Modeling Soil Nitrate Accumulation and Leaching in Conventional and Conservation Agriculture Cropping Systems

water

Article Modeling Soil Nitrate Accumulation and Leaching in Conventional and Conservation Agriculture Cropping Systems

Nicolò Colombani 1 , Micòl Mastrocicco 2,*, Fabio Vincenzi 3 and Giuseppe Castaldelli 3 1 SIMAU-Department of Materials, Environmental Sciences and Urban Planning, Polytechnic University of Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; [email protected] 2 DiSTABiF-Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Campania University “Luigi Vanvitelli”, Via Vivaldi 43, 81100 Caserta, Italy 3 SVeB-Department of Life Sciences and Biotechnology, University of Ferrara, Via L. Borsari 46, 44121 Ferrara, Italy; [email protected] (F.V.); [email protected] (G.C.) * Correspondence: [email protected]; Tel.: +39-0823-274-609

Received: 25 January 2020; Accepted: 29 May 2020; Published: 31 May 2020

Abstract: Nitrate is a major groundwater inorganic contaminant that is mainly due to fertilizer leaching. Compost amendment can increase soils’ organic substances and thus promote denitrification in intensively cultivated soils. In this study, two agricultural plots located in the Padana plain (Ferrara, Italy) were monitored and modeled for a period of 2.7 years. One plot was initially amended with 30 t/ha of compost, not tilled, and amended with standard fertilization practices, while the other one was run with standard fertilization and tillage practices. Monitoring was performed continuously via soil water probes (matric potential) and discontinuously via auger core profiles (major nitrogen species) before and after each cropping season. A HYDRUS-1D numerical model was calibrated and validated versus observed matric potential and nitrate, ammonium, and bromide (used as tracers). Model performance was judged satisfactory and the results provided insights on water and nitrogen balances for the two different agricultural practices tested here. While water balance and retention time in the vadose zone were similar in the two plots, nitrate leaching was less pronounced in the plot amended with compost due to a higher denitrification rate. This study provides clear evidence that compost addition and no-tillage (conservation agriculture) can diminish nitrate leaching to groundwater, with respect to standard agricultural practices.

Keywords: nitrate leaching; tracer; compost; agricultural soils; residence time; numerical modeling

1. Introduction

Groundwater nitrate (NO3−) pollution below croplands is a long-term legacy [1–3], and not surprisingly, NO3− is considered the most widespread inorganic pollutant around the world [4,5]. Moreover, Ascott et al. [6] have recently pointed out that the vadose zone is an important stock of NO3− and this transient stock should be accounted for in N budgets for more robust policymaking. In fact, long residence times in the vadose zone may delay the impact of changes in agricultural practices on groundwater quality even in lowland catchments [7,8]. The mathematical formulation and the solutions of transient transport and transformation from urea hydrolysis, ammonification, nitrification, and denitrification have been used for decades in soil hydrology [9–12]. The numerous reactions involved in the N cycle in soils are substantially driven by different microbial consortia that are usually modeled via first-order reaction kinetics, like in the HYDRUS-1D model [13]. Although, there are many other numerical models that have been specifically developed for N species transformation and transport in variably saturated media, such as DAISY [14]

Water 2020, 12, 1571; doi:10.3390/w12061571 www.mdpi.com/journal/water Water 2020, 12, 1571 2 of 15 or COUP [15], the model choice is often driven by the aims of the research. In fact, agronomic empirical models are usually devoted to quantifying crop yield fertilizer plant-uptake and C and N soil turnover, like the EPIC model [16], while physically based models like HYDRUS-1D are better suited for porewater fluxes, nitrogen balance and leaching calculations (nitrification, denitrification, volatilization, mineralization). In this case, the aim of the research is to quantify the driving mechanisms of nitrate leaching; thus, the physically-based model HYDRUS-1D was selected. However, despite the large capabilities reached by physically-based models, N transformation processes are intrinsically complex and difficult to quantify in field conditions since they are driven by site-specific parameters such as soil water content, temperature, root presence, and redox conditions [17]. For the above-mentioned reasons, physically-based models need to be calibrated and validated against a large amount of field data to be considered robust and to effectively reproduce the ongoing site-specific N transformation processes [18–20]. Thus, the last challenge is to use these well-established numerical models at well-equipped field sites and over long-term periods, e.g., different hydrological years and different crop growing seasons, to quantify the effects induced by different agricultural practices on both soil hydrology and N transformations. Otherwise, without the quantification capacity for a field-validated numerical model, it would be very challenging to infer how much soil N leaching is affected by agricultural practice changes. In this study, two experimental plots located in the lower end of the Po river plain (Italy), were monitored and modeled for a period of 974 days (3 cropping seasons). One plot was amended at the beginning of the study with 30 t/ha of compost, amended with synthetic fertilizers and cultivated using no-tillage practices, following the conservation agriculture indications [21,22], while the other one was run with a standard approach, namely, using synthetic fertilizers and deep tillage practices. Here, the numerical model HYDRUS-1D was calibrated and validated versus field-observed data to infer if soil hydrology and N transformations were substantially changed by the introduction of conservation agriculture practices. As far as the authors are aware, this is the first study that uses a combination of continuous soil moisture sensors, high-resolution tracer profiles, and soil NO3− content to calibrate and validate a numerical model in a compost/no-tillage field experiment. In fact, other similar studies were only calibrated and validated versus observed soil NO3− content [23,24] or discontinuous soil moisture content and soil N content [25].

2. Materials and Methods

2.1. Site Location and Characterization The agricultural test site is located in the Po River plain near the city of Ferrara (Northern Italy) at the following coordinates: 44◦4704100 N and 11◦4202000 E (Figure1). The soil is classified as hypocalcic haplic calcisol. The site has been cultivated with a rotation of cereals (maize and winter wheat) and a precession of beetroot. It is representative of large portions of the lower Po river valley. The site was investigated as part of a research project supported by the Rural Development Programme of the Emilia-Romagna Region (PSR 2014–2020), which aims to reduce the environmental impact of agricultural practices by comparing the use of compost and no-tillage techniques versus the classic fertilization and tillage techniques. A plot of 2500 m2 was amended with 30 t/ha of compost incorporated in the soil with a minimum-tillage technique (15 cm). After that, the plot underwent a no-tillage practice (No-till+CMP plot) for the whole duration of the experiment, while another plot was cultivated with classic fertilization and tillage practices (STD plot). The field was cultivated with (i) winter wheat, seeded on 30 October 2016 and harvested on 26 June 2017; (ii) maize, seeded 7 April 2018 and harvested on 24 August 2018; (iii) winter wheat, seeded on 20 October 2018 and harvested on 28 June 2019. N fertilization was done via different fertilizers, as shown in Table1, while compost was incorporated in the No-till+CMP plot at the beginning of the experiment via minimum tillage moldboard (10–15 cm deep). Water 2020, 12, 1571 3 of 15 Water 2020, 12, x FOR PEER REVIEW 3 of 15

FigureFigure 1. SatelliteSatellite image image sourced sourced from fromGoogle Google Earth on Earth 22 June on 222017, June with 2017, the location with the of STD location (classic of STDfertilization (classic andfertilization tillage practices) and tillage and practices) No-till+CMP and No-till (no-tillage+CMP practice) (no-tillage plots practice) and of plots permanent and of permanentinstrumentation. instrumentation.

