Development and evaluation of a new Canadian spring wheat sub-model for DNDC

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1 Development and evaluation of a new Canadian spring wheat sub-model for DNDC R. Kröbel 1, W. N. Smith 1, B. B. Grant 1, R. L. Desjardins 1, C. A. Campbell 1, N. Tremblay 2, C. S. Li 3, R. P. Zentner 4, and B. G. McConkey 4 1 Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food, Canada, K.W. Neatby Building, 960 Carling Avenue, Ottawa, Ontario, Canada K1A 0C6; 2 Horticulture Research and Development Centre, Agriculture and Agri-Food, Canada, 430, Gouin Blvd, Saint-Jean-sur-Richelieu, Quebec, Canada J3B 3E6; 3 Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, 8 College Road, Durham, NH , USA; and 4 Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food, Canada, PO BO 1030, Swift Current, Saskatchewan, Canada S9H 32. Received 6 December 2010, accepted 27 April Kro bel, R., Smith, W. N., Grant, B. B., Desjardins, R. L., Campbell, C. A., Tremblay, N., Li, C. S., Zentner, R. P. and McConkey, B. G Development and evaluation of a new Canadian spring wheat sub-model for DNDC. Can. J. Soil Sci. 91: In this paper, the ability of the DNDC model (version 93) to predict biomass production, grain yield and plant nitrogen content was assessed using data from experiments at Swift Current, Saskatchewan, and St-Blaise, Quebec, Canada. While predicting wheat grain yields reasonably well, the model overestimated the growth of above-ground plant biomass and nitrogen uptake during the first half of the growing season. A new spring wheat sub-model (DNDC-CSW) was introduced with a modified plant biomass growth curve, dynamic plant C/N ratios and modified plant biomass fractioning curves. DNDC-CSW performed considerably better in simulating plant biomass [modeling efficiency (EF): 0.75, average relative error (ARE): 6.0%] and plant nitrogen content (EF: 0.61, ARE: 2.7%) at Swift Current and St- Blaise (EF of 0.75 and ARE of 2.3%), compared with DNDC 93 (biomass SC: EF 0.49, ARE 17.1%, SB: EF 0.02 ARE 33.4%). In comparison with DNDC 93, DNDC-CSW better captured inter-annual variations in crop growth for a range of wheat rotations, increasing the EF from 0.32 to 0.52 for grain and from 0.35 to 0.39 for straw yields. DNDC-CSW also performed considerably better than DNDC 93 in estimating soil carbon changes at Swift Current. Hence, DNDC-CSW has the potential to improve the performance of DNDC 93 in simulating wheat biomass, plant nitrogen, yield and soil carbon at various Canadian sites. Key words: DNDC, model, spring wheat, crop growth, biomass, plant nitrogen, soil moisture Kro bel, R., Smith, W. N., Grant, B. B., Desjardins, R. L., Campbell, C. A., Tremblay, N., Li, C. S., Zentner, R. P. et McConkey, B. G De veloppement et évaluation d un nouveau module de DNDC pour le ble de printemps canadien. Can. J. Soil Sci. 91: Nous avons e valué la capacite du modèle DNDC (version 93) a` pre dire la production de biomasse, le rendement en grain et la teneur en azote de la plante entière, au moyen d expériences réalise es a` Swift Current et a` Saint- Blaise, au Canada. Le modèle a permis une pre diction acceptable du rendement en grain du blé, mais il a surestimé la croissance en biomasse des parties aériennes ainsi que le prélèvement de N durant la premie` re moitie de la saison de culture. Nous avons élabore pour le ble de printemps un nouveau sous-mode` le (DNDC-CSW), en modifiant la courbe de croissance en biomasse de la plante entie` re, en utilisant des rapports C/N de la plante entière dynamiques et en modifiant les courbes de fractionnement de la biomasse de la plante. Le DNDC-CSW a permis une simulation bien meilleure de la biomasse, avec une efficacite de modélisation (EF) de 0,75 et une erreur relative moyenne (ERM) de 6,0%, ainsi que de la teneur en azote de la plante (EF0,61, ERM2,7%), à Swift Current et à Saint-Blaise (EF0,75, ERM2,3%), par rapport au DNDC 9.3 (biomasse a` Swift Current: EF0,49, ERM17,1%; biomasse a` Saint-Blaise: EF0,02, ERM 33,4%). Par rapport au DNDC 9.3, le DNDC-CSW a mieux capture les variations interannuelles de la croissance de la plante dans diverses rotations de blé, en accroissant l EF de 0,32 a` 0,52 pour le rendement en grain et de 0,35 a` 0,39 pour le rendement en paille. Le DNDC-CSW a e galement permis une estimation bien meilleure des changements survenant dans la teneur en carbone du sol a` Swift Current. Le sous-mode` le DNDC-CSW pourrait donc donner de meilleurs re sultats que le mode` le DNDC 93 pour la simulation de la biomasse du ble et de la teneur en carbone du sol dans les stations canadiennes. Mots clés: DNDC, modèle, ble de printemps, croissance de culture, biomasse, azote dans la plante, humidite du sol Carbon and nitrogen models, which are used to predict N 2 O emissions and soil carbon change, can also provide valuable information on biomass production. Such information is useful for assessing the potential of bioethanol production from crop residues (Saha et al. 2005; Fan et al. 2006). Crop residues have been shown to have a distinct influence on nutrient cycling, carbon Abbreviations: ARE, average relative error; Cont W, continuous wheat; CSW, Canadian spring wheat; DM, dry matter; EF, modeling efficiency; F, summer fallow; F-W, fallow-wheat; F-W-W, fallow-wheat-wheat; PGI, plant growth index; SOC, soil organic carbon; TD, temperature degree; TDD, temperature degree days Can. J. Soil Sci. (2011) 91: doi: /cjss

