “PM” corresponds to physiological maturity which is when the final sample of the growing season was taken. Error bars depict \u003c/em\u003e±\u003cem\u003e one SE. Nitrogen fertilizer application was applied in two doses, one at planting and one at V7 which is indicated by the dotted black line.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/5dd45f2156208a9552278a27.png"},{"id":57835817,"identity":"01ce17a4-275c-49fb-9774-62409e5af79a","added_by":"auto","created_at":"2024-06-06 08:53:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58327,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePanel A) Soil EIN in the top 0 – 15 cm by nitrogen fertilizer and irrigation treatment averaged across the two growing seasons from approximately VT – PM. Panel B) Net Nmin rates in top 0 – 15 cm by nitrogen fertilizer and irrigation treatment averaged across the two growing seasons from approximately VT – PM. In both panels error bars depict \u003c/em\u003e± \u003cem\u003eone SE.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/0e39fedc59288b4536d582e5.png"},{"id":57836459,"identity":"ac063983-21e3-44d0-b793-603ddea30af3","added_by":"auto","created_at":"2024-06-06 09:01:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSoil enzyme activity panel A) corresponds to L-leucine amino peptidase (LAP) activity and panel B) corresponds to activity β\u003c/em\u003e-1,4-N-acetyl-glucosaminidase\u003cem\u003e(NAG). Both panel A and B show enzyme activity in the top 0 – 15 cm by nitrogen fertilizer and irrigation treatments averaged across the two growing seasons from approximately VT – PM. Error bars depict \u003c/em\u003e\u0026nbsp;±\u003cem\u003e one SE.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/44e1387b9bbdebe6fd045267.png"},{"id":57837013,"identity":"d469801b-6561-4b01-b971-dbf4ed12c74e","added_by":"auto","created_at":"2024-06-06 09:09:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":67373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePearson’s correlation scatterplot between soil enzyme activity between R1 and R3 and end of season maize nitrogen uptake. Data shown is averaged across the two growing seasons and treatments. Panel A corresponds to LAP activity and panel B corresponds to NAG activity.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/e97b399c94ed59923cb4664c.png"},{"id":57835819,"identity":"fec7c9a6-368f-4fb2-978c-fbd001a9cc94","added_by":"auto","created_at":"2024-06-06 08:53:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":56932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePanel A) Total aboveground maize nitrogen uptake at physiological maturity by nitrogen fertilizer and irrigation treatments across the two growing seasons. Panel B) Maize Grain Yield at physiological maturity by nitrogen fertilizer and irrigation treatments averaged across the two growing seasons. Grain yields were adjusted to 15.5% moisture content. In both panels error bars depict \u003c/em\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003cem\u003e\u0026nbsp;one SE.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/10110b12d6ec166766d75bf8.png"},{"id":81570271,"identity":"53109b40-66b5-441f-a6d3-3c2cf57b3572","added_by":"auto","created_at":"2025-04-28 16:13:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1748161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/aeaef9a8-1f70-4068-afc6-1029dde3faba.pdf"},{"id":57835821,"identity":"f81c9cef-e46d-468a-a28b-de18cf757160","added_by":"auto","created_at":"2024-06-06 08:53:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":607071,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4474023/v1/f7f76f7b0f5092826293c895.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nitrogen and water availability effects dynamics of soil nitrogen mineralization in a maize system","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTurnover of soil organic nitrogen (N) is an important source of N for crops, capable of supplying over 50% of the N crops need in a growing season even under high levels of inorganic N fertilizer applications (Gardner \u0026amp; Drinkwater, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, plants have to compete for inorganic N with other sinks, namely microbial uptake and sorption or chemical associations on soil surfaces (Daly et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If inorganic N is not stabilized in one of these sinks it can be lost to the environment, sometimes \u0026ldquo;cascading\u0026rdquo; through different environments. Agricultural production systems are the primary source of reactive N globally, contributing to soil acidification, surface water eutrophication, and increased atmospheric concentrations of nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO) (Galloway et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Robertson et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given the environmental and soil fertility implications, proper management of soil N is important for sustainable crop production.\u003c/p\u003e \u003cp\u003eWhile extensive research has focused on improving N fertilizer management, less attention has been given to how management influences the turnover of soil organic N. Soil N mineralization (Nmin) is the complex, microbially-mediated conversion of organic N to inorganic N. Microbial extracellular enzymes cleave N containing monomers making them bioavailable for microbial use and, in some cases, plant use (Schimel \u0026amp; Bennett, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Microbes can use these monomers, or substrates, to meet their carbon and N needs. Excess N in the form of ammonium (NH\u003csub\u003e4\u003c/sub\u003e) is released when microbial N needs are met and through microbial predation along the soil food web (Mooshammer et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Whalen et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Once in the soil, NH\u003csub\u003e4\u003c/sub\u003e tends to be rapidly oxidized by nitrifiers to nitrite (NO\u003csub\u003e2\u003c/sub\u003e) and then further oxidized to nitrate (NO\u003csub\u003e3\u003c/sub\u003e) (Kuypers et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with NH\u003csub\u003e4\u003c/sub\u003e and NO\u003csub\u003e3\u003c/sub\u003e being the main forms of N that plants utilize (Asibi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil Nmin rates have been extremely challenging to quantify because they are governed by multiple dynamic factors including the climate, litter quality, microbial access to substrates, and soil physical, chemical, and biological properties (Colman \u0026amp; Schimel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dungait et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mooshammer et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Rates vary across the growing season and are primarily controlled by soil temperature and moisture (Gon\u0026ccedil;alves \u0026amp; Carlyle, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1999b\u003c/span\u003e). Estimating Nmin rates in agroecosystems can be further complicated as management factors including crop rotation, N additions, and tillage also heavily influence Nmin rates (Carpenter-Boggs et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Mahal et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Silgram \u0026amp; Shepherd, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Soil Nmin rates can be quantified as gross N min, which is the total production of NH\u003csub\u003e4\u003c/sub\u003e, or net Nmin, which is the difference between gross Nmin and gross immobilization (Hart et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). While gross Nmin likely better reflects the soil N available for plant uptake, it can be challenging to quantify and requires expensive stable isotope methods. Therefore, more studies rely on net Nmin assays as an indicator of soil organic N turnover.\u003c/p\u003e \u003cp\u003eImproving synchrony of Nmin rates with plant N demand increases plant N use efficiency and reduces N losses and is therefore an important goal of N management in agroecosystems (Ma et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1999b\u003c/span\u003e). Achieving improved synchrony requires understanding the temporal dynamics of Nmin and plant N uptake. In temperate regions, Nmin rates start to increase in the spring as the soil thaws and rates can persist into the summer before declining again in the fall and winter (Cassman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Martinez-Feria et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Plant N uptake is not uniform throughout the growing season. Maize N uptake specifically follows a sigmoid shape where N uptake rates are low early in the season before dramatically increasing and peaking around R1, and thereafter slowing and eventually ceasing (Ma et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1999a\u003c/span\u003e). These seasonal patterns of Nmin and plant N uptake are highly dependent on climate and management conditions, including water and N availability. In irrigated regions, there is potential to improve synchrony through improved N fertilizer and irrigation management (Quemada \u0026amp; Gabriel, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBoth N and water availability affect Nmin with potentially interacting consequences, but are rarely explored together. Different soil-crop combinations do not always respond the same way to N additions making it difficult to know how much N to add (McDaniel et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Puntel et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, it is common for ample N, even to the point of N saturation, to be added to fields to ensure there is sufficient N for high yields (Grandy et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; McSwiney et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This fertilizer N is often added prior to or early in the growing season as producers are often limited in their ability to add N later in the season (Udvardi et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Plant N needs are partially driven by water availability (Farooq et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and wet-dry cycles are common due to variable precipitation patterns and limitations in irrigation water availability (Rudnick et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e). Therefore, N fertilizer applied pre-season can end up being insufficient to meet crop demands in wetter and more productive years, or, more commonly, due to water limitations in semi-arid regions that reduce plant growth and N uptake, can result in excess N added to fields. Both fertilizer N and water can also affect Nmin with studies showing direct and indirect (and positive or negative) effects. Therefore, improving N synchrony in water-limited systems requires a better understanding of Nxwater effects Nmin rates and plant N uptake dynamics over the growing season.