Simulation of water and nitrogen balances in a perennial forage system using the STICS model

Simulation of water and nitrogen balances in a perennial forage system using the STICS model

Field Crops Research 201 (2017) 10–18 Contents lists available at ScienceDirect Field Crops Research journal homepage: www.elsevier.com/locate/fcr ...

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Field Crops Research 201 (2017) 10–18

Contents lists available at ScienceDirect

Field Crops Research journal homepage: www.elsevier.com/locate/fcr

Simulation of water and nitrogen balances in a perennial forage system using the STICS model Qi Jing 1 , Guillaume Jégo ∗ , Gilles Bélanger, Martin H. Chantigny, Philippe Rochette Quebec Research and Development Centre, Agriculture and Agri-Food Canada, 2560 Hochelaga Boulevard, Quebec City, Quebec, G1 V 2J3, Canada

a r t i c l e

i n f o

Article history: Received 21 July 2014 Received in revised form 19 August 2016 Accepted 21 October 2016 Keywords: Grass Soil Mineral N Manure Timothy

a b s t r a c t Soil–crop models can be effective tools for calculating the water and nitrogen (N) balances of agricultural systems, because such models are able to simulate the complex interactions between crop, soil water, and soil N. However, model performance needs to be evaluated against field measurements. Our objectives were to evaluate the STICS model for simulating inorganic N and water fluxes in a perennial timothy (Phleum pratense L.) forage system in Eastern Canada and use the model for estimating water and N balances under three N treatments. Experimental data were collected for three years on a sandy loam soil with three N treatments (0 kg N ha−1 , mineral fertilizer at 140 kg N ha−1 , and raw liquid swine manure at 140 kg total N ha−1 ). The model had good performance for predicting harvested biomass and soil moisture (relative root mean square error below 30%). Total soil mineral N (nitrate + ammonium) was simulated reasonably well, but soil nitrate content was overestimated during the spring growth of timothy. The model simulated N emissions efficiently and reproduced the tendency toward higher emissions in the liquid swine manure treatment. Despite these discrepancies, general trends of soil mineral N and gaseous N emissions were well reproduced. The annual simulated plant–soil N balance varied with fertilizer types, N rates, and weather conditions. Gaseous N emissions were greater with liquid swine manure than with mineral N fertilizer because of higher ammonia volatilization. Nitrate leaching represented a very small component of the N balance. This study is a significant step toward the validation of the STICS model for simulating N and water cycles in perennial forage systems in cold, humid continental climates. Crown Copyright © 2016 Published by Elsevier B.V. All rights reserved.

1. Introduction Timothy (Phleum pratense L.) is one of the major perennial forage species grown in the northern regions of Europe and North America. In Eastern Canada, timothy is commonly harvested twice a year and fertilized with mineral nitrogen (N) fertilizer, animal manure, or both at rates varying from 90 to 180 kg N ha−1 depending on the soil conditions, region, and production objectives (Bélanger and Richards, 1997; Bélanger et al., 1989, 1999, 2008). The use of manure fertilizer can improve crop production and soil quality (Kellogg et al., 2000). However, the application of animal manure often results in high N emissions through ammonia (NH3 ) volatilization (Amon et al., 2006). Chantigny et al. (2007) showed that the harvested timothy yield obtained with raw liquid swine manure (LSM) was more than 90% of the yield obtained with

∗ Corresponding author. E-mail address: [email protected] (G. Jégo). 1 Current address: Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, Ontario, K1A 0C6, Canada. http://dx.doi.org/10.1016/j.fcr.2016.10.017 0378-4290/Crown Copyright © 2016 Published by Elsevier B.V. All rights reserved.

the equivalent rate of mineral fertilizer. However, measurements of gaseous N losses were higher for the LSM treatment than for the mineral N treatment. Other N losses (e.g. nitrate leaching) and complete water balance (e.g. drainage) were not measured in that experiment, but can be quantified using a soil-crop model. Quantifying soil N processes and losses is very challenging because of their large spatial and temporal variability. Soil N status and flux closely interact with soil moisture, increasing the complexity of quantifying N processes and losses. The use of reliable soil-crop models to estimate water and N fluxes can be an effective alternative to measurement, because models can simulate these complex interactions between processes and calculate variables that are difficult to measure at the agro-ecosystem level. Soil-crop models are important research tools for exploring the relationships between the various components of the water and N cycles, gaining a better understanding of the interactions between soil and crop processes, and examining the agronomic and environmental impacts of crop management (Aronsson and Torstensson, 1998; Blombäck et al., 2003; van Keulen, 2001). However, soil-crop models must first be tested and verified against experimental datasets in

Q. Jing et al. / Field Crops Research 201 (2017) 10–18

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Table 1 Nitrogen (N) fertilization and harvest dates along with the cumulative values of rainfall, solar radiation, and degree-days above 0 ◦ C during the growing period (1 April–1 October) in the three years of the experiment (2001–2003). Year

2001 2002 2003 a b

N rate (kg ha−1 )

b

0; 80/60 0; 80/60 0; 80/60

Fertilization date (DOY)a

Harvest date (DOY)