2 504 CANADIAN JOURNAL OF SOIL SCIENCE sequestration in soil, soil quality and soil fertility (Campbell et al. 1997; Soon et al. 2009; Lemke et al. 2010). Additionally, crop residues have been of interest because of their considerable influence on soil erosion (Wall et al. 1997) and soil moisture, and thus their particular importance for semiarid environments (Flury et al. 2009). According to Osborne et al. (2007), accurate simulation of crop biomass is essential when the aim is to represent the complete cycling and storage of plant nutrients. Currently, many agricultural models are developed, either to investigate crop production with simulation of limited soil process mechanisms [e.g., CERES (Ritchie and Otter 1985), DSSAT (Jones et al. 2003), and DAISY (Abrahamsen and Hansen 2000)], or they are mainly soil process models that also simulate crop growth [e.g., DNDC (Li et al. 1992a, b) and DAYCENT (Parton et al. 1998)]. Although accurate estimation of the soil processes is necessary to properly estimate crop growth, simplifications are often made in order to keep the inputs to the models manageable. This can lead to poor estimations of processes that are not directly tied to the desired output, and corrections are often made through model output calibration. The original version of the DNDC model (Li et al. 1992a, b) was developed to simulate soil carbon pools and denitrification processes, but did not include a submodel for crop growth. An empirical crop growth function was later added (Li et al. 1994), the underlying principles of which have been retained up until now. The empirical formulation of potential crop growth in the DNDC model is entirely temperature driven (while water and nitrogen stress are formulated as limiting factors for the potential growth), and therefore requires only minimal data inputs to allow assessment of the impacts of crop growth and management on soil processes. DNDC has frequently been used to simulate soil moisture (Tonitto et al. 2007; Kro bel et al. 2010), soil carbon (Li 2008; Wang et al. 2008), soil nitrogen (Li et al. 1994, 2006; Giltrap et al. 2010) and greenhouse gas emissions (Li 1995, 2000; Li et al. 1996; Grant et al. 2004; Babu et al. 2005; Beheydt et al. 2007). Although a phenological crop growth sub-model was introduced by Zhang et al. (2002), this sub-model was never utilized. In a soil process model, the crop model is required to simulate the exchange of nitrogen and carbon at the soil/ plant interface (Li et al. 1994). According to Li et al. (1994), plant nitrogen uptake from the dissolved inorganic nitrogen pools in the soil is the key crop growth process that links climate and soil status. After harvest, all calculated residue biomass (crop roots and the straw fraction returned to the field), is transferred to the soil carbon pools eventually (through decomposition or tillage events) representing one of the main sources of soil carbon gain in the model (Li et al. 1994). The linkage between simulating crop growth and DNDC greenhouse gas emission estimates has been described extensively (Li et al. 1996; Grant et al. 2004; Babu et al. 2005; Beheydt et al. 2007). Testing of the DNDC 93 model indicated shortcomings in the simulation of above-ground biomass during the first half of the growing season. Therefore, the objective of this study was to construct a new empirical sub-model of DNDC (DNDC-CSW) to allow for a more accurate estimation of spring wheat growth and nitrogen uptake in Canadian agroecosystems, while model performance with regard to soil moisture, soil carbon and soil nitrogen simulation should not be adversely affected. To accomplish this objective, focus was placed on the inclusion of an empirically fitted biomass growth curve, dynamic fractioning of plant biomass pools and plant C/N ratios, as well as dynamic root development and rooting depth. To ensure that the new development is not merely a site-specific adaptation, it was tested on a variety of cropping systems on two distinctly different sites in western and eastern Canada (different climate regime, different soil properties). MATERIALS AND METHODS Crop Growth in DNDC 93 To run the DNDC 93 model, a set of minimum input data is required. Regarding climatic data, daily minimum and maximum temperature and daily precipitation are required, but if wind speed, solar radiation and air humidity are available, this information can be used to better estimate potential evapotranspiration. For soil properties, soil type, clay content, bulk density, field capacity, wilting point, and soil organic carbon (SOC) content at the start of the simulation are essential. Furthermore, nitrogen pool concentrations (nitrate and ammonium) and the C/N ratios of SOC pools should be specified. For the cropping system, field management information (seeding date, harvest date, level of fertilization, date and type of tillage, etc.) should be provided; crop-specific parameters required are the potential yield, heat requirement (degree days), C/N ratios of the biomass fractions, and crop water requirement (see Table 1). Since its first release, the default version of DNDC 93 referred to in this paper has undergone several improvements (Smith et al. 2010). In DNDC 93, empirically calculated crop growth is driven by the accumulation of growth degree days relative to 08C (in DNDC denoted as temperature degree days). Temperature degree days (TDD), therefore, determine the length of crop growth and the plant growth index (PGI). Potential grain yield, biomass fractions (grain, straw and root) and their C/N ratios are used to calculate the total plant nitrogen demand. Furthermore, the potential yield is used to calculate a potential biomass growth curve (Fig. 1a), thus formulating a potential daily crop growth rate (Li et al. 1994). The potential carbon biomass gain is expressed in

3 KRÖBEL ET AL. * CANADIAN SPRING WHEAT SUB-MODEL FOR DNDC 505 Table 1. Soil and calibrated crop parameters used for simulating treatments in Swift Current and St.-Blaise-Sur-Richelieu Parameter Unit Swift Current St.-Blaise Bulk density g cm Soil ph Soil organic carbon g kg Clay fraction % Field capacity % WFPS z Wilting point % WFPS st wheat cultivar ( ) 2nd wheat cultivar( ) Wheat cultivar Yield kg C ha Temperature degree days 8C Water requirement g g DM Grain fraction % Shoot fraction % Root fraction % Treatment Grain C/N Shoot C/N Root C/N Swift Current CONT-W (NP) CONT-W (P) F-W (NP) F-W-W (NP) F-W-W (P) St-Blaise kg N ha kg N ha kg N ha kg N ha z Water-filled pore space. kilograms of carbon per hectare, and is assumed to represent 40% of the actual plant biomass. Daily nitrogen demand is derived from potential daily crop growth and the total plant nitrogen demand (Li et al. 1994), and is adjusted by water and temperature stress fractions. Water stress is calculated on the basis of crop water requirement and potential evapotranspiration. For temperature stress, crop growth for wheat is assumed to be optimal at a surface soil temperature (02 cm