\u003c/p\u003e \u003cp\u003eFertilizer N additions can directly affect Nmin by meeting the microbial community\u0026rsquo;s N demands and changing microbial activity. The stoichiometric decomposition theory predicts that if the microbial community is limited by N then adding N fertilizer will alleviate microbial N limitation, thus, increasing microbial activity and Nmin (Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e). On the other hand, the N mining theory suggests that if the microbial community is N limited then adding N fertilizer will suppress microbial activity and Nmin. This suppression occurs by meeting microbial N demands thereby eliminating the need to decompose soil organic matter in order to acquire N (Moorhead \u0026amp; Sinsabaugh, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, microbial communities in agricultural systems can vary in their relative degree of C- or N-limitation under different fertilizer and irrigation regimes (Lundquist et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Ye et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), leading to inconsistent support for either theory.\u003c/p\u003e \u003cp\u003eWater is another important resource that directly affects the soil microbial community, and, thus, Nmin. As soils dry, microbes experience water stress as their water potential declines leading to loss of cell turgor and eventually loss of cellular and metabolic function or death, contributing to reduced microbial activity and Nmin rates (Schimel, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, at the other end of the moisture spectrum, saturated soils have low oxygen levels, which also leads to decreased microbial activity and mineralization rates. Optimal soil moisture (close to field capacity) thus, is likely to support higher rates of N min than either water limitation or excess (Barakat et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, water and N availability indirectly affect Nmin by altering soil organic matter quantity, accessibility, and quality. Water and N availability affect both above and belowground biomass production (Flynn et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ord\u0026oacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Poffenbarger et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which in turn affects soil organic matter accumulation (N\u0026uacute;\u0026ntilde;ez \u0026amp; Schipanski, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sherrod et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Adequate moisture and N availability also increase litter quality (lower C:N ratio) (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; He \u0026amp; Dijkstra, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ning et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Increased soil organic matter quantity and quality should increase Nmin if the microbial community is not limited by other factors (Liu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mooshammer et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil water indirectly affects Nmin as the soil goes through dry and wet cycles. As the soil dries, diffusion of substrates and extracellular enzymes that break down these substrates can be disrupted as water films are disconnected. These diffusive limitations along with osmotic regulation are two main reasons Nmin is reduced under drier soils (Borken \u0026amp; Matzner, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, Nmin is carried out by a broad range of soil organisms, allowing it to be drought tolerant in some instances (Homyak et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, rewetting after a dry period can cause a \u0026ldquo;flash\u0026rdquo; of Nmin known as the \u0026ldquo;Birch Effect\u0026rdquo; (Borken \u0026amp; Matzner, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). After rewetting, both bacterial necromass and osmolytes becomes available. These N rich substrates can stimulate an increase in Nmin (Schimel, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the importance of soil Nmin for crop nutrition (Yan et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the overuse of N fertilizers (Battye et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al., 2015a), and the increased likelihood of water limitations in the Great Plains Region (Deines et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Derner et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we conducted a field experiment to better understand how different N and water availabilities alter soil N dynamics and plant responses. We hypothesized: (i) Nmin rates would increase early in season and be maintained until physiological maturity approaches, (ii) N and water additions would increase Nmin rates, even later in the season when plant N uptake is high, and (iii) N and water additions would increase maize N uptake and end of season grain production.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSite Description\u003c/p\u003e \u003cp\u003eThe experiment was conducted at the USDA-ARS Great Plains Research Center in Akron, Colorado (40\u0026deg;09 N, 103\u0026deg;09 W, 1,383 m). The dominant soil type is a Weld silt loam (fine, smectitic, mesic Aridic Argiustoll). The field site is within a semiarid climate with average monthly temperatures ranging from 23 ͦ C in the summer to \u0026ndash; 2.5 ͦ C in the winter, and average annual precipitation of 420 mm occurring primarily from April to September (Nielsen et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA field trial was established in 2021 and data was collected during the 2021 and 2022 growing seasons to investigate the effects of nitrogen and water availability on soil inorganic nitrogen dynamics in no-till continuous maize (\u003cem\u003eZea\u003c/em\u003e mays) agroecosystems. On May 6, 2021, a 104-day maturity maize hybrid (DKC 54\u0026ndash;64) was planted at 79,000 seeds ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and on May 10, 2022, the same maize hybrid was planted at 84,000 seeds per ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In 2019 and 2020, the field was managed as a fully irrigated maize system with limited N applications to reduce residual soil N levels for the start of this study. Prior to this, the field had been managed using no-till practices since prior to 1990 for multiple dryland or irrigated crops.\u003c/p\u003e \u003cp\u003eWe utilized a split-split plot experimental design with a total of 36 plots. There were 3 blocks with 2 irrigation treatments randomly assigned within each block and 6 nitrogen fertilizer treatments randomly assigned within each of those irrigation treatments. Irrigation treatments consisted of full water (100% ET), and limited water (70% ET), which is near average seasonal precipitation for the region (dryland conditions). The fertilizer treatments started at 22 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and increased in increments of 50\u0026ndash;51 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with the highest rate being 275 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e capturing low to excessive N fertilizer. For the current study we included only 3 N treatments, the N1 (Low N, 22 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), N5 (optimal N, 224 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and N6 (excess N, 275 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) treatments. Nitrogen fertilizer was applied as banded urea ammonium nitrate (UAN) and liquid ammonium phosphate with 22 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 44kg P ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e added at planting as starter and the remaining N side dressed at V6 to V7. The plots were the same from one year to the next, so treatments were repeated in the same locations for both growing seasons.\u003c/p\u003e \u003cp\u003eSoil Sampling\u003c/p\u003e \u003cp\u003eSoil samples were taken from both water treatments in the low, optimal, and excess N (N1, N5, \u0026amp; N6) plots approximately every two weeks from germination to approximately R3 and then again at physiological maturity as we expected the soil N pool to be less dynamic during the later reproductive stages. An additional soil sampling event took place when the final set of Nmin incubation tubes were removed to measure the residual soil inorganic N pool. At each sampling, four soil samples were taken per plot with a 1.75 cm diameter hand probe to a depth of 15 cm. Two samples were taken approximately 6\u0026ndash;8 cm away from each side of one of the two middle rows of the plot. Soil samples were composited in the field and stored in a cooler with ice packs before being transported to the lab for analysis. Once back in the lab duplicate subsamples were taken for gravimetric water content, two more subsamples were taken for mineral N extractions (T0 for N mineralization estimates), and two more were taken for enzyme activity assays.