Table 1. N fertilization events and rate in the STD and No-till+CMP experimental plots. Table 1. N fertilization events and rate in the STD and No-till+CMP experimental plots. STD Plot No-till+CMP Plot Crops Date Fertilizer STD Plot No-till+CMP Plot Crops Date Fertilizer (kg-N/ha) (kg-N/ha) (kg-N/ha) (kg-N/ha) 13 October 2016 NH4NO3 40 92* * 13 October 2016 NH4NO3 40 92 Winter wheat 23 February 2017 NH4NO3 85 40 Winter wheat 23 February 2017 NH4NO3 85 40 0303 April April 2017 2017 UreaUrea 4646 46 46 29 March 2018 (NH4)3PO4 36 36 29 March 2018 (NH4)3PO4 36 36 0707 April April 2018 2018 NHNHH4HPO2PO4 33 3 3 MaizeMaize 4 2 4 3030 April April 2018 2018 UreaUrea 115115 115 115 1111 May May 2018 2018 UreaUrea 115115 115 115

10 10October October 2018 2018 (NH(NH4)43)PO3PO4 4 3636 36 36 WinterWinter wheat wheat 27 27February February 2019 2019 NHNH4NO4NO3 3 5454 54 54 0404 April April 2019 2019 UreaUrea 6464 64 64 * considering* considering the the N N use use efficiency eﬃciency from from compost compost equals equal tos 40%. to 40%.

AA series series of of 5 soil 5 soil samples samples (1 cm (1 diameter cm diameter and 20 and cm long-cores) 20 cm long were-cores) collected were using collected an Eijkelkamp using an SoilEijkelkamp & Water Soil M04 & model Water hand-driven M04 model gouge hand- augerdriven corer gouge (Giesbeek, auger corer The Netherlands)(Giesbeek, The with Netherlands) a diameter ofwith 30 mm. a diameter As it causes of 30 minimal mm. As disturbance it causes minimal of the sample, disturbance the gouge of the auger sample, is frequently the gouge applied auger in is profilefrequently research. applied Core in samplesprofile research. were taken Core randomly samples in were the field taken 10 randomly m from each in the other field at a10 depth m from of 0–20,each 50–70other andat a depth 90–110 of cm 0– below20, 50– ground70 and 90 level–110 (b.g.l.) cm below to assess ground the horizontallevel (b.g.l.) and to assess vertical the variability horizontal of and the + soil’svertical physical variability properties, of the NO soil’s3−, and physical NH4 concentrations properties, NO in3 both−, and experimental NH4+ concentrations plots. The collected in both soilexperimental samples were plots. stored The in collectedpolyethylene soil (PE) samples bottles were and immediately stored in polyethylene frozen at 18 ◦ (PE)C until bottles analyzed. and + − NOimmediately3− was analyzed frozen withat −18 a Technicon°C until analyzed Autoanalyser. NO3−II, was while analyzed NH4 waswith analyzed a Technicon using Autoanalyser a double beam II, Jascowhile V-550 NH4+ UV–viswas analyzed spectrophotometer. using a double beam Jasco V-550 UV–vis spectrophotometer. FigureFigure2 2depicts depicts the the meteorological meteorological conditions conditions recorded recorded during during the studythe study period, period, using using data from data afrom meteorological a meteorological station station located located near the near field the site. field Two site irrigation. Two irrigationevents of 50 events mm/ day, of 50 performed mm/day, duringperformed maize during flowering maize with flowering sprinklers with (28 sprinkler June ands (28 13 JulyJune 2018), and 13 were July also 2018 included.), were also The referenceincluded. potentialThe reference evapotranspiration potential evapotranspiration rate (Et0) was calculatedrate (Et0) was using calculated the Penman–Monteith using the Penman equation–Monteith [26]. Theequation groundwater [26]. The heads groundwater were monitored heads w everyere monitor 30 minutesed every and then 30 minutes averaged and on athen daily averaged basis using on aa daily basis using a Soil & Water Diver Micro® water level data-logger (Eijkelkamp, Giesbeek, The

Water 2020, 12, 1571 4 of 15

Water 2020, 12, x FOR PEER REVIEW 4 of 15 Soil & Water Diver Micro® water level data-logger (Eijkelkamp, Giesbeek, The Netherlands), corrected Netherlands), corrected for atmospheric pressure changes and placed in a 4.0-m deep piezometer for atmospheric pressure changes and placed in a 4.0-m deep piezometer situated between the two plots. situated between the two plots.

FigureFigure 2. Rainfall 2. Rainfall and and irrigation irrigation events events (blue (blue line), line), groundwater groundwater head head (red (red line) line),, and and reference reference evapotranspirationevapotranspiration (black (black line) line) observed observed at at thethe ﬁeldfield site.

An arrayAn array of Watermark of Watermark® soil® soil moisture moisture sensorssensors (Irrormeter (Irrormeter Company Company,, Riverside, Riverside, CA, CA,USA USA)) was was verticallyvertically inserted inserted at 30,at 30 60,, 60 and, and 90 90 cm cm b.g.l. b.g.l. intointo augered holes holes in in each each experimen experimentaltal plot plot.. The s Theoil soil moisturemoisture sensors sensors were were used used to to monitor monitor the matric matric potential potential (measurement (measurement range range 0–25 m 0–25 of water m of). water).A copper-constantan thermocouple was inserted adjacent to each soil moisture probe to compensate A copper-constantan thermocouple was inserted adjacent to each soil moisture probe to compensate for the raw measurements of soil temperature since the probes are sensitive to temperature changes. for theMoreover, raw measurements to better observe of soil the matric temperature potential since variation the probes induced are by sensitive the plants’ to roots temperature transpiration, changes. Moreover,a TEROS to better21® soil observe water potential the matric sensor potential (Meter Environment variation induced, Pullman, by WA the, plants’ USA) was roots placed transpiration, at 30 ® a TEROScm b.g.l. 21 insoil both water experimental potential plots sensor (measurement (Meter Environment, range 0.9–100 Pullman, m of water WA,). The USA) principal was placed soil at 30 cmparameters b.g.l. in both(Table experimental 2) were sourced plots from (measurement a previous study range [27], 0.9–100while the m mineralogical of water). The composition principal soil parametersof these (Table soils 2 is) were quartz, sourced K-feldspar, from a plagioc previouslase,study and calcite [ 27],, while associated the mineralogical with small amounts composition of of thesedolomite soils is; quartz, the clay K-feldspar, minerals are plagioclase, mainly smectite, and chlorite, calcite, serpentine associated, and with mixed small layers amounts [28]. Bromide of dolomite; (Br−) was applied at the beginning of the project (30 October 2016) at a concentration of 1000 mg/l. the clay minerals are mainly smectite, chlorite, serpentine, and mixed layers [28]. Bromide (Br−) was More details can be found in Colombani et al. [8]. applied at the beginning of the project (30 October 2016) at a concentration of 1000 mg/l. More details can be foundTable in 2. ColombaniSediment parameters et al. [ and8]. their standard deviation from quintuplicate samples. Data sourced from Mastrocicco et al. [27]. Table 2. Sediment parameters and their standard deviation from quintuplicate samples. Data sourced from MastrociccoParameter et al. [27]. 0–20 (cm b.g.l.) 50–70 (cm b.g.l.) 90–110 (cm b.g.l.) Grain size (%) FineParameter Sand (0.63–2 mm) 0–2010.1 (cm ± 1.8 b.g.l.) 50–708.1 ± (cm0.8 b.g.l.) 90–1109.0 ± 1.3 (cm b.g.l.) GrainSilt size(2–63 (%) μm) 55.8 ± 2.5 56.9 ± 3.5 47.5 ± 4.1 Fine SandClay (0.63–2 (<2 μm) mm) 34.1 10.1 ± 3.01.8 35.0 8.1 ± 2.40.8 43.5 9.0± 3.1 1.3 ± ± ± Dry Siltbulk (2–63 densityµm) (kg/dm3) 1.01 55.8 ± 0.052.5 1.31 56.9 ± 0.113.5 1.57 47.5± 0.20 4.1 ± ± ± ClaySoil (< 2pHµm) (−) 34.17.7 ± 0.13.0 7.8 35.0 ± 0.1 2.47.9 43.5± 0.1 3.1 ± ± ± Dry bulk densitySOM (%) (kg /dm3) 1.011.8 ± 0.40.05 0.5 1.31 ± 0.10.11 0.4 1.57 ± 0.1 0.20 ± ± ± SoilCarbonates pH ( ) (%) 9.1 7.7 ± 0.60.1 11.5 7.8 ± 0.40.1 4.0 ± 7.9 0.3 0.1 − ± ± ± SOM (%) 1.8 0.4 0.5 0.1 0.4 0.1 ± ± ± Carbonates (%) 9.1 0.6 11.5 0.4 4.0 0.3 2.2. Flow and Transport Modelling Approach ± ± ± The finite element model HYDRUS-1D [13] was selected for simulating the one-dimensional 2.2. Flowmovement and Transport of water Modellingin variably Approach saturated media. The code solves Richards’ equation for saturated– Theunsaturated finite element water model flow via HYDRUS-1D a numerical [13 algorithm] was selected under for thesimulating assumption the one-dimensional that the air phase movement is of water in variably saturated media. The code solves Richards’ equation for saturated–unsaturated water ﬂow via a numerical algorithm under the assumption that the air phase is negligible and thermal Water 2020, 12, x FOR PEER REVIEW 5 of 15 Water 2020, 12, 1571 5 of 15

