Adaption of the AmaizeN model for nitrogen management in sweet corn (Zea mays L.)

Field Crops Research 209 (2017) 27–38

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Adaption of the AmaizeN model for nitrogen management in sweet corn (Zea mays L.)

MARK

⁎

Mingwei Yuan , Matthew D. Ruark, William L. Bland Dep. of Soil Science, Univ. of Wisconsin-Madison, 1525 Observatory Dr., Madison, WI 53706, United States

A R T I C L E I N F O

A B S T R A C T

Keywords: Adaptive nitrogen management AmaizeN model Nitrate leaching Sweet corn Sandy soil

Production of irrigated sweet corn on sandy soils requires ample N inputs and results in substantial leaching of nitrate to groundwater. The irrigation infrastructure of this system provides the opportunity for fertilizer additions throughout the season, opening the prospect for adaptive, real-time and site-specific management that might synchronize N availability with crop demand, minimizing leaching. This requires validated process-based and mechanistic simulations of the crop N budget and soil N cycling. We adapted the AmaizeN model to sweet corn, validated the model for groundwater NO3-N leaching estimation on sandy soils, and assessed the potential of model application for adaptive, in-season N management in this cropping system. The model was calibrated and tested with a two-season dataset by comparing predicted and measured leaf area index (LAI), above ground biomass (AGB), yield, and cumulative crop N uptake (CNUP). The model prediction and measurement comparisons yielded high coefficients of determination (R2, 0.82–0.95) and low root-mean-square errors (RMSE, 6.0–9.5%) for the whole range of the target crop attributes across years and wide-ranging N treatments. The difference between simulated and measured groundwater NO3-N loadings from lysimeter experiments ranged from 2.1–19.8% as relative absolute errors in 2014, and 1.7–7.2% in 2015. The adaptive N management strategy was proposed and demonstrated in a moderate-N treatment using the model prediction for real-time soil N availability and crop N demand dynamics. This approach significantly enhanced crop productivity by approximately 40% compared to conventional practice, while reducing N fertilizer inputs and NO3-N loading by 30–48% and 27–52%, respectively, relative to the highest N input treatments. The adaptive strategy shows potential to achieve target crop yields while minimizing NO3-N leaching.

1. Introduction Irrigated sweet corn (Zea mays L.) is an important vegetable crop in Central Sands region of Wisconsin, a region that leads to the state ranking second in processing vegetable production and third in processing sweet corn production (USDA National Agricultural Statistics Service, 2013). The deep coarse-textured soils (90% sand, < 10 g kg−1 organic matter content) in this region have poor nutrient and water retention, and as a result, typical N fertilizer rates are 200 kg N ha−1 in order to optimize crop yield, quality attributes and economic return to the grower (Stites and Kraft, 2001a). Synchronizing the supply of N to sweet corn demand in order to reduce nitrate leaching to groundwater poses a challenge (West et al., 2016). As a result, nitrate leaching losses can be substantial. For example, Bundy and Andraski (2005) estimated

nitrate leaching losses ranging from 38 to 100 kg ha−1 in irrigated sweet corn with a mineral fertilizer application rate of 190 kg N ha−1. Conventional N management strategies based on spatial and temporal generalizations, like the maximum return to N (MRTN) approach (Sawyer et al., 2015), or plant-based diagnostic tests and soil N tests (Zebarth et al., 2009; Tremblay et al., 2011), lack timedependent soil and crop N dynamics, which largely restrict the level of precision and efficiency of N management decisions. An option not fully explored is the use of well-calibrated and tested dynamic simulation models of soil N balance and crop N. The dynamic modeling method provides potential for improvement from generalized and static recommendations to adaptive, real-time and site-specific management depending on process-based and mechanistic simulations of the crop N budget and soil N cycling. One example of dynamic modeling in N

Abbreviations: LAI, leaf area index (m2 of leaf/m2 of ground area); AGB, above ground biomass (kg ha−1); CNUP, cumulative crop N uptake (kg ha−1); RUE, radiation use efficiency (g MJ−1); PAR, photosynthetically active radiation (400–700 nm); FINT, the radiation intercepted by the crop (unitlless); K, extinction coefficient of the canopy (unitless); SLN, specific leaf N (g N m−2); PCU, polymer-coated urea; CSA, conventional split applications of soluble N fertilizers; PAN, plant-available N in the soil (g N m−2 d−1); DemNmax, daily maximum crop N demand (g N m−2 d−1) ⁎ Corresponding author. Present address: Dep. of Crop Science, Univ. of Illinois Urbana-Champaign, 1102 South Goodwin Avenue, Urbana, IL 61801, United States. E-mail address: [email protected] (M. Yuan). http://dx.doi.org/10.1016/j.fcr.2017.04.007 Received 30 October 2016; Received in revised form 14 February 2017; Accepted 10 April 2017 0378-4290/ Published by Elsevier B.V.

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2011; Kuipers et al., 2014) is also possible. These approaches are generally effective for comparison of management practices on a small plot scale, but are limited in their ability to provide robust estimates of nitrate loading at a field scale due to the high spatial variation in soil physical and biochemical processes as well as the difficulty in quantifying preferential flow. Model simulations yield estimates of loading and allow investigation of the effects of parameters that vary in space and time such as land use, irrigation and fertilizer management strategies, and climate change on nitrate leaching (Li et al., 2006a, 2014; Panagopoulos et al., 2007; Conrad and Fohrer, 2009; He et al., 2011; Roelsma and Hendriks, 2014). However, large amounts of input data are needed for model calibration and validation. The goal of this study is to evaluate the ability of the AmaizeN model to simulate sweet corn production on the sandy soils in Central Sands region of Wisconsin with respect to yield and N leaching over different N fertilizer levels. The objectives are, i) adapt and calibrate the AmaizeN model for sweet corn growth, yield and N deficiency effects using two years of N field experiments; ii) validate the model simulations of NO3-N loading with the measurements from lysimeter experiments; iii) apply the model to evaluate the potential for adaptive, in-season N management in this cropping system.