\u003c/p\u003e \u003cp\u003eN mineralization methods\u003c/p\u003e \u003cp\u003eTo measure net N mineralization, undisturbed soil cores were incubated in-situ using PVC tubes (DiStefano \u0026amp; Gholz, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) with ion exchange resin (IER) lysimeters (Susfalk \u0026amp; Johnson, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) attached at the bottom (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Briefly, 6 cm outer diameter PVC tubes were cut into 25 cm long pieces for incubating soil cores, and 7.5 cm long sections for the resin lysimeters. Each lysimeter contained two sandbags with an IER bag in the middle. The sandbags and IER bags were held in place with cheese cloth and secured with zip ties. To prevent the sand and IER bags from falling out of the bottom of the lysimeter, a 1 cm tall section of 5 cm outer diameter PVC wrapped in cheese cloth was inserted and glued to the bottom of the lysimeter PVC. The sandbags had 25 g of sand each and the resin bags had 10 g of IER beads. Due to supply chain issues, in 2021 Lewatit\u0026reg; NM 60 (Thermo Fisher Scientific) IER was used in 2021, and AmberLite\u0026reg; MB20 (Simga-Aldrich) IER was used in 2022. The bags were triple rinsed with DI water and stored at 4 ͦ C until being brought to the field in a cooler with ice for installation. A lathe machine was used to reduce the thickness of the bottom of the incubation PVC tube and the top of the PVC lysimeter. Reducing the thickness of the PVC allowed the two pieces to slide over each other and be joined together with a screw.\u003c/p\u003e \u003cp\u003eTwo incubation\u0026thinsp;+\u0026thinsp;lysimeter PVC tubes were installed in low, optimal, and excess N plots in both water treatments on the same day that soil sampling occurred. The PVC tubes straddled one of the middle rows of maize in the plots and were approximately 6\u0026ndash;8 cm off the row. Cylinders were installed using a drop hammer to hammer the PVC 15 cm into the soil. The cylinders containing the top 15 cm of soil were carefully removed to avoid spilling and soil and the resin lysimeter bottoms were attached. A longer piece of PVC was then hammered into the hole to remove soil between 15\u0026ndash;22.5 cm so that when the soil core plus lysimeter were returned to the hole for the incubation period the top surface of the soil inside the core was flush with the soil surface. The open-top soil cores and resin bags incubated in the field for approximately 2 weeks, except for the final incubation period which lasted about 1 month. After the incubation the tubes were removed from the field and transported back to the lab in a cooler with ice. Once back in the lab resin bags were removed and frozen until analysis. The soil was removed from each PVC tube, homogenized, and two subsamples were taken from each PVC tube: one for gravimetric water content, and one for mineral N extractions.\u003c/p\u003e \u003cp\u003eGravimetric water content was measured for both the initial soil samples and incubated soils by oven-drying a 12 g subsample of soil at 105 ͦ C for 48 hours and then reweighing it. Mineral N extractions were performed on the initial soil samples and incubated soils immediately after they got to the lab by shaking approximately 12 g of field-moist soil in 100 mL of 2M KCl for 1 hour on a reciprocal shaker. After shaking, the samples were filtered through Whatman filter paper No. 1, and stored at \u0026ndash; 20 ͦ C until analysis. Extractions were also performed on the previously frozen resin bags following the same procedure (using resin bag instead of soil).\u003c/p\u003e \u003cp\u003eChemical analysis for inorganic N\u003c/p\u003e \u003cp\u003eMineral N KCl extracts (both initial and incubated soil as well as IER) were analyzed colorimetrically on a microplate reader (Cytation 5, BioTek Instruments, Winooski, VT) to determine nitrate (NO\u003csub\u003e3\u003c/sub\u003e-N) and ammonium (NH\u003csub\u003e4\u003c/sub\u003e-N) concentrations. The Griess method using vanadium (III) chloride (VCl\u003csub\u003e3\u003c/sub\u003e) was used to determine NO\u003csub\u003e3\u003c/sub\u003e-N concentrations in the extracts (Doane \u0026amp; Horw\u0026aacute;th, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and NH\u003csub\u003e4\u003c/sub\u003e-N was determined via the Berthelot method using salicylate-hypochlorite and citrate (Sims et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). All inorganic N values are reported as the sum of NO\u003csub\u003e3\u003c/sub\u003e-N\u0026thinsp;+\u0026thinsp;NH\u003csub\u003e4\u003c/sub\u003e-N.\u003c/p\u003e \u003cp\u003eNet nitrogen mineralization was calculated using Eq.\u0026nbsp;1:\u003c/p\u003e \u003cp\u003eEquation 1: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Net{N}_{{min}}=(\\left(I{N}_{t}+I{N}_{r}\\right)-I{N}_{0})/D\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere Net N\u003csub\u003emin\u003c/sub\u003e refers to net nitrogen mineralization (mg N day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e ), IN\u003csub\u003et\u003c/sub\u003e refers to inorganic N from the incubated soil samples, IN\u003csub\u003er\u003c/sub\u003e refers to inorganic N from the IER, IN\u003csub\u003e0\u003c/sub\u003e refers to inorganic N from the initial soil sample, and D refers to the number of days of the incubation. Soil inorganic N was converted to kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e based on the extractable N, the weight of the dry soil sample, the depth of the soil sample, and the bulk density of the soil. Bulk density data from a previous experiment at this field was used for this calculation. Briefly, 2 replicate 5.1 cm soil cores were taken from each block to a depth of 0\u0026ndash;15 cm. The soil cores were oven dried at 105 ͦ C for 48 hours and the dry weight and volume was used to determine bulk density. Inorganic N from the IER was converted to kg ha\u003csup\u003e\u0026minus;1\u003c/sup\u003e using the same calculations as for the soil core and the assumption that the N in the IER was leached from that same volume of soil.\u003c/p\u003e \u003cp\u003eSoil Enzyme Activity\u003c/p\u003e \u003cp\u003ePotential soil enzyme activity was measured for two enzymes related to nitrogen acquisition; namely NAG (\u003cem\u003eβ\u003c/em\u003e-1,4-N-acetyl-glucosaminidase), which degrades chitin, and LAP (L-leucine amino peptidase), which degrades proteins. We analyzed two lab replicates at field moisture conditions the day after sampling following the protocol outlined in (Saiya-Cork et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In 2021, soil slurries were made by homogenizing 1 g of each sample in approximately 60 ml of 50 mM, pH 8.1, sodium acetate buffer. In 2022, the same protocol was used, but a tris buffer was used instead because we determined it was more appropriate for our alkaline soil conditions. After homogenizing, the slurry was pipetted into black, 96-well microplates and mixed with substrate. Slurries were also mixed with buffer only or with standards (10 mM 4 methylumbelliferone, or 7-amino-4methyl coumarin) as negative quenching controls. Samples were incubated for 4 h at 25 ͦ C in the dark, and the fluorescence was read on a microplate reader (Cytation 5, BioTek Instruments, Vermont, USA) at 365 nm excitation and 450 nm emission wavelengths.\u003c/p\u003e \u003cp\u003eGrain Yield and Plant Nitrogen Accumulation\u003c/p\u003e \u003cp\u003eAlong with the soil nitrogen measurements, aboveground plant N uptake rate, end of season aboveground nitrogen accumulation, and end of season grain yield were measured. Plant N uptake rate was determined by collecting 5 whole plants from all low, optimal, and excess N plots. In 2021 samples were collected at V3, V5, V11, R1, and R6. In 2022 samples were collected at V5, V12, R1, and R6. The samples from R6 were used to determine end of season total N uptake. Unfortunately, no data was available for V11 in 2021 and V5 in 2022. The V11 samples became moldy prior to being dried and analyzed making them unusable. The V5 samples were misplaced and never found before analysis occurred. Plants were separated into leaves, stalks, grain, and cobs, dried at 60 ͦ C and weighed. Plant samples were then ground, homogenized, and a subsample was analyzed for total N concentration using a dry combustion elemental analysis (LECO Tru-SPEC, St. Joseph, MI, USA). Plant N uptake at each growth stage was calculated by multiplying the oven dry biomass of each plant component by its corresponding N concentration and summing all the aboveground parts. Plant N uptake rate was estimated by finding the change in plant N content between growth stages divided by the number of days between growth stages. Due to missing samples from both growing seasons, we combined the two years to estimate plant N uptake at different growth stages. Grain yield was determined at R6 for each year. The grain was separated from the ears, weighed, and adjusted to 15.5% moisture. Plant N uptake rates, total N uptake at the end of the season, and grain yields were converted to a per hectare basis using the harvested area.