negligible and thermal gradients can be ignored in the water flow process. Since an undisturbed soil gradientscolumn collected can be ignoredat this field in the site water [27] flowwas process.already successfully Since an undisturbed simulated soilusing column the dual collected-porosity at thisapproach field site proposed [27] was by already Durner successfully [29], the same simulated approach using was the used dual-porosity to simulate approach this field proposedexperiment by. DurnerThis method [29], thesplit sames the approachporous medium was used into to two simulate overlapping this field regions experiment., and for This each method of these splits regions the, porousa van Genuchten medium into–Mualem two overlapping type function regions, [30] andof the for soil each hydraulic of these regions,properties a van is used. Genuchten–Mualem type functionThe standard [30] ofadvection the soil hydraulic–dispersion properties equation is (ADE) used. for dissolved species transport with linear sorptionThe standardwas selected advection–dispersion to simulate the fate equation and transport (ADE) of for N dissolvedspecies in species the vadose transport zone. withThe actual linear sorptionroot water was uptake selected was to simulated simulate the using fate the and Feddes transport water of N stress species response in the vadose function zone. [13]. The Variable actual crop root waterheights uptake depending was simulated on the using phenological the Feddes state water and stress maximum response rooting function depths, [13]. Variable defined crop by heights visual dependinginspection onduring the phenological core sampling, state were and used maximum as input rooting values. depths, Passive defined nutrient by visual uptake inspection was simulated during coreby multiplying sampling, were root used water as input uptake values. with Passive the dissolved nutrient uptake nutrient was concentration simulated by for multiplying soil solution root waterconcentration uptake withvalues the below dissolved a threshold nutrient concentration concentration (Cr formax soil). Passive solution nutrient concentration uptake values is thus below zero awhen threshold Crmax concentrationequals to zero, (Cr whilemax ).when Passive the nutrientconcentration uptake is islower thus than zero Cr whenmax, the Crmax soluteequals is removed to zero, whilefrom the when model, the concentrationor when the concentration is lower than is Crhighermax, thethan solute Crmax is, the removed solute is from left in the the model, soil. The or when Crmax 3 − 3 thewas concentration set very high isfor higher nutrients than (1000 Crmax mg, the-N/ solutedm ) and is leftlow iner thefor soil.Br (1 The00 mg/dm Crmax was). set very high for 3 3 nutrients (1000 mg-N/dm ) and lower for Br− (100 mg/dm ). 2.3. Flow and Transport Boundary Conditions 2.3. Flow and Transport Boundary Conditions The numerical grid was discretized in 101 nodes of 40 mm each to form a regular grid 4.0 m deep (FigureThe 3) numerical. At the soil grid surface, was discretized a variable in pressure 101 nodes head/flux of 40 mm boundary each to formcondition a regular was selectedgrid 4.0 m to deepreplicate (Figure variation3). Ats the in soilthe infiltration surface, a variable rate due pressure to different head rainfall/flux boundary intensities condition. As a bottom was selected boundary to replicatecondition, variations a variable in pressure the infiltration head/flux rate boundary due to di alsofferent was rainfall specified. intensities. As a bottom boundary condition,In the atransport variable pressuremodel, a headthird/-fluxtype boundary (Cauchy) alsoconcentration was specified. flux boundary condition, with user- specifiedIn the liquid transport phase model, concentration a third-type (Cauchy) of the infconcentrationiltrating water, flux boundary was selected condition, as withupper user-specified and lower liquidboundar phaseies. concentration of the infiltrating water, was selected as upper and lower boundaries.

Figure 3.3. Scheme of the numerical model setup used used to to simulate simulate both both the the STD STD and and No-till No-till+CMP+CMP experimental plots.plots.

The reactive network used to simulate the sequential transformations ofof the N species (from urea + toto NHNH44+,, NO NO22−,− and, and NO NO3−3)− was) was the the one one initially initially proposed proposed by by van van Genuchten Genuchten [ [3131]],, coupled coupled with linear + sorption forfor NHNH44+ andand NH NH33 volatilizationvolatilization via via a a first ﬁrst-order-order reaction [13] [13].. The The or organicganic N mineralization rate ofof bothboth compostcompost and and soil soil organic organic matter matter was was accounted accounted for for using using a zero-order a zero-order production production rate rate for + urea,for urea here, assumedhere assumed as a generalized as a generalized organic N organic source. NH N 4 sourceand.NO NH3−4+deposition and NO3 from− deposition precipitations from wasprecipitations included to was account included for ato rate account of 25 kg-Nfor a /rateha/year of 25 [32 kg],-N/ha/y which is [32 an], intermediatewhich is an intermediate value between value the lowerbetween 13 kg-Nthe lower/ha/year 13 kg [33-N/ha/y] and upper [33] and 34 kg-N upper/ha 34/year kg- [N/ha/y34] estimates [34] estimates for northern for n Italy.orthern The Italy. nutrient The sourcesnutrient and sources amount and wereamount speciﬁed were specified following following the data shownthe data in shown Table1 .in Tillage Table operations1. Tillage operations can alter can alter the Ks of the topsoil immediately after, but there was not enough data on Ks variations to