1st time

2nd time

1st time

2nd time

143 128 135

170 176 169

165 171 167

207 213 211

Rainfall (mm)

Radiation (MJ m−2 )

Degree-days (◦ C-d)

571 527 551

3479 3317 3012

2726 2490 2472

DOY, day of year. Split fertilizer rates (total of 140 kg N ha−1 ) with either a mineral fertilizer or liquid swine manure.

time and space in order to ensure the models’ efficiency in correctly simulating reality. Several models simulate soil–crop systems for annual and perennial crops, such as STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard; Brisson et al., 1998, 2003), APSIM (Agricultural Production Systems sIMulator; Keating et al., 2003), DSSAT (Decision Support System for Agrotechnology Transfer; Jones et al., 2003), CropSyst (Cropping Systems simulation model; Stöckle et al., 2003), and the Wageningen crop models (van Ittersum et al., 2003). Most of those models were developed initially for annual crop systems, and some models have been extended to perennial crop systems in recent years. Models specific to forage crops have also been developed, such as CATIMO (Canadian Timothy Model; Bonesmo and Bélanger, 2002; Jing et al., 2012), but those models do not usually simulate the complete water and N balances of agricultural fields. Initially parameterized and evaluated for wheat and corn, the STICS soil–crop model was subsequently adapted for other annual crops (Brisson et al., 2003) and for forage crops such as alfalfa, tall fescue, and orchard grass (Durand et al., 2010; Juin et al., 2004; Ruget et al., 2001, 2006, 2009). The STICS model simulates crop development and soil water and N processes, such as drainage and N leaching (Brisson et al., 1998, 2003). The STICS model has been calibrated and validated for simulating timothy growth and nutritive value in Canada with annual reinitialization of the model (Jégo et al., 2013). Snow-cover equations have been successfully tested in STICS to accurately simulate the two main soil variables (temperature and moisture) for climates that have long periods of snow cover (Jégo et al., 2014), so that STICS can be run continuously for several years in cold, humid continental climates. This is an important asset because the simulation with annual re-initialization may miss the soil processes during winter and hamper us to well quantify the water and N balances in the complete cycle of the perennial forage system. However, the ability of the STICS model, with snow-cover equations integrated, to predict water and N losses over multiple years for perennial forage systems in northern areas needs to be evaluated. This study is part of a project aimed at improving the capacity of STICS to accurately simulate forage yield and nutritive value as well as the environmental impacts of perennial forage crops in cold, humid continental climates. The STICS model, as calibrated for timothy by Jégo et al. (2013), was applied to simulate soil water and N processes in timothy systems on a sandy loam soil in Eastern Canada for three consecutive years. Our objectives were (1) to evaluate model performance in simulating soil water and inorganic N fluxes and (2) to use the model for estimating complete water and N balances including variables that were not monitored in the study of Chantigny et al. (2007).

2. Materials and methods The STICS soil–crop model was evaluated for simulating biomass, crop N uptake, soil moisture, soil mineral N content, and gaseous N emissions on a timothy field for three consecutive years

(2001–2003) using data from an experiment with three fertilizer treatments. In this study, unlike the previous one by Jégo et al. (2013), STICS was run for three consecutive years without annual reinitialization. The model was then used to estimate the complete water and N input–output balances of this perennial forage system.

2.1. Field experiment The experiment was conducted on a sandy loam field near Quebec City, Quebec, Canada (46◦ 48 N, 71◦ 23 W). The soil characteristics were measured in April 2000. The soil texture was 17.1% clay and 72.6% sand, with 2.1% total carbon and 0.14% organic N. The volumetric soil moisture at field capacity (23%) and wilting point (9%) were calculated using a pedotransfer function (Saxton and Rawls, 2006). The soil bulk density was 1.3 g cm−3 , and the soil pH was 6.5. Timothy (cultivar Champ) was seeded in 2000. Fertilizer treatments were applied annually from 2001 to 2003 and consisted of a control (0 kg N ha−1 ; 0N), a mineral fertilizer (140 kg N ha−1 ; 140N), and raw liquid swine manure (140 kg total N ha−1 ; LSM). The mineral N fertilizer and LSM were broadcast on the soil surface, with 60% of the N applied in the first week of May and 40% applied after the first cut (Table 1). The experiment was a randomized complete block design with four replicates. The plot size was 3 × 7 m. Field data were collected from 2001 to 2003. Soil moisture (0–20 cm) was measured using a TDR (time-domain reflectometry) moisture meter during the timothy growing seasons. Soil mineral N content (0–20 cm) was measured approximately 1, 3, 7, 14, and 21 d after each N application. Three soil cores (0–20 cm) were collected manually with a Dutch auger (5.3 cm in diameter). Soil samples were immediately brought to the laboratory and manually broken to make homogeneous samples. Exchangeable ammonium (NH4 ) and nitrate (NO3 ) were extracted on the day of soil sampling using a 1 M KCl solution and then analyzed by colorimetry. Nitrous oxide (N2 O) emissions were also measured approximately 1, 3, 7, 14, and 21 d after each N application using non-flow-through, non-steady-state chambers (Rochette and Hutchinson, 2005). Air samples (20 mL) were collected from the chamber headspace 0, 10, 20, and 30 min after chamber deployment. The gas samples were analyzed for N2 O concentration within 10 d using a gas chromatograph (Rochette and Hutchinson, 2005). The soil surface N2 O losses were calculated using the equation and nonlinear model proposed by Rochette and Hutchinson (2005). Nitrous oxide losses were estimated for 21 d after every N application by linearly interpolating emission rates between measurement dates. Cumulative N2 O losses were calculated as the sum of the two 21-d measurement periods in that year. Ammonia volatilization losses were monitored for 8 d after each N application using wind tunnels (Rochette et al., 2001). The amount of NH3 emitted during each sampling period was calculated as the difference between the concentration of NH3 in the air entering the tunnel and the concentration in the air leaving it. Cumulative NH3 losses were calculated as the sum of the two 8-d measurement periods in that year. Total gaseous N emissions (N2 O and NH3 ) were