management is the Web-based Adapt-N tool (http://www.adapt-n.com) which provides improved in-season N recommendations based on simulation of soil N dynamics and maize N uptake (based on the Precision Nitrogen Management model; Sogbedji et al., 2006) for conditions in Northeast U.S.A along with inputs of near-real time high resolution climate data (Melkonian et al., 2008). This approach has not been widely extended to other crops. AmaizeN is a daily-time-step decision-support system for optimizing nitrogen management developed for maize crops (Li et al., 2006b). It is an extension of the maize potential production model of Muchow et al. (1990) as modified for cool-temperate conditions by Wilson et al. (1995). It incorporates a mechanistic model of maize growth and development and simulates the response of maize to water and N deficit. The potential growth sub-model is based on a phenomenological framework by Sinclair (1986), which uses the radiation use efficiency (RUE) concept (Monteith, 1977, 1994) instead of simulation of detailed photosynthesis, growth and maintenance respiration to predict net dry matter assimilation (Boote et al., 2013). In quantifying crop N demand and N-limitation effects on crop growth, instead of “critical N mechanism” which predicts shoot N concentration decline as a function of aerial biomass accumulation, AmaizeN includes the “leaf N mechanism” strategy, wherein four N pools approach for assessing effects of N limitation on growth and yield through simulation of N dynamics in four plant organ pools (Li et al., 2009). These approaches have been applied to spring wheat, sorghum, chickpea and peanut (Sinclair, 1986; Sinclair and Amir, 1992; Hammer and Muchow, 1994; Sinclair and Muchow, 1995; Soltani et al., 1999; Jamieson and Semenov, 2000; Sinclair et al., 2003; Soltani and Sinclair, 2011). However, AmaizeN has not been adapted to sweet corn, and no study to date has applied this model for estimation of groundwater NO3-N leaching. Lizaso et al. (2007) developed a sweet corn simulation model by modifying CERES-Maize (Jones et al., 2003) to predict fresh market yield and quality of sweet corn. He et al. (2011) also calibrated the CERES-Maize for sweet corn yield and N leaching estimations. Compared with AmaizeN, one of the major differences is that CERES-Maize utilized the “critical N mechanism” to quantify crop N demand and N deficiency effects on crop growth and development. Li et al. (2009) compared the “critical N mechanism” and “leaf N mechanism” approaches for their predictive accuracy for crop N uptake in maize, and found higher R2 and lower root mean square deviation (RSMD) with the “leaf N” strategy. For predictions of biomass and grain yield, the two strategies yielded similar accuracy. Therefore, the AmaizeN model, with its leaf N mechanism approach, appears to be the most appropriate model to calibrate for sweet corn. A variety of techniques have been used for estimation of nitrate-N (NO3-N) loss from root zones in the Central Sands region of Wisconsin. The N budget method (Stites and Kraft, 2001b; Liang et al., 2005; He et al., 2011) ascribes the residual of the root zone N budget to NO3-N loading, e.g., the mass flux per unit area of NO3-N escaping beyond the rooted depth. However it is unclear how reliable this approach is in providing quantitative estimates of nitrate loading due to the large uncertainties in some components of the N budget (e.g., plant uptake, denitrification). Nitrate loading has also been estimated by back-calculation based on measured NO3-N concentration at the water table from monitoring wells and piezometer. The NO3-N loading was estimated by multiplying estimated groundwater recharge with measured NO3-N concentration (Kraft and Stites, 2003; Bero et al., 2014; Kuipers et al., 2014). This approach provides better integration across time and space, and hence has the potential to provide a more robust estimate of nitrate loading. However, when making measurements at the water table, it can be more difficult to link nitrate loading to groundwater with the specific field or management area under study (Bero et al., 2014). Combining measurements of NO3-N concentration using passive (capillary wick samplers and pan lysimeters) or active (suction cup and plates) with estimates of recharge rate/drainage water (Venterea et al.,

2. Methods and materials 2.1. Field experiment design and data collection A two-year (2014 and 2015) field experiment for model calibration was conducted at the University of Wisconsin Hancock Agricultural Research Station (HARS; latitude: 44°8’23”N; longitude: 89°31’23”W; elevation: 328 m) on overhead irrigated Sparta loamy sand soil (sandy, mixed, mesic Entic Hapludoll). Sparta loamy sand is composed of deep, well-drained sands, with weak subangular blocky structure, and hydraulic conductivity at saturation is rapid or very rapid. The soil organic matter is 1% and pH is 6.2. Sweet corn (Hybrid yellow, Del Monte variety DMC 21–84) was planted on 27 June 2014 and 7 June 2015 at a target density of 59,300 plants ha−1 using a John Deere 7200 four-row planter. The experimental design was a randomized complete block (four high N rate treatments in 2014 and 2015, and four additional low N rate treatments in 2015) with three replications (Table 1). N source was urea for T0-T6, and polymer-coated urea (PCU) for T7. The PCU product used in this study was Environment Smart Nitrogen (ESN) (Agrium, Inc., Calgary, AB). All blocks received starter fertilizer at a rate of 22 kg N ha−1 plus 88 kg P ha−1 and 88 kg K ha−1 band applied 5 cm below and 5 cm to the side of the seed. The size of the experimental field was 0.054 ha, with plot size of 5 × 6 m. Irrigation was managed by the HARS staff to avoid any Table 1 Rate and application timing for eight N treatments for sweet corn in 2014 and 2015 at the Hancock Agricultural Research Station. Year

Treatment

N ratea

Timing PP, V4, R4b

kg ha−1 2015

T0 T1 T2 T3

0 50 75 75

0, 0, 0 50, 0, 0 50, 25, 0 25, 25, 25

2014 and 2015

T4 T5 T6 T7

150 150 200 150

150, 100, 150, 150,

a

0, 0 25, 25 25, 25 0, 0

N source is urea for T0-T6, and polymer-coated urea for T7. PP is preplant, and V4 and R4 are the developmental growth stages. For field 1, three fertilization dates were July 9, 16, 23, 2015; for Field 2, three dates were August 6, 13, 20, 2015. b