Water 2020, 12, 1571 6 of 15 the Ks of the topsoil immediately after, but there was not enough data on Ks variations to include such complexity; thus, Ks was treated as constant throughout the project. The initial species concentrations were linearly interpolated throughout the model profile using the observed concentrations collected every 50 cm. The linear sorption coefficient for NH + (2.14 10 5 dm3/mg-N), ammonification 4 × − rate (0.28 1/day), nitritation rate (0.12 1/day) and nitratation rate (5.9 1/day) were sourced from Mastrocicco et al. [27], while the first-order volatilization rate (0.66 1/day) was sourced from laboratory experiments on NH3 volatilization conducted in nearby fields [12]. Diffusion values such as molecular 5 2 2 diffusion in water (1.0e− m /day) and in air (1.52 m /day) and the Henry equilibrium distribution constant between liquid and gaseous phases for NH3 (0.01) were kept constant in all horizons.

2.4. Model Calibration and Validation Procedure The model calibration target was to minimize the differences between the observed and calculated + values for matric potential and Br−, NO3−, and NH4 concentrations from the plots in the first two cropping years (from October 2016 to September 2018). The model was then validated using the observations of the last cropping season (from September 2018 to June 2019). The flow and transport parameters were assumed to be homogeneous throughout the soil column; thus, they were kept constant within each model, except for vertical dispersivity (λv). The flow and transport model parameters were automatically adjusted via an inverse modeling procedure present in HYDRUS-1D. The latter uses a Marquardt–Levenberg type algorithm to optimize each parameter versus an objective function made by all the available observations and their respective weights [13]. The optimized flow parameters were residual soil water content (Qr) and the saturated soil water content (Qs) of the van Genuchten [30] parametric functions in the 4 layers constituting the model domain, for a total of 8 parameters. The optimized transport parameters were λv (m) and denitrification rate (1/day). Other reaction parameters listed in Table3 were sourced by a similar model previously developed for this soil [27]. The degree of model calibration was assessed by calculating and comparing the determination coefficient (R2) and modeling efficiency (EF) calculated according to Nash and Sutcliffe [35]:

Pn 2 j=1 Oj Pj EF = 1 − , (1) P 2 − n O O j=1 j − where Pj is the calculated value corresponding to the observed Oj, and O is the mean value of the observed data.

3. Results and Discussion

3.1. Flow Model Results The flow model was calibrated versus the continuous monitoring of in situ matric potential (Figure4). Given that the observed values in the two di fferent experimental plots were not dissimilar, it was decided to average the daily values at the same depth to calibrate a single flow model capable of reproducing the soil water fluxes in both plots. The dual-domain approach employed here was indispensable to avoid water ponding and runoff, which was not observed after extreme rainfall events, like the one recorded in September 2017 (see Figure2). The latter caused a sudden decrease of matric potential at all monitored depths (Figure4) that would not have been as well-represented by single porosity, which cannot allow rapid infiltration in low hydraulic conductivity (Ks) soil despite the elevated precipitation rates [36]. The soil retention curve parameters (Table3) are essentially the ones retrieved from a previous intact soil column experiment performed in the same field site [27], and the good model performance (R2 = 0.90 and EF = 0.85) proves that undisturbed soil column experiments can be used to provide reliable estimates of soil retention parameters provided the size of the column is large enough to account for macroporosity at the plot Water 2020, 12, 1571 7 of 15

scaleWater [37]. 2020 From, 12, x FigureFOR PEER4, REVIEW it is also evident that the matric potential was inﬂuenced not only7 of 15 by the roots’ water uptake but also by meteorological conditions that indeed contributed to decreasing matric potentialcontributed during to summer decreasing 2017 matric and potential spring 2018 during via summer frequent 2017 rainfall and spring events. 2018 via frequent rainfall events.

FigureFigure 4. Comparison 4. Comparison of of the the calculated calculated (lines)(lines) and and observed observed (open (open circles) circles) pressure pressure heads heads at different at diﬀ erent depths (cm b.g.l.). The −30 cm observation point belongs to Layer 2, while -60 cm and -90 cm depths (cm b.g.l.). The 30 cm observation point belongs to Layer 2, while 60 cm and 90 cm observation points belong− to Layer 3. Rainfall and irrigation events (blue line) are also− plotted. − observation points belong to Layer 3. Rainfall and irrigation events (blue line) are also plotted. Table 3. Flow parameters obtained by inverse modeling in the STD and No-till+CMP experimental Table3.plots.Flow parameters obtained by inverse modeling in the STD and No-till+CMP experimental plots. Parameter Layer 1 Layer 2 Layer 3 Layer 4 Parameter Layer 1 Layer 2 Layer 3 Layer 4 Qr 0.03 0.04 0.05 0.05 Qr 0.03 0.04 0.05 0.05 Qs 0.44 0.54 0.53 0.35 Qs 0.44 0.54 0.53 0.35 n (-)(-) 1.1.71711 1 1.711.711 1 1.711.711 1 2.802.80 1 1 1 α (1(1/m)/m) 0.50.5111 0.50.5221 0.0. 52 521 0.050.05 2 2 Ks (m/day) 0.08 0.07 0.03 0.90 2 2 Ks (m/dw (-)ay) 0.00.58 1 0.00.57 1 0.00.53 1 0.900.5 nw2 (- (-)) 0.51.51 1 0.51.51 1 0.51.51 1 0.52.80 − 1 1 1 α 2 (1/m) 0.201 0.201 0.201 0.05 −n−2 (-) 1.5 1.5 1.5 2.80 1 2 α−Initial2 (1/m) value taken0.20 from1 [27].0.20Initial1 value0.20 taken1 from0.05 [8]. 1 Initial value taken from [27]. 2 Initial value taken from [8].

Water 2020, 12, 1571 8 of 15 Water 2020, 12, x FOR PEER REVIEW 8 of 15

3.2. Non Nonreactivereactive Transport Model Results FigFigureure5 5 shows shows thethe resultsresults ofof thethe naturalnatural gradientgradient tracertracer testtest startedstarted atat thethe beginningbeginning ofof thethe projectproject and the Br Br− concentrations simulated simulated by the nonreactive transport model.model. The The model model was fittedfitted versus the observed Br− concentrationsconcentrations sourced sourced from from a a previous previous study study on on environmental environmental tracers tracers [8]. [8]. The center mass was recovered at approximately 0.4 m b.g.l. in both experimental plots, near the The center mass was recovered at approximately −−0.4 m b.g.l. in both experimental plots, near the maximum plo plowingwing depth depth.. Thus, Thus, i itt was was decided decided to to calibrate a single nonreactive transport model capable ofof reproducingreproducingthe the solute solute advection advection and and dispersion dispersion in in both both plots. plots. It shouldIt should be noticedbe noticed that that the water table was always lower than 0.8 m b.g.l. and vertical solute transport in the vadose zone was the water table was always lower than− −0.8 m b.g.l. and vertical solute transport in the vadose zone wamuchs much slower slower than than in saturated in saturated conditions. conditions. Moreover, Moreover, the data the data and theirand their modeling modeling allow allow us to us infer to inferthat the that conservation the conservation agriculture agricultur practicese practices did not did significantly not significantly alter the alter solute the transportsolute transpor in thet vadose in the vadosezone with zone respect with torespect standard to standard practices, practices at least,according at least according to the available to the available data. Achange data. A in change physical in parametersphysical parameters influencing influencing solute transport, solute e.g.,transport, Ks, should e.g., beKs expected, should onlybe expected after diff erentonly after years different from the yearsintroduction from the of introduction the conservation of the agriculture conservati practiceson agriculture [38,39 ].practices [38,39].