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eA linear mixed effects model was used for all analyses to determine how the response variables (extractable soil inorganic N, net Nmin, enzyme activity, plant N uptake (total and rate), grain yield) were affected by irrigation treatment, nitrogen fertilizer rate, year, sampling event (where applicable), and their interactions. The split plot design and repeated measures analyses were integrated into the mixed model structure through the inclusion of block and its interactions with irrigation and N rate as random factors. The predictors were treated as categorical variables with fixed effects. Response variables were checked for normality, equal variance, and homogeneity. Inorganic soil N and enzyme activity were log-transformed to meet model assumptions. An ANOVA with the \"Kenward-Roger\" degrees of freedom adjustment was used to assess the significance of the fixed effects and their interactions within the context of this mixed-effects model. Post hoc means comparisons (Tukey) were conducted to assess treatment differences when treatment effects were significant within the ANOVAs. Analyses were performed in R version 4.2.2 using the lme4, lmeTest, and emmeans packages (R Core Team, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen looking at soil N dynamics we wanted to understand the response variable dynamics over the entire season as well as our treatment effects, which were not implemented until V6 or V7. Therefore, we have 2 separate analyses, one which includes the entire growing season, and the other with only the last 3 sampling events (3 incubations and 4 soil samples) thatoccurred after our treatments were officially initiated, and cover before, during, and after peak plant N uptake. Both analyses used the same statistical approach as described above.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePlant N uptake rates\u003c/p\u003e \u003cp\u003ePlant N uptake rates varied across the season and increased with N fertilizer rate, especially later in the season, resulting in a Growth stage x N fertilizer interaction (Fig.\u0026nbsp;2A, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For all N fertilizer treatment groups, plant N uptake rates increased substantially from vegetative stage 3 to vegetative stage 5 (V3 and V5) and peaked at reproductive stage 1 (R1) after which plant N uptake rates declined. Plant N uptake rates increased from V5 to R1 by 29%, 349%, and 530% for the low, optimal, and excess N rates respectively.\u003c/p\u003e \u003cp\u003ePlant N uptake rates also varied by irrigation treatment later in the growing season resulting in a Growing season x Irrigation interaction (Fig.\u0026nbsp;2A, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Plant N uptake rates were 32% higher in the limited water than in the full water treatment at V12. However, the full water treatment had greater plant N uptake rates during reproduction than the limited water treatment (79% and 49% higher at R1 and R6, respectively).\u003c/p\u003e \u003cp\u003eSeasonal dynamics of Inorganic N and Net N min\u003c/p\u003e \u003cp\u003eExtractable inorganic N (EIN) varied across the season from germination through physiological maturity (PM) and tended to be higher in the limited water treatments, especially later in the growing season of 2022. This resulted in a three-way Irrigation x Sampling Event x Year interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;.031). The Sampling Event x Year interaction was consistent whether analyzing the full season of EIN data or just the sampling events after treatment initiation (tassel (VT) \u0026ndash; PM), (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The EIN nearly doubled from the 2021 growing season to the 2022 growing season, and EIN tended to be lower at physiological maturity. When averaged over both growing years and treatments, EIN decreased by ~\u0026thinsp;22.5% at each sampling event from V10 \u0026ndash; PM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the entire growing season, net Nmin rates varied within and across the two growing seasons and changed with N fertilizer rate, resulting in a N fertilizer x Sampling Event x Year interaction (Fig.\u0026nbsp;2B, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.005). Net Nmin rates ranged from strong net immobilization to strong net mineralization with the highest rates of mineralization typically occurring earlier in the growing season before the main application of N fertilizer and when plant N demand was lower (Fig.\u0026nbsp;2). When considering only VT \u0026ndash; PM, net Nmin also varied across and within each growing season and exhibited a strong Sampling Event x Year interaction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When averaged across both growing seasons and treatments, the net Nmin rates declined from close to 0.3 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at VT to essentially 0 (-0.004 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at physiological maturity (Fig.\u0026nbsp;2B).\u003c/p\u003e \u003cp\u003eN x Water effects on Inorganic N and N min\u003c/p\u003e \u003cp\u003eOver the course of the entire season EIN exhibited an N fertilizer x irrigation interaction where EIN values were nearly identical at the low N treatment for both water treatments but were much higher in the limited water for the optimal and excess N rates (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.018). We found a similar trend when looking at VT \u0026ndash; PM. The EIN was 27%, 141%, and 80% greater in the limited water treatment compared to the full water treatment for the low, optimal, and excess N rates respectively (Fig.\u0026nbsp;4A, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.068).\u003c/p\u003e \u003cp\u003eOver the entire season net Nmin was not affected by irrigation, N fertilizer, or an N fertilizer x irrigation interaction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), but when focusing on VT \u0026ndash; PM we found an N fertilizer x irrigation interaction (Fig.\u0026nbsp;4B, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.034). For the full water treatment, net Nmin was highest in the low N treatment (0.524 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and declined by 67% and 85% as the N fertilizer rate increased to optimal N and excess N, respectively. The limited water treatment had a different trend with virtually no Nmin occurring in the low N and optimal N, but 0.34 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the excess N treatment (Fig.\u0026nbsp;4B).\u003c/p\u003e \u003cp\u003eN x Water Effects and seasonal dynamics of Soil Enzyme Activity\u003c/p\u003e \u003cp\u003eBoth LAP and NAG enzyme activity increased with N rate regardless of irrigation treatment (Fig.\u0026nbsp;5, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.018 \u0026amp; 0.008 respectively). Nitrogen fertilizer increased LAP activity by 39% and 22% in the optimal N and excess N treatments compared to the low N treatment, respectively. Soil NAG activity increased by 43% for both the optimal and excess N treatments compared to the low N treatment. Enzyme activity varied across the growing season and years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When focusing on LAP specifically, we found that average end of season (between R1 and R3) LAP activity was positively correlated with end of season plant N uptake (cor\u0026thinsp;=\u0026thinsp;0.429, p\u0026thinsp;=\u0026thinsp;0.009, Fig.\u0026nbsp;6A). There was no correlation between NAG and plant N uptake (cor\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.126, Fig.\u0026nbsp;6B).\u003c/p\u003e \u003cp\u003eN x Water Effects on Cumulative Plant Nitrogen Uptake and Yields\u003c/p\u003e \u003cp\u003eTotal plant N uptake was higher in the 2022 growing season compared to the 2021 growing season (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.077) with average N uptake being 131 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 147 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the 2021 and 2022 growing season respectively. Across both years, plant N uptake increased independently with increases in both N fertilizer and water availability (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 \u0026amp; 0.037). Fertilizer N increased plant N uptake by 146% and 169% when comparing the low N treatment to the optimal and excess N treatments, respectively. Full water availability increased N uptake by 26% relative to the limited water availability (Fig.\u0026nbsp;7A).\u003c/p\u003e \u003cp\u003eThe effect of N fertilizer on grain yield was dependent on irrigation treatment resulting in an N fertilizer x irrigation interaction (Fig.\u0026nbsp;7B, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.027). Under full water, yields increased by 68% and 78% for the optimal and excess N treatments relative to the low N treatment, respectively. In the limited water treatment, grain yields increased by only 36% and 32% as N fertilizer rates increased (Fig.\u0026nbsp;7B). There was no difference between grain yields across the two growing seasons (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.115).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a field experiment to better understand how N fertilizer and irrigation affect soil Nmin dynamics and plant responses. We found that plant N uptake peaked around R1 and increased with additions of water and fertilizer. In contrast, net Nmin rates peaked relatively early in the growing season, often before treatments were initiated, and steadily declined as physiological maturity approached. This resulted in asynchrony between plant N demands and Nmin especially with increased N and water availability. We hypothesized that both N fertilizer and irrigation would independently increase net Nmin rates, but instead we found that net Nmin rates were governed by an N fertilizer x irrigation interaction. With full water availability, Nmin was highest in the low N fertilizer treatment, while under limited water availability, N min was highest under excess N fertilization.