28

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using 2-L plastic Nalgene bottles. Subsamples were taken from each bottle for nitrate and ammonium concentration analysis, and total volume of soil solution from each lysimeter was recorded. Lysimeters were pumped out every 3–10 days after rainfall events (13 collections in 2014, and 16 collections in 2015) during the growing seasons. Upon collection, samples were transported to the laboratory, filtered within 24 h after collection, and stored at 4 °C until analysis could be performed. Nitrate was determined using the single vanadium chloride reagent method (Doane and Horwath, 2003) and ammonium was determined using the revised Berthelot method from Stackpoole et al. (2008). Seasonal NO3–N loading subjected to an ANOVA based on experimental design using the PROC MIXED model procedure for SAS (version 9.3, SAS Institute, 2011)

drought stress, and insecticide, fungicide, and herbicide applications were applied by the station staff as needed to manage pest and disease pressures. The number of days until 50% crop emergence, 50% silking, and harvest were recorded. Leaf trait measurements included the total plant leaf number, rates of leaf appearance (tips), leaf area index (LAI) using LAI-2000 Plant Canopy Analyzer (Licor, Lincoln, NE, USA), and area of each leaf using LI-300 Portable Area Meter (Licor, Lincoln, NE, USA). The measurements were taken every 7–10 days on five tagged plants on each treatment. Biomass separated into leaf, stem, root and ears was monitored and determined every 7–10 days after emergence. The measurements were carried out on three random plants on each N treatment. Sweet corn was harvested at maturity (for the fresh crop), between 1400 and 1600 GDD. Six plants per plot were collected from center harvest rows. Ears (kernel, cob and husk), leaves and stems were separated from stalks. Additionally, all remaining fully-developed marketable ears from harvest rows were hand-harvested, counted and immediately weighed in the field (unhusked) for fresh weight yield derivation. Samples were oven-dried at 70 °C to constant weight and then weighed. Dried organs were then finely ground and analyzed for nitrogen content [N] using an Elementar Vario Macro CHN analyzer (Elementar Analysensyteme GmbH, Hanau, Germany). Treatment effects on sweet corn dry ear yield were determined by ANOVA using PROC MIXED and correlation among of dry ear yield versus pre-silking, post-silking and total N uptakes with N rate was determined by Pearson’s correlation analysis using Proc CORR in SAS version 9.3 (SAS Institute, 2011). Daily maximum (TMAX, °C) and minimum temperatures (TMIN, °C), rainfall (RAIN, mm) and solar radiation (SRAD, MJ m−2 d−1) were measured at the HARS weather station located a few hundred meters from the experimental field. Irrigation (IRRI, mm) was also obtained from HARS record. The NO3-N concentration in irrigation water was reported as 18 mg L−1 at HARS (Bero et al., 2014; and verified in 2015). Thermal time units (TT, °C d) were calculated using the modified method by averaging TMAX and TMIN with a lower threshold of base temperature (TB = 8 °C from sowing to silking, TB = 0 °C from silking to harvest) and upper threshold of ceiling temperature (TC = 30 °C) (Eq. (1)) (Fletcher and Moot, 2006).

TT =

1 2

2.3. Calibration and description of AmaizeN model Our implementation of the AmaizeN model included the potential growth module, the crop N budget and the soil N balance module, all re-coded in Matlab (Mathworks, Natick, MA, USA). Critical input data included daily weather data (TMAX, TMIN, RAIN, and SRAD), soil hydrological and chemical parameters, and crop management inputs (i.e., N fertilization, irrigation, and plant density). The model simulated phenology, LAI, biomass production and distribution, crop [N] dynamics, soil water and N balances, on a daily step during the growing seasons. 2.3.1. Calibration of the potential maize growth sub-model Several modifications were made to adapt the AmaizeN model for sweet corn under N deficiency using the data from field experiments described above (Section 2.1). (1) Phenology: The thermal time units (TT) for different growth stages were calibrated based on the two-year observations; the relatively late planting date in 2014 and the more typical planting date in 2015 provided a range of development conditions. From sowing to emergence, the TT was fixed as 68 °C d (standard deviation, SD = 5.2 °C d); from sowing to end of the juvenile stage (end of leaf growth when leaf number reaches 11), leaf number (LN) was determined by an exponential model as a function of TT (Eq. (2)); from sowing to silking, TT was fixed as 527 °C d (SD = 12.9 °C d); and from silking to harvest, TT was fixed as 696 °C d (SD=13.8 °C d).

× (TMAX + TMIN ) − TB,

if TMAX > TC , TMAX = TC; if TMAX or TMIN < TB, TMAX or TMIN = TB

LN = 0.9267 × exp(0.0061 × TT), R2 = 0.88

(1)

(2)

(2) Leaf area development: The fully expanded leaf area (MLA, cm2) of each leaf was calculated from the largest leaf area (AM = 894 cm2), the leaf number with AM (LNM = 6), and LN (Eq. (3)) using the method from Muchow et al. (1990); and the leaf expansion rate (LER, cm2 (°C d)−1) was modeled as a function of LN (Eq. (4)) using the method from Jones and Kiniry (1986).

2.2. Lysimeter experiment The tube lysimeter experiment was conducted under the center rows of the four highest N rate treatments (T5–T7, Table 1) in both years. The use of porous cylindrical stainless steel tubes as lysimeters to measure drainage and chemical loadings was documented by Miguez (2014). Twelve soil pits of 2 m long by 0.8 m wide (across sweet corn rows) by 0.8 m deep were excavated for the lysimeter facility. Each pit was lined with a 0.8 mm impermeable PVC geomembrane, The porous stainless steel tubes were installed at the bottom at a 10° angle to measure leachate below the root zone. A sample collection container (3 L) was installed 200 mm below each lysimeter, and connected with 12.7-mm diameter stainless steel tubing. The lysimeters trapped and directed drainage from the plot to the collection tubes, but allowed the majority of this flow (about 80%) to leak downward. Soil solution extracted from the tubes provided an averaged concentration of the leaching, including macropore flow. Thus the lysimeters did not yield quantitative drainage estimates but did yield useful N leachate concentrations. A 373 W (0.5 HP) vacuum pump was connected to the lysimeter system at the soil surface to maintain suction between −40 to −50 kPa (just below the air entry suction of the tubes) for soil solution collection

MLA(LN) = AM × exp[−0.048 × (LN − LNM)2 + 0.00084 × (LN − LNM)3], R2 = 0.98

(3)

LER(LN) = 0.0022 × LN 3 + 0.0123 × LN2 + 0.140 × LN + 0.0565, R2 = 0.86

(4)