− Figure 5.5. Comparison of the calculated (black line) andand observedobserved (open(open circles)circles) BrBr− concentrationsconcentrations − proﬁlesprofiles collected onon 27 March 2018 in both the experimental plots.plots. Calculated Br− concentrationsconcentrations using a Crmax ofof ±15%15% (dashed (dashed black black lines) lines) and and minimum minimum and and maximum maximum groundwater groundwater head headss ( (dasheddashed blue ± lines) are also plotted.

The vertical distribution of Br− concentrationconcentration was was well well fitted fitted using the standard ADE equation 2 (Figure(Figure 55),), withwith aacalculated calculated RR2 of 0.880.88 and an EF ofof 0.81.0.81. Thus, for the principle of parsimony in numerical models [40],], there was no need to use more complex approaches like the mobile−immobileimmobile − physical nonequilibrium approach [ 41]].. The The obtained λv valuevaluess are are relatively large in the upper soil layers (Tab (Tablele4 4),),indicating indicatinga a greatergreatersoil soil heterogeneityheterogeneity in in thethe plowedplowed horizon;horizon; thesethese valuesvalues areare inin lineline with the the value value of of 20.3 20.3 mm mm previously previously obtained obtained for the for column the column experiment experiment [27]. In [addition,27]. In addition, the obtained the valuesobtained are value alsoswithin are also the within range the of therange observed of the observedλv values λv for values tilled for agricultural tilled agricultural soils [39 ].soil Finally,s [39]. Finally,it should it beshould mentioned be mentioned that Cr maxthatfor Crmax Br− forlargely Br− largely influences influenc thees simulation the simulation results, results so after, so after a trial a 3 trialand error and error calibration calibration (since ( Crsincemax Crcannotmax cannot be automatically be automatically calibrated), calibrated) a best-fit, a value best-fit of value 103 mg of/dm 103 wasmg/dm obtained.3 was obtained. The cumulative The cumulative mass taken mass up taken by roots up wasby roots 22.1% was of the22.1 Br%− ofmass, the Br a value− mass, generally a value generallyconsistent consistent with the literaturewith the literature data of 8.1% data [ 41of ],8.1% 10.0% [41 []42, 10.0%], or even [42], higheror even (from higher 11% (from to 38%)11% to [43 38%,44].) [Nevertheless,43,44]. Nevertheless this parameter, this parameter should be should better characterizedbe better characterized in futuretracer in future studies tracer and studies included and in includedinverse modeling in inverse procedures, modeling given procedures, that a simple given sensitivity that a simple analysis sensitivity variating analysis the calibrated variating Cr max the valuecalibrated of Cr15%maxshows value of very ± 15% diff erentshows calculated very different Br− concentration calculated Br profiles− concentration (Figure 5pr).ofiles (Figure 5). ±

Water 2020, 12, x FOR PEER REVIEW 9 of 15 Water 2020, 12, 1571 9 of 15 Table 4. Vertical dispersivity values obtained by inverse modeling in the STD and No-till+CMP experimental plots. Table 4. Vertical dispersivity values obtained by inverse modeling in the STD and No-till+CMP experimental plots. Parameter Name Optimized Value

λvParameter layer 1 (mm) Name Optimized11.9 Value λv layer 2 (mm) 6.1 λv layer 1 (mm) 11.9 λv λlayerv layer 3 (mm) 2 (mm) 6.13.0 λv layer 3 (mm) 3.0 λv layer 4 (mm) 3.0 λv layer 4 (mm) 3.0

3.3. Reactive Transport Model Results 3.3. Reactive Transport Model Results From Figure 6, the vertical profiles of NO3− in the STD experimental plot were reproduced with From Figure6, the vertical proﬁles of NO in the STD experimental plot were reproduced with sufficient quality by the numerical model, with3 a− calculated R2 of 0.68 and an EF of 0.59, although the suﬃcient quality by the numerical model, with a calculated R2 of 0.68 and an EF of 0.59, although the model failed to accurately simulate NO3− concentrations between 0.5 and 0.7 m b.g.l., which is below model failed to accurately simulate NO concentrations between 0.5 and 0.7 m b.g.l., which is below the interface between the tilled and untilled3− soil horizons. the interface between the tilled and untilled soil horizons.

Figure 6. Comparison of the calculated (lines) and observed (open symbols) NO3− and NH4+ Figure 6. Comparison of the calculated (lines) and observed (open symbols) NO3− and NH4 concentrationsconcentrations profiles proﬁles collected collected at at selected selected dates dates in in the the STD STD experimental experimental plot. plot. Error Error bars bars represent represent thethe standard standard deviation deviation over over 5 5 replicates replicates..

InIn addition, addition, the the No No-till-till+CMP+CMP model model suffered suﬀered from from poor poor model model performance performance (Fig (Figureure 6)6, ),with with a calculateda calculated R2 Rof2 of0.6 0.699 and and an an EF EF of of 0. 0.58.58. This This could could be be due due to to a anumber number of of reasons, reasons, such such as as root root distributiondistribution heterogeneities heterogeneities in in both both space space and and time, time, seasonal seasonal variation variation of of organic organic N N mineralization mineralization raterate that that was was taken taken as as constant constant here here,, complex complex behavior behavior of of the the uptake uptake rate rate by by plants’ plants’ roots roots modeled modeled herehere as as passive passive root root water water uptake, uptake, variations variations of of soil soil denitrification denitriﬁcation determined determined by by seasonal seasonal variation variation