depth) of 188C, and inhibited at an increasing rate as temperature deviates from 188C. In general, nitrogen uptake by crops occurs from the soil nitrogen pools [nitrate and ammonium and ammonia (Li et al. 1994)], as well as nitrogen fixed by microbes, uptake of clay-adsorbed nitrogen and uptake of NH 3 from the atmosphere. Soil nitrogen uptake is derived from an exponential function which assumes that 50% of the crop nitrogen demand comes from the soil surface layer (02 cm depth). Nitrogen deficits are, in part, alleviated by nitrogen supplied from clay-fixed NH 4 and plant NH 3 uptake. The remaining calculated nitrogen deficit is either diverted to lower soil layers or cannot be met until the next day (Li et al. 1994). Daily plant biomass is derived from the cumulative daily nitrogen uptake and the estimated plant C/N ratio (Fig. 1b) (Li et al. 1994). Using the default calculated biomass fractions (Fig. 1c), root, grain and straw biomass are derived. Root mass is distributed over all soil layers (050 cm) using a logarithmic function (50% in the surface layer). Therefore, roots are present in all soil layers as long as there is root biomass. Single fraction gains are calculated on a daily basis. DNDC-CSW Development Development of the new crop growth routine began with the replacement of potential biomass growth and adjustment of biomass fraction C/N ratios (Table 2). Additionally, the biomass fraction estimates were modified (influencing daily crop nitrogen demand through differing C/N ratios) and root and grain fraction became subject to water stress. Next, we accounted for nitrogen in the grain seed and introduced a new concept to calculate root biomass distribution over depth and time. Finally, the simulated soil depth in the DNDC model was increased from 50 to 90 cm to more accurately account for wheat root growth. In order to make these changes in DNDC more flexible, we have kept the original crop growth model, but added a submodel as a stand-alone section in the DNDC source code. This submodel can easily be duplicated to create other crop-specific submodels, provided that the data are available for empirically fitting the described relationships.

4 506 CANADIAN JOURNAL OF SOIL SCIENCE Fig. 1. Conceptual developments in DNDC-CSW in comparison to DNDC 93 calculations. (a) potential biomass growth, (b) C/N ratio of plant and biomass fractions, (c) biomass fractioning in DNDC 93, (d) biomass fractioning in DNDC-CSW, (e) leaf fraction of the simulated straw fraction, (f) distribution of root biomass in DNDC-CSW over depth and time. Potential Biomass Growth A lysimeter study conducted at Swift Current, in which the growth of spring wheat as a function of nitrogen fertility and water availability were assessed (Campbell 1977a, b; Campbell and Paul 1978), was used to derive a maximum potential plant growth formulation, i.e., no water and no nitrogen stress. For each of the six biomass measurements in time, the highest measured value was used. A polynomial trend-line-fitting exercise was used to obtain Eq. 1 in order to replace, in the CSW submodel, the polynomial equation that is presently employed in the DNDC 93 crop growth submodel (Li et al. 1994). As is the case in DNDC 93, the potential gain in biomass was based on growth degree days relative to 08C, and calculated for every accumulated temperature degree (TD). The plant growth index (PGI) was used to represent the fraction of total accumulated TDD from the (input) maximum required plant growth TDD.

5 KRÖBEL ET AL. * CANADIAN SPRING WHEAT SUB-MODEL FOR DNDC 507 Table 2. Differences between DNDC-CSW and the default DNDC crop growth model Crop model equation Potential biomass growth Water stress on biomass growth Dynamic biomass fraction C/N ratio Flexible plant C/N ratio Crop N demand N provided through seeds Grain fraction Water stress influence on grain fraction development N stress influence on grain fraction development Leaf fraction Root fraction Water stress influence on root fraction development Root mass per layer Rooting depth Crop N uptake in accordance to root presence N uptake due to grain filling Replaced New (for PGI 50.32) 2 ffpgi TDD (3:69E10TD 4 i )(3:09E07TD3 i ) 3 4(1:29E04TD 2 i )(1:88E02TD i ) 5 i1 1:71 (for PGI 0.32) 2 ffpgi TDD (3:33E11TD 4 i )(1:71E07TD3 i ) 3 4(3:13E04TD 2 i )(2:34E01TD i ) 5 (1) i1 48:3 where PGI is the plant growth index (01); ffpgi is the daily potential biomass growth per temperature degree (kg C ha 1 d 1 ) and TD is 1 temperature degree (e.g., if one day contributes 29 TDD, then this calculation is repeated for 29 TD in 8C). Using the newly developed equation, the initial biomass development shortly after emergence (Fig. 1a) was much slower than for the default calculation, representing the delayed emergence and growth of spring wheat in Canadian agricultural ecosystems [as indicated by Wang et al. (2009)]. Accordingly, higher growth rates were needed in the vegetative stage in order to fit the measured data more precisely. In DNDC-CSW (in contrast to DNDC 93), the potential growth curve was designed to be adjustable using the potential yield as input. Additionally, the water and temperature stresses were applied to the potential daily crop growth rate directly, before calculating the crop nitrogen demand. The water stress in DNDC-CSW was based on a dynamic crop water requirement for each day of the growing season, using the daily plant biomass. In contrast, DNDC 93 utilized a stable water requirement for every temperature degree, using the maximum potential (seasonal) biomass. C/N Ratios of the Biomass Fractions Using measured nitrogen content of plant parts from the lysimeter study of Campbell et al. (1977a, b) and Campbell and Paul (1978), dynamic C/N ratios for each of the biomass fractions were calculated using Eq. 2 (Fig. 1b). The average plant C/N ratio was calculated from the C/N ratios of the biomass fractions and their current partition of the total biomass. C=Nstraw(71PGI 3 )(13:8PGI 2 )(14:3PGI)6:69 (if PGI 50.25) C=Nroot(15:7PGI 2 )(16:6PGI)20 (for PGI0.25) C=Nroot(82:8PGI 2 )(134PGI)3:22 (for PGI0.50) C=Ngrain(11:98PGI 2 )(16:69PGI)10:2 (2) where C/N-root is the root C/N ratio, C/N-straw is the straw C/N ratio, C/N-grain is the grain