(3) Radiation use efficiency (RUE): Muchow et al. (1990) used a fixed RUE of 1.6 g MJ−1 from emergence until 500 °C d after silking, and 1.2 g MJ−1 thereafter. In APSIM-Maize, the default RUE was 1.6 g MJ−1 from emergence to start of grain-filling and 1.06 g MJ−1 thereafter (Archontoulis et al., 2014). For sweet corn, Fletcher et al. (2008a) found RUE was 0.66 g MJ−1 with less than 10 fully expanded leaves, and 1.34 g MJ−1 thereafter, while Stone et al. (2001) reported a constant RUE of 1.875 g MJ−1 in fully irrigated sweet corn. In this study, the RUE was calculated as the slope of linear regression between cumulative above ground biomass (AGB) (measured data, see 2.1) versus cumulative intercepted photosynthetically active radiation 29

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Fig. 1. Calibration parameters using 2-year field experiment data, a) Relationship between cumulative above ground biomass (ABG) and cumulative intercepted PAR. Slopes of the fitted lines indicated the radiation use efficiency (RUE, g MJ−1); b) Relationship between harvest index (HI) versus thermal time. The slopes indicate dHI/dTT; c) Effect of nitrogen (N) supply on leaf sizes at all leaf positions; d) RUE as a function of the specific leaf N (SLN, g m−2).

study, and after leaf growth has ceased, biomass is partitioned between ear and stem only.

Table 2 Maximum and minimum N concentrations for leaf, stem, root and ear. Sweet corn

SLN (g m−2) Nstem (%) Nroot (%) Near (%)

LAR = 1.390 × exp(−0.002×TT), R2 = 0.91

AmaizeN

Maximum

Minimum

Maximum

Minimum

1.45 3.00 – 3.60

0.5 0.50 0.55 1.60

1.50 1.50 – 1.60

0.4 0.25 0.25 1.10

(5)

Harvest index increased linearly at 0.0007 (°C d)−1 (Fig. 1b). Maximum HI reached 0.48, which was comparable to that used in AmaizeN (0.5) and APSIM (0.55) (Li et al., 2006b; Archontoulis et al., 2014). (5) Leaf N mechanism: Changes of [N] in plant organs versus thermal time were modeled based on data from N fertility treatments T4–T7 (Table 1). Maximum and minimum [N] in stem, pod, root and specific leaf N (SLN, g N m−2) SLN (Table 2) for sweet corn were determined based on the 95% confidence limits of the regression at V1 and harvest (Soltani et al., 2006). (6) N deficiency effects: N deficiency was found to significantly reduce leaf area (Fig. 1c), leaf life span and RUE through the dilution of SLN (Fig. 1d). The N limitation factors for leaf area (Eq. (6a)), leaf life span (Eq. (6b)) and RUE (Eq. (6c)) (Vos et al., 2005; Li et al., 2009) were modeled based on data from T0–T6.

(PAR) across five N treatments (T0-3, and T6). Cumulative intercepted PAR was calculated by multiplying PAR and the radiation intercepted by the crop (FINT) with measured SR, LAI and the constant extinction coefficient of the canopy (K, set as 0.65 in this study) (Fletcher et al., 2008b). On days where LAI was not directly measured, it was estimated by linear interpolation between measured values. The experimental values of RUE ranged from 1.44 to 1.82 g MJ−1 from lowest to highest N supply (Fig. 1a), comparable to previous studies. The largest value was selected as the potential RUE, reflecting non-limiting N conditions. The RUE was modeled by multiplying the potential RUE with a temperature effect factor (TFrue) and N-limitation factor (NFrue) (discussed below). The temperature effect on RUE (TFrue) has been described by a 3-segment function (Soltani and Sinclair, 2012), and the base, lower optimum, upper optimum and ceiling temperatures of 10, 20, 35, 45 °C, respectively, were used as suggested in literature to determine the TFRUE (Mishra and Kler, 2000; Soltani and Sinclair, 2012). (4) Biomass partitioning and harvest index (HI): In this study, root/ shoot ratio was found to decrease from 0.5 at plant emergence to 0.12 at harvest. Between emergence and end of leaf growth, the leaf: aboveground biomass ratio (LAR) decreased exponentially (Eq. (5)) in this

NFarea = α + (1 − α ) ×

SLN − SLNmin , α = 0.761 SLNmax − SLNmin

(6a)

NFspan = β + (1 − β ) ×

SLN − SLNmin , β = 0.870 SLNmax − SLNmin

(6b)

NFRUE =

2 −1 [1 + exp(−2.823 × SLN )]

(6c)

2.3.2. Description of the soil water budget The water budget of the crop system was modeled following the Sirius model (Jamieson et al., 1998). The balance between water input 30

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(Jamieson et al., 1998; Jamieson and Semenov, 2000; Sinclair et al., 2003; Soltani et al., 2006). In these models the crop N absorption rate was the lesser of NMXdem and PAN. The daily value of PAN is taken as 10% of the soil mineral N dissolved in the plant available water (PAW) (Li et al., 2009), in line with the often-used assumption of a maximum of 10% water uptake in any day (Dardanelli et al., 2004).

Table 3 Physical and chemical attributes of the four soil layers. Measurements were taken 23th July 2014, at HARS. Layer

1

2

3

4

Thickness (cm) θLL θDUL θSAT Bulk density (g m−2) Initial NO3-N (ppm) Initial NH4-N (ppm) Organic N%

5 0.08 0.20 0.32 1.6 3 1.5 0.02

20 0.06 0.18 0.30 1.6

5 0.05 0.15 0.22 1.8

50 0.05 0.20 0.30 1.6

2.4. Model testing and application 2.4.1. Testing for crop growth, development and yield The model was tested by comparing critical simulated outcomes (LAI, AGB, ear dry matter and cumulative N uptake (CNUP)) with observed values in 2014 and 2015 across the various N treatments. Coefficients of determination (R2) (Eq. (9a)) and root-mean-square error (RMSE) (Eq. (9b)) were calculated for assessing the model robustness.