Water 2020, 12, 1571 10 of 15 Water 2020, 12, x FOR PEER REVIEW 10 of 15 ofof substrates’ substrates’ availability availability,, and and soil moisture and saturation.saturation. Nevertheless, Nevertheless, both both simulati simulationsons were were capable of capturing the overall NO3− and NH4+ +fate in the subsurface, and in particular, the NO3− capable of capturing the overall NO3− and NH4 fate in the subsurface, and in particular, the NO3− concentrationsconcentrations were were always always lower lower in in the the No No-till-till+CMP+CMP respect respect to to the the STD STD experimental experimental plot, plot, except except forfor the the initial initial season season when when the the compost compost was was incorpo incorporatedrated into into the the topsoil topsoil (Fig (Figureure 7). This is a clear indicationindication that that the the conservation conservation agriculture agriculture practices practices have have less less impact impact on on the the environment environment than than the the standard ones, in agreement with previous studies [24,25]. In fact, NO3− concentrations below the standard ones, in agreement with previous studies [24,25]. In fact, NO3− concentrations below the maximummaximum rooting rooting zone zone (0.9 (0.9 m m b.g.l.) b.g.l.) were were always always lower lower in in the the No No-till-till+CMP+CMP experimental experimental plot. plot. Moreover,Moreover, thethe organicorganic N N mineralization mineralization rates rates (Table (Tab5) werele 5) capablewere capable of producing of produc45 Kg-Ning ≈/45ha /Kgyear- ≈ N/ha/yfor theear No-till for +theCMP No plot-till+CMP and plot5 Kg-N and/ha ≈/5year Kg- forN/ha/y theear STD for plot. theThe STD large plot. di Thefference large between difference the ≈ betweentwo N mineralization the two N mineralization rates is due to therates low is reactivitydue to the of soillow organic reactivity matter of insoil intensively organic matter cultivated in intensivelyfields [45]. cultivated In the No-till fields+CMP, [45]. In despite the No the-till+CMP, higher organic despite N the mineralization higher organic rate N inducedmineralization by the ratecompost induced addition, by the the co largempost pool addition, of available the large organic pool carbon of available also triggered organic denitrification.carbon also triggered In fact, denitrification.the calibrated valuesIn fact, forthe thecalibrated No-till +valuesCMP for plot the are No nearly-till+CMP two plot times are higher nearly than two thetimes denitrification higher than therate denitrification used for the STD rate plot. used Besides, for the toSTD achieve plot. Besides, a good calibration, to achieve denitrificationa good calibration, was also denitrification activated in wasLayer also 3 ofactivated the No-till in L+ayerCMP 3 model of the (TableNo-till+CMP6). model (Table 6).

− ++ FigureFigure 7. 7. ComparisonComparison of of the the calculated calculated (lines) (lines) and observed (open symbols)symbols) NONO33− and NH4 concentrationsconcentrations profiles proﬁles collected collected at at selected selected dates dates in in the No No-till-till+CMP+CMP experimental experimental plot. plot. Error Error bars bars representrepresent the the standard standard deviation deviation over over 5 5 replicates. replicates.

Water 2020, 12, 1571 11 of 15

Table 5. Mean yearly N balance obtained in the STD and No-till+CMP experimental plots.

STD plot (kg-N/ha/year) N input N output Fertilizer N input 223 Crop N uptake 139 N deposition 25 Denitrification 20 N mineralized 5 N volatilized 2 Sum of N input 254 Sum of N output 161 In-out difference 93 No-till+CMP plot (kg-N/ha/year) N input N output Fertilizer N input 191 Crop N uptake 141 N deposition 25 Denitrification 67 N mineralized 45 N volatilized 2 Sum of N input 262 Sum of N output 210 In-out difference 52

Table 6. N mineralization rates and denitriﬁcation rates (1/day) obtained by inverse modelling in the STD and No-till+CMP experimental plots.

Parameter Name Optimized Value STD Optimized Value No-till+CMP 3 Organic N NO3− Layer 1 1.03 10− 0.11 → × 3 Organic N NO3− Layer 2 1.00 10− 0.10 → × 3 3 Organic N NO − Layer 3 1.00 10 1.00 10 → 3 × − × − Organic N NO3− Layer 4 0.0 0.0 → 3 3 NO3− N2 Layer 1 1.93 10− 3.36 10− → × 3 × 3 NO3− N2 Layer 2 1.93 10− 3.36 10− → × × 3 NO − N Layer 3 0.0 3.36 10 3 → 2 × − NO N Layer 4 0.0 0.0 3−→ 2

These values are in agreement with previous experiments that have reported limited denitrification for STD soil [27] and enhanced denitrification rates when the same soil is amended with labile organic substrates, such as acetate [45] produced by roots exudates, mineralization of crop residues, or organic soil improvers such as compost. Finally, Figure8 shows the cumulative N mineralization and denitrification rates calculated for both experimental plots. Here, it is evident that denitrification was able to remove up to 180 kg-N/ha during the 3 cropping seasons in the No-till+CMP plot, while only 120 kg-N/ha was produced by N mineralization processes. In the STD plot, denitrification was able to remove only 53 kg-N/ha, while less than 13 kg-N/ha was produced by N mineralization processes. Water 2020, 12, 1571 12 of 15 Water 2020, 12, x FOR PEER REVIEW 12 of 15

FigureFigure 8.8. ComparisonComparison of theof the calculated calculated cumulative cumulative N mineralization N mineralizatio rate (uppern rate panel) (upper and denitrificationpanel) and denitrificationrate (lower panel) rate for (lower the No-till panel)+CMP for the plot No (black-till+CMP line) and plot for (black the STD line) plot and (red for line).the STD plot (red line).

AnAn elevatedelevateddenitrification denitrification rate rate in organic-matter-amendedin organic-matter-amended soils issoils seen is as aseen positive as a mechanism positive mechanismfor the protection for the protection of surface of and surfac grounde and waters ground from waters di fffromused diffuse NO3−dpollution. NO3− pollution. However, However, from froman agronomical an agronomical perspective, perspective it is, it evidently is evidently a negative a negative feature, feature, as as this this also also meansmeans aa net loss loss of of N N fertilizerfertilizer from from the the field. field. Therefore, Therefore, with with the the aim aim of of develop developinging management management strategies strategies for for both both environmentalenvironmental andand economic economic sustainability, sustainability, the the model model results results shown shown in Figure in 8F haveigure to 8 be have considered to be consideredby taking intoby accounttaking into both account perspectives. both perspectives. To do so, it mustTo do be so, considered it must thatbe considered denitrification that is denitrificationnot an independent is not a process,n independent but is fullyprocess, integrated but is fully into integrated the N cycle into in the agricultural N cycle in soils.agricultural In fact, soilin soil,s. In when fact, in water soil, saturationwhen water leads saturation to O2 scarcity, leads to anaerobic O2 scarcity, processes anaerobic are processes favored and are denitrification favored and denitrificationbecomes important becomes [45]. important The heterotrophic [45]. The denitrificationheterotrophic d rateenitrification is then constrained rate is then by constrain the availabilityed by theof bothavailability organic of substrates both organic and substrates NO3−. Thus, and in NO the3−. presenceThus, in ofthe great presence availability of great of availability labile organic of labilesubstrates, organic such substrates, as in the such No-till as+ inCMP the plot,No-till+CMP the mean plot yearly, the denitrification mean yearly denitrification rate is higher thanrate foris higherstandard than agricultural for standard practices agricultural (Table practices6). However, (Table from6). However, a long-term from perspective, a long-term perspective the adoption, the of adoptionagriculturally of agricultural conservatively conservative practices, aimed practices, at increasing aimed at and increasing maintaining and soilmaintaining organic matter, soil organic would matter,lead to awould slow butlead continuous to a slow but release continuous of mineral release N (Table of mineral6). This N would (Table allow 6). This the would reduction allow of the the reducintensitytion ofof syntheticthe intensity fertilizer of synthetic applications, fertilizer leading applications to basal, leading N concentrations to basal N concentration much lower thans much the lowerpeakvalues than the that peak usually values are recorded that usually below are agricultural recorded landsbelow fertilized agricultural with syntheticlands fertili urea,zed as with done syntheticin conventional urea, as agriculture. done in conventional A situation ofagriculture. this type, characterizedA situation of by this low type, basal characterized NO3− concentrations, by low basalwould NO correspond3− concentration to a limitations, would of correspond electron acceptors to a limitation for denitrification of electron acceptors and, therefore, for denitrification to a reduction and,of N therefore, losses from to thea reduction field. Therefore, of N losses the from increase the field. of soil Therefore, organic matterthe incr hasease a of dual soil positiveorganic matter role: in hasthe shorta dual term, positive to enhance role: in denitrificationthe short term, as to a enhance buffer to denitrification prevent losses as of a NO buffer3− to to surface prevent and losses ground of NOwaters,3- to andsurface in the and long ground term, waters to increase, and thein t eheffi ciencylong term, of N to fertilization increase the in agricultureefficiency of by N increasing fertilization the in agriculture by increasing the basal N supply, a feature that allows the reduction of the intensity of synthetic fertilizers distribution. This could avoid the formation of NO3− excess in topsoil,