C/N ratio, and PGI is the plant growth index. Although the model was enabled to calculate the grain C/N ratio for low values of PGI, the average plant C/N ratio was not influenced until PGI reached 0.5 when grain biomass became available. The increasing C/ N ratios of straw and roots allowed for nitrogen to later translocate into the grain. Since not all the nitrogen required by the grain was made available through a widening of the C/N ratio of the other two fractions, nitrogen demand from soil will increase during the grain-filling process. As was the case for the biomass fractions, inputting specific C/N ratio target values automatically adjusts the C/N ratio calculation. When water stress inhibited grain fraction development, grain C/N ratio was designed to decrease simultaneously. Biomass Fractioning In DNDC 93 the above-ground biomass constituted the major portion of total plant biomass; roots were a minor fraction (Fig. 1c). Using data published by Campbell and de Jong (2001) for spring wheat, a new root fraction equation was derived (Eq. 3). The new root fraction equation was conceptualized to change the slope of its curve according to the target root fraction input (Fig. 1d). The adjustment of root fractioning was also delayed under water stress. This should better represent natural plant growth behavior where the plant would grow more roots than above-ground biomass under water stress (Campbell et al. 1983). Grain fractioning was adjusted in a similar manner as root fraction (Fig. 1d). An equation for grain dry matter (DM) accumulation was employed (Bauer et al. 1985), and reformulated for DNDC (Eq. 3). For the calculation of the grain fraction in the model, the slope of the curve was also adjusted according to the target grain fraction input. As was the case for the root fractioning, grain fractioning was also delayed by water stress. Moreover,

6 508 CANADIAN JOURNAL OF SOIL SCIENCE in order to represent the process of nitrogen transfer from vegetative plant biomass to the grain, in addition to further nitrogen uptake, the grain fractioning was also inhibited by nitrogen stress. Straw fraction was calculated as the difference between the total and the sum of root and grain fraction (Eq. 3). f _root(110:4pgi 3 )(145:44PGI 2 )(40:61PGI) 90:87 f _straw1(f _rootf _grain) f _leaf(41:45pgi 6 )(126:00PGI 5 ) (136:32PGI 4 )(58:07PGI 3 ) (5:42PGI 2 )(0:02PGI)1 (if PGI]0.5) f _grain35:36 PGI 5 39:26 PGI :55 PGI 3 2:46 PGI :47 PGI (3) 2 where f_root is the daily calculated root fraction (in%), f_grain is the daily calculated grain fraction (in%), f_straw is the daily calculated straw (stemleaf) fraction (in%), f_leaf is the daily calculated leaf fraction from the straw biomass (in%), and PGI is the plant growth index The straw fraction was subdivided into stem and leaf fractions. The leaf fraction was used to calculate plant NH 3 uptake from the atmosphere. In DNDC 93, leaf fraction constituted about 40% of the straw biomass at the beginning of the growing season, declining slightly until PGI 0.5, then decreasing rapidly to a target fraction of about 18% (Fig. 1e). In reality, the aboveground wheat biomass consists almost entirely of leaves at the beginning of the growing season. There is a lack of published data in this regard; thus, leaf fraction as percent of straw biomass was estimated (Eq. 3) based on personal communication (Dr. C. A. Campbell, ECORC, Agriculture and Agri-Food Canada). Differences in leaf fraction estimates by DNDC 93 and DNDC-CSW were most pronounced in the vegetative stage, but fairly similar during the reproductive phase (Fig. 1e). Crop Nitrogen Uptake In DNDC 93, crop nitrogen demand combined the potential crop growth, the potential daily nitrogen demand, and the temperature and water stresses, while in DNDC-CSW the average plant C/N ratio was used to calculate the crop nitrogen demand from the (already stress inhibited) potential biomass growth. As a new concept in DNDC-CSW, nitrogen initially provided in the seeds was accounted for. The latter was set at 2.5 kg Nha 1, representing a rough average of the seed C/N ratio and quantity of grains seeded in Canadian prairie agricultural wheat cropping systems. Similar to DNDC 93, the crop nitrogen demand was subdivided into nitrogen demand by soil layer. However, in DNDC- CSW the amount of roots in the soil profile was used to subdivide nitrogen demand into specific soil layers, whereas in DNDC 93 crop nitrogen demand was distributed by an exponential function. Root Growth over Time and Depth A new concept in DNDC-CSW was added to estimate root growth over time and depth. In DNDC 93, roots were distributed exponentially over the soil layers throughout the growing period and crop nitrogen demand was calculated independently of the root biomass present in the layer. No data were available for root mass with rooting depth from the Swift Current rotation experiment. Consequently, data published by Chaudhary and Bhatnagar (1980) were used as a reference to develop an equation for wheat root mass distribution at approximately 50% of the growth period (Eq. 4). While the first part of the equation described the shape of the curve, the second part specified the point where the curve crosses the y axis. In this respect, the point on the x axis moved with increasing PGI from zero to its target value. Since this means that negative values would be calculated for deeper soil layers, the model was instructed to consider them as zero. root_fraction (1:04E05SD 3 ) 4@ (3:45E03SD 2 ) AGrowthstage5 (4) (0:38SD)15 where Growthstagemin(1;PGI 2); root_fraction is the root fraction per cm of soil depth from the total root biomass (in percent), SD is the soil depth (in cm), PGI is the plant growth index and Growthstage is a plant growth index reduction factor (with a maximum value of 1). Note that the root_fraction results are normalized afterwards to add up to 100%. The relative root biomass distribution was then used to calculate the actual root biomass distribution from the calculated root fraction/biomass (Fig. 1f). Note that a PGI of 0.5 was set as maximum for this calculation, which is why the root distribution remains unchanged for the remainder of the growing period. Parameter Sensitivities The UCODE_2005 software was used to conduct an automated analysis of the input parameter sensitivity in a manner similar to that employed by Kro bel et al. (2010). The analysis investigated the effect of crop growth and soil parameters (using a perturbation of 5%) on biomass, plant N, as well as grain and straw yield for the treatments at Swift Current. Results presented here are not directly transferable to other