(precipitation and irrigation) and output (evapotranspiration and drainage) was simulated. Potential evapotranspiration was calculated using the Priestley and Taylor equation (Priestley and Taylor, 1972) following Diak et al. (1998). Details on calculation of transpiration (ET) and soil evaporation (ES) were given in Jamieson et al. (1995). Water percolation was calculated using a simple ‘cascading bucket approach’ (Jamieson et al., 1998). The soil is described as a cascade of four layers, with variations of physical and chemical properties among them (Table 3).

R2 = 1 −

RMSE =

where CNR is the cumulative N release, A0 is the initial N content (assumed at 100%), t is the time (d) after ESN application tL (assumed 0) is the lag time between the application date and the date when N fertilizer release began.

⎡ ⎛ 0.2 + θ1 ⎞ F (TMP, θ ) = exp ⎢3.35 × ⎜1 − 1.3 × TMP × ⎟ ⎢⎣ 55 ⎠ ⎝

(7b) −2

−1

Daily net mineralization rate (NMIN, g N m d , Eq. (8a)) depends on the potentially mineralizable soil N (MNORG, g N m−2), a temperature coefficient (KT) (Eq. (8b)) and a soil moisture coefficient (KW) (Sinclair and Amir, 1992; Sinclair and Muchow, 1995) (Eq. (8c)).

NMIN = MNORG × KT × Kw

(8a)

⎛ ⎞ 6350.5 KT = 24 × exp ⎜17.753 − ⎟ ⎝ TMP + 273.15 ⎠

(8b)

K w = 1.111 × FTSW 1, if FTSW 1 < 0.9; = 10 − 10 × FTSW 1, if FTSW 1 ≥ 0.9

(yO − yO )2

(9a)

(yP − yO )2 n

(9b)

2.4.2. Estimation for groundwater NO3–N loading On a daily basis, the soil N budget sub-model simulated leachate [N], and the soil water budget sub-model predicted volume of drainage (as equivalent water depth), which together yielded simulated NO3–N loading (ka ha−1). To validate this uncalibrated model output, the measured NO3–N concentration (mg L−1) from the lysimeters (not used in the model construction or calibration) and predicted cumulative water drainage (mm) during the sampling period estimated from the soil water budget sub-model were used to calculate the periodic groundwater NO3–N loading estimate. An independent check on simulated loading was not possible because the lysimeters did not provide quantitative drainage estimates. However, the predicted drainage volume from the soil water budget sub-model is arguably a relatively robust prediction in this irrigated sandy soil. Additionally, the drainage estimate entered into the calculation of leachate [N] in the soil N budget sub-model, so comparison between measured and simulated leachate [N] lends confidence to both the N and water budgets. The cumulative NO3–N loading during the growing season from model simulation and estimation based on lysimeter measurements were compared using relative absolute error (RAE, percentage of the absolute error between the two values of the average of the two) to assess the model simulation of NO3–N leaching.

(7a)

⎤ × exp(−38 × θ1 − 0.4) + 1.8⎥ ⎥⎦

∑

(yO − yP )2

where yP is the predicted value, yO is the observed value of the same sample, yO is the average of the observed values, and n is the number of samples.

2.3.3. Description of the soil N balance The soil N dynamics were simulated in order to estimate daily plantavailable N (PAN, g m−2). For N input from controlled released fertilizers like ESN, the N release pattern was simulated using the first-order kinetic model (Eq. (7a)) with the assumption that both soil temperature (TMP) and moisture (θ1, actual soil water content at the top layer) would affect the N release (Eq. (7b)) (Fujinuma et al., 2009).

⎧ ⎡ t − tL ⎤ ⎫ CNR = A0 × ⎨1 − exp ⎢ − ⎥⎬ ⎣ F (TMP, θ ) ⎦ ⎭ ⎩

∑ ∑

3. Results 3.1. Crop growth, development and yield

(8c)

Combing the models for potential growth, soil N, and water balance we simulated phenology, LAI, biomass production and distribution, crop [N] dynamics, and soil water and N balances, across the growing seasons. Simulations of LAI, AGB, ear dry matter and cumulative crop N uptake (CNUP) were compared with observations from all N treatments in 2014 and 2015 (Fig. 2a–d). Generally, all four predictions showed good agreement with measurements (R2: 0.82–0.95, RMSE: 6.0–9.5% of the whole range of the target crop attributes). The two extreme N rate treatments (T0 and T6) in 2015 were highlighted specifically to illustrate the model robustness across N supplies. In the simulation of LAI, ear dry matter and CNUP, both T0 and T6 have high R2

where FTSW1 is the fraction of transpirable soil water in the top layer determined in the soil water budget model, used as an indicator of soil moisture status. Volitilization loss was simulated as a single pulse as a fraction of applied N (10% for urea as surface applied) (Meisinger and Randall, 1991). Denitrification was calculated based on soil moisture and temperature (Sinclair and Amir, 1992). Leaching of nitrate was coupled with water percolation. Crop N uptake simulation followed Li et al. (2009), based on the 4-N-pool model in Sinclair and Amir (1992), which has been widely used in modeling N processes in various crops 31

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Fig. 2. Predicted versus measured values of (a) LAI, (b) above ground biomass (AGB), (c) yield reported as ear dry matter and (d) cumulative crop N uptake (CNUP) with 2-year field experiment data. Treatments T0 and T6 in 2015 were used to set critical leaf N concentrations in the model.

funnel flow (Kung, 1990). Miguez (2014) reported very low recovery of Br-tracer (4–6%) at a near-by field site in an installation of the tubes that did not include a membrane, demonstrating how significant preferential flow can be in this soil. The N balance including fertilization and irrigation inputs, plant uptake, leaching, mineralization, volatilization, denitrification and net changes in the soil was simulated by the soil N budget sub-model (Table 4). The predicted crop N uptake (136.1–144.5 kg ha−1) matched well with the measured data (133.5–166.2 kg ha−1 for T4-T7, Fig. 2d). The predictions for mineralization and gaseous loss (volatilization and denitrification) were comparable to previous studies in sandy soil (e.g. 0 kg ha−1 net mineralization and 11.1 kg ha−1 of gaseous loss; He et al., 2011). The change in storage of soil N at the end of the season was estimated as the differences in nitrate and ammonium N concentrations between the initial and final soil samples, which were collected on the day of planting and the day of harvest, respectively (Table 4). The daily N leaching patterns (Fig. 5) were in accordance with rainfall and soil [N] patterns in both years and across treatments. The time courses of calculated NO3–N loading from the lysimeter experiment generally compared favorably with the predicted N loading (Fig. 5), but cumulative measured losses typically exceeded simulated losses during much of the crop. This was greatest in 2014 in T4, T5, and T6 in 2014 (Fig. 5a, c and e), in which the model underestimated the NO3–N leaching by 10–20%. These discrepancies may have arisen because the lysimeters were put into service immediately after construction. The soil disturbance would likely have increased mineralization somewhat, causing the model estimates of this to be erroneously small. Additionally, surface runoff from the lysimeter plots may have occurred during heavy rainfall in 2014 (Fig. 5a, c and e). As a result, the simulated NO3–N concentration in soil solution would be diluted due to overestimation of drainage in the soil profile, and ultimately the NO3–N loading was underestimated by the model. Both the predicted and estimated NO3–N loading during the growing seasons were significantly