Water 2020, 12, 1571 13 of 15 basal N supply, a feature that allows the reduction of the intensity of synthetic fertilizers distribution. This could avoid the formation of NO3− excess in topsoil, preventing denitriﬁcation and N losses to the atmosphere in the form of both N2 gas and hazardous N protoxide. Finally, it must be mentioned that the main limitation of the proposed approach, and in general of the physically-based models, is that the large amount of data necessary to calibrate and validate these models are extremely expensive and time-consuming, although, without such models, the quantiﬁcation of mutual biogeochemical processes is extremely challenging. Future studies should focus on increasing the vertical resolution of samples to better capture the sharp concentration gradient fronts that can develop in these soils.

4. Conclusions

In this study, a HYDRUS-1D simulation of soil NO3− accumulation and leaching in conventional and conservation agriculture cropping systems was successfully calibrated and validated against + continuously observed matric potentials, and against Br−, NO3−, and NH4 soil profiles. The model provided insights on the water and nitrogen balance in the two monitored cropping systems, especially quantifying the long residence times in the vadose zone. This approach could be applied in many other lowland agricultural environments with similar soil and climate characteristics. One of the main outcomes is that the water balance and retention time in the vadose zone were similar in the two agricultural plots. However, to better identify advective and dispersive mechanisms, future studies should pay more attention to Br− uptake rates by roots since this parameter largely controls Br− soil concentrations. This study provides clear evidence that compost addition and no-tillage practices (conservation agriculture), but using the standard fertilization practices, can trigger the denitrification rate and thus diminish nitrate leaching into groundwater. Future studies should also focus on carbon cycling to add more information to the effective availability of electron donors for denitrification.

Author Contributions: Conceptualization, N.C. and M.M.; methodology, N.C. and M.M.; software, N.C.; validation, M.M.; formal analysis, F.V.; investigation, N.C. and F.V.; resources, G.C.; data curation, N.C. and M.M.; writing—original draft preparation, N.C.; writing—review and editing, M.M. and G.C.; visualization, N.C.; supervision, M.M. and G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript. Funding: This work was financially supported by the Emilia-Romagna Region with the Project “Agronomic techniques for the prevention of nitrate pollution and the conservation of the organic matter” of the EIP-AGRI Operational Group “Nitrates Ferrara”, within the Rural Development Programme (PSR) 2014–2020. Acknowledgments: The authors would like to thank Paolo Mantovi (CRPA) for providing the data on fertilizer type and quantity, and Marco Rivaroli and Luca Davì (Navarra Foundation for Agriculture) for sowing and harvest dates and tillage practices. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

1. Exner, M.E.; Hirsh, A.J.; Spalding, R.F. Nebraska’s groundwater legacy: Nitrate contamination beneath irrigated cropland. Water Resour. Res. 2014, 50, 4474–4489. [CrossRef][PubMed] 2. Tesoriero, A.J.; Duff, J.H.; Saad, D.A.; Spahr, N.E.; Wolock, D.M. Vulnerability of streams to legacy nitrate sources. Environ. Sci. Technol. 2013, 47, 3623–3629. [CrossRef] 3. Jessen, S.; Postma, D.; Thorling, L.; Müller, S.; Leskelä, J.; Engesgaard, P. Decadal variations in groundwater quality: A legacy from nitrate leaching and denitrification by pyrite in a sandy aquifer. Water Resour. Res. 2017, 53, 184–198. [CrossRef] 4. Fowler, D.; Coyle, M.; Skiba, U.; Sutton, M.A.; Cape, J.N.; Reis, S.; Sheppard, L.J.; Jenkins, A.; Grizzetti, B.; Galloway, J.N.; et al. The global nitrogen cycle in the twenty-first century. Philos. Trans. R. Soc. B 2013, 368, 20130164. [CrossRef][PubMed] 5. Gruber, N.; Galloway, J.N. An Earth-system perspective of the global nitrogen cycle. Nature 2008, 451, 293. [CrossRef] Water 2020, 12, 1571 14 of 15

6. Ascott, M.J.; Gooddy, D.C.; Wang, L.; Stuart, M.E.; Lewis, M.A.; Ward, R.S.; Binley, A.M. Global patterns of nitrate storage in the vadose zone. Nat. Commun. 2017, 8, 1416. [CrossRef] 7. Gerber, C.; Purtschert, R.; Hunkeler, D.; Hug, R.; Sültenfuss, J. Using environmental tracers to determine the relative importance of travel times in the unsaturated and saturated zones for the delay of nitrate reduction measures. J. Hydrol. 2018, 561, 250–266. [CrossRef] 8. Colombani, N.; Mastrocicco, M.; Castaldelli, G.; Aravena, R. Contrasting biogeochemical processes revealed