7 KRÖBEL ET AL. * CANADIAN SPRING WHEAT SUB-MODEL FOR DNDC 509 climates or sites, but only serve as a relative indicator of model behavior. All sensitivities are reported as a fraction of 1, indicating their importance with respect to the most sensitive parameter. A full Monte Carlo analysis across climatic zones and soils was not within the scope of this paper. Of the input parameters that are directly related to the submodel development, the parameters for growing degree day requirement (first and second crop) were among the most sensitive (0.61 and 0.95, respectively). This should be expected as the TDD requirement determines the length of the crop growth curve, and therefore subsequently the increase in daily potential crop growth and the crop nitrogen and water requirement. The shoot C/N ratios of the three treatments were very sensitive (ranging from 0.3 to 0.71, depending on the treatment). This parameter has a large influence on crop N requirement, which is a driver for crop biomass growth, and subsequently (through biomass fractioning) the grain yield. Additional parameters that were sensitive included grain C/N ratio (ranging from 0.1 to 0.3), grain fraction (0.28 and 0.44 for first and second crop, respectively), potential crop yield (0.12 and 0.59 for first and second crop, respectively), and (depending on the presence of N fertilization) crop water requirement (0.34 when water was the limiting factor). Root fraction and root C/N ratios were amongst the least sensitive parameters (B0.19), due to the fact that the N requirement created through root growth is small compared with straw and grain requirements. Of the input parameters unrelated to the submodel development, soil ph (1.0), field capacity (0.91), and soil porosity (0.63) were among the most sensitive parameters. Soil ph has a sizeable influence on the calculation of N cycling processes in the soil, and therefore N availability for crop growth. Field capacity and soil porosity are important parameters for determining water movement and availability; thus, they can strongly influence crop water stress. Soil organic carbon content (0.53), bulk density (0.47), clay fraction (0.24), and wilting point (0.23) also substantially influenced crop growth. Bulk density and wilting point both influence crop water availability, while clay fraction and SOC influence N availability. Site Descriptions of Experiments For the calibration and testing of DNDC-CSW, we used a detailed dataset from an ongoing long-term field experiment that was initiated in 1967 at Swift Current, Saskatchewan, Canada (Campbell et al. 1983a Campbell et al. 1983b). The soil is a Swinton loam to clay loam (Orthic Brown Chernozem), which had been under a fallow-wheat rotation from 1922 to 1966 (Campbell et al. 1983a). The climate of Swift Current is one of the driest in the Canadian prairies (Pelton et al. 1967), having an average precipitation of around 362 mm yr 1 during the study period (Campbell et al. 2007b). Twelve crop rotation-fertility treatments were included in the experiment, organized in a randomized complete block design with three replicates and each phase of all rotations present every year (Kersebaum et al. 2008). The following rotations were selected for model development and testing: continuous wheat receiving NP [Cont-W (NP)] continuous wheat receiving P [Cont-W (P)] fallow-wheat-wheat receiving NP [F-W-W (NP)] fallow-wheat-wheat receiving P [F-W-W (P)] fallow-wheat receiving NP [F-W (NP)] The site and the experiment have been described extensively in prior publications (Campbell et al. 1983a Campbell et al. 1983b; Zentner and Campbell 1988; Campbell and Zentner 1993; Campbell et al. 2004; Campbell et al. 2007a Campbell et al. 2007b). Soil properties (Table 1) were provided by Biederbeck et al. (1984) and (Campbell et al. 1977a), and precipitation data were discussed by both Campbell et al. (1988) and Campbell et al. (2007a) Campbell et al. (2007b). The investigated rotations and management practices were described in detail by Campbell et al. (1983) and grain and straw yields were reported in Campbell et al. (1992), Campbell and Zentner (1993), Paul et al. (1997) and Campbell et al. (2007a) Campbell et al. (2007b). Wheat root fractions were developed from Campbell and de Jong (2001), on the basis of a lysimeter study (Campbell et al. 1977aCampbell et al. 1977a, b; Campbell and Paul 1978) that investigated biomass growth and its fractioning of the chosen wheat variety at an adjacent site to the rotation study. The second site chosen for model testing was St.- Blaise-sur-Richelieu, Quebec, Canada (Tremblay et al. 2009; St-Jean in Jégo et al. 2010). The site is under the regime of a temperate continental climate with over 1100 mm precipitation per year. The experiment at St.- Blaise-sur-Richelieu (St.-Blaise) was conducted on a fine sandy to loam soil (Table 1). Spring wheat growth was investigated under four nitrogen fertilisation treatments (Tremblay et al. 2009) in the years Nitrogen was applied before seeding and again at boot stage (Je go et al. 2010). The resulting data were characterized by a high variability for both soil and crops, which is why only above-ground plant biomass data were used in this study to evaluate simulation results. However, given the differences in climate and soil properties in comparison with the Swift Current site, the St.-Blaise site was chosen to test for site-specific adaptation of the model development. Model Calibration and Evaluation Measured values from the rotation treatments (soil parameters, but also C/N ratios of the biomass fractions) were used as inputs when available (Table 1). In order to better fit simulation results to measurements, a number of parameters which were not measured were