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Q. Jing et al. / Field Crops Research 201 (2017) 10–18

calculated for each N application by summing the cumulative N2 O and NH3 emissions for the 21 d following fertilizer application. Forage was harvested twice each year, and dry matter (DM) content and forage N concentrations were measured at each cut. The harvest dates are indicated in Table 1. Dry matter yield was determined by harvesting forage at a 5-cm height above the soil from an area measuring 0.9 × 7 m in each plot using a self-propelled harvester. The harvested forage was weighed, and a fresh sample of approximately 500 g was taken from each plot. This sample was weighed and then dried at 55 ◦ C in a forced-draft oven for 3 d for the determination of DM percentage. Forage N concentration was determined using a method adapted from Isaac and Johnson (1976). More details about the field experiment, measurement methods, and calculations can be found in Chantigny et al. (2007). 2.2. STICS model and simulations The STICS model was initially parameterized and evaluated for bare soil, wheat, and corn (Brisson et al., 1998, 2002) and was later adapted for other annual and perennial crops (Brisson et al., 2003). The model has been calibrated and validated to simulate the growth and nutritive value in Canada of two timothy cultivars, Champ (used in this study) and Climax (Jégo et al., 2013). Two of the experimental timothy datasets used for model validation were obtained from locations near Quebec City. The STICS model was also integrated into the modeling platform RECORD, and new processes were added to address environmental issues (Bergez et al., 2014). More details about the theory and general parameterization underlying this model are provided in Brisson et al. (2008); only a simple summary related to our objectives is given here. The STICS model can simulate the soil–crop system over one crop cycle or several successive cycles with a daily time step. Crop phenological stages are simulated using a sum of degree-days (◦ Cd). The crop is described by its shoot dry biomass (carbon and N), leaf area index, and biomass of harvested crop organs. Vegetative organs (leaves, stems, branches or tillers, and roots) are functionally separated in terms of radiation, water, and nutrient sink or reservoir role. The soil is described as a sequence of horizontal layers, each of which is characterized in terms of its water content and mineral and organic N contents. The soil and the crop interact via the roots, which are defined in terms of root density distribution in the soil profile. The soil description includes four compartments: microporosity (or textural porosity), macroporosity (or structural porosity), fissures (in the case of swelling clay soils), and stones (various types of stones according to their porosity and water storage). The soil is divided into a maximum of five horizons, but calculations of microporosity are done per 1-cm layer, which is the resolution required to derive the NO3 concentration with relevance (Mary et al., 1999). Water transport in soil micropores is calculated for each 1-cm layer using a tipping-bucket approach. The daily water budget calculates the water status of the soil and the crop as well as the water stress indices that reduce leaf growth and canopy net photosynthesis in plants. The daily N budget takes into account mineralization, denitrification, nitrification, NH3 volatilization, and N absorption. Soil N supply is calculated per 1-cm layer along the rooting depth. Net mineralization in the soil is the sum of humus mineralization and the mineralization of organic residues. Denitrification and nitrification are assumed to occur in the biologically active layer (15 cm in the present study). Concerning nitrification, the fraction of NH4 transformed into NO3 − every day depends on soil temperature, soil moisture, and soil pH. A constant proportion of the nitrified NH4 is emitted as N2 O. Denitrification is simulated using the model proposed by Hénault et al. (2005). The potential daily denitrification rate is affected by soil temperature, NO3 content, and water content. As is the case for nitrification, a constant proportion of the