(0.93–0.99) and low RMSE (4.2–13.4% of the whole range of the target crop attributes). However, the model generally overestimated the AGB, especially under low N supply (T0: R2 = 0.51, RMSE: 33.7% of the whole range of AGB; T6: R2 = 0.90, RMSE: 11.3% of the whole range of AGB). The ear dry matter was reported as yield in this study. To guarantee the predictive accuracy with respective to fresh yield for processing sweet corn, conversion was made based on measurements of ear moisture content. In 2013 and 2014, the measured ear moisture contents ranged from 75% to 85%, thus the converted sweet corn fresh yield ranged from 14.8 to 20.8 kg ha−1. Bussler (2015) reported state averages for fresh yield of processing sweet corn of 19.6 and 18.4 kg ha−1 in 2013 and 2014, respectively, comparable to the converted values based on simulated ear dry matter. 3.2. Simulation for groundwater NO3–N loading The soil water budget sub-model calculated daily drainage within each soil layer, which directly influenced the NO3–N leaching patterns. The cumulative drainage in 2014 (390 mm) was significantly higher than that in 2015 (295 mm; Fig. 3), largely attributable to an extremely heavy rainfall (91 mm) 52 days after planting in 2014. The soil solution NO3–N concentrations collected from each lysimeter (Fig. 4) were never used in the model calibration and so provided a check on simulations of this component of groundwater NO3–N loading. The good agreement between predicted (solid lines in Fig. 4) and measured NO3–N concentrations demonstrated the robustness of the model to simulate the N dynamics in soil profile. The equivalent depth of collected drainage from lysimeters was not utilized for calculation of NO3–N loading, because the water recovery was low (15–45% of the estimated drainage for 2014 and 2015). However, the lysimeter membrane acted to direct all leachate to the tubes, eliminating the potential by-pass of the samplers because of macropores or 32

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Fig. 3. Daily (indicated in bars) and cumulative rainfall plus irrigation (in solid lines), and simulative cumulative drainage (in dash lines) in 2014 and 2015.

ranged from 31% to 174% across three years. Conrad and Fohrer (2009) modeled NO3–N leaching using CoupModel under winter wheat and red clover rotation, and the RAE between predicted and measured leaching in the drainage discharge ranged from 21% to 60% across four time periods. Across our treatments, the averaged RAE was 10.6% between model simulation and estimation, apparently better agreement of modeled and simulated data than in comparable studies. It is also interesting to note how low the RAE values were for the PCU treatments across years indicating that prediction of N release by ESN works well for this soil and climate.

higher in 2014 than those in 2015 due to the higher cumulative rainfall and irrigation inputs (Table 5). The RAE between the simulation and estimation ranged from 1.7% to 19.8% (Table 5), which were within the acceptable level of accuracy.

4. Discussion 4.1. Assessment of model simulations of crop growth parameters and NO3–N leaching In this study, the predictions of LAI, AGB, dry ear yield, and CNUP showed good agreement with measurements (R2: 0.82–0.95, RMSE: 6.0–9.5% of the whole range of the target crop attributes). Other sweet corn simulation models show comparable accuracy for yield prediction. With CERES-Sweet Corn model, R2 and RMSE for prediction of ear dry matter were reported as 0.91 and 7.1% of the whole range (Lizaso et al., 2007). With the adapted CERES-Maize model for sweet corn, the RAE between simulated and measured dry yield across six N treatments ranged from 3% to 29% (He et al., 2011). However, other critical crop growth parameters like LAI and CNUP were not reported, thus the robustness of these models in predicting crop and soil N dynamics are unknown. For simulation of seasonal NO3–N leaching, the RAE between the simulation and estimation ranged from 1.7% to 19.8% (Table 5), which were comparable to other reports. He et al. (2011) applied the adapted CERES-Maize model to predict the NO3–N leaching under sweet corn in sandy soil (three N levels and two irrigation levels, and N was applied through micro-irrigation, split across 9 timings during the growing season), and evaluated the NO3–N leaching prediction with estimation using a seasonal N balance method. The reported RAE ranged from 4.6% to 26.4% (average, 15.3%), implying a somewhat close fit between the two estimation methods. However, comparisons between leaching models and measured data have shown more variable results. Wolf et al. (2005) simulated NO3–N leaching using the ANIMO model in sandy soils, and the RAE between measured and predicted values

4.2. The effects of PCU on NO3–N leaching In both years, there were no significant differences in dry ear yield between N treatments with 150 kg N ha−1 of fertilizer (T4, T5, T7) (Table 6). Measured NO3–N leaching from the PCU-based treatment (T7) was not significantly lower than that from the split-applied urea treatment (T5) with the same N (Table 5). However, the simulated NO3–N leaching in T7 was 9.7–18.5% higher than that in T5. Both simulated and measured NO3–N leaching in T7 were lower than that from the single application urea treatment (T4) (4.4–9.0% lower in simulation, and 18.7–27.4% lower in measurement). These findings were in accord with Venterea et al. (2011), who concluded that single PCU applications did not significantly reduce the NO3–N leaching during the growing season in irrigated potato, compared with multiple conventional split application (CSA) of soluble N fertilizers. Maharjan et al. (2014) found CSA increased yield and N uptake compared with preplant-applied PCU or one-time applied urea and decreased NO3–N leaching compared with PCU in irrigated corn. However, Wilson et al. (2010) found that nitrate leaching with PCU (21.3 kg N ha−1) was significantly lower than with split-applied soluble N (26.9 kg N ha−1) in potato, and Zvomuya et al. (2003) reported a significant reduction in NO3–N leaching (34–49%), improved total and marketable tuber yield (12–19%) and N recovery (7%) with PCU compared with CSA. Bero et al. (2014) was unable to demonstrate effects of fertilizer treatment 33