8 510 CANADIAN JOURNAL OF SOIL SCIENCE calibrated using expert opinion. These include potential yield, plant water requirement (Zhang and Oweis 1999; Singh and Kumar 2010), required TDD (Miller et al. 2002; Carew et al. 2009), and the biomass fractioning. The treatment Cont-W (NP) at the Swift Current site was used for calibration. Because a change in crop variety was reported during the experiment, two crop parameter sets ( and ) were developed (Table 1). Once calibration simulations of the Cont-W (NP) treatment were complete, these model input values were used to simulate crop growth in the remaining simulations for Swift Current, and the parameter set for the second crop was also used in the simulations of the St.-Blaise treatments. To test the model using the new and modified equations after calibration, DNDC-CSW was run on four additional crop rotation treatments: Cont-W (P), F-W (NP), F-W-W (NP) and F-W-W (P). Note that for F-W-W (P) no biomass and plant nitrogen data were available. The Swift Current site comprises one of the most complete long-term soil carbon and crop growth data sites in Canada. Across the treatments there was a total of 1458 soil moisture measurements, 326 biomass and plant nitrogen measurements, 228 grain and 224 straw yield measurements, 80 soil carbon measurements, 1562 soil nitrate and 864 soil ammonium measurements over the years Approximately 15% of the total measurements were used during model calibration. To evaluate model performance, the Nash-Sutcliffe efficiency (Nash and Sutcliffe 1970) was computed, here referred to as the modeling efficiency (EF): EF 1:0 N i1 N i1 (O i P i ) 2 (O i O) 2 (5) where O denotes observed values at a point in time i, O is the average of the observed values, and P denotes predicted values at a point in time i. The EF has a maximum value of 1, and a value below 0 indicates that the deviation of the simulation results from the measurements is larger than the deviation of the average of the measurements. Accordingly, an EF value that is above zero and close to 1 denotes greater accuracy. Root mean square error [RMSE (Eq. 6) (Loague and Green 1991; Moriasi et al. 2007)], average relative error [ARE (Eq. 7) (Gold 1977; Mayer and Butler 1993)] and coefficient of determination [R 2 (Eq. 8)] were also calculated, as were the regression slope m, and the intercept of the y axis b; however, following the arguments of Willmott (1981), no statistical significance calculations were undertaken [(regarding the shortcomings of use of R 2, see also Legates and McCabe (1999)]. RMSE N 1 N ARE N i1 i1 i1 (P i O i ) NO N i1 (P i O i ) 2 0:5 (6) 100 (7) (O i O)(P i P) R 2 N 0:5 N (8) 0:5 (O i O) 2 (P i P) 2 RESULTS AND DISCUSSION Initial testing of DNDC 93 against the long-term crop rotation treatments data at Swift Current, Saskatchewan, indicated that the model did well in simulating the inter-annual variations in wheat yields. Further investigation, however, revealed that the form of the biomass and nitrogen uptake responses, as well as the partitioning of plant biomass fractions, resulted in over-prediction of above-ground biomass and nitrogen content early in the growing season. In the Canadian prairies, cooler spring temperatures can delay crop emergence and growth early in the season (de Jong and Best 1979; Arshad et al. 2002; Wang et al. 2009). In order to account for differing crop growth responses under cold growing season conditions, it was necessary to modify the crop growth routine in the DNDC 93 model. The rates of plant biomass production and nitrogen uptake can influence model outputs, particularly seasonal trace gas emissions for which DNDC is mainly used. Above-ground Plant Biomass DNDC 93 overestimated above-ground biomass at Swift Current in the initial growth stages and underestimated biomass in the later growth stage (Fig. 2a). This occurred because the initial growth rates in the potential biomass growth curve were too high for cool weather conditions that predominate in the Canadian prairies (Fig. 1a). This model simulated a rapid increase in crop growth shortly after seeding, and reached its highest growth rate at a PGI of 0.1. In comparison, through the newly incorporated potential plant biomass curve, crop growth in DNDC- CSW was delayed considerably until after a PGI of 0.2 (Fig. 1a). Modification of the plant biomass fractioning also reduced above-ground biomass estimates in the early growth stages. This was because DNDC-CSW estimated a much higher ratio of root growth in the beginning, while straw biomass (and therefore aboveground biomass in the early stages) was a small fraction of total biomass until half way through the growing season (Fig. 1c, d). Therefore, the modified growth i1

9 KRÖBEL ET AL. * CANADIAN SPRING WHEAT SUB-MODEL FOR DNDC 511 Fig. 2. Regression analysis of the simulated above-ground biomass (at Swift Current and St.-Blaise) and plant nitrogen content (at Swift Current). (a) simulated vs. measured above-ground biomass of all treatments at Swift Current (DNDC 93), (b) simulated vs. measured above-ground biomass of all treatments at Swift Current (DNDC-CSW), (c) simulated vs. measured above-ground plant nitrogen content of all treatments at Swift Current (DNDC-CSW), (d) simulated vs. measured above-ground biomass of all treatments at St.-Blaise (DNDC-CSW). curve in DNDC-CSW, coupled with adapted biomass fraction routines, resulted in a more precise fit of measured and simulated above-ground biomass in the first half of crop development. Nevertheless, there still was a tendency to underestimate the above-ground biomass when the measurements were high (Fig. 2b). In order to match the measurements late in the season, an overall increase in plant biomass growth rate would be required. This would, however, cause an over-prediction of yield estimate unless a late-season senescence function for leaf biomass was included. This addition was not considered at this time as the data were insufficient to allow us to properly quantify this process. For the DNDC 93 simulations (Table 3), the ARE revealed a considerable overestimation of the aboveground biomass (with a mean of 17.1%) and the RMSE was high (with a mean of 1395). The R 2 was largest for F-W (NP), and it also showed the highest RMSE and ARE. Cont-W (P) had