\u003c/p\u003e \u003cp\u003eAn important consideration is that we measured net N min, which can differ substantially from gross N min rates (Booth et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Plant N uptake and soil N enzyme activity data suggest that gross N min dynamics may have differed from our net N min measurements. Enzyme activity, which can be an indicator of gross mineralization rates (Elrys et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), increased with N fertilizer and was not effected by irrigation regime. Plant N uptake, an integrated indicator of gross soil N availability from N min and N fertilizer, was higher as both N additions and water availability increased. This supported our final hypothesis that plant N accumulation and grain yields would increase with N and water availability. Increased N uptake did not translate directly to increased grain yields in the limited water treatment. Therefore, yields exhibited an N fertilizer x irrigation interaction. In the full water treatment grain yields were highest at the excess N rate, and for the limited water treatment the optimal N treatment led to the highest yield.\u003c/p\u003e \u003cp\u003ePlant N uptake rates and Net Nmin over the entire season:\u003c/p\u003e \u003cp\u003eWe found that plant N uptake rates were higher later in the growing season, often peaking at R1, and were higher with increased N fertilizer and water. Interestingly, with low N fertilizer availability, N uptake rates showed much less variation across plant growth stages. Our findings are in line with the literature as many studies have found that maize N uptake peaks around R1 and increases with N and water availability (Guo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1999a\u003c/span\u003e; Osterholz et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNet Nmin rates over the entire season were not affected by N fertilizer, irrigation, or an N fertilizer x irrigation interaction, and rates peaked relatively early in the growing season resulting in asynchrony between Nmin rates and plant N demand. Although this decline in Nmin after the initiation of N fertilizer could suggest that fertilizer addition suppressed Nmin, we also found a decline in the low N treatment, suggesting the decline was due to other temporal dynamics. Many studies have found Nmin rates vary within a growing season and across multiple growing seasons (Loecke et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Maysson M. Mikha et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Studt et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and our findings of Nmin rates being higher in the earlier vegetative stages than the latter is in line with other studies (Ma et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1999b\u003c/span\u003e; Mahal et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There is often a flush of Nmin early in the season as soil temperatures rise and microbial activity increases (Cassman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Miller \u0026amp; Geisseler, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This can lead to asynchrony between soil N supply and plant N needs contributing to high springtime nitrogen losses, specifically nitrate leaching (Danalatos et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Martinez-Feria et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Variations of Nmin rates across and within growing seasons could be due to root exudation, precipitation, temperature, and the soil inorganic pool size (Gon\u0026ccedil;alves \u0026amp; Carlyle, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1999b\u003c/span\u003e; Mahal et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Somewhat surprisingly, climate variables (soil moisture or temperature) did not explain the seasonal patterns in Nmin rates. While rewetting soil after a dry period can cause a flash of Nmin (Fierer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Xiang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), high magnitude perturbations in soil moisture can negatively affect Nmin rates (Barakat et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the current study, it is possible that soil moisture was more consistent earlier in the season when temperatures and plant water use was lower. As temperatures and plant water use increased later in the season, more extreme soil drying-rewetting cycles could have also occurred, thus, decreasing Nmin rates.\u003c/p\u003e \u003cp\u003ePost fertilizer soil N dynamics\u003c/p\u003e \u003cp\u003eMaize N uptake fluctuates throughout the growing season with a relatively short window of high N uptake (Udvardi et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The onset of rapid N accumulation starts around V5, the midpoint is around V12, and peak N uptake happens near R1 or R2 before declining again (Mahal et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Osterholz et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, we wanted to focus on the effects of N fertilizer and irrigation on soil N dynamics before, during, and after peak N uptake.\u003c/p\u003e \u003cp\u003eRepeated fertilizer additions, as done here, have shown to increase total soil N over multiple seasons (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dhillon et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) including in the inorganic N pool (Fujita et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Grandy et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Our results of higher EIN in the 2022 growing season compared to the 2021 growing season are consistent with this literature. A decline in EIN as the growing season progressed, as found here, was also consistent with that observed by others as plant N uptake increased (Mahal et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Finally, increased EIN with water deficits due to reduced plant N uptake is also consistent with previous studies (He \u0026amp; Dijkstra, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current study, Net Nmin rates varied within each growing season and across the two growing seasons post fertilizer application (VT \u0026ndash; PM). We hypothesized that high Nmin rates would be maintained later in the season when plant N uptake was high, especially with warmer soil temperatures and under full irrigation. Although Nmin rates peaked early in the growing season, there was still Nmin occurring when plant N uptake was highest. Consequently, Nmin and subsequent plant N uptake during grain fill can be beneficial as leaf N content and photosynthetic rates are maintained leading to higher yields (Osterholz et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Subedi \u0026amp; Ma, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The methods used in this study do not allow us to determine how much plant accumulated N came from Nmin, which would require an additional technique, such as a \u003csup\u003e15\u003c/sup\u003eN tracer study, to better understand the contribution of soil N turnover to plant N uptake under different nitrogen fertilizer and water rates.\u003c/p\u003e \u003cp\u003eBased on the results from our study, we have to reject our hypothesis that N fertilizer and irrigation would both increase net N min rates. Instead, our results show an N fertilizer x irrigation interactive effect on Nmin rates. When looking at N fertilizer and water alone we see that N fertilizer additions yield different responses in the literature using varying methods. Additions of N fertilizer have increased Nmin rates relative to zero N (Biau et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1999b\u003c/span\u003e; Maysson M. Mikha et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), though the highest N fertilizer rates do not always yield the highest Nmin rates (Al-kaisi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fujita et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ouyang \u0026amp; Norton, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nitrogen fertilizer additions can also suppress Nmin (Carpenter-Boggs et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Mahal et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u0026ldquo;destabilize\u0026rdquo; Nmin where N fertilizer has both the highest and lowest Nmin rates (Studt et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and yield different responses based on the cropping system (Breza et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When considering water alone, Nmin rates tend to be highest when there is adequate soil moisture (Barakat et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At the global scale, Nmin rates increase with precipitation (Colman \u0026amp; Schimel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Elrys et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and irrigation tends to increase Nmin rates (Val\u0026eacute; et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). While Nmin can be fairly drought tolerant due to flashes of Nmin after rewetting dry soils and the broad range of microbes carrying out Nmin (Homyak et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Y. Wang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the flashes cannot always compensate for the reduction in mineralization that occurred during the dry period (Maysoon M Mikha et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and high magnitude perturbations of soil moisture are adverse to Nmin (Barakat et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are few Nitrogen x Water experiments that report Nmin rates, especially in agronomic field settings. However, the N fertilizer x irrigation interaction on Nmin that we found is consistent with other Nitrogen x Water experiments (Wang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the current study the N fertilizer x irrigation interaction observed could have been due to the differing effects N fertilizer and irrigation have on root growth and root exudation (Flynn et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ord\u0026oacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). If root exudation and root growth, especially fine roots that turnover quickly, increased in the limited water excess N treatment relative to other limited water treatments, Nmin rates could have increased (Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e; Jilling et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Given that N fertilizer x irrigation studies on Nmin are limited, further research is needed.