denitrified NO3 is emitted as N2 O. The current version of STICS simulates NH3 volatilization explicitly only following an application of liquid animal manure. For mineral fertilization, a simpler approach based on the concept of competition between the soil and the crop is used (Limaux et al., 1999) to calculate the amount of N lost through volatilization immediately after fertilizer application. The daily absorption of N is equal to the minimum supply available through the soil–root system and crop requirements. Crop requirements correspond to a relationship established from the upper envelope of N dilution curves (Lemaire and Gastal, 1997). The weather data used to run the model are daily minimum and maximum temperatures, solar radiation, rainfall, wind speed, and relative humidity. Daily minimum and maximum temperatures and precipitation are preprocessed with the snow model to account for snow cover. In this study, potential evapotranspiration was calculated using the equation proposed by Shuttleworth and Wallace (1985). The soil properties required to run the model are the organic N, clay, and carbonate contents in the surface soil layer. Field capacity, wilting point, and bulk density are required for all simulated soil layers. Soil input parameters were obtained from soil analyses, with the exception of volumetric soil moisture at field capacity and wilting point, which were derived from those analyses using pedotransfer functions (Saxton and Rawls, 2006). Soil depth and maximum rooting depth were set at 1 m. Actual management practices such as dates and rates of N fertilization and dates of harvesting required in the management file were used. This information is summarized in Table 1, and more details can be found in Chantigny et al. (2007). The model was initialized in April 2000, one year before the start of the comparison between the simulation and measurements. Initial soil water content (April 2000) was set at field capacity, which is representative of the soil water status after snow melt. Initial soil NO3 -N and NH4 -N were set at 5 kg N ha−1 per 20-cm depth up to 100 cm, corresponding to the average contents measured in fall after the last harvest.

2.3. Model evaluation The model was evaluated by comparing the measured data collected over the three years (2001, 2002, and 2003) and for the three treatments (0N, 140N, and LSM) with the simulated values. The variables included in the comparison were dynamics of soil inorganic N as either NO3 or NH4 , cumulative N emissions (NO2 + NH3 ), soil moisture, crop N uptake, and forage DM yield. Several statistical criteria were used to evaluate the model’s performance and were calculated as follows: Model efficiency (EF) with an optimal value of 1.0: n 

(Pi − Oi)

2

i=1

EF = 1 −

n 

(1) (Oi − O)

2

i=1

Mean error (ME) with an optimal value of 0, and its relative value in percent (ME%): 1 (Oi − Pi) n n

ME =

(2)

i=1

 ME% =

ME O

 × 100

(3)

Q. Jing et al. / Field Crops Research 201 (2017) 10–18

13

Root mean square error (RMSE) with an optimal value of 0, and its relative value (RMSE%):

3. Results and discussion

  n 1 2 RMSE =  (Oi − Pi)

3.1. Model evaluation (4)

n

n=1

 RMSE% =

RMSE

 × 100

(5)

O where n is the number of measurements, Oi is the measured value, O is the mean of the measured values, and Pi is the value simulated by the model.

2.4. Nitrogen and water balances After the model evaluation, the simulated annual N and water balances were analyzed for each year of the simulation. The following N inputs were considered in the analysis: N fertilizer, mineralized N, initial inorganic soil N, and initial plant N. The following N outputs were considered: crop N uptake, N emissions, immobilized N, leached N, final soil inorganic N, and final plant N. For the water balance, the inputs were rainfall and initial soil water, and the outputs were transpiration, evaporation, infiltration, and final soil water.

3.1.1. Dry matter yields The harvested DM yields from the first cut were higher than those from the second cut for both the simulated values and the measured values (Fig. 1). In most cases, the measured yields were close to the simulated values. The average annual simulated (6.4 t ha−1 ) and measured (6.2 t ha−1 ) DM yields were very close (Table 2). The model successfully simulated the annual DM yields, with a low ME (0.1 t ha−1 , 2%) and RMSE (1.1 t ha−1 , 17.1%) and a positive EF. However, as already noted by Jégo et al. (2013), the DM yield of the regrowth (second cut) was slightly overestimated (0.8 t ha−1 on average and 0.9 t ha−1 reported by Jégo et al., 2013). For primary growth, while Jégo et al. (2012) reported almost no bias (<0.2 t ha−1 ), a slight underestimation (0.6 t ha−1 ) was found in this study. 3.1.2. Soil moisture The temporal changes in soil moisture were generally simulated well, with the observed values close to the simulated values (Fig. 2). The average measured (22.8%) and simulated (23.3%) soil moisture values were close (Table 2). The model’s performance was fairly good, with RMSE% values of 22.8% (Table 2). The RMSE% values were comparable to those reported in other studies (6%–27%) that used STICS to simulate soil moisture (Beaudoin et al., 2008; Constantin et al., 2012; Coucheney et al., 2015; Jégo et al., 2012). As shown

10

50 Soil moisture (% vol)

0N

8

-1

DM yield (t ha )

0N

6 4 2 0

20 10

50

140N

Soil moisture (% vol)

8

-1

DM yield (t ha )

30

0

10

6 4 2 0

140N

40 30 20 10 0

10

50

LSM

Soil moisture (% vol)

8

-1

DM yield (t ha )

40

6 4 2 0

LSM

40 30 20 10 0

90

120 150 180 210 90 120 150 180 210 90

120 150 180 210

90

120

150

Day of the year

2001

2002

180

90

120

150

180

90

120 150 180 210

Day of the year

2003

Fig. 1. Simulated (lines) and measured (symbols) temporal dynamics of dry matter (DM) yield under three fertilizer treatments applied annually from 2001 to 2003. N, nitrogen; 0N, 0 kg N ha−1 (control); 140N, mineral fertilizer at 140 kg N ha−1 ; LSM, raw liquid swine manure at 140 kg total N ha−1 .