by stable isotopes of H2O, N, C and S in shallow aquifers underlying agricultural lowlands. Sci. Total Environ. 2019, 691, 1282–1296. [CrossRef] 9. Rodríguez, S.B.; Alonso-Gaite, A.; Álvarez-Benedí, J. Characterization of nitrogen transformations, sorption and volatilization processes in urea fertilized soils. Vadose Zone J. 2005, 4, 329–336. [CrossRef] 10. Hanson, B.R.; Šim ˚unek,J.; Hopmans, J.W. Evaluation of urea—Ammonium—Nitrate fertigation with drip irrigation using numerical modeling. Agric. Water Manag. 2006, 86, 102–113. [CrossRef] 11. Li, Y.; Šim ˚unek,J.; Zhang, Z.; Jing, L.; Ni, L. Evaluation of nitrogen balance in a direct-seeded-rice field experiment using Hydrus-1D. Agric. Water Manag. 2015, 148, 213–222. [CrossRef] 12. Castaldelli, G.; Colombani, N.; Tamburini, E.; Vincenzi, F.; Mastrocicco, M. Soil type and microclimatic conditions as drivers of urea transformation kinetics in maize plots. Catena 2018, 166, 200–208. [CrossRef] 13. Šimunek, J.; Šejna, M.; Saito, H.; Sakai, M.; van Genuchten, M.T. The HYDRUS-1D software package for simulating the movement of water, heat, and multiple solutes in variably saturated media, Version 4.0. In HYDRUS Software Series 3; Department of Environmental Sciences, University of California Riverside: Riverside, CA, USA, 2008; Volume 3, p. 315. 14. Hansen, S.; Abrahamsen, P.; Petersen, C.T.; Styczen, M. Daisy: Model use, calibration, and validation. Trans. ASABE 2012, 55, 1317–1333. [CrossRef] 15. Jansson, P.-E.; Karlberg, L. Coupled Heat and Mass Transfer Model for Soil–Plant–Atmosphere Systems; Department of Civil and Environmental Engineering, Royal Institute of Technology: Stockholm, Sweden, 2001; p. 321. 16. Izaurralde, R.C.; Williams, J.R.; McGill, W.B.; Rosenberg, N.J.; Jakas, M.Q. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecol. Model. 2006, 192, 362–384. [CrossRef] 17. Zhu, Q.; Castellano, M.J.; Yang, G. Coupling soil water processes and nitrogen cycle across spatial scales: Potentials, bottlenecks and solutions. Earth-Sci. Rev. 2018, 187, 248–258. [CrossRef] 18. Liang, H.; Qi, Z.; Hu, K.; Li, B.; Prasher, S.O. Modelling subsurface drainage and nitrogen losses from artificially drained cropland using coupled DRAINMOD and WHCNS models. Agric. Water Manag. 2018, 195, 201–210. [CrossRef] 19. Banger, K.; Nafziger, E.D.; Wang, J.; Pittelkow, C.M. Modeling Inorganic Soil Nitrogen Status in Maize Agroecosystems. Soil Sci. Soc. Am. J. 2019, 83, 1564–1574. [CrossRef] 20. Marjerison, R.D.; Melkonian, J.; Hutson, J.L.; van Es, H.M.; Sela, S.; Geohring, L.D.; Vetsch, J. Drainage and nitrate leaching from artificially drained maize fields simulated by the Precision Nitrogen Management model. J. Environ. Qual. 2016, 45, 2044–2052. [CrossRef] 21. Knowler, D.; Bradshaw, B. Farmers’ adoption of conservation agriculture: A review and synthesis of recent research. Food Policy 2007, 32, 25–48. [CrossRef] 22. Hobbs, P.R.; Sayre, K.; Gupta, R. The role of conservation agriculture in sustainable agriculture. Philos. Trans. R. Soc. B 2007, 363, 543–555. [CrossRef] 23. Gerke, H.H.; Arning, M.; Stöppler-Zimmer, H. Modeling long-term compost application effects on nitrate leaching. Plant Soil 1999, 213, 75–92. [CrossRef] 24. Iqbal, S.; Guber, A.K.; Khan, H.Z. Estimating nitrogen leaching losses after compost application in furrow irrigated soils of Pakistan using HYDRUS-2D software. Agric. Water Manag. 2016, 168, 85–95. [CrossRef] 25. Asada, K.; Eguchi, S.; Tsunekawa, A.; Tsuji, M.; Itahashi, S.; Katou, H. Predicting nitrogen leaching with the modified LEACHM model: Validation in soils receiving long-term application of animal manure composts. Nutr. Cycl. Agroecosyst. 2015, 102, 209–225. [CrossRef] 26. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements; FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998; p. 15. 27. Mastrocicco, M.; Colombani, N.; Soana, E.; Vincenzi, F.; Castaldelli, G. Intense rainfalls trigger nitrite leaching in agricultural soils depleted in organic matter. Sci. Total Environ. 2019, 665, 80–90. [CrossRef][PubMed] 28. Di Giuseppe, D.; Tessari, U.; Faccini, B.; Coltorti, M. The use of particle density in sedimentary provenance studies: The superficial sediment of Po Plain (Italy) case study. Geosci. J. 2014, 18, 449–458. [CrossRef] Water 2020, 12, 1571 15 of 15

29. Durner, W. Hydraulic conductivity estimation for soils with heterogeneous pore structure. Water Resour. Res. 1994, 30, 211–223. [CrossRef] 30. Van Genuchten, M.T. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 1980, 44, 892–898. [CrossRef] 31. Van Genuchten, M.T. Convective-dispersive transport of solutes involved in sequential first-order decay reactions. Comput. Geosci. 1985, 11, 129–147. [CrossRef] 32. Rogora, M.; Arisci, S.; Marchetto, A. The role of nitrogen deposition in the recent nitrate decline in lakes and rivers in Northern Italy. Sci. Total Environ. 2012, 417, 214–223. [CrossRef] 33. Castaldelli, G.; Soana, E.; Racchetti, E.; Pierobon, E.; Mastrocicco, M.; Tesini, E.; Fano, E.A.; Bartoli, M. Nitrogen budget in a lowland coastal area within the Po river basin (Northern Italy): Multiple evidences of equilibrium between sources and internal sinks. Environ. Manag. 2012, 52, 567–580. [CrossRef] 34. Gumiero, B.; Candoni, F.; Boz, B.; Da Borso, F.; Colombani, N. Nitrogen Budget of Short Rotation Forests Amended with Digestate in Highly Permeable Soils. Appl. Sci. 2019, 9, 4326. [CrossRef] 35. Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 1970, 10, 282–290. [CrossRef] 36. Nachabe, M.H. Estimating hydraulic conductivity for models of soils with macropores. J. Irrig. Drain. Eng. 1995, 121, 95–102. [CrossRef] 37. Høgh Jensen, K.; Refsgaard, J.C. Spatial Variability of Physical Parameters and Processes in Two Field Soils Part II: Water Flow at Field Scale. Hydrol. Res. 1991, 22, 303–326. [CrossRef] 38. Ismail, I.; Blevins, R.L.; Frye, W.W. Long-term no-tillage effects on soil properties and continuous corn yields. Soil Sci. Soc. Am. J. 1994, 58, 193–198. [CrossRef] 39. Cade-Menun, B.J.; Bainard, L.D.; LaForge, K.; Schellenberg, M.; Houston, B.; Hamel, C. Long-term agricultural land use affects chemical and physical properties of soils from southwest Saskatchewan. Can. J. Soil Sci. 2017, 97, 650–666. [CrossRef] 40. Hill, M.C. The practical use of simplicity in developing ground water models. Groundwater 2006, 44, 775–781. [CrossRef] 41. Šim ˚unek,J.; Jarvis, N.J.; Van Genuchten, M.T.; Gärdenäs, A. Review and comparison of models for describing non-equilibrium and preferential flow and transport in the vadose zone. J. Hydrol. 2003, 272, 14–35. [CrossRef] 42. Tilahun, K.; Botha, J.F.; Bennie, A.T.P. Bromide movement and uptake under bare and cropped soil conditions at Dire Dawa, Ethiopia. S. Afr. J. Plant Soil 2006, 23, 1–6. [CrossRef] 43. Iragavarapu, T.K.; Posner, J.L.; Bubenzer, G.D. The effect of various crops on bromide leaching to shallow groundwater under natural rainfall conditions. J. Soil Water Conserv. 1998, 53, 146–151. 44. Jemison, J.M., Jr.; Fox, R.H. Corn uptake of bromide under greenhouse and field conditions. Commun. Soil Sci. Plan. 1991, 22, 283–297. [CrossRef] 45. Mastrocicco, M.; Colombani, N.; Castaldelli, G. Direct measurement of dissolved dinitrogen to refine reactive modelling of denitrification in agricultural soils. Sci. Total Environ. 2019, 647, 134–140. [CrossRef][PubMed]

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