\u003c/p\u003e \u003cp\u003eBoth soil N cycling enzyme activities assayed in this study (LAP and NAG) increased with N fertilizer and were not affected by irrigation (Fig.\u0026nbsp;5). Water deficits and retirement of irrigated lands have shown to decrease microbial biomass and enzyme activity (Flynn et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; N\u0026uacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, some soil microbial communities can also be quite drought tolerant (Fierer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Homyak et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which may have been the reason there was no irrigation effect in the current study. The effect of N additions on the direction and magnitude of LAP and NAG is variable in the literature with positive (Chen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fujita et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Saiya-Cork et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), neutral (Jian et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and time x N fertilizer interactions being reported (Grandy et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Microbial biomass and composition can elicit different responses to N additions (Geisseler \u0026amp; Scow, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Treseder, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and depend on the ecosystem, N fertilizer rates, and duration of the study (Jia et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the current study, N additions increased both LAP and NAG activity which is with other studies (Chen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNet Nmin is the difference between two opposite and simultaneous microbial processes; namely gross mineralization and gross immobilization, and the net N left over from these processes is available for plant use (Liu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Incubating soil cores in the field is a common method for measuring net Nmin and provides some advantages over lab incubation methods and other field methods (Hart et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). However, there are some limitations associated with our method of measuring net Nmin. First, we excluded living plant roots which influence N cycling via rhizodeposition (Meier et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, Net Nmin is not well correlated with gross mineralization especially as the duration of the incubation increases as most soil inorganic N pools have a turnover time of ~\u0026thinsp;1 day (Booth et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While gross mineralization (total production of NH\u003csub\u003e4\u003c/sub\u003e) measurements can be costly, time consuming, and complicated relative to other methods, some argue that is a better measure of soil N supplying capabilities especially since plants can compete effectively against soil microbes for inorganic N and do not need to wait for \u0026ldquo;leftover net N\u0026rdquo; (Elrys et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osterholz et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nevertheless, net Nmin is still commonly used and is considered a good \u0026ldquo;index\u0026rdquo; of plant available N (Hart et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schimel \u0026amp; Bennett, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil enzyme activities are closely correlated to gross N mineralization, so increased LAP and NAG activity could suggest increased N turnover (Elrys et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our results then suggest that N fertilizer may have enhanced N turnover and, thereby, gross N min that was not reflected by our net N min measurements. If true, this would support our hypothesis that N fertilizer enhanced gross N min. Depolymerization of N containing compounds is considered a rate-limiting step for soil N cycling as depolymerization makes N bioavailable (Geisseler et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mooshammer et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schimel \u0026amp; Bennett, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). LAP and NAG, are extracellular enzymes excreted by soil microorganisms to depolymerize N containing substrates specifically proteins and chitin (Jian et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Depolymerization of proteins is especially a rate-limiting step as proteins account for 60% of the organic N in plant and microbial cells meaning LAP activity should have a greater effect on soil N cycling than NAG (Chen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Given that net Nmin and gross Nmin are not well correlated, it is not surprising that enzyme activity and net Nmin showed different responses to the treatments.\u003c/p\u003e \u003cp\u003ePlant N uptake and Grain Yield\u003c/p\u003e \u003cp\u003eWe hypothesized that both irrigation and N fertilizer would lead to increased N uptake, and based on our results, we can confirm this hypothesis. In the current study, N uptake increased under the full water treatment and as N fertilizer rates increased (Fig.\u0026nbsp;7A). It is well documented that water limitations reduce N uptake (Djaman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hammad et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; He \u0026amp; Dijkstra, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Plant N uptake is reduced under water limitations due to decreased transpiration (Rudnick et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e), and therefore mass flow (Lambers et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), as well as decreased soil N supply from decreased Nmin rates (Elrys et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maysoon M Mikha et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Plant N uptake can still be relatively high due to drought increasing root biomass (Flynn et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hammad et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and expression of root genes associate with N uptake (H. Wang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, shoot growth potential is a driver of plant N uptake, thus reductions of aboveground growth limit N accumulation (Peng et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Y. Wang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nitrogen fertilizer has been shown to increase plant N uptake (Barbieri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dordas \u0026amp; Sioulas, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and sufficient N fertilizer can increase shoot biomass, root biomass, and evapotranspiration in maize (Ord\u0026oacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rudnick et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e) all of which can increase N uptake. In the current study N fertilizer increased the soil inorganic N pool (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; 4A) and soil N-acquiring enzyme activity (Fig.\u0026nbsp;5) which could have increased N availability for the plants (Chen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Geisseler et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Plant N uptake is an integrated indicator of soil N availability and we found that plant N uptake was positively correlated with enzyme activity around peak N uptake (Fig.\u0026nbsp;6A) suggesting enzyme activity did in fact increase bioavailable N.\u003c/p\u003e \u003cp\u003eWe hypothesized that N fertilizer and irrigation would increase grain yields. We found this to be true, but the effect of N fertilizer on grain yield was dependent on water limitations. Under water limitation, grain yields did not differ between the optimal N fertilizer treatment and the excess N treatment. In the current study it is possible that N additions increased vegetative growth and therefore transpiration which lead to insufficient water during grain fill for the limited water treatments (Rudnick et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). This could explain why the limited water, optimal and excess N treatments doubled N uptake relative to the full water low N treatment, but still had lower yields. This highlights the importance of water resources in the Great Plains Region of the U.S. It is well documented that plant growth and yields decline with water limitations, but the extent of the decline depends on timing and severity of water limitations (Comas et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Westgate \u0026amp; Boyer, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Plant response to water and N is greater than its response to each resource in isolation, and maximum plant growth is achieved when both resources are non-limiting (Quemada \u0026amp; Gabriel, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is important to properly manage resources such as N fertilizer in order to optimize economic return and avoid environmental consequences (Zhang et al., 2015a), and here we find that N