2001

2002

2003

Fig. 2. Simulated (lines) and measured (symbols) temporal dynamics of soil moisture (0-to-20-cm depth) under three fertilizer treatments applied annually from 2001 to 2003. N, nitrogen; 0N, 0 kg N ha−1 (control); 140N, mineral fertilizer at 140 kg N ha−1 ; LSM, raw liquid swine manure at 140 kg total N ha−1 .

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Q. Jing et al. / Field Crops Research 201 (2017) 10–18

Table 2 Average simulated (SIMav) and measured (MEASav) values, absolute mean error (ME), relative ME (ME%), root mean square error (RMSE), relative RMSE (RMSE%), and model efficiency (EF) for soil ammonium (NH4 -N) content, soil nitrate (NO3 -N) content, total mineral nitrogen (N) content, soil moisture content, and dry matter (DM) yield used to evaluate the performance of the STICS model. Variable

Unit

n

SIMava

MEASava

MEa

ME%

RMSEa

RMSE%

EF

NH4 -N NO3 -N Total N Moisture DM yield

kg ha−1 kg ha−1 kg ha−1 % vol t ha−1

90 90 90 75 9

11.2 19.5 30.4 23.3 6.4

13.0 8.0 21.0 22.8 6.2

1.8 −11.4 −9.5 0.5 0.1

14.1 −143.2 −45.1 2.0 2.0

10.2 17.0 19.6 5.3 1.1

78.4 213.3 93.7 22.8 17.1

0.60 −1.11 0.30 0.14 0.25

a

With the same unit as each variable.

by Frolking et al. (1998), an accurate simulation of soil moisture appears to be a key requirement for the reliable simulation of soil N flows.

3.1.3. Soil nitrogen The measured soil NH4 -N contents were very low and stable over time when no N fertilizer was applied (Fig. 3). Following the application of mineral fertilizer or LSM, the soil NH4 -N content increased immediately and thereafter decreased quickly owing to crop uptake and nitrification. These trends in soil NH4 -N content were reproduced well by model simulations for the three N fertilizer treatments (Fig. 3). There were some discrepancies between the simulated and measured values for the 140N and LSM treatments.

120

NH4-N (kg ha-1)

100

0N

80 60 40 20 0 -20 120

140N NH4-N (kg ha-1)

100 80 60 40 20 0 -20 120

NH4-N (kg ha-1)

100

LSM

80 60 40 20 0 -20 90

120

150

180

90

120

150

180

90 120 150 180 210

Day of the year

2001

2002

2003

Fig. 3. Simulated (lines) and measured (symbols) temporal dynamics of soil ammonium (NH4 -N) content (0-to-20-cm depth) under three fertilizer treatments applied annually from 2001 to 2003. Whiskers indicate the standard deviation of measurements. N, nitrogen; 0N, 0 kg N ha−1 (control); 140N, mineral fertilizer at 140 kg N ha−1 ; LSM, raw liquid swine manure at 140 kg total N ha−1 .

The average simulated NH4 -N content (11.2 kg NH4 -N ha−1 ) during the study was close to the measured value (13.0 kg NH4 N ha−1 ) (Table 2). The bias was small (ME = 1.8 kg NH4 -N ha−1 ), but the scattering was high (RMSE = 10.2 kg NH4 -N ha−1 ). Model efficiency was good (EF = 0.60), indicating that the model was able to capture the temporal dynamics well. However, RMSE% values were very high (>78%) indicating that some model improvements might be required. Simulations of soil NH4 -N content with high deviations from measured data have often been reported. Blombäck et al. (1995) reported a poor relationship in an annual cropping system between simulated and measured soil NH4 -N contents, with a regression coefficient of determination of less than 0.26. Similarly, a RMSE% greater than 140% was reported by Antonopoulos and Wyseure (1998) in a perennial crop system. However, all these results were considered to be reasonably accurate simulations of soil NH4 -N content, because the temporal dynamics of the simulated and measured NH4 -N contents were consistent. Soil NH4 -N content is difficult to simulate precisely, because the rapid oxidation rate of soil NH4 results in deviations among replicates in field measurements. The measured and simulated soil NO3 -N contents in the 0N treatment were very low throughout the three years of the study (Fig. 4). When mineral N fertilizer was applied, the measured and simulated soil NO3 -N contents increased immediately. Thereafter, the measured soil NO3 -N contents generally decreased faster than the simulated values did. In the LSM treatment, the measured soil NO3 -N contents did not increase significantly after the first application in the spring, whereas the simulated values increased. After the second application, which was done after the first cut, the measured and simulated values were better correlated, with an immediate increase followed by a slow decrease over approximately 30 d. The very fast decrease in soil NO3 -N content after application indicates fast crop N uptake that was probably associated with some N losses through denitrification or leaching. The average simulated soil NO3 -N content (19.5 kg N ha−1 ) was greater than the average measured value (8.0 kg N ha−1 ) (Table 2). The statistical criteria showed that soil NO3 -N contents were simulated poorly, with negative ME and EF and a high RMSE% (213%). The RMSEs between the simulated and measured soil NO3 -N contents were 19.1 kg N ha−1 (RMSE% = 275%) for timothy primary growth and 12.3 kg N ha−1 (RMSE% = 118%) for timothy regrowth (data not shown). Several hypotheses could explain this overestimation of soil NO3 -N content. For the primary growth, the overestimation may have been caused by the underestimation of biomass of the first cut (0.6 t ha−1 on average; Fig. 1) leading to an underestimation of crop N uptake. According to Fig. 5, the N uptake underestimation was between 20 and 60 kg N ha−1 , which was close to the overestimation of simulated soil NO3 -N (up to almost 50 kg N ha−1 in some cases; Fig. 4). This hypothesis, however, cannot explain overestimations of soil NO3 -N content during the regrowth period (Figs. 3 and 5), because simulated biomass and N uptake were not underestimated during the regrowth (second cut; Figs. 1 and 5). Another possible cause of the overestimation of soil NO3 -N content is that the STICS model, calibrated for nonconsecutive simulations