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Fig. 4. Measured and simulated N concentration below the rootzone from T4–T7 in 2014 and 2015. Vertical error bars indicated the differences between lysimeter replicates and horizontal bars indicated the sampling time intervals. Measurements are time averages and so are unlikely to match the peak concentrations in the simulations.

test this assumption (values plotted as the average (the dots) over the interval that a sample represented (the horizontal bars)). Observed uptake in the low-N T0, high-N T6 (Fig. 6a and b) and moderate-N T2 (Fig. 6c) treatments tracked the root zone plant-available N, lending support to the assumption a maximum of 10% of soil solution N could be absorbed each day. In these treatments, DemNmax exceeded PAN for parts of the season. With the ample-N T6 treatment, observed uptake (ANUPR) was always well below the simulated PAN, generally following the modeled DemNmax (Fig. 6a, b-T6). The critical period for crop N uptake (15–40 DAP) occurs during the vegetative stage (DAP: 15–50 d) (Fig. 6), when the leaf expansion process requires large amounts of N for photosynthetic proteins (Gastal and Lemaire, 2002). After that, crop N demand decreases and N is transferred from leaves to seeds to support the growth of grains (Jeuffroy et al., 2002). In this study, the pre-silking N uptakes accounted for 55–73% of the total crop N uptakes across various N supply levels (Table 6). The pre-silking N uptake was more strongly correlated with yield than that which occurred post-silking, confirming the significant role of this early N uptake for crop productivity (Table 6). We also fitted linear regressions between dry ear yield versus the three N uptake parameters. The steepest slope was found between dry ear yield and pre-silking N uptake (14.66 kg kg−1), and the slopes were smaller with total (11.76 kg kg−1) and post-silking N uptake (6.84 kg kg−1), which also proved that lack of N early in the crop life leads to lower yield (data not shown). Ciampitti and Vyn (2011) also validated this N uptake pattern in field maize. They reported the ratio

Table 4 Simulations of the soil N budgets in 2014 and 2015. Year

2014

2015

Treatment

T4 T5 kg ha−1

T6

T7

T4

T5

T6

T7

Fertilization Irrigation Crop Uptake Mineralization Vol+Denitri. N leaching Change in soil N storage

190.4 48 137.5 1.6 11.5 97.2 −6.2

246.4 48 139.8 1.5 15 118.3 22.8

190.4 48 144.5 1.6 11.8 92.9 −9.2

190.4 48.2 140.6 1.2 11.4 78.6 9.2

190.4 48.2 141.2 1.2 11.4 65.2 22

246.4 48.2 144.5 1.1 14.8 99 37.4

190.4 48.2 141.2 1.2 11.6 71.5 15.5

190.4 48 136.1 1.6 11.4 78.4 14.1

(PCU and CSA) on NO3–N leaching in this soil because of great variability in groundwater NO3–N concentrations. 4.3. Implication for in-season N management The model predicted daily maximum crop N demand (DemNmax, g N m−2 d−1, dashed lines in Fig. 6) and PAN in the root zone (solid lines in Fig. 6). The plants were assumed to take up (actual crop N uptake rate, ANUPR, g N m−2 d−1) the smaller of the two on any given day. Measured ANUPR for the two extreme and one intermediate N rate treatments (T0 and T6 in both years, and T2 in 2015) were plotted to 34

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Fig. 5. Daily simulated loadings (kg N ha−1), and simulated and measured cumulative N loadings from T4–T7 in 2014 and 2015. Table 5 Comparisons between N loading estimations across the growing season from lysimeters and model simulations. Year

Treatment

Simulated

Measured

RAE %

kg ha−1 2014

2015

T4 T5 T6 T7

97.2ba 78.4c 118.3a 92.9b

116.7b 95.7c 131.2a 94.9c

18.2 19.8 10.3 2.1

T4 T5 T6 T7

78.6bb 65.2c 99a 71.5b

73.1b 64.1c 92.2c 66.9c

7.2 1.7 7.1 6.6

Table 6 Measured dry ear yield, pre/post-silking, and total N uptake, and ratio of pre-silking/total N uptake under different N treatments in 2014 and 2015. Year

Treatment

Dry ear yield

Presilking N uptake

Post-silking N uptake

Total N uptake

Ratio of presilking/ total N uptake %

kg ha−1 2014

T4 T5 T6 T7

3925ca 3997b 4125a 4032b

93 91 113 109

59 47 42 42

152 138 155 151

61.0 66.3 72.9 71.9

2015

T0 T1 T2 T3 T4 T5 T6 T7

1640gb 2168f 2756e 3107d 3815c 3861b 3912a 3794c

59 82 86 78 98 99 107 96

47 40 44 43 39 48 59 37

106 122 130 121 137 147 166 133

55.4 67.1 66.5 64.3 71.3 67.4 64.5 72.0

0.88

0.17

0.84

a

Least significant difference (LSD) test within N treatments in 2014, means followed by the same letter are not significantly different (a = 0.05). b LSD test within N treatments in 2015.

of N uptake pre-silking versus cumulative post-silking ranged from 0.56 to 0.71 across N treatments, plant densities and hybrids. For an adaptive management N strategy, this reinforces that the critical period is early, and that later additions cannot make up for early-season lost growth. The effects of N fertilizer management on NO3–N leaching may be highly correlated with rainfall timing, amounts and frequency. For instance, an extreme rainfall event (105 mm) occurred one day after the third fertilizer application in 2014 (Fig. 5a), leading to a large NO3–N

Correlationc

a Least significant difference (LSD) test within N treatments in 2014, means followed by the same letter are not significantly different (a = 0.05). b LSD test within N treatments in 2015. c Correlations were calculated between dry ear yield versus pre-silking, post-silking, and total N uptake, respectively.