the highest EF, as well as the lowest RMSE and ARE. The intercept b was relatively high across all four rotation treatments (highest value: 2144). For the simulation of the above-ground biomass, DNDC-CSW performed considerably better than DNDC 93 (Table 3). The mean EF was 0.75, and varied only slightly between the different treatments, and the RMSE was reduced by roughly 30% for all treatments. The mean ARE across rotation treatments was slightly overestimated at 6.0%, slightly under-predicting Cont-W (P). The R 2 was almost equal for all four rotation treatments, aggregating to This was also the case for the slopes, which were also relatively close to the 1:1-line. There was also a considerable reduction of the intercept b in comparison to the DNDC 93 estimate. Kersebaum et al. (2008) simulated Cont-W (NP) and F-W (NP) treatments at the Swift Current site, using the HERMES model (Kersebaum 1995). The model had difficulties in simulating soil water content; consequently, they re-initialized soil moisture each year using the first measurement. On the basis of this adjustment, the authors were able to increase the EF

10 512 CANADIAN JOURNAL OF SOIL SCIENCE Table 3. Modeling efficiency (EF), root mean squared error (RMSE), average relative error (ARE,%), and coefficient of determination R 2 (with slope m and intercept b) of simulated and measured plant biomass (kg DM ha 1 ) and plant N content (kg N ha 1 ) of four treatments in Swift Current ( ), using DNDC 93 and DNDC-CSW Treatment (all phases) Default CSW Default CSW Default CSW Aboveground biomass EF RMSE ARE CONT-W (NP) z CONT-W (P) F-W (NP) F-W-W (NP) Mean R 2 Slope m Intercept b CONT-W (NP) z CONT-W (P) F-W (NP) F-W-W (NP) Aggregated y Aboveground plant nitrogen content EF RMSE ARE CONT-W (NP) z NA x 0.59 NA 13.0 NA 6.9 CONT-W (P) NA 0.63 NA 9.0 NA 18.2 F-W (NP) NA 0.63 NA 16.0 NA 5.9 F-W-W (NP) NA 0.61 NA 12.6 NA 6.5 Mean NA 0.61 NA 12.7 NA 2.7 R 2 Slope m Intercept b CONT-W (NP) z NA 0.64 NA 0.81 NA 7.9 CONT-W (P) NA 0.70 NA 0.75 NA 1.5 F-W (NP) NA 0.64 NA 0.70 NA 10.3 F-W-W (NP) NA 0.68 NA 0.89 NA 5.4 Aggregated y NA 0.68 NA 0.92 NA 2.1 z Calibration treatment. y Note that for R 2, slope m and intercept b all data were pooled to take an aggregated measure instead of taking the mean. x Not applicable. for the simulation of above-ground plant biomass from 0.5 to 0.88, and the R 2 from 0.57 to While the adjusted HERMES model achieved better results than the DNDC-CSW calculations (R vs and EF 0.88 vs. 0.75, respectively), DNDC-CSW did not require re-initializations, and still achieved almost as good precision. Kersebaum et al. (2008) indicated that the HERMES model tended to underestimate aboveground plant biomass. Using the two versions of the model (DNDC 93 and DNDC-CSW) to simulate plant biomass at the St.- Blaise site in Quebec, very similar results were obtained as for the Swift Current site (Table 4). DNDC-CSW performed much better than DNDC 93 (EF0.75 vs. 0.02, respectively, and ARE 2.3% vs. 33.4%, respectively). Furthermore, the RMSE and intercept b were reduced markedly, and there was a clear increase in R 2 and the slope m approached unity. It is noteworthy that the intercepts were close to zero for all treatments (Fig. 2d). In a simulation study reporting on spring wheat biomass production in Canada, three different sites in Eastern Canada were simulated using the crop growth model Stics (Je go et al. 2010). Reported RMSE values ranged from 500 (Ottawa site) to 1000 (St-Blaise site). In this respect, DNDC-CSW performed equally well as the Stics model, considering that the RMSE values for St-Blaise are of the same order of magnitude. Plant Nitrogen Content For the Swift Current simulation with DNDC 93, no comparison between measured and simulated aboveground plant nitrogen content was undertaken due to the fact that the model only calculates the total plant nitrogen content. However, there was a good fit between DNDC-CSW estimates and measurements in early growth stages at low plant nitrogen content, but in the later growth stages the scatter between measured and predicted was larger (Fig. 2c). Generally, the trendline revealed a good fit with the 1:1-line. The EF-values for plant nitrogen content as predicted by DNDC-CSW were close to 0.6 for all rotation treatments (Table 3). The ARE was underestimated for the Cont-W (P) and F-W (NP) treatment. Overall, however, the average relative error across rotations was only 2.7%. The coefficient of determination was lower than for biomass, but still acceptable at about The slope of the trendline was close to the 1:1-line for all rotation

11 KRÖBEL ET AL. * CANADIAN SPRING WHEAT SUB-MODEL FOR DNDC 513 Table 4. Modeling efficiency (EF), root mean squared error (RMSE), average relative error (ARE,%), and coefficient of determination (with slope m and intercept b) of simulated and measured biomass (in kg DM ha 1 ) of four different treatments at St.-Blaise-sur-Richelieu ( ), using DNDC 93 and DNDC-CSW Treatment (fertilisation) Default CSW Default CSW Default CSW EF RMSE ARE 030 kg N ha kg N ha kg N ha kg N ha Mean R 2 Slope m Intercept b 030 kg N ha kg N ha kg N ha kg N ha Aggregated z z Note that for R 2, slope m and intercept b all data were pooled to take an aggregated measure instead of taking the mean. treatments and the intercept b was close to zero, except for F-W (NP). The fact that the aggregated slope for both plant nitrogen content and aboveground biomass were identical (0.92) confirm that the biomass is the major factor determining the plant nitrogen content (Clarke et al. 1990). Considering that the simulated plant nitrogen content is strongly dependent on an accurate simulation of the plant biomass (Clarke et al. 1990), and given the results above, the dynamic plant and biomass fraction C/N ratios were able to capture the development of the plant nitrogen content quite accurately. However, there is room for improvement, particularly for the later growth stages when soil nitrogen was depleted. A large proportion of available soil nitrogen was used for plant growth in all simulated rotations and plant nitrogen content was especially underestimated for the Cont-W (P) treatment. Comparing DNDC-CSW with other simulation models, the