Q. Jing et al. / Field Crops Research 201 (2017) 10–18

40 20 0 -20 80

NO3-N (kg ha-1)

140N

60 40

140N-Cut 1 140N-Cut 2

LSM-Cut 1 LSM-Cut 2

100

80

60

40

20

20 0

0 0

-20 80

NO3-N (kg ha-1)

0N-Cut 1 0N-Cut 2

120

0N

60

Simulated soil mineral N (kg ha−1)

NO3-N (kg ha-1)

80

15

20

40

60

80

100

120

Measured soil mineral N (kg ha−1) LSM

60

Fig. 6. Measured versus simulated soil mineral nitrogen (N; nitrate + ammonium) content (0-to-20-cm depth) during timothy primary growth (Cut 1) and regrowth (Cut 2) with three N treatments applied for three consecutive years (2001–2003). The solid line is the 1:1 line between simulated and measured values.

40 20 0 -20 90

120

150

180

90

120

150

180

90

120

150

180

210

Day of the year

Fig. 4. Simulated (lines) and measured (symbols) temporal dynamics of soil nitrate (NO3 -N) content (0-to-20-cm depth) under three fertilizer treatments applied annually from 2001 to 2003. Whiskers indicate the standard deviation of measurements. N, nitrogen; 0N, 0 kg N ha−1 (control); 140N, mineral fertilizer at 140 kg N ha−1 ; LSM, raw liquid swine manure at 140 kg total N ha−1 .

140

Simulated N uptake (kg ha−1)

120

0N-Cut 1

140N-Cut 1

LSM-Cut 1

0N-Cut 2

140N-Cut 2

LSM-Cut 2

100 80 60 40

ME = 13.4 ME% = 23.3 RMSE = 30.6 RMSE% =53.2 EF = 0.29

20 0 0

20

40

60

80

Meausred N uptake (kg

100

120

140

ha−1)

Fig. 5. Measured versus simulated nitrogen (N) uptake for the first and second cuts of timothy with three N treatments applied for three consecutive years (2001–2003). The solid line is the 1:1 line between simulated and measured values.

with annual reinitializations (Jégo et al., 2013), was applied for consecutive simulations in the present study; that situation may explain the discrepancy between the measured and simulated crop N uptake values during primary growth. In a previous study with annual reinitialization, Jégo et al. (2013) noted that the performance of STICS for timothy was better for primary growth than for regrowth. This previous study suggested that this poorer model performance for regrowth than for primary growth could be due to an inaccurate simulation of plant reserves. When the model is run continuously this accurate simulation of plant reserve could affect both primary growth and regrowth and explain why the bias

of simulated DM yield at first cut (0.6 t ha−1 ) was greater than the value reported by Jégo et al. (2012) (0.2 t ha−1 ). The use of equations proposed by Strullu et al. (2014) for improving the simulation of perennial organs and their relationship with non-perennial ones of Miscanthus giganteus in STICS could be a promising way for improving the simulation of other perennial crops, such as timothy, in STICS. These hypotheses, however, cannot explain alone the overestimation of soil NO3 -N content, especially during the regrowth. A new calibration of the parameters of the maximum and critical N dilution curve may also be envisaged. Finally, several other hypotheses, such as, an overestimation of soil N mineralization or an underestimation of gaseous N emissions could explain this discrepancy between the simulated and measured soil NO3 -N contents. This study is one of the first evaluations of STICS for the separate simulation of NO3 − and NH4 -N. Previous studies evaluated mainly the performance of the STICS model in simulating total soil mineral N (NO3 + NH4 ). In those studies, the RMSE% ranged from 6% to 74% and the EF ranged from −20 to 0.90 for various soils under annual crops receiving mineral N fertilization (Beaudoin et al., 2008; Constantin et al., 2012; Jégo et al., 2008, 2012). Other studies, using different crop models, also reported poor simulations of soil NO3 -N content (Blombäck et al., 1995), with high RMSE% values ranging from 150% to 200% (Antonopoulos and Wyseure, 1998). The average simulated soil mineral N (NO3 -N + NH4 -N) content (30.4 kg ha−1 ) was greater than the average measured value (21.0 kg ha−1 ) (Fig. 6; Table 2). This overestimation was due mainly to NO3 -N overestimation and was more pronounced during primary growth than during the regrowth period. Beaudoin et al. (2008) also reported a tendency toward the overestimation of soil mineral N using STICS for long-term consecutive simulations. The statistical criteria of our study (RMSE = 19.6 kg N ha−1 ; RMSE% = 93.7%; EF = 0.30) showed that the model’s performance was comparable with the performance reported by Beaudoin et al. (2008), Constantin et al. (2012), and Coucheney et al. (2015) (RMSE ranging from 16 to 40 kg ha−1 ; RMSE% ranging from 36.4% to 103.4%; EF ranging from −2.6 to 0.66). The model tended to slightly underestimate the gaseous N emissions, a tendency that could partially explain the overestimation of soil mineral N (Fig. 7). The simulated values were significantly correlated with the measured values with a slope 1.26, indicating that the trend in N emissions was reproduced well by the model. The model’s performance was good, with