35

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Fig. 6. Simulated daily plant available N in the rootzone, maximum crop N demand and measured actual crop N uptake of T0 and T6 (in 2014 and 2015), and T2 (in 2015). Horizontal bars indicated the time interval over which the leachate [N] sample averaged.

single-day leaching loss (up to 22 kg N ha−1, around 20% of the cumulative N loading during the growing season). Had this rainfall event not occurred, or been smaller or had happened later in the season, the loss it caused would have been reduced. Relatively low-rainfall years (like 2015) produce equal N leaching and crop yield with PCU (T7) and conventional split-applied fertilizers (T5), while in years with higher rainfall (like 2014), PCU has the potential to reduce leaching and generate higher yield. Our observations and simulations support the low-rainfall hypothesis, but cannot substantiate the higher rainfall case (Table 6). Comparing the curves of DemNmax and PAN in the rootzone of T0 and T6 (Fig. 6a and b), PAN was far greater than DemNmax the entire growing seasons in T6, indicating an over-application of N fertilizers for optimum crop productivity. This was also proven by the field experiment observations. In our experiments, though the measured dry ear yields of T6 were significantly higher than those in other high N rate treatments (T4, T5 and T7) in both years (Table 6), they were only 2.3–4.9% higher, while measured NO3–N leaching in T6 was 12.3–43.8% higher than in other treatments (Table 5). This implies that with comparable crop productivity, T6 had significantly higher NO3–N leaching potential compared with that of T4, T5 and T7, because of this excessive N supply. However, in T0, the crop became short of N after 17 DAP (PAN was less than DemNmax), which directly led to the lowest crop yield (Table 6). In the moderate-N T2 treatment

(Fig. 6c), prior to 27 DAP, N in the soil was sufficient to sustain optimum crop productivity; however, during the time from 27 to 38 DAP (within the critical N uptake period) PAN in the soil dropped below DemNmax, precluding optimum crop growth. From 51–64 DAP, there was another period of N deficiency that presumably affected cob development and grain-filling. This crop demand and soil supply dynamic resulted in a 31% and 28% reduction in fresh and dry ear yields for T2, compared to T6. The model revealed specifically when N deficiencies/excesses in the soil solution occurred and impacted crop growth. Though this was retrospective, a similar analysis based on model simulation can also be made in real time to determine the optimal N application rate and timing. Adaptive management (Walters, 1986) is generally considered to be the best available approach for managing systems in the presence of high uncertainty (Westgate et al., 2013). Our usage of “adaptive management” refers to dynamic in-season adjustments of N fertilizer application rate and timing, adapting to the effects of growing season temperature and precipitation on plant and soil N balances. Running the validated model on the real-time basis with weather and management inputs supports learning with continuous assessment, which serves as the basis for adaptive N management and further provides stakeholders with a capability to sustain yield goals while minimizing groundwater NO3–N leaching in sandy soil during the growing season. Similar to the methodology proposed by Van Alphen and Stoorvogel 36

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Bero, Paul Sytsma, and the crew at the Hancock Agricultural Research Station for their assistance with field work on this project.

(2000), this could be implemented based on the simulated PAN and DemNmax dynamics as discussed above. In this study, with real-time weather and management inputs, the daily PAN in the soil and DemNmax could be predicted, and whenever the two curves intercepted, indicating N depletion for optimum crop yield, in-season N fertilizer additions should be applied as appropriate to minimize differences between PAN and DemNmax. Additional rules derived from temperature and precipitation climatology could “look ahead” in time to optimize application amount. Such an optimization would naturally favor applications be made within the vegetative growth stage because later applications would not significantly increase the yield. Additional field experiments are needed to evaluate this application of the model. The value of this adaptive management strategy can be visualized for T2 in Fig. 6c. It appears that N fertilization at 20 DAP was appropriate, but the N rate should have been 80 instead of 50 kg N ha−1 to avoid the N deficiency during the critical N uptake period. We modeled this and found that at about 31 DAP the model simulation again predicted that PAN fell below DemNmax, so a second application would also be beneficial at this time instead of 36 DAP as in the former T2 treatment. The PAN and DemNmax values did not indicate any N deficiency later on, thus no extra N fertilizer was needed. With this optimal N application scheme, though the total N rates increased from 75 to 105 kg N ha−1 and predicted NO3-N loading increased from 40.6 to 47.8 kg ha−1 (18% increase), the predicted dry ear yield increased from 2700 to 3743 kg ha−1 (39% increase). However, predicted NO3-N loading was reduced by 27–52% relative to T4-T7 in 2015 (Table 5), while the predicted dry yield was not significantly different (p < 0.05, Table 6).

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5. Conclusions This study validated the predictive capacity of the AmaizeN model for sweet corn yield in response to N and N leaching on irrigated sandy soils. The adapted AmaizeN model accurately (R2, 0.82–0.95; RMSE, 6.0–9.5% of the whole range of the target crop attributes) predicted variations in LAI, AGB, dry ear yield and CNUP across different N levels. The general agreement between measured and daily simulated leachate [N] shows that this model is a useful tool for estimation of groundwater NO3–N leaching in this cropping system. The application of this model for daily prediction as well as continuous assessment of soil N availability and crop N demand dynamics substantiated the potential for adaptive, in-season N management. With the adaptive N management strategy, the moderate-N (T2) treatment increased N fertilizer input from 75 to 105 kg N ha−1 to prevent any N deficiency, causing predicted dry ear yield to increase by approximately 40% relative to the measured yield. The enhanced yield was not statistically different from those in high-N treatments (T4–T7), but N fertilizer inputs were reduced by 30–48%, and predicted NO3–N loading was decreased by 27–52%. Field experiments can be conducted to assess the robustness and feasibility of this adaptive N management strategy, and model application with long-term climate data can be conducted to explore how alternative N management strategies can increase crop yield while reducing fertilizer N input and groundwater NO3–N loading under various climate scenarios. Funding This work was supported by USDA-NIFA- Specialty Crop Research Initiative [grant number: 2012-51181-20001], USDA-NRCSConservation Innovation Grant [reference number 69-3A75-11-211], Champ Tanner Agricultural Physics Award Fund, and the Department of Soil Science, University of Wisconsin-Madison. Acknowledgements The authors would like to thank Jaimie West, Mack Naber, Nick 37

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