HERMES model simulation (Kersebaum et al. 2008) performed slightly better after adaptation than DNDC-CSW. For HERMES vs. DNDC-CSW, R 2 (0.71 vs. 0.68), EF (0.69 vs. 0.61) and RMSE (12.6 vs. 12.7) were generally similar in precision. However, for both the slope (0.8 vs. 0.92) and the intercept (12 vs. 2.1) of the trendline, DNDC-CSW achieved more precise results. This suggests a slightly higher precision of the HERMES model (less scatter around the trendline) for estimating plant nitrogen content, but greater accuracy by DNDC-CSW (better fit of the trendline). Grain and Straw Yield For Cont-W (NP), as well as wheat grown on fallow and wheat grown on stubble for the F-W-W (NP) treatment, the two versions of the DNDC model accurately simulated the average magnitude of grain and straw yields (Fig. 3). Both model versions also simulated the inter-annual variations due to changes in weather. For the grain yield simulation of the calibration treatment Cont-W (NP), the two versions of the model performed equally well, the difference being that DNDC-CSW, in general, slightly overestimated grain yield, which was to be expected since only water and nitrogen stresses were simulated (Fig. 3a). DNDC 93 tended to underestimate grain yields. For the grain simulation in the wheat on fallow year (Fig. 3b), simulations with DNDC 93 showed a tendency to overestimate the measured yields; DNDC-CSW performed slightly better than DNDC 93. The straw simulation with DNDC-CSW in the wheat grown on fallow phase was also good, although there were some underestimations from 1990 to 2000 (Fig. 3c). In the simulation of grain and straw yield in the wheat grown on stubble phase (Fig. 3d, e), both versions of DNDC were less effective than for their simulation of wheat grown on fallow. While DNDC 93 sometimes tended to underestimate grain yield (Fig. 3d) and straw yields (Fig. 3e), DNDC-CSW tended to overestimate them sometimes. With regard to the simulation of the Swift Current grain yield, DNDC 93 achieved an acceptable mean EF of 0.32, but was poor in simulating Cont-W (P) (Table 5). The RMSE was approximately 521, and the mean ARE was 2.3%. The coefficient of determination was generally above 0.5 and for F-W-W (P) the slope was close to the 1:1-line. The intercept b was fairly low for most of the treatments. The grain yield simulations of DNDC-CSW provided a slight improvement over the simulations by DNDC 93 (Table 5). This was most evident in the calculated mean EF of 0.52, compared with 0.32 for DNDC 93. The RMSE was slightly lower for DNDC-CSW than for DNDC 93. The mean ARE was 5.3%, but slightly negative for the F-W (NP) rotation. For DNDC-CSW, the R 2 was only slightly improved over DNDC 93 for most treatments, but slope values were much lower. The intercept, although higher than for DNDC 93, was generally similar across all

12 514 CANADIAN JOURNAL OF SOIL SCIENCE Fig. 3. Measured vs. simulated grain yield of Cont-W (NP) and grain and straw yields of treatment F-W-W (NP) from 1967 to (a) measured vs. simulated Cont-W (NP) grain yield (a) measured vs. simulated F-W-W (NP) grain yield (wheat following fallow), (b) measured vs. simulated F-W-W (NP) straw yield (wheat following fallow), (c) measured vs. simulated F-W-W (NP) grain yield (wheat following wheat), (d) measured vs. simulated F-W-W (NP) straw yield (wheat following wheat).

13 KRÖBEL ET AL. * CANADIAN SPRING WHEAT SUB-MODEL FOR DNDC 515 Table 5. Modeling efficiency (EF), root mean squared error (RMSE), average relative error (ARE,%), and coefficient of determination R 2 (with slope m and intercept b) of simulated and measured grain and straw yields (kg DM ha 1 ) of five treatments in Swift Current ( ), using DNDC 93 and DNDC-CSW Treatment (all phases) Default CSW Default CSW Default CSW Grain yield EF RMSE ARE CONT-W (NP) z CONT-W (P) F-W (NP) F-W-W (NP) F-W-W (P) Mean R 2 Slope m Intercept b CONT-W (NP) z CONT-W (P) F-W (NP) F-W-W (NP) F-W-W (P) Aggregate y Straw yield EF RMSE ARE CONT-W (NP) z CONT-W (P) F-W (NP) F-W-W (NP) F-W-W (P) Mean R 2 Slope m Intercept b CONT-W (NP) z CONT-W (P) F-W (NP) F-W-W (NP) F-W-W (P) Aggregated y z Calibration treatment. treatments. Both DNDC 93 and DNDC-CSW were able to capture inter annual grain yield variations across different rotations. In the HERMES simulation, Kersebaum et al. (2008) achieved a grain yield modeling efficiency of 0.28 and a coefficient of determination of 0.57 (after adaptation). While the coefficient of determination is comparable with the grain yield results of this study (0.65), the DNDC-CSW achieved a considerably higher modeling efficiency (0.52). DNDC-CSW also achieved a slightly lower RMSE, but the HERMES model provided more precise estimates of slope and intercept with regard to the grain yield simulation. Roloff et al. (1998a) used the EPIC model (Williams 1995) to simulate yield response for the same rotation treatments at Swift Current. They reported two simulation approaches, and the results from their better simulation were compared with the results from this study. While the EPIC model achieved better results simulating the unfertilized treatments (better modeling efficiency and coefficient of determination), it performed less effectively in simulating the fertilized rotations (EF close to zero). Chipanshi et al. (1997) used CERES, a model mainly focusing on biomass production, to predict Swift Current wheat grain yields and found that the model was able to predict higher grain yield quite accurately (93%), while it under-predicted yields in drought years ( 23%). While their methodology of analysis is not very comparable with our study, the ARE for simulating grain yield with DNDC-CSW was about 5.3%, which is only slightly higher than the deviation of the best results for the CERES model. With regard to Swift Current straw yields, DNDC- CSW achieved a slightly higher EF value than DNDC 93 (0.39 and 0.35, respectively), but RMSE were of a comparable magnitude for the two versions of the model. Nevertheless, straw yields were clearly underestimated by DNDC 93, as is indicated through the ARE results ( 12.2%). Coefficients of determination and slopes were roughly equal, the slope being slightly

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