fertilizer rates need to be adjusted based on water availability and yield potential. Finding the optimal N rate is challenging (McDaniel et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Puntel et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), especially when water availability is highly variable as it is in the Great Plains (Derner et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rudnick et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Schlegel et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Being able to adjust N fertilizer throughout the growing season based on water availability and plant N needs may help increase economic returns and resource use efficiency (Quemada \u0026amp; Gabriel, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we found an apparent asynchrony between Nmin rates and peak plant N demand regardless of N fertilizer and irrigation treatment. Improving synchrony between soil N supply and plant N demand is an important goal for agroecosystems as it improves yields and sustainability. When focusing on the later part of the growing season when plant N uptake is higher we found that Nmin rates were governed by an N fertilizer x irrigation interaction. Further research is necessary to understand what is underlying this interaction, as well as understanding whether gross N min patterns follow the net Nmin patterns we found. Plant N uptake was high when N fertilizer rates were high, but yields were still low when water was limiting. The effect of N fertilizer on grain yields was dependent on water availability and too much N was marginally detrimental to grain yields when water was limiting. Being able to adjust N fertilizer based on water availability and plant N needs, especially throughout the season if possible, may help improve economic returns, grain yields, and reduce N losses simultaneously.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTD collected and analyzed the data and wrote the manuscript. All authors contributed to the study design, data interpretation, and reviewing and editing the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Paul Campbell and Cody Hardy, at the USDA-ARS in Akron for their help managing field operations; Stacey Poland, Tyler Pokoski, and Tyler Untiedt and the team at the USDA-ARS for their help with data collection; Dr. Bo Stevens and Josh Wenz and the team at the USDA-ARS in Fort Collins for their help with data collection and processing; Brittani Meis, Oliver Hoffman, and the Agroecology Lab at Colorado State University for their help with sample collection and processing; and Dr. Ann Hess and the Franklin A. Graybill Statistics and Data Science Laboratory at Colorado State University for statistical consulting. This work was supported with funds from USDA-ARS NP211 project 3012-13210-001-000D. The USDA is an equal opportunity provider and employer. TD was additionally supported by funds from Los Alamos National Lab DR Program, award no. 89233120SNA00.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be available at dryad.org at the time of publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-kaisi, M. M., Kruse, M. L., Sawyer, J. E., \u0026amp; State, I. (2008). Effect of Nitrogen Fertilizer Application on Growing Season Soil Carbon Dioxide Emission in a Corn \u0026ndash; Soybean Rotation. \u003cem\u003eJournal of Environment Quality\u003c/em\u003e. https://doi.org/10.2134/jeq2007.0240\u003c/li\u003e\n\u003cli\u003eAsibi, A. E., Chai, Q., \u0026amp; Coulter, A. (2019). \u003cem\u003eMechanisms of Nitrogen Use in Maize\u003c/em\u003e. 1\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eBarakat, M., Cheviron, B., \u0026amp; Angulo-jaramillo, R. (2016). 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Global meta-analysis of nitrogen fertilizer use efficiency in rice, wheat and maize. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e338\u003c/em\u003e, 108089. https://doi.org/https://doi.org/10.1016/j.agee.2022.108089\u003c/li\u003e\n\u003cli\u003eZhang, Xin, Davidson, E. A., Mauzerall, D. L., Searchinger, T. D., Dumas, P., \u0026amp; Shen, Y. (2015a). Managing nitrogen for sustainable development. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e528\u003c/em\u003e(7580), 51\u0026ndash;59. https://doi.org/10.1038/nature15743\u003c/li\u003e\n\u003cli\u003eZhang, Xinyu, Dong, W., Dai, X., Schaeffer, S., Yang, F., Radosevich, M., Xu, L., Liu, X., \u0026amp; Sun, X. (2015b). Responses of absolute and specific soil enzyme activities to long term additions of organic and mineral fertilizer. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e536\u003c/em\u003e, 59\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eZhu, S., Vivanco, J. M., \u0026amp; Manter, D. K. (2016). Nitrogen fertilizer rate affects root exudation, the rhizosphere microbiome and nitrogen-use-efficiency of maize. \u003cem\u003eApplied Soil Ecology\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e, 324\u0026ndash;333. https://doi.org/https://doi.org/10.1016/j.apsoil.2016.07.009\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Analysis of variance summary statistics for plant nitrogen uptake rates (Fig. 2A). Bold values indicate significance at p \u0026lt; 0.05.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"398\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003ePlant Nitrogen Uptake Rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.531486146095716%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource of Variance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3727959697733%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.861460957178842%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.6095717884131%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.624685138539043%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.531486146095716%\" valign=\"bottom\"\u003e\n \u003cp\u003eIrrigation (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3727959697733%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.861460957178842%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.6095717884131%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.624685138539043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.531486146095716%\" valign=\"bottom\"\u003e\n \u003cp\u003eN Fertilizer (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3727959697733%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.861460957178842%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.6095717884131%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.624685138539043%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.531486146095716%\" valign=\"bottom\"\u003e\n \u003cp\u003eGrowth Stage (GS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3727959697733%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.861460957178842%\" valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.6095717884131%\" valign=\"bottom\"\u003e\n \u003cp\u003e78.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.624685138539043%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.531486146095716%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3727959697733%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.861460957178842%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.6095717884131%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.624685138539043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.531486146095716%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x GS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3727959697733%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.861460957178842%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.6095717884131%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.624685138539043%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.531486146095716%\" valign=\"bottom\"\u003e\n \u003cp\u003eN x GS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3727959697733%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.861460957178842%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.6095717884131%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.624685138539043%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Analysis of variance summary statistics for soil extractable inorganic nitrogen (EIN) for the entire season (Fig. 3), net nitrogen mineralization (Nmin) rates over the entire season (Fig. 2B), EIN from approximately tassel (VT) \u0026nbsp;\u0026ndash; physiological maturity (PM) (Fig. 4A), Net Nmin rates from approximately VT - PM (Fig. 4B), soil N-acquiring enzyme activity leucine amino peptidase (LAP) from approximately VT - PM (Fig. 5A) and\u0026nbsp;b-1,4-N-acetyl-glucosaminidase (NAG) from\u0026nbsp;approximately VT - PM\u0026nbsp;(Fig. 5B). Bold values indicate significance at p \u0026lt; 0.05.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"632\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.22784810126582%\" colspan=\"5\"\u003e\n \u003cp\u003eFull Season soil extractable inorganic N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.272151898734177%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"37.5%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eFull Season net N mineralization rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource of Variance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eIrr (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN Fertilizer (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eSampling Event (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e156\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e18.