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Q. Jing et al. / Field Crops Research 201 (2017) 10–18

50

Simulated N emissions (kg ha−1)

0N

140N

LSM

y = 1.26x - 0.70 R² = 0.82

40

30 ME = -1.1 ME% = -15.7 RMSE = 5.2 RMSE% =75.6 EF = 0.5

20

10

0 0

10

20

30

40

50

Measured N emissions (kg ha−1) Fig. 7. Measured versus simulated cumulative gaseous nitrogen (N) emissions (nitrous oxide + ammonia) in a sandy loam soil with three N treatments applied for three consecutive years (2001–2003). Cumulative emissions were calculated for the 21-d period following each fertilizer application in spring and summer (Chantigny et al., 2007). The bold line is the regression trend line between measured and simulated values. The solid line is the 1:1 line between simulated and measured values. ME, mean error; ME%, relative mean error; RMSE, root mean square error; RMSE%, relative root mean square error; EF, model efficiency.

an EF of 0.56, but the scattering was high (RMSE = 5.2 kg N ha−1 ; RMSE% = 75.6%). The same ranges of statistical criteria were also reported in studies using other models (Frolking et al., 1998). The DNDC (DeNitrification-DeComposition) and DayCent models were reported to overestimate N emissions in comparison with the measurements, with RMSE% values greater than 132% and 57%, respectively (Abdalla et al., 2010). Our results were obtained with the current version of STICS (v8.3), and improvements concerning gaseous N emissions in the next version should allow the separate estimation of N2 O and NH3 emissions. Although the statistical criteria for the simulation of soil N attributes were not very good, our simulation results were generally better than or comparable to those reported in other studies using STICS (Beaudoin et al., 2008; Jégo et al., 2008, 2012) or other models (Abdalla et al., 2010; Antonopoulos and Wyseure, 1998; Blombäck et al., 1995; Frolking et al., 1998). The poor statistical criteria for simulations of soil mineral N and gaseous N emissions may be explained by the rapid rates of N transformation, which can lead to large differences between measured and simulated values. This result also suggest that, although the use of the snow-cover model to preprocess the weather data improved the simulation of soil temperature and soil moisture (Jégo et al., 2014), further development and improvement of the STICS model might be required to accurately simulate N processes in cold, humid continental climates. Additional field data, including winter measurements, are required to propose and test model improvements more precisely. 3.2. Water and nitrogen balances From the evaluation of the model’s performance, the changes over time and the effects of the N fertilizer treatments were generally captured well by the model. Although the discrepancies between simulations and measurements were high for some variables, especially soil mineral N and N gaseous emissions, the simulation uncertainty of these variables, reflected by RMSE values comprised between 5 and 20 kg N ha−1 , remained significantly lower than the total annual N input and output, which were between 90 and 290 kg N ha−1 depending on the year and the fertilizer treatment. We considered this level of performance to be sufficient for analyzing the complete water and N balances simulated by STICS.

Fig. 8. Annual (January–December) simulated water balance in a timothy cropping system under a control treatment (0N), mineral fertilization at 140 kg N ha−1 (140N), and fertilization with liquid swine manure at 140 kg total N ha−1 (LSM). Total water input is indicated as T. For each treatment and T, the left bar indicates 2001 results, the middle bar indicates 2002 results, and the right bar indicates 2003 results.

Several components of the water balance were analyzed (Fig. 8). Drainage represented a large fraction of the water balance and occurred mostly during the timothy primary growth period, owing mainly to the snow melt in early spring. Total drainage varied among years and was the greatest in 2003, in response to the greater rainfall in that year. The fertilizer treatments had only minor effects on water output components. Plant biomass was smaller, so plant transpiration was slightly lower and soil evaporation and drainage were slightly higher in the 0N treatment than in the other two treatments. The simulations showed that the interannual variations in rainfall had a greater effect on the water balance than the N treatments did. Nitrogen inputs included the initial crop N, soil mineral N, soil N mineralization, N fertilization, and N from rainfall. The simulated initial plant N was greater with N fertilizer input than without (Fig. 9). Initial soil mineral N was similar across N treatments, but it varied from year to year and was smaller in 2003 than in the other two years. Nitrogen mineralization varied between 25 and 47 kg N ha−1 among years, with a lower rate in 2002 than in the other two years. The simulated values of N mineralization were comparable with those (12–82 kg N ha−1 yr−1 ) reported by Gabrielle et al. (2005) using the CERES-N model for wheat–maize rotation systems. However, annual N mineralization of 180–381 kg N ha−1 was reported in forage grass systems in the Netherlands (Verloop et al., 2014). The smaller amounts of mineralized N in our study could be explained by the shorter period with temperatures favorable for soil N mineralization (Kirschbaum, 1995) in our study area, where the soil temperature stays below the freezing point for about five months during the winter (Environment Canada, https://weather.gc.ca/). Dessureault-Rompré et al. (2013) reported that potential soil mineralization is less than 60 kg N ha−1 in Eastern Canada from May to August, which represents most of the growing season. The mineralization of soil N was greater in the 0N and 140N plots than in the LSM plots (Fig. 8). This difference could be explained by the slower decomposition of the added residue in the LSM treatment, by the use of some mineralized N by microorganisms to decompose the added residue, or by both factors combined. Crop N uptake, when averaged across years, was highest with mineral fertilization (total uptake [sum of two cuts] of 137 kg N ha−1 ), intermediate with LSM (90 kg N ha−1 ), and lowest with the 0N treatment (38 kg N ha−1 ). The ratio of N uptake to available mineral N (sum of N derived from fertilization, mineralization

Q. Jing et al. / Field Crops Research 201 (2017) 10–18

Fig. 9. Annual (January–December) simulated mineral nitrogen (N) balance in a timothy cropping system under a control treatment (0N), mineral fertilization at 140 kg N ha−1 (140N), and fertilization with liquid swine manure at 140 kg total N ha−1 (LSM). For each treatment, the left bar indicates 2001 results, the middle bar indicates 2002 results, and the right bar indicates 2003 results.

of soil organic matter, rainfall, and initial soil inorganic N) was similar for the mineral fertilizer treatment (0.63) and the LSM treatment (0.61) and lower for the control treatment (0.52). This difference may be explained by the greater N uptake ability of plants when fertilized with N. A previous study showed that certain N fertilizer rates were required to maximize timothy growth, resulting in shoot N concentration equal to or greater than optimal N concentration (Bélanger and Richards, 1997). Nitrogen leaching losses, which was the only N loss not experimentally measured, were very small compared with other output components. The low leaching could be explained by the rapid crop N uptake in the spring (Figs. 2 and 3), when most drainage occurred. In the summer, soil N mineralization increased owing to increased soil temperature, but during that period, crop N uptake was high and drainage was low because of high evapotranspiration. Final plant N was greater in the 140N treatment than in the LSM treatment. Nitrogen emissions were greater in the LSM treatment than in the 140N treatment. The higher N emissions under LSM were caused mainly by greater NH3 volatilization. In the one to two weeks after application, fertilizer volatilization declined exponentially to 0, suggesting that deep LSM application instead of a surface application would reduce N volatilization, as suggested by Chantigny et al. (2007). 4. Conclusions The present study showed that the STICS model, calibrated for simulating timothy growth and coupled with a simple snow-cover model, did a good job of simulating biomass and soil moisture dynamics in a timothy cropping system in a cold, humid continental climate. The model’s performance in simulating N gaseous emissions and soil mineral N (NO3 − -N + NH4 + -N) content was not

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as good, especially for soil NO3 − , which was overestimated for the primary growth of timothy for the three consecutive years. This overestimation was possibly caused by the underestimation of biomass (and thus crop N uptake) during the primary growth of timothy, or by the poor simulation of soil N processes under this climate. Concerning biomass and N uptake, two directions for model improvements can be envisaged. First, the use of better equations for predicting perennial organs as proposed by Strullu et al. (2014) and secondly, a verification of the maximum and critical N dilution curve used to calculate the timothy N uptake for various N rate and multiple years. Concerning soil N processes, the poor simulations of soil mineral N and gaseous N emissions have often been reported (Abdalla et al., 2010; Blombäck et al., 1995; Constantin et al., 2012; Frolking et al., 1998), and may be explained by the rapid rates of N transformation, which could lead to large differences between measured and simulated values. Additional experimental data, including data from winter periods, would be required to better understand and characterize these processes in perennial crop fields under a northern climate. The use and evaluation of an improved version of the STICS model for N2 O simulation, which is currently under development, should also be considered. Despite these inaccuracies of the model, gaseous N emissions were simulated efficiently (EF = 0.56), and the model reproduced well the trends toward higher emissions in the LSM treatment than in the mineral N or control treatment. The simulation results also complete the results of the previous study by Chantigny et al. (2007) by showing that NO3 leaching was very low for all treatments and represented a small portion of the total N balance. The overestimation of simulated soil nitrate content would have no effect on this conclusion since a lower soil nitrate concentration would have probably resulted in an even lower nitrate leaching. The STICS model has been evaluated mainly in annual cropping systems for yield, soil N content, and soil moisture (Beaudoin et al., 2008) but has not been extensively tested on perennial crops such as timothy. The present study, which is a significant step in the overall objective to validate the STICS model for simulating water and N processes in northern perennial forage systems, paves the way for future research aimed at using this model to improve the management of forage systems in the face of climate change. In addition, these results could serve as a reference to evaluate the next version of STICS, which should include improved N2 O emission predictions.

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