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eYear (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e156\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n 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width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n 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width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e156\u003c/strong\u003e\u003c/p\u003e\n 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valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x N x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n 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\u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN x S x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e156\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x N x S x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.22784810126582%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003eSoil extractable inorganic N (VT - PM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.272151898734177%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"37.5%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eNet N Mineralization (VT - PM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource of Variance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eIrr (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN Fertilizer (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eSampling Event (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eYear (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e61.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.091\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN x S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eS x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.626582278481013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.60126582278481%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eLeucine Amino Peptidase (LAP)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.272151898734177%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"37.5%\" colspan=\"4\"\u003e\n \u003cp\u003eb-1,4-N-acetyl-glucosaminidase (NAG)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource of Variance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eIrr (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN Fertilizer (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eSampling Event (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eYear (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e182.20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e184.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.92\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN x S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eW x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eN x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.59083728278041%\" valign=\"bottom\"\u003e\n \u003cp\u003eS x Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.372827804107425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.265402843601896%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.794628751974724%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.846761453396525%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.320695102685624%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.636650868878357%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Analysis of variance summary statistics end of season aboveground plant N uptake (Fig. 7A) and end of season grain yield adjusted to 15.5% moisture content (Fig. 7B). Bold values indicate significance at p \u0026lt; 0.05.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"657\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.99088145896656%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal Aboveground Plant Nitrogen Uptake\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.103343465045593%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"38.90577507598784%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eEnd of Season Grain Yield\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.188449848024316%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource of Variance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.206686930091186%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.662613981762918%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.103343465045593%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.182370820668693%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.188449848024316%\" valign=\"bottom\"\u003e\n \u003cp\u003eIrrigation (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.206686930091186%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.662613981762918%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.103343465045593%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.182370820668693%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.188449848024316%\" valign=\"bottom\"\u003e\n \u003cp\u003eN Fertilizer (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.206686930091186%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.662613981762918%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e76.81\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.103343465045593%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.182370820668693%\" valign=\"bottom\"\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.182370820668693%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrient-cycling-in-agroecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fres","sideBox":"Learn more about [Nutrient Cycling in Agroecosystems](http://link.springer.com/journal/10705)","snPcode":"10705","submissionUrl":"https://submission.nature.com/new-submission/10705/3","title":"Nutrient Cycling in Agroecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Net nitrogen mineralization, water limitation, water deficit, soil N-acquiring enzymes, β-1,4-N-acetyl-glucosaminidase (NAG), L-leucine amino peptidase (LAP), nitrogen x water interaction, nitrogen cycling, plant nitrogen uptake","lastPublishedDoi":"10.21203/rs.3.rs-4474023/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4474023/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNitrogen (N) fertilizer and water availability can independently stimulate or limit soil N dynamics through direct and indirect processes. Importantly, soil N mineralization (Nmin) is a major N source for maize but affected by N fertilization and water availability. We examined in-situ net Nmin, soil enzyme activity, and maize N uptake in a semiarid region of North America in response to two levels of water availability (100% and 70% crop evapotranspiration, ET) and three levels of N fertilization (22\u0026ndash;275 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e capturing low, optimal, and excess N fertilization. Nitrogen mineralization rates peaked relatively early in the growing season leading to asynchrony between soil N supply and plant demand. Later in the season when plant N uptake was highest, Nmin rates were high under low N with full water supply, and high under high N with limited water supply, resulting in an N fertilizer and water interaction. Soil L-leucine amino peptidase (LAP) and \u003cem\u003eβ\u003c/em\u003e-1,4-N-acetyl-glucosaminidase (NAG), which can be indicators of gross Nmin, increased with N fertilizer additions but were not affected by water supply. Further research is needed to understand the mechanisms underlying this interaction as well as exploring if gross Nmin has a similar response. Maize N uptake increased with N fertilizer additions under both levels of water availability but was higher in the full water supply. In the limited water availability, increased plant N uptake with increased N fertilization did not translate to large grain yield increases highlighting the impact of water stress, especially during grain fill.\u003c/p\u003e","manuscriptTitle":"Nitrogen and water availability effects dynamics of soil nitrogen mineralization in a maize system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 08:53:53","doi":"10.21203/rs.3.rs-4474023/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-18T22:55:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-18T17:05:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5570214017066543966728603866196008869","date":"2024-08-27T19:34:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-24T12:14:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194806677642089906068216723144150115151","date":"2024-06-12T11:29:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-25T08:15:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-25T06:28:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-25T06:28:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrient Cycling in Agroecosystems","date":"2024-05-24T18:31:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrient-cycling-in-agroecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fres","sideBox":"Learn more about [Nutrient Cycling in Agroecosystems](http://link.springer.com/journal/10705)","snPcode":"10705","submissionUrl":"https://submission.nature.com/new-submission/10705/3","title":"Nutrient Cycling in Agroecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fb010090-02d1-4115-9fce-caed1ed8c16c","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T16:09:55+00:00","versionOfRecord":{"articleIdentity":"rs-4474023","link":"https://doi.org/10.1007/s10705-025-10406-8","journal":{"identity":"nutrient-cycling-in-agroecosystems","isVorOnly":false,"title":"Nutrient Cycling in Agroecosystems"},"publishedOn":"2025-04-22 15:58:01","publishedOnDateReadable":"April 22nd, 2025"},"versionCreatedAt":"2024-06-06 08:53:53","video":"","vorDoi":"10.1007/s10705-025-10406-8","vorDoiUrl":"https://doi.org/10.1007/s10705-025-10406-8","workflowStages":[]},"version":"v1","identity":"rs-4474023","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4474023","identity":"rs-4474023","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}